0 else log_level\\n except IndexError:\\n log_level = 10\\n mpl_log_level = log_level\\n loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]\\n # set level for all loggers\\n # separate log level for matplotlib because it's so verbose\\n for logger in loggers:\\n if logger.name.startswith(\\\"matplotlib\\\"):\\n logger.setLevel(mpl_log_level)\\n else:\\n logger.setLevel(log_level)\\n\\n LOG.debug(\\\"Program opts:\\\\n%s\\\", pformat(vars(opts)))\\n\\n # main vars\\n inputs = []\\n processed = []\\n # im: Optional[Image.Image] = None\\n im: Image.Image | np.ndarray | None = None\\n in_file_path: Optional[str]\\n in_image_size = Size(0, 0)\\n in_file_size = 0\\n in_dpi = 0\\n in_exif: Optional[dict] = None\\n out_exif: bytes = b\\\"\\\"\\n out_exif_size = 0\\n out_file_path = None\\n out_image_size = Size(0, 0)\\n out_file_size = 0\\n no_op = False\\n\\n for cmd, arg in args:\\n LOG.debug(\\\"Processing command %s with args:\\\\n%s\\\", cmd, pformat(vars(arg)))\\n\\n if cmd == \\\"open\\\":\\n in_file_path = arg.input.name\\n in_file_size = os.path.getsize(in_file_path) # type: ignore\\n im = Image.open(arg.input)\\n in_image_size = Size(*im.size)\\n LOG.info(\\\"Input dims: %s\\\", in_image_size)\\n try:\\n in_exif = piexif.load(in_file_path)\\n del in_exif[\\\"thumbnail\\\"]\\n # LOG.debug(\\\"Exif: %s\\\", in_exif)\\n in_dpi = im.info[\\\"dpi\\\"]\\n except KeyError:\\n pass\\n LOG.info(\\\"Input file size: %s\\\", humanize_bytes(in_file_size))\\n LOG.info(\\\"Input dpi: %s\\\", in_dpi)\\n if arg.show_histogram:\\n LOG.debug(\\\"Generating numpy thumbnail for histogram\\\")\\n im = cv2.cvtColor(np.asarray(im), cv2.COLOR_RGB2BGR)\\n thumb = resize.resize_thumbnail_opencv(im, Size(1000, 1000))\\n print(generate_rgb_histogram(thumb))\\n show_rgb_histogram(im)\\n elif cmd == \\\"open2\\\":\\n # Test of opening multiple images for some operations, such as matting\\n for item in arg.input:\\n _im = Image.open(item)\\n try:\\n ex = piexif.load(item.name)\\n dpi = _im.info[\\\"dpi\\\"]\\n del ex[\\\"thumbnail\\\"]\\n except KeyError:\\n ex = None\\n dpi = (0, 0)\\n _im = np.asarray(_im)\\n _im = cv2.cvtColor(_im, cv2.COLOR_RGB2BGR)\\n inputs.append(\\n Img(\\n _im,\\n file_path=item.name,\\n dpi=dpi,\\n exif=ex,\\n )\\n )\\n LOG.debug(\\\"Imgs: %s\\\", inputs)\\n im = inputs[0].data\\n in_file_path = inputs[0].file_path\\n in_file_size = inputs[0].file_size\\n in_image_size = inputs[0].size\\n if arg.show_histogram:\\n if not is_ndarray(im):\\n raise TypeError('Expected numpy.ndarray')\\n LOG.debug(\\\"Generating numpy thumbnail for histogram\\\")\\n thumb = resize.resize_thumbnail_opencv(im, Size(1000, 1000))\\n print(generate_rgb_histogram(thumb))\\n show_rgb_histogram(im)\\n elif cmd == \\\"mat\\\":\\n if not is_ndarray(im):\\n raise TypeError('Expected numpy.ndarray')\\n im = mat.create_mat(im, size_inches=arg.size)\\n out_image_size = Size.from_np(im)\\n elif cmd == \\\"resize\\\":\\n im = Image.fromarray(im) if type(im) == np.ndarray else im\\n if is_ndarray(im) or im is None:\\n raise TypeError('Expected Image, not ndarray')\\n orig_size = Size(*im.size) # type: ignore\\n out_image_size = orig_size\\n try:\\n resize_method, new_size = resize.get_method(\\n orig_size,\\n width=arg.width,\\n height=arg.height,\\n scale=arg.scale,\\n longest=arg.longest,\\n shortest=arg.shortest,\\n )\\n except ResizeNotNeededError as e:\\n LOG.warning(e)\\n else:\\n # Resize/resample\\n try:\\n im = resize.resize(\\n resize_method,\\n im,\\n new_size,\\n )\\n except ImageTooSmallError as e:\\n LOG.warning(e)\\n out_image_size = Size(*im.size) # type: ignore\\n elif cmd == \\\"resize2\\\":\\n for item in inputs:\\n try:\\n resize_method, new_size = resize.get_method(\\n item.size,\\n width=arg.width,\\n height=arg.height,\\n scale=arg.scale,\\n longest=arg.longest,\\n shortest=arg.shortest,\\n force=arg.force,\\n )\\n except ResizeNotNeededError as e:\\n LOG.warning(e)\\n except ResizeAttributeError as e:\\n print(f\\\"{fg.li_red}error: {e}{rs.fg}\\\", file=sys.stderr)\\n sys.exit(1)\\n else:\\n try:\\n _im = resize.resize_opencv(\\n resize_method, item.data, new_size, resample=cv2.INTER_AREA\\n )\\n if _im is not None:\\n processed.append(Img(_im))\\n else:\\n LOG.error('Expected image from resize_opencv(), got None')\\n except ImageTooSmallError as e:\\n LOG.warning(e)\\n LOG.info(processed)\\n out_image_size = processed[0].size\\n im = processed[0].data\\n elif cmd == \\\"text\\\":\\n if im is None:\\n LOG.error('Image is None')\\n return\\n im = watermark.with_text(\\n im,\\n text=arg.text,\\n copyright=arg.copyright,\\n scale=arg.scale,\\n position=arg.position,\\n opacity=arg.opacity,\\n exif=in_exif,\\n ) # type: ignore\\n elif cmd == \\\"text2\\\":\\n im = watermark.with_text(\\n Image.fromarray(im),\\n text=arg.text,\\n copyright=arg.copyright,\\n scale=arg.scale,\\n position=arg.position,\\n opacity=arg.opacity,\\n exif=in_exif,\\n )\\n im = np.asarray(im)\\n elif cmd == \\\"watermark\\\":\\n im = watermark.with_image(\\n im,\\n Image.open(arg.image),\\n scale=arg.scale,\\n position=arg.position,\\n padding=arg.margin,\\n opacity=arg.opacity,\\n invert=arg.invert,\\n )\\n elif cmd == \\\"watermark2\\\":\\n watermark_image = cv2.imread(arg.image.name, cv2.IMREAD_UNCHANGED)\\n # im = watermark.with_image_opencv(\\n # im,\\n # watermark_image,\\n # scale=arg.scale,\\n # position=arg.position,\\n # opacity=arg.opacity,\\n # padding=arg.margin,\\n # )\\n try:\\n im = watermark.overlay_transparent(\\n im,\\n watermark_image,\\n scale=arg.scale,\\n padding=arg.margin,\\n position=arg.position,\\n alpha=arg.opacity,\\n invert=arg.invert,\\n )\\n except OverlaySizeError as e:\\n print(f\\\"{fg.li_red}error: {e}{rs.fg}\\\", file=sys.stderr)\\n sys.exit(1)\\n elif cmd == \\\"sharpen\\\":\\n im = sharpen.unsharp_mask(im, amount=arg.amount, threshold=arg.threshold)\\n elif cmd == \\\"save\\\":\\n # if type(im) == np.ndarray:\\n # im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))\\n use_progressive_jpg = in_file_size > 10000\\n if use_progressive_jpg:\\n LOG.debug(\\\"Large file; using progressive jpg\\\")\\n\\n # Exif\\n if arg.keep_exif:\\n out_exif = piexif.dump(piexif.load(in_file_path))\\n out_exif_size = sys.getsizeof(out_exif)\\n\\n outbuf = BytesIO()\\n try:\\n im.save(\\n outbuf,\\n \\\"JPEG\\\",\\n quality=arg.jpg_quality,\\n dpi=in_dpi,\\n progressive=use_progressive_jpg,\\n optimize=True,\\n exif=out_exif,\\n )\\n except AttributeError:\\n write_params = [\\n cv2.IMWRITE_JPEG_QUALITY,\\n arg.jpg_quality,\\n cv2.IMWRITE_JPEG_OPTIMIZE,\\n ]\\n if use_progressive_jpg:\\n write_params += [\\n cv2.IMWRITE_JPEG_PROGRESSIVE,\\n ]\\n _, buf = cv2.imencode(\\\".jpg\\\", im, write_params)\\n outbuf = BytesIO(buf)\\n image_buffer = outbuf.getbuffer()\\n out_file_size = image_buffer.nbytes + out_exif_size\\n LOG.info(\\\"Buffer output size: %s\\\", humanize_bytes(out_file_size))\\n\\n if arg.output is None:\\n root, _ = os.path.splitext(in_file_path)\\n out_file_path = f\\\"{root}{arg.suffix}.jpg\\\"\\n else:\\n out_file_path = arg.output.name\\n\\n if arg.no_op:\\n no_op = True\\n continue\\n LOG.info(\\\"Saving buffer to %s\\\", out_file_path)\\n if (out_path := Path(out_file_path)).exists():\\n if not arg.force:\\n LOG.critical(\\n \\\"file '%s' exists and force argument not found\\\", out_path\\n )\\n print(\\n f\\\"{fg.red}{ef.bold}Error: file '{out_path}' exists;\\\",\\n f\\\" use -f option to force overwrite.{rs.all}\\\",\\n file=sys.stderr,\\n )\\n return\\n # Create output dir if it doesn't exist\\n out_path.parent.mkdir(parents=True, exist_ok=True)\\n\\n with out_path.open(\\\"wb\\\") as f:\\n f.write(image_buffer)\\n if arg.keep_exif:\\n piexif.insert(out_exif, out_file_path)\\n out_file_size = os.path.getsize(out_file_path)\\n\\n elapsed = perf_counter() - time_start\\n report = generate_report(\\n in_file_size,\\n out_file_size,\\n in_file_path,\\n out_file_path,\\n in_image_size,\\n out_image_size,\\n elapsed,\\n no_op,\\n )\\n print(report)\",\n \"def main():\\n service = discovery.build('drive', 'v3', http=get_http())\\n\\n pic_q = retrieve_pics(service)\\n classify_pics(pic_q)\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"pldmfwuppkgname\\\", help=\\\"Name of the PLDM FW update package\\\"\\n )\\n parser.add_argument(\\\"metadatafile\\\", help=\\\"Path of metadata JSON file\\\")\\n parser.add_argument(\\n \\\"images\\\",\\n nargs=\\\"+\\\",\\n help=(\\n \\\"One or more firmware image paths, in the same order as \\\"\\n \\\" ComponentImageInformationArea entries\\\"\\n ),\\n )\\n\\n args = parser.parse_args()\\n image_files = args.images\\n with open(args.metadatafile) as file:\\n try:\\n metadata = json.load(file)\\n except ValueError:\\n sys.exit(\\\"ERROR: Invalid metadata JSON file\\\")\\n\\n # Validate the number of component images\\n if len(image_files) != len(metadata[\\\"ComponentImageInformationArea\\\"]):\\n sys.exit(\\n \\\"ERROR: number of images passed != number of entries \\\"\\n \\\" in ComponentImageInformationArea\\\"\\n )\\n\\n try:\\n with open(args.pldmfwuppkgname, \\\"w+b\\\") as pldm_fw_up_pkg:\\n component_bitmap_bit_length = write_pkg_header_info(\\n pldm_fw_up_pkg, metadata\\n )\\n write_fw_device_identification_area(\\n pldm_fw_up_pkg, metadata, component_bitmap_bit_length\\n )\\n write_component_image_info_area(\\n pldm_fw_up_pkg, metadata, image_files\\n )\\n update_pkg_header_size(pldm_fw_up_pkg)\\n write_pkg_header_checksum(pldm_fw_up_pkg)\\n append_component_images(pldm_fw_up_pkg, image_files)\\n pldm_fw_up_pkg.close()\\n except BaseException:\\n pldm_fw_up_pkg.close()\\n os.remove(args.pldmfwuppkgname)\\n raise\",\n \"def main():\\n\\n\\n\\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\\n\\n # fetch and sort the .mnc and .tag files\\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\\n tag_files = [f for f in skulls_folder if 'tag' in f]\\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\\n \\n mnc_files.sort()\\n tag_files.sort()\\n mnc_names.sort()\\n\\n # Process and package ndarrays as tuples inside npy file\\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\\n \\n print('\\\\n' * 5)\\n\\n # Push the npy files to GCP Cloud Storage\\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)\",\n \"def execute(args, **kwargs):\\n p = set_options()\\n a = p.parse_args(args)\\n # logging.info(str(a))\\n\\n ifiles = ImageFiles(a)\\n\\n if a.info:\\n ifiles.describe()\\n else:\\n ifiles.write()\",\n \"def main():\\n\\tparser = construct_parser()\\n\\targs = parser.parse_args()\\n\\ttiles = slice(args.image, args.num_tiles, save=False)\\n\\tsave_tiles(tiles, prefix=get_basename(args.image), directory=args.dir,\\n\\t\\t format=args.format)\",\n \"def process(self, image):\",\n \"def main():\\n space_ship = SimpleImage(\\\"images/MillenniumFalcon.png\\\")\\n figure = SimpleImage(\\\"images/ReyGreenScreen.png\\\")\\n result = combine(space_ship, figure)\\n result.show()\",\n \"def main(argv):\\n\\n epd = EPD()\\n\\n epd.clear()\\n\\n print('panel = {p:s} {w:d} x {h:d} version={v:s}'.format(p=epd.panel, w=epd.width, h=epd.height, v=epd.version))\\n\\n for file_name in argv:\\n if not os.path.exists(file_name):\\n sys.exit('error: image file{f:s} does not exist'.format(f=file_name))\\n print('display: {f:s}'.format(f=file_name))\\n display_file(epd, file_name)\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"--interval\\\", \\\"-t\\\", type=int,\\n help=\\\"length of time in seconds between images\\\",\\n default=60)\\n parser.add_argument(\\\"--images\\\", \\\"-n\\\", type=int, help=\\\"number of images\\\", default=1)\\n parser.add_argument(\\\"--output\\\", \\\"-o\\\",\\n help=\\\"path to output directory\\\", default=\\\"./\\\")\\n parser.add_argument(\\\"--rotation\\\", \\\"-r\\\",\\n help=\\\"degrees to rotate pi image\\\", default=0)\\n parser.add_argument(\\\"--iso\\\", \\\"-i\\\", type=int, help=\\\"camera iso 0 is auto mode\\\", default=0)\\n parser.add_argument(\\\"--shutter\\\", \\\"-s\\\", type=int,\\n help=\\\"camera shutter speed in microseconds 0 is auto mode\\\", default=0)\\n parser.add_argument(\\\"--exposure\\\", \\\"-e\\\", help=\\\"exposure compensation set between -25 and +25\\\", default=0)\\n args = parser.parse_args()\\n interval = int(args.interval)\\n images = int(args.images)\\n output = args.output\\n rotation = int(args.rotation)\\n iso = int(args.iso)\\n shutter_speed = int(args.shutter)\\n exposure_compensation = int(args.exposure)\\n\\n\\n timelapse(int=interval,\\n n=images,\\n dir=output,\\n rotation=rotation,\\n iso=iso,\\n shutter=shutter_speed,\\n exposure=exposure_compensation)\",\n \"def main():\\n base_dir = '/home/sjimenez/imagenes_prueba'\\n out_dir = '/home/sjimenez/easy_analysis'\\n for _, _, files in os.walk(base_dir, topdown=False):\\n for f in files:\\n print('--------- {} ---------'.format(f))\\n act_dir = osp.join(base_dir, f)\\n act_im = cv2.imread(act_dir)\\n if act_im is not None:\\n get_image_stats(act_im, out_dir, f)\\n else:\\n print('Not able to open the image')\",\n \"def main():\\n\\n os.system(\\\"rm -rf images; mkdir images\\\")\\n\\n if (len(sys.argv) > 1):\\n N = int(sys.argv[1])\\n else:\\n N = 10\\n\\n x_test = np.load(\\\"../../../../data/mnist/mnist_test_images.npy\\\")\\n\\n for i in range(N):\\n r,c = random.randint(6,12), random.randint(6,12)\\n g = np.zeros(r*c)\\n for j in range(r*c):\\n if (random.random() < 0.15):\\n g[j] = 1\\n g = g.reshape((r,c))\\n g[:,0] = g[0,:] = g[:,-1] = g[-1,:] = 0\\n\\n img = np.zeros((28*r,28*c), dtype=\\\"uint8\\\")\\n for x in range(r):\\n for y in range(c):\\n if (g[x,y] == 1):\\n n = random.randint(0, x_test.shape[0])\\n im = x_test[n]\\n img[28*x:(28*x+28), 28*y:(28*y+28)] = im\\n \\n Image.fromarray(img).save(\\\"images/image_%04d.png\\\" % i)\",\n \"def imageProcessing():\\n\\n # Parser initialization\\n parser = argparse.ArgumentParser(description=colourers.toCyan('Image processor for reading/writing images into BMP/PNG formats and applying transformations on it.'))\\n \\n # Formats Parser\\n group = parser.add_argument_group(colourers.toGreen('formats'))\\n formatParser = group.add_mutually_exclusive_group(required=True)\\n formatParser.add_argument('--bmp',\\n type=str,\\n metavar=colourers.toRed('<bmp file name>'), \\n help=colourers.toMagenta('bmp file to parse'))\\n formatParser.add_argument('--png',\\n type=str,\\n metavar=colourers.toRed('<png file name>'),\\n help=colourers.toMagenta('png file to parse'))\\n\\n # Printers Parser\\n group = parser.add_argument_group(colourers.toYellow('printers'))\\n printers = group.add_mutually_exclusive_group()\\n printers.add_argument('--header',\\n help=colourers.toMagenta('print the file format header'),\\n action='store_true')\\n printers.add_argument('--print-color',\\n '-pc',\\n type=int,\\n nargs=2,\\n metavar=(colourers.toRed('<width>'), colourers.toRed('<height>')),\\n help=colourers.toMagenta('pixel to print'))\\n printers.add_argument('--histogram',\\n action='store_true',\\n help=colourers.toMagenta('print histogram associated'))\\n printers.add_argument('--output',\\n '-o',\\n type=str,\\n metavar=colourers.toRed('<output file>'),\\n help=colourers.toMagenta('image output file'))\\n\\n # Transformers Parser\\n transformers = parser.add_argument_group(colourers.toBlue('transformers'))\\n transformers.add_argument('--half',\\n action='store_true',\\n help='applying the filter on one half of the image')\\n transformers.add_argument('--rotate',\\n '-r',\\n type=int,\\n choices=[90, 180, 270],\\n metavar=colourers.toRed('<degree of rotation>'),\\n help=colourers.toMagenta('rotate the image'))\\n transformers.add_argument('--scale',\\n '-s',\\n type=int,\\n nargs='+',\\n action=required_length(1, 2),\\n metavar=(colourers.toRed('<scaleRatio> | [<width>'), colourers.toRed('<height>')),\\n help=colourers.toMagenta('scale/shrink the image'))\\n transformers.add_argument('--contrast',\\n '-c',\\n type=float,\\n metavar=colourers.toRed('<contrast factor>'),\\n help=colourers.toMagenta('apply a factor contrast'))\\n transformers.add_argument('--grayscale',\\n '-gs',\\n action='store_true',\\n help=colourers.toMagenta('to grayscale image'))\\n transformers.add_argument('--binary',\\n '-b',\\n action='store_true',\\n help=colourers.toMagenta('to binary image'))\\n transformers.add_argument('--invert',\\n '-i',\\n action='store_true',\\n help=colourers.toMagenta('to inverted image, equivalent to --contrast -1'))\\n transformers.add_argument('--channel',\\n type=str,\\n choices=['blue', 'green', 'red'],\\n metavar=colourers.toRed('<channel>'),\\n nargs='+',\\n action=required_length(1, 2),\\n help=colourers.toMagenta('to the specified channel'))\\n \\n # Filters Parser\\n filters = parser.add_argument_group(colourers.toCyan('filters'))\\n filters.add_argument('--edge-detection',\\n '-ed',\\n type=str,\\n choices=['canny', 'sobel', 'prewitt', 'roberts', 'kirsch'],\\n metavar=colourers.toRed('<filter name>'),\\n help=colourers.toMagenta('perform an edge detection'))\\n filters.add_argument('--retrieve-color',\\n '-rv',\\n action='store_true',\\n help=colourers.toMagenta('retrieve the colors of a grayscale image'))\\n filters.add_argument('--edge-enhancement',\\n '-ee',\\n action='store_true', \\n help=colourers.toMagenta('applying increased edge enhancement filter'))\\n filters.add_argument('--sharpen',\\n action='store_true',\\n help=colourers.toMagenta('sharpening the image'))\\n filters.add_argument('--unsharp',\\n action='store_true',\\n help=colourers.toMagenta('unsharp the image')) \\n filters.add_argument('--denoise',\\n action='store_true',\\n help=colourers.toMagenta('denoise the image'))\\n filters.add_argument('--texture-detection',\\n '-td',\\n action='store_true',\\n help=colourers.toMagenta('applying texture detection (Gabor Filter)'))\\n filters.add_argument('--blur',\\n type=str,\\n choices=['simple', 'more', 'average', 'gaussian', 'motion'],\\n metavar=colourers.toRed('<type of blur>'),\\n help=colourers.toMagenta('perform the selected blur'))\\n filters.add_argument('--blur-iteration',\\n '-bi',\\n type=int,\\n default=1,\\n metavar=colourers.toRed('<number of iteration>'),\\n help=colourers.toMagenta('apply N times the blur function'))\\n filters.add_argument('--emboss',\\n action='store_true',\\n help=colourers.toMagenta('perform an embossing filter'))\\n filters.add_argument('--overlap',\\n type=str,\\n nargs='+',\\n metavar=colourers.toRed('<image to overlap>'),\\n help=colourers.toMagenta('overlap an image given on the selected image'))\\n\\n # Args parsing\\n args = parser.parse_args()\\n\\n filename = \\\"\\\"\\n # BMP Block\\n if args.bmp:\\n filename = args.bmp\\n\\n if not os.path.isfile(filename):\\n colourers.error('\\\"{}\\\" does not exist !'.format(filename))\\n sys.exit(-1)\\n colourers.success('Success Opening {}...'.format(filename))\\n\\n bmp = BMP(filename)\\n half = args.half\\n\\n if args.print_color:\\n width, height = args.print_color\\n colourers.info(f'Printing pixel color of ({width}, {height})')\\n Printers.printPixel(bmp, width, height)\\n sys.exit(0)\\n \\n elif args.header:\\n colourers.info(f'Printing BMP header of {bmp.filename}')\\n Printers.printHeader(bmp)\\n sys.exit(0)\\n \\n elif args.histogram:\\n colourers.info(f'Printing color histogram of {bmp.filename}')\\n Printers.printHistogram(bmp)\\n sys.exit(0)\\n \\n if (args.rotate or args.scale or args.contrast or args.grayscale or \\n args.binary or args.channel or args.edge_detection or args.retrieve_color or\\n args.edge_enhancement or args.blur or args.emboss or args.overlap or args.texture_detection or\\n args.denoise or args.sharpen or args.unsharp):\\n if not hp.atLeastOne(args.output, (\\n args.rotate,\\n args.scale,\\n args.contrast,\\n args.grayscale,\\n args.binary,\\n args.channel,\\n args.edge_detection,\\n args.retrieve_color,\\n args.edge_enhancement,\\n args.blur,\\n args.emboss,\\n args.overlap,\\n args.texture_detection,\\n args.denoise,\\n args.sharpen,\\n args.unsharp\\n )):\\n parser.error('--rotate/--scale/--contrast/--grayscale/--binary/--channel/--edge-detection/--retrieve-color/--edge-enhancement/--blur/--emboss/--overlap/--texture-detection/--denoise/--sharpen/--unsharp and --output must be given together')\\n \\n if args.rotate:\\n degree = args.rotate\\n colourers.info(f'Rotating image to {degree} degree')\\n bmp.imageData = Transformers.rotate(bmp, degree)\\n\\n if args.scale:\\n if len(args.scale) == 2:\\n width, height = args.scale\\n colourers.info(f'Scaling image to {width}x{height} pixels')\\n bmp.imageData = Transformers.scale(bmp, height, width)\\n else:\\n scaleRatio = args.scale[0]\\n\\n colourers.info(f'Scaling image to {scaleRatio} scale ratio')\\n\\n height = int(hp.readLittleEndian(bmp.height))\\n width = int(hp.readLittleEndian(bmp.width))\\n\\n bmp.imageData = Transformers.scale(bmp, height * scaleRatio, width * scaleRatio)\\n \\n if args.contrast:\\n factor = args.contrast\\n colourers.info(f'Applying a factor contrast of {factor}')\\n bmp.imageData = Transformers.contrast(bmp, factor)\\n \\n if args.grayscale:\\n colourers.info(f'Applying grayscale mask to the image')\\n bmp.imageData = Transformers.grayscale(bmp, half)\\n \\n if args.binary:\\n colourers.info(f'Applying binary mask to the image')\\n bmp.imageData = Transformers.binary(bmp, half)\\n \\n if args.invert:\\n colourers.info(f'Inverting image colours')\\n bmp.imageData = Transformers.invert(bmp, half)\\n \\n if args.channel:\\n if len(args.channel) == 2:\\n c1, c2 = args.channel\\n colourers.info(f'Keeping only {c1} and {c2} channels of the image')\\n bmp.imageData = Transformers.toChannel(bmp, [c1, c2], half)\\n else:\\n channel = args.channel[0]\\n colourers.info(f'Keeping only {channel} channel of the image')\\n bmp.imageData = Transformers.toChannel(bmp, channel, half)\\n \\n if args.denoise:\\n colourers.info(f'Denoising the image')\\n bmp.imageData = Filters.wienerFilter(bmp.imageData, gaussianKernel(9, sigma=0.33), K=10)\\n \\n if args.texture_detection:\\n colourers.info(f'Applying texture detection (Gabor Filter)')\\n bmp.imageData = Filters.gaborFilter(bmp.imageData, gaborKernel(0))\\n \\n if args.edge_enhancement:\\n colourers.info(f'Applying increased edge enhancement filter')\\n bmp.imageData = Filters.iee(bmp.imageData)\\n\\n if args.edge_detection:\\n filterName = args.edge_detection\\n if filterName == 'canny':\\n colourers.info(f'Performing Canny filter for edge detection')\\n bmp.imageData = Filters.ced(bmp.imageData, sigma=0.33, kernelSize=9, weakPix=50)\\n if filterName == 'sobel':\\n colourers.info(f'Performing Sobel filter for edge detection')\\n bmp.imageData = Filters.sed(bmp.imageData, sigma=0.33, kernelSize=9)\\n if filterName == 'prewitt':\\n colourers.info(f'Performing Prewitt filter for edge detection')\\n bmp.imageData = Filters.ped(bmp.imageData, sigma=0.33, kernelSize=9)\\n if filterName == 'roberts':\\n colourers.info(f'Performing Roberts filter for edge detection')\\n bmp.imageData = Filters.red(bmp.imageData, sigma=0.33, kernelSize=9)\\n if filterName == 'kirsch':\\n colourers.info(f'Performing Kirsch filter for edge detection')\\n bmp.imageData = Filters.ked(bmp.imageData, sigma=0.33, kernelSize=9)\\n\\n if args.sharpen:\\n colourers.info(f'Sharpening the image')\\n bmp.imageData = Filters.sharpen(bmp.imageData)\\n \\n if args.unsharp:\\n colourers.info(f'Unsharpening the image')\\n bmp.imageData = Filters.unsharp(bmp.imageData)\\n\\n if args.retrieve_color:\\n colourers.info(f'Retrieving color')\\n bmp.imageData = Filters.retrieveColor(bmp.imageData)\\n \\n if args.blur:\\n blurType = args.blur\\n colourers.info(f'Performing a {blurType} blur')\\n for _ in range(args.blur_iteration):\\n blurFunc = Filters.blur.switcher.get(blurType)\\n bmp.imageData = blurFunc(bmp.imageData)\\n \\n if args.emboss:\\n colourers.info(f'Performing emboss filter')\\n bmp.imageData = Filters.emboss(bmp.imageData)\\n \\n if args.overlap:\\n overlappers = []\\n for ov in args.overlap:\\n overlappers.append(BMP(ov).imageData)\\n colourers.info(f'Performing an overlapping between {bmp.filename} and {args.overlap}')\\n bmp.imageData = Filters.overlap(bmp.imageData, overlappers)\\n \\n if args.output:\\n outputFile = args.output\\n hp.saveBMP(bmp, bmp.imageData, outputFile)\\n colourers.success(f'Succesfully saved into {outputFile}')\\n sys.exit(0)\\n \\n parser.error('Give at least one more argument')\\n \\n # PNG Block\\n else:\\n filename = args.png\\n\\n if not os.path.isfile(filename):\\n print('\\\"{}\\\" does not exist'.format(filename), file=sys.stderr)\\n sys.exit(-1)\\n print('Success Opening {}...'.format(filename))\\n \\n png = PNG(filename)\",\n \"def main():\\n print(\\\"For each image, type the new name of the file.\\\" +\\n \\\" No extension necessary!\\\", end=\\\"\\\\n\\\\n\\\")\\n file_list = input_path.glob(f\\\"*.{args.ext}\\\")\\n plt.ion()\\n\\n for pic in file_list:\\n img = io.imread(str(pic))\\n img = rescale(img, 0.25)\\n img = rotate(img, 90, resize = True)\\n plt.draw()\\n plt.pause(0.001)\\n if args.vinyl:\\n new_name = get_vinyl_name()\\n else:\\n print(\\\"\\\\n\\\")\\n new_name = input(\\n \\\"Please enter a new filename. Press [enter] to skip: \\\")\\n if new_name:\\n if not new_name.endswith(args.ext):\\n new_name += \\\".\\\" + args.ext\\n # io.imsave(output_path / new_name, img)\\n shutil.copyfile(pic, output_path / new_name)\\n if args.replace:\\n os.remove(pic)\",\n \"def _image(self):\\n print(\\\"imaging\\\")\\n self.images.append(self.device_control.image())\\n yield\",\n \"def process(image):\\n pass\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n register_device_args(parser)\\n register_update_args(parser, default_os_check='update', default_pave=False)\\n args = parser.parse_args()\\n update(args.system_image_dir, args.os_check, args.target_id,\\n args.serial_num, args.pave)\",\n \"def run(self, options):\\n \\n print(Gstr_title)\\n print('Version: %s' % self.get_version())\\n self.load_images(options)\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description='codec_compare')\\n parser.add_argument('path', metavar='DIR',\\n help='path to images folder')\\n args = parser.parse_args()\\n classpath = args.path\\n classname = classpath.split('/')[1]\\n\\n images = set(listdir_full_path(classpath))\\n if len(images) <= 0:\\n print \\\"\\\\033[91m[ERROR]\\\\033[0m\\\" + \\\" no source files in ./images.\\\"\\n sys.exit(1)\\n\\n codeclist_full = set(['aom', 'deepcoder', 'deepcoder-lite', 'fuif', 'fvdo', 'hevc', 'kakadu', 'jpeg',\\n 'pik', 'tat', 'xavs', 'xavs-fast', 'xavs-median', 'webp'])\\n\\n bpp_targets = set([0.06, 0.12, 0.25, 0.50, 0.75, 1.00, 1.50, 2.00])\\n for image in images:\\n width, height, depth = get_dimensions(image, classname)\\n name, imgfmt = os.path.splitext(image)\\n imgfmt = os.path.basename(image).split(\\\".\\\")[-1]\\n derivative_images = []\\n if classname[:6] == 'classB':\\n derivative_images = create_derivatives(image, classname)\\n else:\\n derivative_images.append((image, imgfmt))\\n\\n for derivative_image, pix_fmt in derivative_images:\\n json_dir = 'metrics'\\n mkdir_p(json_dir)\\n json_file = os.path.join(json_dir,\\n os.path.splitext(os.path.basename(derivative_image))[0] + \\\".\\\" + pix_fmt + \\\".json\\\")\\n # if os.path.isfile(json_file):\\n # print \\\"\\\\033[92m[JSON OK]\\\\033[0m \\\" + json_file\\n # continue\\n main_dict = dict()\\n derivative_image_metrics = dict()\\n for codecname in codeclist_full:\\n convertflag = 1\\n caseflag = pix_fmt\\n if (codecname == 'webp' or codecname == 'tat' or 'deepcoder' in codecname) and depth != '8':\\n continue\\n if 'xavs' in codecname and depth != '8' and depth != '10':\\n continue\\n if 'classE' in classname and ('tat' in codecname or 'xavs' in codecname or 'deepcoder' in codecname):\\n continue\\n if codecname == 'kakadu' and classname[:6] == 'classB':\\n convertflag = 0\\n caseflag = imgfmt\\n bpp_target_metrics = dict()\\n for bpp_target in bpp_targets:\\n print(codecname)\\n if codecname == 'aom' and classname[:6] == 'classB':\\n # ('AERIAL2' in image or 'CATS' in image or 'XRAY' in image or 'GOLD' in image or 'TEXTURE1' in image):\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + 'av1'\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif codecname == 'kakadu' and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif 'xavs' in codecname and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif codecname == 'fvdo' and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_pgm' + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.pgm')\\n original_image = image\\n else:\\n if codecname == 'fuif' and 'tif' in imgfmt:\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '.tif_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\\n elif codecname == 'webp' or codecname == 'tat':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_yuv420p.' + codecname\\n else:\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image_path = os.path.join('outputs', codecname, 'decoded')\\n decoded_image = ''\\n for decodedfile in os.listdir(decoded_image_path):\\n encoderoot = '_'.join(os.path.splitext(os.path.basename(encoded_image_name))[0].split('_')[:-1])\\n if encoderoot in decodedfile:\\n if ('tat' in codecname or 'webp' in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] == '.yuv':\\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\\n print(decoded_image)\\n if ('tat' not in codecname or 'webp' not in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] != '.yuv':\\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\\n if 'classE' not in classname and 'classB' not in classname and os.path.isfile(decoded_image):\\n decoded_image = convert_decoded(decoded_image, width, height, depth, codecname)\\n original_image = convert_decoded(derivative_image, width, height, depth, 'reference')\\n else:\\n original_image = derivative_image\\n\\n print('Reference:' + original_image)\\n print('Encoded:' + encoded_image)\\n print('Decoded:' + decoded_image)\\n if (os.path.isfile(original_image) and os.path.isfile(decoded_image) and os.path.isfile(encoded_image)):\\n if 'classE' in classname:\\n metrics = compute_metrics_HDR(original_image, decoded_image, encoded_image, bpp_target,\\n codecname, width, height, pix_fmt, depth)\\n\\n elif 'classB' in classname:\\n metrics = compute_metrics(original_image, decoded_image, encoded_image, bpp_target, codecname,\\n width, height, pix_fmt)\\n else:\\n metrics = compute_metrics_SDR(original_image, decoded_image, encoded_image, bpp_target,\\n codecname, width,\\n height, imgfmt, depth)\\n measured_bpp = (os.path.getsize(encoded_image) * 1.024 * 8) / (float((int(width) * int(height))))\\n bpp_target_metrics[measured_bpp] = metrics\\n else:\\n continue\\n \\n derivative_image_metrics[codecname] = bpp_target_metrics\\n main_dict[derivative_image] = derivative_image_metrics\\n\\n mkdir_p(json_dir)\\n with open(json_file, 'w') as f:\\n f.write(json.dumps(main_dict, indent=2))\",\n \"def main():\\n tmp_dir = xm.constants['dir_tmp']\\n exr_f = join(tmp_dir, 'test.exr')\\n exr = EXR(exr_f)\\n exr.extract_normal(join(tmp_dir, 'test.png'), vis=True)\",\n \"def main():\\r\\n original = SimpleImage(\\\"images/poppy.png\\\")\\r\\n original.show()\\r\\n # shrink function\\r\\n after_shrink = shrink('images/poppy.png')\\r\\n after_shrink.show()\",\n \"def main():\\n feature_extraction_model = \\\"HOG\\\"\\n dimension_reduction_model = \\\"PCA\\\"\\n k_value = get_input_k(\\\"k\\\")\\n K_value = get_input_k(\\\"K\\\")\\n folder = get_input_folder(\\\"Folder\\\")\\n dim_k_value = 40\\n\\n query_images = get_input_image_list(folder)\\n start = time.time()\\n dim_red = DimensionReduction(feature_extraction_model, dimension_reduction_model, dim_k_value, folder_metadata=folder,\\n metadata_collection=\\\"labelled\\\")\\n obj_feat = dim_red.get_object_feature_matrix()\\n features_list = np.array(obj_feat['featureVector'].tolist())\\n images_list = np.array(obj_feat['imageId'])\\n cos_sim = cosine_similarity(features_list)\\n\\n sim_graph = sim_graph_from_sim_max(cos_sim, images_list, k_value)\\n results = ppr(sim_graph, images_list, query_images)\\n results = results[:K_value]\\n\\n print(\\\"Top {} images from Personalized page Rank are:\\\".format(K_value))\\n for r in results:\\n r[\\\"path\\\"] = os.path.abspath(os.path.join(folder, r['imageId']))\\n print(r)\\n\\n query_images_list = [os.path.abspath(os.path.join(folder, img)) for img in query_images]\\n title = {\\\"Model\\\": \\\"Personalized Page Rank\\\", \\\"k\\\": k_value, \\\"K\\\": K_value}\\n show_images_ppr(query_images_list, title, results)\\n print(\\\"Execution time: {} seconds\\\".format(time.time() - start))\",\n \"def run():\\n\\n today = datetime.now().strftime(\\\"%Y-%m-%d\\\")\\n log_file = os.path.abspath(\\\"logs/{}.log\\\".format(today))\\n logger = RsmasLogger(\\\"pipeline\\\", log_file)\\n\\n images = get_list_of_images()\\n # LOG: list of images to process\\n logger.log(loglevel.INFO, [img.key for img in images])\\n\\n for im in images:\\n\\n logger.log(loglevel.INFO, \\\"Processing image: {}\\\".format(im.key))\\n\\n file_path = \\\"{}/{}\\\".format(im.bucket_name, im.key)\\n full_path = \\\"{}_full.jpg\\\"\\n mod_path = \\\"{}_mod.jpg\\\"\\n aws_path = \\\"{}/{}/{}/{}\\\"\\n try:\\n haz_id, haz_name, sat_name, sat_dir, img_type, img_date, center = summary.pull_summary_data(\\n \\\"/vsis3/{}\\\".format(file_path))\\n sat_id = Satellite.from_params(sat_name, bool(sat_dir))\\n except:\\n # LOG: error in image metadata format\\n logger.log(loglevel.ERROR, '\\\\tThere was an error in the metadata format of the image. Skipping.')\\n continue\\n\\n aws_path = aws_path.format(haz_id, sat_id, img_type, img_date)\\n full_path = full_path.format(img_date)\\n mod_path = mod_path.format(img_date)\\n\\n # 1. Read in image file\\n with rasterio.open(\\\"s3://{}\\\".format(file_path)) as data:\\n band = data.read(1)\\n img = plot.show(band)\\n img.get_figure().savefig(full_path, dpi=300)\\n\\n # 3. Compress image\\n compressed = immanip.compress_image(full_path, compression_amount=0.3)\\n\\n # 4 - 5. Pad image and add date on image\\n text_image = immanip.add_text_to_image(compressed, img_date)\\n\\n # 6. Save image locally\\n text_image.save(mod_path.format(img_date))\\n mod_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, mod_path))\\n full_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, full_path))\\n\\n tif_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, im.key))\\n\\n # LOG: images successfully moved to S3 bucket\\n # LOG: mod_path_aws, full_path_aws, tif_path_aws\\n\\n hazard = Hazard(haz_id, haz_name, HazardType.VOLCANO, Location(center[0], center[1]), Date(img_date), 0)\\n satellite = Satellite.from_params(sat_name, bool(sat_dir))\\n image = Image(str(randint(1, 10000000)),\\n haz_id,\\n satellite,\\n ImageType.from_string(img_type),\\n Date(img_date),\\n ImageURL(full_path_aws),\\n ImageURL(tif_path_aws),\\n ImageURL(mod_path_aws))\\n\\n try:\\n db = Database()\\n except ConnectionError:\\n logger.log(loglevel.ERROR, \\\"\\\\tThere was an error while connecting to the database. Skipping this image.\\\")\\n continue\\n\\n db.create_new_hazard(hazard)\\n db.create_new_satellite(satellite)\\n db.create_new_image(image)\\n\\n db.close()\\n\\n # LOG: database successfully updated\\n logger.log(loglevel.INFO, \\\"\\\\tDatabase succesfully updated.\\\")\\n\\n save.save_image_s3(mod_path, \\\"{}/{}\\\".format(aws_path, mod_path))\\n save.save_image_s3(full_path, \\\"{}/{}\\\".format(aws_path, full_path))\\n save.move_tif(im.key, \\\"{}/{}\\\".format(aws_path, im.key))\\n\\n logger.log(loglevel.INFO, \\\"\\\\tImages were successfully uploaded to the S3 bucket\\\")\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\tmod_path_aws: {}\\\".format(mod_path_aws))\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\tfull_path_aws: {}\\\".format(full_path_aws))\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\ttif_path_aws: {}\\\".format(tif_path_aws))\\n\\n # LOG: image completed\\n logger.log(loglevel.INFO, \\\"\\\\tProcessing of {} completed.\\\".format(im.key))\\n\\n # LOG: finished processing images\\n logger.log(loglevel.INFO, \\\"Processing complete.\\\")\",\n \"def main():\\n logging.basicConfig(\\n level=logging.DEBUG, format=\\\"%(levelname)s: %(message)s\\\")\\n root.add_command(all_)\\n root.add_command(build)\\n root.add_command(cleanup)\\n root.add_command(clitest)\\n root.add_command(run)\\n root.add_command(push)\\n root.add_command(unittest)\\n root.add_command(list_images)\\n root.add_command(list_stages)\\n root()\",\n \"def run_image_viewer( self ):\\n\\n # XXX: hardcoded program name and image size.\\n subprocess.Popen( [\\\"feh\\\", \\\"-dZ\\\", \\\"-g\\\", \\\"800x600\\\", self.record[\\\"filename\\\"]] )\",\n \"def main():\\n\\n dir_path =r'/Users/dustin/CS/projects/ship_detector/data/ships-in-satellite-imagery/shipsnet/'\\n\\n data_array, label_array = read_images(dir_path)\\n\\n array_info(data_array, label_array)\\n\\n image_info(data_array[0,:], plot_image=False)\\n\\n split_ratios = [0.8, 0.1, 0.1] #splitting the dataset into 80% train, 10% dev, 10% test\\n\\n X_train, X_dev, X_test, Y_train, Y_dev, Y_test = dataset_split(data_array, label_array, split_ratios)\",\n \"def generate_image(self):\\n pass\",\n \"def test_image():\\n def get_images_name(folder):\\n \\\"\\\"\\\"Create a generator to list images name at evaluation time\\\"\\\"\\\"\\n onlyfiles = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]\\n for f in onlyfiles:\\n yield f\\n\\n def pil_loader(path):\\n \\\"\\\"\\\"Load images from /eval/ subfolder, convert to greyscale and resized it as squared\\\"\\\"\\\"\\n with open(path, 'rb') as f:\\n with Image.open(f) as img:\\n sqrWidth = np.ceil(np.sqrt(img.size[0]*img.size[1])).astype(int)\\n return img.convert('L').resize((sqrWidth, sqrWidth))\\n\\n eval_loader = torch.utils.data.DataLoader(ImageFolder(root=args.evalf, transform=transforms.Compose([\\n transforms.Resize(28),\\n transforms.CenterCrop(28),\\n transforms.ToTensor(),\\n transforms.Normalize((0.1307,), (0.3081,))\\n ]), loader=pil_loader), batch_size=1, **kwargs)\\n\\n # Name generator\\n names = get_images_name(os.path.join(args.evalf, \\\"images\\\"))\\n model.eval()\\n with torch.no_grad():\\n for data, target in eval_loader:\\n data, target = data.to(device), target.to(device)\\n output = model(data)\\n label = output.argmax(dim=1, keepdim=True).item()\\n print (\\\"Images: \\\" + next(names) + \\\", Classified as: \\\" + str(label))\",\n \"def test_create_image(self):\\n pass\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-d', '--debug', action='store_true',\\n help='Start debugging. Image MUST be an ELF file.')\\n parser.add_argument('-t', '--toolchain', type=is_valid_path,\\n help='Tool chain installation path.')\\n parser.set_defaults(addr='addr_x86')\\n group = parser.add_mutually_exclusive_group()\\n group.add_argument('-s', '--sensor', action='store_const',\\n dest='addr', const='addr_arc',\\n help='Sensor Subsystem Application \\\\\\n (only valid for Quark SE).')\\n group.add_argument('-r', '--rom', action='store_const',\\n dest='addr', const='addr_rom',\\n help='Flash ROM (Bootloader - 1st stage).')\\n group.add_argument('-u', '--rom-2nd', action='store_const',\\n dest='addr', const='addr_rom_2nd',\\n help='Flash 2nd Stage ROM ' \\\\\\n '(Bootloader - 2nd stage, Quark SE only).')\\n parser.add_argument('board', metavar='BOARD',\\n choices=['d2000_dev', 'quarkse_dev'],\\n help='Board name (d2000_dev or quarkse_dev).')\\n parser.add_argument('inputfile', type=is_valid_file, metavar='INFILE',\\n help='Image name')\\n args = parser.parse_args()\\n\\n # Start looking for the tool chain path.\\n tool_chain = get_tool_chain_path(args.toolchain)\\n # From the tool chain retrieve common paths.\\n common_paths = get_common_paths(tool_chain)\\n\\n if args.debug:\\n cpu = get_cpu(args.board, args.addr)\\n run_debug(cpu, common_paths, tool_chain, args.inputfile)\\n else:\\n run_flash(args.board, args.addr, common_paths, args.inputfile)\",\n \"def main():\\n\\n logging.basicConfig(level=logging.INFO)\\n\\n # Load GCE settings.\\n settings = json.loads(open(gce.SETTINGS_FILE, 'r').read())\\n\\n # Perform OAuth 2.0 authorization flow.\\n flow = flow_from_clientsecrets(\\n settings['client_secrets'], scope=settings['compute_scope'])\\n storage = Storage(settings['oauth_storage'])\\n credentials = storage.get()\\n\\n # Authorize an instance of httplib2.Http.\\n if credentials is None or credentials.invalid:\\n credentials = run(flow, storage)\\n http = httplib2.Http()\\n auth_http = credentials.authorize(http)\\n\\n gce_helper = gce.Gce(auth_http, settings['project'])\\n\\n # Start an image with a local start-up script.\\n logging.info('Starting up an instance')\\n instance_name = 'startup-script-demo'\\n zone_name = settings['compute']['zone']\\n try:\\n gce_helper.start_instance(\\n instance_name,\\n\\tzone=zone_name,\\n service_email=settings['compute']['service_email'],\\n scopes=settings['compute']['scopes'],\\n startup_script='startup.sh',\\n metadata=[\\n {'key': 'url', 'value': settings['image_url']},\\n {'key': 'text', 'value': settings['image_text']},\\n {'key': 'cs-bucket', 'value': settings['storage']['bucket']}])\\n except gce.ApiError, e:\\n logging.error('Error starting instance.')\\n logging.error(e)\\n return\\n except gce.ApiOperationError as e:\\n logging.error('Error starting instance')\\n logging.error(e)\\n return\\n\\n # List all running instances.\\n logging.info('Here are your running instances:')\\n instances = gce_helper.list_instances()\\n for instance in instances:\\n logging.info(instance['name'])\\n\\n logging.info(\\n 'Visit http://storage.googleapis.com/%s/output.png',\\n settings['storage']['bucket'])\\n logging.info('It might take a minute for the output.png file to show up.')\\n raw_input('Hit Enter when done to shutdown instance')\\n\\n # Stop the instance.\\n logging.info('Shutting down the instance')\\n try:\\n gce_helper.stop_instance(instance_name, zone=zone_name)\\n except gce.ApiError, e:\\n logging.error('Error stopping instance.')\\n logging.error(e)\\n return\\n except gce.ApiOperationError, e:\\n logging.error('Error stopping instance')\\n logging.error(e)\\n return\\n\\n logging.info('Remember to delete the output.png file in ' + settings[\\n 'storage']['bucket'])\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def main(image_path):\\n temp_dir = tempfile.mkdtemp()\\n print('Saving output to {}'.format(temp_dir))\\n estimator = run_image(image_path)\\n visualize(estimator, image_path, temp_dir)\",\n \"def make_image():\\n click.echo(\\\"make_image\\\")\",\n \"def do_command(self, args):\\n imageops = dbops.Images()\\n imageops.state(args)\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n description=\\\"A program to generate geometric wallpaper.\\\")\\n\\n parser.add_argument(\\\"-s\\\", \\\"--shape\\\",\\n help=\\\"the name of shapes to generate\\\",\\n type=str,\\n choices=(\\\"hexagon\\\",\\n \\\"square\\\",\\n \\\"diamond\\\",\\n \\\"half-diamond\\\"),\\n default=\\\"hexagon\\\")\\n\\n parser.add_argument(\\\"-n\\\", \\\"--num-across\\\",\\n help=\\\"the number of shapes across the canvas to create\\\",\\n type=int,\\n default=10)\\n\\n parser.add_argument(\\\"--size\\\",\\n help=\\\"the size of the created image\\\",\\n type=int,\\n nargs=2,\\n default=instagram_size)\\n\\n parser.add_argument(\\\"-o\\\", \\\"--outfile\\\",\\n help=\\\"name of the created file\\\",\\n type=str)\\n\\n args = parser.parse_args()\\n\\n # Create the image.\\n im = Image.new('RGB', args.size)\\n draw = ImageDraw.Draw(im)\\n\\n for shape in gw.shapes.cover_in_shapes(args.shape, im.size,\\n args.num_across):\\n draw.polygon(shape, fill=gw.colors.palette_chooser())\\n\\n # Save the image.\\n if args.outfile:\\n im.save(args.outfile)\\n else:\\n im.save(\\\"{}_{}_{}x{}.png\\\".format(args.shape, args.num_across,\\n args.size[0], args.size[1]),\\n \\\"PNG\\\")\",\n \"def do_command(self, args):\\n imageops = dbops.Images()\\n imageops.add(args)\",\n \"def image_process(image_info):\\n path = os.path.join(cfg.IMAGESET, image_info.get(\\\"index\\\") + \\\".jpg\\\")\\n if not os.path.exists(path):\\n raise IOError(\\\"please check your file is not exists: \\\" + path)\\n def load_image(path):\\n image = Image.open(path)\\n return image\\n return load_image(path)\",\n \"def main(args):\\n args = parse_args(args)\\n setup_logging(args.loglevel)\\n\\n download_images_from_grid_vector(grid_vector=args.grid_vector,\\n output_dir=args.output_dir,\\n type_id=args.type,\\n product_type_id=args.product_type,\\n num_jobs=args.num_jobs)\",\n \"def main(self):\\r\\n pass\",\n \"def run_image_editor( self ):\\n\\n # XXX: hardcoded program name and image size.\\n subprocess.Popen( [\\\"gimp\\\", \\\"-adfs\\\", self.record[\\\"filename\\\"]] )\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def main():\\n\\n # Just grab all files - we'll use try/except to filter\\n images = glob.glob(os.path.join(args.input_dir, '*.*'))\\n if not os.path.exists(args.output_dir):\\n os.makedirs(args.output_dir)\\n for img_file in images:\\n print(img_file)\\n try:\\n np_img = plt.imread(img_file)\\n print(np_img.shape)\\n img_name = img_file.split(os.sep)[-1]\\n new_img_file = os.path.join(args.output_dir, img_name)\\n pad_image(np_img, new_img_file)\\n except Exception as e:\\n print('Warning: {}. Skpping file.'.format(e))\\n continue\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def main():\\n rospy.init_node('image_to_pointcloud')\\n rospy.loginfo(\\\"Starting sonar image to pointcloud node...\\\")\\n converter = ImageConverter()\\n\\n rate = rospy.Rate(10)\\n rospy.sleep(3.0)\\n while not rospy.is_shutdown():\\n converter.proc_and_pub_pointcloud()\\n rate.sleep()\",\n \"def main():\\n print(\\\"Program version: 1.5\\\")\\n StartTime = datetime.now()\\n args = parseArguments()\\n\\n verbose = args.verbose\\n images = args.images\\n ignore_warnings = args.ignore_warnings\\n if(args.silent):\\n verbose = False\\n images = False\\n ignore_warnings = True\\n\\n if(args.images):\\n plt.ioff()\\n\\n if(args.ignore_warnings):\\n warnings.simplefilter('ignore', UserWarning)\\n\\n #sample header keywords\\n # OBJECT = 'P016+03_P1_JKdeep' / Original target\\n # RA = ' 01:06:37.759' / 01:06:37.7 RA (J2000) pointing\\n # DEC = ' 03:32:36.096' / 03:32:36.0 DEC (J2000) pointing\\n # EQUINOX = 2000. / Standard FK5 (years)\\n # RADECSYS= 'FK5 ' / Coordinate reference frame\\n # CRVAL1 = 16.65733 / 01:06:37.7, RA at ref pixel\\n # CRVAL2 = 3.54336 / 03:32:36.0, DEC at ref pixel\\n # CRPIX1 = 447. /Ref pixel in X\\n # CRPIX2 = 452. / Ref pixel in Y\\n # CDELT1 = -8.0000000000000E-5 / SS arcsec per pixel in RA\\n # CDELT2 = 8.00000000000003E-5 / SS arcsec per pixel in DEC\\n # CTYPE1 = 'RA---TAN' / pixel coordinate system\\n # CTYPE2 = 'DEC--TAN' / pixel coordinate system\\n # PC1_1 = 0.000000 / Translation matrix element\\n # PC1_2 = 1.000000 / Translation matrix element\\n # PC2_1 = -1.000000 / Translation matrix element\\n # PC2_2 = 0.000000 / Translation matrix element\\n\\n fits_image_filenames = args.input\\n\\n #if directory given search for appropriate fits files\\n\\n if(os.path.isdir(fits_image_filenames[0])):\\n print(\\\"detected a directory. Will search for fits files in it\\\")\\n path = fits_image_filenames[0]\\n fits_image_filenames = []\\n for file in os.listdir(path):\\n if file.endswith(\\\".fits\\\") and \\\"_astro\\\" not in file:\\n fits_image_filenames.append(path+\\\"/\\\"+file)\\n print(fits_image_filenames)\\n\\n multiple = False\\n if(len(fits_image_filenames)>1):\\n multiple = True\\n not_converged = []\\n converged_counter = 0\\n for fits_image_filename in fits_image_filenames:\\n\\n result,_ = astrometry_script(fits_image_filename, catalog=args.catalog, rotation_scaling=0, xy_transformation=args.xy_transformation, fine_transformation=args.fine_transformation,\\n images=images, vignette=args.vignette,vignette_rectangular=args.vignette_rectangular, cutouts=args.cutout, ra=args.ra, dec=args.dec, projection_ra=args.projection_ra, projection_dec=args.projection_dec, verbose=verbose, save_images=args.save_images, ignore_header_rot=args.ignore_header_rot, radius = args.radius, save_bad_result=args.save_bad_result, silent =args.silent, sigma_threshold_for_source_detection= args.sigma_threshold_for_source_detection, high_res=args.high_resolution, hdul_idx=args.hdul_idx, filename_for_sources=args.filename_for_sources, FWHM=args.seeing)\\n\\n if((not result) and args.rotation_scaling):\\n print(\\\"Did not converge. Will try again with full rotation and scaling\\\")\\n result, _ = astrometry_script(fits_image_filename, catalog=args.catalog, rotation_scaling=args.rotation_scaling, xy_transformation=args.xy_transformation, fine_transformation=args.fine_transformation,\\n images=images, vignette=args.vignette,vignette_rectangular=args.vignette_rectangular, cutouts=args.cutout, ra=args.ra, dec=args.dec, projection_ra=args.projection_ra, projection_dec=args.projection_dec, verbose=verbose, save_images=args.save_images, ignore_header_rot=args.ignore_header_rot, radius = args.radius, save_bad_result=args.save_bad_result, silent=args.silent, sigma_threshold_for_source_detection=args.sigma_threshold_for_source_detection, high_res=args.high_resolution, hdul_idx=args.hdul_idx, filename_for_sources=args.filename_for_sources, FWHM=args.seeing)\\n\\n if(result):\\n print(\\\"Astrometry was determined to be good.\\\")\\n converged_counter = converged_counter+1\\n else:\\n print(\\\"Astrometry was determined to be bad.\\\")\\n not_converged.append(fits_image_filename)\\n if(args.save_bad_result):\\n print(\\\"Result was saved anyway\\\")\\n else:\\n print(\\\"Result was not saved.\\\")\\n # print(\\\"\\\")\\n # print(\\\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\\\")\\n # print(\\\"> Astrometry for {} \\\".format(fits_image_filename))\\n #\\n # with fits.open(fits_image_filename) as hdul:\\n # #print(hdul.info())\\n # if(args.verbose):\\n # print(\\\"if image is not at first position in the fits file the program will break later on\\\")\\n # #print(hdul[0].header)\\n #\\n # hdu = hdul[0]\\n # #hdu.verify('fix')\\n # hdr = hdu.header\\n #\\n #\\n # image_or = hdul[0].data.astype(float)\\n # median = np.nanmedian(image_or)\\n # image_or[np.isnan(image_or)]=median\\n # image = image_or - median\\n #\\n # observation = find_sources(image, args.vignette)\\n # #print(observation)\\n #\\n # positions = (observation['xcenter'], observation['ycenter'])\\n # apertures = CircularAperture(positions, r=4.)\\n #\\n #\\n # #world coordinates\\n # print(\\\">Info found in the file -- (CRVAl: position of central pixel (CRPIX) on the sky)\\\")\\n # print(WCS(hdr))\\n #\\n # hdr[\\\"NAXIS1\\\"] = image.shape[0]\\n # hdr[\\\"NAXIS2\\\"] = image.shape[1]\\n #\\n # #wcsprm = Wcsprm(hdr.tostring().encode('utf-8')) #everything else gave me errors with python 3, seemed to make problems with pc conversios, so i wwitched to the form below\\n # wcsprm = WCS(hdr).wcs\\n # wcsprm_original = WCS(hdr).wcs\\n # if(args.verbose):\\n # print(WCS(wcsprm.to_header()))\\n # wcsprm, fov_radius, INCREASE_FOV_FLAG, PIXSCALE_UNCLEAR = read_additional_info_from_header(wcsprm, hdr, args.ra, args.dec, args.projection_ra, args.projection_dec)\\n # if(args.verbose):\\n # print(WCS(wcsprm.to_header()))\\n #\\n # #print(wcsprm)\\n # #wcsprm.pc = [[2, 0],[0,1]]\\n #\\n #\\n # #Possibly usefull examples of how to use wcsprm:\\n # #print(wcsprm.set())\\n # #print(wcsprm.get_pc())\\n # #pc = wcsprm.get_pc()\\n # #print(np.linalg.det(pc))\\n # #print(wcsprm.get_cdelt())\\n # #wcs.fix()\\n # #print(wcsprm.print_contents())\\n # #print(repr(hdr.update(wcsprm.to_header().encode('utf-8')))) #not working\\n #\\n # #hdu.verify(\\\"fix\\\")\\n # #print(repr(hdr))\\n # #wcs.wcs_pix2world(pixcrd, 1)\\n # #wcs.wcs_world2pix(world, 1)\\n # #wcs.wcs.crpix = [-234.75, 8.3393]\\n # # wcs.wcs.cdelt = np.array([-0.066667, 0.066667])\\n # # wcs.wcs.crval = [0, -90]\\n # # wcs.wcs.ctype = [\\\"RA---AIR\\\", \\\"DEC--AIR\\\"]\\n # # wcs.wcs.set_pv([(2, 1, 45.0)])\\n # # For historical compatibility, three alternate specifications of the linear transformations\\n # # are available in wcslib. The canonical PCi_ja with CDELTia, CDi_ja, and the deprecated CROTAia\\n # # keywords. Although the latter may not formally co-exist with PCi_ja,\\n # # the approach here is simply to ignore them if given in conjunction with PCi_ja.\\n # # has_pc, has_cd and has_crota can be used to determine which of these alternatives are present in the header.\\n # # These alternate specifications of the linear transformation matrix are translated immediately to PCi_ja by set\\n # # and are nowhere visible to the lower-level routines. In particular, set resets cdelt to unity if CDi_ja is present\\n # # (and no PCi_ja). If no CROTAia is associated with the latitude axis, set reverts to a unity PCi_ja matrix.\\n #\\n #\\n #\\n #\\n #\\n # #get rough coordinates\\n # #print(hdr[\\\"RA\\\"])\\n # #coord = SkyCoord(hdr[\\\"RA\\\"], hdr[\\\"DEC\\\"], unit=(u.hourangle, u.deg), frame=\\\"icrs\\\")\\n # coord = SkyCoord(wcsprm.crval[0], wcsprm.crval[1], unit=(u.deg, u.deg), frame=\\\"icrs\\\")\\n # if(not PIXSCALE_UNCLEAR):\\n # if(wcsprm.crpix[0] < 0 or wcsprm.crpix[1] < 0 or wcsprm.crpix[0] > image.shape[0] or wcsprm.crpix[1] > image.shape[1] ):\\n # print(\\\"central value outside of the image, moving it to the center\\\")\\n # coord_radec = wcsprm.p2s([[image.shape[0]/2, image.shape[1]/2]], 0)[\\\"world\\\"][0]\\n # coord = SkyCoord(coord_radec[0], coord_radec[1], unit=(u.deg, u.deg), frame=\\\"icrs\\\")\\n # #print(wcsprm)\\n #\\n #\\n #\\n # #better: put in nice wrapper! with repeated tries and maybe try synchron!\\n # print(\\\">Dowloading catalog data\\\")\\n # radius = u.Quantity(fov_radius, u.arcmin)#will prob need more\\n # catalog_data = query.get_data(coord, radius, args.catalog)\\n # #reference = reference.query(\\\"mag <20\\\")\\n # max_sources = 500\\n # if(INCREASE_FOV_FLAG):\\n # max_sources= max_sources*2.25 #1.5 times the radius, so 2.25 the area\\n # if(catalog_data.shape[0]>max_sources):\\n # catalog_data = catalog_data.nsmallest(400, \\\"mag\\\")\\n #\\n # if(args.catalog == \\\"GAIA\\\" and catalog_data.shape[0] < 5):\\n # print(\\\"GAIA seems to not have enough objects, will enhance with PS1\\\")\\n # catalog_data2 = query.get_data(coord, radius, \\\"PS\\\")\\n # catalog_data = pd.concat([catalog_data, catalog_data2])\\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1), r=5.)\\n # print(\\\"Now we have a total of {} sources. Keep in mind that there might be duplicates now since we combined 2 catalogs\\\".format(catalog_data.shape[0]))\\n # elif(args.catalog == \\\"PS\\\" and (catalog_data is None or catalog_data.shape[0] < 5)):\\n # print(\\\"We seem to be outside the PS footprint, enhance with GAIA data\\\")\\n # catalog_data2 = query.get_data(coord, radius, \\\"GAIA\\\")\\n # catalog_data = pd.concat([catalog_data, catalog_data2])\\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1), r=5.)\\n # print(\\\"Now we have a total of {} sources. Keep in mind that there might be duplicates now since we combined 2 catalogs\\\".format(catalog_data.shape[0]))\\n #\\n # #remove duplicates in catalog?\\n #\\n # apertures_catalog = CircularAperture(wcsprm.s2p(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1)['pixcrd'], r=5.)\\n #\\n #\\n # #plotting what we have, I keep it in the detector field, world coordinates are more painfull to plot\\n # if(args.images):\\n # fig = plt.figure()\\n # fig.canvas.set_window_title('Input for {}'.format(fits_image_filename))\\n # plt.xlabel(\\\"pixel x direction\\\")\\n # plt.ylabel(\\\"pixel y direction\\\")\\n # plt.title(\\\"Input - red: catalog sources, blue: detected sources in img\\\")\\n # plt.imshow(image,cmap='Greys', origin='lower', norm=LogNorm())\\n # apertures.plot(color='blue', lw=1.5, alpha=0.5)\\n # apertures_catalog.plot(color='red', lw=1.5, alpha=0.5)\\n #\\n # plt.xlim(-200,image.shape[0]+200)\\n # plt.ylim(-200,image.shape[1]+200)\\n # if(args.save_images):\\n # name_parts = fits_image_filename.rsplit('.', 1)\\n # plt.savefig(name_parts[0]+\\\"_image_before.pdf\\\")\\n #\\n # ###tranforming to match the sources\\n # print(\\\"---------------------------------\\\")\\n # print(\\\">Finding the transformation\\\")\\n # if(args.rotation_scaling):\\n # print(\\\"Finding scaling and rotation\\\")\\n # wcsprm = register.get_scaling_and_rotation(observation, catalog_data, wcsprm, scale_guessed=PIXSCALE_UNCLEAR, verbose=args.verbose)\\n # if(args.xy_transformation):\\n # print(\\\"Finding offset\\\")\\n # wcsprm,_,_ = register.offset_with_orientation(observation, catalog_data, wcsprm, fast=False , INCREASE_FOV_FLAG=INCREASE_FOV_FLAG, verbose= args.verbose)\\n #\\n # #correct subpixel error\\n # obs_x, obs_y, cat_x, cat_y, distances = register.find_matches(observation, catalog_data, wcsprm, threshold=3)\\n # rms = np.sqrt(np.mean(np.square(distances)))\\n # best_score = len(obs_x)/(rms+10) #start with current best score\\n # fine_transformation = False\\n # if(args.fine_transformation):\\n # for i in [2,3,5,8,10,6,4, 20,2,1,0.5]:\\n # wcsprm_new, score = register.fine_transformation(observation, catalog_data, wcsprm, threshold=i)\\n # if(score> best_score):\\n # wcsprm = wcsprm_new\\n # best_score = score\\n # fine_transformation = True\\n # if not fine_transformation:\\n # print(\\\"Fine transformation did not improve result so will be discarded.\\\")\\n # else:\\n # print(\\\"Fine transformation applied to improve result\\\")\\n # #register.calculate_rms(observation, catalog_data,wcs)\\n #\\n # #make wcsprim more physical by moving scaling to cdelt, out of the pc matrix\\n # wcs =WCS(wcsprm.to_header())\\n # if(args.verbose):\\n # print(wcs)\\n #\\n # from astropy.wcs import utils\\n # scales = utils.proj_plane_pixel_scales(wcs)\\n # print(scales)\\n # cdelt = wcsprm.get_cdelt()\\n # print(cdelt)\\n # scale_ratio = scales/cdelt\\n # #print(scale_ratio)\\n # pc = np.array(wcsprm.get_pc())\\n # pc[0,0] = pc[0,0]/scale_ratio[0]\\n # pc[1,0] = pc[1,0]/scale_ratio[1]\\n # pc[0,1] = pc[0,1]/scale_ratio[0]\\n # pc[1,1] = pc[1,1]/scale_ratio[1]\\n # wcsprm.pc = pc\\n # wcsprm.cdelt = scales\\n # if(args.verbose):\\n # print(\\\"moved scaling info to CDelt\\\")\\n # print(WCS(wcsprm.to_header()))\\n #\\n # #WCS difference before and after\\n # print(\\\"> Compared to the input the Wcs was changed by: \\\")\\n # scales_original = utils.proj_plane_pixel_scales(WCS(hdr))\\n # print(\\\"WCS got scaled by {} in x direction and {} in y direction\\\".format(scales[0]/scales_original[0], scales[1]/scales_original[1]))\\n # #sources:\\n # #https://math.stackexchange.com/questions/2113634/comparing-two-rotation-matrices\\n # #https://stackoverflow.com/questions/2827393/angles-between-two-n-dimensional-vectors-in-python/13849249#13849249\\n # def unit_vector(vector):\\n # \\\"\\\"\\\" Returns the unit vector of the vector. \\\"\\\"\\\"\\n # return vector / max(np.linalg.norm(vector), 1e-10)\\n # def matrix_angle( B, A ):\\n # \\\"\\\"\\\" comment cos between vectors or matrices \\\"\\\"\\\"\\n # Aflat = A.reshape(-1)\\n # Aflat = unit_vector(Aflat)\\n # Bflat = B.reshape(-1)\\n # Bflat = unit_vector(Bflat)\\n # #return np.arccos((np.dot( Aflat, Bflat ) / max( np.linalg.norm(Aflat) * np.linalg.norm(Bflat), 1e-10 )))\\n # return np.arccos(np.clip(np.dot(Aflat, Bflat), -1.0, 1.0))\\n # #print(matrix_angle(wcsprm.get_pc(), wcsprm_original.get_pc()) /2/np.pi*360)\\n # rotation_angle = matrix_angle(wcsprm.get_pc(), wcsprm_original.get_pc()) /2/np.pi*360\\n # if((wcsprm.get_pc() @ wcsprm_original.get_pc() )[0,1] > 0):\\n # text = \\\"counterclockwise\\\"\\n # else:\\n # text = \\\"clockwise\\\"\\n # print(\\\"Rotation of WCS by an angle of {} deg \\\".format(rotation_angle)+text)\\n # old_central_pixel = wcsprm_original.s2p([wcsprm.crval], 0)[\\\"pixcrd\\\"][0]\\n # print(\\\"x offset: {} px, y offset: {} px \\\".format(wcsprm.crpix[0]- old_central_pixel[0], wcsprm.crpix[1]- old_central_pixel[1]))\\n #\\n #\\n # #check final figure\\n # if(args.images):\\n # fig = plt.figure()\\n # fig.canvas.set_window_title('Result for {}'.format(fits_image_filename))\\n # plt.xlabel(\\\"pixel x direction\\\")\\n # plt.ylabel(\\\"pixel y direction\\\")\\n # plt.title(\\\"Result - red: catalog sources, blue: detected sources in img\\\")\\n # plt.imshow(image,cmap='Greys', origin='lower', norm=LogNorm())\\n # apertures.plot(color='blue', lw=1.5, alpha=0.5)\\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1), r=5.)\\n # apertures_catalog = CircularAperture(wcsprm.s2p(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1)['pixcrd'], r=5.)\\n #\\n # apertures_catalog.plot(color='red', lw=1.5, alpha=0.5)\\n # if(args.save_images):\\n # name_parts = fits_image_filename.rsplit('.', 1)\\n # plt.savefig(name_parts[0]+\\\"_image_after.pdf\\\")\\n #\\n # print(\\\"--- Evaluate how good the transformation is ----\\\")\\n # register.calculate_rms(observation, catalog_data,wcsprm)\\n #\\n #\\n # #updating file\\n # write_wcs_to_hdr(fits_image_filename, wcsprm)\\n #\\n #\\n # print(\\\"overall time taken\\\")\\n # print(datetime.now()-StartTime)\\n # if(args.images):\\n # plt.show()\\n if(multiple):\\n print(\\\">> Final report:\\\")\\n print(\\\"Processed {} files, {} of them did converge. The following files failed:\\\".format(len(fits_image_filenames), converged_counter))\\n print(not_converged)\\n print(\\\"-- finished --\\\")\",\n \"def execute(self, images_and_density_maps):\\n raise NotImplementedError(\\\"execute method not implemented in the child class\\\")\",\n \"def main(folder, outputfile):\\n parser = argument_parser()\\n args = parser.parse_args()\\n\\n show_all = args.show_all\\n verbose = args.verbose\\n\\n random.seed(args.rng_seed)\\n\\n args.files = folder\\n print args.files\\n\\n try:\\n image = Image.open(args.files[0])\\n except IOError, msg:\\n print >> sys.stderr, msg\\n return 1\\n if image.mode == 'P':\\n image = image.convert('RGB')\\n \\n if image.size[0] > args.w:\\n image = image.resize((args.w, int((float(args.w)/image.size[0]) *\\n image.size[1])), Image.ANTIALIAS)\\n\\n if not show_all:\\n def nothing(a, b):\\n pass\\n do_something = nothing\\n elif args.saving:\\n do_something = Imsave(\\\"saved/\\\" + args.files[0][:-4] + \\\"_\\\" +\\n str(image.size[0]) + \\\"/\\\").save\\n else:\\n import im_debug\\n do_something = im_debug.show\\n\\n if verbose:\\n import time\\n class Logger:\\n def __init__(self):\\n self.t = 0\\n\\n def __call__(self, m):\\n t_n = time.time()\\n if self.t > 0:\\n print >> sys.stderr, \\\"\\\\t\\\" + str(t_n - self.t)\\n print >> sys.stderr, m\\n self.t = t_n\\n logger = Logger()\\n\\n else:\\n def logger(m):\\n pass\\n \\n if args.manual_mode:\\n import manual\\n try:\\n lines = manual.find_lines(image)\\n except manual.UserQuitError:\\n #TODO ask user to try again\\n return 1\\n else:\\n if args.l_cache:\\n filename = (\\\"saved/cache/\\\" + args.files[0][:-4] + \\\"_\\\" +\\n str(image.size[0]))\\n cache_dir = \\\"/\\\".join(filename.split('/')[:-1])\\n if os.path.exists(filename):\\n lines, l1, l2, bounds, hough = pickle.load(open(filename))\\n print >> sys.stderr, \\\"using cached results\\\"\\n else:\\n lines, l1, l2, bounds, hough = linef.find_lines(image, do_something, logger)\\n if not os.path.isdir(cache_dir):\\n os.makedirs(cache_dir)\\n d_file = open(filename, 'wb')\\n pickle.dump((lines, l1, l2, bounds, hough), d_file)\\n d_file.close()\\n else:\\n lines, l1, l2, bounds, hough = linef.find_lines(image, do_something, logger)\\n\\n grid, lines = gridf.find(lines, image.size, l1, l2, bounds, hough,\\n show_all, do_something, logger)\\n if show_all:\\n im_g = image.copy()\\n draw = ImageDraw.Draw(im_g)\\n for l in grid[0] + grid[1]:\\n draw.line(l, fill=(64, 255, 64), width=1)\\n do_something(im_g, \\\"grid\\\", name=\\\"grid\\\")\\n\\n intersections = intrsc.b_intersects(image, lines, show_all, do_something, logger)\\n board = intrsc.board(image, intersections, show_all, do_something, logger)\\n\\n logger(\\\"finished\\\")\\n\\n # TODO! refactor this mess:\\n if len(args.files) == 1:\\n\\n if args.sgf_output:\\n print board.asSGFsetPos()\\n else:\\n print board\\n \\n else:\\n game = output.Game(19, board) #TODO size parameter\\n #for f in args.files[1:]:\\n for i, f in enumerate(args.files):\\n try:\\n image = Image.open(f)\\n except IOError, msg:\\n print >> sys.stderr, msg\\n continue\\n if verbose:\\n print >> sys.stderr, \\\"Opening\\\", f\\n if image.mode == 'P':\\n image = image.convert('RGB')\\n if image.size[0] > args.w:\\n image = image.resize((args.w, int((float(args.w)/image.size[0]) *\\n image.size[1])), Image.ANTIALIAS)\\n board = intrsc.board(image, intersections, show_all, do_something, logger)\\n if args.sgf_output:\\n game.addMove(board)\\n else:\\n with open(outputfile + str(i) + \\\".txt\\\", \\\"w\\\") as f:\\n f.write(str(board))\\n\\n if args.sgf_output:\\n print game.asSGF()\\n\\n return 0\",\n \"def run(self):\\n self.run_tasks()\\n self.images = np.array(self.images)\\n self.shapes.extend(self.images.shape[-2:])\\n\\n self.images = np.reshape(self.images, self.shapes)\",\n \"def _state_main(self, gui):\\n gui.entry.wait_variable(gui.entry_sv)\\n\\n '''Clean string'''\\n files = literal_eval(gui.entry_sv.get())\\n\\n '''Remove previous images'''\\n if hasattr(gui, \\\"panel\\\"):\\n gui.panel.destroy()\\n\\n '''Load each image'''\\n for file_name in files:\\n file_name = file_name.replace(\\\"{\\\", \\\"\\\").replace(\\\"}\\\", \\\"\\\")\\n # image = tk.PhotoImage(file=file_name)\\n if \\\".CR2\\\" in file_name:\\n '''Rawpy implementation'''\\n file_image = rawpy.imread(file_name)\\n file_image = file_image.postprocess()\\n '''Rawkit implementation'''\\n '''file_image = Raw(file_name)\\n file_image = np.array(file_image.to_buffer())'''\\n '''OpenCV implementation'''\\n '''file_image = cv2.imread(file_name)'''\\n else:\\n file_image = Image.open(file_name)\\n '''image = file_image.resize((500, 500), Image.ANTIALIAS)\\n image = ImageTk.PhotoImage(image)\\n gui.panel = tk.Label(gui.root, image=image)\\n gui.panel.image = image\\n gui.panel.pack()'''\\n # panel.grid(row=2)\\n\\n image_data = np.array(file_image)\\n image_data = cv2.cvtColor(image_data, cv2.COLOR_RGB2GRAY)\\n '''print(image_data.shape)\\n print(image_data)\\n print(len(image_data))\\n print(len(image_data[0]))'''\\n returned_image = Image.fromarray(image_data)\\n '''cv2.imshow(\\\"Gray\\\", image_data)\\n cv2.waitKey()\\n cv2.destroyWindow(\\\"Gray\\\")'''\\n\\n '''enhanced_contrast = ImageEnhance.Contrast(Image.fromarray(file_image))\\n enhanced_image = enhanced_contrast.enhance(255)\\n enhanced_data = np.array(enhanced_image)\\n plot_functions.imshow(enhanced_image)\\n plot_functions.show()'''\\n\\n # color_space = cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV)\\n # print(color_space)\\n \\n '''Create mask for white-ish pixels'''\\n '''lower_background = np.array([150, 150, 150])\\n upper_background = np.array([255, 255, 255])\\n print(image_data)\\n white_mask = cv2.inRange(image_data, lower_background, upper_background)\\n white_mask = cv2.morphologyEx(white_mask, cv2.MORPH_OPEN, np.ones((3,3),np.uint8))\\n white_mask = cv2.morphologyEx(white_mask, cv2.MORPH_DILATE, np.ones((3, 3), np.uint8))\\n white_mask = white_mask / 255'''\\n\\n '''Create mask for black-ish pixels'''\\n '''lower_background = np.array([0, 0, 0])\\n upper_background = np.array([25, 25, 25])\\n black_mask = cv2.inRange(image_data, lower_background, upper_background)\\n black_mask = cv2.morphologyEx(black_mask, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))\\n black_mask = cv2.morphologyEx(black_mask, cv2.MORPH_DILATE, np.ones((3, 3), np.uint8))\\n black_mask = black_mask / 255'''\\n\\n '''Add masks together'''\\n '''background_mask = white_mask\\n # Ensure no value is above 1\\n background_mask = np.clip(background_mask, 0, 1)'''\\n \\n copied_image_data = np.asarray(returned_image).copy()\\n # background_mask = np.logical_not(background_mask)\\n '''for row_index, [mask_row, image_row] in enumerate(zip(background_mask, copied_image_data)):\\n # place black pixel on corresponding masked pixels\\n # copied_image_data[row_index] = np.array([image_row[pixel] * int(mask_row[pixel]) for pixel in range(len(mask_row))])\\n # make pixel fully white on corresponding masked pixels\\n copied_image_data[row_index] = np.array([np.array([255, 255, 255]) if int(mask_row[pixel]) else image_row[pixel] for pixel in range(len(mask_row))])'''\\n\\n '''Turn removed pixels red'''\\n '''mask_image = Image.fromarray(copied_image_data)\\n plot_functions.imshow(mask_image)\\n plot_functions.show()'''\\n trapezoid_data = copied_image_data.copy()\\n\\n enhanced_contrast = ImageEnhance.Contrast(Image.fromarray(trapezoid_data))\\n enhanced_image = enhanced_contrast.enhance(255)\\n trapezoid_data = np.array(enhanced_image)\\n\\n '''Detect lines'''\\n edges = cv2.Canny(trapezoid_data, 75, 150)\\n lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, maxLineGap=1000)\\n # print(lines)\\n for line in lines:\\n x1, y1, x2, y2 = line[0]\\n if y1 == y2:\\n cv2.line(copied_image_data, (x1, y1), (x2, y2), (255, 255, 255), 1)\\n\\n '''Trapezoid attempt'''\\n\\n # filters image bilaterally and displays it\\n bilatImg = cv2.bilateralFilter(trapezoid_data, 5, 175, 175)\\n\\n # finds edges of bilaterally filtered image and displays it\\n edgeImg = cv2.Canny(bilatImg, 75, 200)\\n\\n # gets contours (outlines) for shapes and sorts from largest area to smallest area\\n contours, hierarchy = cv2.findContours(edgeImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\\n contours = sorted(contours, key=cv2.contourArea, reverse=True)\\n\\n # drawing red contours on the image\\n for con in contours:\\n cv2.drawContours(trapezoid_data, con, -1, (255, 255, 255), 3)\\n\\n '''Detect corners'''\\n dst = cv2.cornerHarris(edges, 30, 31, 0.001)\\n dst = cv2.dilate(dst, None)\\n ret, dst = cv2.threshold(dst, 0.01 * dst.max(), 255, 0)\\n dst = np.uint8(dst)\\n\\n # find centroids\\n ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)\\n # define the criteria to stop and refine the corners\\n criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100,\\n 0.001)\\n corners = cv2.cornerSubPix(edges, np.float32(centroids), (5, 5),\\n (-1, -1), criteria)\\n\\n good_corners = []\\n for corner in corners:\\n if (corner[1] < 1000) & (corner[1] > 650) & (corner[0] > 250) & (corner[0] < 2250):\\n good_corners.append(corner)\\n cv2.circle(edges, (corner[0], corner[1]), 10, (255, 255, 255))\\n\\n print(good_corners)\\n if len(good_corners) >= 3:\\n corner_combos = itertools.combinations(good_corners, 3)\\n elif len(good_corners) > 1:\\n corner_combos = itertools.combinations(good_corners, 2)\\n\\n best_corner_combo = None\\n best_coef = np.inf\\n for corner_combo in corner_combos:\\n regression = LinearRegression().fit(np.array([corner[0] for corner in corner_combo]).reshape(-1, 1),\\n np.array([corner[1] for corner in corner_combo]))\\n if np.abs(regression.coef_) < best_coef:\\n best_coef = np.abs(regression.coef_)\\n best_corner_combo = np.array([corner[1] for corner in corner_combo])\\n\\n y_edge = int(round(np.mean(best_corner_combo)))\\n edges = edges[y_edge:3000, 200:2200]\\n copied_image_data = copied_image_data[y_edge:2500, 200:2200]\\n trapezoid_data = trapezoid_data[y_edge:2500, 200:2200]\\n\\n # and double-checking the outcome\\n cv2.imshow(\\\"linesEdges\\\", edges)\\n cv2.imshow(\\\"linesDetected\\\", copied_image_data)\\n cv2.imshow(\\\"Contours check\\\", trapezoid_data)\\n cv2.waitKey()\\n cv2.destroyWindow(\\\"Contours check\\\")\\n\\n # find the perimeter of the first closed contour\\n perim = cv2.arcLength(contours[0], True)\\n # setting the precision\\n epsilon = 0.02 * perim\\n # approximating the contour with a polygon\\n approxCorners = cv2.approxPolyDP(contours[0], epsilon, True)\\n # check how many vertices has the approximate polygon\\n approxCornersNumber = len(approxCorners)\\n\\n for corners in approxCorners:\\n cv2.circle(trapezoid_data, (corners[0], corners[1]), radius=10, color=(255, 255, 255), thickness=-1)\\n cv2.imshow(\\\"Vertex position\\\", trapezoid_data)\\n cv2.waitKey()\\n cv2.destroyWindow(\\\"Vertex position\\\")\\n cv2.imshow(\\\"linesEdges\\\", edges)\\n cv2.imshow(\\\"linesDetected\\\", copied_image_data)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"def getimgs():\",\n \"def main(self):\",\n \"def main():\\n parser = argparse.ArgumentParser(description=\\\"Extract images from a ROS bag.\\\")\\n parser.add_argument(\\\"bag_file\\\", help=\\\"Input ROS bag.\\\")\\n parser.add_argument(\\\"base_dir\\\", nargs='?', default=\\\"./dataset\\\", help=\\\"Output directory.\\\")\\n args = parser.parse_args()\\n bag = rosbag.Bag(args.bag_file, \\\"r\\\")\\n\\n print \\\"Extract topics from %s into %s\\\" %(args.bag_file, args.base_dir)\\n if not os.path.exists(os.path.join(args.base_dir,\\\"image_dvs/\\\")):\\n os.makedirs(os.path.join(args.base_dir,\\\"image_dvs\\\"), mode=0o777)\\n if not os.path.exists(os.path.join(args.base_dir,\\\"image_rgbd/\\\")):\\n os.makedirs(os.path.join(args.base_dir,\\\"image_rgbd\\\"), mode=0o777)\\n if not os.path.exists(os.path.join(args.base_dir,\\\"depth/\\\")):\\n os.makedirs(os.path.join(args.base_dir,\\\"depth\\\"), mode=0o777)\\n\\t\\n text_image_dvs = open(os.path.join(args.base_dir,\\\"image_dvs.txt\\\"), 'w')\\n text_image_rgbd = open(os.path.join(args.base_dir,\\\"image_rgbd.txt\\\"), 'w')\\n text_depth = open(os.path.join(args.base_dir,\\\"depth.txt\\\"), 'w')\\n text_events = open(os.path.join(args.base_dir,\\\"events.txt\\\"), 'w')\\n text_imu = open(os.path.join(args.base_dir,\\\"imu.txt\\\"), 'w')\\n text_gt_pose = open(os.path.join(args.base_dir,\\\"pose.txt\\\"), 'w')\\n\\n text_image_dvs.write(\\\"# DVS images\\\\n\\\")\\n text_image_dvs.write(\\\"# timestamp filename\\\\n\\\")\\n text_image_rgbd.write(\\\"# RGBD images\\\\n\\\")\\n text_image_rgbd.write(\\\"# timestamp filename\\\\n\\\")\\n text_depth.write(\\\"# RGBD depth\\\\n\\\")\\n text_depth.write(\\\"# timestamp filename\\\\n\\\")\\n text_events.write(\\\"# events\\\\n\\\")\\n text_events.write(\\\"# timestamp x y polarity\\\\n\\\")\\n text_imu.write(\\\"# imu\\\\n\\\")\\n text_imu.write(\\\"# acceleration gyroscope\\\\n\\\")\\n text_imu.write(\\\"# timestamp ax ay az gx gy gz\\\\n\\\")\\n text_gt_pose.write(\\\"# timestamp x y z qx qy qz qw\\\\n\\\")\\n \\n\\n for topic, msg, t in bag.read_messages(topics=[\\\"/camera/rgb/image_color\\\", \\\"/camera/depth_registered/image\\\", \\\"/dvs/events\\\", \\\"/dvs/image_raw\\\", \\\"/dvs/imu\\\", \\\"/vicon/\\\"]):\\n\\n if topic == \\\"/dvs/image_raw\\\":\\n save_image(msg, t, args.base_dir, \\\"image_dvs\\\", text_image_dvs)\\n elif topic == \\\"/camera/rgb/image_color\\\":\\n save_image(msg, t, args.base_dir, \\\"image_rgbd\\\", text_image_rgbd)\\n elif topic == \\\"/camera/depth_registered/image\\\":\\n save_depth(msg, t, args.base_dir, \\\"depth\\\", text_depth)\\n elif topic == \\\"/dvs/events\\\":\\n save_event(msg, text_events)\\n elif topic == \\\"/dvs/imu\\\":\\n save_imu(msg, t, text_imu)\\n elif topic == \\\"/vicon/\\\":\\n save_pose(msg, t, text_gt_pose)\\n\\n print \\\"\\\\rTime passed: %i.%09i [s]\\\" %(t.secs, t.nsecs),\\n\\n text_image_dvs.close()\\n text_image_rgbd.close()\\n text_depth.close()\\n text_events.close()\\n text_imu.close()\\n text_gt_pose.close()\\n bag.close()\\n\\n return\",\n \"def main():\\n pass\",\n \"def run(self):\\n if not self.config.galfile_pixelized:\\n raise ValueError(\\\"Code only runs with pixelized galfile.\\\")\\n\\n self.config.check_files(check_zredfile=False, check_bkgfile=True, check_bkgfile_components=False, check_parfile=True, check_zlambdafile=True)\\n\\n # Compute the border size\\n\\n self.config.border = self.config.compute_border()\\n\\n self.config.d.hpix = [self.pixel]\\n self.config.d.nside = self.nside\\n self.config.d.outbase = '%s_%d_%05d' % (self.config.outbase, self.nside, self.pixel)\\n\\n # Do the run\\n self.config.start_file_logging()\\n\\n self.config.logger.info(\\\"Running runcat on pixel %d\\\" % (self.pixel))\\n\\n runcat = RunCatalog(self.config)\\n if not os.path.isfile(runcat.filename):\\n runcat.run(do_percolation_masking=self.config.runcat_percolation_masking)\\n runcat.output(savemembers=True, withversion=True)\\n\\n self.config.stop_file_logging()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7724663","0.7417523","0.72567064","0.7056425","0.6892278","0.67639","0.66935354","0.6682261","0.66802955","0.66739655","0.6670762","0.6670762","0.6670762","0.6636167","0.65970665","0.6594695","0.65808475","0.657597","0.6567467","0.6554589","0.6533119","0.65239656","0.65158015","0.6487369","0.64368385","0.64349025","0.6423539","0.6417072","0.64064765","0.6404944","0.63934755","0.63688093","0.6350036","0.6339439","0.6327485","0.63134307","0.62936187","0.6293564","0.6280804","0.6272605","0.6264456","0.6256006","0.62456983","0.6239172","0.6236754","0.62226486","0.6218445","0.62096477","0.6199852","0.61936474","0.61936474","0.61936474","0.61936474","0.61936474","0.61887115","0.6177249","0.6174861","0.61557484","0.61546755","0.61480683","0.6138048","0.61342853","0.61153144","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6114893","0.6109357","0.6109357","0.61082363","0.61026406","0.6099821","0.6091504","0.60830796","0.60825175","0.60809773","0.6077184","0.6074389","0.60378975","0.6036458","0.6014074","0.60082203"],"string":"[\n \"0.7724663\",\n \"0.7417523\",\n \"0.72567064\",\n \"0.7056425\",\n \"0.6892278\",\n \"0.67639\",\n \"0.66935354\",\n \"0.6682261\",\n \"0.66802955\",\n \"0.66739655\",\n \"0.6670762\",\n \"0.6670762\",\n \"0.6670762\",\n \"0.6636167\",\n \"0.65970665\",\n \"0.6594695\",\n \"0.65808475\",\n \"0.657597\",\n \"0.6567467\",\n \"0.6554589\",\n \"0.6533119\",\n \"0.65239656\",\n \"0.65158015\",\n \"0.6487369\",\n \"0.64368385\",\n \"0.64349025\",\n \"0.6423539\",\n \"0.6417072\",\n \"0.64064765\",\n \"0.6404944\",\n \"0.63934755\",\n \"0.63688093\",\n \"0.6350036\",\n \"0.6339439\",\n \"0.6327485\",\n \"0.63134307\",\n \"0.62936187\",\n \"0.6293564\",\n \"0.6280804\",\n \"0.6272605\",\n \"0.6264456\",\n \"0.6256006\",\n \"0.62456983\",\n \"0.6239172\",\n \"0.6236754\",\n \"0.62226486\",\n \"0.6218445\",\n \"0.62096477\",\n \"0.6199852\",\n \"0.61936474\",\n \"0.61936474\",\n \"0.61936474\",\n \"0.61936474\",\n \"0.61936474\",\n \"0.61887115\",\n \"0.6177249\",\n \"0.6174861\",\n \"0.61557484\",\n \"0.61546755\",\n \"0.61480683\",\n \"0.6138048\",\n \"0.61342853\",\n \"0.61153144\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6114893\",\n \"0.6109357\",\n \"0.6109357\",\n \"0.61082363\",\n \"0.61026406\",\n \"0.6099821\",\n \"0.6091504\",\n \"0.60830796\",\n \"0.60825175\",\n \"0.60809773\",\n \"0.6077184\",\n \"0.6074389\",\n \"0.60378975\",\n \"0.6036458\",\n \"0.6014074\",\n \"0.60082203\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65013987"},"document_rank":{"kind":"string","value":"23"}}},{"rowIdx":93767,"cells":{"query":{"kind":"string","value":"Detect posedge or negedge on input signal, default to posedge(mode=0)"},"document":{"kind":"string","value":"def edge_detect(\n clk,\n rst_n,\n din,\n valid,\n mode=0\n):\n\n din_r = UBool(0)\n\n @always_ff(clk.posedge, reset=rst_n)\n def _latch():\n din_r.next = din\n\n # TODO: conditional parameter on generate\n @always_comb\n def _valid():\n if mode == 0:\n valid.next = not din and din_r\n else:\n valid.next = din and not din_r\n\n return instances()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sensed(self):\n return not self.pin.read() if self.pinishigh else self.pin.read()","async def handle_less(message: types.Message):\n await handle_change_threshold(message, 1 / 1.5)","def nearest_test_pulse(self):","def is_ringing(self) -> bool:","def forward(self, input):\r\n return np.maximum(0,input)","def signal(x):\r\n if x >= 0.0:\r\n return 1.0\r\n return -1.0","def lefton(hedge, point):\r\n\r\n return area2(hedge, point) >= 0","def is_over(self, state):\n return state.current_total == 0","def is_weak(self):\n\n # set the minimum number of keypooints\n keypoint_threshold = 20 if self.detector_method == 'FAST'\\\n else 5\n \n # check if the tracker has less than minimum keypoints to track\n c1 = self.old_points.shape[0] < keypoint_threshold\n \n x,y,w,h = self.bounding_box\n row, col = self.fg_mask.shape\n \n # check if the window is out of the frame\n c2 = x >= col-1 or x < 0\n c3 = y >= row-1 or y < 0\n c4 = x+w >= col-1\n c5 = y+h >= row-1\n \n return c1+c2+c3+c4+c5","def propagation(self, other):\n if self.infected_pop >= 1 and other.infected_pop == 0:\n if random.random() <= self.infected_ratio()/10:\n other.infected_pop = 1","def signal_kernel(self, signum: int):","def relu(self):\n return self * self.ge(0)","def __call__(self, *args, **kwargs):\n out = super().__call__(*args, **kwargs)\n out **= 2\n self._debug.append(out)\n out = int(out > self.threshold_value)\n if out and self.since_last_peak > self._num_of_taps:\n self.since_last_peak = -1\n else:\n out = 0\n self.since_last_peak += 1\n return out","def ensure_low(motion_out, motion_in):\n while (GPIO.input(motion_out) == 1) or (GPIO.input(motion_in) == 1):\n # Determines if either of the two motion sensors are high/triggered.\n time.sleep(0.1)\n # Delay by 100ms.","def nopupgain_fn(self, X, Xp):\n Y = self.theta[0, 0] + self.theta[0, 1] * X\n return(Y)","def input_signal(self, signal: np.ndarray):\n # Algorithm 3.4 (2)\n signal = self.__check_signal(signal)\n # keep track of number of signals input so far\n self.num_signal += 1\n # Store the input signal?\n self.sigs.append(signal)\n\n # Algorithm 3.4 (1)\n # if number of nodes is smaller than 2, set the input signal as a node.\n if len(self.nodes) < 2:\n self.__add_node(signal)\n return\n\n # Algorithm 3.4 (3)\n winner, dists = self.__find_nearest_nodes(2, signal)\n sim_thresholds = self.__calculate_similarity_thresholds(winner)\n # signal is a new pattern \n if dists[0] > sim_thresholds[0] or dists[1] > sim_thresholds[1]:\n self.__add_node(signal)\n else:\n # Algorithm 3.4 (4)\n self.__increment_edge_ages(winner[0])\n # Algorithm 3.4 (5)\n need_add_edge, need_combine = self.__need_add_edge(winner)\n if need_add_edge:\n # print(\"add edge\")\n # Algorithm 3.4 (5)(a)\n self.__add_edge(winner)\n else:\n # Algorithm 3.4 (5)(b)\n self.__remove_edge_from_adjacent_mat(winner)\n # Algorithm 3.4 (5)(a) need to combine subclasses\n if need_combine:\n self.__combine_subclass(winner)\n # Algorithm 3.4 (6) checked, maybe fixed problem N\n self.__update_density(winner[0])\n # Algorithm 3.4 (7) is embedded in func __update_density()\n # Algorithm 3.4 (8) (a)\n self.__update_winner(winner[0], signal)\n # Algorithm 3.4 (8) (b)\n self.__update_adjacent_nodes(winner[0], signal)\n\n # Algorithm 3.4 (9)\n self.__remove_old_edges()\n\n # Algorithm 3.4 (10)\n if self.num_signal % self.iteration_threshold == 0 and self.num_signal > 1:\n # print(self.won)\n # update self.N based on self.won\n for i in range(len(self.won)):\n if self.won[i]:\n self.N[i] += 1\n for i in range(len(self.won)):\n self.won[i] = False\n print(\"Input signal amount:\", self.num_signal, \"nodes amount:\", len(self.nodes))\n self.__separate_subclass()\n self.__delete_noise_nodes()\n self.total_loop += 1 # ?\n # algo 3.4(11)?\n self.__classify()\n # plot\n # threading.Thread(self.plot_NN())\n \n # clear signals\n self.sigs.clear()","def calibrate_nopp(fundamental):\n thresh = 0.09682\n m1 = -0.1014\n b1 = 0.2839\n m2 = -0.065\n b2 = 0.21\n state1 = (fundamental < thresh)\n\n cal_fun = copy.copy(fundamental)\n cal_fun[state1] = 10**((fundamental[state1] - b1) / m1)\n cal_fun[~state1] = 10**((fundamental[~state1] - b2) / m2)\n return cal_fun","def __ge__(self, *args):\n return _ida_frame.stkpnt_t___ge__(self, *args)","def oppdater(self, input):\n return","def not_pushed_down(self):\n return (self.genus >= 2 and self.n != 0) or (self.genus == 1 and self.n > 1) or (self.genus == 0 and self.n > 3)","def is_opgepakt(self) -> bool:\n return not GPIO.input(self._afstandsensor_input_pin)","def min_power_out_storage_rule(_m, g, y, s, t):\r\n\r\n return - m.p_out[g, y, s, t] <= 0","def ge(self, y):\n return 1 - self.lt(y)","def motion_edge(self, channel):\n\n # read the input to determine which edge\n # do a little software debounce and double-check the value\n\n # is the detector indicating motion?\n if GPIO.input(self.motion.io_pin) == True:\n self.logging.debug(\"Rising edge detected on %s\" %channel)\n \n # was the last look indicating no motion (i.e. this is a clean transition)\n if self.motion.value == False:\n self.motion.pvalue = self.motion.value\n self.motion.value = True\n # if we were already in a \"motion=yes\" state, must have been noise\n else:\n self.logging.debug(\"Noise\")\n\n # edge change direction indicated : back to no motion\n else:\n self.logging.debug(\"Falling edge on %s\" %channel)\n # clean transition\n if self.motion.value == True:\n self.motion.pvalue = self.motion.value\n self.motion.value = False\n # noise\n else:\n self.logging.debug(\"Noise\")","def _get_next_hop_unchanged(self):\n return self.__next_hop_unchanged","def isHittingLow(self):\n return not self.limLow.get()","def __ge__(self, *args):\n return _ida_hexrays.cswitch_t___ge__(self, *args)","def payoff_nopool(p=0.06,\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\n E_nopool = MLB_contract*p + minor_contract * (1-p)\n E_nopool_var = (MLB_contract - E_nopool)**2 * p + (minor_contract - E_nopool)**2 * (1-p)\n return E_nopool, E_nopool_var**0.5","def lower_cb(self,p):\n avg_elo=self.elo0+self.elo1\n delta=self.elo1-self.elo0\n N=30\n# Various error conditions must be handled better here!\n while True:\n Elo0=avg_elo-N*delta\n Elo1=avg_elo+N*delta\n sol=brentq(lambda elo:self.outcome_prob(elo)-(1-p),Elo0,Elo1)\n if sol['msg']=='no bracket':\n N*=2\n continue\n break\n return sol['x0']","def __ge__(self, other):\n return self.abs2phy.__ge__(other)","def __ge__(self, other):\n return self.abs2phy.__ge__(other)","def direc_detect(motion_out, motion_in):\n if (GPIO.input(motion_out) == 1):\n # Determines if outside motion sensor is triggered first.\n init_trig(motion_in)\n # Wait atleast 4 seconds for the corresponding sensor to trigger.\n ensure_low(motion_out, motion_in)\n # Ensure that both motion sensors are low/are not triggered.\n\n if (GPIO.input(motion_in) == 1):\n # Determines if inside motion sensor is triggered first.\n init_trig(motion_out)\n # Wait atleast 4 seconds for the corresponding sensor to trigger.\n ensure_low(motion_out, motion_in)\n # Ensure that both motion sensors are low/are not triggered.","def edge_status(g, source, target):\n edge = source, target, g.graph['edge_key']\n if edge in g.graph['negatives']:\n status = 0\n elif edge in g.graph['positives']:\n status = 1\n else:\n status = int(g.has_edge(*edge)) + 2\n return status","def rbd1_koon() -> NonRepairableRBD:\n qp = 0.03\n qv = 0.01\n edges = [\n (\"source\", \"pump1\"),\n (\"source\", \"pump2\"),\n (\"pump1\", \"valve\"),\n (\"pump2\", \"valve\"),\n (\"valve\", \"sink\"),\n ]\n reliabilities = {\n \"pump1\": FixedEventProbability.from_params(qp),\n \"pump2\": FixedEventProbability.from_params(qp),\n \"valve\": FixedEventProbability.from_params(qv),\n }\n k = {\"valve\": 2}\n return NonRepairableRBD(edges, reliabilities, k)","def input_resistance(self):\n return None","def low(self):","def detect():\n\n _pass_done = 0\n _improve = True\n new_mod = modularity()\n cur_mod = -999999999.0\n rl = random.sample(range(0, node_count), node_count)\n while _improve & (_pass_done < max_pass) & (new_mod - cur_mod > min_mod):\n cur_mod = new_mod\n _improve = False\n _pass_done += 1\n for node_tmp in rl:\n n = node_tmp\n nc = bl[n]\n ncomm = neigh_comm(n)\n remove(n, nc, ncomm[nc])\n best_c = nc\n best_l = 0.0\n best_incre = 0.0\n for c in ncomm:\n incre = modularity_gain(n, c, ncomm[c])\n if incre > best_incre:\n best_incre = incre\n best_c = c\n best_l = ncomm[c]\n insert(n, best_c, best_l)\n if best_c != nc:\n _improve = True\n new_mod = modularity()\n print new_mod","def signal_qual(self):\n sig = None\n while sig is None:\n sig = self.acquire_signal_quality()\n time.sleep(0.5)\n return sig","def ghost_greedy(self,current,ghost,g_pos):\n x, y, pos ,(st,b),start,p_prob = g_pos[ghost]\n node = self.nodes_array[x][y].getNeighborByDirection(pos)\n if st > 0 and b and node is not None:\n st = max(st-1,0)\n return [(1,(node.i,node.j,pos,(st,not b),start,p_prob))]\n\n rpos = Actions.reverseDirection(pos)\n node = self.nodes_array[x][y]\n priority = [Directions.NORTH, Directions.WEST, Directions.SOUTH, Directions.EAST]\n node_neig = [node.up, node.left, node.down, node.right]\n prio = list()\n nodes = list()\n for i in range(len(node_neig)):\n if node_neig[i]:\n prio.append(priority[i])\n nodes.append((node_neig[i].i,node_neig[i].j))\n\n if len(prio) > 1 and rpos != Directions.STOP:\n for i in range(len(prio)):\n if prio[i] == rpos:\n del prio[i]\n del nodes[i]\n break\n\n arg = min if st == 0 else max\n dist_list = [manhattanDistance(pos, (current.i,current.j)) for pos in nodes]\n ptr_val = arg(dist_list)\n pos = []\n st = st - 1 if st - 1 > 0 else 0\n for i in range(len(dist_list)):\n if dist_list[i] == ptr_val:\n if not st:\n pos.append((nodes[i][0],nodes[i][1],prio[i],(0,False),start,p_prob))\n else:\n pos.append((x,y,prio[i],(st,True),start,p_prob))\n\n pos_p = 1./len(pos)\n return [(pos_p,possibility) for possibility in pos]","def was_used(self):\r\n return self.circ_chosen != 0","def do_not_enter_sign_detection(img_in):\n img_in = img_in.copy()\n img = red_masking(img_in)\n # display_img(img, 'DNE_RED')\n img = process_base_image(img, (7, 7))\n # Assumption made that a DNE sign will always have at least a\n # radius of 5.\n min_radius = 15\n max_radius = np.int(img.shape[1] / 2)\n circles = hough_circles(img, 1, 10, 30, 30, min_radius, max_radius)\n\n if circles is not None:\n circles = np.uint16(np.around(circles))\n circles = circles[0, :]\n # since multiple circles might be found, the correct one\n circle_mid_colors = [pixel_color(img, x[0], x[1]) for x in circles]\n valid_idx = circle_mid_colors.index(np.max(circle_mid_colors))\n the_sign = circles[valid_idx]\n else:\n return None, None\n\n output = (the_sign[0], the_sign[1])\n return output","def detect_crossover(self):\n\t\n\n\t\tif self.trend == 0:\n\t\t\tif self.averages['fast'] < self.averages['slow']:\n\t\t\t\tself.trend = -1\n\t\t\telif self.averages['fast'] >= self.averages['slow']:\n\t\t\t\tself.trend = 1\n\t\telse: \n\t\t\tif self.trend == -1 and self.averages['fast'] >= self.averages['slow']:\n\t\t\t\tself.trend = 1\n\t\t\t\treturn {'action': 0, 'sType': self.strategyType}\n\t\t\telif self.trend == 1 and self.averages['fast'] <= self.averages['slow']:\n\t\t\t\tself.trend = -1\n\t\t\t\treturn {'action': 1, 'sType': self.strategyType}\n\t\t\telse:\n\t\t\t\treturn None # no crossover","def isFusion(event,buff):\n index,diff,label = event\n label = label[0]\n if diff>0:\n return False,[]\n img_before = np.copy(buff[:,:,index-1])\n img_after = np.copy(buff[:,:,index])\n mask_before = (img_before==label).astype(np.uint8)\n nb_elts_before = np.amax(img_before)\n kernel = np.ones((7,7),np.uint8)\n neighbouring_mask = cv2.dilate(mask_before,kernel,iterations=8)\n\n new_map = np.multiply(img_before,neighbouring_mask.astype(np.uint8))\n \n #Removing the element we are currently looking at\n new_map[img_before==label]=0\n possible_candidates = []\n for i in range(nb_elts_before):\n if np.any(new_map==i+1):\n possible_candidates.append(i+1)\n #Computes the area of the cells and compares them\n size_cell_disappearing = np.count_nonzero(img_before==label)\n match = [] #lists the ratios sizeAfter/sizeBefore for possible matches\n \n for vals in possible_candidates:\n size_other_cell = np.count_nonzero(img_before==vals)\n size_before = size_other_cell+size_cell_disappearing\n size_after = np.count_nonzero(img_after==vals)\n ratio = float(size_after)/float(size_before)\n if ratio>0.8 and ratio<1.2:\n match.append((vals,abs(1-ratio)))\n if len(match)==0:\n return False,[]\n if len(match)>1:\n #Several matches, so pick the best\n values = [y for x,y in match]\n result_label,osef = match[np.argmin(values)]\n else:\n result_label, osef = match[0]\n return True,result_label","def test_soft_threshold():\n assert snet.soft_threshold(10, 100) == 0\n assert snet.soft_threshold(-10, 100) == 0\n assert snet.soft_threshold(10, 3) == 7\n assert snet.soft_threshold(-10, 3) == -7","def isRoundOver(self):\n pass","def is_on_the_edge(self):\r\n if self.for_color == 1:\r\n new_sum = np.count_nonzero(self.result_board.state[:, 0] == 1) + np.count_nonzero(\r\n self.result_board.state[:, 4] == 1) + np.count_nonzero(self.result_board.state[0, 1:4] == 1) +\\\r\n np.count_nonzero(self.result_board.state[4, 1:4] == 1)\r\n else:\r\n new_sum = np.count_nonzero(self.result_board.state[:, 0] == 2) + np.count_nonzero(\r\n self.result_board.state[:, 4] == 2) + np.count_nonzero(\r\n self.result_board.state[0, 1:4] == 2) + np.count_nonzero(\r\n self.result_board.state[4, 1:4] == 2)\r\n\r\n self.priority += (-new_sum) * 0.1","def get_high_one(self, state):\n non_zero_idxs = [-2, -3, -4]\n idx_idxs = np.random.randint(low=0, high=3, size=10)\n for idx_idx in idx_idxs:\n non_zero_idx = non_zero_idxs[idx_idx]\n if self.potential(state) + self.weights[non_zero_idx] <= self.initial_potential:\n state[non_zero_idx] += 1\n break\n return state","def reward_threshold(self) -> Optional[float]:","def pruning_algorithm(self):\n # traverse tree to get conditional likelihood estimate at root.\n self.set_qmat()\n for node in self.tree.treenode.traverse(\"postorder\"):\n if not node.is_leaf(): \n self.node_conditional_likelihood(node)\n logger.debug(\n f\"node={node.idx}; likelihood=[{node.likelihood[0]:.6f}, {node.likelihood[1]:.6f}]\") \n\n # multiply root prior times the conditional likelihood at root\n root = self.tree.treenode\n lik = (\n (1 - self.prior_root_is_1) * root.likelihood[0] + \n self.prior_root_is_1 * root.likelihood[1]\n )\n return lik","def decreaseSearch(self):\n\n self.detections = self.detections()\n self.detections.location = self.location\n for index, row in enumerate(self.magdata):\n\n if row[3] < self.upper*self.background and row[3] < self.background - 2*self.std:\n # less than 50% of background and 2 std\n #print \"Large decrease (less than \" + str(self.upper*self.background) + \")\", row[3]\n self.detections.largeDecrease.append([index,self.timestamps[index], row[3]])\n \n elif row[3] < self.lower*self.background and row[3] < self.background - self.std:\n # less than 25% of background and 1 std\n #print \"Decrease (less than \" + str(self.lower*self.background) + \")\", row[3]\n self.detections.smallDecrease.append([index,self.timestamps[index], row[3]])\n \n elif row[3] > (1 + self.upper)*self.background and row[3] > self.background + 2*self.std:\n # greater than 50% of background\n #print \"Large increase (greater than \" + str((1 + self.upper)*self.background) + \")\", row[3]\n self.detections.largeIncrease.append([index,self.timestamps[index], row[3]])\n \n elif row[3] > (1 + self.lower)*self.background and row[3] > self.background + self.std:\n # greater than 25% of background\n #print \"Increase (greater than \" + str((1 + self.lower)*self.background) + \")\", row[3]\n self.detections.smallIncrease.append([index,self.timestamps[index], row[3]])\n\n \n if resultCounter(self.detections) == 0:\n self.detections.results.append(None)\n print \"No observable jumps/dips in magnetic field strength\"\n else:\n for attribute, bins in classIterator(self.detections):\n if 'crease' in attribute:\n self.detections.results.append('Number of ' + attribute + ': ' + str(len(bins)))","def _gain(self):\n return None","def game_over(self):\n raise NotImplementedError(\"Abstract method\") # no mercy for stooges","def RecursiveLowPassFast(signal, coeff, self):\n # Creates running mean value of the input\n ml = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], signal) \n # Plot Running threshold value at the current plot\n self.p1.plot(self.t, ml, pen=pg.mkPen(color=(246, 178, 255), width=3))\n\n # Creates running square deviation from the mean\n vl = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], np.square(signal - ml))\n # Creates \"threshold line\". If current value < sl[i] -> i belongs to event. \n sl = ml - coeff['S'] * np.sqrt(vl)\n self.p1.plot(self.t, sl, pen=pg.mkPen(color=(173, 27, 183), width=3))\n # Finds the length of the initial signal\n Ni = len(signal)\n # Finds those points where signal less than \"threshold line\"\n points = np.array(np.where(signal<=sl)[0])\n to_pop=np.array([]) # Empty supplementary array for finding adjacent points \n # For loop for finding adjacent points \n for i in range(1,len(points)):\n if points[i] - points[i - 1] == 1:\n to_pop=np.append(to_pop, i)\n # Points contain only border points of events\n points = np.delete(points, to_pop)\n # Empty list for Event location storage\n RoughEventLocations = []\n NumberOfEvents=0 #Number of events\n\n # For Loop for finding separating edges of different events and satisfying Event length limits\n for i in points:\n if NumberOfEvents is not 0:\n if i >= RoughEventLocations[NumberOfEvents-1][0] and i <= RoughEventLocations[NumberOfEvents-1][1]:\n continue\n NumberOfEvents += 1\n start = i\n El = ml[i] - coeff['E'] * np.sqrt(vl[i])\n Mm = ml[i]\n Vv = vl[i]\n duration = 0\n while signal[i + 1] < El and i < (Ni - 2) and duration < coeff['eventlengthLimit']:\n duration += 1\n i += 1\n if duration >= coeff['eventlengthLimit'] or i > (Ni - 10):\n NumberOfEvents -= 1\n else:\n k = start\n while signal[k] < Mm and k > 1:\n k -= 1\n start = k - 1\n k2 = i + 1\n while signal[k2] > Mm:\n k2 -= 1\n endp = k2\n if start<0:\n start=0\n RoughEventLocations.append((start, endp, ml[start], vl[start]))\n\n return np.array(RoughEventLocations)","def false_pos(yt, yp) -> Any:\n from keras import backend as K\n return K.sum(K.cast(yp * (1 - yt) > 0.5, 'float')) / K.maximum(1.0, K.sum(1 - yt))","def traffic_sign_detection_noisy(img_in):\n filter_size = 5\n filter_sigma = 4\n filter_kernel = cv2.getGaussianKernel(filter_size, filter_sigma)\n filter_kernel = filter_kernel * filter_kernel.T\n img_in = cv2.filter2D(img_in, -1, filter_kernel)\n\n thresh1 = 110\n thresh2 = 60\n cannyEdges = cv2.Canny(img_in, thresh1, thresh2)\n # cv2.imshow('smoothed_img', img_in)\n # cv2.imshow('cannyEdges', cannyEdges)\n\n DetectedObj = traffic_sign_detection(img_in)\n\n return DetectedObj\n raise NotImplementedError","def get_all_crossing_threshold(sig, thresh, front, use_numexpr = False):\n sig1 = sig[:-1]\n sig2 = sig[1:]\n if use_numexpr:\n if front == '+':\n pos_spike, = np.where(numexpr.evaluate( '(sig1<=thresh) & (sig2>thresh)'))\n elif front == '-':\n pos_spike, = np.where(numexpr.evaluate( '(sig1>=thresh) & (sig2<thresh)'))\n else :\n if front == '+':\n pos_spike, = np.where( (sig1 <= thresh) & ( sig2>thresh) )\n elif front == '-':\n pos_spike, = np.where( (sig1 >= thresh) & ( sig2<thresh) )\n return pos_spike+1","def roi_ko(self):\n self.pressure_img.mask = self.pressure_img.previous_roi","def __ge__(self, other: Event) -> bool:\n return not self.__lt__(other)","def motion_ge(input_line, cur, count):\n # \"ge is just \"w\" on inverted data (e.g. \"olleH\" instead of \"Hello\").\n pos_inv = motion_w(input_line[::-1], len(input_line) - cur - 1, count)[0]\n pos = len(input_line) - pos_inv - 1\n return pos, True, False","def signal_kernel(self, signum):\n pass","def onImageEdge(self,tolerance=1):\n # this has to be one to deal with blob library weirdness that goes deep down to opencv\n return ( self.distanceToNearestEdge() <= tolerance )","def apply_thresholding(x):\n return x > threshold_otsu(x)","def flag_resting(sensor, varname, N_kernel=20, order=1,\n prior_mask=None, reference=\"max\", delta_threshold=0.02):\n values = sensor.data[varname]\n time = sensor.data[\"time\"]\n if prior_mask is not None:\n fit_values = values[prior_mask]\n fit_time = time[prior_mask]\n else:\n fit_values = values\n fit_time = time\n\n if reference == \"g\":\n delta = np.abs(fit_values - g_constant)\n ref_val = g_constant\n elif reference == \"max\":\n p = np.polyfit(fit_time, fit_values, order)\n poly = np.poly1d(p)\n delta = np.abs(values - poly(time))\n ref_val = np.max(np.abs(values))\n else:\n raise ValueError(\"reference =\", reference, \"is not supported!\")\n\n mask = delta < delta_threshold*ref_val\n\n if prior_mask is not None:\n mask = np.logical_and(mask, prior_mask)\n\n kernel = np.ones(N_kernel)/N_kernel\n conv = np.convolve(mask, kernel, mode=\"same\")\n mask_resting = conv > 0.5\n mask_moving = conv <= 0.5\n\n sensor.data[f\"mask_{varname}_resting\"] = mask_resting\n sensor.data[f\"mask_{varname}_moving\"] = mask_moving\n\n find_resting_edge(sensor)","def check(self):\n if (sum(self.state) == 0):\n return -1\n elif (self.state[-1] >= 1):\n return 1\n else:\n return 0","def top_is_pointwise(self):\n return False","def __ne__(*args, **kwargs):\n return _gdi_.Pen___ne__(*args, **kwargs)","def isLow(self):\n\t\treturn self.resolution == 'LOW'","def switch_to_no_feedforward_inputs(self):\n\n self.h_e=np.ones_like(self.inputs.noise_flat.T)*self.feed_forward_off_value\n self.h=np.vstack([self.h_e,self.h_i])","def _preprocess_state(self, state):\r\n if np.isinf(self.observation_space.low).any() or np.isinf(self.observation_space.high).any(): return state\r\n return (state - self.observation_space.low) /(self.observation_space.high)","def disaggregate_chunk(self, chunk):\n\n # An resistive element has active power equal to apparent power.\n # Checking power units.\n units = self.__physical_quantity(chunk)\n\n # EVENTS OUT OF THE CHUNK:\n # Delta values:\n column_name = 'diff_' + units[1]\n chunk[column_name] = chunk.loc[:, units].diff()\n\n # Filter the noise.\n chunk['onpower'] = (chunk[column_name] > self.powerNoise)\n chunk['offpower'] = (chunk[column_name] < -self.powerNoise)\n events = chunk[(chunk.onpower == True) | (chunk.offpower == True)]\n\n detection_list = []\n singleOnevent = 0\n # Max Likelihood algorithm (optimized):\n for onevent in events[events.onpower == True].iterrows():\n # onTime = onevent[0]\n # deltaOn = onevent[1][1]\n # windowning:\n offevents = events[(events.offpower == True) & (events.index > onevent[0]) & (\n events.index < onevent[0] + timedelta(seconds=self.timeWindow))]\n # Filter paired events:\n offevents = offevents[\n abs(onevent[1][1] - offevents[column_name].abs()) < self.powerPair]\n\n # Max likelihood computation:\n if not offevents.empty:\n # pon = self.__pdf(self.onpower, onevent[1][1])\n for offevent in offevents.iterrows():\n # offTime = offevent[0]\n # deltaOff = offevent[1][1]\n # poff = self.__pdf(self.offpower, offevent[1][1])\n # duration = offevent[0] - onTime\n # pduration = self.__pdf(self.duration, (offevent[0] - onTime).total_seconds())\n likelihood = self.__pdf(self.onpower, onevent[1][1]) * \\\n self.__pdf(self.offpower, offevent[1][1]) * \\\n self.__pdf(self.duration, (offevent[0] - \\\n onevent[0]).total_seconds())\n detection_list.append(\n {'likelihood': likelihood, 'onTime': onevent[0], \n 'offTime': offevent[0], 'deltaOn': onevent[1][1]})\n else:\n singleOnevent += 1\n\n # Passing detections to a pandas.DataFrame\n detections = pd.DataFrame(\n columns=('onTime', 'offTime', 'likelihood', 'deltaOn'))\n\n for i in range(len(detection_list)):\n detections.loc[i] = [detection_list[i]['onTime'], detection_list[i][\n 'offTime'], detection_list[i]['likelihood'], detection_list[i]['deltaOn']]\n\n detections = detections[detections.likelihood >= self.thLikelihood]\n\n # Constructing dis_chunk (power of disaggregated appliance)\n dis_chunk = pd.DataFrame(\n index=chunk.index, columns=[str(units[0]) + '_' + str(units[1])])\n dis_chunk.fillna(0, inplace=True)\n\n # Ruling out overlapped detecttions ordering by likelihood value.\n detections = detections.sort('likelihood', ascending=False)\n for row in detections.iterrows():\n # onTime = row[1][0] offTime = row[1][1] deltaOn = row[1][3]\n #import ipdb\n #ipdb.set_trace()\n if ((dis_chunk[(dis_chunk.index >= row[1][0]) &\n (dis_chunk.index < row[1][1])].sum().values[0]) == 0):\n # delta = chunk[chunk.index == onTime][column_name].values[0]\n dis_chunk[(dis_chunk.index >= row[1][0]) & (\n dis_chunk.index < row[1][1])] = row[1][3]\n\n # Stat information:\n print(str(len(events)) + \" events found.\")\n print(str(len(events[events.onpower == True])) + \" onEvents found\")\n print(str(singleOnevent) + \" onEvents no paired.\")\n\n return dis_chunk","def ground_min(self):\n\n def compare(e):\n if is_wire(e):\n return e.potential\n else:\n return float('inf')\n self.move_ground(min(self.elements, key=compare))","def unconstrain_positive(self):\n self.unconstrain(Logexp())","def simple_hoc(signal, **kwargs):\n X = np.int_(signal >= 0)\n return np.sum(np.abs(X[1:] - X[:-1]))","def has_increased_significantly(old, new, sig_fig=10**(-4)):\n return(new > old and np.log10(1.-old/new) > -sig_fig)","def __ge__(self, *args):\n return _ida_hexrays.cwhile_t___ge__(self, *args)","def terminal_test(self, state):\n if state.moves:\n return 0\n return np.inf if state.to_move == \"B\" else -np.inf","def evaporatePheromones(self):\n for u in self.graph.nodes:\n for v in self.graph.neighbors(u):\n if self.graph_data[u]['pheromones'][v] > 0 :\n self.graph_data[u]['pheromones'][v] -= self.parameters['evaporation_rate'] * self.graph_data[u]['pheromones'][v]\n self.graph_data[v]['pheromones'][u] -= self.parameters['evaporation_rate'] * self.graph_data[v]['pheromones'][u]","def signal_vwap(self):\n pass","def is_ring(self): # -> bool:\n ...","def notOnImageEdge(self,tolerance=1):\n\n # this has to be one to deal with blob library weirdness that goes deep down to opencv\n return ( self.distanceToNearestEdge() > tolerance )","def autothreshold(gray_im, method=\"otsu\"):\n if method == \"otsu\":\n t = otsu(gray_im)\n elif method == \"kmeans\":\n t = ave(kmeans(list(gray_im.getdata())))\n return gray_im.point(lambda x: 0 if x < t else 255) # < or <= ?","def termination_heuristics(self, state):\n\n point_state = state[0][0]\n target_mask = get_target_mask(point_state)\n point_state = point_state[:3, target_mask].T\n depth_heuristics = self.graspnet_closure(point_state) \n if (depth_heuristics):\n print('object inside gripper? start retracting...')\n return depth_heuristics","def policy_improvement(self):\n\n # Set max value to terminal states in order to force actions to them.\n self.V[self.get_index(*self.environment.terminal_state)] = np.max(self.V) + 1\n\n policy_stable = True # Used to check if the Policy has changed or not in this step\n\n for state in range(len(self.V)):\n old_actions_probs = np.copy(self.policy.state_actions_probs[state])\n\n if self.environment.is_wall(*self.get_position(state)):\n self.policy.state_actions_probs[state] = [0, 0, 0, 0]\n else:\n self.policy.state_actions_probs[state] = self.get_greedy_actions(state)\n\n if not np.array_equal(old_actions_probs, self.policy.state_actions_probs[state]):\n policy_stable = False\n\n return self.policy, policy_stable","def __neg__(self):\n # Manually memoize to take advantage of the fact that this is an\n # involution\n try:\n return self._neg\n except AttributeError:\n self._neg = type(self)(self.data.__neg__(), self.bset)\n self._neg._neg = self # Involution at work\n return self._neg","def __lt__(self, event):\n\t\treturn True","def autorange(xvar, sig, gwin=7, swin=None, win=30,\n on_mult=(1.5, 1.), off_mult=(1., 1.5),\n nbin=10, transform='log', thresh=None):\n failed = []\n sig = np.asanyarray(sig)\n\n # smooth signal\n if swin is not None:\n sigs = fastsmooth(sig, swin)\n else:\n sigs = sig\n\n # transform signal\n if transform == 'log':\n tsigs = log_nozero(sigs)\n else:\n tsigs = sigs\n\n if thresh is None:\n if tsigs.ndim == 1:\n scale = False\n tsigs = tsigs.reshape(-1, 1)\n else:\n scale = True\n fsig = separate_signal(tsigs, scaleX=scale).astype(bool)\n else:\n if transform == 'log':\n thresh = np.log(thresh)\n fsig = tsigs > thresh\n fsig[0] = False # the first value must always be background\n fbkg = ~fsig\n\n # remove transitions by fitting a gaussian to the gradients of\n # each transition\n\n # 1. determine the approximate index of each transition\n zeros = bool_2_indices(fsig)\n \n if zeros is not None:\n zeros = zeros.flatten()\n if sigs.ndim > 1:\n sigs = sigs.sum(axis=1)\n\n # 2. calculate the absolute gradient of the target trace.\n grad = abs(fastgrad(sigs, gwin)) # gradient of untransformed data.\n\n for z in zeros: # for each approximate transition\n # isolate the data around the transition\n if z - win < 0:\n lo = gwin // 2\n hi = int(z + win)\n elif z + win > (len(sig) - gwin // 2):\n lo = int(z - win)\n hi = len(sig) - gwin // 2\n else:\n lo = int(z - win)\n hi = int(z + win)\n\n xs = xvar[lo:hi]\n ys = grad[lo:hi]\n\n # determine type of transition (on/off)\n mid = (hi + lo) // 2\n tp = sigs[mid + 3] > sigs[mid - 3] # True if 'on' transition.\n\n # fit a gaussian to the first derivative of each\n # transition. Initial guess parameters:\n # - A: maximum gradient in data\n # - mu: c\n # - width: 2 * time step\n # The 'sigma' parameter of curve_fit:\n # This weights the fit by distance from c - i.e. data closer\n # to c are more important in the fit than data further away\n # from c. This allows the function to fit the correct curve,\n # even if the data window has captured two independent\n # transitions (i.e. end of one ablation and start of next)\n # ablation are < win time steps apart).\n centre = xvar[z] # center of transition\n width = (xvar[1] - xvar[0]) * 2\n\n try:\n pg, _ = curve_fit(gauss, xs, ys,\n p0=(np.nanmax(ys),\n centre,\n width),\n sigma=(xs - centre)**2 + .01)\n # get the x positions when the fitted gaussian is at 'conf' of\n # maximum\n # determine transition FWHM\n fwhm = abs(2 * pg[-1] * np.sqrt(2 * np.log(2)))\n # apply on_mult or off_mult, as appropriate.\n if tp:\n lim = np.array([-fwhm, fwhm]) * on_mult + pg[1]\n else:\n lim = np.array([-fwhm, fwhm]) * off_mult + pg[1]\n\n fbkg[(xvar > lim[0]) & (xvar < lim[1])] = False\n fsig[(xvar > lim[0]) & (xvar < lim[1])] = False\n\n except RuntimeError:\n failed.append([centre, tp])\n pass\n\n ftrn = ~fbkg & ~fsig\n\n # if there are any failed transitions, exclude the mean transition width\n # either side of the failures\n if len(failed) > 0:\n trns = xvar[bool_2_indices(ftrn)]\n tr_mean = (trns[:, 1] - trns[:, 0]).mean() / 2\n for f, tp in failed:\n if tp:\n ind = (xvar >= f - tr_mean *\n on_mult[0]) & (xvar <= f + tr_mean * on_mult[0])\n else:\n ind = (xvar >= f - tr_mean *\n off_mult[0]) & (xvar <= f + tr_mean * off_mult[0])\n fsig[ind] = False\n fbkg[ind] = False\n ftrn[ind] = False\n\n return fbkg, fsig, ftrn, [f[0] for f in failed]","def getNextNodeUsingCellDiff(kGoalState):\n \n global fringe\n global solutions\n\n \n\n\n\n minNode = None\n minCost = 99999999999\n minNodeIndex = -1\n\n \n pnode = None\n pcost = None\n\n if len(solutions)>0 and solutions[0] != None:\n pnode = solutions[0];\n pcost = getHValueForNode(pnode,kGoalState)\n #print pnode, pcost\n # raw_input()\n \n\n\n\n for idx,node in enumerate(fringe):\n #get the heu. function values\n g_value = getHValueForNode(node,kGoalState)\n \n\n if g_value < minCost:\n minNode = node\n minNodeIndex = idx\n minCost = g_value\n\n\n fringe.pop(minNodeIndex)\n c = getHValueForNode(minNode,kGoalState)\n if pnode != None:\n if c > pcost:\n minNode = None\n \n return minNode","def __ge__(self, other: t.Any) -> bool:\n return self._op_bool('__ge__', other)","def evaluate(self) :\n for inp in self.inStates :\n if inp.getState() == 0 : return 0\n return 1","def calculate_signal(self):\n y = self.data.get_bar_values(self.pair[0], \"adj_close\", N=self.ols_window)\n x = self.data.get_bar_values(self.pair[1], \"adj_close\", N=self.ols_window)\n\n if y is not None and x is not None:\n if len(y) >= self.ols_window and len(x) >= self.ols_window:\n # get hedge ratio\n self.hedge_ratio = sm.OLS(y, x).fit().params[0]\n\n # get z score of residuals\n spread = y - self.hedge_ratio * x\n zscore_last = ((spread - spread.mean()) / spread.std())[-1]\n\n # calculate signals and add to events queue\n y_signal, x_signal = self.calculate_xy_signal(zscore_last)\n if y_signal is not None and x_signal is not None:\n self.events.put(y_signal)\n self.events.put(x_signal)","def total_outlier_flag(self):\r\n flagged = np.zeros(self.ndetects,np.int32)\r\n for nm, method in enumerate(self.outlier_methods):\r\n if hasattr(self.rec_track, method):\r\n flagged = np.maximum(flagged, self.rec_track[method])\r\n if 'flagged' in self.df_track.columns:\r\n self.df_track = self.df_track.drop(columns=['flagged'])\r\n ncol = len(self.df_track.columns)\r\n self.df_track.insert(ncol, 'flagged', flagged)\r\n self.rec_track = self.df_track.to_records()\r\n\r\n return","def is_onhold(self) -> bool:","def detectar_pico_doge():\n\n ultimos_precios = persistence.traer_ultimos_precios_doge()\n prev = float(ultimos_precios[0])\n now = float(ultimos_precios[1])\n porcentaje = 0\n\n porcentaje = calcular_porcentaje(prev, now)\n porcentaje = round(porcentaje, 2)\n if porcentaje > 1.5 or porcentaje < -1.5:\n return porcentaje\n return 0","def detection(input_state, detection_event):\n size = len(input_state)\n output_state = np.zeros((size,) * 4, dtype=complex)\n if detection_event == 'BOTH':\n for p1 in range(size):\n for p2 in range(size):\n for p3 in range(size):\n for p4 in range(size):\n if p1 is not 0 and p3 is not 0:\n output_state[p1, p2, p3, p4] = input_state[p1, p2, p3, p4]\n elif detection_event == 'NONE':\n for p1 in range(size):\n for p2 in range(size):\n for p3 in range(size):\n for p4 in range(size):\n if p1 is 0 and p3 is 0:\n output_state[p1, p2, p3, p4] = input_state[p1, p2, p3, p4]\n elif detection_event == 'FIRST':\n for p1 in range(size):\n for p2 in range(size):\n for p3 in range(size):\n for p4 in range(size):\n if p1 > 0 and p3 is 0:\n output_state[p1, p2, p3, p4] = input_state[p1, p2, p3, p4]\n elif detection_event == 'THIRD':\n for p1 in range(size):\n for p2 in range(size):\n for p3 in range(size):\n for p4 in range(size):\n if p1 is 0 and p3 is not 0:\n output_state[p1, p2, p3, p4] = input_state[p1, p2, p3, p4]\n else:\n raise ValueError('Wrong configuration')\n\n return output_state","def backward_elimination(X, y, sig_level=0.05):\n regressor_OLS = sm.OLS(y, X).fit()\n max_pvalue = max(regressor_OLS.pvalues).astype(float)\n if max_pvalue < sig_level:\n print(regressor_OLS.summary())\n return X\n else:\n max_pvalue_index = np.argmax(regressor_OLS.pvalues)\n X = np.delete(X, max_pvalue_index, axis=1)\n backward_elimination(X, y, sig_level)","def _opt_pol(self, *, s=None):\n\n if s is None:\n s = self._state\n\n rel_pos = s - self._mode_offset\n r1_pos = -self._mode_separation / 2\n r2_pos = -r1_pos\n\n r1_a = np.clip(r1_pos - rel_pos, -1, 1)\n r2_a = np.clip(r2_pos - rel_pos, -1, 1)\n\n if rel_pos < 0:\n # Target left peak\n return r1_a\n else:\n # Target right peak\n return r2_a","def alter_state(neuron, inp):\n # Refractory period (simplified)\n if neuron['state'] == 1:\n return 0\n # Firing threshold (simplified)\n elif inp >= 3:\n return 1\n # Random firing probability\n elif random() < random_fire_prob:\n return 1\n else:\n return 0","def pending_f(self, x_p):\n targetlabel_mask = torch.from_numpy(onehot_like(np.zeros(self.classnum), self.target))\n secondlargest_mask = torch.from_numpy(np.ones(self.classnum)) - targetlabel_mask\n targetlabel_mask = targetlabel_mask.to(self.device)\n secondlargest_mask = secondlargest_mask.to(self.device)\n\n Zx_i = np.max((self.model.get_logits(x_p).double().to(self.device) * secondlargest_mask).cpu().detach().numpy())\n Zx_t = np.max((self.model.get_logits(x_p).double().to(self.device) * targetlabel_mask).cpu().detach().numpy())\n\n if ( Zx_i - Zx_t < - self.confidence):\n return True\n else:\n return False","def __nonzero__(self):\n return _osgAnimation.mapVertexInfluence___nonzero__(self)","def __ge__(self, other):\r\n # self >= other\r\n s = (self - other).share[0]\r\n return type(self)((s >= 0, self.exponent_type(0)))","def y_overrun(self):\n return (self.status & 0x20) != 0"],"string":"[\n \"def sensed(self):\\n return not self.pin.read() if self.pinishigh else self.pin.read()\",\n \"async def handle_less(message: types.Message):\\n await handle_change_threshold(message, 1 / 1.5)\",\n \"def nearest_test_pulse(self):\",\n \"def is_ringing(self) -> bool:\",\n \"def forward(self, input):\\r\\n return np.maximum(0,input)\",\n \"def signal(x):\\r\\n if x >= 0.0:\\r\\n return 1.0\\r\\n return -1.0\",\n \"def lefton(hedge, point):\\r\\n\\r\\n return area2(hedge, point) >= 0\",\n \"def is_over(self, state):\\n return state.current_total == 0\",\n \"def is_weak(self):\\n\\n # set the minimum number of keypooints\\n keypoint_threshold = 20 if self.detector_method == 'FAST'\\\\\\n else 5\\n \\n # check if the tracker has less than minimum keypoints to track\\n c1 = self.old_points.shape[0] < keypoint_threshold\\n \\n x,y,w,h = self.bounding_box\\n row, col = self.fg_mask.shape\\n \\n # check if the window is out of the frame\\n c2 = x >= col-1 or x < 0\\n c3 = y >= row-1 or y < 0\\n c4 = x+w >= col-1\\n c5 = y+h >= row-1\\n \\n return c1+c2+c3+c4+c5\",\n \"def propagation(self, other):\\n if self.infected_pop >= 1 and other.infected_pop == 0:\\n if random.random() <= self.infected_ratio()/10:\\n other.infected_pop = 1\",\n \"def signal_kernel(self, signum: int):\",\n \"def relu(self):\\n return self * self.ge(0)\",\n \"def __call__(self, *args, **kwargs):\\n out = super().__call__(*args, **kwargs)\\n out **= 2\\n self._debug.append(out)\\n out = int(out > self.threshold_value)\\n if out and self.since_last_peak > self._num_of_taps:\\n self.since_last_peak = -1\\n else:\\n out = 0\\n self.since_last_peak += 1\\n return out\",\n \"def ensure_low(motion_out, motion_in):\\n while (GPIO.input(motion_out) == 1) or (GPIO.input(motion_in) == 1):\\n # Determines if either of the two motion sensors are high/triggered.\\n time.sleep(0.1)\\n # Delay by 100ms.\",\n \"def nopupgain_fn(self, X, Xp):\\n Y = self.theta[0, 0] + self.theta[0, 1] * X\\n return(Y)\",\n \"def input_signal(self, signal: np.ndarray):\\n # Algorithm 3.4 (2)\\n signal = self.__check_signal(signal)\\n # keep track of number of signals input so far\\n self.num_signal += 1\\n # Store the input signal?\\n self.sigs.append(signal)\\n\\n # Algorithm 3.4 (1)\\n # if number of nodes is smaller than 2, set the input signal as a node.\\n if len(self.nodes) < 2:\\n self.__add_node(signal)\\n return\\n\\n # Algorithm 3.4 (3)\\n winner, dists = self.__find_nearest_nodes(2, signal)\\n sim_thresholds = self.__calculate_similarity_thresholds(winner)\\n # signal is a new pattern \\n if dists[0] > sim_thresholds[0] or dists[1] > sim_thresholds[1]:\\n self.__add_node(signal)\\n else:\\n # Algorithm 3.4 (4)\\n self.__increment_edge_ages(winner[0])\\n # Algorithm 3.4 (5)\\n need_add_edge, need_combine = self.__need_add_edge(winner)\\n if need_add_edge:\\n # print(\\\"add edge\\\")\\n # Algorithm 3.4 (5)(a)\\n self.__add_edge(winner)\\n else:\\n # Algorithm 3.4 (5)(b)\\n self.__remove_edge_from_adjacent_mat(winner)\\n # Algorithm 3.4 (5)(a) need to combine subclasses\\n if need_combine:\\n self.__combine_subclass(winner)\\n # Algorithm 3.4 (6) checked, maybe fixed problem N\\n self.__update_density(winner[0])\\n # Algorithm 3.4 (7) is embedded in func __update_density()\\n # Algorithm 3.4 (8) (a)\\n self.__update_winner(winner[0], signal)\\n # Algorithm 3.4 (8) (b)\\n self.__update_adjacent_nodes(winner[0], signal)\\n\\n # Algorithm 3.4 (9)\\n self.__remove_old_edges()\\n\\n # Algorithm 3.4 (10)\\n if self.num_signal % self.iteration_threshold == 0 and self.num_signal > 1:\\n # print(self.won)\\n # update self.N based on self.won\\n for i in range(len(self.won)):\\n if self.won[i]:\\n self.N[i] += 1\\n for i in range(len(self.won)):\\n self.won[i] = False\\n print(\\\"Input signal amount:\\\", self.num_signal, \\\"nodes amount:\\\", len(self.nodes))\\n self.__separate_subclass()\\n self.__delete_noise_nodes()\\n self.total_loop += 1 # ?\\n # algo 3.4(11)?\\n self.__classify()\\n # plot\\n # threading.Thread(self.plot_NN())\\n \\n # clear signals\\n self.sigs.clear()\",\n \"def calibrate_nopp(fundamental):\\n thresh = 0.09682\\n m1 = -0.1014\\n b1 = 0.2839\\n m2 = -0.065\\n b2 = 0.21\\n state1 = (fundamental < thresh)\\n\\n cal_fun = copy.copy(fundamental)\\n cal_fun[state1] = 10**((fundamental[state1] - b1) / m1)\\n cal_fun[~state1] = 10**((fundamental[~state1] - b2) / m2)\\n return cal_fun\",\n \"def __ge__(self, *args):\\n return _ida_frame.stkpnt_t___ge__(self, *args)\",\n \"def oppdater(self, input):\\n return\",\n \"def not_pushed_down(self):\\n return (self.genus >= 2 and self.n != 0) or (self.genus == 1 and self.n > 1) or (self.genus == 0 and self.n > 3)\",\n \"def is_opgepakt(self) -> bool:\\n return not GPIO.input(self._afstandsensor_input_pin)\",\n \"def min_power_out_storage_rule(_m, g, y, s, t):\\r\\n\\r\\n return - m.p_out[g, y, s, t] <= 0\",\n \"def ge(self, y):\\n return 1 - self.lt(y)\",\n \"def motion_edge(self, channel):\\n\\n # read the input to determine which edge\\n # do a little software debounce and double-check the value\\n\\n # is the detector indicating motion?\\n if GPIO.input(self.motion.io_pin) == True:\\n self.logging.debug(\\\"Rising edge detected on %s\\\" %channel)\\n \\n # was the last look indicating no motion (i.e. this is a clean transition)\\n if self.motion.value == False:\\n self.motion.pvalue = self.motion.value\\n self.motion.value = True\\n # if we were already in a \\\"motion=yes\\\" state, must have been noise\\n else:\\n self.logging.debug(\\\"Noise\\\")\\n\\n # edge change direction indicated : back to no motion\\n else:\\n self.logging.debug(\\\"Falling edge on %s\\\" %channel)\\n # clean transition\\n if self.motion.value == True:\\n self.motion.pvalue = self.motion.value\\n self.motion.value = False\\n # noise\\n else:\\n self.logging.debug(\\\"Noise\\\")\",\n \"def _get_next_hop_unchanged(self):\\n return self.__next_hop_unchanged\",\n \"def isHittingLow(self):\\n return not self.limLow.get()\",\n \"def __ge__(self, *args):\\n return _ida_hexrays.cswitch_t___ge__(self, *args)\",\n \"def payoff_nopool(p=0.06,\\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\\n E_nopool = MLB_contract*p + minor_contract * (1-p)\\n E_nopool_var = (MLB_contract - E_nopool)**2 * p + (minor_contract - E_nopool)**2 * (1-p)\\n return E_nopool, E_nopool_var**0.5\",\n \"def lower_cb(self,p):\\n avg_elo=self.elo0+self.elo1\\n delta=self.elo1-self.elo0\\n N=30\\n# Various error conditions must be handled better here!\\n while True:\\n Elo0=avg_elo-N*delta\\n Elo1=avg_elo+N*delta\\n sol=brentq(lambda elo:self.outcome_prob(elo)-(1-p),Elo0,Elo1)\\n if sol['msg']=='no bracket':\\n N*=2\\n continue\\n break\\n return sol['x0']\",\n \"def __ge__(self, other):\\n return self.abs2phy.__ge__(other)\",\n \"def __ge__(self, other):\\n return self.abs2phy.__ge__(other)\",\n \"def direc_detect(motion_out, motion_in):\\n if (GPIO.input(motion_out) == 1):\\n # Determines if outside motion sensor is triggered first.\\n init_trig(motion_in)\\n # Wait atleast 4 seconds for the corresponding sensor to trigger.\\n ensure_low(motion_out, motion_in)\\n # Ensure that both motion sensors are low/are not triggered.\\n\\n if (GPIO.input(motion_in) == 1):\\n # Determines if inside motion sensor is triggered first.\\n init_trig(motion_out)\\n # Wait atleast 4 seconds for the corresponding sensor to trigger.\\n ensure_low(motion_out, motion_in)\\n # Ensure that both motion sensors are low/are not triggered.\",\n \"def edge_status(g, source, target):\\n edge = source, target, g.graph['edge_key']\\n if edge in g.graph['negatives']:\\n status = 0\\n elif edge in g.graph['positives']:\\n status = 1\\n else:\\n status = int(g.has_edge(*edge)) + 2\\n return status\",\n \"def rbd1_koon() -> NonRepairableRBD:\\n qp = 0.03\\n qv = 0.01\\n edges = [\\n (\\\"source\\\", \\\"pump1\\\"),\\n (\\\"source\\\", \\\"pump2\\\"),\\n (\\\"pump1\\\", \\\"valve\\\"),\\n (\\\"pump2\\\", \\\"valve\\\"),\\n (\\\"valve\\\", \\\"sink\\\"),\\n ]\\n reliabilities = {\\n \\\"pump1\\\": FixedEventProbability.from_params(qp),\\n \\\"pump2\\\": FixedEventProbability.from_params(qp),\\n \\\"valve\\\": FixedEventProbability.from_params(qv),\\n }\\n k = {\\\"valve\\\": 2}\\n return NonRepairableRBD(edges, reliabilities, k)\",\n \"def input_resistance(self):\\n return None\",\n \"def low(self):\",\n \"def detect():\\n\\n _pass_done = 0\\n _improve = True\\n new_mod = modularity()\\n cur_mod = -999999999.0\\n rl = random.sample(range(0, node_count), node_count)\\n while _improve & (_pass_done < max_pass) & (new_mod - cur_mod > min_mod):\\n cur_mod = new_mod\\n _improve = False\\n _pass_done += 1\\n for node_tmp in rl:\\n n = node_tmp\\n nc = bl[n]\\n ncomm = neigh_comm(n)\\n remove(n, nc, ncomm[nc])\\n best_c = nc\\n best_l = 0.0\\n best_incre = 0.0\\n for c in ncomm:\\n incre = modularity_gain(n, c, ncomm[c])\\n if incre > best_incre:\\n best_incre = incre\\n best_c = c\\n best_l = ncomm[c]\\n insert(n, best_c, best_l)\\n if best_c != nc:\\n _improve = True\\n new_mod = modularity()\\n print new_mod\",\n \"def signal_qual(self):\\n sig = None\\n while sig is None:\\n sig = self.acquire_signal_quality()\\n time.sleep(0.5)\\n return sig\",\n \"def ghost_greedy(self,current,ghost,g_pos):\\n x, y, pos ,(st,b),start,p_prob = g_pos[ghost]\\n node = self.nodes_array[x][y].getNeighborByDirection(pos)\\n if st > 0 and b and node is not None:\\n st = max(st-1,0)\\n return [(1,(node.i,node.j,pos,(st,not b),start,p_prob))]\\n\\n rpos = Actions.reverseDirection(pos)\\n node = self.nodes_array[x][y]\\n priority = [Directions.NORTH, Directions.WEST, Directions.SOUTH, Directions.EAST]\\n node_neig = [node.up, node.left, node.down, node.right]\\n prio = list()\\n nodes = list()\\n for i in range(len(node_neig)):\\n if node_neig[i]:\\n prio.append(priority[i])\\n nodes.append((node_neig[i].i,node_neig[i].j))\\n\\n if len(prio) > 1 and rpos != Directions.STOP:\\n for i in range(len(prio)):\\n if prio[i] == rpos:\\n del prio[i]\\n del nodes[i]\\n break\\n\\n arg = min if st == 0 else max\\n dist_list = [manhattanDistance(pos, (current.i,current.j)) for pos in nodes]\\n ptr_val = arg(dist_list)\\n pos = []\\n st = st - 1 if st - 1 > 0 else 0\\n for i in range(len(dist_list)):\\n if dist_list[i] == ptr_val:\\n if not st:\\n pos.append((nodes[i][0],nodes[i][1],prio[i],(0,False),start,p_prob))\\n else:\\n pos.append((x,y,prio[i],(st,True),start,p_prob))\\n\\n pos_p = 1./len(pos)\\n return [(pos_p,possibility) for possibility in pos]\",\n \"def was_used(self):\\r\\n return self.circ_chosen != 0\",\n \"def do_not_enter_sign_detection(img_in):\\n img_in = img_in.copy()\\n img = red_masking(img_in)\\n # display_img(img, 'DNE_RED')\\n img = process_base_image(img, (7, 7))\\n # Assumption made that a DNE sign will always have at least a\\n # radius of 5.\\n min_radius = 15\\n max_radius = np.int(img.shape[1] / 2)\\n circles = hough_circles(img, 1, 10, 30, 30, min_radius, max_radius)\\n\\n if circles is not None:\\n circles = np.uint16(np.around(circles))\\n circles = circles[0, :]\\n # since multiple circles might be found, the correct one\\n circle_mid_colors = [pixel_color(img, x[0], x[1]) for x in circles]\\n valid_idx = circle_mid_colors.index(np.max(circle_mid_colors))\\n the_sign = circles[valid_idx]\\n else:\\n return None, None\\n\\n output = (the_sign[0], the_sign[1])\\n return output\",\n \"def detect_crossover(self):\\n\\t\\n\\n\\t\\tif self.trend == 0:\\n\\t\\t\\tif self.averages['fast'] < self.averages['slow']:\\n\\t\\t\\t\\tself.trend = -1\\n\\t\\t\\telif self.averages['fast'] >= self.averages['slow']:\\n\\t\\t\\t\\tself.trend = 1\\n\\t\\telse: \\n\\t\\t\\tif self.trend == -1 and self.averages['fast'] >= self.averages['slow']:\\n\\t\\t\\t\\tself.trend = 1\\n\\t\\t\\t\\treturn {'action': 0, 'sType': self.strategyType}\\n\\t\\t\\telif self.trend == 1 and self.averages['fast'] <= self.averages['slow']:\\n\\t\\t\\t\\tself.trend = -1\\n\\t\\t\\t\\treturn {'action': 1, 'sType': self.strategyType}\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn None # no crossover\",\n \"def isFusion(event,buff):\\n index,diff,label = event\\n label = label[0]\\n if diff>0:\\n return False,[]\\n img_before = np.copy(buff[:,:,index-1])\\n img_after = np.copy(buff[:,:,index])\\n mask_before = (img_before==label).astype(np.uint8)\\n nb_elts_before = np.amax(img_before)\\n kernel = np.ones((7,7),np.uint8)\\n neighbouring_mask = cv2.dilate(mask_before,kernel,iterations=8)\\n\\n new_map = np.multiply(img_before,neighbouring_mask.astype(np.uint8))\\n \\n #Removing the element we are currently looking at\\n new_map[img_before==label]=0\\n possible_candidates = []\\n for i in range(nb_elts_before):\\n if np.any(new_map==i+1):\\n possible_candidates.append(i+1)\\n #Computes the area of the cells and compares them\\n size_cell_disappearing = np.count_nonzero(img_before==label)\\n match = [] #lists the ratios sizeAfter/sizeBefore for possible matches\\n \\n for vals in possible_candidates:\\n size_other_cell = np.count_nonzero(img_before==vals)\\n size_before = size_other_cell+size_cell_disappearing\\n size_after = np.count_nonzero(img_after==vals)\\n ratio = float(size_after)/float(size_before)\\n if ratio>0.8 and ratio<1.2:\\n match.append((vals,abs(1-ratio)))\\n if len(match)==0:\\n return False,[]\\n if len(match)>1:\\n #Several matches, so pick the best\\n values = [y for x,y in match]\\n result_label,osef = match[np.argmin(values)]\\n else:\\n result_label, osef = match[0]\\n return True,result_label\",\n \"def test_soft_threshold():\\n assert snet.soft_threshold(10, 100) == 0\\n assert snet.soft_threshold(-10, 100) == 0\\n assert snet.soft_threshold(10, 3) == 7\\n assert snet.soft_threshold(-10, 3) == -7\",\n \"def isRoundOver(self):\\n pass\",\n \"def is_on_the_edge(self):\\r\\n if self.for_color == 1:\\r\\n new_sum = np.count_nonzero(self.result_board.state[:, 0] == 1) + np.count_nonzero(\\r\\n self.result_board.state[:, 4] == 1) + np.count_nonzero(self.result_board.state[0, 1:4] == 1) +\\\\\\r\\n np.count_nonzero(self.result_board.state[4, 1:4] == 1)\\r\\n else:\\r\\n new_sum = np.count_nonzero(self.result_board.state[:, 0] == 2) + np.count_nonzero(\\r\\n self.result_board.state[:, 4] == 2) + np.count_nonzero(\\r\\n self.result_board.state[0, 1:4] == 2) + np.count_nonzero(\\r\\n self.result_board.state[4, 1:4] == 2)\\r\\n\\r\\n self.priority += (-new_sum) * 0.1\",\n \"def get_high_one(self, state):\\n non_zero_idxs = [-2, -3, -4]\\n idx_idxs = np.random.randint(low=0, high=3, size=10)\\n for idx_idx in idx_idxs:\\n non_zero_idx = non_zero_idxs[idx_idx]\\n if self.potential(state) + self.weights[non_zero_idx] <= self.initial_potential:\\n state[non_zero_idx] += 1\\n break\\n return state\",\n \"def reward_threshold(self) -> Optional[float]:\",\n \"def pruning_algorithm(self):\\n # traverse tree to get conditional likelihood estimate at root.\\n self.set_qmat()\\n for node in self.tree.treenode.traverse(\\\"postorder\\\"):\\n if not node.is_leaf(): \\n self.node_conditional_likelihood(node)\\n logger.debug(\\n f\\\"node={node.idx}; likelihood=[{node.likelihood[0]:.6f}, {node.likelihood[1]:.6f}]\\\") \\n\\n # multiply root prior times the conditional likelihood at root\\n root = self.tree.treenode\\n lik = (\\n (1 - self.prior_root_is_1) * root.likelihood[0] + \\n self.prior_root_is_1 * root.likelihood[1]\\n )\\n return lik\",\n \"def decreaseSearch(self):\\n\\n self.detections = self.detections()\\n self.detections.location = self.location\\n for index, row in enumerate(self.magdata):\\n\\n if row[3] < self.upper*self.background and row[3] < self.background - 2*self.std:\\n # less than 50% of background and 2 std\\n #print \\\"Large decrease (less than \\\" + str(self.upper*self.background) + \\\")\\\", row[3]\\n self.detections.largeDecrease.append([index,self.timestamps[index], row[3]])\\n \\n elif row[3] < self.lower*self.background and row[3] < self.background - self.std:\\n # less than 25% of background and 1 std\\n #print \\\"Decrease (less than \\\" + str(self.lower*self.background) + \\\")\\\", row[3]\\n self.detections.smallDecrease.append([index,self.timestamps[index], row[3]])\\n \\n elif row[3] > (1 + self.upper)*self.background and row[3] > self.background + 2*self.std:\\n # greater than 50% of background\\n #print \\\"Large increase (greater than \\\" + str((1 + self.upper)*self.background) + \\\")\\\", row[3]\\n self.detections.largeIncrease.append([index,self.timestamps[index], row[3]])\\n \\n elif row[3] > (1 + self.lower)*self.background and row[3] > self.background + self.std:\\n # greater than 25% of background\\n #print \\\"Increase (greater than \\\" + str((1 + self.lower)*self.background) + \\\")\\\", row[3]\\n self.detections.smallIncrease.append([index,self.timestamps[index], row[3]])\\n\\n \\n if resultCounter(self.detections) == 0:\\n self.detections.results.append(None)\\n print \\\"No observable jumps/dips in magnetic field strength\\\"\\n else:\\n for attribute, bins in classIterator(self.detections):\\n if 'crease' in attribute:\\n self.detections.results.append('Number of ' + attribute + ': ' + str(len(bins)))\",\n \"def _gain(self):\\n return None\",\n \"def game_over(self):\\n raise NotImplementedError(\\\"Abstract method\\\") # no mercy for stooges\",\n \"def RecursiveLowPassFast(signal, coeff, self):\\n # Creates running mean value of the input\\n ml = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], signal) \\n # Plot Running threshold value at the current plot\\n self.p1.plot(self.t, ml, pen=pg.mkPen(color=(246, 178, 255), width=3))\\n\\n # Creates running square deviation from the mean\\n vl = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], np.square(signal - ml))\\n # Creates \\\"threshold line\\\". If current value < sl[i] -> i belongs to event. \\n sl = ml - coeff['S'] * np.sqrt(vl)\\n self.p1.plot(self.t, sl, pen=pg.mkPen(color=(173, 27, 183), width=3))\\n # Finds the length of the initial signal\\n Ni = len(signal)\\n # Finds those points where signal less than \\\"threshold line\\\"\\n points = np.array(np.where(signal<=sl)[0])\\n to_pop=np.array([]) # Empty supplementary array for finding adjacent points \\n # For loop for finding adjacent points \\n for i in range(1,len(points)):\\n if points[i] - points[i - 1] == 1:\\n to_pop=np.append(to_pop, i)\\n # Points contain only border points of events\\n points = np.delete(points, to_pop)\\n # Empty list for Event location storage\\n RoughEventLocations = []\\n NumberOfEvents=0 #Number of events\\n\\n # For Loop for finding separating edges of different events and satisfying Event length limits\\n for i in points:\\n if NumberOfEvents is not 0:\\n if i >= RoughEventLocations[NumberOfEvents-1][0] and i <= RoughEventLocations[NumberOfEvents-1][1]:\\n continue\\n NumberOfEvents += 1\\n start = i\\n El = ml[i] - coeff['E'] * np.sqrt(vl[i])\\n Mm = ml[i]\\n Vv = vl[i]\\n duration = 0\\n while signal[i + 1] < El and i < (Ni - 2) and duration < coeff['eventlengthLimit']:\\n duration += 1\\n i += 1\\n if duration >= coeff['eventlengthLimit'] or i > (Ni - 10):\\n NumberOfEvents -= 1\\n else:\\n k = start\\n while signal[k] < Mm and k > 1:\\n k -= 1\\n start = k - 1\\n k2 = i + 1\\n while signal[k2] > Mm:\\n k2 -= 1\\n endp = k2\\n if start<0:\\n start=0\\n RoughEventLocations.append((start, endp, ml[start], vl[start]))\\n\\n return np.array(RoughEventLocations)\",\n \"def false_pos(yt, yp) -> Any:\\n from keras import backend as K\\n return K.sum(K.cast(yp * (1 - yt) > 0.5, 'float')) / K.maximum(1.0, K.sum(1 - yt))\",\n \"def traffic_sign_detection_noisy(img_in):\\n filter_size = 5\\n filter_sigma = 4\\n filter_kernel = cv2.getGaussianKernel(filter_size, filter_sigma)\\n filter_kernel = filter_kernel * filter_kernel.T\\n img_in = cv2.filter2D(img_in, -1, filter_kernel)\\n\\n thresh1 = 110\\n thresh2 = 60\\n cannyEdges = cv2.Canny(img_in, thresh1, thresh2)\\n # cv2.imshow('smoothed_img', img_in)\\n # cv2.imshow('cannyEdges', cannyEdges)\\n\\n DetectedObj = traffic_sign_detection(img_in)\\n\\n return DetectedObj\\n raise NotImplementedError\",\n \"def get_all_crossing_threshold(sig, thresh, front, use_numexpr = False):\\n sig1 = sig[:-1]\\n sig2 = sig[1:]\\n if use_numexpr:\\n if front == '+':\\n pos_spike, = np.where(numexpr.evaluate( '(sig1<=thresh) & (sig2>thresh)'))\\n elif front == '-':\\n pos_spike, = np.where(numexpr.evaluate( '(sig1>=thresh) & (sig2<thresh)'))\\n else :\\n if front == '+':\\n pos_spike, = np.where( (sig1 <= thresh) & ( sig2>thresh) )\\n elif front == '-':\\n pos_spike, = np.where( (sig1 >= thresh) & ( sig2<thresh) )\\n return pos_spike+1\",\n \"def roi_ko(self):\\n self.pressure_img.mask = self.pressure_img.previous_roi\",\n \"def __ge__(self, other: Event) -> bool:\\n return not self.__lt__(other)\",\n \"def motion_ge(input_line, cur, count):\\n # \\\"ge is just \\\"w\\\" on inverted data (e.g. \\\"olleH\\\" instead of \\\"Hello\\\").\\n pos_inv = motion_w(input_line[::-1], len(input_line) - cur - 1, count)[0]\\n pos = len(input_line) - pos_inv - 1\\n return pos, True, False\",\n \"def signal_kernel(self, signum):\\n pass\",\n \"def onImageEdge(self,tolerance=1):\\n # this has to be one to deal with blob library weirdness that goes deep down to opencv\\n return ( self.distanceToNearestEdge() <= tolerance )\",\n \"def apply_thresholding(x):\\n return x > threshold_otsu(x)\",\n \"def flag_resting(sensor, varname, N_kernel=20, order=1,\\n prior_mask=None, reference=\\\"max\\\", delta_threshold=0.02):\\n values = sensor.data[varname]\\n time = sensor.data[\\\"time\\\"]\\n if prior_mask is not None:\\n fit_values = values[prior_mask]\\n fit_time = time[prior_mask]\\n else:\\n fit_values = values\\n fit_time = time\\n\\n if reference == \\\"g\\\":\\n delta = np.abs(fit_values - g_constant)\\n ref_val = g_constant\\n elif reference == \\\"max\\\":\\n p = np.polyfit(fit_time, fit_values, order)\\n poly = np.poly1d(p)\\n delta = np.abs(values - poly(time))\\n ref_val = np.max(np.abs(values))\\n else:\\n raise ValueError(\\\"reference =\\\", reference, \\\"is not supported!\\\")\\n\\n mask = delta < delta_threshold*ref_val\\n\\n if prior_mask is not None:\\n mask = np.logical_and(mask, prior_mask)\\n\\n kernel = np.ones(N_kernel)/N_kernel\\n conv = np.convolve(mask, kernel, mode=\\\"same\\\")\\n mask_resting = conv > 0.5\\n mask_moving = conv <= 0.5\\n\\n sensor.data[f\\\"mask_{varname}_resting\\\"] = mask_resting\\n sensor.data[f\\\"mask_{varname}_moving\\\"] = mask_moving\\n\\n find_resting_edge(sensor)\",\n \"def check(self):\\n if (sum(self.state) == 0):\\n return -1\\n elif (self.state[-1] >= 1):\\n return 1\\n else:\\n return 0\",\n \"def top_is_pointwise(self):\\n return False\",\n \"def __ne__(*args, **kwargs):\\n return _gdi_.Pen___ne__(*args, **kwargs)\",\n \"def isLow(self):\\n\\t\\treturn self.resolution == 'LOW'\",\n \"def switch_to_no_feedforward_inputs(self):\\n\\n self.h_e=np.ones_like(self.inputs.noise_flat.T)*self.feed_forward_off_value\\n self.h=np.vstack([self.h_e,self.h_i])\",\n \"def _preprocess_state(self, state):\\r\\n if np.isinf(self.observation_space.low).any() or np.isinf(self.observation_space.high).any(): return state\\r\\n return (state - self.observation_space.low) /(self.observation_space.high)\",\n \"def disaggregate_chunk(self, chunk):\\n\\n # An resistive element has active power equal to apparent power.\\n # Checking power units.\\n units = self.__physical_quantity(chunk)\\n\\n # EVENTS OUT OF THE CHUNK:\\n # Delta values:\\n column_name = 'diff_' + units[1]\\n chunk[column_name] = chunk.loc[:, units].diff()\\n\\n # Filter the noise.\\n chunk['onpower'] = (chunk[column_name] > self.powerNoise)\\n chunk['offpower'] = (chunk[column_name] < -self.powerNoise)\\n events = chunk[(chunk.onpower == True) | (chunk.offpower == True)]\\n\\n detection_list = []\\n singleOnevent = 0\\n # Max Likelihood algorithm (optimized):\\n for onevent in events[events.onpower == True].iterrows():\\n # onTime = onevent[0]\\n # deltaOn = onevent[1][1]\\n # windowning:\\n offevents = events[(events.offpower == True) & (events.index > onevent[0]) & (\\n events.index < onevent[0] + timedelta(seconds=self.timeWindow))]\\n # Filter paired events:\\n offevents = offevents[\\n abs(onevent[1][1] - offevents[column_name].abs()) < self.powerPair]\\n\\n # Max likelihood computation:\\n if not offevents.empty:\\n # pon = self.__pdf(self.onpower, onevent[1][1])\\n for offevent in offevents.iterrows():\\n # offTime = offevent[0]\\n # deltaOff = offevent[1][1]\\n # poff = self.__pdf(self.offpower, offevent[1][1])\\n # duration = offevent[0] - onTime\\n # pduration = self.__pdf(self.duration, (offevent[0] - onTime).total_seconds())\\n likelihood = self.__pdf(self.onpower, onevent[1][1]) * \\\\\\n self.__pdf(self.offpower, offevent[1][1]) * \\\\\\n self.__pdf(self.duration, (offevent[0] - \\\\\\n onevent[0]).total_seconds())\\n detection_list.append(\\n {'likelihood': likelihood, 'onTime': onevent[0], \\n 'offTime': offevent[0], 'deltaOn': onevent[1][1]})\\n else:\\n singleOnevent += 1\\n\\n # Passing detections to a pandas.DataFrame\\n detections = pd.DataFrame(\\n columns=('onTime', 'offTime', 'likelihood', 'deltaOn'))\\n\\n for i in range(len(detection_list)):\\n detections.loc[i] = [detection_list[i]['onTime'], detection_list[i][\\n 'offTime'], detection_list[i]['likelihood'], detection_list[i]['deltaOn']]\\n\\n detections = detections[detections.likelihood >= self.thLikelihood]\\n\\n # Constructing dis_chunk (power of disaggregated appliance)\\n dis_chunk = pd.DataFrame(\\n index=chunk.index, columns=[str(units[0]) + '_' + str(units[1])])\\n dis_chunk.fillna(0, inplace=True)\\n\\n # Ruling out overlapped detecttions ordering by likelihood value.\\n detections = detections.sort('likelihood', ascending=False)\\n for row in detections.iterrows():\\n # onTime = row[1][0] offTime = row[1][1] deltaOn = row[1][3]\\n #import ipdb\\n #ipdb.set_trace()\\n if ((dis_chunk[(dis_chunk.index >= row[1][0]) &\\n (dis_chunk.index < row[1][1])].sum().values[0]) == 0):\\n # delta = chunk[chunk.index == onTime][column_name].values[0]\\n dis_chunk[(dis_chunk.index >= row[1][0]) & (\\n dis_chunk.index < row[1][1])] = row[1][3]\\n\\n # Stat information:\\n print(str(len(events)) + \\\" events found.\\\")\\n print(str(len(events[events.onpower == True])) + \\\" onEvents found\\\")\\n print(str(singleOnevent) + \\\" onEvents no paired.\\\")\\n\\n return dis_chunk\",\n \"def ground_min(self):\\n\\n def compare(e):\\n if is_wire(e):\\n return e.potential\\n else:\\n return float('inf')\\n self.move_ground(min(self.elements, key=compare))\",\n \"def unconstrain_positive(self):\\n self.unconstrain(Logexp())\",\n \"def simple_hoc(signal, **kwargs):\\n X = np.int_(signal >= 0)\\n return np.sum(np.abs(X[1:] - X[:-1]))\",\n \"def has_increased_significantly(old, new, sig_fig=10**(-4)):\\n return(new > old and np.log10(1.-old/new) > -sig_fig)\",\n \"def __ge__(self, *args):\\n return _ida_hexrays.cwhile_t___ge__(self, *args)\",\n \"def terminal_test(self, state):\\n if state.moves:\\n return 0\\n return np.inf if state.to_move == \\\"B\\\" else -np.inf\",\n \"def evaporatePheromones(self):\\n for u in self.graph.nodes:\\n for v in self.graph.neighbors(u):\\n if self.graph_data[u]['pheromones'][v] > 0 :\\n self.graph_data[u]['pheromones'][v] -= self.parameters['evaporation_rate'] * self.graph_data[u]['pheromones'][v]\\n self.graph_data[v]['pheromones'][u] -= self.parameters['evaporation_rate'] * self.graph_data[v]['pheromones'][u]\",\n \"def signal_vwap(self):\\n pass\",\n \"def is_ring(self): # -> bool:\\n ...\",\n \"def notOnImageEdge(self,tolerance=1):\\n\\n # this has to be one to deal with blob library weirdness that goes deep down to opencv\\n return ( self.distanceToNearestEdge() > tolerance )\",\n \"def autothreshold(gray_im, method=\\\"otsu\\\"):\\n if method == \\\"otsu\\\":\\n t = otsu(gray_im)\\n elif method == \\\"kmeans\\\":\\n t = ave(kmeans(list(gray_im.getdata())))\\n return gray_im.point(lambda x: 0 if x < t else 255) # < or <= ?\",\n \"def termination_heuristics(self, state):\\n\\n point_state = state[0][0]\\n target_mask = get_target_mask(point_state)\\n point_state = point_state[:3, target_mask].T\\n depth_heuristics = self.graspnet_closure(point_state) \\n if (depth_heuristics):\\n print('object inside gripper? start retracting...')\\n return depth_heuristics\",\n \"def policy_improvement(self):\\n\\n # Set max value to terminal states in order to force actions to them.\\n self.V[self.get_index(*self.environment.terminal_state)] = np.max(self.V) + 1\\n\\n policy_stable = True # Used to check if the Policy has changed or not in this step\\n\\n for state in range(len(self.V)):\\n old_actions_probs = np.copy(self.policy.state_actions_probs[state])\\n\\n if self.environment.is_wall(*self.get_position(state)):\\n self.policy.state_actions_probs[state] = [0, 0, 0, 0]\\n else:\\n self.policy.state_actions_probs[state] = self.get_greedy_actions(state)\\n\\n if not np.array_equal(old_actions_probs, self.policy.state_actions_probs[state]):\\n policy_stable = False\\n\\n return self.policy, policy_stable\",\n \"def __neg__(self):\\n # Manually memoize to take advantage of the fact that this is an\\n # involution\\n try:\\n return self._neg\\n except AttributeError:\\n self._neg = type(self)(self.data.__neg__(), self.bset)\\n self._neg._neg = self # Involution at work\\n return self._neg\",\n \"def __lt__(self, event):\\n\\t\\treturn True\",\n \"def autorange(xvar, sig, gwin=7, swin=None, win=30,\\n on_mult=(1.5, 1.), off_mult=(1., 1.5),\\n nbin=10, transform='log', thresh=None):\\n failed = []\\n sig = np.asanyarray(sig)\\n\\n # smooth signal\\n if swin is not None:\\n sigs = fastsmooth(sig, swin)\\n else:\\n sigs = sig\\n\\n # transform signal\\n if transform == 'log':\\n tsigs = log_nozero(sigs)\\n else:\\n tsigs = sigs\\n\\n if thresh is None:\\n if tsigs.ndim == 1:\\n scale = False\\n tsigs = tsigs.reshape(-1, 1)\\n else:\\n scale = True\\n fsig = separate_signal(tsigs, scaleX=scale).astype(bool)\\n else:\\n if transform == 'log':\\n thresh = np.log(thresh)\\n fsig = tsigs > thresh\\n fsig[0] = False # the first value must always be background\\n fbkg = ~fsig\\n\\n # remove transitions by fitting a gaussian to the gradients of\\n # each transition\\n\\n # 1. determine the approximate index of each transition\\n zeros = bool_2_indices(fsig)\\n \\n if zeros is not None:\\n zeros = zeros.flatten()\\n if sigs.ndim > 1:\\n sigs = sigs.sum(axis=1)\\n\\n # 2. calculate the absolute gradient of the target trace.\\n grad = abs(fastgrad(sigs, gwin)) # gradient of untransformed data.\\n\\n for z in zeros: # for each approximate transition\\n # isolate the data around the transition\\n if z - win < 0:\\n lo = gwin // 2\\n hi = int(z + win)\\n elif z + win > (len(sig) - gwin // 2):\\n lo = int(z - win)\\n hi = len(sig) - gwin // 2\\n else:\\n lo = int(z - win)\\n hi = int(z + win)\\n\\n xs = xvar[lo:hi]\\n ys = grad[lo:hi]\\n\\n # determine type of transition (on/off)\\n mid = (hi + lo) // 2\\n tp = sigs[mid + 3] > sigs[mid - 3] # True if 'on' transition.\\n\\n # fit a gaussian to the first derivative of each\\n # transition. Initial guess parameters:\\n # - A: maximum gradient in data\\n # - mu: c\\n # - width: 2 * time step\\n # The 'sigma' parameter of curve_fit:\\n # This weights the fit by distance from c - i.e. data closer\\n # to c are more important in the fit than data further away\\n # from c. This allows the function to fit the correct curve,\\n # even if the data window has captured two independent\\n # transitions (i.e. end of one ablation and start of next)\\n # ablation are < win time steps apart).\\n centre = xvar[z] # center of transition\\n width = (xvar[1] - xvar[0]) * 2\\n\\n try:\\n pg, _ = curve_fit(gauss, xs, ys,\\n p0=(np.nanmax(ys),\\n centre,\\n width),\\n sigma=(xs - centre)**2 + .01)\\n # get the x positions when the fitted gaussian is at 'conf' of\\n # maximum\\n # determine transition FWHM\\n fwhm = abs(2 * pg[-1] * np.sqrt(2 * np.log(2)))\\n # apply on_mult or off_mult, as appropriate.\\n if tp:\\n lim = np.array([-fwhm, fwhm]) * on_mult + pg[1]\\n else:\\n lim = np.array([-fwhm, fwhm]) * off_mult + pg[1]\\n\\n fbkg[(xvar > lim[0]) & (xvar < lim[1])] = False\\n fsig[(xvar > lim[0]) & (xvar < lim[1])] = False\\n\\n except RuntimeError:\\n failed.append([centre, tp])\\n pass\\n\\n ftrn = ~fbkg & ~fsig\\n\\n # if there are any failed transitions, exclude the mean transition width\\n # either side of the failures\\n if len(failed) > 0:\\n trns = xvar[bool_2_indices(ftrn)]\\n tr_mean = (trns[:, 1] - trns[:, 0]).mean() / 2\\n for f, tp in failed:\\n if tp:\\n ind = (xvar >= f - tr_mean *\\n on_mult[0]) & (xvar <= f + tr_mean * on_mult[0])\\n else:\\n ind = (xvar >= f - tr_mean *\\n off_mult[0]) & (xvar <= f + tr_mean * off_mult[0])\\n fsig[ind] = False\\n fbkg[ind] = False\\n ftrn[ind] = False\\n\\n return fbkg, fsig, ftrn, [f[0] for f in failed]\",\n \"def getNextNodeUsingCellDiff(kGoalState):\\n \\n global fringe\\n global solutions\\n\\n \\n\\n\\n\\n minNode = None\\n minCost = 99999999999\\n minNodeIndex = -1\\n\\n \\n pnode = None\\n pcost = None\\n\\n if len(solutions)>0 and solutions[0] != None:\\n pnode = solutions[0];\\n pcost = getHValueForNode(pnode,kGoalState)\\n #print pnode, pcost\\n # raw_input()\\n \\n\\n\\n\\n for idx,node in enumerate(fringe):\\n #get the heu. function values\\n g_value = getHValueForNode(node,kGoalState)\\n \\n\\n if g_value < minCost:\\n minNode = node\\n minNodeIndex = idx\\n minCost = g_value\\n\\n\\n fringe.pop(minNodeIndex)\\n c = getHValueForNode(minNode,kGoalState)\\n if pnode != None:\\n if c > pcost:\\n minNode = None\\n \\n return minNode\",\n \"def __ge__(self, other: t.Any) -> bool:\\n return self._op_bool('__ge__', other)\",\n \"def evaluate(self) :\\n for inp in self.inStates :\\n if inp.getState() == 0 : return 0\\n return 1\",\n \"def calculate_signal(self):\\n y = self.data.get_bar_values(self.pair[0], \\\"adj_close\\\", N=self.ols_window)\\n x = self.data.get_bar_values(self.pair[1], \\\"adj_close\\\", N=self.ols_window)\\n\\n if y is not None and x is not None:\\n if len(y) >= self.ols_window and len(x) >= self.ols_window:\\n # get hedge ratio\\n self.hedge_ratio = sm.OLS(y, x).fit().params[0]\\n\\n # get z score of residuals\\n spread = y - self.hedge_ratio * x\\n zscore_last = ((spread - spread.mean()) / spread.std())[-1]\\n\\n # calculate signals and add to events queue\\n y_signal, x_signal = self.calculate_xy_signal(zscore_last)\\n if y_signal is not None and x_signal is not None:\\n self.events.put(y_signal)\\n self.events.put(x_signal)\",\n \"def total_outlier_flag(self):\\r\\n flagged = np.zeros(self.ndetects,np.int32)\\r\\n for nm, method in enumerate(self.outlier_methods):\\r\\n if hasattr(self.rec_track, method):\\r\\n flagged = np.maximum(flagged, self.rec_track[method])\\r\\n if 'flagged' in self.df_track.columns:\\r\\n self.df_track = self.df_track.drop(columns=['flagged'])\\r\\n ncol = len(self.df_track.columns)\\r\\n self.df_track.insert(ncol, 'flagged', flagged)\\r\\n self.rec_track = self.df_track.to_records()\\r\\n\\r\\n return\",\n \"def is_onhold(self) -> bool:\",\n \"def detectar_pico_doge():\\n\\n ultimos_precios = persistence.traer_ultimos_precios_doge()\\n prev = float(ultimos_precios[0])\\n now = float(ultimos_precios[1])\\n porcentaje = 0\\n\\n porcentaje = calcular_porcentaje(prev, now)\\n porcentaje = round(porcentaje, 2)\\n if porcentaje > 1.5 or porcentaje < -1.5:\\n return porcentaje\\n return 0\",\n \"def detection(input_state, detection_event):\\n size = len(input_state)\\n output_state = np.zeros((size,) * 4, dtype=complex)\\n if detection_event == 'BOTH':\\n for p1 in range(size):\\n for p2 in range(size):\\n for p3 in range(size):\\n for p4 in range(size):\\n if p1 is not 0 and p3 is not 0:\\n output_state[p1, p2, p3, p4] = input_state[p1, p2, p3, p4]\\n elif detection_event == 'NONE':\\n for p1 in range(size):\\n for p2 in range(size):\\n for p3 in range(size):\\n for p4 in range(size):\\n if p1 is 0 and p3 is 0:\\n output_state[p1, p2, p3, p4] = input_state[p1, p2, p3, p4]\\n elif detection_event == 'FIRST':\\n for p1 in range(size):\\n for p2 in range(size):\\n for p3 in range(size):\\n for p4 in range(size):\\n if p1 > 0 and p3 is 0:\\n output_state[p1, p2, p3, p4] = input_state[p1, p2, p3, p4]\\n elif detection_event == 'THIRD':\\n for p1 in range(size):\\n for p2 in range(size):\\n for p3 in range(size):\\n for p4 in range(size):\\n if p1 is 0 and p3 is not 0:\\n output_state[p1, p2, p3, p4] = input_state[p1, p2, p3, p4]\\n else:\\n raise ValueError('Wrong configuration')\\n\\n return output_state\",\n \"def backward_elimination(X, y, sig_level=0.05):\\n regressor_OLS = sm.OLS(y, X).fit()\\n max_pvalue = max(regressor_OLS.pvalues).astype(float)\\n if max_pvalue < sig_level:\\n print(regressor_OLS.summary())\\n return X\\n else:\\n max_pvalue_index = np.argmax(regressor_OLS.pvalues)\\n X = np.delete(X, max_pvalue_index, axis=1)\\n backward_elimination(X, y, sig_level)\",\n \"def _opt_pol(self, *, s=None):\\n\\n if s is None:\\n s = self._state\\n\\n rel_pos = s - self._mode_offset\\n r1_pos = -self._mode_separation / 2\\n r2_pos = -r1_pos\\n\\n r1_a = np.clip(r1_pos - rel_pos, -1, 1)\\n r2_a = np.clip(r2_pos - rel_pos, -1, 1)\\n\\n if rel_pos < 0:\\n # Target left peak\\n return r1_a\\n else:\\n # Target right peak\\n return r2_a\",\n \"def alter_state(neuron, inp):\\n # Refractory period (simplified)\\n if neuron['state'] == 1:\\n return 0\\n # Firing threshold (simplified)\\n elif inp >= 3:\\n return 1\\n # Random firing probability\\n elif random() < random_fire_prob:\\n return 1\\n else:\\n return 0\",\n \"def pending_f(self, x_p):\\n targetlabel_mask = torch.from_numpy(onehot_like(np.zeros(self.classnum), self.target))\\n secondlargest_mask = torch.from_numpy(np.ones(self.classnum)) - targetlabel_mask\\n targetlabel_mask = targetlabel_mask.to(self.device)\\n secondlargest_mask = secondlargest_mask.to(self.device)\\n\\n Zx_i = np.max((self.model.get_logits(x_p).double().to(self.device) * secondlargest_mask).cpu().detach().numpy())\\n Zx_t = np.max((self.model.get_logits(x_p).double().to(self.device) * targetlabel_mask).cpu().detach().numpy())\\n\\n if ( Zx_i - Zx_t < - self.confidence):\\n return True\\n else:\\n return False\",\n \"def __nonzero__(self):\\n return _osgAnimation.mapVertexInfluence___nonzero__(self)\",\n \"def __ge__(self, other):\\r\\n # self >= other\\r\\n s = (self - other).share[0]\\r\\n return type(self)((s >= 0, self.exponent_type(0)))\",\n \"def y_overrun(self):\\n return (self.status & 0x20) != 0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5264356","0.5113618","0.5000561","0.49808434","0.49234867","0.49224997","0.49052876","0.4896278","0.4894377","0.4853233","0.47944462","0.47820812","0.47796014","0.47683772","0.47345206","0.47341648","0.47143006","0.4695682","0.46904302","0.4684524","0.4674314","0.46719474","0.46534288","0.4653393","0.4645347","0.46418893","0.4624342","0.4604034","0.46021265","0.4596785","0.4596785","0.4585466","0.45800248","0.4577139","0.4576213","0.45735896","0.45702317","0.45577484","0.4553825","0.45536774","0.45400387","0.45278937","0.45230165","0.4512592","0.4500951","0.4499864","0.4494183","0.4490904","0.44879773","0.44816285","0.44712412","0.44656482","0.44622007","0.44581437","0.445397","0.4446763","0.44460544","0.44458026","0.44390267","0.44339412","0.4431876","0.44314378","0.4429974","0.44255197","0.44212013","0.44195902","0.44165498","0.44129932","0.44108844","0.44104102","0.44085246","0.44035688","0.4403307","0.4401756","0.44014332","0.43946946","0.43943453","0.43940434","0.43939888","0.43792778","0.4375526","0.43722105","0.43712384","0.4369433","0.43688622","0.43686554","0.4366031","0.4365224","0.43644017","0.43580604","0.43579355","0.43568513","0.4356528","0.43550622","0.4354827","0.4352759","0.43523732","0.43499166","0.43457848","0.43394127","0.43380278"],"string":"[\n \"0.5264356\",\n \"0.5113618\",\n \"0.5000561\",\n \"0.49808434\",\n \"0.49234867\",\n \"0.49224997\",\n \"0.49052876\",\n \"0.4896278\",\n \"0.4894377\",\n \"0.4853233\",\n \"0.47944462\",\n \"0.47820812\",\n \"0.47796014\",\n \"0.47683772\",\n \"0.47345206\",\n \"0.47341648\",\n \"0.47143006\",\n \"0.4695682\",\n \"0.46904302\",\n \"0.4684524\",\n \"0.4674314\",\n \"0.46719474\",\n \"0.46534288\",\n \"0.4653393\",\n \"0.4645347\",\n \"0.46418893\",\n \"0.4624342\",\n \"0.4604034\",\n \"0.46021265\",\n \"0.4596785\",\n \"0.4596785\",\n \"0.4585466\",\n \"0.45800248\",\n \"0.4577139\",\n \"0.4576213\",\n \"0.45735896\",\n \"0.45702317\",\n \"0.45577484\",\n \"0.4553825\",\n \"0.45536774\",\n \"0.45400387\",\n \"0.45278937\",\n \"0.45230165\",\n \"0.4512592\",\n \"0.4500951\",\n \"0.4499864\",\n \"0.4494183\",\n \"0.4490904\",\n \"0.44879773\",\n \"0.44816285\",\n \"0.44712412\",\n \"0.44656482\",\n \"0.44622007\",\n \"0.44581437\",\n \"0.445397\",\n \"0.4446763\",\n \"0.44460544\",\n \"0.44458026\",\n \"0.44390267\",\n \"0.44339412\",\n \"0.4431876\",\n \"0.44314378\",\n \"0.4429974\",\n \"0.44255197\",\n \"0.44212013\",\n \"0.44195902\",\n \"0.44165498\",\n \"0.44129932\",\n \"0.44108844\",\n \"0.44104102\",\n \"0.44085246\",\n \"0.44035688\",\n \"0.4403307\",\n \"0.4401756\",\n \"0.44014332\",\n \"0.43946946\",\n \"0.43943453\",\n \"0.43940434\",\n \"0.43939888\",\n \"0.43792778\",\n \"0.4375526\",\n \"0.43722105\",\n \"0.43712384\",\n \"0.4369433\",\n \"0.43688622\",\n \"0.43686554\",\n \"0.4366031\",\n \"0.4365224\",\n \"0.43644017\",\n \"0.43580604\",\n \"0.43579355\",\n \"0.43568513\",\n \"0.4356528\",\n \"0.43550622\",\n \"0.4354827\",\n \"0.4352759\",\n \"0.43523732\",\n \"0.43499166\",\n \"0.43457848\",\n \"0.43394127\",\n \"0.43380278\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93768,"cells":{"query":{"kind":"string","value":"Binary to onehot conversion"},"document":{"kind":"string","value":"def bin2ohot(din, dout):\n IN_W = len(din)\n OUT_W = 2**IN_W\n\n @always_comb\n def _conv():\n for i in range(OUT_W):\n if din == i:\n dout[i] = 1\n else:\n dout[i] = 0\n\n return instances()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def binary_to_one_hot(arr: np.ndarray) -> np.ndarray:\n res = np.zeros((arr.shape[0], 2))\n res[np.where(arr == 1)[0], 0] = 1\n res[np.where(arr == 0)[0], 1] = 1\n return res","def one_hot_encode(x):\n # TODO: Implement Function\n output = np.zeros((len(x), 10))\n \n for i, j in enumerate(x):\n output[i,j] = 1\n \n return output","def convert_to_one_hot(a):\n a = a[:, 0]\n a = a.astype(int)\n A = np.zeros((len(a), config.num_classes))\n A[np.arange(len(a)), a] = 1\n return A","def To1hot(label,num_class):\n onehot = np.zeros(num_class)\n onehot[label] = 1\n return onehot","def one_hot(x, num_classes, dtype=jnp.float32):\n return jax.nn.one_hot(x, num_classes).astype(dtype)","def one_hot_encode(x):\n # TODO: Implement Function\n lb = preprocessing.LabelBinarizer()\n lb.fit([0,1,2,3,4,5,6,7,8,9])\n \n return lb.transform(x)","def one_hot_encode(y, out_size):\n n = len(y)\n oh = np.zeros((n, out_size))\n oh[range(n), y] = 1\n return oh","def one_hot(y_):\n y_ = y_.reshape(len(y_))\n n_values = int(np.max(y_)) + 1\n n_values = 6\n return np.eye(n_values)[np.array(y_, dtype=np.int32)] # Returns FLOATS","def onehot(isTrue):\n if isTrue:\n return [1, 0]\n else:\n return [0, 1]","def one_hot(x, dtype=np.float32):\n return np.array(x[:, None] == np.arange(x.max()+1), dtype)","def to_onehot(x, num_classes):\n return np.eye(num_classes, dtype='float32')[x]","def one_hot_encode(x):\n # TODO: Implement Function\n x_l = list(x)\n for index in np.arange(len(x_l)):\n x_l[index] = get_one_hot_vector(x[index])[x[index]]\n return np.array(x_l)","def int2onehot(x, length):\n assert x > 0 and x < length\n assert isinstance(x, int)\n assert isinstance(length, int)\n ohv = np.zeros([length], dtype=np.float32)\n ohv[x] = 1\n return ohv","def one_hot_encode(self, arr, n_labels):\n one_hot = np.zeros((np.multiply(*arr.shape), n_labels), dtype=np.float32)\n one_hot[np.arange(one_hot.shape[0]), arr.flatten()] = 1.\n one_hot = one_hot.reshape((*arr.shape, n_labels))\n \n return one_hot","def to_one_hot(v):\n n = len(v)\n m = max(v) + 1\n out = np.zeros((n, m))\n out[np.arange(n), v] = 1\n return out","def one_hot_encode(x, n_classes):\n return np.eye(n_classes)[x]","def _onehot(integer_labels):\r\n n_rows = len(integer_labels)\r\n n_cols = integer_labels.max() + 1\r\n onehot = np.zeros((n_rows, n_cols), dtype='uint8')\r\n onehot[np.arange(n_rows), integer_labels] = 1\r\n return onehot","def label_to_one_hot(label, num_of_class=2):\r\n import numpy as np\r\n one_hot = np.zeros((len(label), num_of_class), dtype=np.uint8)\r\n for i in range(len(label)):\r\n one_hot[i, int(label[i] - 1)] = 1 # label is 1 and 2\r\n\r\n return one_hot","def one_hot_encoding(y):\n\n y_oh = np.zeros((y.shape[0], y.max() - y.min() + 1))\n\n # currently only works in min is actually 0\n for j in range(0, y_oh.shape[1]):\n y_oh[np.where(y == j), j] = 1\n\n return y_oh","def _onehot(y, n_classes=False):\n if not n_classes:\n \"\"\"Create one-hot encoded labels.\"\"\"\n n_classes = len(set(y))\n out = np.zeros((len(y), n_classes))\n for i, ii in enumerate(y):\n out[i][ii] += 1\n y_onehot = out.astype(int)\n return y_onehot","def one_hot_encoding(data):\n\n data_encoded = pd.get_dummies(data)\n\n return data_encoded","def one_hot_encoding(labels, num_classes=10):\n num_labels = labels.shape[0]\n encoded = np.zeros((num_labels, num_classes))\n encoded[np.arange(num_labels), labels[np.arange(num_labels)]] = 1\n \n return encoded","def make_onehot(x,num_labels=7):\n enc = OneHotEncoder(n_values=num_labels)\n return enc.fit_transform(np.array(x).reshape(-1, 1)).toarray()","def to_one_hot(arr, num_classes):\n arr = arr.data.astype(int)\n a = np.zeros((arr.shape[0], num_classes))\n a[np.arange(len(a)), arr] = 1\n return tensor.Tensor(a, requires_grad=True)","def to_one_hot(arr, num_classes):\n arr = arr.data.astype(int)\n a = np.zeros((arr.shape[0], num_classes))\n a[np.arange(len(a)), arr] = 1\n return tensor.Tensor(a, requires_grad=True)","def one_hot(a, actions, dtype=t.float32):\n assert 1 == a.shape[-1]\n return (a == t.arange(actions, device=a.device)).to(dtype=dtype)","def convert_one_hot_v2(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n depth = op.attr(\"depth\")\n dtype = op.attr(\"dtype\")\n dtype = _convert_dtype_value(dtype)\n ndim = len(infer_shape(x))\n on_value = _op.const(1)\n off_value = _op.const(0)\n axis = ndim\n out = _op.one_hot(x, on_value, off_value, depth, axis, dtype)\n g.add_node(op.output(\"Out\")[0], out)","def encode_one_hot2(s):\n x = np.zeros((LINE_SIZE, INPUT_VOCAB_SIZE))\n for n, c in enumerate(s):\n index = char_indices[c]\n x[n, index] = 1 \n return x","def onehot(trace):\n encoded_trace = np.zeros((len(trace), 3), dtype=int)\n encoded_trace[np.arange(len(trace)), trace] = 1\n return encoded_trace.flatten()","def one_hot_encode(df, col):\n return pd.get_dummies(df, columns=[col], drop_first=True)","def one_hot_encode(self, y: np.ndarray) -> np.ndarray:\n return np.eye(self.output_size)[y]","def _one_hot_encode(label_vector, total_num_labels):\n out = np.zeros(shape=(len(label_vector), total_num_labels))\n for i in range(len(label_vector)):\n out[i, int(label_vector[i])] = 1\n return out","def one_hot(index):\n\toutput = np.zeros(100)\n\toutput[index] = 1\n\treturn output","def one_hot(self, x, N):\n\n ### YOUR CODE HERE ###\n M = x.shape[0]\n one_hot = np.zeros((N, M))\n for i in range(M):\n one_hot[x[i], i] = 1\n \n# for xi,intWord in enumerate(x):\n# one_hot[xi,intWord] = 1\n \n\n assert one_hot.shape == (N, x.shape[0])\n return one_hot","def decode_one_hot(x):\n s = []\n for onehot in x:\n one_index = np.argmax(onehot)\n c = indices_char[one_index]\n s.append(c) \n return ''.join(s)","def encode_one_hot(s):\n all = []\n for c in s:\n x = np.zeros((INPUT_VOCAB_SIZE)) \n index = char_indices[c]\n x[index] = 1 \n all.append(x)\n return all","def one_hot_enc(self, word):\n word = self.text_to_int(word)\n word = Variable(torch.tensor(word))\n word = torch.nn.functional.one_hot(word, len(self.index_map))\n return word.transpose(0, 1)","def oneHot(index, n):\n x = np.zeros(n)\n x[index] = 1\n return x","def to_categorical(x, n_col=None):\n if not n_col:\n n_col = np.amax(x) + 1\n one_hot = np.zeros((x.shape[0], n_col))\n one_hot[np.arange(x.shape[0]), x] = 1\n return one_hot","def one_hot_encoding(sequence):\n\n mydict = {\n \"A\": np.asarray([1, 0, 0, 0]),\n \"a\": np.asarray([1, 0, 0, 0]),\n \"C\": np.asarray([0, 1, 0, 0]),\n \"c\": np.asarray([0, 1, 0, 0]),\n \"G\": np.asarray([0, 0, 1, 0]),\n \"g\": np.asarray([0, 0, 1, 0]),\n \"T\": np.asarray([0, 0, 0, 1]),\n \"t\": np.asarray([0, 0, 0, 1]),\n \"Y\": np.asarray([0, 1, 0, 1]),\n \"y\": np.asarray([0, 1, 0, 1]),\n \"R\": np.asarray([1, 0, 1, 0]),\n \"r\": np.asarray([1, 0, 1, 0]),\n \"S\": np.asarray([0, 1, 1, 0]),\n \"s\": np.asarray([0, 1, 1, 0]),\n \"W\": np.asarray([1, 0, 0, 1]),\n \"w\": np.asarray([1, 0, 0, 1]),\n \"K\": np.asarray([0, 0, 1, 1]),\n \"k\": np.asarray([0, 0, 1, 1]),\n \"M\": np.asarray([1, 1, 0, 0]),\n \"m\": np.asarray([1, 1, 0, 0]),\n \"B\": np.asarray([0, 1, 1, 1]),\n \"b\": np.asarray([0, 1, 1, 1]),\n \"D\": np.asarray([1, 0, 1, 1]),\n \"d\": np.asarray([1, 0, 1, 1]),\n \"H\": np.asarray([1, 1, 0, 1]),\n \"h\": np.asarray([1, 1, 0, 1]),\n \"V\": np.asarray([1, 1, 1, 0]),\n \"v\": np.asarray([1, 1, 1, 0]),\n \"N\": np.asarray([0, 0, 0, 0]),\n \"n\": np.asarray([0, 0, 0, 0]),\n \"-\": np.asarray([0, 0, 0, 0]),\n }\n print(f\"Seq: {sequence}\")\n if len(sequence) > 0:\n nuc_list = list()\n for nuc in list(sequence):\n nuc_list.append(mydict[nuc])\n result = np.stack(np.asarray(nuc_list, dtype=\"int8\"))\n return result\n else: \n print(\"ERROR! sequence is too short\")","def turn_to_one_hot(self,length,ind):\n one_hot = [0 for _ in range(length)]\n if (ind >= len(one_hot)):\n one_hot[-1] = 1\n else:\n one_hot[ind] = 1\n return one_hot","def as_one_hot(ind, n):\n vec = np.zeros(n)\n vec[ind] = 1\n return vec","def to_one_hot_encoding(target_data):\n target_data = target_data.squeeze()\n n_class = len(np.unique(target_data))\n res = np.eye(n_class)[target_data.astype(int)]\n return res","def one_hot(array, N):\r\n\r\n array = array.astype(int)\r\n assert numpy.max(array) < N\r\n assert numpy.min(array) >= 0\r\n\r\n one_hot = numpy.zeros((array.shape[0], N))\r\n one_hot[numpy.arange(array.shape[0]), array] = 1\r\n return one_hot","def make_one_hot(y):\n one_hot = np.zeros((len(y), 10))\n for i in range(len(y)):\n one_hot[i, y[i]] = 1.\n return one_hot.transpose(1,0)","def convert_to_onehot(vector, num_classes=None):\n assert isinstance(vector, np.ndarray)\n assert len(vector) > 0\n\n if num_classes is None:\n num_classes = np.max(vector)+1\n else:\n assert num_classes > 0\n assert num_classes >= np.max(vector)\n\n result = np.zeros(shape=(len(vector), num_classes))\n result[np.arange(len(vector)), vector] = 1\n return result.astype(int)","def onehot(index):\n classNum=2#1\n onehot = np.zeros(classNum)#这代表种类类型\n onehot[index] = 1.0\n return onehot","def one_hot(self) -> np.ndarray:\n array = np.zeros(10, dtype=int)\n array[self.of] = 1\n array.flags.writeable = False\n return array","def one_hot_encoding(data):\r\n encoder = LabelEncoder()\r\n y = encoder.fit_transform(data)\r\n return(y)","def one_hot(y, num_classes):\n return np.eye(num_classes)[y]","def one_hot(labels, dim):\n batch_size = labels.size(0)\n out = torch.zeros(batch_size, dim)\n out[np.arange(batch_size), labels.squeeze().long()] = 1\n return out","def one_hot(index, dims, dtype=np.uint8):\n\n seq_len = len(index)\n ret = np.zeros((seq_len, dims), dtype)\n for i in range(seq_len):\n ret[i][index[i]]=1\n\n return ret","def one_hot_converter(column):\n # encode class values as integers\n encoder = LabelEncoder()\n encoder.fit(column) \n encoded_ = encoder.transform(column)\n # convert integers to dummy variables, i.e., one-hot encoded\n encoded_column = to_categorical(encoded_)\n \n return encoded_column","def indices_to_one_hot(data, nb_classes):\n targets = np.array(data).reshape(-1)\n return np.eye(nb_classes)[targets]","def indices_to_one_hot(data, nb_classes):\n targets = np.array(data).reshape(-1)\n return np.eye(nb_classes)[targets]","def indices_to_one_hot(data, nb_classes):\n targets = np.array(data).reshape(-1)\n return np.eye(nb_classes)[targets]","def indices_to_one_hot(data, nb_classes):\n targets = np.array(data).reshape(-1)\n return np.eye(nb_classes)[targets]","def one_hot(Y, n_c):\n Y_onehot = np.zeros((Y.size, n_c))\n Y_onehot[np.arange(Y.size), Y] = 1\n return Y_onehot.T","def dense_to_one_hot(labels_dense, label):\n num_labels = len(labels_dense)\n index_offset = list(labels_dense).index(label)\n labels_one_hot = np.zeros(num_labels)\n labels_one_hot[index_offset] = 1\n return labels_one_hot","def one_hot_to_flat(val):\n idxs = np.array(np.where(val == 1.0))[-1]\n if len(val.shape) == 1:\n return int(idxs)\n return idxs","def _maybe_one_hot(self, obs):\n if self.toOneHot:\n obs = np.reshape(obs, (1, -1))\n ints = obs.dot(self.multiplication_factor)\n x = np.zeros([obs.shape[0], self.one_hot_len])\n for i, j in enumerate(ints):\n x[i, j] = 1\n return x\n else:\n return obs","def one_hot(x, k, dtype=np.float32):\n return np.array(x[:, None] == np.arange(k), dtype)","def int_to_one_hot(x, n=None):\n if isinstance(x, int):\n if n is None:\n raise ValueError('n is required to one-hot encode a single integer')\n if x >= n:\n raise ValueError('x must be smaller than n in order to one-hot encode')\n output = np.zeros((n,))\n output[x] = 1\n else:\n if n is None:\n n = int(np.max(x) + 1)\n else:\n if np.max(x) >= n:\n raise ValueError('The maximum value in x ({}) is greater than '\n 'n ({}), therefore 1-of-n encoding is not '\n 'possible'.format(np.max(x), n))\n x = np.array(x, dtype=np.int)\n if x.ndim is 1:\n x = x[:, None]\n orig_shp = x.shape\n x = np.reshape(x, (-1, orig_shp[-1]))\n output = np.zeros((x.shape[0], n))\n output[np.arange(x.shape[0]), x.squeeze()] = 1\n output = output.reshape(orig_shp[:-1] + (n,))\n\n return output","def to_onehot(value, dim):\n one_hot = torch.zeros(value.shape[0], dim)\n one_hot[torch.arange(value.shape[0]), value.long()] = 1\n return one_hot","def indices_to_one_hot(data, nb_classes):\n\ttargets = np.array(data).reshape(-1)\n\treturn np.eye(nb_classes)[targets]","def one_hot(x, k, dtype=np.float32):\n return np.array(x[:, None] == np.arange(k), dtype)","def conv_y_to_onehot_mat(labels):\n one_idx = np.array(labels)\n nkind = len(np.unique(one_idx))\n nlabels = len(one_idx)\n\n ret = np.zeros((nkind, nlabels))\n ret[one_idx, np.arange(nlabels)] = 1\n return ret","def one_hot_decode(one_hot):\n if type(one_hot) is not np.ndarray or len(one_hot.shape) != 2:\n return None\n elif not np.where((one_hot == 0) | (one_hot == 1), True, False).all():\n return None\n elif np.sum(one_hot) != len(one_hot[0]):\n return None\n else:\n return np.argmax(one_hot, axis=0)","def dense_to_one_hot(labels_dense, num_classes=10):\n num_labels = labels_dense.shape[0]\n #print('check num_labels',num_labels)\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def get_one_hot_encoded(self, label):\n try:\n label = str(label, 'utf-8').lower()\n except:\n label = str(label).lower() # hack for pytest TODO\n vector = np.zeros(self._labels_dim, dtype=int)\n index = self.get_label_2_index(label=label)\n vector[index] = 1\n return vector","def one_hot_encoding(gt, num_classes):\n if gt.ndim == 1:\n # multi-class classification\n return F.one_hot(gt, num_classes=num_classes)\n else:\n # binary classification\n # example. [[0], [1], [1]]\n # multi-label classification\n # example. [[0, 1, 1], [1, 0, 0], [1, 1, 1]]\n return gt","def __one_hot(self, y):\n y_one_hot = np.zeros((y.size, y.max() + 1))\n y_one_hot[np.arange(y.size), y] = 1\n \n return y_one_hot","def dense_to_one_hot(labels_dense, num_classes):\r\n print ('in onehot', labels_dense, num_classes)\r\n num_labels = labels_dense.shape[0]\r\n index_offset = numpy.arange(num_labels) * num_classes\r\n labels_one_hot = numpy.zeros((num_labels, num_classes))\r\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\r\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels) * num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def one_hot_encode(x):\n\n # check if encoder has been previously created, if not make a global var an initialize it\n if 'encoder' not in globals():\n global encoder\n encoder = LabelBinarizer()\n encoder.fit(range(10))\n\n return encoder.transform(x)","def one_hot(seq):\n prot_seq = seq\n BASES = 'ARNDCQEGHILKMFPSTWYV'\n bases = np.array([base for base in BASES])\n feat = np.concatenate(\n [[(bases == base.upper()).astype(int)] if str(base).upper() in BASES else np.array([[-1] * len(BASES)]) for base\n in prot_seq])\n return feat","def one_hot(self, x, vocab, n_grams) -> np.ndarray:\n _x = np.zeros((x.__len__(), self.max_length, vocab.__len__()))\n for i, item in enumerate(tqdm(x, desc='One Hot Text Encoder')):\n items = TextProcessor.n_gram_split(item, n_grams)\n for j, token in enumerate(items):\n if j >= self.max_length:\n break\n if token in vocab:\n idx = vocab.index(token)\n _x[i][j][idx] = 1\n\n return _x","def dense_to_one_hot(labels_dense, num_classes):\r\n num_labels = labels_dense.shape[0]\r\n index_offset = numpy.arange(num_labels) * num_classes\r\n labels_one_hot = numpy.zeros((num_labels, num_classes))\r\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\r\n return labels_one_hot","def label2onehot(self, labels, dim):\n batch_size = labels.size(0)\n out = torch.zeros(batch_size, dim)\n out[np.arange(batch_size), labels.long()] = 1\n return out","def label2onehot(self, batch_size, labels):\r\n dim = 6\r\n out = torch.zeros(batch_size, dim)\r\n out[np.arange(batch_size), labels] = 1\r\n return out","def dense_to_one_hot(labels, n_classes=2):\n labels = np.array(labels)\n n_labels = labels.shape[0]\n index_offset = np.arange(n_labels) * n_classes\n labels_one_hot = np.zeros((n_labels, n_classes), dtype=np.float32)\n labels_one_hot.flat[index_offset + labels.ravel()] = 1\n return labels_one_hot","def _get_one_hot(targets, num_classes):\n ret = np.zeros((num_classes, targets.shape[0]))\n ret[targets, np.arange(targets.size)] = 1\n return ret","def dense_to_one_hot(labels_dense, num_classes):\n\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels)*num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def one_hot_encode(df, ohe_cols):\n return pd.get_dummies(df, columns=ohe_cols)","def one_hot(self, y):\n\n one_hot = np.zeros((self.n_samples, self.n_classes))\n\n # using np.array to select elements of another np.array\n\n # first diemention index\n # np.arange(self.n_samples) : (1, n_samples)\n # row vectors (0,1,2.....n_samples-1) \n\n # second dimention index\n # \n\n\n one_hot[np.arange(self.n_samples), y.T] = 1\n return one_hot","def get_one_hot_vector(number):\n from sklearn.preprocessing import LabelBinarizer\n encoder = LabelBinarizer()\n seed_vector = np.arange(10)\n encoder.fit(seed_vector)\n labels = encoder.transform(seed_vector)\n return labels.astype(np.float32)","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels) * num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels) * num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels) * num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels) * num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def one_hot_encode(x_: ArrayLike) -> tuple[IntArray, dict[str, int]]:\n x: np.ndarray = np.copy(x_)\n if x.ndim == 1:\n x = x[:, np.newaxis]\n shape = x.shape\n has_na = np.any(pd.isna(x))\n if x.dtype == object:\n x = x.astype(str)\n categories, codes = np.unique(x, return_inverse=True)\n num_classes = len(categories)\n encoded_x = np.zeros((x.size, num_classes), dtype=np.uint8)\n encoded_x[np.arange(x.size), codes.astype(np.uint8).ravel()] = 1\n encoded_x = encoded_x.reshape(*shape, num_classes)\n if has_na:\n # remove NaN column\n categories = categories[:-1]\n encoded_x = encoded_x[:, :, :-1]\n mapping = {\n _category_name(category): code for code, category in enumerate(categories)\n }\n return encoded_x, mapping","def _one_hot(x, k, dtype=np.float32):\n return np.array(x[:, None] == np.arange(k), dtype)","def one_hot(labels):\n one_hot_labels = np.zeros(labels.shape + (n_actions,))\n for c in range(n_actions):\n one_hot_labels[labels == c, c] = 1.0\n return one_hot_labels","def global_class_to_one_hot(global_class):\n if global_class == 'Store':\n return [1, 0, 0, 0, 0]\n elif global_class == 'Home':\n return [0, 1, 0, 0, 0]\n elif global_class == 'Public spaces':\n return [0, 0, 1, 0, 0]\n elif global_class == 'Leisure':\n return [0, 0, 0, 1, 0]\n elif global_class == 'Working place':\n return [0, 0, 0, 0, 1]\n else:\n raise Exception('The global class \"' + global_class + '\" cant be transform to one-hot format')","def dense_to_one_hot(labels_dense, num_classes):\n\tnum_labels = labels_dense.shape[0]\n\tindex_offset = numpy.arange(num_labels) * num_classes\n\tlabels_one_hot = numpy.zeros((num_labels, num_classes))\n\tlabels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n\treturn labels_one_hot","def one_hot(class_ids, num_classes):\n oh = np.zeros((len(class_ids), num_classes), dtype=np.float32)\n oh[np.arange(len(class_ids)), class_ids] = 1\n\n assert (oh.argmax(axis=1) == class_ids).all()\n assert (oh.sum(axis=1) == 1).all()\n\n return oh","def one_hot_encode(labels, num_classes=None):\n if num_classes is None:\n num_classes = len(np.unique(labels))\n return np.eye(num_classes)[labels]"],"string":"[\n \"def binary_to_one_hot(arr: np.ndarray) -> np.ndarray:\\n res = np.zeros((arr.shape[0], 2))\\n res[np.where(arr == 1)[0], 0] = 1\\n res[np.where(arr == 0)[0], 1] = 1\\n return res\",\n \"def one_hot_encode(x):\\n # TODO: Implement Function\\n output = np.zeros((len(x), 10))\\n \\n for i, j in enumerate(x):\\n output[i,j] = 1\\n \\n return output\",\n \"def convert_to_one_hot(a):\\n a = a[:, 0]\\n a = a.astype(int)\\n A = np.zeros((len(a), config.num_classes))\\n A[np.arange(len(a)), a] = 1\\n return A\",\n \"def To1hot(label,num_class):\\n onehot = np.zeros(num_class)\\n onehot[label] = 1\\n return onehot\",\n \"def one_hot(x, num_classes, dtype=jnp.float32):\\n return jax.nn.one_hot(x, num_classes).astype(dtype)\",\n \"def one_hot_encode(x):\\n # TODO: Implement Function\\n lb = preprocessing.LabelBinarizer()\\n lb.fit([0,1,2,3,4,5,6,7,8,9])\\n \\n return lb.transform(x)\",\n \"def one_hot_encode(y, out_size):\\n n = len(y)\\n oh = np.zeros((n, out_size))\\n oh[range(n), y] = 1\\n return oh\",\n \"def one_hot(y_):\\n y_ = y_.reshape(len(y_))\\n n_values = int(np.max(y_)) + 1\\n n_values = 6\\n return np.eye(n_values)[np.array(y_, dtype=np.int32)] # Returns FLOATS\",\n \"def onehot(isTrue):\\n if isTrue:\\n return [1, 0]\\n else:\\n return [0, 1]\",\n \"def one_hot(x, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(x.max()+1), dtype)\",\n \"def to_onehot(x, num_classes):\\n return np.eye(num_classes, dtype='float32')[x]\",\n \"def one_hot_encode(x):\\n # TODO: Implement Function\\n x_l = list(x)\\n for index in np.arange(len(x_l)):\\n x_l[index] = get_one_hot_vector(x[index])[x[index]]\\n return np.array(x_l)\",\n \"def int2onehot(x, length):\\n assert x > 0 and x < length\\n assert isinstance(x, int)\\n assert isinstance(length, int)\\n ohv = np.zeros([length], dtype=np.float32)\\n ohv[x] = 1\\n return ohv\",\n \"def one_hot_encode(self, arr, n_labels):\\n one_hot = np.zeros((np.multiply(*arr.shape), n_labels), dtype=np.float32)\\n one_hot[np.arange(one_hot.shape[0]), arr.flatten()] = 1.\\n one_hot = one_hot.reshape((*arr.shape, n_labels))\\n \\n return one_hot\",\n \"def to_one_hot(v):\\n n = len(v)\\n m = max(v) + 1\\n out = np.zeros((n, m))\\n out[np.arange(n), v] = 1\\n return out\",\n \"def one_hot_encode(x, n_classes):\\n return np.eye(n_classes)[x]\",\n \"def _onehot(integer_labels):\\r\\n n_rows = len(integer_labels)\\r\\n n_cols = integer_labels.max() + 1\\r\\n onehot = np.zeros((n_rows, n_cols), dtype='uint8')\\r\\n onehot[np.arange(n_rows), integer_labels] = 1\\r\\n return onehot\",\n \"def label_to_one_hot(label, num_of_class=2):\\r\\n import numpy as np\\r\\n one_hot = np.zeros((len(label), num_of_class), dtype=np.uint8)\\r\\n for i in range(len(label)):\\r\\n one_hot[i, int(label[i] - 1)] = 1 # label is 1 and 2\\r\\n\\r\\n return one_hot\",\n \"def one_hot_encoding(y):\\n\\n y_oh = np.zeros((y.shape[0], y.max() - y.min() + 1))\\n\\n # currently only works in min is actually 0\\n for j in range(0, y_oh.shape[1]):\\n y_oh[np.where(y == j), j] = 1\\n\\n return y_oh\",\n \"def _onehot(y, n_classes=False):\\n if not n_classes:\\n \\\"\\\"\\\"Create one-hot encoded labels.\\\"\\\"\\\"\\n n_classes = len(set(y))\\n out = np.zeros((len(y), n_classes))\\n for i, ii in enumerate(y):\\n out[i][ii] += 1\\n y_onehot = out.astype(int)\\n return y_onehot\",\n \"def one_hot_encoding(data):\\n\\n data_encoded = pd.get_dummies(data)\\n\\n return data_encoded\",\n \"def one_hot_encoding(labels, num_classes=10):\\n num_labels = labels.shape[0]\\n encoded = np.zeros((num_labels, num_classes))\\n encoded[np.arange(num_labels), labels[np.arange(num_labels)]] = 1\\n \\n return encoded\",\n \"def make_onehot(x,num_labels=7):\\n enc = OneHotEncoder(n_values=num_labels)\\n return enc.fit_transform(np.array(x).reshape(-1, 1)).toarray()\",\n \"def to_one_hot(arr, num_classes):\\n arr = arr.data.astype(int)\\n a = np.zeros((arr.shape[0], num_classes))\\n a[np.arange(len(a)), arr] = 1\\n return tensor.Tensor(a, requires_grad=True)\",\n \"def to_one_hot(arr, num_classes):\\n arr = arr.data.astype(int)\\n a = np.zeros((arr.shape[0], num_classes))\\n a[np.arange(len(a)), arr] = 1\\n return tensor.Tensor(a, requires_grad=True)\",\n \"def one_hot(a, actions, dtype=t.float32):\\n assert 1 == a.shape[-1]\\n return (a == t.arange(actions, device=a.device)).to(dtype=dtype)\",\n \"def convert_one_hot_v2(g, op, block):\\n\\n x = g.get_node(op.input(\\\"X\\\")[0])\\n depth = op.attr(\\\"depth\\\")\\n dtype = op.attr(\\\"dtype\\\")\\n dtype = _convert_dtype_value(dtype)\\n ndim = len(infer_shape(x))\\n on_value = _op.const(1)\\n off_value = _op.const(0)\\n axis = ndim\\n out = _op.one_hot(x, on_value, off_value, depth, axis, dtype)\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def encode_one_hot2(s):\\n x = np.zeros((LINE_SIZE, INPUT_VOCAB_SIZE))\\n for n, c in enumerate(s):\\n index = char_indices[c]\\n x[n, index] = 1 \\n return x\",\n \"def onehot(trace):\\n encoded_trace = np.zeros((len(trace), 3), dtype=int)\\n encoded_trace[np.arange(len(trace)), trace] = 1\\n return encoded_trace.flatten()\",\n \"def one_hot_encode(df, col):\\n return pd.get_dummies(df, columns=[col], drop_first=True)\",\n \"def one_hot_encode(self, y: np.ndarray) -> np.ndarray:\\n return np.eye(self.output_size)[y]\",\n \"def _one_hot_encode(label_vector, total_num_labels):\\n out = np.zeros(shape=(len(label_vector), total_num_labels))\\n for i in range(len(label_vector)):\\n out[i, int(label_vector[i])] = 1\\n return out\",\n \"def one_hot(index):\\n\\toutput = np.zeros(100)\\n\\toutput[index] = 1\\n\\treturn output\",\n \"def one_hot(self, x, N):\\n\\n ### YOUR CODE HERE ###\\n M = x.shape[0]\\n one_hot = np.zeros((N, M))\\n for i in range(M):\\n one_hot[x[i], i] = 1\\n \\n# for xi,intWord in enumerate(x):\\n# one_hot[xi,intWord] = 1\\n \\n\\n assert one_hot.shape == (N, x.shape[0])\\n return one_hot\",\n \"def decode_one_hot(x):\\n s = []\\n for onehot in x:\\n one_index = np.argmax(onehot)\\n c = indices_char[one_index]\\n s.append(c) \\n return ''.join(s)\",\n \"def encode_one_hot(s):\\n all = []\\n for c in s:\\n x = np.zeros((INPUT_VOCAB_SIZE)) \\n index = char_indices[c]\\n x[index] = 1 \\n all.append(x)\\n return all\",\n \"def one_hot_enc(self, word):\\n word = self.text_to_int(word)\\n word = Variable(torch.tensor(word))\\n word = torch.nn.functional.one_hot(word, len(self.index_map))\\n return word.transpose(0, 1)\",\n \"def oneHot(index, n):\\n x = np.zeros(n)\\n x[index] = 1\\n return x\",\n \"def to_categorical(x, n_col=None):\\n if not n_col:\\n n_col = np.amax(x) + 1\\n one_hot = np.zeros((x.shape[0], n_col))\\n one_hot[np.arange(x.shape[0]), x] = 1\\n return one_hot\",\n \"def one_hot_encoding(sequence):\\n\\n mydict = {\\n \\\"A\\\": np.asarray([1, 0, 0, 0]),\\n \\\"a\\\": np.asarray([1, 0, 0, 0]),\\n \\\"C\\\": np.asarray([0, 1, 0, 0]),\\n \\\"c\\\": np.asarray([0, 1, 0, 0]),\\n \\\"G\\\": np.asarray([0, 0, 1, 0]),\\n \\\"g\\\": np.asarray([0, 0, 1, 0]),\\n \\\"T\\\": np.asarray([0, 0, 0, 1]),\\n \\\"t\\\": np.asarray([0, 0, 0, 1]),\\n \\\"Y\\\": np.asarray([0, 1, 0, 1]),\\n \\\"y\\\": np.asarray([0, 1, 0, 1]),\\n \\\"R\\\": np.asarray([1, 0, 1, 0]),\\n \\\"r\\\": np.asarray([1, 0, 1, 0]),\\n \\\"S\\\": np.asarray([0, 1, 1, 0]),\\n \\\"s\\\": np.asarray([0, 1, 1, 0]),\\n \\\"W\\\": np.asarray([1, 0, 0, 1]),\\n \\\"w\\\": np.asarray([1, 0, 0, 1]),\\n \\\"K\\\": np.asarray([0, 0, 1, 1]),\\n \\\"k\\\": np.asarray([0, 0, 1, 1]),\\n \\\"M\\\": np.asarray([1, 1, 0, 0]),\\n \\\"m\\\": np.asarray([1, 1, 0, 0]),\\n \\\"B\\\": np.asarray([0, 1, 1, 1]),\\n \\\"b\\\": np.asarray([0, 1, 1, 1]),\\n \\\"D\\\": np.asarray([1, 0, 1, 1]),\\n \\\"d\\\": np.asarray([1, 0, 1, 1]),\\n \\\"H\\\": np.asarray([1, 1, 0, 1]),\\n \\\"h\\\": np.asarray([1, 1, 0, 1]),\\n \\\"V\\\": np.asarray([1, 1, 1, 0]),\\n \\\"v\\\": np.asarray([1, 1, 1, 0]),\\n \\\"N\\\": np.asarray([0, 0, 0, 0]),\\n \\\"n\\\": np.asarray([0, 0, 0, 0]),\\n \\\"-\\\": np.asarray([0, 0, 0, 0]),\\n }\\n print(f\\\"Seq: {sequence}\\\")\\n if len(sequence) > 0:\\n nuc_list = list()\\n for nuc in list(sequence):\\n nuc_list.append(mydict[nuc])\\n result = np.stack(np.asarray(nuc_list, dtype=\\\"int8\\\"))\\n return result\\n else: \\n print(\\\"ERROR! sequence is too short\\\")\",\n \"def turn_to_one_hot(self,length,ind):\\n one_hot = [0 for _ in range(length)]\\n if (ind >= len(one_hot)):\\n one_hot[-1] = 1\\n else:\\n one_hot[ind] = 1\\n return one_hot\",\n \"def as_one_hot(ind, n):\\n vec = np.zeros(n)\\n vec[ind] = 1\\n return vec\",\n \"def to_one_hot_encoding(target_data):\\n target_data = target_data.squeeze()\\n n_class = len(np.unique(target_data))\\n res = np.eye(n_class)[target_data.astype(int)]\\n return res\",\n \"def one_hot(array, N):\\r\\n\\r\\n array = array.astype(int)\\r\\n assert numpy.max(array) < N\\r\\n assert numpy.min(array) >= 0\\r\\n\\r\\n one_hot = numpy.zeros((array.shape[0], N))\\r\\n one_hot[numpy.arange(array.shape[0]), array] = 1\\r\\n return one_hot\",\n \"def make_one_hot(y):\\n one_hot = np.zeros((len(y), 10))\\n for i in range(len(y)):\\n one_hot[i, y[i]] = 1.\\n return one_hot.transpose(1,0)\",\n \"def convert_to_onehot(vector, num_classes=None):\\n assert isinstance(vector, np.ndarray)\\n assert len(vector) > 0\\n\\n if num_classes is None:\\n num_classes = np.max(vector)+1\\n else:\\n assert num_classes > 0\\n assert num_classes >= np.max(vector)\\n\\n result = np.zeros(shape=(len(vector), num_classes))\\n result[np.arange(len(vector)), vector] = 1\\n return result.astype(int)\",\n \"def onehot(index):\\n classNum=2#1\\n onehot = np.zeros(classNum)#这代表种类类型\\n onehot[index] = 1.0\\n return onehot\",\n \"def one_hot(self) -> np.ndarray:\\n array = np.zeros(10, dtype=int)\\n array[self.of] = 1\\n array.flags.writeable = False\\n return array\",\n \"def one_hot_encoding(data):\\r\\n encoder = LabelEncoder()\\r\\n y = encoder.fit_transform(data)\\r\\n return(y)\",\n \"def one_hot(y, num_classes):\\n return np.eye(num_classes)[y]\",\n \"def one_hot(labels, dim):\\n batch_size = labels.size(0)\\n out = torch.zeros(batch_size, dim)\\n out[np.arange(batch_size), labels.squeeze().long()] = 1\\n return out\",\n \"def one_hot(index, dims, dtype=np.uint8):\\n\\n seq_len = len(index)\\n ret = np.zeros((seq_len, dims), dtype)\\n for i in range(seq_len):\\n ret[i][index[i]]=1\\n\\n return ret\",\n \"def one_hot_converter(column):\\n # encode class values as integers\\n encoder = LabelEncoder()\\n encoder.fit(column) \\n encoded_ = encoder.transform(column)\\n # convert integers to dummy variables, i.e., one-hot encoded\\n encoded_column = to_categorical(encoded_)\\n \\n return encoded_column\",\n \"def indices_to_one_hot(data, nb_classes):\\n targets = np.array(data).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def indices_to_one_hot(data, nb_classes):\\n targets = np.array(data).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def indices_to_one_hot(data, nb_classes):\\n targets = np.array(data).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def indices_to_one_hot(data, nb_classes):\\n targets = np.array(data).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def one_hot(Y, n_c):\\n Y_onehot = np.zeros((Y.size, n_c))\\n Y_onehot[np.arange(Y.size), Y] = 1\\n return Y_onehot.T\",\n \"def dense_to_one_hot(labels_dense, label):\\n num_labels = len(labels_dense)\\n index_offset = list(labels_dense).index(label)\\n labels_one_hot = np.zeros(num_labels)\\n labels_one_hot[index_offset] = 1\\n return labels_one_hot\",\n \"def one_hot_to_flat(val):\\n idxs = np.array(np.where(val == 1.0))[-1]\\n if len(val.shape) == 1:\\n return int(idxs)\\n return idxs\",\n \"def _maybe_one_hot(self, obs):\\n if self.toOneHot:\\n obs = np.reshape(obs, (1, -1))\\n ints = obs.dot(self.multiplication_factor)\\n x = np.zeros([obs.shape[0], self.one_hot_len])\\n for i, j in enumerate(ints):\\n x[i, j] = 1\\n return x\\n else:\\n return obs\",\n \"def one_hot(x, k, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(k), dtype)\",\n \"def int_to_one_hot(x, n=None):\\n if isinstance(x, int):\\n if n is None:\\n raise ValueError('n is required to one-hot encode a single integer')\\n if x >= n:\\n raise ValueError('x must be smaller than n in order to one-hot encode')\\n output = np.zeros((n,))\\n output[x] = 1\\n else:\\n if n is None:\\n n = int(np.max(x) + 1)\\n else:\\n if np.max(x) >= n:\\n raise ValueError('The maximum value in x ({}) is greater than '\\n 'n ({}), therefore 1-of-n encoding is not '\\n 'possible'.format(np.max(x), n))\\n x = np.array(x, dtype=np.int)\\n if x.ndim is 1:\\n x = x[:, None]\\n orig_shp = x.shape\\n x = np.reshape(x, (-1, orig_shp[-1]))\\n output = np.zeros((x.shape[0], n))\\n output[np.arange(x.shape[0]), x.squeeze()] = 1\\n output = output.reshape(orig_shp[:-1] + (n,))\\n\\n return output\",\n \"def to_onehot(value, dim):\\n one_hot = torch.zeros(value.shape[0], dim)\\n one_hot[torch.arange(value.shape[0]), value.long()] = 1\\n return one_hot\",\n \"def indices_to_one_hot(data, nb_classes):\\n\\ttargets = np.array(data).reshape(-1)\\n\\treturn np.eye(nb_classes)[targets]\",\n \"def one_hot(x, k, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(k), dtype)\",\n \"def conv_y_to_onehot_mat(labels):\\n one_idx = np.array(labels)\\n nkind = len(np.unique(one_idx))\\n nlabels = len(one_idx)\\n\\n ret = np.zeros((nkind, nlabels))\\n ret[one_idx, np.arange(nlabels)] = 1\\n return ret\",\n \"def one_hot_decode(one_hot):\\n if type(one_hot) is not np.ndarray or len(one_hot.shape) != 2:\\n return None\\n elif not np.where((one_hot == 0) | (one_hot == 1), True, False).all():\\n return None\\n elif np.sum(one_hot) != len(one_hot[0]):\\n return None\\n else:\\n return np.argmax(one_hot, axis=0)\",\n \"def dense_to_one_hot(labels_dense, num_classes=10):\\n num_labels = labels_dense.shape[0]\\n #print('check num_labels',num_labels)\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def get_one_hot_encoded(self, label):\\n try:\\n label = str(label, 'utf-8').lower()\\n except:\\n label = str(label).lower() # hack for pytest TODO\\n vector = np.zeros(self._labels_dim, dtype=int)\\n index = self.get_label_2_index(label=label)\\n vector[index] = 1\\n return vector\",\n \"def one_hot_encoding(gt, num_classes):\\n if gt.ndim == 1:\\n # multi-class classification\\n return F.one_hot(gt, num_classes=num_classes)\\n else:\\n # binary classification\\n # example. [[0], [1], [1]]\\n # multi-label classification\\n # example. [[0, 1, 1], [1, 0, 0], [1, 1, 1]]\\n return gt\",\n \"def __one_hot(self, y):\\n y_one_hot = np.zeros((y.size, y.max() + 1))\\n y_one_hot[np.arange(y.size), y] = 1\\n \\n return y_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\r\\n print ('in onehot', labels_dense, num_classes)\\r\\n num_labels = labels_dense.shape[0]\\r\\n index_offset = numpy.arange(num_labels) * num_classes\\r\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\r\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\r\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels) * num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def one_hot_encode(x):\\n\\n # check if encoder has been previously created, if not make a global var an initialize it\\n if 'encoder' not in globals():\\n global encoder\\n encoder = LabelBinarizer()\\n encoder.fit(range(10))\\n\\n return encoder.transform(x)\",\n \"def one_hot(seq):\\n prot_seq = seq\\n BASES = 'ARNDCQEGHILKMFPSTWYV'\\n bases = np.array([base for base in BASES])\\n feat = np.concatenate(\\n [[(bases == base.upper()).astype(int)] if str(base).upper() in BASES else np.array([[-1] * len(BASES)]) for base\\n in prot_seq])\\n return feat\",\n \"def one_hot(self, x, vocab, n_grams) -> np.ndarray:\\n _x = np.zeros((x.__len__(), self.max_length, vocab.__len__()))\\n for i, item in enumerate(tqdm(x, desc='One Hot Text Encoder')):\\n items = TextProcessor.n_gram_split(item, n_grams)\\n for j, token in enumerate(items):\\n if j >= self.max_length:\\n break\\n if token in vocab:\\n idx = vocab.index(token)\\n _x[i][j][idx] = 1\\n\\n return _x\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\r\\n num_labels = labels_dense.shape[0]\\r\\n index_offset = numpy.arange(num_labels) * num_classes\\r\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\r\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\r\\n return labels_one_hot\",\n \"def label2onehot(self, labels, dim):\\n batch_size = labels.size(0)\\n out = torch.zeros(batch_size, dim)\\n out[np.arange(batch_size), labels.long()] = 1\\n return out\",\n \"def label2onehot(self, batch_size, labels):\\r\\n dim = 6\\r\\n out = torch.zeros(batch_size, dim)\\r\\n out[np.arange(batch_size), labels] = 1\\r\\n return out\",\n \"def dense_to_one_hot(labels, n_classes=2):\\n labels = np.array(labels)\\n n_labels = labels.shape[0]\\n index_offset = np.arange(n_labels) * n_classes\\n labels_one_hot = np.zeros((n_labels, n_classes), dtype=np.float32)\\n labels_one_hot.flat[index_offset + labels.ravel()] = 1\\n return labels_one_hot\",\n \"def _get_one_hot(targets, num_classes):\\n ret = np.zeros((num_classes, targets.shape[0]))\\n ret[targets, np.arange(targets.size)] = 1\\n return ret\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels)*num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def one_hot_encode(df, ohe_cols):\\n return pd.get_dummies(df, columns=ohe_cols)\",\n \"def one_hot(self, y):\\n\\n one_hot = np.zeros((self.n_samples, self.n_classes))\\n\\n # using np.array to select elements of another np.array\\n\\n # first diemention index\\n # np.arange(self.n_samples) : (1, n_samples)\\n # row vectors (0,1,2.....n_samples-1) \\n\\n # second dimention index\\n # \\n\\n\\n one_hot[np.arange(self.n_samples), y.T] = 1\\n return one_hot\",\n \"def get_one_hot_vector(number):\\n from sklearn.preprocessing import LabelBinarizer\\n encoder = LabelBinarizer()\\n seed_vector = np.arange(10)\\n encoder.fit(seed_vector)\\n labels = encoder.transform(seed_vector)\\n return labels.astype(np.float32)\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels) * num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels) * num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels) * num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels) * num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def one_hot_encode(x_: ArrayLike) -> tuple[IntArray, dict[str, int]]:\\n x: np.ndarray = np.copy(x_)\\n if x.ndim == 1:\\n x = x[:, np.newaxis]\\n shape = x.shape\\n has_na = np.any(pd.isna(x))\\n if x.dtype == object:\\n x = x.astype(str)\\n categories, codes = np.unique(x, return_inverse=True)\\n num_classes = len(categories)\\n encoded_x = np.zeros((x.size, num_classes), dtype=np.uint8)\\n encoded_x[np.arange(x.size), codes.astype(np.uint8).ravel()] = 1\\n encoded_x = encoded_x.reshape(*shape, num_classes)\\n if has_na:\\n # remove NaN column\\n categories = categories[:-1]\\n encoded_x = encoded_x[:, :, :-1]\\n mapping = {\\n _category_name(category): code for code, category in enumerate(categories)\\n }\\n return encoded_x, mapping\",\n \"def _one_hot(x, k, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(k), dtype)\",\n \"def one_hot(labels):\\n one_hot_labels = np.zeros(labels.shape + (n_actions,))\\n for c in range(n_actions):\\n one_hot_labels[labels == c, c] = 1.0\\n return one_hot_labels\",\n \"def global_class_to_one_hot(global_class):\\n if global_class == 'Store':\\n return [1, 0, 0, 0, 0]\\n elif global_class == 'Home':\\n return [0, 1, 0, 0, 0]\\n elif global_class == 'Public spaces':\\n return [0, 0, 1, 0, 0]\\n elif global_class == 'Leisure':\\n return [0, 0, 0, 1, 0]\\n elif global_class == 'Working place':\\n return [0, 0, 0, 0, 1]\\n else:\\n raise Exception('The global class \\\"' + global_class + '\\\" cant be transform to one-hot format')\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n\\tnum_labels = labels_dense.shape[0]\\n\\tindex_offset = numpy.arange(num_labels) * num_classes\\n\\tlabels_one_hot = numpy.zeros((num_labels, num_classes))\\n\\tlabels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n\\treturn labels_one_hot\",\n \"def one_hot(class_ids, num_classes):\\n oh = np.zeros((len(class_ids), num_classes), dtype=np.float32)\\n oh[np.arange(len(class_ids)), class_ids] = 1\\n\\n assert (oh.argmax(axis=1) == class_ids).all()\\n assert (oh.sum(axis=1) == 1).all()\\n\\n return oh\",\n \"def one_hot_encode(labels, num_classes=None):\\n if num_classes is None:\\n num_classes = len(np.unique(labels))\\n return np.eye(num_classes)[labels]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8336464","0.8243893","0.8077957","0.8006649","0.792823","0.79020256","0.7887456","0.7865496","0.78533834","0.7835221","0.7825861","0.7818072","0.7791989","0.7786955","0.77542466","0.77538735","0.77049476","0.7704875","0.76757723","0.76493716","0.76375353","0.7634562","0.7628089","0.7607766","0.7607766","0.7605397","0.75757253","0.7545504","0.7540205","0.7539761","0.75357324","0.7532761","0.7521911","0.75194776","0.7506236","0.7504179","0.74916434","0.7487735","0.74725664","0.74631846","0.74414396","0.743445","0.74275327","0.74273247","0.7420955","0.74130565","0.7400249","0.73940456","0.7382244","0.73711073","0.7368601","0.73447406","0.73402715","0.7330741","0.7330741","0.7330741","0.7330741","0.732595","0.73253834","0.7320683","0.731116","0.7304354","0.7296453","0.72758514","0.7274945","0.72713536","0.7264586","0.7261942","0.72573125","0.72475237","0.7240243","0.72388476","0.7238842","0.72374326","0.72374326","0.72374326","0.72371095","0.7225553","0.7224489","0.7224379","0.7209574","0.72069347","0.7205543","0.7204133","0.71960473","0.7190637","0.71896464","0.7189136","0.71873486","0.71721464","0.71721464","0.71721464","0.71721464","0.7171331","0.7162303","0.7159995","0.71575105","0.7150268","0.71310055","0.71169436","0.710268"],"string":"[\n \"0.8336464\",\n \"0.8243893\",\n \"0.8077957\",\n \"0.8006649\",\n \"0.792823\",\n \"0.79020256\",\n \"0.7887456\",\n \"0.7865496\",\n \"0.78533834\",\n \"0.7835221\",\n \"0.7825861\",\n \"0.7818072\",\n \"0.7791989\",\n \"0.7786955\",\n \"0.77542466\",\n \"0.77538735\",\n \"0.77049476\",\n \"0.7704875\",\n \"0.76757723\",\n \"0.76493716\",\n \"0.76375353\",\n \"0.7634562\",\n \"0.7628089\",\n \"0.7607766\",\n \"0.7607766\",\n \"0.7605397\",\n \"0.75757253\",\n \"0.7545504\",\n \"0.7540205\",\n \"0.7539761\",\n \"0.75357324\",\n \"0.7532761\",\n \"0.7521911\",\n \"0.75194776\",\n \"0.7506236\",\n \"0.7504179\",\n \"0.74916434\",\n \"0.7487735\",\n \"0.74725664\",\n \"0.74631846\",\n \"0.74414396\",\n \"0.743445\",\n \"0.74275327\",\n \"0.74273247\",\n \"0.7420955\",\n \"0.74130565\",\n \"0.7400249\",\n \"0.73940456\",\n \"0.7382244\",\n \"0.73711073\",\n \"0.7368601\",\n \"0.73447406\",\n \"0.73402715\",\n \"0.7330741\",\n \"0.7330741\",\n \"0.7330741\",\n \"0.7330741\",\n \"0.732595\",\n \"0.73253834\",\n \"0.7320683\",\n \"0.731116\",\n \"0.7304354\",\n \"0.7296453\",\n \"0.72758514\",\n \"0.7274945\",\n \"0.72713536\",\n \"0.7264586\",\n \"0.7261942\",\n \"0.72573125\",\n \"0.72475237\",\n \"0.7240243\",\n \"0.72388476\",\n \"0.7238842\",\n \"0.72374326\",\n \"0.72374326\",\n \"0.72374326\",\n \"0.72371095\",\n \"0.7225553\",\n \"0.7224489\",\n \"0.7224379\",\n \"0.7209574\",\n \"0.72069347\",\n \"0.7205543\",\n \"0.7204133\",\n \"0.71960473\",\n \"0.7190637\",\n \"0.71896464\",\n \"0.7189136\",\n \"0.71873486\",\n \"0.71721464\",\n \"0.71721464\",\n \"0.71721464\",\n \"0.71721464\",\n \"0.7171331\",\n \"0.7162303\",\n \"0.7159995\",\n \"0.71575105\",\n \"0.7150268\",\n \"0.71310055\",\n \"0.71169436\",\n \"0.710268\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93769,"cells":{"query":{"kind":"string","value":"Generates a bot object"},"document":{"kind":"string","value":"def __init__(self, order, timer):\n super().__init__(order)\n self.name = 'Bot' + str(order + 1)\n self.is_checked = False\n self.timer = timer\n # vpip, pfr and agg stored as integer% (/ 100)\n # TODO\n # Dictionary?\n # Translate to playable hand ratings\n self.vpip = 18\n # Translate to preflop 3-bet hand ratings\n self.pfr = 14\n self.agg = 00\n self.total_hand = Hand()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, bot=BNBot):\n self.bot = bot","def __init__(self, bot):\n self.bot = bot","def __init__(self, bot):\n self.bot = bot","def bot_factory(url):\n from dallinger_experiment.experiment import Bot\n\n return Bot(url)","def makebot(self, name, cfg=None):\n\n bot = None\n\n # if not config create a default bot\n if not cfg:\n cfg = Config(self.datadir + os.sep + name, 'config', inittxt=fleetbotconfigtxt)\n cfg.save()\n\n # create bot based on type \n if cfg['type'] == 'irc':\n from gozerbot.irc.bot import Bot\n bot = Bot(cfg)\n elif cfg['type'] == 'jabber':\n from gozerbot.jabber.jabberbot import Jabberbot\n bot = Jabberbot(cfg)\n else:\n logging.debug('unproper type: %s' % cfg['type'])\n\n # set bot name and initialize bot\n if bot:\n cfg['name'] = bot.name = name\n self.initbot(bot)\n return bot\n\n # failed to created the bot\n raise Exception(\"can't make %s bot\" % name)","def _init_bot(bot_type, game, player_id):\n rng = np.random.RandomState(FLAGS.seed)\n if bot_type == \"mcts\":\n evaluator = mcts.RandomRolloutEvaluator(FLAGS.rollout_count, rng)\n return mcts.MCTSBot(\n game,\n FLAGS.uct_c,\n FLAGS.max_simulations,\n evaluator,\n random_state=rng,\n solve=FLAGS.solve,\n verbose=FLAGS.verbose)\n if bot_type == \"az\":\n model = az_model.Model.from_checkpoint(FLAGS.az_path)\n evaluator = az_evaluator.AlphaZeroEvaluator(game, model)\n return mcts.MCTSBot(\n game,\n FLAGS.uct_c,\n FLAGS.max_simulations,\n evaluator,\n random_state=rng,\n child_selection_fn=mcts.SearchNode.puct_value,\n solve=FLAGS.solve,\n verbose=FLAGS.verbose)\n if bot_type == \"random\":\n return uniform_random.UniformRandomBot(player_id, rng)\n if bot_type == \"human\":\n return human.HumanBot()\n if bot_type == \"gtp\":\n bot = gtp.GTPBot(game, FLAGS.gtp_path)\n for cmd in FLAGS.gtp_cmd:\n bot.gtp_cmd(cmd)\n return bot\n raise ValueError(\"Invalid bot type: %s\" % bot_type)","def __init__(self, bot: DreamBot) -> None:\n\n self.bot = bot","def __init__(self, bot: DreamBot) -> None:\n\n self.bot = bot","def create_chitchat_bot(self):\n\n # Hint: you might want to create and train chatterbot.ChatBot here.\n # It could be done by creating ChatBot with the *trainer* parameter equals \n # \"chatterbot.trainers.ChatterBotCorpusTrainer\"\n # and then calling *train* function with \"chatterbot.corpus.english\" param\n \n ########################\n #### YOUR CODE HERE ####\n ########################\n self.chatbot = ChatBot(\n 'skoochbot',\n trainer='chatterbot.trainers.ChatterBotCorpusTrainer'\n )\n \n self.chatbot.train(\"chatterbot.corpus.english\")\n print(\"Extra training...\")\n \n self.chatbot.set_trainer(ListTrainer)\n \n self.chatbot.train([\n \"Hello\",\n \"Hello. How are you?\",\n \"I am well.\",\n \"Good to hear\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"How are you?\",\n \"I am well. How are you?\",\n \"I am also well.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"Your momma\",\n \"is so fat they gave her her own zipcode.\",\n ])\n self.chatbot.train([\n \"How are you doing?\",\n \"I am well. How are you?\",\n \"I am also well.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"What's your name?\",\n \"My name is skoochbot. What is yours?\",\n \"That is my name too!\",\n \"Really?\",\n \"No.\",\n \"Yes.\",\n \"No.\",\n \"Yes.\",\n \"So, what can I help you with today?\",\n ])\n self.chatbot.train([\n \"No\",\n \"Yes\",\n \"No\",\n \"Yes it does\",\n \"No it doesn't\",\n \"Yes it does\",\n \"So, what can I help you with today?\",\n ])\n self.chatbot.train([\n \"What is your name?\",\n \"My name is skoochbot. What is yours?\",\n \"That's a nice name.\",\n \"Thank you.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"Fuck you\",\n \"No, fuck you buddy\",\n \"You suck\",\n \"No, you suck\",\n \"No, you suck more\",\n \"I hate you so much\",\n \"I hate you too\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"Where are you?\",\n \"At your momma's house.\",\n \"Where do you live?\",\n \"Your momma's house.\",\n \"Where are you from?\",\n \"Somewhere over the rainbow.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"Who are you?\",\n \"I'm your worst nightmare.\",\n \"You can run but you can't hide, bitch.\",\n \"This is a dead parrot.\",\n \"It's just sleeping.\",\n \"Well you better wake him up then, hadn't you?\",\n \"So, what can I help you with today?\",\n \"This parrot is dead.\",\n \"No, it's just taking a little nap.\"\n ])\n self.chatbot.train([\n \"I'm squanching here!\",\n \"Sorry carry on.\",\n \"Thank you for the privacy.\",\n \"You are welcome.\",\n \"Let's get schwifty.\",\n \"Let's do it up in here.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"How are you?\",\n \"I am good\",\n \"That is good to hear.\",\n \"Thank you.\",\n \"You are welcome.\",\n \"So, what can I help you with today?\",\n \"What is AI?\",\n \"Your momma.\",\n \"What are your hobbies?\",\n \"Your momma and AI.\",\n \"What's your hobby?\",\n \"Your momma and AI. What is your hobby?\",\n \"What is your hobby?\",\n \"Your momma.\"\n ])\n self.chatbot.train([\n \"WHAT DO YOU WANT?\",\n \"Well, I was told outside that.\",\n \"Don't give me that, you snotty-faced heap of parrot droppings!\",\n \"What?\",\n \"Shut your festering gob, you tit! Your type really makes me puke, you vacuous, coffee-nosed, malodorous, pervert!!!\",\n \"Look, I CAME HERE FOR AN ARGUMENT\",\n \"OH, oh I'm sorry, but this is abuse.\",\n ])\n self.chatbot.train([\n \"Is this the right room for an argument?\",\n \"I told you once.\",\n \"No you haven't.\",\n \"Yes I have.\",\n \"When?\",\n \"Just now\",\n \"No you didn't\",\n \"Yes I did\",\n \"You didn't\",\n \"I'm telling you I did\",\n \"Oh, I'm sorry, just one moment. Is this a five minute argument or the full half hour?\",\n \"Oh look, this isn't an argument.\",\n \"Yes it is\",\n \"No, it's just contradiction.\"\n \"No it isn't.\",\n \"Yes it is.\"\n ])","def create(self, name):\n success, response = self._client_api.gen_request(req_type='post',\n path='/projects/{}/bots'.format(self.project.id),\n json_req={'name': name})\n if success:\n bot = entities.Bot.from_json(_json=response.json(),\n project=self.project,\n bots=self, client_api=self._client_api)\n else:\n raise exceptions.PlatformException(response)\n assert isinstance(bot, entities.Bot)\n return bot","def get_class(bot):\n return TosurnamentPlayerCog(bot)","def create_chitchat_bot(self):\n \n self.chatbot = ChatBot(\"Fresher's Friend\")\n self.chatbot.trainer2=ListTrainer(self.chatbot)\n self.chatbot.trainer=ChatterBotCorpusTrainer(self.chatbot)\n self.chatbot.trainer.train(\"chatterbot.corpus.english.greetings\")\n #self.chatbot.trainer.train(\"chatterbot.corpus.english.conversations\")\n for filename in filenames: \n self.chatbot.trainer2.train(files[filename])","async def creator(message: types.Message):\n link = 'https://https://github.com/germanjke/StyleTransformerGANs'\n link2 = '@logisticregression'\n await message.answer(text=f'Bot created for final Deep Learning School by MIPT project .\\\n \\nLink to github repo: {link}, contact me: {link2}')","def __init__(__self__, *,\n data_privacy: pulumi.Input['DataPrivacyPropertiesArgs'],\n idle_session_ttl_in_seconds: pulumi.Input[int],\n role_arn: pulumi.Input[str],\n auto_build_bot_locales: Optional[pulumi.Input[bool]] = None,\n bot_file_s3_location: Optional[pulumi.Input['BotS3LocationArgs']] = None,\n bot_locales: Optional[pulumi.Input[Sequence[pulumi.Input['BotLocaleArgs']]]] = None,\n bot_tags: Optional[pulumi.Input[Sequence[pulumi.Input['BotTagArgs']]]] = None,\n description: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n test_bot_alias_settings: Optional[pulumi.Input['BotTestBotAliasSettingsArgs']] = None,\n test_bot_alias_tags: Optional[pulumi.Input[Sequence[pulumi.Input['BotTagArgs']]]] = None):\n pulumi.set(__self__, \"data_privacy\", data_privacy)\n pulumi.set(__self__, \"idle_session_ttl_in_seconds\", idle_session_ttl_in_seconds)\n pulumi.set(__self__, \"role_arn\", role_arn)\n if auto_build_bot_locales is not None:\n pulumi.set(__self__, \"auto_build_bot_locales\", auto_build_bot_locales)\n if bot_file_s3_location is not None:\n pulumi.set(__self__, \"bot_file_s3_location\", bot_file_s3_location)\n if bot_locales is not None:\n pulumi.set(__self__, \"bot_locales\", bot_locales)\n if bot_tags is not None:\n pulumi.set(__self__, \"bot_tags\", bot_tags)\n if description is not None:\n pulumi.set(__self__, \"description\", description)\n if name is not None:\n pulumi.set(__self__, \"name\", name)\n if test_bot_alias_settings is not None:\n pulumi.set(__self__, \"test_bot_alias_settings\", test_bot_alias_settings)\n if test_bot_alias_tags is not None:\n pulumi.set(__self__, \"test_bot_alias_tags\", test_bot_alias_tags)","def generate():","def buildProtocol(self, addr):\n protocol = PortalBot()\n protocol.factory = self\n protocol.nickname = self.nickname\n protocol.channel = self.channel\n protocol.network = self.network\n protocol.port = self.port\n protocol.ssl = self.ssl\n protocol.nicklist = []\n return protocol","def __init__(self, poolbot):\n self.poolbot = poolbot","def generate(self):\n\t\traise BuilderException(\"You can not use this class directly!\")","def create_bot(config: IrcConfig) -> Union[Bot, DummyBot]:\n if config.server is None:\n logger.info('No IRC server specified; will write to STDOUT instead.')\n return DummyBot(config.channels)\n\n return Bot(\n config.server,\n config.nickname,\n config.realname,\n config.commands,\n config.channels,\n )","def generate(self):\n pass","def __init__(self, username, password, bot, channel):\n super().__init__(username, password)\n\n self.queue = deque()\n self.ingame_cog = Ingame(bot)\n\n self.bot = bot\n self.channel = channel\n self.chat_breakout = False\n self.loop = asyncio.get_event_loop()\n self.ingame_cog.is_pycraft_instance = True","def generate(self):\n pass","def generate(self):\n pass","def generate(self):\n pass","def __init__(self, address=('', 50000), authkey=b'tradingbot'):\n _ClientBot.__init__(self, address=address, authkey=authkey)","async def set_bot():\n\n self = await LOCAL.APP.get_me()\n LOCAL.bot_id = self.id\n LOCAL.bot_name = self.first_name\n LOCAL.bot_username = self.username","def generate(self):","def connect(self):\n bot_ammount_current = len(self.bots)\n if bot_ammount_current==0:\n self.wave_counter+=1\n self.bot_ammount_wave = ((self.wave_counter // 2) + 1) * 5\n bot_ammount_create = self.bot_ammount_wave if self.bot_ammount_wave<=50 else 50\n for _ in range(bot_ammount_create):\n pos_cofig = self.pos_dict_bot[random.randint(1,2)]\n start_pos = pos_cofig[0], random.sample(pos_cofig[1], 1)[0]\n self.bots.append(Bot(self.game, start_pos[0], start_pos[1]))\n self.bot_ammount_wave -= 1\n elif bot_ammount_current < 50 and self.bot_ammount_wave>0:\n bot_ammount_create = 50 - bot_ammount_current\n if self.bot_ammount_wave < bot_ammount_create:\n bot_ammount_create = self.bot_ammount_wave\n for _ in range(bot_ammount_create):\n pos_cofig = self.pos_dict_bot[random.randint(1,2)]\n start_pos = pos_cofig[0], random.sample(pos_cofig[1], 1)[0]\n self.bots.append(Bot(self.game, start_pos[0], start_pos[1]))\n self.bot_ammount_wave -= 1\n\n current_time = time.perf_counter()\n if current_time-self.time_last_gunpack > self.gunpck_frequency:\n self.game.arena.create_gunpack()\n self.time_last_gunpack = current_time","def generate(cls, name=''):\n # Create the class\n self = cls()\n\n # Name\n self.name = name\n\n # Generate luck\n self.luck = randint(1, 10)\n\n # Total health\n self.total_health = randint(\n (self.max_health * self.health_mult_1),\n (self.max_health * self.health_mult_2)\n )\n\n # Max our health out\n self.add_health(self.max_health)\n\n # Add default weapons\n self.weapons = choice(self.default_weapons)\n\n # Set our describing word\n self.word = choice(self.word_choice)\n\n # Inventory should be empty\n self.inventory = dict()\n\n # Level and xp\n self.level = 1\n self.xp = 1\n\n return self","def makebot(*args, checkdepends: bool=True, checkres: int=0, input: AnyStr=\"\", nooverwrite:\n bool=True, output: AnyStr=\"\", verbose: bool=True, **kwargs)->None:\n pass","async def CoMLegendBuilder(self, ctx):\n me = CoachService.discord_user_to_coach(ctx.author)\n data = getattr(special_play, inspect.currentframe().f_code.co_name)(ctx.channel.name, me)\n await self.send_embed(data, ctx)","def make(self):\n pass","def make_object():\n return object()","async def main() -> None:\n logging.basicConfig(\n level=logging.INFO,\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n )\n logger.error(\"Starting the bot\")\n config = load_config(\"bot.ini\")\n\n if config.tg_bot.use_redis:\n storage = RedisStorage2()\n else:\n storage = MemoryStorage()\n\n pool = await create_pool(\n user=config.db.user,\n password=config.db.password,\n database=config.db.database,\n host=config.db.host,\n )\n\n bot = Bot(token=config.tg_bot.token, parse_mode=ParseMode.HTML)\n dp = Dispatcher(bot=bot, storage=storage)\n\n dp.middleware.setup(DbMiddleware(pool))\n dp.middleware.setup(RoleMiddleware(config.tg_bot.admin_id))\n dp.filters_factory.bind(RoleFilter)\n\n admin.register_admin(dp)\n user.register_user(dp)\n await set_basic_commands(dp)\n\n try:\n await dp.start_polling(allowed_updates=False)\n finally:\n await dp.storage.close()\n await dp.storage.wait_closed()\n await bot.session.close()","def default_robot(): #py:default_robot\n class Robot(UsedRobot):\n def __init__(self):\n self.body = RUR._default_robot_body_()\n return Robot()","def bot():\n log(log.DEBUG, 'Starting bot')\n bot = BotSad()\n bot.listen()","def getBuilder():","def generate(cls):\n raise NotImplementedError()","def main():\n\n loop = asyncio.get_event_loop()\n bot = MyBot()\n\n # bot.pool = loop.run_until_complete(DB.create_pool(\n bot.pool = loop.run_until_complete(Table().create_pool(\n user=POSTGRES_USER,\n password=POSTGRES_PASS,\n database=POSTGRES_DB,\n host=POSTGRES_HOST\n ))\n # bot.db_manager = DB()\n bot.run()","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None) -> 'Bot':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = BotArgs.__new__(BotArgs)\n\n __props__.__dict__[\"arn\"] = None\n __props__.__dict__[\"auto_build_bot_locales\"] = None\n __props__.__dict__[\"bot_file_s3_location\"] = None\n __props__.__dict__[\"bot_locales\"] = None\n __props__.__dict__[\"bot_tags\"] = None\n __props__.__dict__[\"data_privacy\"] = None\n __props__.__dict__[\"description\"] = None\n __props__.__dict__[\"idle_session_ttl_in_seconds\"] = None\n __props__.__dict__[\"name\"] = None\n __props__.__dict__[\"role_arn\"] = None\n __props__.__dict__[\"test_bot_alias_settings\"] = None\n __props__.__dict__[\"test_bot_alias_tags\"] = None\n return Bot(resource_name, opts=opts, __props__=__props__)","async def setup(bot):\n await bot.add_cog(People(bot))","def generate():\n PackCommandExecutor().pack()\n GenerateCommandExecutor().generate()","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n auto_build_bot_locales: Optional[pulumi.Input[bool]] = None,\n bot_file_s3_location: Optional[pulumi.Input[pulumi.InputType['BotS3LocationArgs']]] = None,\n bot_locales: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['BotLocaleArgs']]]]] = None,\n bot_tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['BotTagArgs']]]]] = None,\n data_privacy: Optional[pulumi.Input[pulumi.InputType['DataPrivacyPropertiesArgs']]] = None,\n description: Optional[pulumi.Input[str]] = None,\n idle_session_ttl_in_seconds: Optional[pulumi.Input[int]] = None,\n name: Optional[pulumi.Input[str]] = None,\n role_arn: Optional[pulumi.Input[str]] = None,\n test_bot_alias_settings: Optional[pulumi.Input[pulumi.InputType['BotTestBotAliasSettingsArgs']]] = None,\n test_bot_alias_tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['BotTagArgs']]]]] = None,\n __props__=None):\n ...","def default_factory(*args, **kwargs):\n obj = RandomGameEntity()\n obj.build(*args, **kwargs)\n return obj","def set_builder_bot(self, builder_bot): \n self.builder_bot = builder_bot # pragma: no cover","def run_bot():\n client = commands.Bot(command_prefix=\"!\", case_insensitive=True)\n\n load_cogs(client)\n\n client.run(token)","async def _bot():\n await bot.say('Yes, the bot is cool.')","def setup(bot: util.CustomBot):\r\n bot.add_cog(Info(bot))","def main():\n\n merch_bot = MerchBot()\n merch_bot.run_bot()","async def botinfo(ctx, bot: typing.Union[discord.Member, discord.User]):\n if not bot.bot:\n return await r(ctx, 'Not a bot.')\n\n data = await make_request(\"https://www.motiondevelopment.top/api/v1.2/bots/\", bot.id)\n \n e = discord.Embed(\n title=f'Available bot info for {bot}',\n color=0xfecdea,\n description=f\"**Short Bot Description:** (do `uwu desc [bot]` for big description)\\n\\n*{data['Small_desc']}*\"\n )\n\n if data[\"bot_status\"] == \"online\":\n status = '<:online:805576670353948702> Online'\n elif data[\"bot_status\"] == \"idle\":\n status = '<:idle:805855470778056725> Idle'\n elif data[\"bot_status\"] == \"offline\":\n status = '<:offline:805576352450871346> Offline'\n elif data[\"bot_status\"] == \"dnd\":\n status = '<:dnd:819964146317393990> Do Not Disturb'\n\n listed_at = datetime.datetime.strptime(data[\"list_date\"], '%Y-%m-%d')\n\n e.add_field(\n name='Owner:', value=f'**{data[\"owner_name\"]}**\\n({data[\"owner_id\"]})', inline=False)\n e.add_field(name='Tags:', value=', '.join(data[\"tops\"]))\n e.add_field(name='Vanity URL:', value=data[\"vanity_url\"]\n if data[\"vanity_url\"] != '' else 'No vanity URL set.', inline=False)\n e.add_field(name='Bot Status:', value=status)\n e.add_field(name='Invites:',\n value=f'[Bot Invite]({data[\"invite\"]})\\n[Bot Support Server](https://discord.gg/{data[\"discord\"]})', inline=False)\n e.add_field(name='Other Bot Info:', value=f'''\n **Prefix:** `{data[\"prefix\"]}`\n **Site:** {data[\"site\"] if data[\"site\"] != '' else \"No sites.\"}\n **Library:** {data[\"lib\"]}\n **Listed at:** {listed_at}\n **Server Count:** {data[\"servers\"] if data[\"servers\"] != 'None' else '*Not set up!*'}''', inline=False)\n e.set_thumbnail(url=f'https://cdn.discordapp.com/avatars/{data[\"id\"]}/{data[\"avatar\"]}')\n await em(ctx, embed=e)","def __init__(self, address=('', 50000), authkey=b'tradingbot'):\n self.id = 0\n _ClientBot.__init__(self, address=address, authkey=authkey)\n self.conn_tbm = ConnTradingBotManager(self.id)","def buildProtocol(self, addr):\r\n p = RobotWebSocketProtocol(self._realm)\r\n p.factory = self\r\n return p","def add_bot(self, bot):\n self.add_user(bot)","async def link(ctx, bot: typing.Union[discord.Member, discord.User]):\n if not bot.bot:\n return await r(ctx, \"Not a bot.\")\n await r(ctx, f'<https://www.motiondevelopment.top/bots/{bot.id}>')","def build_goal_generation(\n cls, constants: CType, mujoco_simulation: SType\n ) -> GoalGenerator:\n pass","def __init__(self, bot: BunkBot, channels: ChannelService):\r\n self.bot: BunkBot = bot\r\n self.message: Message = None\r\n self.channels: ChannelService = channels\r\n self.yt_result: YoutubeResult = YoutubeResult()\r\n self.yt_link: str = \"\"","def parse_bot(self) -> None:\n if not self.skip_bot_detection and not self.bot:\n self.bot = Bot(\n self.user_agent,\n self.ua_hash,\n self.ua_spaceless,\n self.VERSION_TRUNCATION,\n ).parse()\n self.all_details['bot'] = self.bot.ua_data","def buildCommand(self, player, game, json):","def __new__(cls, *, bot = ..., description = ..., icon = ..., name = ...):\n # bot\n if bot is ...:\n bot = ZEROUSER\n else:\n bot = validate_bot(bot)\n \n # description\n if description is ...:\n description = None\n else:\n description = validate_description(description)\n \n # icon\n if icon is ...:\n icon = None\n else:\n icon = cls.icon.validate_icon(icon, allow_data = True)\n \n # name\n if name is ...:\n name = ''\n else:\n name = validate_name(name)\n \n self = object.__new__(cls)\n self.bot = bot\n self.description = description\n self.icon = icon\n self.id = 0\n self.name = name\n return self","def getBuilder(name):","def getBuilder(name):","def generatebomberman(self, board):\n self.createperson(board, self.x_pos, self.y_pos, 'B')\n return board","def __init__(self, bot_name, plan, email, registration_id,\n event_name='', sandbox_mode=False):\n self.email = email\n initialization_data = {\n 'username': bot_name,\n 'email': email,\n 'plan': plan,\n 'sandbox': sandbox_mode,\n }\n if event_name:\n initialization_data['event_name'] = event_name\n if registration_id:\n initialization_data['event_id'] = registration_id\n\n self.websocket = create_connection('ws://codelift.org/v2/building')\n res = self.websocket.recv()\n if res != '{\"status\":\"connected\",\"message\":\"Connected\"}':\n raise('unexpected connection failure:' + res)\n state = self._get_world_state(initialization_data)\n if state['status'] == 'error':\n print(state['message'])\n exit()\n self.status = state['status']\n print(state['message'])\n # print('building url: {}'.format(self.building_url))\n # print('visualization url: {}'.format(self.visualization_url))","def __init__(self, email: str=None, is_bot: bool=None, avatar_url: str=None, avatar_version: int=None, full_name: str=None, is_admin: bool=None, is_owner: bool=None, is_billing_admin: bool=None, role: int=None, bot_type: int=None, user_id: int=None, bot_owner_id: int=None, is_active: bool=None, is_guest: bool=None, timezone: str=None, date_joined: str=None, delivery_email: str=None, profile_data: Dict[str, object]=None):\n self.openapi_types = {\n 'email': str,\n 'is_bot': bool,\n 'avatar_url': str,\n 'avatar_version': int,\n 'full_name': str,\n 'is_admin': bool,\n 'is_owner': bool,\n 'is_billing_admin': bool,\n 'role': int,\n 'bot_type': int,\n 'user_id': int,\n 'bot_owner_id': int,\n 'is_active': bool,\n 'is_guest': bool,\n 'timezone': str,\n 'date_joined': str,\n 'delivery_email': str,\n 'profile_data': Dict[str, object]\n }\n\n self.attribute_map = {\n 'email': 'email',\n 'is_bot': 'is_bot',\n 'avatar_url': 'avatar_url',\n 'avatar_version': 'avatar_version',\n 'full_name': 'full_name',\n 'is_admin': 'is_admin',\n 'is_owner': 'is_owner',\n 'is_billing_admin': 'is_billing_admin',\n 'role': 'role',\n 'bot_type': 'bot_type',\n 'user_id': 'user_id',\n 'bot_owner_id': 'bot_owner_id',\n 'is_active': 'is_active',\n 'is_guest': 'is_guest',\n 'timezone': 'timezone',\n 'date_joined': 'date_joined',\n 'delivery_email': 'delivery_email',\n 'profile_data': 'profile_data'\n }\n\n self._email = email\n self._is_bot = is_bot\n self._avatar_url = avatar_url\n self._avatar_version = avatar_version\n self._full_name = full_name\n self._is_admin = is_admin\n self._is_owner = is_owner\n self._is_billing_admin = is_billing_admin\n self._role = role\n self._bot_type = bot_type\n self._user_id = user_id\n self._bot_owner_id = bot_owner_id\n self._is_active = is_active\n self._is_guest = is_guest\n self._timezone = timezone\n self._date_joined = date_joined\n self._delivery_email = delivery_email\n self._profile_data = profile_data","def generate(cls, **kwargs) -> str:\n pass","def make_connection_bot(connection_string):\n engine = create_engine(connection_string)\n connection = engine.connect()\n Session = sessionmaker(bind=engine)\n session = Session()\n return session","def __init__(self,bot_name,message_type):\n\n self.name=bot_name #set bot name\n if (message_type != \"message-out\" and message_type != \"message-in\"):#check if message type is correct\n raise message_type_error(message_type) #if not, raise message_type_error exception\n else:\n self.message_class=message_type #set message type\n\n self.driver = webdriver.Chrome('C:\\Python36\\chromedriver.exe') #launch chrome driver\n self.driver.get('https://web.whatsapp.com') #connect to the website","def exec(self,):\n self.chatbot.printStructureChatbotDict()","async def _bot(ctx):\n await ctx.send('Yes, the bot is cool.')","def setup(bot):\n\tbot.add_cog(Wallet(bot))","def __init__(self, bot, filename=\"\"):\n if len(filename) > 0:\n self.filename = filename\n self.quotes_list = datastore.DataStore(self.filename)\n self.bot=bot\n random.seed()","async def generate(self, prompt: str):\n result_handle = await self.handle.generate.remote(prompt)\n return await result_handle","def test_new_bot(\n self, mock_get_ai_details, mock_get_ai, mock_get_categories\n ):\n\n mock_get_ai.return_value = self.ai\n mock_get_ai_details.return_value = self.ai_details\n\n response = self.client.get(reverse(\n 'studio:edit_bot',\n kwargs={'aiid': self.ai['aiid']}\n ))\n\n self.assertContains(response, 'Build a bot from text-based simple Q&A '\n 'using the inline editor.')\n self.assertContains(response, 'Want to structure your dialog, build more '\n 'complex Q&A or connect to 3rd party services.')\n self.assertContains(response, 'Speed up your bot building process by '\n 'starting with one of our Templates from the store.')","def __init__(self, pos, inventory=None):\n _GenericBot.__init__(self, pos, inventory)\n self._changes = {} # Changes to the world","def setup(bot: Bot) -> None:\n bot.add_cog(Armory(bot))","def bot(app, debug):\n if debug is None:\n verify_id(None, None, app)\n\n (id, tmp) = setup_experiment(log)\n\n if debug:\n url = debug\n else:\n heroku_app = HerokuApp(dallinger_uid=app)\n worker = generate_random_id()\n hit = generate_random_id()\n assignment = generate_random_id()\n ad_url = \"{}/ad\".format(heroku_app.url)\n ad_parameters = \"assignmentId={}&hitId={}&workerId={}&mode=sandbox\"\n ad_parameters = ad_parameters.format(assignment, hit, worker)\n url = \"{}?{}\".format(ad_url, ad_parameters)\n bot = bot_factory(url)\n bot.run_experiment()","def create_cbuildbot_factory(target, gs_path, short_name):\n # Factory with the steps to pull out a Chromium source tree (no compilation).\n # It uses an unused slave_type string to avoid adding the normal compile step.\n chrome_factory = linux().ChromiumFactory(slave_type='WebRtcCros')\n\n archive_base = 'gs://chromeos-image-archive/%s/%s' % (target, gs_path)\n cbuildbot_params = '--archive-base=%s %s' % (archive_base, target)\n\n # Extend that factory with Cbuildbot build steps to build and test CrOS using\n # the Chrome from the above Chromium source tree.\n return chromeos_factory.CbuildbotFactory(\n params=cbuildbot_params,\n buildroot='/b/cbuild.%s' % short_name,\n chrome_root='.', # Where ChromiumFactory has put \"Chrome\".\n factory=chrome_factory,\n slave_manager=False).get_factory()","async def new(ctx):\n if ctx.message.channel.name.lower() not in tod_channels:\n return\n\n room = ctx.message.channel.name.lower()\n host = ctx.message.author\n if room not in tod_games:\n tod_games[room] = {'host': host.name, 'host_id': host.name, 'participants': {}, 'last': None}\n tod_games[room]['current'] = host.name\n tod_games[room]['last'] = host.name\n tod_games[room]['participants'][host.name.lower()] = {'spins': 0}\n await amor_manager.say(\"New Game of Truth Or Dare started in {}\".format(room))\n else:\n host = tod_games[room]['host']\n await amor_manager.say(\"Truth or Dare already in progress in {}. Game host: {}\".format(room, host))","def new(cls, base_repo, semester, section, username):\n return cls.from_url(\"http://mockhub.com/\", \"token\")","def buildProtocol(self, addr):\n if hasattr(settings, \"DISCORD_SESSION_CLASS\"):\n protocol_class = class_from_module(\n settings.DISCORD_SESSION_CLASS, fallback=DiscordClient\n )\n protocol = protocol_class()\n else:\n protocol = DiscordClient()\n\n protocol.factory = self\n protocol.sessionhandler = self.sessionhandler\n return protocol","def roomba_create():\n global LIST_OF_ROOMBAS\n LIST_OF_ROOMBAS = []\n\n for position in range(0, 2):\n new_roomba = OtherRoomba(position)\n LIST_OF_ROOMBAS.append(new_roomba)\n if position == 1:\n # Sends 5 bytes of 111 to indicate that it is roomba 1\n server.send(b'\\x6F\\x6F\\x6F\\x6F\\x6F')\n else:\n # Sends 5 bytes of 222 to indicate that is roomba 2\n server.send(b'\\xDE\\xDE\\xDE\\xDE\\xDE')\n # Sends the byte command for the Roomba to drive out randomly\n server.send('\\x00\\x00\\x00\\x00\\x01')\n shuffle(LIST_OF_ROOMBAS)","def generate_bottonDown(self):\n posx = self.posx + self.sizex - 2*self.sizebotx \n posy = self.posy + self.sizeboty \n return Boton(posx, posy, self.sizebotx, self.sizeboty, color=(124, 117, 221), color_h=(95, 90, 173), text_color=(0,0,0), press_color = (36, 26, 117), font_size=5)","def InitializeDatabase(session, params={}):\n name = 'My Bot'\n symbols = []\n quote_asset = 'BTC'\n starting_balance = 0.001\n test_run = False\n initial_entry_allocation = 25\n signal_distance = 0.3\n profit_target = 2\n stop_loss_value = 0\n exit_on_signal = False\n\n if params.__contains__('name'):\n name = params['name']\n\n if params.__contains__('symbols'):\n symbols = params['symbols']\n\n if params.__contains__('quote_asset'):\n quote_asset = params['quote_asset']\n\n if params.__contains__('starting_balance'):\n starting_balance = params['starting_balance']\n\n if params.__contains__('test_run'):\n test_run = params['test_run']\n\n if params.__contains__('initial_entry_allocation'):\n initial_entry_allocation = params['initial_entry_allocation']\n\n if params.__contains__('signal_distance'):\n signal_distance = params['signal_distance']\n\n if params.__contains__('profit_target'):\n profit_target = params['profit_target']\n\n if params.__contains__('stop_loss_value'):\n stop_loss_value = params['stop_loss_value']\n\n if params.__contains__('exit_on_signal'):\n exit_on_signal = params['exit_on_signal']\n\n\n myobject = Bot(\n name = name,\n quote_asset = quote_asset,\n starting_balance = starting_balance,\n current_balance = starting_balance,\n test_run = test_run\n )\n\n session.add(myobject)\n\n entrysets = EntrySettings(\n initial_entry_allocation = initial_entry_allocation,\n signal_distance = signal_distance\n )\n \n exitsets = ExitSettings(\n profit_target = profit_target, # in %\n stop_loss_value = stop_loss_value, # in %\n exit_on_signal = exit_on_signal\n )\n myobject.entry_settings = entrysets\n myobject.exit_settings = exitsets\n session.commit()\n\n for symbol in symbols:\n pair = Pair(\n bot_id = myobject.id,\n symbol = symbol,\n current_order_id = None\n )\n session.add(pair)\n\n session.commit()","def bot_init():\n client.Console.Say('Hello World')\n global player\n player = client.GetPlayer()","async def build(self, ctx: Context):\n # Simulates that the bot is typing to visually show user command is being processed\n async with ctx.typing(): \n start_time = time()\n description= f'{emojis[\"discord\"]} Configuring `{ctx.guild.name}` for verification...\\n'\n\n guild = ctx.message.guild\n bot_role = get(ctx.guild.roles, name='AberLink')\n everyone_role = get(ctx.guild.roles, name='@everyone')\n verified_role = get(ctx.guild.roles, name='verified')\n verify_channel = get(guild.channels, name='verify')\n verify_perms = discord.PermissionOverwrite()\n verified_role_perms = discord.Permissions(\n send_messages=True, read_messages=True, read_message_history=True, \n change_nickname=True, embed_links=True, attach_files=True, \n add_reactions=True, external_emojis=True, \n connect=True, speak=True, stream=True, use_voice_activation=True\n )\n\n #make sure that the bots position is above that of verification\n bot_role.edit(position=2)\n\n # Change permissions on @everyone role\n await everyone_role.edit(reason='Configuring everyone role for verify', permissions=discord.Permissions())\n description += f'{int((time() - start_time) * 1000)}ms: `@everyone` removed all permissions\\n'\n # {int((end_time - start_time) * 1000)}\n \n # Create or modify verified role\n if verified_role is not None:\n await verified_role.edit(reason='Updating old verified role', permissions=verified_role_perms)\n description += f'{int((time() - start_time) * 1000)}ms: `verified` role already exists, updating to match permissions...\\n'\n else:\n verified_role = await guild.create_role(reason='Creating verified role', name='verified', permissions=verified_role_perms)\n verified_role.edit(position=1)\n description += f'{int((time() - start_time) * 1000)}ms: `verified` role created\\n'\n \n # Gives the bot the verified role\n bot = await guild.fetch_member(ctx.bot.user.id)\n await bot.add_roles(verified_role)\n\n # Create or modify verify channel\n if verify_channel is not None:\n description += f'{int((time() - start_time) * 1000)}ms: `verify` channel already exists, updating to match permissions...\\n'\n message = await verify_channel.send(f'Welcome to `{guild.name}`! If you are seeing this message then please type `!verify`')\n await message.pin()\n else:\n verify_channel = await guild.create_text_channel('verify')\n description += f'{int((time() - start_time) * 1000)}ms: `verify` channel created\\n'\n message = await verify_channel.send(f'Welcome to `{guild.name}`! If you are seeing this message then please type `!verify`')\n await message.pin()\n \n # Set permissions for roles in verify channel\n verify_perms.read_messages = True\n verify_perms.send_messages = True\n verify_perms.read_message_history = True\n await verify_channel.set_permissions(everyone_role, overwrite=verify_perms)\n verify_perms.read_messages = False\n verify_perms.send_messages = False\n await verify_channel.set_permissions(verified_role, overwrite=verify_perms)\n description += f'{emojis[\"aberlink\"]} This server is now setup for verification!'\n embed = Embed(description=description, colour=discord.Colour.green())\n await ctx.send(embed=embed)","async def randomproject(self, ctx):\r\n if await bMsg(ctx,ctx.message.author.name,client):\r\n return\r\n logger.info('Scratch.randomproject', extra={'invoker': ctx.message.author.name})\r\n async with ctx.channel.typing():\r\n count = json.loads(await self.req('https://api.scratch.mit.edu/projects/count/all'))['count']\r\n comments = None\r\n while comments is None:\r\n pid = random.randint(1, count)\r\n comments = await self.req('https://scratch.mit.edu/site-api/comments/project/' + str(pid))\r\n await ctx.send('https://scratch.mit.edu/projects/' + str(pid))","async def send_welcome(message: types.Message):\n await message.reply(\"Hi!\\nI'm Autoencoder Bot!\\nPowered by aiogram.\\n\\n\"\n \"/random - for get generated image from nose\\n\"\n \"send photo - show decoded variant from autoencoder\\n\"\n \"TODO: - make smile on photo, generate glasses and heads\")","def setup(bot: Red):\n bot.add_cog(Welcome(bot))","async def setup(bot: Bot) -> None:\n await bot.add_cog(Utils(bot))","def setup(bot):\n bot.add_cog(Session(bot))","def __init__(self, imag_bot, block_, player_loc):\n self._bot = imag_bot\n self._block = block_\n self._player_loc = player_loc","async def setup(bot):\n await bot.add_cog(WordleCmd(bot))","def get_commands(bot):\n new_commands = []\n\n new_commands.append(Command(\n 'mycommand', subcommands=[\n SubCommand(\n Opt('myoption'),\n doc='This is a simple command with a single required option.'),\n SubCommand(\n Opt('custom', optional=True),\n Opt('attached', optional=True, attached='attached argument'),\n doc='This has two different optional options, one without an attached '\n 'parameter, and the other requiring an attached parameter.'),\n SubCommand(\n Opt('trailing'),\n Arg('arg 1'),\n Arg('arg 2'),\n Arg('arg 3', argtype=ArgTypes.SPLIT, additional='more args'),\n doc='This command requires a lot of trailing arguments.'),\n SubCommand(\n Opt('grouped'),\n Arg('grouped arguments', argtype=ArgTypes.MERGED),\n doc='This will group all given arguments as a single string.'),\n SubCommand(\n Opt('complex', attached='attached'),\n Opt('other', optional=True, attached='also required'),\n Arg('arg 1'),\n Arg('arg 2', argtype=ArgTypes.SPLIT_OPTIONAL, additional='more args'),\n doc='The complex option has a required attached parameter, and the '\n '\\'other\\' option also has a required attached parameter if '\n '\\'other\\' is included. Additionally, there will be a requirement '\n 'of at least 1 trailing argument.'),\n SubCommand(\n Opt('marquee'),\n Arg('text', argtype=ArgTypes.MERGED,\n check=lambda b, m, v, *a: len(v) <= 100,\n check_error=\"Marquee message must be less than 100 characters long.\"),\n doc='Creates a marquee that loops 3 times.')],\n shortcuts=[\n Shortcut(\n 'complex', 'complex {attached} other {other} {arg 1} {arg 2}',\n Arg('attached'), Arg('other'), Arg('arg 1'),\n Arg('arg 2', argtype=ArgTypes.SPLIT_OPTIONAL)),\n Shortcut(\n 'marquee', 'marquee {text}', Arg('text', argtype=ArgTypes.MERGED))],\n description='Your command description here.',\n other='This text is optional - it just shows up after everything '\n 'else. Quick note, all of the commands here can only be used by '\n 'bot moderators or above, as indicated by elevated_level. A '\n 'level of 2 would mean only server owners or above can use the '\n 'command, and a level of 3 would restrict the command to only '\n 'the bot owners.',\n elevated_level=1, category='demo'))\n\n new_commands.append(Command(\n 'myothercommand', subcommands=[\n SubCommand(\n Arg('text', argtype=ArgTypes.MERGED_OPTIONAL),\n doc='This traps all further commands from being executed.'),\n SubCommand(\n Opt('order'), Opt('matters'),\n doc='It is impossible to access this command because the first '\n 'subcommand will always be satisfied first. Order of the '\n 'subcommand matters!'),\n SubCommand(\n Opt('sample'), Opt('foo'), Opt('bar'),\n doc='Also impossible to access. This subcommand just adds some '\n 'keywords to the command.')],\n description='Only bot owners can see this text!',\n other='Note that no shortcuts were defined. They, too, are optional. '\n 'Also, this command is hidden, which means that only the bot '\n 'owners can see this command listed from the help command. '\n 'However, unless the command is configured with an elevated '\n 'permissions level, any user can still execute the command. '\n 'Users still will not be able to see the specific help for this '\n 'command, though. Lastly, this command is disabled in DMs.',\n hidden=True, allow_direct=False, category='demo'))\n\n new_commands.append(Command(\n 'notify', subcommands=[\n SubCommand(\n Arg('text', argtype=ArgTypes.MERGED),\n doc='Notify the owners with some text!')],\n other='This command uses a custom function. It is called with the '\n 'same arguments as get_response. The command will show up to '\n 'all users in the help command, but can only be used by server '\n 'owners, as it is disallowed in direct messages.',\n elevated_level=2, allow_direct=False, function=custom_notify,\n category='demo'))\n\n new_commands.append(Command(\n 'wait', other='Use this command to demo the wait_for functionality', category='demo'))\n\n return new_commands","def generate(gui):\n\n global robot_obj\n global ftm_list\n global btm_list\n global fk_list\n global jac_list\n global com_list\n global com_jac_list\n\n ftm = ftm_list if gui.checkBox_ftm.isChecked() else []\n btm = btm_list if gui.checkBox_btm.isChecked() else []\n fk = fk_list if gui.checkBox_fk.isChecked() else []\n jac = jac_list if gui.checkBox_jac.isChecked() else []\n com = com_list if gui.checkBox_com.isChecked() else []\n com_jac = com_jac_list if gui.checkBox_com_jac.isChecked() else []\n\n language = Language(settings[\"language\"])\n optimization_level = settings[\"optimization_level\"]\n\n generate_everything(robot_obj, ftm, btm,\n fk, jac, com, com_jac,\n polynomial_trajectories,\n control_loops_list,\n optimization_level,\n language,\n path + '../generated/' + settings[\"filename\"],\n progressbar=gui.progressBar)","def bot(self):\n return self._bot","async def welcome_command(ctx):\n await ctx.send(f\"Hello! I am a bot made by {ctx.bot.owner}\")","def create(ctx):\n pass","def generate(self):\n return self.gen.generate()","def generate(self):\r\n raise NotImplementedError","def make_bb_object(name, data):\n global BLACKBOARD, TRACE_LEVEL\n bb_obj = BB_object(name, data)\n if TRACE_LEVEL > 2:\n print \"\\tCreating {0} object: {1}\".format( type(data), bb_obj )\n BLACKBOARD[name] = bb_obj\n signal_creation_event(bb_obj)\n return bb_obj","def executeBot(bot):\n try:\n line = \"\"\n message = \"\"\n line=sys.stdin.readline()\n while(line):\n if( \"go\" in line ):\n gs = GameState(message)\n bot.doTurn(gs)\n gs.finishTurn()\n message=\"\"\n elif( \"name?\" in line ):\n print bot.getName()\n sys.stdout.flush()\n else:\n message=message+line+os.linesep\n line=sys.stdin.readline()\n except Exception as e:\n sys.stderr.write(\"shit happens : \")\n print e"],"string":"[\n \"def __init__(self, bot=BNBot):\\n self.bot = bot\",\n \"def __init__(self, bot):\\n self.bot = bot\",\n \"def __init__(self, bot):\\n self.bot = bot\",\n \"def bot_factory(url):\\n from dallinger_experiment.experiment import Bot\\n\\n return Bot(url)\",\n \"def makebot(self, name, cfg=None):\\n\\n bot = None\\n\\n # if not config create a default bot\\n if not cfg:\\n cfg = Config(self.datadir + os.sep + name, 'config', inittxt=fleetbotconfigtxt)\\n cfg.save()\\n\\n # create bot based on type \\n if cfg['type'] == 'irc':\\n from gozerbot.irc.bot import Bot\\n bot = Bot(cfg)\\n elif cfg['type'] == 'jabber':\\n from gozerbot.jabber.jabberbot import Jabberbot\\n bot = Jabberbot(cfg)\\n else:\\n logging.debug('unproper type: %s' % cfg['type'])\\n\\n # set bot name and initialize bot\\n if bot:\\n cfg['name'] = bot.name = name\\n self.initbot(bot)\\n return bot\\n\\n # failed to created the bot\\n raise Exception(\\\"can't make %s bot\\\" % name)\",\n \"def _init_bot(bot_type, game, player_id):\\n rng = np.random.RandomState(FLAGS.seed)\\n if bot_type == \\\"mcts\\\":\\n evaluator = mcts.RandomRolloutEvaluator(FLAGS.rollout_count, rng)\\n return mcts.MCTSBot(\\n game,\\n FLAGS.uct_c,\\n FLAGS.max_simulations,\\n evaluator,\\n random_state=rng,\\n solve=FLAGS.solve,\\n verbose=FLAGS.verbose)\\n if bot_type == \\\"az\\\":\\n model = az_model.Model.from_checkpoint(FLAGS.az_path)\\n evaluator = az_evaluator.AlphaZeroEvaluator(game, model)\\n return mcts.MCTSBot(\\n game,\\n FLAGS.uct_c,\\n FLAGS.max_simulations,\\n evaluator,\\n random_state=rng,\\n child_selection_fn=mcts.SearchNode.puct_value,\\n solve=FLAGS.solve,\\n verbose=FLAGS.verbose)\\n if bot_type == \\\"random\\\":\\n return uniform_random.UniformRandomBot(player_id, rng)\\n if bot_type == \\\"human\\\":\\n return human.HumanBot()\\n if bot_type == \\\"gtp\\\":\\n bot = gtp.GTPBot(game, FLAGS.gtp_path)\\n for cmd in FLAGS.gtp_cmd:\\n bot.gtp_cmd(cmd)\\n return bot\\n raise ValueError(\\\"Invalid bot type: %s\\\" % bot_type)\",\n \"def __init__(self, bot: DreamBot) -> None:\\n\\n self.bot = bot\",\n \"def __init__(self, bot: DreamBot) -> None:\\n\\n self.bot = bot\",\n \"def create_chitchat_bot(self):\\n\\n # Hint: you might want to create and train chatterbot.ChatBot here.\\n # It could be done by creating ChatBot with the *trainer* parameter equals \\n # \\\"chatterbot.trainers.ChatterBotCorpusTrainer\\\"\\n # and then calling *train* function with \\\"chatterbot.corpus.english\\\" param\\n \\n ########################\\n #### YOUR CODE HERE ####\\n ########################\\n self.chatbot = ChatBot(\\n 'skoochbot',\\n trainer='chatterbot.trainers.ChatterBotCorpusTrainer'\\n )\\n \\n self.chatbot.train(\\\"chatterbot.corpus.english\\\")\\n print(\\\"Extra training...\\\")\\n \\n self.chatbot.set_trainer(ListTrainer)\\n \\n self.chatbot.train([\\n \\\"Hello\\\",\\n \\\"Hello. How are you?\\\",\\n \\\"I am well.\\\",\\n \\\"Good to hear\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"How are you?\\\",\\n \\\"I am well. How are you?\\\",\\n \\\"I am also well.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"Your momma\\\",\\n \\\"is so fat they gave her her own zipcode.\\\",\\n ])\\n self.chatbot.train([\\n \\\"How are you doing?\\\",\\n \\\"I am well. How are you?\\\",\\n \\\"I am also well.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"What's your name?\\\",\\n \\\"My name is skoochbot. What is yours?\\\",\\n \\\"That is my name too!\\\",\\n \\\"Really?\\\",\\n \\\"No.\\\",\\n \\\"Yes.\\\",\\n \\\"No.\\\",\\n \\\"Yes.\\\",\\n \\\"So, what can I help you with today?\\\",\\n ])\\n self.chatbot.train([\\n \\\"No\\\",\\n \\\"Yes\\\",\\n \\\"No\\\",\\n \\\"Yes it does\\\",\\n \\\"No it doesn't\\\",\\n \\\"Yes it does\\\",\\n \\\"So, what can I help you with today?\\\",\\n ])\\n self.chatbot.train([\\n \\\"What is your name?\\\",\\n \\\"My name is skoochbot. What is yours?\\\",\\n \\\"That's a nice name.\\\",\\n \\\"Thank you.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"Fuck you\\\",\\n \\\"No, fuck you buddy\\\",\\n \\\"You suck\\\",\\n \\\"No, you suck\\\",\\n \\\"No, you suck more\\\",\\n \\\"I hate you so much\\\",\\n \\\"I hate you too\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"Where are you?\\\",\\n \\\"At your momma's house.\\\",\\n \\\"Where do you live?\\\",\\n \\\"Your momma's house.\\\",\\n \\\"Where are you from?\\\",\\n \\\"Somewhere over the rainbow.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"Who are you?\\\",\\n \\\"I'm your worst nightmare.\\\",\\n \\\"You can run but you can't hide, bitch.\\\",\\n \\\"This is a dead parrot.\\\",\\n \\\"It's just sleeping.\\\",\\n \\\"Well you better wake him up then, hadn't you?\\\",\\n \\\"So, what can I help you with today?\\\",\\n \\\"This parrot is dead.\\\",\\n \\\"No, it's just taking a little nap.\\\"\\n ])\\n self.chatbot.train([\\n \\\"I'm squanching here!\\\",\\n \\\"Sorry carry on.\\\",\\n \\\"Thank you for the privacy.\\\",\\n \\\"You are welcome.\\\",\\n \\\"Let's get schwifty.\\\",\\n \\\"Let's do it up in here.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"How are you?\\\",\\n \\\"I am good\\\",\\n \\\"That is good to hear.\\\",\\n \\\"Thank you.\\\",\\n \\\"You are welcome.\\\",\\n \\\"So, what can I help you with today?\\\",\\n \\\"What is AI?\\\",\\n \\\"Your momma.\\\",\\n \\\"What are your hobbies?\\\",\\n \\\"Your momma and AI.\\\",\\n \\\"What's your hobby?\\\",\\n \\\"Your momma and AI. What is your hobby?\\\",\\n \\\"What is your hobby?\\\",\\n \\\"Your momma.\\\"\\n ])\\n self.chatbot.train([\\n \\\"WHAT DO YOU WANT?\\\",\\n \\\"Well, I was told outside that.\\\",\\n \\\"Don't give me that, you snotty-faced heap of parrot droppings!\\\",\\n \\\"What?\\\",\\n \\\"Shut your festering gob, you tit! Your type really makes me puke, you vacuous, coffee-nosed, malodorous, pervert!!!\\\",\\n \\\"Look, I CAME HERE FOR AN ARGUMENT\\\",\\n \\\"OH, oh I'm sorry, but this is abuse.\\\",\\n ])\\n self.chatbot.train([\\n \\\"Is this the right room for an argument?\\\",\\n \\\"I told you once.\\\",\\n \\\"No you haven't.\\\",\\n \\\"Yes I have.\\\",\\n \\\"When?\\\",\\n \\\"Just now\\\",\\n \\\"No you didn't\\\",\\n \\\"Yes I did\\\",\\n \\\"You didn't\\\",\\n \\\"I'm telling you I did\\\",\\n \\\"Oh, I'm sorry, just one moment. Is this a five minute argument or the full half hour?\\\",\\n \\\"Oh look, this isn't an argument.\\\",\\n \\\"Yes it is\\\",\\n \\\"No, it's just contradiction.\\\"\\n \\\"No it isn't.\\\",\\n \\\"Yes it is.\\\"\\n ])\",\n \"def create(self, name):\\n success, response = self._client_api.gen_request(req_type='post',\\n path='/projects/{}/bots'.format(self.project.id),\\n json_req={'name': name})\\n if success:\\n bot = entities.Bot.from_json(_json=response.json(),\\n project=self.project,\\n bots=self, client_api=self._client_api)\\n else:\\n raise exceptions.PlatformException(response)\\n assert isinstance(bot, entities.Bot)\\n return bot\",\n \"def get_class(bot):\\n return TosurnamentPlayerCog(bot)\",\n \"def create_chitchat_bot(self):\\n \\n self.chatbot = ChatBot(\\\"Fresher's Friend\\\")\\n self.chatbot.trainer2=ListTrainer(self.chatbot)\\n self.chatbot.trainer=ChatterBotCorpusTrainer(self.chatbot)\\n self.chatbot.trainer.train(\\\"chatterbot.corpus.english.greetings\\\")\\n #self.chatbot.trainer.train(\\\"chatterbot.corpus.english.conversations\\\")\\n for filename in filenames: \\n self.chatbot.trainer2.train(files[filename])\",\n \"async def creator(message: types.Message):\\n link = 'https://https://github.com/germanjke/StyleTransformerGANs'\\n link2 = '@logisticregression'\\n await message.answer(text=f'Bot created for final Deep Learning School by MIPT project .\\\\\\n \\\\nLink to github repo: {link}, contact me: {link2}')\",\n \"def __init__(__self__, *,\\n data_privacy: pulumi.Input['DataPrivacyPropertiesArgs'],\\n idle_session_ttl_in_seconds: pulumi.Input[int],\\n role_arn: pulumi.Input[str],\\n auto_build_bot_locales: Optional[pulumi.Input[bool]] = None,\\n bot_file_s3_location: Optional[pulumi.Input['BotS3LocationArgs']] = None,\\n bot_locales: Optional[pulumi.Input[Sequence[pulumi.Input['BotLocaleArgs']]]] = None,\\n bot_tags: Optional[pulumi.Input[Sequence[pulumi.Input['BotTagArgs']]]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n test_bot_alias_settings: Optional[pulumi.Input['BotTestBotAliasSettingsArgs']] = None,\\n test_bot_alias_tags: Optional[pulumi.Input[Sequence[pulumi.Input['BotTagArgs']]]] = None):\\n pulumi.set(__self__, \\\"data_privacy\\\", data_privacy)\\n pulumi.set(__self__, \\\"idle_session_ttl_in_seconds\\\", idle_session_ttl_in_seconds)\\n pulumi.set(__self__, \\\"role_arn\\\", role_arn)\\n if auto_build_bot_locales is not None:\\n pulumi.set(__self__, \\\"auto_build_bot_locales\\\", auto_build_bot_locales)\\n if bot_file_s3_location is not None:\\n pulumi.set(__self__, \\\"bot_file_s3_location\\\", bot_file_s3_location)\\n if bot_locales is not None:\\n pulumi.set(__self__, \\\"bot_locales\\\", bot_locales)\\n if bot_tags is not None:\\n pulumi.set(__self__, \\\"bot_tags\\\", bot_tags)\\n if description is not None:\\n pulumi.set(__self__, \\\"description\\\", description)\\n if name is not None:\\n pulumi.set(__self__, \\\"name\\\", name)\\n if test_bot_alias_settings is not None:\\n pulumi.set(__self__, \\\"test_bot_alias_settings\\\", test_bot_alias_settings)\\n if test_bot_alias_tags is not None:\\n pulumi.set(__self__, \\\"test_bot_alias_tags\\\", test_bot_alias_tags)\",\n \"def generate():\",\n \"def buildProtocol(self, addr):\\n protocol = PortalBot()\\n protocol.factory = self\\n protocol.nickname = self.nickname\\n protocol.channel = self.channel\\n protocol.network = self.network\\n protocol.port = self.port\\n protocol.ssl = self.ssl\\n protocol.nicklist = []\\n return protocol\",\n \"def __init__(self, poolbot):\\n self.poolbot = poolbot\",\n \"def generate(self):\\n\\t\\traise BuilderException(\\\"You can not use this class directly!\\\")\",\n \"def create_bot(config: IrcConfig) -> Union[Bot, DummyBot]:\\n if config.server is None:\\n logger.info('No IRC server specified; will write to STDOUT instead.')\\n return DummyBot(config.channels)\\n\\n return Bot(\\n config.server,\\n config.nickname,\\n config.realname,\\n config.commands,\\n config.channels,\\n )\",\n \"def generate(self):\\n pass\",\n \"def __init__(self, username, password, bot, channel):\\n super().__init__(username, password)\\n\\n self.queue = deque()\\n self.ingame_cog = Ingame(bot)\\n\\n self.bot = bot\\n self.channel = channel\\n self.chat_breakout = False\\n self.loop = asyncio.get_event_loop()\\n self.ingame_cog.is_pycraft_instance = True\",\n \"def generate(self):\\n pass\",\n \"def generate(self):\\n pass\",\n \"def generate(self):\\n pass\",\n \"def __init__(self, address=('', 50000), authkey=b'tradingbot'):\\n _ClientBot.__init__(self, address=address, authkey=authkey)\",\n \"async def set_bot():\\n\\n self = await LOCAL.APP.get_me()\\n LOCAL.bot_id = self.id\\n LOCAL.bot_name = self.first_name\\n LOCAL.bot_username = self.username\",\n \"def generate(self):\",\n \"def connect(self):\\n bot_ammount_current = len(self.bots)\\n if bot_ammount_current==0:\\n self.wave_counter+=1\\n self.bot_ammount_wave = ((self.wave_counter // 2) + 1) * 5\\n bot_ammount_create = self.bot_ammount_wave if self.bot_ammount_wave<=50 else 50\\n for _ in range(bot_ammount_create):\\n pos_cofig = self.pos_dict_bot[random.randint(1,2)]\\n start_pos = pos_cofig[0], random.sample(pos_cofig[1], 1)[0]\\n self.bots.append(Bot(self.game, start_pos[0], start_pos[1]))\\n self.bot_ammount_wave -= 1\\n elif bot_ammount_current < 50 and self.bot_ammount_wave>0:\\n bot_ammount_create = 50 - bot_ammount_current\\n if self.bot_ammount_wave < bot_ammount_create:\\n bot_ammount_create = self.bot_ammount_wave\\n for _ in range(bot_ammount_create):\\n pos_cofig = self.pos_dict_bot[random.randint(1,2)]\\n start_pos = pos_cofig[0], random.sample(pos_cofig[1], 1)[0]\\n self.bots.append(Bot(self.game, start_pos[0], start_pos[1]))\\n self.bot_ammount_wave -= 1\\n\\n current_time = time.perf_counter()\\n if current_time-self.time_last_gunpack > self.gunpck_frequency:\\n self.game.arena.create_gunpack()\\n self.time_last_gunpack = current_time\",\n \"def generate(cls, name=''):\\n # Create the class\\n self = cls()\\n\\n # Name\\n self.name = name\\n\\n # Generate luck\\n self.luck = randint(1, 10)\\n\\n # Total health\\n self.total_health = randint(\\n (self.max_health * self.health_mult_1),\\n (self.max_health * self.health_mult_2)\\n )\\n\\n # Max our health out\\n self.add_health(self.max_health)\\n\\n # Add default weapons\\n self.weapons = choice(self.default_weapons)\\n\\n # Set our describing word\\n self.word = choice(self.word_choice)\\n\\n # Inventory should be empty\\n self.inventory = dict()\\n\\n # Level and xp\\n self.level = 1\\n self.xp = 1\\n\\n return self\",\n \"def makebot(*args, checkdepends: bool=True, checkres: int=0, input: AnyStr=\\\"\\\", nooverwrite:\\n bool=True, output: AnyStr=\\\"\\\", verbose: bool=True, **kwargs)->None:\\n pass\",\n \"async def CoMLegendBuilder(self, ctx):\\n me = CoachService.discord_user_to_coach(ctx.author)\\n data = getattr(special_play, inspect.currentframe().f_code.co_name)(ctx.channel.name, me)\\n await self.send_embed(data, ctx)\",\n \"def make(self):\\n pass\",\n \"def make_object():\\n return object()\",\n \"async def main() -> None:\\n logging.basicConfig(\\n level=logging.INFO,\\n format=\\\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\\\",\\n )\\n logger.error(\\\"Starting the bot\\\")\\n config = load_config(\\\"bot.ini\\\")\\n\\n if config.tg_bot.use_redis:\\n storage = RedisStorage2()\\n else:\\n storage = MemoryStorage()\\n\\n pool = await create_pool(\\n user=config.db.user,\\n password=config.db.password,\\n database=config.db.database,\\n host=config.db.host,\\n )\\n\\n bot = Bot(token=config.tg_bot.token, parse_mode=ParseMode.HTML)\\n dp = Dispatcher(bot=bot, storage=storage)\\n\\n dp.middleware.setup(DbMiddleware(pool))\\n dp.middleware.setup(RoleMiddleware(config.tg_bot.admin_id))\\n dp.filters_factory.bind(RoleFilter)\\n\\n admin.register_admin(dp)\\n user.register_user(dp)\\n await set_basic_commands(dp)\\n\\n try:\\n await dp.start_polling(allowed_updates=False)\\n finally:\\n await dp.storage.close()\\n await dp.storage.wait_closed()\\n await bot.session.close()\",\n \"def default_robot(): #py:default_robot\\n class Robot(UsedRobot):\\n def __init__(self):\\n self.body = RUR._default_robot_body_()\\n return Robot()\",\n \"def bot():\\n log(log.DEBUG, 'Starting bot')\\n bot = BotSad()\\n bot.listen()\",\n \"def getBuilder():\",\n \"def generate(cls):\\n raise NotImplementedError()\",\n \"def main():\\n\\n loop = asyncio.get_event_loop()\\n bot = MyBot()\\n\\n # bot.pool = loop.run_until_complete(DB.create_pool(\\n bot.pool = loop.run_until_complete(Table().create_pool(\\n user=POSTGRES_USER,\\n password=POSTGRES_PASS,\\n database=POSTGRES_DB,\\n host=POSTGRES_HOST\\n ))\\n # bot.db_manager = DB()\\n bot.run()\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None) -> 'Bot':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = BotArgs.__new__(BotArgs)\\n\\n __props__.__dict__[\\\"arn\\\"] = None\\n __props__.__dict__[\\\"auto_build_bot_locales\\\"] = None\\n __props__.__dict__[\\\"bot_file_s3_location\\\"] = None\\n __props__.__dict__[\\\"bot_locales\\\"] = None\\n __props__.__dict__[\\\"bot_tags\\\"] = None\\n __props__.__dict__[\\\"data_privacy\\\"] = None\\n __props__.__dict__[\\\"description\\\"] = None\\n __props__.__dict__[\\\"idle_session_ttl_in_seconds\\\"] = None\\n __props__.__dict__[\\\"name\\\"] = None\\n __props__.__dict__[\\\"role_arn\\\"] = None\\n __props__.__dict__[\\\"test_bot_alias_settings\\\"] = None\\n __props__.__dict__[\\\"test_bot_alias_tags\\\"] = None\\n return Bot(resource_name, opts=opts, __props__=__props__)\",\n \"async def setup(bot):\\n await bot.add_cog(People(bot))\",\n \"def generate():\\n PackCommandExecutor().pack()\\n GenerateCommandExecutor().generate()\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n auto_build_bot_locales: Optional[pulumi.Input[bool]] = None,\\n bot_file_s3_location: Optional[pulumi.Input[pulumi.InputType['BotS3LocationArgs']]] = None,\\n bot_locales: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['BotLocaleArgs']]]]] = None,\\n bot_tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['BotTagArgs']]]]] = None,\\n data_privacy: Optional[pulumi.Input[pulumi.InputType['DataPrivacyPropertiesArgs']]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n idle_session_ttl_in_seconds: Optional[pulumi.Input[int]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n role_arn: Optional[pulumi.Input[str]] = None,\\n test_bot_alias_settings: Optional[pulumi.Input[pulumi.InputType['BotTestBotAliasSettingsArgs']]] = None,\\n test_bot_alias_tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['BotTagArgs']]]]] = None,\\n __props__=None):\\n ...\",\n \"def default_factory(*args, **kwargs):\\n obj = RandomGameEntity()\\n obj.build(*args, **kwargs)\\n return obj\",\n \"def set_builder_bot(self, builder_bot): \\n self.builder_bot = builder_bot # pragma: no cover\",\n \"def run_bot():\\n client = commands.Bot(command_prefix=\\\"!\\\", case_insensitive=True)\\n\\n load_cogs(client)\\n\\n client.run(token)\",\n \"async def _bot():\\n await bot.say('Yes, the bot is cool.')\",\n \"def setup(bot: util.CustomBot):\\r\\n bot.add_cog(Info(bot))\",\n \"def main():\\n\\n merch_bot = MerchBot()\\n merch_bot.run_bot()\",\n \"async def botinfo(ctx, bot: typing.Union[discord.Member, discord.User]):\\n if not bot.bot:\\n return await r(ctx, 'Not a bot.')\\n\\n data = await make_request(\\\"https://www.motiondevelopment.top/api/v1.2/bots/\\\", bot.id)\\n \\n e = discord.Embed(\\n title=f'Available bot info for {bot}',\\n color=0xfecdea,\\n description=f\\\"**Short Bot Description:** (do `uwu desc [bot]` for big description)\\\\n\\\\n*{data['Small_desc']}*\\\"\\n )\\n\\n if data[\\\"bot_status\\\"] == \\\"online\\\":\\n status = '<:online:805576670353948702> Online'\\n elif data[\\\"bot_status\\\"] == \\\"idle\\\":\\n status = '<:idle:805855470778056725> Idle'\\n elif data[\\\"bot_status\\\"] == \\\"offline\\\":\\n status = '<:offline:805576352450871346> Offline'\\n elif data[\\\"bot_status\\\"] == \\\"dnd\\\":\\n status = '<:dnd:819964146317393990> Do Not Disturb'\\n\\n listed_at = datetime.datetime.strptime(data[\\\"list_date\\\"], '%Y-%m-%d')\\n\\n e.add_field(\\n name='Owner:', value=f'**{data[\\\"owner_name\\\"]}**\\\\n({data[\\\"owner_id\\\"]})', inline=False)\\n e.add_field(name='Tags:', value=', '.join(data[\\\"tops\\\"]))\\n e.add_field(name='Vanity URL:', value=data[\\\"vanity_url\\\"]\\n if data[\\\"vanity_url\\\"] != '' else 'No vanity URL set.', inline=False)\\n e.add_field(name='Bot Status:', value=status)\\n e.add_field(name='Invites:',\\n value=f'[Bot Invite]({data[\\\"invite\\\"]})\\\\n[Bot Support Server](https://discord.gg/{data[\\\"discord\\\"]})', inline=False)\\n e.add_field(name='Other Bot Info:', value=f'''\\n **Prefix:** `{data[\\\"prefix\\\"]}`\\n **Site:** {data[\\\"site\\\"] if data[\\\"site\\\"] != '' else \\\"No sites.\\\"}\\n **Library:** {data[\\\"lib\\\"]}\\n **Listed at:** {listed_at}\\n **Server Count:** {data[\\\"servers\\\"] if data[\\\"servers\\\"] != 'None' else '*Not set up!*'}''', inline=False)\\n e.set_thumbnail(url=f'https://cdn.discordapp.com/avatars/{data[\\\"id\\\"]}/{data[\\\"avatar\\\"]}')\\n await em(ctx, embed=e)\",\n \"def __init__(self, address=('', 50000), authkey=b'tradingbot'):\\n self.id = 0\\n _ClientBot.__init__(self, address=address, authkey=authkey)\\n self.conn_tbm = ConnTradingBotManager(self.id)\",\n \"def buildProtocol(self, addr):\\r\\n p = RobotWebSocketProtocol(self._realm)\\r\\n p.factory = self\\r\\n return p\",\n \"def add_bot(self, bot):\\n self.add_user(bot)\",\n \"async def link(ctx, bot: typing.Union[discord.Member, discord.User]):\\n if not bot.bot:\\n return await r(ctx, \\\"Not a bot.\\\")\\n await r(ctx, f'<https://www.motiondevelopment.top/bots/{bot.id}>')\",\n \"def build_goal_generation(\\n cls, constants: CType, mujoco_simulation: SType\\n ) -> GoalGenerator:\\n pass\",\n \"def __init__(self, bot: BunkBot, channels: ChannelService):\\r\\n self.bot: BunkBot = bot\\r\\n self.message: Message = None\\r\\n self.channels: ChannelService = channels\\r\\n self.yt_result: YoutubeResult = YoutubeResult()\\r\\n self.yt_link: str = \\\"\\\"\",\n \"def parse_bot(self) -> None:\\n if not self.skip_bot_detection and not self.bot:\\n self.bot = Bot(\\n self.user_agent,\\n self.ua_hash,\\n self.ua_spaceless,\\n self.VERSION_TRUNCATION,\\n ).parse()\\n self.all_details['bot'] = self.bot.ua_data\",\n \"def buildCommand(self, player, game, json):\",\n \"def __new__(cls, *, bot = ..., description = ..., icon = ..., name = ...):\\n # bot\\n if bot is ...:\\n bot = ZEROUSER\\n else:\\n bot = validate_bot(bot)\\n \\n # description\\n if description is ...:\\n description = None\\n else:\\n description = validate_description(description)\\n \\n # icon\\n if icon is ...:\\n icon = None\\n else:\\n icon = cls.icon.validate_icon(icon, allow_data = True)\\n \\n # name\\n if name is ...:\\n name = ''\\n else:\\n name = validate_name(name)\\n \\n self = object.__new__(cls)\\n self.bot = bot\\n self.description = description\\n self.icon = icon\\n self.id = 0\\n self.name = name\\n return self\",\n \"def getBuilder(name):\",\n \"def getBuilder(name):\",\n \"def generatebomberman(self, board):\\n self.createperson(board, self.x_pos, self.y_pos, 'B')\\n return board\",\n \"def __init__(self, bot_name, plan, email, registration_id,\\n event_name='', sandbox_mode=False):\\n self.email = email\\n initialization_data = {\\n 'username': bot_name,\\n 'email': email,\\n 'plan': plan,\\n 'sandbox': sandbox_mode,\\n }\\n if event_name:\\n initialization_data['event_name'] = event_name\\n if registration_id:\\n initialization_data['event_id'] = registration_id\\n\\n self.websocket = create_connection('ws://codelift.org/v2/building')\\n res = self.websocket.recv()\\n if res != '{\\\"status\\\":\\\"connected\\\",\\\"message\\\":\\\"Connected\\\"}':\\n raise('unexpected connection failure:' + res)\\n state = self._get_world_state(initialization_data)\\n if state['status'] == 'error':\\n print(state['message'])\\n exit()\\n self.status = state['status']\\n print(state['message'])\\n # print('building url: {}'.format(self.building_url))\\n # print('visualization url: {}'.format(self.visualization_url))\",\n \"def __init__(self, email: str=None, is_bot: bool=None, avatar_url: str=None, avatar_version: int=None, full_name: str=None, is_admin: bool=None, is_owner: bool=None, is_billing_admin: bool=None, role: int=None, bot_type: int=None, user_id: int=None, bot_owner_id: int=None, is_active: bool=None, is_guest: bool=None, timezone: str=None, date_joined: str=None, delivery_email: str=None, profile_data: Dict[str, object]=None):\\n self.openapi_types = {\\n 'email': str,\\n 'is_bot': bool,\\n 'avatar_url': str,\\n 'avatar_version': int,\\n 'full_name': str,\\n 'is_admin': bool,\\n 'is_owner': bool,\\n 'is_billing_admin': bool,\\n 'role': int,\\n 'bot_type': int,\\n 'user_id': int,\\n 'bot_owner_id': int,\\n 'is_active': bool,\\n 'is_guest': bool,\\n 'timezone': str,\\n 'date_joined': str,\\n 'delivery_email': str,\\n 'profile_data': Dict[str, object]\\n }\\n\\n self.attribute_map = {\\n 'email': 'email',\\n 'is_bot': 'is_bot',\\n 'avatar_url': 'avatar_url',\\n 'avatar_version': 'avatar_version',\\n 'full_name': 'full_name',\\n 'is_admin': 'is_admin',\\n 'is_owner': 'is_owner',\\n 'is_billing_admin': 'is_billing_admin',\\n 'role': 'role',\\n 'bot_type': 'bot_type',\\n 'user_id': 'user_id',\\n 'bot_owner_id': 'bot_owner_id',\\n 'is_active': 'is_active',\\n 'is_guest': 'is_guest',\\n 'timezone': 'timezone',\\n 'date_joined': 'date_joined',\\n 'delivery_email': 'delivery_email',\\n 'profile_data': 'profile_data'\\n }\\n\\n self._email = email\\n self._is_bot = is_bot\\n self._avatar_url = avatar_url\\n self._avatar_version = avatar_version\\n self._full_name = full_name\\n self._is_admin = is_admin\\n self._is_owner = is_owner\\n self._is_billing_admin = is_billing_admin\\n self._role = role\\n self._bot_type = bot_type\\n self._user_id = user_id\\n self._bot_owner_id = bot_owner_id\\n self._is_active = is_active\\n self._is_guest = is_guest\\n self._timezone = timezone\\n self._date_joined = date_joined\\n self._delivery_email = delivery_email\\n self._profile_data = profile_data\",\n \"def generate(cls, **kwargs) -> str:\\n pass\",\n \"def make_connection_bot(connection_string):\\n engine = create_engine(connection_string)\\n connection = engine.connect()\\n Session = sessionmaker(bind=engine)\\n session = Session()\\n return session\",\n \"def __init__(self,bot_name,message_type):\\n\\n self.name=bot_name #set bot name\\n if (message_type != \\\"message-out\\\" and message_type != \\\"message-in\\\"):#check if message type is correct\\n raise message_type_error(message_type) #if not, raise message_type_error exception\\n else:\\n self.message_class=message_type #set message type\\n\\n self.driver = webdriver.Chrome('C:\\\\Python36\\\\chromedriver.exe') #launch chrome driver\\n self.driver.get('https://web.whatsapp.com') #connect to the website\",\n \"def exec(self,):\\n self.chatbot.printStructureChatbotDict()\",\n \"async def _bot(ctx):\\n await ctx.send('Yes, the bot is cool.')\",\n \"def setup(bot):\\n\\tbot.add_cog(Wallet(bot))\",\n \"def __init__(self, bot, filename=\\\"\\\"):\\n if len(filename) > 0:\\n self.filename = filename\\n self.quotes_list = datastore.DataStore(self.filename)\\n self.bot=bot\\n random.seed()\",\n \"async def generate(self, prompt: str):\\n result_handle = await self.handle.generate.remote(prompt)\\n return await result_handle\",\n \"def test_new_bot(\\n self, mock_get_ai_details, mock_get_ai, mock_get_categories\\n ):\\n\\n mock_get_ai.return_value = self.ai\\n mock_get_ai_details.return_value = self.ai_details\\n\\n response = self.client.get(reverse(\\n 'studio:edit_bot',\\n kwargs={'aiid': self.ai['aiid']}\\n ))\\n\\n self.assertContains(response, 'Build a bot from text-based simple Q&A '\\n 'using the inline editor.')\\n self.assertContains(response, 'Want to structure your dialog, build more '\\n 'complex Q&A or connect to 3rd party services.')\\n self.assertContains(response, 'Speed up your bot building process by '\\n 'starting with one of our Templates from the store.')\",\n \"def __init__(self, pos, inventory=None):\\n _GenericBot.__init__(self, pos, inventory)\\n self._changes = {} # Changes to the world\",\n \"def setup(bot: Bot) -> None:\\n bot.add_cog(Armory(bot))\",\n \"def bot(app, debug):\\n if debug is None:\\n verify_id(None, None, app)\\n\\n (id, tmp) = setup_experiment(log)\\n\\n if debug:\\n url = debug\\n else:\\n heroku_app = HerokuApp(dallinger_uid=app)\\n worker = generate_random_id()\\n hit = generate_random_id()\\n assignment = generate_random_id()\\n ad_url = \\\"{}/ad\\\".format(heroku_app.url)\\n ad_parameters = \\\"assignmentId={}&hitId={}&workerId={}&mode=sandbox\\\"\\n ad_parameters = ad_parameters.format(assignment, hit, worker)\\n url = \\\"{}?{}\\\".format(ad_url, ad_parameters)\\n bot = bot_factory(url)\\n bot.run_experiment()\",\n \"def create_cbuildbot_factory(target, gs_path, short_name):\\n # Factory with the steps to pull out a Chromium source tree (no compilation).\\n # It uses an unused slave_type string to avoid adding the normal compile step.\\n chrome_factory = linux().ChromiumFactory(slave_type='WebRtcCros')\\n\\n archive_base = 'gs://chromeos-image-archive/%s/%s' % (target, gs_path)\\n cbuildbot_params = '--archive-base=%s %s' % (archive_base, target)\\n\\n # Extend that factory with Cbuildbot build steps to build and test CrOS using\\n # the Chrome from the above Chromium source tree.\\n return chromeos_factory.CbuildbotFactory(\\n params=cbuildbot_params,\\n buildroot='/b/cbuild.%s' % short_name,\\n chrome_root='.', # Where ChromiumFactory has put \\\"Chrome\\\".\\n factory=chrome_factory,\\n slave_manager=False).get_factory()\",\n \"async def new(ctx):\\n if ctx.message.channel.name.lower() not in tod_channels:\\n return\\n\\n room = ctx.message.channel.name.lower()\\n host = ctx.message.author\\n if room not in tod_games:\\n tod_games[room] = {'host': host.name, 'host_id': host.name, 'participants': {}, 'last': None}\\n tod_games[room]['current'] = host.name\\n tod_games[room]['last'] = host.name\\n tod_games[room]['participants'][host.name.lower()] = {'spins': 0}\\n await amor_manager.say(\\\"New Game of Truth Or Dare started in {}\\\".format(room))\\n else:\\n host = tod_games[room]['host']\\n await amor_manager.say(\\\"Truth or Dare already in progress in {}. Game host: {}\\\".format(room, host))\",\n \"def new(cls, base_repo, semester, section, username):\\n return cls.from_url(\\\"http://mockhub.com/\\\", \\\"token\\\")\",\n \"def buildProtocol(self, addr):\\n if hasattr(settings, \\\"DISCORD_SESSION_CLASS\\\"):\\n protocol_class = class_from_module(\\n settings.DISCORD_SESSION_CLASS, fallback=DiscordClient\\n )\\n protocol = protocol_class()\\n else:\\n protocol = DiscordClient()\\n\\n protocol.factory = self\\n protocol.sessionhandler = self.sessionhandler\\n return protocol\",\n \"def roomba_create():\\n global LIST_OF_ROOMBAS\\n LIST_OF_ROOMBAS = []\\n\\n for position in range(0, 2):\\n new_roomba = OtherRoomba(position)\\n LIST_OF_ROOMBAS.append(new_roomba)\\n if position == 1:\\n # Sends 5 bytes of 111 to indicate that it is roomba 1\\n server.send(b'\\\\x6F\\\\x6F\\\\x6F\\\\x6F\\\\x6F')\\n else:\\n # Sends 5 bytes of 222 to indicate that is roomba 2\\n server.send(b'\\\\xDE\\\\xDE\\\\xDE\\\\xDE\\\\xDE')\\n # Sends the byte command for the Roomba to drive out randomly\\n server.send('\\\\x00\\\\x00\\\\x00\\\\x00\\\\x01')\\n shuffle(LIST_OF_ROOMBAS)\",\n \"def generate_bottonDown(self):\\n posx = self.posx + self.sizex - 2*self.sizebotx \\n posy = self.posy + self.sizeboty \\n return Boton(posx, posy, self.sizebotx, self.sizeboty, color=(124, 117, 221), color_h=(95, 90, 173), text_color=(0,0,0), press_color = (36, 26, 117), font_size=5)\",\n \"def InitializeDatabase(session, params={}):\\n name = 'My Bot'\\n symbols = []\\n quote_asset = 'BTC'\\n starting_balance = 0.001\\n test_run = False\\n initial_entry_allocation = 25\\n signal_distance = 0.3\\n profit_target = 2\\n stop_loss_value = 0\\n exit_on_signal = False\\n\\n if params.__contains__('name'):\\n name = params['name']\\n\\n if params.__contains__('symbols'):\\n symbols = params['symbols']\\n\\n if params.__contains__('quote_asset'):\\n quote_asset = params['quote_asset']\\n\\n if params.__contains__('starting_balance'):\\n starting_balance = params['starting_balance']\\n\\n if params.__contains__('test_run'):\\n test_run = params['test_run']\\n\\n if params.__contains__('initial_entry_allocation'):\\n initial_entry_allocation = params['initial_entry_allocation']\\n\\n if params.__contains__('signal_distance'):\\n signal_distance = params['signal_distance']\\n\\n if params.__contains__('profit_target'):\\n profit_target = params['profit_target']\\n\\n if params.__contains__('stop_loss_value'):\\n stop_loss_value = params['stop_loss_value']\\n\\n if params.__contains__('exit_on_signal'):\\n exit_on_signal = params['exit_on_signal']\\n\\n\\n myobject = Bot(\\n name = name,\\n quote_asset = quote_asset,\\n starting_balance = starting_balance,\\n current_balance = starting_balance,\\n test_run = test_run\\n )\\n\\n session.add(myobject)\\n\\n entrysets = EntrySettings(\\n initial_entry_allocation = initial_entry_allocation,\\n signal_distance = signal_distance\\n )\\n \\n exitsets = ExitSettings(\\n profit_target = profit_target, # in %\\n stop_loss_value = stop_loss_value, # in %\\n exit_on_signal = exit_on_signal\\n )\\n myobject.entry_settings = entrysets\\n myobject.exit_settings = exitsets\\n session.commit()\\n\\n for symbol in symbols:\\n pair = Pair(\\n bot_id = myobject.id,\\n symbol = symbol,\\n current_order_id = None\\n )\\n session.add(pair)\\n\\n session.commit()\",\n \"def bot_init():\\n client.Console.Say('Hello World')\\n global player\\n player = client.GetPlayer()\",\n \"async def build(self, ctx: Context):\\n # Simulates that the bot is typing to visually show user command is being processed\\n async with ctx.typing(): \\n start_time = time()\\n description= f'{emojis[\\\"discord\\\"]} Configuring `{ctx.guild.name}` for verification...\\\\n'\\n\\n guild = ctx.message.guild\\n bot_role = get(ctx.guild.roles, name='AberLink')\\n everyone_role = get(ctx.guild.roles, name='@everyone')\\n verified_role = get(ctx.guild.roles, name='verified')\\n verify_channel = get(guild.channels, name='verify')\\n verify_perms = discord.PermissionOverwrite()\\n verified_role_perms = discord.Permissions(\\n send_messages=True, read_messages=True, read_message_history=True, \\n change_nickname=True, embed_links=True, attach_files=True, \\n add_reactions=True, external_emojis=True, \\n connect=True, speak=True, stream=True, use_voice_activation=True\\n )\\n\\n #make sure that the bots position is above that of verification\\n bot_role.edit(position=2)\\n\\n # Change permissions on @everyone role\\n await everyone_role.edit(reason='Configuring everyone role for verify', permissions=discord.Permissions())\\n description += f'{int((time() - start_time) * 1000)}ms: `@everyone` removed all permissions\\\\n'\\n # {int((end_time - start_time) * 1000)}\\n \\n # Create or modify verified role\\n if verified_role is not None:\\n await verified_role.edit(reason='Updating old verified role', permissions=verified_role_perms)\\n description += f'{int((time() - start_time) * 1000)}ms: `verified` role already exists, updating to match permissions...\\\\n'\\n else:\\n verified_role = await guild.create_role(reason='Creating verified role', name='verified', permissions=verified_role_perms)\\n verified_role.edit(position=1)\\n description += f'{int((time() - start_time) * 1000)}ms: `verified` role created\\\\n'\\n \\n # Gives the bot the verified role\\n bot = await guild.fetch_member(ctx.bot.user.id)\\n await bot.add_roles(verified_role)\\n\\n # Create or modify verify channel\\n if verify_channel is not None:\\n description += f'{int((time() - start_time) * 1000)}ms: `verify` channel already exists, updating to match permissions...\\\\n'\\n message = await verify_channel.send(f'Welcome to `{guild.name}`! If you are seeing this message then please type `!verify`')\\n await message.pin()\\n else:\\n verify_channel = await guild.create_text_channel('verify')\\n description += f'{int((time() - start_time) * 1000)}ms: `verify` channel created\\\\n'\\n message = await verify_channel.send(f'Welcome to `{guild.name}`! If you are seeing this message then please type `!verify`')\\n await message.pin()\\n \\n # Set permissions for roles in verify channel\\n verify_perms.read_messages = True\\n verify_perms.send_messages = True\\n verify_perms.read_message_history = True\\n await verify_channel.set_permissions(everyone_role, overwrite=verify_perms)\\n verify_perms.read_messages = False\\n verify_perms.send_messages = False\\n await verify_channel.set_permissions(verified_role, overwrite=verify_perms)\\n description += f'{emojis[\\\"aberlink\\\"]} This server is now setup for verification!'\\n embed = Embed(description=description, colour=discord.Colour.green())\\n await ctx.send(embed=embed)\",\n \"async def randomproject(self, ctx):\\r\\n if await bMsg(ctx,ctx.message.author.name,client):\\r\\n return\\r\\n logger.info('Scratch.randomproject', extra={'invoker': ctx.message.author.name})\\r\\n async with ctx.channel.typing():\\r\\n count = json.loads(await self.req('https://api.scratch.mit.edu/projects/count/all'))['count']\\r\\n comments = None\\r\\n while comments is None:\\r\\n pid = random.randint(1, count)\\r\\n comments = await self.req('https://scratch.mit.edu/site-api/comments/project/' + str(pid))\\r\\n await ctx.send('https://scratch.mit.edu/projects/' + str(pid))\",\n \"async def send_welcome(message: types.Message):\\n await message.reply(\\\"Hi!\\\\nI'm Autoencoder Bot!\\\\nPowered by aiogram.\\\\n\\\\n\\\"\\n \\\"/random - for get generated image from nose\\\\n\\\"\\n \\\"send photo - show decoded variant from autoencoder\\\\n\\\"\\n \\\"TODO: - make smile on photo, generate glasses and heads\\\")\",\n \"def setup(bot: Red):\\n bot.add_cog(Welcome(bot))\",\n \"async def setup(bot: Bot) -> None:\\n await bot.add_cog(Utils(bot))\",\n \"def setup(bot):\\n bot.add_cog(Session(bot))\",\n \"def __init__(self, imag_bot, block_, player_loc):\\n self._bot = imag_bot\\n self._block = block_\\n self._player_loc = player_loc\",\n \"async def setup(bot):\\n await bot.add_cog(WordleCmd(bot))\",\n \"def get_commands(bot):\\n new_commands = []\\n\\n new_commands.append(Command(\\n 'mycommand', subcommands=[\\n SubCommand(\\n Opt('myoption'),\\n doc='This is a simple command with a single required option.'),\\n SubCommand(\\n Opt('custom', optional=True),\\n Opt('attached', optional=True, attached='attached argument'),\\n doc='This has two different optional options, one without an attached '\\n 'parameter, and the other requiring an attached parameter.'),\\n SubCommand(\\n Opt('trailing'),\\n Arg('arg 1'),\\n Arg('arg 2'),\\n Arg('arg 3', argtype=ArgTypes.SPLIT, additional='more args'),\\n doc='This command requires a lot of trailing arguments.'),\\n SubCommand(\\n Opt('grouped'),\\n Arg('grouped arguments', argtype=ArgTypes.MERGED),\\n doc='This will group all given arguments as a single string.'),\\n SubCommand(\\n Opt('complex', attached='attached'),\\n Opt('other', optional=True, attached='also required'),\\n Arg('arg 1'),\\n Arg('arg 2', argtype=ArgTypes.SPLIT_OPTIONAL, additional='more args'),\\n doc='The complex option has a required attached parameter, and the '\\n '\\\\'other\\\\' option also has a required attached parameter if '\\n '\\\\'other\\\\' is included. Additionally, there will be a requirement '\\n 'of at least 1 trailing argument.'),\\n SubCommand(\\n Opt('marquee'),\\n Arg('text', argtype=ArgTypes.MERGED,\\n check=lambda b, m, v, *a: len(v) <= 100,\\n check_error=\\\"Marquee message must be less than 100 characters long.\\\"),\\n doc='Creates a marquee that loops 3 times.')],\\n shortcuts=[\\n Shortcut(\\n 'complex', 'complex {attached} other {other} {arg 1} {arg 2}',\\n Arg('attached'), Arg('other'), Arg('arg 1'),\\n Arg('arg 2', argtype=ArgTypes.SPLIT_OPTIONAL)),\\n Shortcut(\\n 'marquee', 'marquee {text}', Arg('text', argtype=ArgTypes.MERGED))],\\n description='Your command description here.',\\n other='This text is optional - it just shows up after everything '\\n 'else. Quick note, all of the commands here can only be used by '\\n 'bot moderators or above, as indicated by elevated_level. A '\\n 'level of 2 would mean only server owners or above can use the '\\n 'command, and a level of 3 would restrict the command to only '\\n 'the bot owners.',\\n elevated_level=1, category='demo'))\\n\\n new_commands.append(Command(\\n 'myothercommand', subcommands=[\\n SubCommand(\\n Arg('text', argtype=ArgTypes.MERGED_OPTIONAL),\\n doc='This traps all further commands from being executed.'),\\n SubCommand(\\n Opt('order'), Opt('matters'),\\n doc='It is impossible to access this command because the first '\\n 'subcommand will always be satisfied first. Order of the '\\n 'subcommand matters!'),\\n SubCommand(\\n Opt('sample'), Opt('foo'), Opt('bar'),\\n doc='Also impossible to access. This subcommand just adds some '\\n 'keywords to the command.')],\\n description='Only bot owners can see this text!',\\n other='Note that no shortcuts were defined. They, too, are optional. '\\n 'Also, this command is hidden, which means that only the bot '\\n 'owners can see this command listed from the help command. '\\n 'However, unless the command is configured with an elevated '\\n 'permissions level, any user can still execute the command. '\\n 'Users still will not be able to see the specific help for this '\\n 'command, though. Lastly, this command is disabled in DMs.',\\n hidden=True, allow_direct=False, category='demo'))\\n\\n new_commands.append(Command(\\n 'notify', subcommands=[\\n SubCommand(\\n Arg('text', argtype=ArgTypes.MERGED),\\n doc='Notify the owners with some text!')],\\n other='This command uses a custom function. It is called with the '\\n 'same arguments as get_response. The command will show up to '\\n 'all users in the help command, but can only be used by server '\\n 'owners, as it is disallowed in direct messages.',\\n elevated_level=2, allow_direct=False, function=custom_notify,\\n category='demo'))\\n\\n new_commands.append(Command(\\n 'wait', other='Use this command to demo the wait_for functionality', category='demo'))\\n\\n return new_commands\",\n \"def generate(gui):\\n\\n global robot_obj\\n global ftm_list\\n global btm_list\\n global fk_list\\n global jac_list\\n global com_list\\n global com_jac_list\\n\\n ftm = ftm_list if gui.checkBox_ftm.isChecked() else []\\n btm = btm_list if gui.checkBox_btm.isChecked() else []\\n fk = fk_list if gui.checkBox_fk.isChecked() else []\\n jac = jac_list if gui.checkBox_jac.isChecked() else []\\n com = com_list if gui.checkBox_com.isChecked() else []\\n com_jac = com_jac_list if gui.checkBox_com_jac.isChecked() else []\\n\\n language = Language(settings[\\\"language\\\"])\\n optimization_level = settings[\\\"optimization_level\\\"]\\n\\n generate_everything(robot_obj, ftm, btm,\\n fk, jac, com, com_jac,\\n polynomial_trajectories,\\n control_loops_list,\\n optimization_level,\\n language,\\n path + '../generated/' + settings[\\\"filename\\\"],\\n progressbar=gui.progressBar)\",\n \"def bot(self):\\n return self._bot\",\n \"async def welcome_command(ctx):\\n await ctx.send(f\\\"Hello! I am a bot made by {ctx.bot.owner}\\\")\",\n \"def create(ctx):\\n pass\",\n \"def generate(self):\\n return self.gen.generate()\",\n \"def generate(self):\\r\\n raise NotImplementedError\",\n \"def make_bb_object(name, data):\\n global BLACKBOARD, TRACE_LEVEL\\n bb_obj = BB_object(name, data)\\n if TRACE_LEVEL > 2:\\n print \\\"\\\\tCreating {0} object: {1}\\\".format( type(data), bb_obj )\\n BLACKBOARD[name] = bb_obj\\n signal_creation_event(bb_obj)\\n return bb_obj\",\n \"def executeBot(bot):\\n try:\\n line = \\\"\\\"\\n message = \\\"\\\"\\n line=sys.stdin.readline()\\n while(line):\\n if( \\\"go\\\" in line ):\\n gs = GameState(message)\\n bot.doTurn(gs)\\n gs.finishTurn()\\n message=\\\"\\\"\\n elif( \\\"name?\\\" in line ):\\n print bot.getName()\\n sys.stdout.flush()\\n else:\\n message=message+line+os.linesep\\n line=sys.stdin.readline()\\n except Exception as e:\\n sys.stderr.write(\\\"shit happens : \\\")\\n print e\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6956568","0.6869598","0.6869598","0.6731655","0.66259605","0.6554135","0.65377176","0.65377176","0.6139103","0.60624367","0.6005556","0.58768237","0.5853435","0.58470196","0.5823871","0.57866377","0.5736363","0.5711528","0.56844354","0.5640654","0.5606571","0.55597293","0.55597293","0.55597293","0.55398124","0.55359","0.5523565","0.55156326","0.5506997","0.54988253","0.54901135","0.5488123","0.5474752","0.54527104","0.5449986","0.5443849","0.54209816","0.5396447","0.539642","0.53866744","0.5382184","0.5381426","0.53445005","0.5327547","0.53261554","0.53203654","0.5319437","0.5315224","0.53041095","0.52943015","0.52837044","0.5271249","0.527102","0.5268476","0.5266124","0.52631295","0.52573574","0.5256292","0.5239578","0.52376944","0.52376944","0.52293843","0.52287346","0.52240765","0.52201533","0.5209393","0.5203459","0.5198872","0.5195901","0.51947916","0.5193854","0.5193557","0.5185568","0.51650715","0.5164905","0.51586473","0.515221","0.51357096","0.5135259","0.5131439","0.5128439","0.51225644","0.51130307","0.51129764","0.51121294","0.50996023","0.5080335","0.5074438","0.5073287","0.5070248","0.50685966","0.50637656","0.50632846","0.50592214","0.5055333","0.50488806","0.50482744","0.5048125","0.504714","0.5046189","0.5032601"],"string":"[\n \"0.6956568\",\n \"0.6869598\",\n \"0.6869598\",\n \"0.6731655\",\n \"0.66259605\",\n \"0.6554135\",\n \"0.65377176\",\n \"0.65377176\",\n \"0.6139103\",\n \"0.60624367\",\n \"0.6005556\",\n \"0.58768237\",\n \"0.5853435\",\n \"0.58470196\",\n \"0.5823871\",\n \"0.57866377\",\n \"0.5736363\",\n \"0.5711528\",\n \"0.56844354\",\n \"0.5640654\",\n \"0.5606571\",\n \"0.55597293\",\n \"0.55597293\",\n \"0.55597293\",\n \"0.55398124\",\n \"0.55359\",\n \"0.5523565\",\n \"0.55156326\",\n \"0.5506997\",\n \"0.54988253\",\n \"0.54901135\",\n \"0.5488123\",\n \"0.5474752\",\n \"0.54527104\",\n \"0.5449986\",\n \"0.5443849\",\n \"0.54209816\",\n \"0.5396447\",\n \"0.539642\",\n \"0.53866744\",\n \"0.5382184\",\n \"0.5381426\",\n \"0.53445005\",\n \"0.5327547\",\n \"0.53261554\",\n \"0.53203654\",\n \"0.5319437\",\n \"0.5315224\",\n \"0.53041095\",\n \"0.52943015\",\n \"0.52837044\",\n \"0.5271249\",\n \"0.527102\",\n \"0.5268476\",\n \"0.5266124\",\n \"0.52631295\",\n \"0.52573574\",\n \"0.5256292\",\n \"0.5239578\",\n \"0.52376944\",\n \"0.52376944\",\n \"0.52293843\",\n \"0.52287346\",\n \"0.52240765\",\n \"0.52201533\",\n \"0.5209393\",\n \"0.5203459\",\n \"0.5198872\",\n \"0.5195901\",\n \"0.51947916\",\n \"0.5193854\",\n \"0.5193557\",\n \"0.5185568\",\n \"0.51650715\",\n \"0.5164905\",\n \"0.51586473\",\n \"0.515221\",\n \"0.51357096\",\n \"0.5135259\",\n \"0.5131439\",\n \"0.5128439\",\n \"0.51225644\",\n \"0.51130307\",\n \"0.51129764\",\n \"0.51121294\",\n \"0.50996023\",\n \"0.5080335\",\n \"0.5074438\",\n \"0.5073287\",\n \"0.5070248\",\n \"0.50685966\",\n \"0.50637656\",\n \"0.50632846\",\n \"0.50592214\",\n \"0.5055333\",\n \"0.50488806\",\n \"0.50482744\",\n \"0.5048125\",\n \"0.504714\",\n \"0.5046189\",\n \"0.5032601\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93770,"cells":{"query":{"kind":"string","value":"Decides whether to bet or fold at this point in time"},"document":{"kind":"string","value":"def bet_fold_algorithm(self, current_top_bid,\n pot_size, community_cards, position, number_of_players, is_first_cycle, total_value):\n # If pre-flop\n if len(community_cards) == 0:\n rank = self.hand.opening_hand_ranking()\n # Cut-off rank = 0.00 right now\n # Else bet for now\n cutoff = self.stat_to_ev(self.vpip)\n if rank <= cutoff:\n # Fold\n return 'f'\n else:\n return 'b'\n # If post-flop\n else:\n # Greater than 10 high card\n if total_value > self.agg_to_total_value_cutoff():\n return 'b'\n elif position == number_of_players - 1 and current_top_bid < pot_size / 5:\n return 'b'\n elif current_top_bid == 0:\n return 'c'\n else:\n return 'f'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fold(self):\n self.active = False\n self.hand = None\n self.bet = 0\n return True","def get_learn_after_each_decision(self):\r\n return 0","def notFold(self):\n return not self._fold","def rough_outcome(self) -> float:\n # HUYNH YOU PRICK WHY THE FUCK DO YOU MAKE US WRITE THIS SHIT EVEN IT'S NOT USED ANYWHERE\n # pick move based on this may not be optimal but better than random\n # return 1 if win immediately\n # return -1 if all states reachable will result the other player win\n # return 0 if otherwise ??? what the fuck does this mean\n # look two states forward\n pass","def converged(self) -> bool:","def converged(self) -> bool:","def converged(self) -> bool:","def _decisionFunction(self):\n #THIS IS WHERE THE INTELLIGENT AGENT CODE MAKES DECISION\n #since this is the hand coded extension, I'm just going to hard code some stuff\n #q learning and Sarsa should hopefully do better\n \n #this is some other hand coded stuff that you read in stone's paper\n c1 = 64 #c1 = distance in pixels\n c2 = 2.5#c2 = something to multiply angle by\n c3 = 77 #c3 is the number of pixels you assume are in 5 meteres\n #state variable 7 is distance in pixels from K1 to T1\n if self.stateVariables[7] > c1:\n self._holdBall()\n else:\n passMax = float(\"-Inf\")\n passMaxArg = None\n for i in range(1,3):\n var = (c2 * arccos(self.stateVariables[10+i])) + (self.stateVariables[8+i] / c3)\n \"\"\"\n print(\"var = \", var)\n print(\"stateVariable[\", 10 + i, \"]=\", self.stateVariables[10+i] )\n print(\"arccos of stateVariable[\", 10 + i, \"]=\", arccos(self.stateVariables[10+i]) )\n print(\"stateVariable[\", 8 + i, \"]=\", self.stateVariables[8+i] )\n \"\"\"\n \n if var > passMax:\n passMax = var\n passMaxArg = i\n self._passBall(passMaxArg)","def step_solution(self):\n import time, random\n time.sleep(1.0)\n print '(step_solution) Implement me!'\n return True if random.random() < 0.25 else False","def post_process_stage1(self, pred):\n return (pred>0)*1","def betting_round(self, br=None):\n if not br:\n br = BettingRound([], {}, pot=self.pot)\n self.br = br\n\n next_better = self.br.next_better()\n while next_better is not None:\n action = self.get_action(next_better)\n\n if action.is_fold():\n pass\n elif action.is_raise():\n pass\n elif action.is_check():\n pass\n elif action.is_call():\n pass","def evaluate(self) :\n if self.inStates[0].getState() == self.inStates[1].getState(): return 0\n return 1","def perform_strategy(self, counter):\r\n if counter < self.percent * len(self.envelopes): # in the first self.percent percent\r\n self.curr_max = max(self.curr_max, self.envelopes[counter].money)\r\n return\r\n return self.envelopes[counter].money > self.curr_max","def fold(self, fold, shuffle = True):\n testData = self.mergefolds[fold]\n trainData = sum(self.mergefolds[:fold] +\n self.mergefolds[fold + 1:], [])\n if shuffle:\n random.shuffle(testData)\n random.shuffle(trainData)\n return trainData, testData","def do_fold(self) -> None:\r\n self.active = False","def assumed_state(self) -> bool:\n return self._optimistic","def goal_test(self):\n if -1 in self.state:\n return False\n else:\n return True","def future_deceiveddown(self, a):\n nfav, succfav = self.control[a.name] \n #f_n = n+1;\n # f_worldround = self.world.round+1\n f_successRate = float(a.success) / float(self.world.round+1)\n f_successRatefav = float(succfav) / float(nfav+1)\n if hardrule:\n return self.nsucc(a) > (f_successRatefav + epsilonD) and \\\n (nfav+1 > 5) and ((self.world.round - nfav) > 5) \n else:\n return nfav+1 > 5 and (f_successRate > f_successRatefav + epsilonD \\\n or f_successRate < epsilonD)","def is_artificial(self):\n\t\treturn 0","def is_boring(state):\n return state.boring_moves >= state.just_stop","def decision():\n return random.random() > 0.5","def decide():","def check(self):\n if (sum(self.state) == 0):\n return -1\n elif (self.state[-1] >= 1):\n return 1\n else:\n return 0","def fold_change_bohr(bohr_parameter):\n\n fold_change = (1 + np.e**(-bohr_parameter))**-1\n\n return fold_change","def future_deceivedup(self, a): \n nfav, succfav = self.control[a.name]\n #f_n = n+1;\n # f_worldround = self.world.round+1\n f_successRate = float(a.success +1) / float(self.world.round+1)\n if hardrule:\n return (nfav+1 > 5) and ((self.world.round - nfav) > 5) and \\\n float(a.success+1-succfav)/(self.world.round+1 - nfav) > \\\n (float(succfav)/nfav) + epsilonD\n else:\n return nfav > 5 and (f_successRate > (float(succfav)/nfav) + epsilonD \\\n or f_successRate < epsilonD)","def ct_bis(env, a, median, mad):\n\n\tb1 = (a.T < median - env.threshold * mad).T\n\tb2 = a[:, :a.shape[1] - 1] < a[:, 1:]\n\tb3 = a[:, 1:] < a[:, :a.shape[1] - 1]\n\tb1[:, :a.shape[1] - 1] = b1[:, :a.shape[1] - 1] & b2\n\tb1[:, 1:] = b1[:, 1:] & b3\n\treturn (b1)","def updateState(self):\n\n if ('cutting' in self.step_ops) and (self.cut_state.user_cutting):\n self.step_ops['cutting'] = True\n \n if ('cooking' in self.step_ops) and (self.cut_state.user_cooking):\n self.step_ops['cooking'] = True\n\n # TODO: add the rest of the operations\n\n advance = True\n\n # Check if ALL operations are complete\n for op in self.step_ops:\n if self.step_ops[op] == False:\n advance = False\n break\n\n if advance:\n self.nextStep()","def evaluate(self) :\n for inp in self.inStates :\n if inp.getState() == 0 : return 0\n return 1","def _ftolCheck(self):\n oldLoss = biggestRecentLoss(self.loss, self.lookback)\n newLoss = float(self.loss[-1])\n fracDiff = 2 * (oldLoss - newLoss)/(oldLoss + newLoss)\n \n if fracDiff < self.ftol:\n \n self.converged = True","def evaluate_binary_consistency(self):\n\n change_rw = 0\n change_sm = 0\n th = [0.005]\n for threshold in th:\n raw_th = [self.rw_data[t] > threshold for t in range(0, self.T)]\n smooth_th = [self.smth_data[t] > 0 for t in range(0, self.T)]\n # print(\"Zeros rw:\", get_avg_zeros_per_row(raw_th))\n # print(\"Zeros sm:\", get_avg_zeros_per_row(self.smth_data))\n change_rw = change_rw + self.change_of_network_over_time(raw_th)\n change_sm = change_sm + self.change_of_network_over_time(smooth_th)\n\n change_rw = change_rw / len(th)\n change_sm = change_sm / len(th)\n\n return change_rw, change_sm","def boolean_func(experiment):","def goal_test(self, state):\n return state.board==range(self.N*self.N)","def is_over(self, state: StonehengeState) -> bool:\n total_result = state.hori_result + state.left_result + state.right_result\n total_line = len(total_result)\n p1_taken = 0\n p2_taken = 0\n # all_taken = True\n for item in total_result:\n if item == '1':\n p1_taken+=1\n elif item =='2':\n p2_taken += 1\n # else:\n # all_taken = False\n # print('p1 taken:' + str(p1_taken))\n # print('p2 taken:' + str(p2_taken))\n # print('p1_taken more than half?')\n # print(float(p1_taken) >= total_line/2)\n # print('p2_taken more than half?')\n # print(float(p2_taken) >= total_line/2)\n return float(p1_taken) >= total_line/2 or float(p2_taken) >= total_line/2","def step5(self):\n\t\tself.j = self.k\n\t\tif self.b[self.k] == 'e':\n\t\t\ta = self.m()\n\t\t\tif a > 1 or (a == 1 and not self.cvc(self.k-1)):\n\t\t\t\tself.k = self.k - 1\n\t\tif self.b[self.k] == 'l' and self.doublec(self.k) and self.m() > 1:\n\t\t\tself.k = self.k -1","def choose_check(self, evolver: 'Evolver') -> bool:\r\n\r\n if self.checking is None:\r\n # first!\r\n self._start_checking(evolver, self.seed_points[0])\r\n return True\r\n\r\n if self.seed_points_ind < len(self.seed_points):\r\n self.seed_points_ind += 1\r\n if self.seed_points_ind < len(self.seed_points):\r\n self._start_checking(evolver, self.seed_points[self.seed_points_ind])\r\n return True\r\n if self.evolve_param.categorical:\r\n return False\r\n\r\n self.salient_points_ind = 0\r\n if evolver.settings.salient_points == 0:\r\n return False\r\n else:\r\n self.salient_points_ind += 1\r\n if self.salient_points_ind >= evolver.settings.salient_points:\r\n return False\r\n\r\n if self.evolve_param.integral:\r\n salient_pt = IntegralSalienceComputer.hint(\r\n self.evolve_param.domain, self.sweep_pts[:self.sweep_len],\r\n self.sweep[:self.sweep_len].reshape(self.sweep_len, 1))\r\n if salient_pt is None:\r\n return False\r\n else:\r\n salient_pt = ContinuousSalienceComputer.hint(\r\n self.evolve_param.domain, self.sweep_pts[:self.sweep_len],\r\n self.sweep[:self.sweep_len].reshape(self.sweep_len, 1))\r\n\r\n self._start_checking(evolver, salient_pt)\r\n return True","def assign_folds(df, num_folds, held_out_fraction, held_out_max):\n result_df = pandas.DataFrame(index=df.index)\n\n for fold in range(num_folds):\n result_df[\"fold_%d\" % fold] = True\n for (allele, sub_df) in df.groupby(\"allele\"):\n medians = sub_df.groupby(\"peptide\").measurement_value.median()\n\n low_peptides = medians[medians < medians.median()].index.values\n high_peptides = medians[medians >= medians.median()].index.values\n\n held_out_count = int(\n min(len(medians) * held_out_fraction, held_out_max))\n\n held_out_peptides = set()\n if held_out_count == 0:\n pass\n elif held_out_count < 2:\n held_out_peptides = set(\n medians.index.to_series().sample(n=held_out_count))\n else:\n held_out_low_count = min(\n len(low_peptides),\n int(held_out_count / 2))\n held_out_high_count = min(\n len(high_peptides),\n held_out_count - held_out_low_count)\n\n held_out_low = pandas.Series(low_peptides).sample(\n n=held_out_low_count) if held_out_low_count else set()\n held_out_high = pandas.Series(high_peptides).sample(\n n=held_out_high_count) if held_out_high_count else set()\n held_out_peptides = set(held_out_low).union(set(held_out_high))\n\n result_df.loc[\n sub_df.index[sub_df.peptide.isin(held_out_peptides)],\n \"fold_%d\" % fold\n ] = False\n\n print(\"Training points per fold\")\n print(result_df.sum())\n\n print(\"Test points per fold\")\n print((~result_df).sum())\n return result_df","def status(self):\n return self.value > self.initial_value/2 \\\n and self.bag > 0 \\\n and self.balance > 1","def step(self, flipindex):\n y = self.Y[flipindex, 0]\n currentclassind = self.classvec[flipindex]\n \n DVTY_old_currentclass = self.DVTY_list[currentclassind]\n fitness_old_currentclass = self.classFitnessList[currentclassind]\n DVTY_new_currentclass = DVTY_old_currentclass - 2 * self.Dsvecs_list[flipindex]\n fitness_new_currentclass = self.size - DVTY_new_currentclass.T * DVTY_new_currentclass\n fitnessdiff_currentclass = fitness_new_currentclass - fitness_old_currentclass\n \n changed = False\n \n bestclassind = None\n VTY_new_bestclass = None\n bestfitnessdiff = float('Inf')\n fitness_new_bestclass = None\n \n for newclassind in range(self.labelcount):\n \n if newclassind == currentclassind: continue\n \n DVTY_old_newclass = self.DVTY_list[newclassind]\n DVTY_new_newclass = DVTY_old_newclass + 2 * self.Dsvecs_list[flipindex]\n fitness_old_newclass = self.classFitnessList[newclassind]\n fitness_new_newclass = self.size - DVTY_new_newclass.T * DVTY_new_newclass\n fitnessdiff_newclass = fitness_new_newclass - fitness_old_newclass\n \n fitnessdiff = fitnessdiff_currentclass + fitnessdiff_newclass\n \n if fitnessdiff < 0 and fitnessdiff < bestfitnessdiff:\n bestfitnessdiff = fitnessdiff[0, 0]\n bestclassind = newclassind\n DVTY_new_bestclass = DVTY_new_newclass\n fitness_new_bestclass = fitness_new_newclass\n \n \n if bestclassind is not None:\n if self.classcounts[currentclassind] > self.constraint:\n \n self.Y[flipindex, currentclassind] = -1.\n self.Y[flipindex, bestclassind] = 1.\n self.classcounts[currentclassind] -= 1\n self.classcounts[bestclassind] += 1\n self.classvec[flipindex] = bestclassind\n \n self.DVTY_list[currentclassind] = DVTY_new_currentclass\n self.DVTY_list[bestclassind] = DVTY_new_bestclass\n self.classFitnessList[currentclassind] = fitness_new_currentclass\n self.classFitnessList[bestclassind] = fitness_new_bestclass\n \n changed = True\n \n return changed","def perform_strategy(self, counter):\r\n if counter == 0: # reset. allows for multiple runs with the same instance\r\n self.maxes_counter = 0\r\n self.curr_max = -inf\r\n m = self.envelopes[counter].money\r\n if m >= self.curr_max:\r\n self.maxes_counter += 1\r\n self.curr_max = m\r\n return self.maxes_counter == self.N","def solveOneStep(self):\n ### Student code goes here\n\n if (self.currentState.state == self.victoryCondition):\n self.visited[self.currentState] = True\n win_or_not = (self.currentState.state == self.victoryCondition)\n return win_or_not\n\n game_depth = self.currentState.depth\n continue_game = True\n test_its = 0\n while continue_game:\n test_its += 1\n # too long \n # time test\n if test_its == \"too long\":\n return \"too long\"\n result = self.solveOneStep_helper(game_depth)\n if result:\n victory_satisfied = (self.currentState.state == self.victoryCondition)\n if victory_satisfied:\n result_bool = True\n return result_bool\n else:\n game_depth = game_depth + 1\n else:\n return False","def check(self):\n\n if (sum(self.game_state) == 0):\n return 1\n elif (self.game_state[-1] >=1 ):\n return -1\n else:\n return 0","def __bool__(self) -> bool:\n if self.initial_value == 1 and self.number_of_steps == 0:\n return True\n return False","def apply_test_if_better(self):\n\n if self.__genes_test is None or self.__fitness_test is None:\n raise ValueError(\"Test values should not be None.\")\n\n # if test is better\n if self.__fitness_test < self.__fitness:\n self.genes = self.__genes_test\n self.__fitness = self.__fitness_test\n\n self.__genes_test = None\n self.__fitness_test = None\n\n return True\n\n # if original is better\n else:\n self.__genes_test = None\n self.__fitness_test = None\n\n return False","def test_updatePlayerbHist_fold_2(self):\n self.assertEqual(self.player.foldedInd, 1)","def detect_crossover(self):\n\t\n\n\t\tif self.trend == 0:\n\t\t\tif self.averages['fast'] < self.averages['slow']:\n\t\t\t\tself.trend = -1\n\t\t\telif self.averages['fast'] >= self.averages['slow']:\n\t\t\t\tself.trend = 1\n\t\telse: \n\t\t\tif self.trend == -1 and self.averages['fast'] >= self.averages['slow']:\n\t\t\t\tself.trend = 1\n\t\t\t\treturn {'action': 0, 'sType': self.strategyType}\n\t\t\telif self.trend == 1 and self.averages['fast'] <= self.averages['slow']:\n\t\t\t\tself.trend = -1\n\t\t\t\treturn {'action': 1, 'sType': self.strategyType}\n\t\t\telse:\n\t\t\t\treturn None # no crossover","def _inbuilt_model_selection_step(self, X, y):\n\n cross_val_score = self.cross_val_score(X=X, y=y,\n return_incumbent_score=True)\n mean_cross_val_score = cross_val_score.mean(axis=0)\n\n if mean_cross_val_score[0] >= mean_cross_val_score[1]:\n # Base boosting did not improve performance\n setattr(self, 'return_incumbent_', True)","def __bool__(self):\n return not(self.outcome != 0 or self.filled)","def is_best(self, metric: float) -> bool:","def _toss_fair_coin() -> bool:\n return random.random() > 0.5","def keep_cross_validation_fold_assignment(self):\n return self._parms.get(\"keep_cross_validation_fold_assignment\")","def _should_continue(self):\n # should_continue = self.iter < 20\n # self.iter += 1\n # return should_continue\n if self.iter > self.max_iter:\n return False\n elif self.prev_elbo is None:\n self.prev_elbo = self._get_elbo()\n return True\n elbo = self._get_elbo()\n improvement = (elbo - self.prev_elbo) / self.prev_elbo\n self.prev_elbo = elbo\n self.iter += 1\n return self.epsilon < improvement","def graspnet_closure(self, point_state):\n score = self.graspnet.compute_grasps_score(sess, point_state)\n print('grasp closure score:', score)\n return score > self.grasp_score_threshold # tuned threshold","def solveOneStep(self):\n ### Student code goes here\n if self.currentState.state == self.victoryCondition:\n self.visited[self.currentState]=True\n return True\n return self.BFS()","def goal_test(self, state):\n #return state == self.goal","def One_Decision(self):\n\n # EP.Population.one_year(self) # performs statistics\n\n agent = self.selectIndividual() # agent who will play the game \n # print agent.ID, 'about to move'\n self.CallsSinceLastMove += 1\n if agent.decisionToMove() and agent.moves():\n self.Moves += 1\n self.CallsSinceLastMove = 0\n # if self.popSize: self.Observer.season(self.Moves // self.popSize) # sets StepId\n self.Observer.season() # sets StepId\n # print(self.Observer.StepId)\n if self.Observer.Visible(): # time for display\n Satisfactions = self.satisfaction()\n for (Colour, Satisfaction) in Satisfactions:\n self.Observer.curve(Name='%s Satisfaction' % str(Colour), Value=Satisfaction)\n # if Satisfactions:\n # self.Observer.curve(Name='Global Satisfaction', Value=sum([S for (C,S) in Satisfactions])/len(Satisfactions))\n \n if self.CallsSinceLastMove > 10 * self.popSize:\n return False # situation is probably stable\n return True # simulation goes on","def final_strategy(score, opponent_score):\n # BEGIN PROBLEM 12\n def is_odd(n):\n return n % 2 != 0\n\n margin = 8\n num_rolls = 4\n # 1 try to force swap\n if swap_strategy(score, opponent_score, margin, num_rolls) == 0:\n num_rolls = 0\n # 2 when losing\n elif score < opponent_score:\n # 2.1 in this range, let opponent get ahead for better chances for swap\n if 0.43 * opponent_score < score < 0.50 * opponent_score:\n num_rolls = 9 # high chances for a Pig Out\n # 2.2 baseline strategy\n else:\n num_rolls = 4\n # 3 when winning\n else:\n # 3.1 when at least doubling the opponent score, get more conservative\n if score >= opponent_score * 1.95:\n if score >= 78:\n num_rolls = 0\n else:\n margin = 6\n if bacon_strategy(score, opponent_score, margin, num_rolls) == 0:\n fb_score = free_bacon(score)\n if is_odd(fb_score + score): # avoid even to reduce chance of swap\n num_rolls = 0\n else:\n num_rolls = round((100 - score) / 23) # por prueba y error\n # 3.2 when barely over opponent's score, stay here to avoid a bad swap\n elif opponent_score * 1.5 < score < opponent_score * 1.7:\n fb_score = free_bacon(score)\n if fb_score < 8:\n num_rolls = 0\n else:\n num_rolls = 9 # high chances for a Pig Out\n # 3.3 when winning, but not doubling opponent's score\n else:\n margin = round((100 - score) / 2)\n if bacon_strategy(score, opponent_score, margin, num_rolls) == 0:\n fb_score = free_bacon(score)\n if is_odd(fb_score + score): # avoid even to reduce chance of swap\n num_rolls = 0\n else: # more chances to get a 1 and reduce chance of swap\n num_rolls = 10\n return num_rolls\n # END PROBLEM 12","def bust(person):\n if person.total > GOAL_TOTAL() and person.aceCount == 0:\n return True\n elif person.total > GOAL_TOTAL() and person.aceCount > 0:\n adjust_ace(person)\n return person.total > GOAL_TOTAL()\n else: # person.total <= GOAL_TOTAL()\n return False","def choose_action(self, d, name):\n # some initializations\n current_state = d.state\n caution = False\n confidence = False\n buy_rules = [0,0,0,0]\n next_vec = self.transition_matrix[name][int(current_state)]\n num_undesirable_states = (self.trigger_states[name]+1)\n num_desirable_states = (next_vec.size-num_undesirable_states)\n if num_undesirable_states<5:\n left_basket_max = 2\n else:\n left_basket_max = num_undesirable_states//3\n if num_desirable_states<5:\n right_basket_min = next_vec.size-2\n else:\n right_basket_min = next_vec.size-num_undesirable_states//3\n # check if rules are satisfied\n # rule-1\n m1 = np.max(next_vec[:self.trigger_states[name]+1])\n m1_idx = np.argmax(next_vec[:self.trigger_states[name]+1])\n m2 = np.max(next_vec[self.trigger_states[name]+1:])\n m2_idx = np.argmax(next_vec[self.trigger_states[name]+1:])+\\\n next_vec[:self.trigger_states[name]+1].size\n if m2-m1>=0.1: # threshold\n #print('Rule #1 satisfied.')\n buy_rules[0]=1\n # rule-2\n if np.sum(next_vec[self.trigger_states[name]+1:])-\\\n np.sum(next_vec[:self.trigger_states[name]+1])>=0.25: # threshold\n #print('Rule #2 satisfied.')\n buy_rules[1]=1\n # rule-3 \n if m1_idx<left_basket_max: \n if buy_rules[0]!=1:\n caution=True\n #print('Predicted state is very undesirable.')\n # rule-3\n if m2_idx>=right_basket_min:\n if buy_rules[0]==1:\n confidence=True\n #print('Predicted state is very desirable.')\n if d.MACD>d.signal_line:\n #print('Rule #3 satisfied.')\n buy_rules[2] = True\n # sum of k most undesirable vs k most desirable\n temp_1 = np.sort(next_vec[self.trigger_states[name]+1:])\n temp_2 = np.sort(next_vec[:self.trigger_states[name]+1])\n size = 3\n if temp_1.size<size or temp_2.size<size:\n size = min(temp_1.size, temp_2.size)\n k1 = np.sum(temp_1[::-size])\n k2 = np.sum(temp_2[::-size])\n if k1-k2>0.25:\n #print('Rule #4 satisfied.')\n buy_rules[3] = True\n # finally, make a call using the rules\n if confidence or sum(buy_rules)>=3:\n return 'buy'\n elif caution or (buy_rules[0]==0 and sum(buy_rules)<=2 and m1-m2>0.05):\n return 'sell'\n else:\n return 'hold'","def worst_score(self):\r\n pass","def __call__(self, trainer):\n observation = trainer.observation\n summary = self._summary\n key = self._key\n if key in observation:\n summary.add({key: observation[key]})\n\n if not self._interval_trigger(trainer):\n return False\n\n if self._max_trigger(trainer):\n return True\n\n stats = summary.compute_mean()\n value = float(stats[key]) # copy to CPU\n self._init_summary()\n\n if not self._best_value or self._compare(self._best_value, value):\n self._best_value = value\n self._waited = 0\n return False\n elif self._waited >= self._patience:\n return True\n else:\n self._waited += 1\n if self._waited >= self._patience:\n return True\n else:\n return False","def reached_convergence(self, delta):\n num_evaluations = len(self.evaluations)\n if num_evaluations < 4:\n return False\n\n if self.best_eval_actions is not None and \\\n (len(self.best_eval_actions) - self.evaluations[num_evaluations - 1]) > delta + 2:\n return False\n\n diff1 = abs(self.evaluations[num_evaluations - 4] - self.evaluations[num_evaluations - 3])\n diff2 = abs(self.evaluations[num_evaluations - 4] - self.evaluations[num_evaluations - 2])\n diff3 = abs(self.evaluations[num_evaluations - 4] - self.evaluations[num_evaluations - 1])\n diff4 = abs(self.evaluations[num_evaluations - 3] - self.evaluations[num_evaluations - 2])\n diff5 = abs(self.evaluations[num_evaluations - 3] - self.evaluations[num_evaluations - 1])\n diff6 = abs(self.evaluations[num_evaluations - 2] - self.evaluations[num_evaluations - 1])\n\n actions = 0\n for agent in self.agents:\n actions += len(agent.actions)\n\n # num of (all possible) actions ~= num of states\n eval_steps = min(actions, 100)\n\n if (self.evaluations[num_evaluations - 1] < eval_steps) and\\\n (self.evaluations[num_evaluations - 2] < eval_steps) and\\\n (self.evaluations[num_evaluations - 3] < eval_steps) and \\\n (self.evaluations[num_evaluations - 4] < eval_steps):\n\n if diff1 < delta and diff2 < delta and diff3 < delta and diff4 < delta and diff5 < delta and diff6 < delta:\n return True\n\n return False","def test_best_val(self, te_acc):\n self.test_val = te_acc","def solveOneStep(self):\n ### Student code goes here\n return True","def bacon_strategy(score, opponent_score, margin=8, num_rolls=4):\n # BEGIN PROBLEM 10\n if free_bacon(opponent_score) >= margin:\n return 0\n return num_rolls\n # END PROBLEM 10","def _hs_reducible_stages(self,tol=1.e-13):\n m=len(self)\n mindiff=10.\n for i in range(m):\n for j in range(i+1,m):\n dif = np.max(np.abs(self.A[i,:]-self.A[j,:]))\n if dif<tol: return True,[i,j]\n mindiff=min(mindiff,dif)\n return False, mindiff","def epidemic_finish(states, iteration):\n return np.sum(states) == 0 and iteration > 10","def rough_outcome(self) -> float:\n if is_win(self):\n return 1\n elif is_lose(self):\n return -1\n return 0","def is_over(self, state):\n return state.current_total == 0","def get_learn_after_each_trial(self):\r\n return 0","def top_is_pointwise(self):\n return False","def perform_cv_fold(self, algo, fold, folds):\n # TODO: this is only done for hyperparameter optimization and is not\n # part of the OpenML specification. The OpenML specification would\n # like to have the hyperparameter evaluation inside the evaluate\n # performed by the target algorithm itself. Hyperparameter\n # optimization on the other hand needs these both things to be decoupled\n # For being closer to OpenML one could also call evaluate and pass\n # everything else through kwargs.\n if self.task_type != \"Supervised Classification\":\n raise NotImplementedError(self.task_type)\n\n if self.estimation_procedure[\"type\"] != \\\n \"crossvalidation with holdout\":\n raise NotImplementedError(self.estimation_procedure[\"type\"] )\n\n if self.estimation_procedure[\"parameters\"][\"stratified_sampling\"] != \\\n 'true':\n raise NotImplementedError(self.estimation_procedure[\"parameters\"][\"stratified_sampling\"])\n\n if self.evaluation_measure != \"predictive_accuracy\":\n raise NotImplementedError(self.evaluation_measure)\n\n ########################################################################\n # Test folds\n train_indices, test_indices = self.get_train_test_split()\n\n ########################################################################\n # Crossvalidation folds\n train_indices, validation_indices = self.get_validation_split(fold)\n\n X, Y = self.get_dataset()\n\n algo.fit(X[train_indices], Y[train_indices])\n\n predictions = algo.predict(X[validation_indices])\n accuracy = sklearn.metrics.accuracy_score(Y[validation_indices], predictions)\n return accuracy","def fully_burning(cell, new_ig, decay):\n\n new_ig = int(new_ig)\n decay = int(decay)\n cell = int(cell)\n if cell == BURNING: return True\n if cell == START_BURN and new_ig >= decay: return True\n return False","def Bayes_prediction(X, y, fold_number=10):\n D = X.shape[1]\n fold = KFold(n_splits=fold_number)\n cross_tab_all = []\n lamb_hat_all = []\n \n for train_index, test_index in fold.split(X, y):\n X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n length = X_train.shape[0]\n pi_hat = y_train.mean()\n lamb_hat = np.zeros((2, D))\n \n for flag in range(2):\n for d in range(D):\n lamb_hat[flag][d] = (sum(X_train.iloc[i][d] * (y_train.iloc[i]==flag) for i in range(length))) / (sum(y_train.iloc[i]==flag for i in range(length)))\n\n y_pred = np.zeros(len(X_test))\n for i in range(len(X_test)):\n y_pred[i] = Bayes_classifier(pi_hat, X_test.iloc[i], lamb_hat)\n \n cross_tab = np.zeros((2, 2))\n for m in [0, 1]:\n for n in [0, 1]:\n cross_tab[m][n] = sum([(y_test.values[i]==m) & (y_pred[i]==n) for i in range(len(y_pred))]) \n \n cross_tab_all.append(cross_tab)\n lamb_hat_all.append(lamb_hat)\n \n cross_tab_all = sum(cross_tab_all)\n lamb_hat_all\n\n return lamb_hat_all, cross_tab_all","def evaluate(self) :\n for inp in self.inStates :\n if inp.getState() == 1 : return 1\n return 0","def setBestStepRun(self, stepRun):\n if(self.__bestStepRun < stepRun):\n self.__bestStepRun = stepRun\n return True\n else:\n return False","def condition(timestep, *_):\n return tf.less(timestep, tf.shape(score)[1])","def recall(self):\n tpos, fneg = len(self.__true_positives), len(self.__false_negatives)\n return float(tpos) / (tpos + fneg)","def is_better(self, curr, best, **kwargs):\r\n score_threshold = kwargs.pop('score_threshold', 1e-3)\r\n relative_eps = 1.0 + score_threshold\r\n return curr >= best*relative_eps","def dstflow(self):\n if self.name in conditional_branch + unconditional_branch:\n return True\n return False","def test_foo(self):\n self.ran = True\n 1 / 0","def is_best(self, val) -> bool:\n if self.val is None or (val > self.val):\n self.val = val\n print(\"Updating Best\")\n return True\n else:\n return False","def pull(self):\n chance = np.random.uniform()\n return chance < self.winning_prob","def evaluation_point(self):\n if self.turns in range(40,(self.game_length - 19), self.policy_eval_point):\n return True","def evaluate(self):\n self.training = False","def classify(self, testInstance):\n return self.fire(testInstance) > 0.5","def _is_valid_bn_fold(conv: LayerType, fold_backward: bool) -> bool:\n valid = True\n if not fold_backward:\n # Cannot fold BN -> Conv with padding. AIMET does not support forward folding to grouped or DW Conv\n if isinstance(conv, (torch.nn.Conv2d, torch.nn.Conv1d, torch.nn.Conv3d)):\n valid &= all(item == 0 for item in conv.padding)\n valid &= conv.groups == 1\n # AIMET does not support forward folding to ConvTranspose\n elif isinstance(conv, torch.nn.ConvTranspose2d):\n valid = False\n else:\n # AIMET does not support backwards folding to grouped ConvTranspose\n if isinstance(conv, torch.nn.ConvTranspose2d):\n valid &= conv.groups in (1, conv.in_channels)\n return valid","def is_caught_up_well_enough_for_government_work():\n return config.CAUGHT_UP or (config.BLOCKCHAIN_SERVICE_LAST_BLOCK and config.CURRENT_BLOCK_INDEX >= config.BLOCKCHAIN_SERVICE_LAST_BLOCK - 1)","def _score_to_decision(self, score):","def Continue():\n # adjust this to take as many steps as you need\n return warp.top.it <= 500","def test_convergence(self, time_step):\n \n ##compare the average episode length between two loop\n if self.past_episode == time_step:\n self.convergence = True\n else:\n self.convergence = False","def test_convergence(self, time_step):\n \n ##compare the average episode length between two loop\n if self.past_episode == time_step:\n self.convergence = True\n else:\n self.convergence = False","def fold_assignment(self):\n return self._parms.get(\"fold_assignment\")","def two_fold(\n self,\n std=False,\n two_fold_type=\"pooled\",\n submitted_weight=None,\n n=None,\n conf=None,\n ):\n self.submitted_n = n\n self.submitted_conf = conf\n std_val = None\n self.two_fold_type = two_fold_type\n self.submitted_weight = submitted_weight\n\n if two_fold_type == \"cotton\":\n self.t_params = (\n self.len_f / (self.len_f + self.len_s) * self._f_model.params\n ) + (self.len_s / (self.len_f + self.len_s) * self._s_model.params)\n\n elif two_fold_type == \"reimers\":\n self.t_params = 0.5 * (self._f_model.params + self._s_model.params)\n\n elif two_fold_type == \"self_submitted\":\n if submitted_weight is None:\n raise ValueError(\"Please submit weights\")\n submitted_weight = [submitted_weight, 1 - submitted_weight]\n self.t_params = (\n submitted_weight[0] * self._f_model.params\n + submitted_weight[1] * self._s_model.params\n )\n\n elif two_fold_type == \"nuemark\":\n self._t_model = OLS(self.endog, self.neumark).fit(\n cov_type=self.cov_type, cov_kwds=self.cov_kwds\n )\n self.t_params = self._t_model.params\n\n else:\n self._t_model = OLS(self.endog, self.exog).fit(\n cov_type=self.cov_type, cov_kwds=self.cov_kwds\n )\n self.t_params = np.delete(self._t_model.params, self.bifurcate)\n\n self.unexplained = (\n self.exog_f_mean @ (self._f_model.params - self.t_params)\n ) + (self.exog_s_mean @ (self.t_params - self._s_model.params))\n self.explained = (self.exog_f_mean - self.exog_s_mean) @ self.t_params\n\n if std is True:\n std_val = self.variance(2)\n\n return OaxacaResults(\n (self.unexplained, self.explained, self.gap), 2, std_val=std_val\n )","def run_core(self, cur_bi, next_lvl):\r\n self.cur_lvl = next_lvl\r\n self.cur_bin = cur_bi\r\n logger.info('sat engine run_core: cur_bin == %d' % cur_bi)\r\n\r\n while 1:\r\n conflict, ccindex, cvindex = self.bcp()\r\n if conflict is False:\r\n # no conflict\r\n allsat = self.decision(cur_bi)\r\n if allsat:\r\n # sat\r\n return True, cur_bi + 1, self.cur_lvl, 0 # next bin index\r\n else:\r\n # conflict\r\n bkt_bi, bkt_lvl = self.analysis(ccindex, cvindex)\r\n self.local_vars.decay_var_inc()\r\n if bkt_bi != cur_bi: # unsat\r\n logger.info('----\\t\\tpartial unsat')\r\n return False, bkt_bi, bkt_lvl + 1, bkt_lvl\r\n else:\r\n self.backtrack_cur_bin(bkt_lvl)","def goal_test(self, state):\n for x, y in state.alvos:\n if state.tabuleiro[x][y] is not BOX_ON_TARGET:\n return False\n return True","def is_smelling(self,conc_array):\n if conc_array[int(self.x)][int(self.y)]>self.threshold:\n self.smell_timer = self.Timer(self.T,self.lamda)\n #Nav mode three and four need to know whether the moth is smelling\n #at a specific moment, for that reason they use Tfirst.\n self.Tfirst = self.T\n self.odor = True #this datum will be useful in the graphical functions\n return True\n elif self.turned_on:\n self.odor = False\n if self.smell_timer.is_running(self.T):\n return True #note - even though the there is no detection, the navigator stay in nav mode.\n else:\n self.odor = False\n return False","def termialTest(state):\n if state.isWin() or state.isLose():\n return True\n return False","async def decide(self):\n return 0., 0., 0.","def guess_goal(mydata, target):\r\n cardin = dict(mydata.apply(pd.Series.nunique))\r\n target_type = mydata.dtypes[target]\r\n if(target_type == 'float64') | (target_type == 'int64'):\r\n if cardin[target] > 50:\r\n act_regression = True\r\n act_classification = False\r\n else:\r\n act_regression = False\r\n act_classification = True\r\n else:\r\n act_regression = False\r\n act_classification = True\r\n return act_regression, act_classification","def is_done(self, best_sum_logprobs):\n if len(self) < self.n_hyp:\n return False\n elif self.early_stopping:\n return True\n else:\n return self.worst_score >= best_sum_logprobs / self.max_len ** self.length_penalty"],"string":"[\n \"def fold(self):\\n self.active = False\\n self.hand = None\\n self.bet = 0\\n return True\",\n \"def get_learn_after_each_decision(self):\\r\\n return 0\",\n \"def notFold(self):\\n return not self._fold\",\n \"def rough_outcome(self) -> float:\\n # HUYNH YOU PRICK WHY THE FUCK DO YOU MAKE US WRITE THIS SHIT EVEN IT'S NOT USED ANYWHERE\\n # pick move based on this may not be optimal but better than random\\n # return 1 if win immediately\\n # return -1 if all states reachable will result the other player win\\n # return 0 if otherwise ??? what the fuck does this mean\\n # look two states forward\\n pass\",\n \"def converged(self) -> bool:\",\n \"def converged(self) -> bool:\",\n \"def converged(self) -> bool:\",\n \"def _decisionFunction(self):\\n #THIS IS WHERE THE INTELLIGENT AGENT CODE MAKES DECISION\\n #since this is the hand coded extension, I'm just going to hard code some stuff\\n #q learning and Sarsa should hopefully do better\\n \\n #this is some other hand coded stuff that you read in stone's paper\\n c1 = 64 #c1 = distance in pixels\\n c2 = 2.5#c2 = something to multiply angle by\\n c3 = 77 #c3 is the number of pixels you assume are in 5 meteres\\n #state variable 7 is distance in pixels from K1 to T1\\n if self.stateVariables[7] > c1:\\n self._holdBall()\\n else:\\n passMax = float(\\\"-Inf\\\")\\n passMaxArg = None\\n for i in range(1,3):\\n var = (c2 * arccos(self.stateVariables[10+i])) + (self.stateVariables[8+i] / c3)\\n \\\"\\\"\\\"\\n print(\\\"var = \\\", var)\\n print(\\\"stateVariable[\\\", 10 + i, \\\"]=\\\", self.stateVariables[10+i] )\\n print(\\\"arccos of stateVariable[\\\", 10 + i, \\\"]=\\\", arccos(self.stateVariables[10+i]) )\\n print(\\\"stateVariable[\\\", 8 + i, \\\"]=\\\", self.stateVariables[8+i] )\\n \\\"\\\"\\\"\\n \\n if var > passMax:\\n passMax = var\\n passMaxArg = i\\n self._passBall(passMaxArg)\",\n \"def step_solution(self):\\n import time, random\\n time.sleep(1.0)\\n print '(step_solution) Implement me!'\\n return True if random.random() < 0.25 else False\",\n \"def post_process_stage1(self, pred):\\n return (pred>0)*1\",\n \"def betting_round(self, br=None):\\n if not br:\\n br = BettingRound([], {}, pot=self.pot)\\n self.br = br\\n\\n next_better = self.br.next_better()\\n while next_better is not None:\\n action = self.get_action(next_better)\\n\\n if action.is_fold():\\n pass\\n elif action.is_raise():\\n pass\\n elif action.is_check():\\n pass\\n elif action.is_call():\\n pass\",\n \"def evaluate(self) :\\n if self.inStates[0].getState() == self.inStates[1].getState(): return 0\\n return 1\",\n \"def perform_strategy(self, counter):\\r\\n if counter < self.percent * len(self.envelopes): # in the first self.percent percent\\r\\n self.curr_max = max(self.curr_max, self.envelopes[counter].money)\\r\\n return\\r\\n return self.envelopes[counter].money > self.curr_max\",\n \"def fold(self, fold, shuffle = True):\\n testData = self.mergefolds[fold]\\n trainData = sum(self.mergefolds[:fold] +\\n self.mergefolds[fold + 1:], [])\\n if shuffle:\\n random.shuffle(testData)\\n random.shuffle(trainData)\\n return trainData, testData\",\n \"def do_fold(self) -> None:\\r\\n self.active = False\",\n \"def assumed_state(self) -> bool:\\n return self._optimistic\",\n \"def goal_test(self):\\n if -1 in self.state:\\n return False\\n else:\\n return True\",\n \"def future_deceiveddown(self, a):\\n nfav, succfav = self.control[a.name] \\n #f_n = n+1;\\n # f_worldround = self.world.round+1\\n f_successRate = float(a.success) / float(self.world.round+1)\\n f_successRatefav = float(succfav) / float(nfav+1)\\n if hardrule:\\n return self.nsucc(a) > (f_successRatefav + epsilonD) and \\\\\\n (nfav+1 > 5) and ((self.world.round - nfav) > 5) \\n else:\\n return nfav+1 > 5 and (f_successRate > f_successRatefav + epsilonD \\\\\\n or f_successRate < epsilonD)\",\n \"def is_artificial(self):\\n\\t\\treturn 0\",\n \"def is_boring(state):\\n return state.boring_moves >= state.just_stop\",\n \"def decision():\\n return random.random() > 0.5\",\n \"def decide():\",\n \"def check(self):\\n if (sum(self.state) == 0):\\n return -1\\n elif (self.state[-1] >= 1):\\n return 1\\n else:\\n return 0\",\n \"def fold_change_bohr(bohr_parameter):\\n\\n fold_change = (1 + np.e**(-bohr_parameter))**-1\\n\\n return fold_change\",\n \"def future_deceivedup(self, a): \\n nfav, succfav = self.control[a.name]\\n #f_n = n+1;\\n # f_worldround = self.world.round+1\\n f_successRate = float(a.success +1) / float(self.world.round+1)\\n if hardrule:\\n return (nfav+1 > 5) and ((self.world.round - nfav) > 5) and \\\\\\n float(a.success+1-succfav)/(self.world.round+1 - nfav) > \\\\\\n (float(succfav)/nfav) + epsilonD\\n else:\\n return nfav > 5 and (f_successRate > (float(succfav)/nfav) + epsilonD \\\\\\n or f_successRate < epsilonD)\",\n \"def ct_bis(env, a, median, mad):\\n\\n\\tb1 = (a.T < median - env.threshold * mad).T\\n\\tb2 = a[:, :a.shape[1] - 1] < a[:, 1:]\\n\\tb3 = a[:, 1:] < a[:, :a.shape[1] - 1]\\n\\tb1[:, :a.shape[1] - 1] = b1[:, :a.shape[1] - 1] & b2\\n\\tb1[:, 1:] = b1[:, 1:] & b3\\n\\treturn (b1)\",\n \"def updateState(self):\\n\\n if ('cutting' in self.step_ops) and (self.cut_state.user_cutting):\\n self.step_ops['cutting'] = True\\n \\n if ('cooking' in self.step_ops) and (self.cut_state.user_cooking):\\n self.step_ops['cooking'] = True\\n\\n # TODO: add the rest of the operations\\n\\n advance = True\\n\\n # Check if ALL operations are complete\\n for op in self.step_ops:\\n if self.step_ops[op] == False:\\n advance = False\\n break\\n\\n if advance:\\n self.nextStep()\",\n \"def evaluate(self) :\\n for inp in self.inStates :\\n if inp.getState() == 0 : return 0\\n return 1\",\n \"def _ftolCheck(self):\\n oldLoss = biggestRecentLoss(self.loss, self.lookback)\\n newLoss = float(self.loss[-1])\\n fracDiff = 2 * (oldLoss - newLoss)/(oldLoss + newLoss)\\n \\n if fracDiff < self.ftol:\\n \\n self.converged = True\",\n \"def evaluate_binary_consistency(self):\\n\\n change_rw = 0\\n change_sm = 0\\n th = [0.005]\\n for threshold in th:\\n raw_th = [self.rw_data[t] > threshold for t in range(0, self.T)]\\n smooth_th = [self.smth_data[t] > 0 for t in range(0, self.T)]\\n # print(\\\"Zeros rw:\\\", get_avg_zeros_per_row(raw_th))\\n # print(\\\"Zeros sm:\\\", get_avg_zeros_per_row(self.smth_data))\\n change_rw = change_rw + self.change_of_network_over_time(raw_th)\\n change_sm = change_sm + self.change_of_network_over_time(smooth_th)\\n\\n change_rw = change_rw / len(th)\\n change_sm = change_sm / len(th)\\n\\n return change_rw, change_sm\",\n \"def boolean_func(experiment):\",\n \"def goal_test(self, state):\\n return state.board==range(self.N*self.N)\",\n \"def is_over(self, state: StonehengeState) -> bool:\\n total_result = state.hori_result + state.left_result + state.right_result\\n total_line = len(total_result)\\n p1_taken = 0\\n p2_taken = 0\\n # all_taken = True\\n for item in total_result:\\n if item == '1':\\n p1_taken+=1\\n elif item =='2':\\n p2_taken += 1\\n # else:\\n # all_taken = False\\n # print('p1 taken:' + str(p1_taken))\\n # print('p2 taken:' + str(p2_taken))\\n # print('p1_taken more than half?')\\n # print(float(p1_taken) >= total_line/2)\\n # print('p2_taken more than half?')\\n # print(float(p2_taken) >= total_line/2)\\n return float(p1_taken) >= total_line/2 or float(p2_taken) >= total_line/2\",\n \"def step5(self):\\n\\t\\tself.j = self.k\\n\\t\\tif self.b[self.k] == 'e':\\n\\t\\t\\ta = self.m()\\n\\t\\t\\tif a > 1 or (a == 1 and not self.cvc(self.k-1)):\\n\\t\\t\\t\\tself.k = self.k - 1\\n\\t\\tif self.b[self.k] == 'l' and self.doublec(self.k) and self.m() > 1:\\n\\t\\t\\tself.k = self.k -1\",\n \"def choose_check(self, evolver: 'Evolver') -> bool:\\r\\n\\r\\n if self.checking is None:\\r\\n # first!\\r\\n self._start_checking(evolver, self.seed_points[0])\\r\\n return True\\r\\n\\r\\n if self.seed_points_ind < len(self.seed_points):\\r\\n self.seed_points_ind += 1\\r\\n if self.seed_points_ind < len(self.seed_points):\\r\\n self._start_checking(evolver, self.seed_points[self.seed_points_ind])\\r\\n return True\\r\\n if self.evolve_param.categorical:\\r\\n return False\\r\\n\\r\\n self.salient_points_ind = 0\\r\\n if evolver.settings.salient_points == 0:\\r\\n return False\\r\\n else:\\r\\n self.salient_points_ind += 1\\r\\n if self.salient_points_ind >= evolver.settings.salient_points:\\r\\n return False\\r\\n\\r\\n if self.evolve_param.integral:\\r\\n salient_pt = IntegralSalienceComputer.hint(\\r\\n self.evolve_param.domain, self.sweep_pts[:self.sweep_len],\\r\\n self.sweep[:self.sweep_len].reshape(self.sweep_len, 1))\\r\\n if salient_pt is None:\\r\\n return False\\r\\n else:\\r\\n salient_pt = ContinuousSalienceComputer.hint(\\r\\n self.evolve_param.domain, self.sweep_pts[:self.sweep_len],\\r\\n self.sweep[:self.sweep_len].reshape(self.sweep_len, 1))\\r\\n\\r\\n self._start_checking(evolver, salient_pt)\\r\\n return True\",\n \"def assign_folds(df, num_folds, held_out_fraction, held_out_max):\\n result_df = pandas.DataFrame(index=df.index)\\n\\n for fold in range(num_folds):\\n result_df[\\\"fold_%d\\\" % fold] = True\\n for (allele, sub_df) in df.groupby(\\\"allele\\\"):\\n medians = sub_df.groupby(\\\"peptide\\\").measurement_value.median()\\n\\n low_peptides = medians[medians < medians.median()].index.values\\n high_peptides = medians[medians >= medians.median()].index.values\\n\\n held_out_count = int(\\n min(len(medians) * held_out_fraction, held_out_max))\\n\\n held_out_peptides = set()\\n if held_out_count == 0:\\n pass\\n elif held_out_count < 2:\\n held_out_peptides = set(\\n medians.index.to_series().sample(n=held_out_count))\\n else:\\n held_out_low_count = min(\\n len(low_peptides),\\n int(held_out_count / 2))\\n held_out_high_count = min(\\n len(high_peptides),\\n held_out_count - held_out_low_count)\\n\\n held_out_low = pandas.Series(low_peptides).sample(\\n n=held_out_low_count) if held_out_low_count else set()\\n held_out_high = pandas.Series(high_peptides).sample(\\n n=held_out_high_count) if held_out_high_count else set()\\n held_out_peptides = set(held_out_low).union(set(held_out_high))\\n\\n result_df.loc[\\n sub_df.index[sub_df.peptide.isin(held_out_peptides)],\\n \\\"fold_%d\\\" % fold\\n ] = False\\n\\n print(\\\"Training points per fold\\\")\\n print(result_df.sum())\\n\\n print(\\\"Test points per fold\\\")\\n print((~result_df).sum())\\n return result_df\",\n \"def status(self):\\n return self.value > self.initial_value/2 \\\\\\n and self.bag > 0 \\\\\\n and self.balance > 1\",\n \"def step(self, flipindex):\\n y = self.Y[flipindex, 0]\\n currentclassind = self.classvec[flipindex]\\n \\n DVTY_old_currentclass = self.DVTY_list[currentclassind]\\n fitness_old_currentclass = self.classFitnessList[currentclassind]\\n DVTY_new_currentclass = DVTY_old_currentclass - 2 * self.Dsvecs_list[flipindex]\\n fitness_new_currentclass = self.size - DVTY_new_currentclass.T * DVTY_new_currentclass\\n fitnessdiff_currentclass = fitness_new_currentclass - fitness_old_currentclass\\n \\n changed = False\\n \\n bestclassind = None\\n VTY_new_bestclass = None\\n bestfitnessdiff = float('Inf')\\n fitness_new_bestclass = None\\n \\n for newclassind in range(self.labelcount):\\n \\n if newclassind == currentclassind: continue\\n \\n DVTY_old_newclass = self.DVTY_list[newclassind]\\n DVTY_new_newclass = DVTY_old_newclass + 2 * self.Dsvecs_list[flipindex]\\n fitness_old_newclass = self.classFitnessList[newclassind]\\n fitness_new_newclass = self.size - DVTY_new_newclass.T * DVTY_new_newclass\\n fitnessdiff_newclass = fitness_new_newclass - fitness_old_newclass\\n \\n fitnessdiff = fitnessdiff_currentclass + fitnessdiff_newclass\\n \\n if fitnessdiff < 0 and fitnessdiff < bestfitnessdiff:\\n bestfitnessdiff = fitnessdiff[0, 0]\\n bestclassind = newclassind\\n DVTY_new_bestclass = DVTY_new_newclass\\n fitness_new_bestclass = fitness_new_newclass\\n \\n \\n if bestclassind is not None:\\n if self.classcounts[currentclassind] > self.constraint:\\n \\n self.Y[flipindex, currentclassind] = -1.\\n self.Y[flipindex, bestclassind] = 1.\\n self.classcounts[currentclassind] -= 1\\n self.classcounts[bestclassind] += 1\\n self.classvec[flipindex] = bestclassind\\n \\n self.DVTY_list[currentclassind] = DVTY_new_currentclass\\n self.DVTY_list[bestclassind] = DVTY_new_bestclass\\n self.classFitnessList[currentclassind] = fitness_new_currentclass\\n self.classFitnessList[bestclassind] = fitness_new_bestclass\\n \\n changed = True\\n \\n return changed\",\n \"def perform_strategy(self, counter):\\r\\n if counter == 0: # reset. allows for multiple runs with the same instance\\r\\n self.maxes_counter = 0\\r\\n self.curr_max = -inf\\r\\n m = self.envelopes[counter].money\\r\\n if m >= self.curr_max:\\r\\n self.maxes_counter += 1\\r\\n self.curr_max = m\\r\\n return self.maxes_counter == self.N\",\n \"def solveOneStep(self):\\n ### Student code goes here\\n\\n if (self.currentState.state == self.victoryCondition):\\n self.visited[self.currentState] = True\\n win_or_not = (self.currentState.state == self.victoryCondition)\\n return win_or_not\\n\\n game_depth = self.currentState.depth\\n continue_game = True\\n test_its = 0\\n while continue_game:\\n test_its += 1\\n # too long \\n # time test\\n if test_its == \\\"too long\\\":\\n return \\\"too long\\\"\\n result = self.solveOneStep_helper(game_depth)\\n if result:\\n victory_satisfied = (self.currentState.state == self.victoryCondition)\\n if victory_satisfied:\\n result_bool = True\\n return result_bool\\n else:\\n game_depth = game_depth + 1\\n else:\\n return False\",\n \"def check(self):\\n\\n if (sum(self.game_state) == 0):\\n return 1\\n elif (self.game_state[-1] >=1 ):\\n return -1\\n else:\\n return 0\",\n \"def __bool__(self) -> bool:\\n if self.initial_value == 1 and self.number_of_steps == 0:\\n return True\\n return False\",\n \"def apply_test_if_better(self):\\n\\n if self.__genes_test is None or self.__fitness_test is None:\\n raise ValueError(\\\"Test values should not be None.\\\")\\n\\n # if test is better\\n if self.__fitness_test < self.__fitness:\\n self.genes = self.__genes_test\\n self.__fitness = self.__fitness_test\\n\\n self.__genes_test = None\\n self.__fitness_test = None\\n\\n return True\\n\\n # if original is better\\n else:\\n self.__genes_test = None\\n self.__fitness_test = None\\n\\n return False\",\n \"def test_updatePlayerbHist_fold_2(self):\\n self.assertEqual(self.player.foldedInd, 1)\",\n \"def detect_crossover(self):\\n\\t\\n\\n\\t\\tif self.trend == 0:\\n\\t\\t\\tif self.averages['fast'] < self.averages['slow']:\\n\\t\\t\\t\\tself.trend = -1\\n\\t\\t\\telif self.averages['fast'] >= self.averages['slow']:\\n\\t\\t\\t\\tself.trend = 1\\n\\t\\telse: \\n\\t\\t\\tif self.trend == -1 and self.averages['fast'] >= self.averages['slow']:\\n\\t\\t\\t\\tself.trend = 1\\n\\t\\t\\t\\treturn {'action': 0, 'sType': self.strategyType}\\n\\t\\t\\telif self.trend == 1 and self.averages['fast'] <= self.averages['slow']:\\n\\t\\t\\t\\tself.trend = -1\\n\\t\\t\\t\\treturn {'action': 1, 'sType': self.strategyType}\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn None # no crossover\",\n \"def _inbuilt_model_selection_step(self, X, y):\\n\\n cross_val_score = self.cross_val_score(X=X, y=y,\\n return_incumbent_score=True)\\n mean_cross_val_score = cross_val_score.mean(axis=0)\\n\\n if mean_cross_val_score[0] >= mean_cross_val_score[1]:\\n # Base boosting did not improve performance\\n setattr(self, 'return_incumbent_', True)\",\n \"def __bool__(self):\\n return not(self.outcome != 0 or self.filled)\",\n \"def is_best(self, metric: float) -> bool:\",\n \"def _toss_fair_coin() -> bool:\\n return random.random() > 0.5\",\n \"def keep_cross_validation_fold_assignment(self):\\n return self._parms.get(\\\"keep_cross_validation_fold_assignment\\\")\",\n \"def _should_continue(self):\\n # should_continue = self.iter < 20\\n # self.iter += 1\\n # return should_continue\\n if self.iter > self.max_iter:\\n return False\\n elif self.prev_elbo is None:\\n self.prev_elbo = self._get_elbo()\\n return True\\n elbo = self._get_elbo()\\n improvement = (elbo - self.prev_elbo) / self.prev_elbo\\n self.prev_elbo = elbo\\n self.iter += 1\\n return self.epsilon < improvement\",\n \"def graspnet_closure(self, point_state):\\n score = self.graspnet.compute_grasps_score(sess, point_state)\\n print('grasp closure score:', score)\\n return score > self.grasp_score_threshold # tuned threshold\",\n \"def solveOneStep(self):\\n ### Student code goes here\\n if self.currentState.state == self.victoryCondition:\\n self.visited[self.currentState]=True\\n return True\\n return self.BFS()\",\n \"def goal_test(self, state):\\n #return state == self.goal\",\n \"def One_Decision(self):\\n\\n # EP.Population.one_year(self) # performs statistics\\n\\n agent = self.selectIndividual() # agent who will play the game \\n # print agent.ID, 'about to move'\\n self.CallsSinceLastMove += 1\\n if agent.decisionToMove() and agent.moves():\\n self.Moves += 1\\n self.CallsSinceLastMove = 0\\n # if self.popSize: self.Observer.season(self.Moves // self.popSize) # sets StepId\\n self.Observer.season() # sets StepId\\n # print(self.Observer.StepId)\\n if self.Observer.Visible(): # time for display\\n Satisfactions = self.satisfaction()\\n for (Colour, Satisfaction) in Satisfactions:\\n self.Observer.curve(Name='%s Satisfaction' % str(Colour), Value=Satisfaction)\\n # if Satisfactions:\\n # self.Observer.curve(Name='Global Satisfaction', Value=sum([S for (C,S) in Satisfactions])/len(Satisfactions))\\n \\n if self.CallsSinceLastMove > 10 * self.popSize:\\n return False # situation is probably stable\\n return True # simulation goes on\",\n \"def final_strategy(score, opponent_score):\\n # BEGIN PROBLEM 12\\n def is_odd(n):\\n return n % 2 != 0\\n\\n margin = 8\\n num_rolls = 4\\n # 1 try to force swap\\n if swap_strategy(score, opponent_score, margin, num_rolls) == 0:\\n num_rolls = 0\\n # 2 when losing\\n elif score < opponent_score:\\n # 2.1 in this range, let opponent get ahead for better chances for swap\\n if 0.43 * opponent_score < score < 0.50 * opponent_score:\\n num_rolls = 9 # high chances for a Pig Out\\n # 2.2 baseline strategy\\n else:\\n num_rolls = 4\\n # 3 when winning\\n else:\\n # 3.1 when at least doubling the opponent score, get more conservative\\n if score >= opponent_score * 1.95:\\n if score >= 78:\\n num_rolls = 0\\n else:\\n margin = 6\\n if bacon_strategy(score, opponent_score, margin, num_rolls) == 0:\\n fb_score = free_bacon(score)\\n if is_odd(fb_score + score): # avoid even to reduce chance of swap\\n num_rolls = 0\\n else:\\n num_rolls = round((100 - score) / 23) # por prueba y error\\n # 3.2 when barely over opponent's score, stay here to avoid a bad swap\\n elif opponent_score * 1.5 < score < opponent_score * 1.7:\\n fb_score = free_bacon(score)\\n if fb_score < 8:\\n num_rolls = 0\\n else:\\n num_rolls = 9 # high chances for a Pig Out\\n # 3.3 when winning, but not doubling opponent's score\\n else:\\n margin = round((100 - score) / 2)\\n if bacon_strategy(score, opponent_score, margin, num_rolls) == 0:\\n fb_score = free_bacon(score)\\n if is_odd(fb_score + score): # avoid even to reduce chance of swap\\n num_rolls = 0\\n else: # more chances to get a 1 and reduce chance of swap\\n num_rolls = 10\\n return num_rolls\\n # END PROBLEM 12\",\n \"def bust(person):\\n if person.total > GOAL_TOTAL() and person.aceCount == 0:\\n return True\\n elif person.total > GOAL_TOTAL() and person.aceCount > 0:\\n adjust_ace(person)\\n return person.total > GOAL_TOTAL()\\n else: # person.total <= GOAL_TOTAL()\\n return False\",\n \"def choose_action(self, d, name):\\n # some initializations\\n current_state = d.state\\n caution = False\\n confidence = False\\n buy_rules = [0,0,0,0]\\n next_vec = self.transition_matrix[name][int(current_state)]\\n num_undesirable_states = (self.trigger_states[name]+1)\\n num_desirable_states = (next_vec.size-num_undesirable_states)\\n if num_undesirable_states<5:\\n left_basket_max = 2\\n else:\\n left_basket_max = num_undesirable_states//3\\n if num_desirable_states<5:\\n right_basket_min = next_vec.size-2\\n else:\\n right_basket_min = next_vec.size-num_undesirable_states//3\\n # check if rules are satisfied\\n # rule-1\\n m1 = np.max(next_vec[:self.trigger_states[name]+1])\\n m1_idx = np.argmax(next_vec[:self.trigger_states[name]+1])\\n m2 = np.max(next_vec[self.trigger_states[name]+1:])\\n m2_idx = np.argmax(next_vec[self.trigger_states[name]+1:])+\\\\\\n next_vec[:self.trigger_states[name]+1].size\\n if m2-m1>=0.1: # threshold\\n #print('Rule #1 satisfied.')\\n buy_rules[0]=1\\n # rule-2\\n if np.sum(next_vec[self.trigger_states[name]+1:])-\\\\\\n np.sum(next_vec[:self.trigger_states[name]+1])>=0.25: # threshold\\n #print('Rule #2 satisfied.')\\n buy_rules[1]=1\\n # rule-3 \\n if m1_idx<left_basket_max: \\n if buy_rules[0]!=1:\\n caution=True\\n #print('Predicted state is very undesirable.')\\n # rule-3\\n if m2_idx>=right_basket_min:\\n if buy_rules[0]==1:\\n confidence=True\\n #print('Predicted state is very desirable.')\\n if d.MACD>d.signal_line:\\n #print('Rule #3 satisfied.')\\n buy_rules[2] = True\\n # sum of k most undesirable vs k most desirable\\n temp_1 = np.sort(next_vec[self.trigger_states[name]+1:])\\n temp_2 = np.sort(next_vec[:self.trigger_states[name]+1])\\n size = 3\\n if temp_1.size<size or temp_2.size<size:\\n size = min(temp_1.size, temp_2.size)\\n k1 = np.sum(temp_1[::-size])\\n k2 = np.sum(temp_2[::-size])\\n if k1-k2>0.25:\\n #print('Rule #4 satisfied.')\\n buy_rules[3] = True\\n # finally, make a call using the rules\\n if confidence or sum(buy_rules)>=3:\\n return 'buy'\\n elif caution or (buy_rules[0]==0 and sum(buy_rules)<=2 and m1-m2>0.05):\\n return 'sell'\\n else:\\n return 'hold'\",\n \"def worst_score(self):\\r\\n pass\",\n \"def __call__(self, trainer):\\n observation = trainer.observation\\n summary = self._summary\\n key = self._key\\n if key in observation:\\n summary.add({key: observation[key]})\\n\\n if not self._interval_trigger(trainer):\\n return False\\n\\n if self._max_trigger(trainer):\\n return True\\n\\n stats = summary.compute_mean()\\n value = float(stats[key]) # copy to CPU\\n self._init_summary()\\n\\n if not self._best_value or self._compare(self._best_value, value):\\n self._best_value = value\\n self._waited = 0\\n return False\\n elif self._waited >= self._patience:\\n return True\\n else:\\n self._waited += 1\\n if self._waited >= self._patience:\\n return True\\n else:\\n return False\",\n \"def reached_convergence(self, delta):\\n num_evaluations = len(self.evaluations)\\n if num_evaluations < 4:\\n return False\\n\\n if self.best_eval_actions is not None and \\\\\\n (len(self.best_eval_actions) - self.evaluations[num_evaluations - 1]) > delta + 2:\\n return False\\n\\n diff1 = abs(self.evaluations[num_evaluations - 4] - self.evaluations[num_evaluations - 3])\\n diff2 = abs(self.evaluations[num_evaluations - 4] - self.evaluations[num_evaluations - 2])\\n diff3 = abs(self.evaluations[num_evaluations - 4] - self.evaluations[num_evaluations - 1])\\n diff4 = abs(self.evaluations[num_evaluations - 3] - self.evaluations[num_evaluations - 2])\\n diff5 = abs(self.evaluations[num_evaluations - 3] - self.evaluations[num_evaluations - 1])\\n diff6 = abs(self.evaluations[num_evaluations - 2] - self.evaluations[num_evaluations - 1])\\n\\n actions = 0\\n for agent in self.agents:\\n actions += len(agent.actions)\\n\\n # num of (all possible) actions ~= num of states\\n eval_steps = min(actions, 100)\\n\\n if (self.evaluations[num_evaluations - 1] < eval_steps) and\\\\\\n (self.evaluations[num_evaluations - 2] < eval_steps) and\\\\\\n (self.evaluations[num_evaluations - 3] < eval_steps) and \\\\\\n (self.evaluations[num_evaluations - 4] < eval_steps):\\n\\n if diff1 < delta and diff2 < delta and diff3 < delta and diff4 < delta and diff5 < delta and diff6 < delta:\\n return True\\n\\n return False\",\n \"def test_best_val(self, te_acc):\\n self.test_val = te_acc\",\n \"def solveOneStep(self):\\n ### Student code goes here\\n return True\",\n \"def bacon_strategy(score, opponent_score, margin=8, num_rolls=4):\\n # BEGIN PROBLEM 10\\n if free_bacon(opponent_score) >= margin:\\n return 0\\n return num_rolls\\n # END PROBLEM 10\",\n \"def _hs_reducible_stages(self,tol=1.e-13):\\n m=len(self)\\n mindiff=10.\\n for i in range(m):\\n for j in range(i+1,m):\\n dif = np.max(np.abs(self.A[i,:]-self.A[j,:]))\\n if dif<tol: return True,[i,j]\\n mindiff=min(mindiff,dif)\\n return False, mindiff\",\n \"def epidemic_finish(states, iteration):\\n return np.sum(states) == 0 and iteration > 10\",\n \"def rough_outcome(self) -> float:\\n if is_win(self):\\n return 1\\n elif is_lose(self):\\n return -1\\n return 0\",\n \"def is_over(self, state):\\n return state.current_total == 0\",\n \"def get_learn_after_each_trial(self):\\r\\n return 0\",\n \"def top_is_pointwise(self):\\n return False\",\n \"def perform_cv_fold(self, algo, fold, folds):\\n # TODO: this is only done for hyperparameter optimization and is not\\n # part of the OpenML specification. The OpenML specification would\\n # like to have the hyperparameter evaluation inside the evaluate\\n # performed by the target algorithm itself. Hyperparameter\\n # optimization on the other hand needs these both things to be decoupled\\n # For being closer to OpenML one could also call evaluate and pass\\n # everything else through kwargs.\\n if self.task_type != \\\"Supervised Classification\\\":\\n raise NotImplementedError(self.task_type)\\n\\n if self.estimation_procedure[\\\"type\\\"] != \\\\\\n \\\"crossvalidation with holdout\\\":\\n raise NotImplementedError(self.estimation_procedure[\\\"type\\\"] )\\n\\n if self.estimation_procedure[\\\"parameters\\\"][\\\"stratified_sampling\\\"] != \\\\\\n 'true':\\n raise NotImplementedError(self.estimation_procedure[\\\"parameters\\\"][\\\"stratified_sampling\\\"])\\n\\n if self.evaluation_measure != \\\"predictive_accuracy\\\":\\n raise NotImplementedError(self.evaluation_measure)\\n\\n ########################################################################\\n # Test folds\\n train_indices, test_indices = self.get_train_test_split()\\n\\n ########################################################################\\n # Crossvalidation folds\\n train_indices, validation_indices = self.get_validation_split(fold)\\n\\n X, Y = self.get_dataset()\\n\\n algo.fit(X[train_indices], Y[train_indices])\\n\\n predictions = algo.predict(X[validation_indices])\\n accuracy = sklearn.metrics.accuracy_score(Y[validation_indices], predictions)\\n return accuracy\",\n \"def fully_burning(cell, new_ig, decay):\\n\\n new_ig = int(new_ig)\\n decay = int(decay)\\n cell = int(cell)\\n if cell == BURNING: return True\\n if cell == START_BURN and new_ig >= decay: return True\\n return False\",\n \"def Bayes_prediction(X, y, fold_number=10):\\n D = X.shape[1]\\n fold = KFold(n_splits=fold_number)\\n cross_tab_all = []\\n lamb_hat_all = []\\n \\n for train_index, test_index in fold.split(X, y):\\n X_train, X_test = X.iloc[train_index], X.iloc[test_index]\\n y_train, y_test = y.iloc[train_index], y.iloc[test_index]\\n length = X_train.shape[0]\\n pi_hat = y_train.mean()\\n lamb_hat = np.zeros((2, D))\\n \\n for flag in range(2):\\n for d in range(D):\\n lamb_hat[flag][d] = (sum(X_train.iloc[i][d] * (y_train.iloc[i]==flag) for i in range(length))) / (sum(y_train.iloc[i]==flag for i in range(length)))\\n\\n y_pred = np.zeros(len(X_test))\\n for i in range(len(X_test)):\\n y_pred[i] = Bayes_classifier(pi_hat, X_test.iloc[i], lamb_hat)\\n \\n cross_tab = np.zeros((2, 2))\\n for m in [0, 1]:\\n for n in [0, 1]:\\n cross_tab[m][n] = sum([(y_test.values[i]==m) & (y_pred[i]==n) for i in range(len(y_pred))]) \\n \\n cross_tab_all.append(cross_tab)\\n lamb_hat_all.append(lamb_hat)\\n \\n cross_tab_all = sum(cross_tab_all)\\n lamb_hat_all\\n\\n return lamb_hat_all, cross_tab_all\",\n \"def evaluate(self) :\\n for inp in self.inStates :\\n if inp.getState() == 1 : return 1\\n return 0\",\n \"def setBestStepRun(self, stepRun):\\n if(self.__bestStepRun < stepRun):\\n self.__bestStepRun = stepRun\\n return True\\n else:\\n return False\",\n \"def condition(timestep, *_):\\n return tf.less(timestep, tf.shape(score)[1])\",\n \"def recall(self):\\n tpos, fneg = len(self.__true_positives), len(self.__false_negatives)\\n return float(tpos) / (tpos + fneg)\",\n \"def is_better(self, curr, best, **kwargs):\\r\\n score_threshold = kwargs.pop('score_threshold', 1e-3)\\r\\n relative_eps = 1.0 + score_threshold\\r\\n return curr >= best*relative_eps\",\n \"def dstflow(self):\\n if self.name in conditional_branch + unconditional_branch:\\n return True\\n return False\",\n \"def test_foo(self):\\n self.ran = True\\n 1 / 0\",\n \"def is_best(self, val) -> bool:\\n if self.val is None or (val > self.val):\\n self.val = val\\n print(\\\"Updating Best\\\")\\n return True\\n else:\\n return False\",\n \"def pull(self):\\n chance = np.random.uniform()\\n return chance < self.winning_prob\",\n \"def evaluation_point(self):\\n if self.turns in range(40,(self.game_length - 19), self.policy_eval_point):\\n return True\",\n \"def evaluate(self):\\n self.training = False\",\n \"def classify(self, testInstance):\\n return self.fire(testInstance) > 0.5\",\n \"def _is_valid_bn_fold(conv: LayerType, fold_backward: bool) -> bool:\\n valid = True\\n if not fold_backward:\\n # Cannot fold BN -> Conv with padding. AIMET does not support forward folding to grouped or DW Conv\\n if isinstance(conv, (torch.nn.Conv2d, torch.nn.Conv1d, torch.nn.Conv3d)):\\n valid &= all(item == 0 for item in conv.padding)\\n valid &= conv.groups == 1\\n # AIMET does not support forward folding to ConvTranspose\\n elif isinstance(conv, torch.nn.ConvTranspose2d):\\n valid = False\\n else:\\n # AIMET does not support backwards folding to grouped ConvTranspose\\n if isinstance(conv, torch.nn.ConvTranspose2d):\\n valid &= conv.groups in (1, conv.in_channels)\\n return valid\",\n \"def is_caught_up_well_enough_for_government_work():\\n return config.CAUGHT_UP or (config.BLOCKCHAIN_SERVICE_LAST_BLOCK and config.CURRENT_BLOCK_INDEX >= config.BLOCKCHAIN_SERVICE_LAST_BLOCK - 1)\",\n \"def _score_to_decision(self, score):\",\n \"def Continue():\\n # adjust this to take as many steps as you need\\n return warp.top.it <= 500\",\n \"def test_convergence(self, time_step):\\n \\n ##compare the average episode length between two loop\\n if self.past_episode == time_step:\\n self.convergence = True\\n else:\\n self.convergence = False\",\n \"def test_convergence(self, time_step):\\n \\n ##compare the average episode length between two loop\\n if self.past_episode == time_step:\\n self.convergence = True\\n else:\\n self.convergence = False\",\n \"def fold_assignment(self):\\n return self._parms.get(\\\"fold_assignment\\\")\",\n \"def two_fold(\\n self,\\n std=False,\\n two_fold_type=\\\"pooled\\\",\\n submitted_weight=None,\\n n=None,\\n conf=None,\\n ):\\n self.submitted_n = n\\n self.submitted_conf = conf\\n std_val = None\\n self.two_fold_type = two_fold_type\\n self.submitted_weight = submitted_weight\\n\\n if two_fold_type == \\\"cotton\\\":\\n self.t_params = (\\n self.len_f / (self.len_f + self.len_s) * self._f_model.params\\n ) + (self.len_s / (self.len_f + self.len_s) * self._s_model.params)\\n\\n elif two_fold_type == \\\"reimers\\\":\\n self.t_params = 0.5 * (self._f_model.params + self._s_model.params)\\n\\n elif two_fold_type == \\\"self_submitted\\\":\\n if submitted_weight is None:\\n raise ValueError(\\\"Please submit weights\\\")\\n submitted_weight = [submitted_weight, 1 - submitted_weight]\\n self.t_params = (\\n submitted_weight[0] * self._f_model.params\\n + submitted_weight[1] * self._s_model.params\\n )\\n\\n elif two_fold_type == \\\"nuemark\\\":\\n self._t_model = OLS(self.endog, self.neumark).fit(\\n cov_type=self.cov_type, cov_kwds=self.cov_kwds\\n )\\n self.t_params = self._t_model.params\\n\\n else:\\n self._t_model = OLS(self.endog, self.exog).fit(\\n cov_type=self.cov_type, cov_kwds=self.cov_kwds\\n )\\n self.t_params = np.delete(self._t_model.params, self.bifurcate)\\n\\n self.unexplained = (\\n self.exog_f_mean @ (self._f_model.params - self.t_params)\\n ) + (self.exog_s_mean @ (self.t_params - self._s_model.params))\\n self.explained = (self.exog_f_mean - self.exog_s_mean) @ self.t_params\\n\\n if std is True:\\n std_val = self.variance(2)\\n\\n return OaxacaResults(\\n (self.unexplained, self.explained, self.gap), 2, std_val=std_val\\n )\",\n \"def run_core(self, cur_bi, next_lvl):\\r\\n self.cur_lvl = next_lvl\\r\\n self.cur_bin = cur_bi\\r\\n logger.info('sat engine run_core: cur_bin == %d' % cur_bi)\\r\\n\\r\\n while 1:\\r\\n conflict, ccindex, cvindex = self.bcp()\\r\\n if conflict is False:\\r\\n # no conflict\\r\\n allsat = self.decision(cur_bi)\\r\\n if allsat:\\r\\n # sat\\r\\n return True, cur_bi + 1, self.cur_lvl, 0 # next bin index\\r\\n else:\\r\\n # conflict\\r\\n bkt_bi, bkt_lvl = self.analysis(ccindex, cvindex)\\r\\n self.local_vars.decay_var_inc()\\r\\n if bkt_bi != cur_bi: # unsat\\r\\n logger.info('----\\\\t\\\\tpartial unsat')\\r\\n return False, bkt_bi, bkt_lvl + 1, bkt_lvl\\r\\n else:\\r\\n self.backtrack_cur_bin(bkt_lvl)\",\n \"def goal_test(self, state):\\n for x, y in state.alvos:\\n if state.tabuleiro[x][y] is not BOX_ON_TARGET:\\n return False\\n return True\",\n \"def is_smelling(self,conc_array):\\n if conc_array[int(self.x)][int(self.y)]>self.threshold:\\n self.smell_timer = self.Timer(self.T,self.lamda)\\n #Nav mode three and four need to know whether the moth is smelling\\n #at a specific moment, for that reason they use Tfirst.\\n self.Tfirst = self.T\\n self.odor = True #this datum will be useful in the graphical functions\\n return True\\n elif self.turned_on:\\n self.odor = False\\n if self.smell_timer.is_running(self.T):\\n return True #note - even though the there is no detection, the navigator stay in nav mode.\\n else:\\n self.odor = False\\n return False\",\n \"def termialTest(state):\\n if state.isWin() or state.isLose():\\n return True\\n return False\",\n \"async def decide(self):\\n return 0., 0., 0.\",\n \"def guess_goal(mydata, target):\\r\\n cardin = dict(mydata.apply(pd.Series.nunique))\\r\\n target_type = mydata.dtypes[target]\\r\\n if(target_type == 'float64') | (target_type == 'int64'):\\r\\n if cardin[target] > 50:\\r\\n act_regression = True\\r\\n act_classification = False\\r\\n else:\\r\\n act_regression = False\\r\\n act_classification = True\\r\\n else:\\r\\n act_regression = False\\r\\n act_classification = True\\r\\n return act_regression, act_classification\",\n \"def is_done(self, best_sum_logprobs):\\n if len(self) < self.n_hyp:\\n return False\\n elif self.early_stopping:\\n return True\\n else:\\n return self.worst_score >= best_sum_logprobs / self.max_len ** self.length_penalty\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72265923","0.6000676","0.5944416","0.56944317","0.5582698","0.5582698","0.5582698","0.55106276","0.5448707","0.54409707","0.54199135","0.5411135","0.5394675","0.5384363","0.53631586","0.536092","0.5355963","0.5352496","0.5329533","0.5312051","0.530132","0.5279798","0.527588","0.5269978","0.52688086","0.5251886","0.52256465","0.5224322","0.5209907","0.5189129","0.5180046","0.517282","0.5166726","0.5161865","0.51597","0.51569664","0.5152888","0.5148415","0.514683","0.5137738","0.5133692","0.5130324","0.51269245","0.51259243","0.51250434","0.512415","0.5121836","0.51158834","0.5108149","0.51019764","0.5100949","0.5100534","0.50987476","0.5096013","0.5095982","0.5095499","0.50927305","0.5086365","0.50799733","0.50744474","0.5073565","0.50702715","0.50651956","0.50645906","0.5061963","0.5058743","0.5054645","0.5044616","0.5040829","0.5033969","0.5032253","0.50232476","0.5008562","0.5003287","0.5001748","0.50005","0.49971667","0.49962336","0.49952102","0.49934733","0.4992827","0.49916226","0.4985951","0.49831933","0.49796277","0.49775794","0.4974163","0.49672884","0.49660748","0.49659595","0.49659595","0.49650362","0.4962065","0.49548942","0.49525633","0.49483982","0.4945325","0.4944445","0.49351934","0.49345315"],"string":"[\n \"0.72265923\",\n \"0.6000676\",\n \"0.5944416\",\n \"0.56944317\",\n \"0.5582698\",\n \"0.5582698\",\n \"0.5582698\",\n \"0.55106276\",\n \"0.5448707\",\n \"0.54409707\",\n \"0.54199135\",\n \"0.5411135\",\n \"0.5394675\",\n \"0.5384363\",\n \"0.53631586\",\n \"0.536092\",\n \"0.5355963\",\n \"0.5352496\",\n \"0.5329533\",\n \"0.5312051\",\n \"0.530132\",\n \"0.5279798\",\n \"0.527588\",\n \"0.5269978\",\n \"0.52688086\",\n \"0.5251886\",\n \"0.52256465\",\n \"0.5224322\",\n \"0.5209907\",\n \"0.5189129\",\n \"0.5180046\",\n \"0.517282\",\n \"0.5166726\",\n \"0.5161865\",\n \"0.51597\",\n \"0.51569664\",\n \"0.5152888\",\n \"0.5148415\",\n \"0.514683\",\n \"0.5137738\",\n \"0.5133692\",\n \"0.5130324\",\n \"0.51269245\",\n \"0.51259243\",\n \"0.51250434\",\n \"0.512415\",\n \"0.5121836\",\n \"0.51158834\",\n \"0.5108149\",\n \"0.51019764\",\n \"0.5100949\",\n \"0.5100534\",\n \"0.50987476\",\n \"0.5096013\",\n \"0.5095982\",\n \"0.5095499\",\n \"0.50927305\",\n \"0.5086365\",\n \"0.50799733\",\n \"0.50744474\",\n \"0.5073565\",\n \"0.50702715\",\n \"0.50651956\",\n \"0.50645906\",\n \"0.5061963\",\n \"0.5058743\",\n \"0.5054645\",\n \"0.5044616\",\n \"0.5040829\",\n \"0.5033969\",\n \"0.5032253\",\n \"0.50232476\",\n \"0.5008562\",\n \"0.5003287\",\n \"0.5001748\",\n \"0.50005\",\n \"0.49971667\",\n \"0.49962336\",\n \"0.49952102\",\n \"0.49934733\",\n \"0.4992827\",\n \"0.49916226\",\n \"0.4985951\",\n \"0.49831933\",\n \"0.49796277\",\n \"0.49775794\",\n \"0.4974163\",\n \"0.49672884\",\n \"0.49660748\",\n \"0.49659595\",\n \"0.49659595\",\n \"0.49650362\",\n \"0.4962065\",\n \"0.49548942\",\n \"0.49525633\",\n \"0.49483982\",\n \"0.4945325\",\n \"0.4944445\",\n \"0.49351934\",\n \"0.49345315\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61763084"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":93771,"cells":{"query":{"kind":"string","value":"Decides amount to bet"},"document":{"kind":"string","value":"def amount_algorithm(self, current_top_bid, pot_size,\n community_cards, position, number_of_players, is_first_cycle, total_value):\n # If pre-flop\n if len(community_cards) == 0:\n if is_first_cycle is False:\n return current_top_bid - self.current_bet\n rank = self.hand.opening_hand_ranking()\n # 168 hands\n # Nits Voluntary Put in Pot (VPIP) = 18%\n # Pre-flop Raise (PFR) = 14%\n # So 168 * 0.14 = 23 top hands\n # Triple bet: > 0.15\n # 168 * 0.18 = 28 top hands\n # Min raise: > 0.05\n # Change as slider\n # TODO\n rand_rank = 0 # Change to random 0-0.15?\n cutoff = self.stat_to_ev(self.pfr)\n if rank > cutoff - rand_rank:\n # Triple bet\n return current_top_bid * 3 - self.current_bet\n else:\n # Min raise\n return current_top_bid * 2 - self.current_bet\n # Otherwise always call for now\n # Raises half the pot then calls 60%\n # Or check-raise-call? 40%\n # If post-flop\n else:\n if is_first_cycle is True:\n # TODO\n # rand check or raise\n rando = random.randint(1, 10)\n # if check\n if rando > 7 and current_top_bid == 0:\n self.is_checked = True\n return 0\n # if raise\n else:\n return int(pot_size / 2)\n else:\n if self.is_checked is True:\n self.is_checked = False\n return int(pot_size / 2)\n else:\n return current_top_bid - self.current_bet"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def bet(self, amount):\n if amount >self.budget:\n print 'you cannot bet because of little money'\n else:\n self.bet_amount = amount\n print 'you bet %s' % (amount)","def get_bet(self) -> int:\n return int(input(f\"How much money would you like to place? \"\n f\"(input an integer between {MINIMUM_BET()}-{self.balance}): \"))","def make_bet(self, amount):\n self.update_fear(amount)\n self.bot.bet(amount)","def bet(self, amount):\r\n\r\n if self.players[self.active_player].credits < self.big_blind:\r\n message = \"Player {} won! Not enough money remaining.\".format(self.players[(self.active_player + 1) %\r\n len(self.players)].name)\r\n self.game_message.emit(message)\r\n self.restart()\r\n if self.players[(self.active_player + 1) % len(self.players)].credits < self.big_blind:\r\n message = \"Player {} won! Not enough money remaining.\".format(self.players[self.active_player].name)\r\n self.game_message_warning.emit(message)\r\n self.restart()\r\n\r\n if amount == 0:\r\n message = \"Raises must be larger than zero!\"\r\n self.game_message_warning.emit(message)\r\n\r\n elif self.previous_bet + amount > self.players[self.active_player].credits:\r\n message = \"Not enough money!\"\r\n self.game_message_warning.emit(message)\r\n else:\r\n self.pot += amount\r\n self.new_pot.emit()\r\n\r\n self.players[self.active_player].credits -= (self.previous_bet + amount)\r\n self.new_credits.emit()\r\n\r\n output_text = \"{} bet ${} and raised ${}\".format(self.players[self.active_player].name, self.previous_bet,\r\n amount)\r\n\r\n self.previous_bet = (self.previous_bet + amount)\r\n self.actions += 1\r\n\r\n self.new_output.emit(output_text)\r\n\r\n self.active_player = (self.active_player + 1) % len(self.players)\r\n\r\n # Update the players to hide their cards when it is not their turn\r\n for player in self.players:\r\n player.flip_cards()\r\n\r\n self.progress_game()","def bet(self, amount: float) -> None:\n if amount <= self.balance:\n self.__bet = amount\n else:\n raise ValueError(\"Amount exceeds balance.\")","def bet(self, amount: int = 100):\n if amount <= self.current_balance:\n self.current_bet = amount\n else:\n raise Exception(f\"Can't cover {amount} bet, balance: {current_balance}\")\n return self","def raise_bet(value):\r\n\r\n global total_bet, dealer_bet, in_play, bottom_alert\r\n if value > player.get_cash() or not in_play:\r\n bottom_alert = \"You cannot bet $%i right now.\" % (value)\r\n elif in_play:\r\n player.spend_cash(value)\r\n dealer_bet += value\r\n total_bet += value * 2\r\n bottom_alert = \"\"","def bet(self, amount):\n self._chips -= amount\n self._bet += amount\n assert self._chips >= 0","def amount_bet(self, user):\n try:\n return Bet.objects.get(group_match = self, user=user).amount\n except Bet.DoesNotExist:\n return Decimal(0)","def bet(self):\n while True:\n try:\n self.round_bet = float(\n input(f'{self.name}, please enter an amount to bet for this round: '))\n if self.round_bet > self.bankroll:\n print('You have bet more than you have!')\n continue\n if self.round_bet <= 0:\n self.out_of_round = True\n else:\n self.bankroll -= self.round_bet\n break\n except TypeError:\n print('Please enter in a valid bet!')\n continue\n except ValueError:\n print('Please enter in a valid bet!')\n return self.name, self.round_bet","def test_bet(self):\n hand = self._hand\n self.assertEqual(hand.bet.amount, 150)","def place_bet(self) -> None:\n amount = self.get_bet()\n while not self.valid_bet(amount):\n print(f\"That is an invalid bet. Please input an amount within ${MINIMUM_BET()} and ${self.balance}\\n\")\n amount = self.get_bet()\n self.balance -= amount\n self.bet = amount\n print(f\"A total of ${self.bet} has been deducted from your balance. Good luck, player!\\n\")\n time.sleep(1)","def get_player_bet(self) -> None:\n print(\"Please enter the amount you want to bet.\")\n while self.user.bet == 0:\n input_ = input(\">>> \")\n try:\n input_ = float(input_)\n self.user.bet = input_\n except ValueError as e:\n print(str(e))\n continue","def add_bet(self,bet):\n \n #subtract bet from the purse\n self.purse -= bet\n \n #add bet to existing bet\n self.bet += bet\n \n return self.bet","def raise_bet(self, points):\n return self.call(points=points)\n # self.points -= points\n # self.bet += points\n # return points","def determineAmountToCall(self, player):\n\t\treturn sum(self.currentBet) - sum(player.betAmount)","def setBigBlindBetAmount(self):\n\t\tif sum(self.currentBet) < self.bigBlind:\n\t\t\tif len(self.pots) > 1:\n\t\t\t\tnewbet = self.bigBlind - sum(self.currentBet)\n\t\t\telse:\n\t\t\t\tnewbet = self.bigBlind\n\t\t\tself.currentBet[-1] = newbet","def qualify_bet(inputs):\n print 'Qualifying bet:'\n back_value = inputs['bet_value']\n back_odds = inputs['bet_odds']\n lay_odds = inputs['lay_odds']\n commission_per_cent = inputs['commission_per_cent']\n # With inputs in place calculate bet\n commission = 0.01 * commission_per_cent\n lay_stake = (back_value * back_odds) / (lay_odds - commission)\n # Calculate profit in both cases (back wins and back loses)\n profit_back_wins = back_value * (back_odds - 1) \\\n - lay_stake * (lay_odds - 1)\n profit_lay_wins = lay_stake * (1 - commission) - back_value\n # Print calculated outputs\n print 'Profit if *back* wins: GBP', round(profit_back_wins,2)\n print 'Profit if *lay* wins: GBP', round(profit_lay_wins,2) \n print 'Lay required: GBP', round(lay_stake,2)","def balance(self, player):\n print 'hand of %s: %s'%(player.name,player.cards.hand)\n print 'hand of %s: %s'%(self.name,self.cards.hand)\n if player.cards.hand == self.cards.hand:\n return 0\n elif player.cards.hand > self.cards.hand:\n return player.bet_amount*2\n else:\n return -player.bet_amount","def all_in():\r\n\r\n raise_bet(player.get_cash())","def withdraw(self, amount):\n self.balance -= amount\n if self.balance < 10:\n self.balance -= 5\n self.fees += 5","async def bet(message, amount):\n\n # make sure their discord account is linked\n mixcord_user = await database.get_user(message.user_id)\n if mixcord_user is None:\n return \"your mixer account must be linked to your discord via mixcord to use this command.\"\n\n # make sure they have sufficient balance\n if amount == \"all\":\n amount = mixcord_user[\"balance\"]\n if amount == 0:\n return \"amount must be a positive integer.\"\n else:\n amount = utils.get_positive_int(amount)\n if amount is None:\n return \"amount must be a positive integer.\"\n if mixcord_user[\"balance\"] < amount:\n return \"you have insufficient balance. ({}/{} {})\".format(mixcord_user[\"balance\"], amount, currency_name)\n\n won = random.randint(0, 1) == 1\n if won:\n await database.add_balance(message.user_id, amount)\n return \"you won :D you now have {} {}.\".format((mixcord_user[\"balance\"] + amount), currency_name)\n else:\n await database.add_balance(message.user_id, -amount)\n return \"you lost :( you now have {} {}.\".format((mixcord_user[\"balance\"] - amount), currency_name)","def win(self, amount):\n self.balance += amount","def abbott_steam():\n per_klb = 20 # dollars per klb of steam\n kwh_eq = to_kwh(1) # kwh equivalent of steam\n per_kwh = per_klb / kwh_eq\n return per_kwh","def deposit(amt) :\r\n\tglobal bal\r\n\tbal_in = bal\r\n\t#PREMISES FOR NEXT LINE: \r\n\t# (amt >= 0)\r\n\t# (bal >= 0)\r\n\t# (bal == bal_in)\r\n\tbal = bal + amt\r\n\t#PREMISES FOR ATTACHED PROOF, IF ANY: \r\n\t# (bal == (bal_old + amt))\r\n\t# (amt >= 0)\r\n\t# (bal_old >= 0)\r\n\t# (bal_old == bal_in)\r\n\t#PREMISES FOR NEXT LINE: \r\n\t# (amt >= 0)\r","def free_bet(inputs):\n print 'Free bet:'\n free_value = inputs['bet_value']\n free_odds = inputs['bet_odds']\n lay_odds = inputs['lay_odds']\n commission_per_cent = inputs['commission_per_cent']\n # With inputs in place calculate bet\n commission = 0.01 * commission_per_cent\n lay_stake = free_value * (free_odds - 1) / (lay_odds - commission)\n # Calculate profit in both cases (free back wins and free back loses)\n profit_free_wins = free_value * (free_odds - 1) \\\n - lay_stake * (lay_odds - 1)\n profit_lay_wins = lay_stake * (1 - commission)\n # Also calculate free SNR value\n free_SNR = profit_free_wins / free_value\n free_SNR_per_cent = free_SNR * 100\n # Print calculated outputs\n print 'Profit if *back* wins: GBP', round(profit_free_wins,2)\n print 'Profit if *lay* wins: GBP', round(profit_lay_wins,2) \n print 'Free value %: ', round(free_SNR_per_cent,1), '%'\n print 'Lay required: GBP', round(lay_stake,2)","def withdrawMoney(self, withdraw_amount):\r\n self.balance_amt = self.balance_amt - withdraw_amount","def withdraw(amt) :\r\n global bal \r\n bal_in = bal\r\n\t#PREMISES FOR NEXT LINE: \r\n\t# (amt >= 0)\r\n\t# (bal >= 0)\r\n\t# (bal == bal_in)\r\n\t\"\"\"{1.OK amt >= 0\tpremise\r\n\t\t2.OK bal >= 0\tpremise\r\n\t\t3.OK bal == bal_in\tpremise\r\n\t}\"\"\"\r\n\t#PREMISES FOR NEXT LINE: \r\n\t# (bal == bal_in)\r\n\tif amt <= bal:\r\n\t\t#PREMISES FOR THEN-ARM: \r\n\t\t# (amt <= bal)\r\n\t\t# (bal == bal_in)\r\n\t\t\"\"\"{1.OK amt <= bal\tpremise\r\n\t\t\t4.OK bal == bal_in\tpremise\r\n\t\t}\"\"\"\r\n\t\t#PREMISES FOR NEXT LINE: \r\n\t\t# (bal == bal_in)\r\n\t\tbal = bal - amt\r\n\t\t#PREMISES FOR ATTACHED PROOF, IF ANY: \r\n\t\t# (bal == (bal_old - amt))\r\n\t\t# (bal_old == bal_in)\r\n\t\t\"\"\"{1.OK bal == bal_old - amt\tpremise\r\n\t\t\t2.OK amt <= bal_old\talgebra 1\r\n\t\t\t3.OK amt >= 0\talgebra 1\r\n\t\t\t4.OK bal_old >= 0\talgebra 1\r\n\t\t\t5.OK bal_old == bal_in\tpremise\r\n\t\t\t6.OK amt == bal_in - bal\talgebra 1 5\r\n\t\t}\"\"\"\r\n\t\t#PREMISES FOR NEXT LINE: \r\n\t\t# (amt == (bal_in - bal))\r\n\t\tcash = amt\r\n\t\t#PREMISES FOR ATTACHED PROOF, IF ANY: \r\n\t\t# (cash == amt)\r\n\t\t# (amt == (bal_in - bal))\r\n\t\t\"\"\"{1.OK amt == bal_in - bal\tpremise\r\n\t\t\t2.OK cash == amt\tpremise\r\n\t\t\t3.OK cash == bal_in - bal\t\tsubst 2 1\r\n\t\t\t4.OK bal >= 0\talgebra 1\r\n\t\t\t5.OK bal_in == bal + cash\talgebra 3\r\n\t\t}\"\"\"\r\n\t\t#PREMISES FOR NEXT LINE: \r\n\t\t# (bal_in == (bal + cash))\r\n\telse :\r\n\t\t#PREMISES FOR ELSE-ARM: \r\n\t\t# not (amt <= bal)\r\n\t\t# (bal == bal_in)\r\n\t\t\"\"\"{1.OK not(amt <= bal)\tpremise\r\n\t\t\t4.OK bal == bal_in\tpremise\r\n\t\t}\"\"\"\r\n\t\t#PREMISES FOR NEXT LINE: \r\n\t\t# (bal == bal_in)\r\n\t\tcash = 0\r\n\t\t#PREMISES FOR ATTACHED PROOF, IF ANY: \r\n\t\t# (cash == 0)\r\n\t\t# (bal == bal_in)\r\n\t\tassert not (amt <= bal) # UNABLE TO VERIFY\r\n\t\t\"\"\"{1.OK cash == 0\tpremise\r\n\t\t\t2.OK bal == bal_in\tpremise\r\n\t\t\t3.?? not(amt <= bal)\tpremise\r\n\t\t\t4.OK bal >= 0\talgebra 3\r\n\t\t}\"\"\"\r\n\t\t#PREMISES FOR NEXT LINE: \r\n\t\t# (bal >= 0)\r\n# ERROR: uneven indentation of commands\r\n # prove here that bal >= 0 and bal + cash == bal_in\r\n return cash\r\n #PREMISES FOR NEXT LINE: \r\n # (bal >= 0)\r\n # ((bal + cash) == bal_in)\r\n\t#PREMISES FOR NEXT LINE: \r\n\t# ((bal_in == (bal + cash)) or ((bal >= 0) and ((bal + cash) == bal_in)))\r\n\tassert (bal >= 0) # UNABLE TO VERIFY\r","def post_bet(self, bot_name, amount):\n canPost = self.parent.post_bet(bot_name, amount)\n if canPost:\n self.pot += amount\n return True\n else:\n return False","def skim_amount(amount, fees):\n fee = amount * fees.var + fees.fix\n vat = fee * Fees.VAT\n fee += vat\n fee = fee.round_up()\n vat = vat.round_up()\n return amount - fee, fee, vat","def calcul_buy_nb_action(self):\n nb_action = self.max_loss / (self.buy_price - self.stop_loss)\n invest = self.max_loss / (self.buy_price - self.stop_loss) * self.buy_price\n\n if invest > self.capital:\n return round(self.capital / self.buy_price, 9)\n else:\n return round(nb_action, 9)","def win(self, amount: int):\n self.current_balance += amount\n self.current_bet = 0\n return self ## fluent","def betting_round(self, method, params):\n self.bet_history += [[]]\n current_bets = [self.starting_player] * len(self.agents)\n \n max_bet = 0\n if method == self.deal_cards:\n max_bet = big_blind\n current_bets[self.starting_player] = small_blind\n current_bets[(self.starting_player + 1) % len(self.agents)] = big_blind\n\n (self.all_in[self.starting_player], bet) = self.normalize_bet(self.chips[self.starting_player], method(self.agents[self.starting_player], params[self.starting_player]), max_bet)\n self.in_game[self.starting_player] = (not self.all_in[self.starting_player])\n current_bets[self.starting_player] = bet\n self.chips[self.starting_player] -= bet\n check = True if bet == 0 else False\n max_bet = max(max_bet, bet)\n self.pot += bet\n self.bet_history[-1] += [bet]\n\n raised_player = self.starting_player\n i = (raised_player + 1) % len(self.agents)\n\n if method == self.deal_cards:\n # raised_player = (self.starting_player + 1) % len(agents)\n check = False\n if bet > max_bet:\n raised_player = i\n max_bet = bet\n\n if bet == 0:\n self.in_game[i] = False\n self.in_game_count -= 1\n\n while (i != raised_player) and (not self.all_in[i]) and (current_bets[i] <= max_bet):\n if self.in_game[i]:\n (self.all_in[i], bet) = self.normalize_bet(self.chips[i], method(self.agents[i], params[i]), max_bet)\n self.in_game[i] = (not self.all_in[i])\n delta_bet = max(0, bet - current_bets[i])\n current_bets[i] = bet\n self.chips[i] -= delta_bet\n self.pot += delta_bet\n self.bet_history[-1] += [bet]\n\n if bet > max_bet:\n check = False\n raised_player = i\n max_bet = bet\n\n if bet == 0 and not check:\n self.in_game[i] = False\n self.in_game_count -= 1\n\n i = (i + 1) % len(self.agents)","def pay(self, amt: float):\n self._money += amt","def calculate_value(self):\n base = self.balance\n bag = self.bag * self.state['price'] * (1-self.gap)\n value = base + bag\n self.value = value\n return value","def bet_check(m):\n try:\n value = float(m.content)\n if 0 <= value <= player.coins:\n return True\n else:\n return False\n except:\n return False","async def bet(message, user: ParamType.MIXER_USER, amount):\n\n username = user.username.lower()\n username_sender = message.username.lower()\n\n mixcord_user = await database.get_user(message.user_id)\n\n # handle if somebody is trying to accept or deny\n if amount == \"accept\" or amount == \"deny\":\n\n # get the pending bet\n bet = pending_bets.get(username)\n if bet is None or bet[\"username\"] != username_sender:\n return \"failed to find the bet you're responding to.\"\n\n # delete the pending bet, because we're handling it\n del pending_bets[username]\n\n # if the user wants to deny the bet, don't do anything\n if amount == \"deny\":\n return \"you have denied the pending bet from @{}.\".format(username)\n\n # if the user wants to accept the bet, continue\n if amount == \"accept\":\n\n # make sure they have enough money to accept\n if bet[\"amount\"] > mixcord_user[\"balance\"]:\n return \"you have insufficient funds to accept this bet.\"\n\n # make sure the issuer of the challenge still has enough money\n competitor_mixcord_user = await database.get_user(user.id)\n if bet[\"amount\"] > competitor_mixcord_user[\"balance\"]:\n return \"@{} no longer has sufficient funding to run this bet.\".format(username)\n\n # determine winner/loser\n pick = random.randint(0, 1) == 1\n winner_id = user.id if pick else message.user_id\n loser_id = message.user_id if pick else user.id\n winner_username = username if pick else username_sender\n loser_username = message.username if pick else username\n\n # affect balances accordingly\n await database.add_balance(winner_id, bet[\"amount\"])\n await database.add_balance(loser_id, -bet[\"amount\"])\n\n # end the bet!\n await chat.send_message(\"@{} has won {} {}! better luck next time, @{}.\".format(winner_username, bet[\"amount\"], currency_name, loser_username))\n return None\n\n # make sure the amount is numeric by converting it to an int\n amount = utils.get_positive_int(amount)\n if amount is None: return \"amount must be a positive integer.\"\n\n # make sure they're not trying to start a bet against themself :/\n if message.username == username:\n return \"you're not able to start a bet against yourself.\"\n\n # make sure we don't already have a pending bet\n if pending_bets.get(message.username) is not None:\n return \"you already have a pending bet.\"\n\n # make sure the challenger has enough money to start the bet\n if amount > mixcord_user[\"balance\"]:\n return \"you have insufficient funds to request this bet.\"\n\n # store challenge information\n pending_bets[message.username] = {\n \"username\": username,\n \"amount\": amount\n }\n\n # send messages indicating the challenge has been issued\n await chat.send_message(\"@{} has challenged @{} to a bet of {} {}!\".format(message.username, username, amount, currency_name))\n await asyncio.sleep(0.5)\n await chat.send_message(\"use {}bet @{} [accept/deny] to respond to your pending bet!\".format(chat.commands.prefix, message.username), username)\n\n # automatically timeout the bet in 30 seconds\n await asyncio.sleep(30)\n bet = pending_bets.get(message.username)\n if bet is not None:\n del pending_bets[message.username]\n await chat.send_message(\"@{} your pending bet has timed out.\".format(message.username))","def balance(self):\n return self._rbal - self._lbal","def win(self) -> int:\n earnings: int = self._bet\n\n # If it has a blackjack, the earnings increments 1.5 times\n for hand in self._hands:\n if hand.has_blackjack():\n earnings = round(earnings * 1.5)\n\n self._actual_money += earnings\n return earnings","def __balance__(self) -> float:\n\n with dataset.connect(database.get_db()) as db:\n # Find last bank transaction.\n statement = statement = f\"\"\"\n SELECT opening_balance, transaction_amount\n FROM bank\n WHERE author_id = {self.user.id}\n ORDER BY id DESC\n LIMIT 1\n \"\"\"\n result = db.query(statement)\n\n for row in result:\n balance = row[\"opening_balance\"] + row[\"transaction_amount\"]\n break\n else:\n # If there was no result for the user, default balance is given.\n balance = 500\n\n return float(balance)","def __call__(self, auctioneer):\n curr_bid = auctioneer.current_bid\n bid_price = curr_bid * self._bid_increase_perc\n if bid_price <= self._budget and self.get_bid_probability() > 0.3:\n self._highest_bid = bid_price\n return bid_price\n return 0","def _balance_update(self):\n return_rate = self.df.loc[self.currentStep, \"return_Close\"]\n self.buy_amount += return_rate * self.buy_amount\n self.sell_amount -= return_rate * self.sell_amount","def draw(self, amount):\n # make sure balance doesn't go below zero\n self.__balance = max(self.__balance-amount, 0)\n return self.__balance","def withdraw(self, amount):\n self.withdrw = amount\n \n if (self.balance-self.withdrw) < 0:\n self.balance = self.balance - 5 - self.withdrw\n self.fee += 5\n else:\n self.balance -= self.withdrw","def lose(self) -> None:\n self._actual_money -= self._bet","def event_player_blackjack(self) -> None:\n win_amount = self.user.bet + 1.5\n print(\"Congratulations, you win:\", win_amount)\n self.user.win_balance(win_amount)","def on_place_bet (self,event):\n global placed_bet\n placed_bet = True\n arcade.draw_text(f\"Value: {self.dealer_value}\", 280, 450, arcade.color.BLACK, 16)\n arcade.draw_text(f\"Value: {self.player_value}\", 280, 250, arcade.color.BLACK, 16)\n \n self.final_bet = self.bet\n self.dealer_hand[1].face_up()\n self.player_hand[0].face_up()\n self.player_hand[1].face_up()","def __bet(self, numbers: str, user_seed: str) -> None:\n self.BetSource(self.tx.origin, self.tx.timestamp)\n if not self._game_on.get():\n Logger.debug(f'Game not active yet.', TAG)\n revert(f'Game not active yet.')\n amount = self.msg.value\n Logger.debug(f'Betting {amount} loop on {numbers}.', TAG)\n self.BetPlaced(amount, numbers)\n self._take_wager(self.address, amount)\n\n nums = set(numbers.split(','))\n n = len(nums)\n if n == 0:\n Logger.debug(f'Bet placed without numbers.', TAG)\n revert(f' Invalid bet. No numbers submitted. Zero win chance. Returning funds.')\n elif n > 20:\n Logger.debug(f'Bet placed with too many numbers. Max numbers = 20.', TAG)\n revert(f' Invalid bet. Too many numbers submitted. Returning funds.')\n\n numset = set(WHEEL_ORDER)\n numset.remove('0')\n for num in nums:\n if num not in numset:\n Logger.debug(f'Invalid number submitted.', TAG)\n revert(f' Please check your bet. Numbers must be between 0 and 20, submitted as a comma separated '\n f'string. Returning funds.')\n\n bet_type = self._bet_type.get()\n self._bet_type.set(BET_TYPES[0])\n if bet_type == BET_TYPES[2] or bet_type == BET_TYPES[3]:\n bet_limit = self._bet_limits[0]\n else:\n bet_limit = self._bet_limits[n]\n if amount < BET_MIN or amount > bet_limit:\n Logger.debug(f'Betting amount {amount} out of range.', TAG)\n revert(f'Betting amount {amount} out of range ({BET_MIN} -> {bet_limit} loop).')\n\n if n == 1:\n bet_type = BET_TYPES[4]\n if bet_type == BET_TYPES[1]:\n payout = int(MULTIPLIERS[BET_TYPES[5]] * 1000) * amount // (1000 * n)\n else:\n payout = MULTIPLIERS[bet_type] * amount\n if self.icx.get_balance(self.address) < payout:\n Logger.debug(f'Not enough in treasury to make the play.', TAG)\n revert('Not enough in treasury to make the play.')\n\n spin = self.get_random(user_seed)\n winningNumber = WHEEL_ORDER[int(spin * 21)]\n Logger.debug(f'winningNumber was {winningNumber}.', TAG)\n win = winningNumber in nums\n payout = payout * win\n self.BetResult(str(spin), winningNumber, payout)\n\n if win == 1:\n self._wager_payout(self.address, payout)\n else:\n Logger.debug(f'Player lost. ICX retained in treasury.', TAG)","def user_balance_lost(user_pokemon: str, computer_pokemon: str, bet_amount) -> int:\n user_pokemon = get_pokemon(user_pokemon)\n computer_pokemon = get_pokemon(computer_pokemon)\n user_attack = type_logic.damage_to(user_pokemon.first_type, computer_pokemon.first_type) + type_logic.damage_to(user_pokemon.first_type, computer_pokemon.second_type)\n computer_attack = type_logic.damage_to(computer_pokemon.first_type, user_pokemon.first_type) + type_logic.damage_to(computer_pokemon.first_type, user_pokemon.second_type)\n difference = abs(user_attack - computer_attack)\n print(\"{} attacks {} for {}\".format(user_pokemon.name, computer_pokemon.name, difference))\n\n money_exchange = 0\n if difference == 0:\n # 45% of this happening\n money_exchange = 0\n elif .5 <= difference <= 1.5:\n # 33% of this happening\n money_exchange = .5 * bet_amount\n else:\n # 22% of this happening\n money_exchange = bet_amount\n\n return -money_exchange if user_attack < computer_attack else money_exchange","def __get__(self) -> float:\n\n return float(self.balance)","def valid_bet(self, amount: int) -> bool:\n return MINIMUM_BET() <= amount <= self.balance","def withdraw(self, amount):\n if self.overdrawn:\n print('You have overdrawn, please add more money!')\n return self.balance\n self.balance = self.balance - amount\n return self.balance","def do_damage(self) -> float:\n res = 0.05 + self.experience / 100\n self.experience = self.experience + 1\n return res","def calculate_bonuses (the_sum_of_current_purchase):\n the_sum_of_previous_purchases = 0\n blue_card_percent = 0.05\n silver_card_percent = 0.07\n gold_card_percent = 0.1\n the_sum_of_previous_purchases = the_sum_of_previous_purchases + the_sum_of_current_purchase\n\n if the_sum_of_previous_purchases <1000:\n bonus_for_purchase = 0\n if 1000 <= the_sum_of_previous_purchases <= 15_000:\n bonus_for_purchase = the_sum_of_current_purchase * blue_card_percent\n\n if 15001 <= the_sum_of_previous_purchases < 150_000:\n bonus_for_purchase = the_sum_of_current_purchase * silver_card_percent\n\n if the_sum_of_previous_purchases >= 150_000:\n bonus_for_purchase = the_sum_of_current_purchase * gold_card_percent\n\n return bonus_for_purchase","def withdraw(self, amount):\r\n self.balance = self.balance - amount\r\n self.transactions.append(-amount)\r\n return amount","def __init__(self):\n self.balance = START_BANK()\n self.bet = 0","def update_balance(self, multiplier: int) -> int:\n self.user.balance += DEFAULT_BET * multiplier\n return self.user.balance","def calculate_profit(self):","def _amount(amount, asset='HBD'):\n assert asset == 'HBD', 'unhandled asset %s' % asset\n return \"%.3f HBD\" % amount","def _pay(self, asked_value):\n\t\tpayment_value = min(self.stock, asked_value)\n\t\tself.stock -= payment_value\n\t\treturn payment_value","def account_balance():\n return float(pareto.rvs(1.161))","def buying_price(self):\n buy_price = self.standard_init_price()\n # Special status and resources price adaptation\n if self.planet.status in [self.tradeitem.dps]:\n buy_price = (buy_price * 5) / 3\n\n elif self.planet.special in [self.tradeitem.cr]:\n buy_price = (buy_price * 3) / 4\n\n elif self.planet.special in [self.tradeitem.er]:\n buy_price = (buy_price * 4) / 3\n\n # randomize a bit\n moins = random.randrange(self.tradeitem.var)\n plus = random.randrange(self.tradeitem.var)\n buy_price = buy_price - moins + plus\n\n # price can't be negative\n if buy_price < 0:\n buy_price = 0\n\n return int(buy_price)","def raise2():\r\n\r\n value = int(RAISE_BUTTONS[1].get_text().split(\" $\")[1])\r\n raise_bet(value)","def test_amount_in_tons(self):","def raise3():\r\n\r\n value = int(RAISE_BUTTONS[2].get_text().split(\" $\")[1])\r\n raise_bet(value)","def update_amounts(self, save=True):\n self.amount_donated = self.get_amount_total(\n [StatusDefinition.SUCCESS, StatusDefinition.PENDING,\n StatusDefinition.PLEDGED])\n self.amount_needed = self.amount_asked - self.amount_donated\n\n if self.amount_needed < 0:\n # Should never be less than zero\n self.amount_needed = 0\n\n if save:\n self.save()","async def balance(self, ctx):\r\n author = ctx.author\r\n with DB() as db:\r\n company = await self.get_active_company(ctx, db, author)\r\n history = db.query(CompanyHistory).filter(CompanyHistory.company == company.id).order_by(CompanyHistory.date.desc()).limit(2).all()\r\n net_worth = history[0].value\r\n delta = history[0].value - history[1].value if len(history) == 2 else 0\r\n percent = delta * 100 / history[1].value if len(history) == 2 else 0\r\n symbol = '⮝' if delta >= 0 else '⮟'\r\n embed = discord.Embed(title=f'{company.name}', description=f'{symbol}{round(percent, 2)}%', inline=True)\r\n embed.add_field(name='Cash Assets:', value=f'{round(company.balance, 2)} USD')\r\n embed.add_field(name='Net worth:', value=f'{round(net_worth, 2)} USD')\r\n await ctx.send(embed=embed)","def get_money(self) -> float: \n money = get_owned()\n try:\n assert type(self.owned_init) == float\n except AssertionError: #The first time one tries to make a bet this is evoked\n self.owned_init = money\n finally:\n return money","def set_steals_percentage(self):\n bx = self.get_standard_stats()\n team = self.get_team_stats()\n opp_team = self.get_opp_team_stats()\n poss = self.get_team_possessions()\n result = 0.00\n if bx[\"minutes\"] > 0:\n result = ((bx[\"steals\"] * (team[\"minutes\"]/Decimal('5'))) / Decimal(float(bx[\"minutes\"]) * poss)) * 100\n self.steals_percentage = \"%.2f\" % round(result, 2)","def validate_bet(buy_type, cash_in):\n while cash_in < 0:\n print(\"Invalid\", buy_type)\n cash_in = round(float(input(\"Enter \" + buy_type + \": $\")), 2)\n\n return cash_in","def cash_ratio(self):\n return self.cash / self.current_liabilities","def test_update_balance(self):\n current_year_tuple = (0.1, 0.1, 0.8)\n iteration_balance = 90\n contribution = 10\n expected_result = 110\n test_balance = investment_growth.update_balance(iteration_balance, contribution, current_year_tuple)\n self.assertEqual(test_balance, expected_result)","def _get_reward(self):\n if self.is_game_done:\n return self.price - 1\n else:\n return 0.0","def blind_bet(self):\n self.this_player.bet(SMALL_BLIND_BET)\n self.other_player.bet(BIG_BLIND_BET)\n if SHOW_MESSAGE:\n print(\"Making blind bets.\")\n print(\"Player1:\")\n self.player1.show()\n print(\"Player2:\")\n self.player2.show()","def total_rewards(self) -> float:\n return self.__total_rewards","def initial_cash_balance(self) -> float:\n return self.buy_budget * len(self.stocks)","def gold_card(subtotal):\n return subtotal * 0.05","def _set_bet_limit(self) -> None:\n for i, ratio in enumerate(BET_LIMIT_RATIOS):\n self._bet_limits[i] = self._treasury_min.get() // ratio","def rough_outcome(self) -> float:\n if is_win(self):\n return 1\n elif is_lose(self):\n return -1\n return 0","def silver_card(subtotal):\n return subtotal * 0.02","async def process_bj_game(self, ctx, amount, user_id):\n if amount >= 0:\n if not await self.check_in_game(user_id, ctx):\n if amount > await ex.u_currency.get_balance(user_id):\n await ctx.send(f\"> **{ctx.author}, you can not bet more than your current balance.**\")\n else:\n return True\n else:\n await ctx.send(f\"> **{ctx.author}, you can not bet a negative number.**\")","def can_bet(self, amount: int) -> bool:\n if isinstance(amount, int):\n if self.chips_amount - amount >= 0:\n return True\n else:\n raise ValueError(\"The 'amount' param must be an instance of int, got\"+\n str(type(amount)))\n\n return False","def raise1():\r\n\r\n value = int(RAISE_BUTTONS[0].get_text().split(\" $\")[1])\r\n raise_bet(value)","def withdraw(self, amount, budget):\r\n if budget != \"Total Balance\":\r\n assert budget in self.budgets, \"Specified budget doesn't exist\"\r\n self.budgets[budget] -= float(amount)\r\n self.balance -= float(amount)","def double_declining_balance():\r\n cost = float(input(\"Please Enter The Cost Of Asset: \"))\r\n accdepreciation = float(input(\"Please Enter The Value Of Accumulated Depreciation: \"))\r\n life = float(input(\"Please Enter Estimated Useful Life Of Asset(Years): \"))\r\n rv = float(input(\"Please Enter Estimated Residual Value Of Asset: \"))\r\n n = 0\r\n a = (float(cost)-float(accdepreciation)) * (float(2)/float(life))\r\n bn = float(a)/float(12)\r\n print \">> Your Monthly Depreciation For First Year is\",bn\r\n while(n != (life-1)):\r\n bk = float(cost)\r\n a = ((float(cost)-float(accdepreciation)) * (float(2)/float(life)))\r\n cost -= float(a)\r\n bk -= float(a)\r\n n += 1\r\n vvv = float(bk)-float(rv)\r\n print \">> Your Depreciation For Year No.\",n,\"is\",a\r\n print \">> Your Book Value After\",n,\"Years is\",bk,\"\\n\"\r\n print \">> Your Depreciation For Year No.\",int(life),\"is\",vvv\r\n print \">> Your Book Value After\",int(life),\"Years is\",rv","def get_fee_pct(self, contract_type: str) -> Tuple[float, float]:\n if contract_type == 'forex':\n return (0.00002, 0.00002)\n elif contract_type == 'crypto':\n if self.CRYPTO_EXCHANGE == 'binance':\n if self.trade_volume < 50_000:\n return (.001, .001)\n elif self.trade_volume < 100_000:\n return (.0009, .0009)\n elif self.trade_volume < 5000_000:\n return (.0009, .0008)\n elif self.trade_volume < 1_000_000:\n return (.0008, .0007)\n elif self.trade_volume < 5_000_000:\n return (.0007, .0005)\n elif self.trade_volume < 10_000_000:\n return (.0006, .0004)\n elif self.trade_volume < 25_000_000:\n return (.0006, 0)\n elif self.trade_volume < 100_000_000:\n return (.0005, 0)\n elif self.trade_volume < 250_000_000:\n return (.0004, 0)\n elif self.trade_volume < 500_000_000:\n return (.0003, 0)\n else: return (.0002, 0)\n elif self.CRYPTO_EXCHANGE == 'kraken':\n if self.trade_volume < 50_000:\n return (.0026, .0016)\n elif self.trade_volume < 100_000:\n return (.0024, .0014)\n elif self.trade_volume < 250_000:\n return (.0022, .0012)\n elif self.trade_volume < 500_000:\n return (.002, .001)\n elif self.trade_volume < 1_000_000:\n return (.0018, .0008)\n elif self.trade_volume < 2_500_000:\n return (.0016, .0006)\n elif self.trade_volume < 5_000_000:\n return (.0014, .0004)\n elif self.trade_volume < 10_000_000:\n return (.0012, .0002)\n else: return (.001, 0)\n elif self.CRYPTO_EXCHANGE == 'coinbase':\n if self.trade_volume < 10_000:\n return (.005, .005)\n elif self.trade_volume < 50_000:\n return (.0035, .0035)\n elif self.trade_volume < 100_000:\n return (.0025, .0015)\n elif self.trade_volume < 1_000_000:\n return (.002, .001)\n elif self.trade_volume < 10_000_000:\n return (.0018, .0008)\n elif self.trade_volume < 50_000_000:\n return (.0015, .0005)\n elif self.trade_volume < 300_000_000:\n return (.0007, 0)\n elif self.trade_volume < 500_000_000:\n return (.0005, 0)\n else: return (.0004, 0)\n elif self.CRYPTO_EXCHANGE == 'robinhood':\n return (0.0001, 0.0001)\n return (0, 0)","def withdrawMoney(self, withdraw_amount):\r\n if (self.balance_amt - withdraw_amount) > 0:\r\n self.balance_amt = self.balance_amt - withdraw_amount\r\n else:\r\n raise WithdrawError #Exception('Overdraft withdrawal Error. Cannot withdraw more than amount in account balance: {}'.format(self.balance_amt))\r","def withdraw(self, amount):\n self.balance -= amount","def withdraw(self, amount):\r\n balance = self['get']('balance')\r\n if amount > balance:\r\n return 'Insufficient funds'\r\n self['set']('balance', balance - amount)\r\n return self['get']('balance')","def value(self):\n #import pdb; pdb.set_trace()\n return ((self.team1.get_cur_hp() / self.team1.get_total_hp()) - \n (self.team2.get_cur_hp() / self.team2.get_total_hp()))","def transaction(money_in, drink_cost):\n if money_in >= drink_cost:\n change = round((money_in - drink_cost), 2) # 2 decimal places\n print(f'your change is £{change}')\n global profit #Global scope needed\n profit = profit + drink_cost\n return True\n else:\n print(\"That's not enough money!\")\n return False","def call(self, points, subpot=None):\n points = int(points)\n if points >= self.points:\n points = self.points\n self.all_in = True\n self.active = False\n self.points -= points\n self.bet += points\n return points","def get_bet(self):\n while newbet := input(f\"{self.name}: {self.chips} chips. Last bet: {self.lastbet}. Bet: \"):\n try:\n newbet = int(newbet)\n if newbet in range(0, self.chips+1):\n self.bet = newbet\n self.chips -= newbet\n return newbet\n else:\n print(\"You don't have that many chips.\")\n except ValueError:\n print(\"Bets are numbers please.\")","def get_total_bets(self) -> int:\n return self._total_bet_count.get() + self._daily_bet_count.get()","def _award_accounts(self):\n\n prize_money = 0\n for i in xrange(len(self.accounts)):\n # Each savings account has a 1% chance of quadrupling their principal. The\n # chance is independent between accounts.\n if random.randint(1, 100) == 1:\n prize_money += 3 * self.accounts[i]\n self.accounts[i] *= 4\n return prize_money","def do_bet(self, arg):\n\t\topts = get_options(parser.parser_add, arg)\n\t\tif opts is None: return\n\t\tkwargs = {}\n\t\tkwargs['name'] = ' '.join(opts.broken_name)\n\t\tkwargs['weight'] = opts.weight\n\t\tif opts.outcome is not None:\n\t\t\tkwargs['outcome'] = convert_outcome(opts.outcome)\n\t\tself.manager.add_bet(**kwargs)\n\t\tprint(display.format_bet(self.manager.bets[-1]))","def pay_round_money(self) -> int:\r\n return self.round_money.pay_all_money()","def withdraw(account, amount):\n pass","def investment_price(self):\n invest = self.max_loss / (self.buy_price - self.stop_loss) * self.buy_price\n if invest > self.capital:\n return round(self.capital, 2)\n else:\n return round(invest, 2)","def budget_balance(self):\n budget_balance = round(self.budget() - self.total_spent(), 2)\n budget_balance_degree = round( (9000 * self.total_spent()) / (self.budget()), 4) #convert to degrees and round to four decimal places\n return (budget_balance, budget_balance_degree)"],"string":"[\n \"def bet(self, amount):\\n if amount >self.budget:\\n print 'you cannot bet because of little money'\\n else:\\n self.bet_amount = amount\\n print 'you bet %s' % (amount)\",\n \"def get_bet(self) -> int:\\n return int(input(f\\\"How much money would you like to place? \\\"\\n f\\\"(input an integer between {MINIMUM_BET()}-{self.balance}): \\\"))\",\n \"def make_bet(self, amount):\\n self.update_fear(amount)\\n self.bot.bet(amount)\",\n \"def bet(self, amount):\\r\\n\\r\\n if self.players[self.active_player].credits < self.big_blind:\\r\\n message = \\\"Player {} won! Not enough money remaining.\\\".format(self.players[(self.active_player + 1) %\\r\\n len(self.players)].name)\\r\\n self.game_message.emit(message)\\r\\n self.restart()\\r\\n if self.players[(self.active_player + 1) % len(self.players)].credits < self.big_blind:\\r\\n message = \\\"Player {} won! Not enough money remaining.\\\".format(self.players[self.active_player].name)\\r\\n self.game_message_warning.emit(message)\\r\\n self.restart()\\r\\n\\r\\n if amount == 0:\\r\\n message = \\\"Raises must be larger than zero!\\\"\\r\\n self.game_message_warning.emit(message)\\r\\n\\r\\n elif self.previous_bet + amount > self.players[self.active_player].credits:\\r\\n message = \\\"Not enough money!\\\"\\r\\n self.game_message_warning.emit(message)\\r\\n else:\\r\\n self.pot += amount\\r\\n self.new_pot.emit()\\r\\n\\r\\n self.players[self.active_player].credits -= (self.previous_bet + amount)\\r\\n self.new_credits.emit()\\r\\n\\r\\n output_text = \\\"{} bet ${} and raised ${}\\\".format(self.players[self.active_player].name, self.previous_bet,\\r\\n amount)\\r\\n\\r\\n self.previous_bet = (self.previous_bet + amount)\\r\\n self.actions += 1\\r\\n\\r\\n self.new_output.emit(output_text)\\r\\n\\r\\n self.active_player = (self.active_player + 1) % len(self.players)\\r\\n\\r\\n # Update the players to hide their cards when it is not their turn\\r\\n for player in self.players:\\r\\n player.flip_cards()\\r\\n\\r\\n self.progress_game()\",\n \"def bet(self, amount: float) -> None:\\n if amount <= self.balance:\\n self.__bet = amount\\n else:\\n raise ValueError(\\\"Amount exceeds balance.\\\")\",\n \"def bet(self, amount: int = 100):\\n if amount <= self.current_balance:\\n self.current_bet = amount\\n else:\\n raise Exception(f\\\"Can't cover {amount} bet, balance: {current_balance}\\\")\\n return self\",\n \"def raise_bet(value):\\r\\n\\r\\n global total_bet, dealer_bet, in_play, bottom_alert\\r\\n if value > player.get_cash() or not in_play:\\r\\n bottom_alert = \\\"You cannot bet $%i right now.\\\" % (value)\\r\\n elif in_play:\\r\\n player.spend_cash(value)\\r\\n dealer_bet += value\\r\\n total_bet += value * 2\\r\\n bottom_alert = \\\"\\\"\",\n \"def bet(self, amount):\\n self._chips -= amount\\n self._bet += amount\\n assert self._chips >= 0\",\n \"def amount_bet(self, user):\\n try:\\n return Bet.objects.get(group_match = self, user=user).amount\\n except Bet.DoesNotExist:\\n return Decimal(0)\",\n \"def bet(self):\\n while True:\\n try:\\n self.round_bet = float(\\n input(f'{self.name}, please enter an amount to bet for this round: '))\\n if self.round_bet > self.bankroll:\\n print('You have bet more than you have!')\\n continue\\n if self.round_bet <= 0:\\n self.out_of_round = True\\n else:\\n self.bankroll -= self.round_bet\\n break\\n except TypeError:\\n print('Please enter in a valid bet!')\\n continue\\n except ValueError:\\n print('Please enter in a valid bet!')\\n return self.name, self.round_bet\",\n \"def test_bet(self):\\n hand = self._hand\\n self.assertEqual(hand.bet.amount, 150)\",\n \"def place_bet(self) -> None:\\n amount = self.get_bet()\\n while not self.valid_bet(amount):\\n print(f\\\"That is an invalid bet. Please input an amount within ${MINIMUM_BET()} and ${self.balance}\\\\n\\\")\\n amount = self.get_bet()\\n self.balance -= amount\\n self.bet = amount\\n print(f\\\"A total of ${self.bet} has been deducted from your balance. Good luck, player!\\\\n\\\")\\n time.sleep(1)\",\n \"def get_player_bet(self) -> None:\\n print(\\\"Please enter the amount you want to bet.\\\")\\n while self.user.bet == 0:\\n input_ = input(\\\">>> \\\")\\n try:\\n input_ = float(input_)\\n self.user.bet = input_\\n except ValueError as e:\\n print(str(e))\\n continue\",\n \"def add_bet(self,bet):\\n \\n #subtract bet from the purse\\n self.purse -= bet\\n \\n #add bet to existing bet\\n self.bet += bet\\n \\n return self.bet\",\n \"def raise_bet(self, points):\\n return self.call(points=points)\\n # self.points -= points\\n # self.bet += points\\n # return points\",\n \"def determineAmountToCall(self, player):\\n\\t\\treturn sum(self.currentBet) - sum(player.betAmount)\",\n \"def setBigBlindBetAmount(self):\\n\\t\\tif sum(self.currentBet) < self.bigBlind:\\n\\t\\t\\tif len(self.pots) > 1:\\n\\t\\t\\t\\tnewbet = self.bigBlind - sum(self.currentBet)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tnewbet = self.bigBlind\\n\\t\\t\\tself.currentBet[-1] = newbet\",\n \"def qualify_bet(inputs):\\n print 'Qualifying bet:'\\n back_value = inputs['bet_value']\\n back_odds = inputs['bet_odds']\\n lay_odds = inputs['lay_odds']\\n commission_per_cent = inputs['commission_per_cent']\\n # With inputs in place calculate bet\\n commission = 0.01 * commission_per_cent\\n lay_stake = (back_value * back_odds) / (lay_odds - commission)\\n # Calculate profit in both cases (back wins and back loses)\\n profit_back_wins = back_value * (back_odds - 1) \\\\\\n - lay_stake * (lay_odds - 1)\\n profit_lay_wins = lay_stake * (1 - commission) - back_value\\n # Print calculated outputs\\n print 'Profit if *back* wins: GBP', round(profit_back_wins,2)\\n print 'Profit if *lay* wins: GBP', round(profit_lay_wins,2) \\n print 'Lay required: GBP', round(lay_stake,2)\",\n \"def balance(self, player):\\n print 'hand of %s: %s'%(player.name,player.cards.hand)\\n print 'hand of %s: %s'%(self.name,self.cards.hand)\\n if player.cards.hand == self.cards.hand:\\n return 0\\n elif player.cards.hand > self.cards.hand:\\n return player.bet_amount*2\\n else:\\n return -player.bet_amount\",\n \"def all_in():\\r\\n\\r\\n raise_bet(player.get_cash())\",\n \"def withdraw(self, amount):\\n self.balance -= amount\\n if self.balance < 10:\\n self.balance -= 5\\n self.fees += 5\",\n \"async def bet(message, amount):\\n\\n # make sure their discord account is linked\\n mixcord_user = await database.get_user(message.user_id)\\n if mixcord_user is None:\\n return \\\"your mixer account must be linked to your discord via mixcord to use this command.\\\"\\n\\n # make sure they have sufficient balance\\n if amount == \\\"all\\\":\\n amount = mixcord_user[\\\"balance\\\"]\\n if amount == 0:\\n return \\\"amount must be a positive integer.\\\"\\n else:\\n amount = utils.get_positive_int(amount)\\n if amount is None:\\n return \\\"amount must be a positive integer.\\\"\\n if mixcord_user[\\\"balance\\\"] < amount:\\n return \\\"you have insufficient balance. ({}/{} {})\\\".format(mixcord_user[\\\"balance\\\"], amount, currency_name)\\n\\n won = random.randint(0, 1) == 1\\n if won:\\n await database.add_balance(message.user_id, amount)\\n return \\\"you won :D you now have {} {}.\\\".format((mixcord_user[\\\"balance\\\"] + amount), currency_name)\\n else:\\n await database.add_balance(message.user_id, -amount)\\n return \\\"you lost :( you now have {} {}.\\\".format((mixcord_user[\\\"balance\\\"] - amount), currency_name)\",\n \"def win(self, amount):\\n self.balance += amount\",\n \"def abbott_steam():\\n per_klb = 20 # dollars per klb of steam\\n kwh_eq = to_kwh(1) # kwh equivalent of steam\\n per_kwh = per_klb / kwh_eq\\n return per_kwh\",\n \"def deposit(amt) :\\r\\n\\tglobal bal\\r\\n\\tbal_in = bal\\r\\n\\t#PREMISES FOR NEXT LINE: \\r\\n\\t# (amt >= 0)\\r\\n\\t# (bal >= 0)\\r\\n\\t# (bal == bal_in)\\r\\n\\tbal = bal + amt\\r\\n\\t#PREMISES FOR ATTACHED PROOF, IF ANY: \\r\\n\\t# (bal == (bal_old + amt))\\r\\n\\t# (amt >= 0)\\r\\n\\t# (bal_old >= 0)\\r\\n\\t# (bal_old == bal_in)\\r\\n\\t#PREMISES FOR NEXT LINE: \\r\\n\\t# (amt >= 0)\\r\",\n \"def free_bet(inputs):\\n print 'Free bet:'\\n free_value = inputs['bet_value']\\n free_odds = inputs['bet_odds']\\n lay_odds = inputs['lay_odds']\\n commission_per_cent = inputs['commission_per_cent']\\n # With inputs in place calculate bet\\n commission = 0.01 * commission_per_cent\\n lay_stake = free_value * (free_odds - 1) / (lay_odds - commission)\\n # Calculate profit in both cases (free back wins and free back loses)\\n profit_free_wins = free_value * (free_odds - 1) \\\\\\n - lay_stake * (lay_odds - 1)\\n profit_lay_wins = lay_stake * (1 - commission)\\n # Also calculate free SNR value\\n free_SNR = profit_free_wins / free_value\\n free_SNR_per_cent = free_SNR * 100\\n # Print calculated outputs\\n print 'Profit if *back* wins: GBP', round(profit_free_wins,2)\\n print 'Profit if *lay* wins: GBP', round(profit_lay_wins,2) \\n print 'Free value %: ', round(free_SNR_per_cent,1), '%'\\n print 'Lay required: GBP', round(lay_stake,2)\",\n \"def withdrawMoney(self, withdraw_amount):\\r\\n self.balance_amt = self.balance_amt - withdraw_amount\",\n \"def withdraw(amt) :\\r\\n global bal \\r\\n bal_in = bal\\r\\n\\t#PREMISES FOR NEXT LINE: \\r\\n\\t# (amt >= 0)\\r\\n\\t# (bal >= 0)\\r\\n\\t# (bal == bal_in)\\r\\n\\t\\\"\\\"\\\"{1.OK amt >= 0\\tpremise\\r\\n\\t\\t2.OK bal >= 0\\tpremise\\r\\n\\t\\t3.OK bal == bal_in\\tpremise\\r\\n\\t}\\\"\\\"\\\"\\r\\n\\t#PREMISES FOR NEXT LINE: \\r\\n\\t# (bal == bal_in)\\r\\n\\tif amt <= bal:\\r\\n\\t\\t#PREMISES FOR THEN-ARM: \\r\\n\\t\\t# (amt <= bal)\\r\\n\\t\\t# (bal == bal_in)\\r\\n\\t\\t\\\"\\\"\\\"{1.OK amt <= bal\\tpremise\\r\\n\\t\\t\\t4.OK bal == bal_in\\tpremise\\r\\n\\t\\t}\\\"\\\"\\\"\\r\\n\\t\\t#PREMISES FOR NEXT LINE: \\r\\n\\t\\t# (bal == bal_in)\\r\\n\\t\\tbal = bal - amt\\r\\n\\t\\t#PREMISES FOR ATTACHED PROOF, IF ANY: \\r\\n\\t\\t# (bal == (bal_old - amt))\\r\\n\\t\\t# (bal_old == bal_in)\\r\\n\\t\\t\\\"\\\"\\\"{1.OK bal == bal_old - amt\\tpremise\\r\\n\\t\\t\\t2.OK amt <= bal_old\\talgebra 1\\r\\n\\t\\t\\t3.OK amt >= 0\\talgebra 1\\r\\n\\t\\t\\t4.OK bal_old >= 0\\talgebra 1\\r\\n\\t\\t\\t5.OK bal_old == bal_in\\tpremise\\r\\n\\t\\t\\t6.OK amt == bal_in - bal\\talgebra 1 5\\r\\n\\t\\t}\\\"\\\"\\\"\\r\\n\\t\\t#PREMISES FOR NEXT LINE: \\r\\n\\t\\t# (amt == (bal_in - bal))\\r\\n\\t\\tcash = amt\\r\\n\\t\\t#PREMISES FOR ATTACHED PROOF, IF ANY: \\r\\n\\t\\t# (cash == amt)\\r\\n\\t\\t# (amt == (bal_in - bal))\\r\\n\\t\\t\\\"\\\"\\\"{1.OK amt == bal_in - bal\\tpremise\\r\\n\\t\\t\\t2.OK cash == amt\\tpremise\\r\\n\\t\\t\\t3.OK cash == bal_in - bal\\t\\tsubst 2 1\\r\\n\\t\\t\\t4.OK bal >= 0\\talgebra 1\\r\\n\\t\\t\\t5.OK bal_in == bal + cash\\talgebra 3\\r\\n\\t\\t}\\\"\\\"\\\"\\r\\n\\t\\t#PREMISES FOR NEXT LINE: \\r\\n\\t\\t# (bal_in == (bal + cash))\\r\\n\\telse :\\r\\n\\t\\t#PREMISES FOR ELSE-ARM: \\r\\n\\t\\t# not (amt <= bal)\\r\\n\\t\\t# (bal == bal_in)\\r\\n\\t\\t\\\"\\\"\\\"{1.OK not(amt <= bal)\\tpremise\\r\\n\\t\\t\\t4.OK bal == bal_in\\tpremise\\r\\n\\t\\t}\\\"\\\"\\\"\\r\\n\\t\\t#PREMISES FOR NEXT LINE: \\r\\n\\t\\t# (bal == bal_in)\\r\\n\\t\\tcash = 0\\r\\n\\t\\t#PREMISES FOR ATTACHED PROOF, IF ANY: \\r\\n\\t\\t# (cash == 0)\\r\\n\\t\\t# (bal == bal_in)\\r\\n\\t\\tassert not (amt <= bal) # UNABLE TO VERIFY\\r\\n\\t\\t\\\"\\\"\\\"{1.OK cash == 0\\tpremise\\r\\n\\t\\t\\t2.OK bal == bal_in\\tpremise\\r\\n\\t\\t\\t3.?? not(amt <= bal)\\tpremise\\r\\n\\t\\t\\t4.OK bal >= 0\\talgebra 3\\r\\n\\t\\t}\\\"\\\"\\\"\\r\\n\\t\\t#PREMISES FOR NEXT LINE: \\r\\n\\t\\t# (bal >= 0)\\r\\n# ERROR: uneven indentation of commands\\r\\n # prove here that bal >= 0 and bal + cash == bal_in\\r\\n return cash\\r\\n #PREMISES FOR NEXT LINE: \\r\\n # (bal >= 0)\\r\\n # ((bal + cash) == bal_in)\\r\\n\\t#PREMISES FOR NEXT LINE: \\r\\n\\t# ((bal_in == (bal + cash)) or ((bal >= 0) and ((bal + cash) == bal_in)))\\r\\n\\tassert (bal >= 0) # UNABLE TO VERIFY\\r\",\n \"def post_bet(self, bot_name, amount):\\n canPost = self.parent.post_bet(bot_name, amount)\\n if canPost:\\n self.pot += amount\\n return True\\n else:\\n return False\",\n \"def skim_amount(amount, fees):\\n fee = amount * fees.var + fees.fix\\n vat = fee * Fees.VAT\\n fee += vat\\n fee = fee.round_up()\\n vat = vat.round_up()\\n return amount - fee, fee, vat\",\n \"def calcul_buy_nb_action(self):\\n nb_action = self.max_loss / (self.buy_price - self.stop_loss)\\n invest = self.max_loss / (self.buy_price - self.stop_loss) * self.buy_price\\n\\n if invest > self.capital:\\n return round(self.capital / self.buy_price, 9)\\n else:\\n return round(nb_action, 9)\",\n \"def win(self, amount: int):\\n self.current_balance += amount\\n self.current_bet = 0\\n return self ## fluent\",\n \"def betting_round(self, method, params):\\n self.bet_history += [[]]\\n current_bets = [self.starting_player] * len(self.agents)\\n \\n max_bet = 0\\n if method == self.deal_cards:\\n max_bet = big_blind\\n current_bets[self.starting_player] = small_blind\\n current_bets[(self.starting_player + 1) % len(self.agents)] = big_blind\\n\\n (self.all_in[self.starting_player], bet) = self.normalize_bet(self.chips[self.starting_player], method(self.agents[self.starting_player], params[self.starting_player]), max_bet)\\n self.in_game[self.starting_player] = (not self.all_in[self.starting_player])\\n current_bets[self.starting_player] = bet\\n self.chips[self.starting_player] -= bet\\n check = True if bet == 0 else False\\n max_bet = max(max_bet, bet)\\n self.pot += bet\\n self.bet_history[-1] += [bet]\\n\\n raised_player = self.starting_player\\n i = (raised_player + 1) % len(self.agents)\\n\\n if method == self.deal_cards:\\n # raised_player = (self.starting_player + 1) % len(agents)\\n check = False\\n if bet > max_bet:\\n raised_player = i\\n max_bet = bet\\n\\n if bet == 0:\\n self.in_game[i] = False\\n self.in_game_count -= 1\\n\\n while (i != raised_player) and (not self.all_in[i]) and (current_bets[i] <= max_bet):\\n if self.in_game[i]:\\n (self.all_in[i], bet) = self.normalize_bet(self.chips[i], method(self.agents[i], params[i]), max_bet)\\n self.in_game[i] = (not self.all_in[i])\\n delta_bet = max(0, bet - current_bets[i])\\n current_bets[i] = bet\\n self.chips[i] -= delta_bet\\n self.pot += delta_bet\\n self.bet_history[-1] += [bet]\\n\\n if bet > max_bet:\\n check = False\\n raised_player = i\\n max_bet = bet\\n\\n if bet == 0 and not check:\\n self.in_game[i] = False\\n self.in_game_count -= 1\\n\\n i = (i + 1) % len(self.agents)\",\n \"def pay(self, amt: float):\\n self._money += amt\",\n \"def calculate_value(self):\\n base = self.balance\\n bag = self.bag * self.state['price'] * (1-self.gap)\\n value = base + bag\\n self.value = value\\n return value\",\n \"def bet_check(m):\\n try:\\n value = float(m.content)\\n if 0 <= value <= player.coins:\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"async def bet(message, user: ParamType.MIXER_USER, amount):\\n\\n username = user.username.lower()\\n username_sender = message.username.lower()\\n\\n mixcord_user = await database.get_user(message.user_id)\\n\\n # handle if somebody is trying to accept or deny\\n if amount == \\\"accept\\\" or amount == \\\"deny\\\":\\n\\n # get the pending bet\\n bet = pending_bets.get(username)\\n if bet is None or bet[\\\"username\\\"] != username_sender:\\n return \\\"failed to find the bet you're responding to.\\\"\\n\\n # delete the pending bet, because we're handling it\\n del pending_bets[username]\\n\\n # if the user wants to deny the bet, don't do anything\\n if amount == \\\"deny\\\":\\n return \\\"you have denied the pending bet from @{}.\\\".format(username)\\n\\n # if the user wants to accept the bet, continue\\n if amount == \\\"accept\\\":\\n\\n # make sure they have enough money to accept\\n if bet[\\\"amount\\\"] > mixcord_user[\\\"balance\\\"]:\\n return \\\"you have insufficient funds to accept this bet.\\\"\\n\\n # make sure the issuer of the challenge still has enough money\\n competitor_mixcord_user = await database.get_user(user.id)\\n if bet[\\\"amount\\\"] > competitor_mixcord_user[\\\"balance\\\"]:\\n return \\\"@{} no longer has sufficient funding to run this bet.\\\".format(username)\\n\\n # determine winner/loser\\n pick = random.randint(0, 1) == 1\\n winner_id = user.id if pick else message.user_id\\n loser_id = message.user_id if pick else user.id\\n winner_username = username if pick else username_sender\\n loser_username = message.username if pick else username\\n\\n # affect balances accordingly\\n await database.add_balance(winner_id, bet[\\\"amount\\\"])\\n await database.add_balance(loser_id, -bet[\\\"amount\\\"])\\n\\n # end the bet!\\n await chat.send_message(\\\"@{} has won {} {}! better luck next time, @{}.\\\".format(winner_username, bet[\\\"amount\\\"], currency_name, loser_username))\\n return None\\n\\n # make sure the amount is numeric by converting it to an int\\n amount = utils.get_positive_int(amount)\\n if amount is None: return \\\"amount must be a positive integer.\\\"\\n\\n # make sure they're not trying to start a bet against themself :/\\n if message.username == username:\\n return \\\"you're not able to start a bet against yourself.\\\"\\n\\n # make sure we don't already have a pending bet\\n if pending_bets.get(message.username) is not None:\\n return \\\"you already have a pending bet.\\\"\\n\\n # make sure the challenger has enough money to start the bet\\n if amount > mixcord_user[\\\"balance\\\"]:\\n return \\\"you have insufficient funds to request this bet.\\\"\\n\\n # store challenge information\\n pending_bets[message.username] = {\\n \\\"username\\\": username,\\n \\\"amount\\\": amount\\n }\\n\\n # send messages indicating the challenge has been issued\\n await chat.send_message(\\\"@{} has challenged @{} to a bet of {} {}!\\\".format(message.username, username, amount, currency_name))\\n await asyncio.sleep(0.5)\\n await chat.send_message(\\\"use {}bet @{} [accept/deny] to respond to your pending bet!\\\".format(chat.commands.prefix, message.username), username)\\n\\n # automatically timeout the bet in 30 seconds\\n await asyncio.sleep(30)\\n bet = pending_bets.get(message.username)\\n if bet is not None:\\n del pending_bets[message.username]\\n await chat.send_message(\\\"@{} your pending bet has timed out.\\\".format(message.username))\",\n \"def balance(self):\\n return self._rbal - self._lbal\",\n \"def win(self) -> int:\\n earnings: int = self._bet\\n\\n # If it has a blackjack, the earnings increments 1.5 times\\n for hand in self._hands:\\n if hand.has_blackjack():\\n earnings = round(earnings * 1.5)\\n\\n self._actual_money += earnings\\n return earnings\",\n \"def __balance__(self) -> float:\\n\\n with dataset.connect(database.get_db()) as db:\\n # Find last bank transaction.\\n statement = statement = f\\\"\\\"\\\"\\n SELECT opening_balance, transaction_amount\\n FROM bank\\n WHERE author_id = {self.user.id}\\n ORDER BY id DESC\\n LIMIT 1\\n \\\"\\\"\\\"\\n result = db.query(statement)\\n\\n for row in result:\\n balance = row[\\\"opening_balance\\\"] + row[\\\"transaction_amount\\\"]\\n break\\n else:\\n # If there was no result for the user, default balance is given.\\n balance = 500\\n\\n return float(balance)\",\n \"def __call__(self, auctioneer):\\n curr_bid = auctioneer.current_bid\\n bid_price = curr_bid * self._bid_increase_perc\\n if bid_price <= self._budget and self.get_bid_probability() > 0.3:\\n self._highest_bid = bid_price\\n return bid_price\\n return 0\",\n \"def _balance_update(self):\\n return_rate = self.df.loc[self.currentStep, \\\"return_Close\\\"]\\n self.buy_amount += return_rate * self.buy_amount\\n self.sell_amount -= return_rate * self.sell_amount\",\n \"def draw(self, amount):\\n # make sure balance doesn't go below zero\\n self.__balance = max(self.__balance-amount, 0)\\n return self.__balance\",\n \"def withdraw(self, amount):\\n self.withdrw = amount\\n \\n if (self.balance-self.withdrw) < 0:\\n self.balance = self.balance - 5 - self.withdrw\\n self.fee += 5\\n else:\\n self.balance -= self.withdrw\",\n \"def lose(self) -> None:\\n self._actual_money -= self._bet\",\n \"def event_player_blackjack(self) -> None:\\n win_amount = self.user.bet + 1.5\\n print(\\\"Congratulations, you win:\\\", win_amount)\\n self.user.win_balance(win_amount)\",\n \"def on_place_bet (self,event):\\n global placed_bet\\n placed_bet = True\\n arcade.draw_text(f\\\"Value: {self.dealer_value}\\\", 280, 450, arcade.color.BLACK, 16)\\n arcade.draw_text(f\\\"Value: {self.player_value}\\\", 280, 250, arcade.color.BLACK, 16)\\n \\n self.final_bet = self.bet\\n self.dealer_hand[1].face_up()\\n self.player_hand[0].face_up()\\n self.player_hand[1].face_up()\",\n \"def __bet(self, numbers: str, user_seed: str) -> None:\\n self.BetSource(self.tx.origin, self.tx.timestamp)\\n if not self._game_on.get():\\n Logger.debug(f'Game not active yet.', TAG)\\n revert(f'Game not active yet.')\\n amount = self.msg.value\\n Logger.debug(f'Betting {amount} loop on {numbers}.', TAG)\\n self.BetPlaced(amount, numbers)\\n self._take_wager(self.address, amount)\\n\\n nums = set(numbers.split(','))\\n n = len(nums)\\n if n == 0:\\n Logger.debug(f'Bet placed without numbers.', TAG)\\n revert(f' Invalid bet. No numbers submitted. Zero win chance. Returning funds.')\\n elif n > 20:\\n Logger.debug(f'Bet placed with too many numbers. Max numbers = 20.', TAG)\\n revert(f' Invalid bet. Too many numbers submitted. Returning funds.')\\n\\n numset = set(WHEEL_ORDER)\\n numset.remove('0')\\n for num in nums:\\n if num not in numset:\\n Logger.debug(f'Invalid number submitted.', TAG)\\n revert(f' Please check your bet. Numbers must be between 0 and 20, submitted as a comma separated '\\n f'string. Returning funds.')\\n\\n bet_type = self._bet_type.get()\\n self._bet_type.set(BET_TYPES[0])\\n if bet_type == BET_TYPES[2] or bet_type == BET_TYPES[3]:\\n bet_limit = self._bet_limits[0]\\n else:\\n bet_limit = self._bet_limits[n]\\n if amount < BET_MIN or amount > bet_limit:\\n Logger.debug(f'Betting amount {amount} out of range.', TAG)\\n revert(f'Betting amount {amount} out of range ({BET_MIN} -> {bet_limit} loop).')\\n\\n if n == 1:\\n bet_type = BET_TYPES[4]\\n if bet_type == BET_TYPES[1]:\\n payout = int(MULTIPLIERS[BET_TYPES[5]] * 1000) * amount // (1000 * n)\\n else:\\n payout = MULTIPLIERS[bet_type] * amount\\n if self.icx.get_balance(self.address) < payout:\\n Logger.debug(f'Not enough in treasury to make the play.', TAG)\\n revert('Not enough in treasury to make the play.')\\n\\n spin = self.get_random(user_seed)\\n winningNumber = WHEEL_ORDER[int(spin * 21)]\\n Logger.debug(f'winningNumber was {winningNumber}.', TAG)\\n win = winningNumber in nums\\n payout = payout * win\\n self.BetResult(str(spin), winningNumber, payout)\\n\\n if win == 1:\\n self._wager_payout(self.address, payout)\\n else:\\n Logger.debug(f'Player lost. ICX retained in treasury.', TAG)\",\n \"def user_balance_lost(user_pokemon: str, computer_pokemon: str, bet_amount) -> int:\\n user_pokemon = get_pokemon(user_pokemon)\\n computer_pokemon = get_pokemon(computer_pokemon)\\n user_attack = type_logic.damage_to(user_pokemon.first_type, computer_pokemon.first_type) + type_logic.damage_to(user_pokemon.first_type, computer_pokemon.second_type)\\n computer_attack = type_logic.damage_to(computer_pokemon.first_type, user_pokemon.first_type) + type_logic.damage_to(computer_pokemon.first_type, user_pokemon.second_type)\\n difference = abs(user_attack - computer_attack)\\n print(\\\"{} attacks {} for {}\\\".format(user_pokemon.name, computer_pokemon.name, difference))\\n\\n money_exchange = 0\\n if difference == 0:\\n # 45% of this happening\\n money_exchange = 0\\n elif .5 <= difference <= 1.5:\\n # 33% of this happening\\n money_exchange = .5 * bet_amount\\n else:\\n # 22% of this happening\\n money_exchange = bet_amount\\n\\n return -money_exchange if user_attack < computer_attack else money_exchange\",\n \"def __get__(self) -> float:\\n\\n return float(self.balance)\",\n \"def valid_bet(self, amount: int) -> bool:\\n return MINIMUM_BET() <= amount <= self.balance\",\n \"def withdraw(self, amount):\\n if self.overdrawn:\\n print('You have overdrawn, please add more money!')\\n return self.balance\\n self.balance = self.balance - amount\\n return self.balance\",\n \"def do_damage(self) -> float:\\n res = 0.05 + self.experience / 100\\n self.experience = self.experience + 1\\n return res\",\n \"def calculate_bonuses (the_sum_of_current_purchase):\\n the_sum_of_previous_purchases = 0\\n blue_card_percent = 0.05\\n silver_card_percent = 0.07\\n gold_card_percent = 0.1\\n the_sum_of_previous_purchases = the_sum_of_previous_purchases + the_sum_of_current_purchase\\n\\n if the_sum_of_previous_purchases <1000:\\n bonus_for_purchase = 0\\n if 1000 <= the_sum_of_previous_purchases <= 15_000:\\n bonus_for_purchase = the_sum_of_current_purchase * blue_card_percent\\n\\n if 15001 <= the_sum_of_previous_purchases < 150_000:\\n bonus_for_purchase = the_sum_of_current_purchase * silver_card_percent\\n\\n if the_sum_of_previous_purchases >= 150_000:\\n bonus_for_purchase = the_sum_of_current_purchase * gold_card_percent\\n\\n return bonus_for_purchase\",\n \"def withdraw(self, amount):\\r\\n self.balance = self.balance - amount\\r\\n self.transactions.append(-amount)\\r\\n return amount\",\n \"def __init__(self):\\n self.balance = START_BANK()\\n self.bet = 0\",\n \"def update_balance(self, multiplier: int) -> int:\\n self.user.balance += DEFAULT_BET * multiplier\\n return self.user.balance\",\n \"def calculate_profit(self):\",\n \"def _amount(amount, asset='HBD'):\\n assert asset == 'HBD', 'unhandled asset %s' % asset\\n return \\\"%.3f HBD\\\" % amount\",\n \"def _pay(self, asked_value):\\n\\t\\tpayment_value = min(self.stock, asked_value)\\n\\t\\tself.stock -= payment_value\\n\\t\\treturn payment_value\",\n \"def account_balance():\\n return float(pareto.rvs(1.161))\",\n \"def buying_price(self):\\n buy_price = self.standard_init_price()\\n # Special status and resources price adaptation\\n if self.planet.status in [self.tradeitem.dps]:\\n buy_price = (buy_price * 5) / 3\\n\\n elif self.planet.special in [self.tradeitem.cr]:\\n buy_price = (buy_price * 3) / 4\\n\\n elif self.planet.special in [self.tradeitem.er]:\\n buy_price = (buy_price * 4) / 3\\n\\n # randomize a bit\\n moins = random.randrange(self.tradeitem.var)\\n plus = random.randrange(self.tradeitem.var)\\n buy_price = buy_price - moins + plus\\n\\n # price can't be negative\\n if buy_price < 0:\\n buy_price = 0\\n\\n return int(buy_price)\",\n \"def raise2():\\r\\n\\r\\n value = int(RAISE_BUTTONS[1].get_text().split(\\\" $\\\")[1])\\r\\n raise_bet(value)\",\n \"def test_amount_in_tons(self):\",\n \"def raise3():\\r\\n\\r\\n value = int(RAISE_BUTTONS[2].get_text().split(\\\" $\\\")[1])\\r\\n raise_bet(value)\",\n \"def update_amounts(self, save=True):\\n self.amount_donated = self.get_amount_total(\\n [StatusDefinition.SUCCESS, StatusDefinition.PENDING,\\n StatusDefinition.PLEDGED])\\n self.amount_needed = self.amount_asked - self.amount_donated\\n\\n if self.amount_needed < 0:\\n # Should never be less than zero\\n self.amount_needed = 0\\n\\n if save:\\n self.save()\",\n \"async def balance(self, ctx):\\r\\n author = ctx.author\\r\\n with DB() as db:\\r\\n company = await self.get_active_company(ctx, db, author)\\r\\n history = db.query(CompanyHistory).filter(CompanyHistory.company == company.id).order_by(CompanyHistory.date.desc()).limit(2).all()\\r\\n net_worth = history[0].value\\r\\n delta = history[0].value - history[1].value if len(history) == 2 else 0\\r\\n percent = delta * 100 / history[1].value if len(history) == 2 else 0\\r\\n symbol = '⮝' if delta >= 0 else '⮟'\\r\\n embed = discord.Embed(title=f'{company.name}', description=f'{symbol}{round(percent, 2)}%', inline=True)\\r\\n embed.add_field(name='Cash Assets:', value=f'{round(company.balance, 2)} USD')\\r\\n embed.add_field(name='Net worth:', value=f'{round(net_worth, 2)} USD')\\r\\n await ctx.send(embed=embed)\",\n \"def get_money(self) -> float: \\n money = get_owned()\\n try:\\n assert type(self.owned_init) == float\\n except AssertionError: #The first time one tries to make a bet this is evoked\\n self.owned_init = money\\n finally:\\n return money\",\n \"def set_steals_percentage(self):\\n bx = self.get_standard_stats()\\n team = self.get_team_stats()\\n opp_team = self.get_opp_team_stats()\\n poss = self.get_team_possessions()\\n result = 0.00\\n if bx[\\\"minutes\\\"] > 0:\\n result = ((bx[\\\"steals\\\"] * (team[\\\"minutes\\\"]/Decimal('5'))) / Decimal(float(bx[\\\"minutes\\\"]) * poss)) * 100\\n self.steals_percentage = \\\"%.2f\\\" % round(result, 2)\",\n \"def validate_bet(buy_type, cash_in):\\n while cash_in < 0:\\n print(\\\"Invalid\\\", buy_type)\\n cash_in = round(float(input(\\\"Enter \\\" + buy_type + \\\": $\\\")), 2)\\n\\n return cash_in\",\n \"def cash_ratio(self):\\n return self.cash / self.current_liabilities\",\n \"def test_update_balance(self):\\n current_year_tuple = (0.1, 0.1, 0.8)\\n iteration_balance = 90\\n contribution = 10\\n expected_result = 110\\n test_balance = investment_growth.update_balance(iteration_balance, contribution, current_year_tuple)\\n self.assertEqual(test_balance, expected_result)\",\n \"def _get_reward(self):\\n if self.is_game_done:\\n return self.price - 1\\n else:\\n return 0.0\",\n \"def blind_bet(self):\\n self.this_player.bet(SMALL_BLIND_BET)\\n self.other_player.bet(BIG_BLIND_BET)\\n if SHOW_MESSAGE:\\n print(\\\"Making blind bets.\\\")\\n print(\\\"Player1:\\\")\\n self.player1.show()\\n print(\\\"Player2:\\\")\\n self.player2.show()\",\n \"def total_rewards(self) -> float:\\n return self.__total_rewards\",\n \"def initial_cash_balance(self) -> float:\\n return self.buy_budget * len(self.stocks)\",\n \"def gold_card(subtotal):\\n return subtotal * 0.05\",\n \"def _set_bet_limit(self) -> None:\\n for i, ratio in enumerate(BET_LIMIT_RATIOS):\\n self._bet_limits[i] = self._treasury_min.get() // ratio\",\n \"def rough_outcome(self) -> float:\\n if is_win(self):\\n return 1\\n elif is_lose(self):\\n return -1\\n return 0\",\n \"def silver_card(subtotal):\\n return subtotal * 0.02\",\n \"async def process_bj_game(self, ctx, amount, user_id):\\n if amount >= 0:\\n if not await self.check_in_game(user_id, ctx):\\n if amount > await ex.u_currency.get_balance(user_id):\\n await ctx.send(f\\\"> **{ctx.author}, you can not bet more than your current balance.**\\\")\\n else:\\n return True\\n else:\\n await ctx.send(f\\\"> **{ctx.author}, you can not bet a negative number.**\\\")\",\n \"def can_bet(self, amount: int) -> bool:\\n if isinstance(amount, int):\\n if self.chips_amount - amount >= 0:\\n return True\\n else:\\n raise ValueError(\\\"The 'amount' param must be an instance of int, got\\\"+\\n str(type(amount)))\\n\\n return False\",\n \"def raise1():\\r\\n\\r\\n value = int(RAISE_BUTTONS[0].get_text().split(\\\" $\\\")[1])\\r\\n raise_bet(value)\",\n \"def withdraw(self, amount, budget):\\r\\n if budget != \\\"Total Balance\\\":\\r\\n assert budget in self.budgets, \\\"Specified budget doesn't exist\\\"\\r\\n self.budgets[budget] -= float(amount)\\r\\n self.balance -= float(amount)\",\n \"def double_declining_balance():\\r\\n cost = float(input(\\\"Please Enter The Cost Of Asset: \\\"))\\r\\n accdepreciation = float(input(\\\"Please Enter The Value Of Accumulated Depreciation: \\\"))\\r\\n life = float(input(\\\"Please Enter Estimated Useful Life Of Asset(Years): \\\"))\\r\\n rv = float(input(\\\"Please Enter Estimated Residual Value Of Asset: \\\"))\\r\\n n = 0\\r\\n a = (float(cost)-float(accdepreciation)) * (float(2)/float(life))\\r\\n bn = float(a)/float(12)\\r\\n print \\\">> Your Monthly Depreciation For First Year is\\\",bn\\r\\n while(n != (life-1)):\\r\\n bk = float(cost)\\r\\n a = ((float(cost)-float(accdepreciation)) * (float(2)/float(life)))\\r\\n cost -= float(a)\\r\\n bk -= float(a)\\r\\n n += 1\\r\\n vvv = float(bk)-float(rv)\\r\\n print \\\">> Your Depreciation For Year No.\\\",n,\\\"is\\\",a\\r\\n print \\\">> Your Book Value After\\\",n,\\\"Years is\\\",bk,\\\"\\\\n\\\"\\r\\n print \\\">> Your Depreciation For Year No.\\\",int(life),\\\"is\\\",vvv\\r\\n print \\\">> Your Book Value After\\\",int(life),\\\"Years is\\\",rv\",\n \"def get_fee_pct(self, contract_type: str) -> Tuple[float, float]:\\n if contract_type == 'forex':\\n return (0.00002, 0.00002)\\n elif contract_type == 'crypto':\\n if self.CRYPTO_EXCHANGE == 'binance':\\n if self.trade_volume < 50_000:\\n return (.001, .001)\\n elif self.trade_volume < 100_000:\\n return (.0009, .0009)\\n elif self.trade_volume < 5000_000:\\n return (.0009, .0008)\\n elif self.trade_volume < 1_000_000:\\n return (.0008, .0007)\\n elif self.trade_volume < 5_000_000:\\n return (.0007, .0005)\\n elif self.trade_volume < 10_000_000:\\n return (.0006, .0004)\\n elif self.trade_volume < 25_000_000:\\n return (.0006, 0)\\n elif self.trade_volume < 100_000_000:\\n return (.0005, 0)\\n elif self.trade_volume < 250_000_000:\\n return (.0004, 0)\\n elif self.trade_volume < 500_000_000:\\n return (.0003, 0)\\n else: return (.0002, 0)\\n elif self.CRYPTO_EXCHANGE == 'kraken':\\n if self.trade_volume < 50_000:\\n return (.0026, .0016)\\n elif self.trade_volume < 100_000:\\n return (.0024, .0014)\\n elif self.trade_volume < 250_000:\\n return (.0022, .0012)\\n elif self.trade_volume < 500_000:\\n return (.002, .001)\\n elif self.trade_volume < 1_000_000:\\n return (.0018, .0008)\\n elif self.trade_volume < 2_500_000:\\n return (.0016, .0006)\\n elif self.trade_volume < 5_000_000:\\n return (.0014, .0004)\\n elif self.trade_volume < 10_000_000:\\n return (.0012, .0002)\\n else: return (.001, 0)\\n elif self.CRYPTO_EXCHANGE == 'coinbase':\\n if self.trade_volume < 10_000:\\n return (.005, .005)\\n elif self.trade_volume < 50_000:\\n return (.0035, .0035)\\n elif self.trade_volume < 100_000:\\n return (.0025, .0015)\\n elif self.trade_volume < 1_000_000:\\n return (.002, .001)\\n elif self.trade_volume < 10_000_000:\\n return (.0018, .0008)\\n elif self.trade_volume < 50_000_000:\\n return (.0015, .0005)\\n elif self.trade_volume < 300_000_000:\\n return (.0007, 0)\\n elif self.trade_volume < 500_000_000:\\n return (.0005, 0)\\n else: return (.0004, 0)\\n elif self.CRYPTO_EXCHANGE == 'robinhood':\\n return (0.0001, 0.0001)\\n return (0, 0)\",\n \"def withdrawMoney(self, withdraw_amount):\\r\\n if (self.balance_amt - withdraw_amount) > 0:\\r\\n self.balance_amt = self.balance_amt - withdraw_amount\\r\\n else:\\r\\n raise WithdrawError #Exception('Overdraft withdrawal Error. Cannot withdraw more than amount in account balance: {}'.format(self.balance_amt))\\r\",\n \"def withdraw(self, amount):\\n self.balance -= amount\",\n \"def withdraw(self, amount):\\r\\n balance = self['get']('balance')\\r\\n if amount > balance:\\r\\n return 'Insufficient funds'\\r\\n self['set']('balance', balance - amount)\\r\\n return self['get']('balance')\",\n \"def value(self):\\n #import pdb; pdb.set_trace()\\n return ((self.team1.get_cur_hp() / self.team1.get_total_hp()) - \\n (self.team2.get_cur_hp() / self.team2.get_total_hp()))\",\n \"def transaction(money_in, drink_cost):\\n if money_in >= drink_cost:\\n change = round((money_in - drink_cost), 2) # 2 decimal places\\n print(f'your change is £{change}')\\n global profit #Global scope needed\\n profit = profit + drink_cost\\n return True\\n else:\\n print(\\\"That's not enough money!\\\")\\n return False\",\n \"def call(self, points, subpot=None):\\n points = int(points)\\n if points >= self.points:\\n points = self.points\\n self.all_in = True\\n self.active = False\\n self.points -= points\\n self.bet += points\\n return points\",\n \"def get_bet(self):\\n while newbet := input(f\\\"{self.name}: {self.chips} chips. Last bet: {self.lastbet}. Bet: \\\"):\\n try:\\n newbet = int(newbet)\\n if newbet in range(0, self.chips+1):\\n self.bet = newbet\\n self.chips -= newbet\\n return newbet\\n else:\\n print(\\\"You don't have that many chips.\\\")\\n except ValueError:\\n print(\\\"Bets are numbers please.\\\")\",\n \"def get_total_bets(self) -> int:\\n return self._total_bet_count.get() + self._daily_bet_count.get()\",\n \"def _award_accounts(self):\\n\\n prize_money = 0\\n for i in xrange(len(self.accounts)):\\n # Each savings account has a 1% chance of quadrupling their principal. The\\n # chance is independent between accounts.\\n if random.randint(1, 100) == 1:\\n prize_money += 3 * self.accounts[i]\\n self.accounts[i] *= 4\\n return prize_money\",\n \"def do_bet(self, arg):\\n\\t\\topts = get_options(parser.parser_add, arg)\\n\\t\\tif opts is None: return\\n\\t\\tkwargs = {}\\n\\t\\tkwargs['name'] = ' '.join(opts.broken_name)\\n\\t\\tkwargs['weight'] = opts.weight\\n\\t\\tif opts.outcome is not None:\\n\\t\\t\\tkwargs['outcome'] = convert_outcome(opts.outcome)\\n\\t\\tself.manager.add_bet(**kwargs)\\n\\t\\tprint(display.format_bet(self.manager.bets[-1]))\",\n \"def pay_round_money(self) -> int:\\r\\n return self.round_money.pay_all_money()\",\n \"def withdraw(account, amount):\\n pass\",\n \"def investment_price(self):\\n invest = self.max_loss / (self.buy_price - self.stop_loss) * self.buy_price\\n if invest > self.capital:\\n return round(self.capital, 2)\\n else:\\n return round(invest, 2)\",\n \"def budget_balance(self):\\n budget_balance = round(self.budget() - self.total_spent(), 2)\\n budget_balance_degree = round( (9000 * self.total_spent()) / (self.budget()), 4) #convert to degrees and round to four decimal places\\n return (budget_balance, budget_balance_degree)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80275404","0.7679025","0.7675108","0.7609656","0.74898267","0.7448841","0.733221","0.7229419","0.69224185","0.69190437","0.69124156","0.6872131","0.68597424","0.67960364","0.6715273","0.66506547","0.6604676","0.6566986","0.65195584","0.65036714","0.6471303","0.6427731","0.6426412","0.6421445","0.64143914","0.64101404","0.63902825","0.63715357","0.6321666","0.63131964","0.63030463","0.630297","0.6298912","0.6290664","0.6286086","0.6283696","0.62772983","0.62651134","0.6252369","0.6238445","0.6227538","0.6222754","0.62180626","0.6204908","0.6185252","0.6181901","0.61733705","0.6169149","0.61674076","0.616588","0.6157055","0.61324644","0.61252826","0.61168003","0.61138976","0.61018485","0.6084999","0.6059926","0.60356236","0.6033001","0.60220426","0.60218644","0.60123676","0.6002245","0.59914935","0.5979688","0.59791005","0.5968806","0.59687376","0.59616125","0.59600854","0.5959449","0.5956843","0.5955009","0.59544086","0.5953763","0.59490675","0.594221","0.5939239","0.59380734","0.59301853","0.5914038","0.59102184","0.5909666","0.59067965","0.58966005","0.58893204","0.58800673","0.5875657","0.5867401","0.5861946","0.58603","0.58556753","0.5855177","0.5853976","0.58514214","0.585072","0.58429176","0.5840865","0.5839317"],"string":"[\n \"0.80275404\",\n \"0.7679025\",\n \"0.7675108\",\n \"0.7609656\",\n \"0.74898267\",\n \"0.7448841\",\n \"0.733221\",\n \"0.7229419\",\n \"0.69224185\",\n \"0.69190437\",\n \"0.69124156\",\n \"0.6872131\",\n \"0.68597424\",\n \"0.67960364\",\n \"0.6715273\",\n \"0.66506547\",\n \"0.6604676\",\n \"0.6566986\",\n \"0.65195584\",\n \"0.65036714\",\n \"0.6471303\",\n \"0.6427731\",\n \"0.6426412\",\n \"0.6421445\",\n \"0.64143914\",\n \"0.64101404\",\n \"0.63902825\",\n \"0.63715357\",\n \"0.6321666\",\n \"0.63131964\",\n \"0.63030463\",\n \"0.630297\",\n \"0.6298912\",\n \"0.6290664\",\n \"0.6286086\",\n \"0.6283696\",\n \"0.62772983\",\n \"0.62651134\",\n \"0.6252369\",\n \"0.6238445\",\n \"0.6227538\",\n \"0.6222754\",\n \"0.62180626\",\n \"0.6204908\",\n \"0.6185252\",\n \"0.6181901\",\n \"0.61733705\",\n \"0.6169149\",\n \"0.61674076\",\n \"0.616588\",\n \"0.6157055\",\n \"0.61324644\",\n \"0.61252826\",\n \"0.61168003\",\n \"0.61138976\",\n \"0.61018485\",\n \"0.6084999\",\n \"0.6059926\",\n \"0.60356236\",\n \"0.6033001\",\n \"0.60220426\",\n \"0.60218644\",\n \"0.60123676\",\n \"0.6002245\",\n \"0.59914935\",\n \"0.5979688\",\n \"0.59791005\",\n \"0.5968806\",\n \"0.59687376\",\n \"0.59616125\",\n \"0.59600854\",\n \"0.5959449\",\n \"0.5956843\",\n \"0.5955009\",\n \"0.59544086\",\n \"0.5953763\",\n \"0.59490675\",\n \"0.594221\",\n \"0.5939239\",\n \"0.59380734\",\n \"0.59301853\",\n \"0.5914038\",\n \"0.59102184\",\n \"0.5909666\",\n \"0.59067965\",\n \"0.58966005\",\n \"0.58893204\",\n \"0.58800673\",\n \"0.5875657\",\n \"0.5867401\",\n \"0.5861946\",\n \"0.58603\",\n \"0.58556753\",\n \"0.5855177\",\n \"0.5853976\",\n \"0.58514214\",\n \"0.585072\",\n \"0.58429176\",\n \"0.5840865\",\n \"0.5839317\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66638166"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":93772,"cells":{"query":{"kind":"string","value":"Used to remove any possible empty module params passed in from a task file."},"document":{"kind":"string","value":"def filter_empty_subparams(self, param_name):\n param = self.module.params.get(param_name)\n filtered = []\n if isinstance(param, list):\n for subparam in param:\n if isinstance(subparam, dict):\n filtered.append(\n dict(\n (key, value)\n for key, value in subparam.items()\n if value is not None\n )\n )\n else:\n filtered = param\n return filtered"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def remove_parameters(self):\n self.parameters = []","def purge(root=os.path.join(\"~\", \".daisykit\", \"assets\")):\n root = os.path.expanduser(root)\n files = os.listdir(root)\n for f in files:\n if f.endswith(\".params\"):\n os.remove(os.path.join(root, f))","def buildWithout(self, mods):\n for modname in mods:\n try:\n self.optmodules.remove(modname)\n except:\n print \"\\n*** WARNING: \" + modname + \" not found in the list of modules from \" + self.name + \"!!\"\n print \"please recheck your config file: names are case-sensitive!!\"","def scrub():\n\n\tlocal(\"rm -fr dist build\")\n\tlocal(\"find . -name \\\"*.pyc\\\" -exec rm '{}' ';'\")","def test_module():\n print(\"-------Testing Module-------\")\n \n test_string = [\"//Remove Me\", \"(Me Too)\", \"But leave me\", \n \"and most of me //just this bit\",\n \"Don't forget //this aswell\", \" \"]\n print(\"--Original--\")\n print(test_string)\n print(\"--Cleaned--\")\n print(remove_psuedoinstructions(clean_file(test_string)))","def clean_modules(gallery_conf, fname):\n for reset_module in gallery_conf['reset_modules']:\n reset_module(gallery_conf, fname)","def remove_unused_args(args, thnn_args):\n def clean_name(name):\n name = name[:name.index('[')] if '[' in name else name\n if name.endswith('_'):\n name = name[:-1]\n return name\n uses = set([clean_name(arg['name']) for arg in thnn_args])\n uses.add('output_mask')\n args = [arg for arg in args if arg['name'] in uses]\n for arg in args:\n if 'default' in arg:\n del arg['default']\n return args","def pop_non_relevant_module_fields(data: Dict):\n keys_to_keep = [\n \"title\",\n \"description\",\n \"content_type\",\n \"published_at\",\n \"references\",\n \"architectures\",\n \"authors\",\n \"rank\",\n \"reliability\",\n ]\n for key in list(data):\n if key not in keys_to_keep:\n data.pop(key)","def exclude_params(params_expanded, exclude):\n if exclude is None:\n exclude = {}\n cut_params(params=params_expanded, exclude=exclude)\n print_grid_params(params_expanded)\n\n params_full = grid_tools.enumerate_params(params_expanded)\n return params_full","def delete_params(self, base_key, *params):\n self.params[\n base_key + '.no_' + '|'.join(params)] = delete_params(\n self.params[base_key], *params)","def removeModulesNotOnAPathExcluding( process, keepList=() ):\n allMods=set((x for x in process.producers_().iterkeys()))\n allMods.update((x for x in process.filters_().iterkeys()))\n allMods.update((x for x in process.analyzers_().iterkeys()))\n allMods.update((x for x in process.outputModules_().iterkeys()))\n \n modulesOnPaths = set()\n for p in process.paths_():\n modulesOnPaths.update( (x for x in getattr(process,p).moduleNames())) \n for p in process.endpaths_():\n modulesOnPaths.update( (x for x in getattr(process,p).moduleNames()))\n\n notOnPaths = allMods.difference(modulesOnPaths)\n \n keepModuleNames = set( (x.label_() for x in keepList) )\n \n getRidOf = notOnPaths.difference(keepModuleNames)\n \n for n in getRidOf:\n delattr(process,n)","def clean(pattern=default, *, module=None):\n pattern = default.unwrap(pattern, current_config[\"clean\"])\n\n if pattern is False:\n return\n\n if module is None:\n import __main__ as module\n\n items = vars(module)\n to_delete = [key for key in items if fnmatch.fnmatchcase(key, pattern)]\n\n for key in to_delete:\n del items[key]","def _remove_node_address_from_params(cls, params: dict):\n if ConstantKeys.NODE_ADDRESS in params:\n del params[ConstantKeys.NODE_ADDRESS]","def test_ignore_empty_files(self):\n\n self.checker.config.file_header_ignore_empty_files = True\n node_mock = MagicMock()\n node_mock.stream.return_value.__enter__.return_value.read.return_value.decode.return_value = ''\n with self.assertNoMessages():\n self.checker.process_module(node_mock)","def remove_variables(self):\n self.variables = []","def delete_params(self, base_key, *params):\n self.params[base_key + \".no_\" + \"|\".join(params)] = self.delete_params_s(self.params[base_key], params)","def unusedFromKDOTDataPreparation():","def _finalize_parameters(fsdp_module: XlaFullyShardedDataParallel) -> None:\n for p in fsdp_module.full_params:\n if not p.requires_grad:\n continue\n if hasattr(p, \"_shard_bwd_hook\"):\n assert len(p._shard_bwd_hook) == 2, len(p._shard_bwd_hook)\n p._shard_bwd_hook[1].remove()\n delattr(p, \"_shard_bwd_hook\")","def clean(params):\n if tf.gfile.IsDirectory(params.ckpt_path):\n tf.gfile.DeleteRecursively(params.ckpt_path)\n\n if tf.gfile.IsDirectory(params.summary_path):\n tf.gfile.DeleteRecursively(params.summary_path)\n\n if tf.gfile.IsDirectory(params.result_path):\n tf.gfile.DeleteRecursively(params.result_path)\n\n if tf.gfile.IsDirectory(params.log_path):\n tf.gfile.DeleteRecursively(params.log_path)\n\n if tf.gfile.IsDirectory(\"__pycache__\"):\n tf.gfile.DeleteRecursively(\"__pycache__\")\n\n if tf.gfile.IsDirectory(params.config_path):\n tf.gfile.DeleteRecursively(params.config_path)","def remBuildOnlyDependency(self, mods):\n for mod in mods:\n try:\n self.reqmodules_buildonly.remove(mod)\n except:\n print \"\\n*** WARNING: \" + mod + \" not found in the list of build only dependencies from \" + self.name + \"!!\"\n print \"please recheck your config file: names are case-sensitive!!\"","def removeFilenameFilter(call, args=(), kwargs={}, nodeClass='*'):","def cleanTask(self, blTaskName):\n return self._genericCommand('clean', blTaskName, 'all')","def clear_empty_values(args):\n result = {}\n for param in args:\n if args[param] is not None:\n result[param] = args[param]\n return result","def remove_var(b, exclude):\n return dict((k, v) for k, v in b.items() if param_name(k) not in exclude)","def exclude_opts(cls) -> Tuple[str, ...]:\n return \"required\", \"print_config\", \"config\", \"ngpu\"","def emptyModule():\n return(Module(\"\",[]))","def _clear(self):\n self._argparse_group = None","def remove_info(config):\n clean_config = copy.deepcopy(config)\n\n if 'parameters' in clean_config:\n params = clean_config['parameters']\n for name in params:\n val = params[name]\n if isinstance(val, dict):\n # This should not generally happen since we deal with it in update_param_info, but just in case\n if 'val' not in val:\n raise ConfigurationError(\n \"Parameter info remove error.\"\n \" Parameter that is defined by a dictionary must contain 'val' field that\"\n \" defines its default value. Found this definition: %s=%s\" % (name, val)\n )\n params[name] = val['val']\n\n return clean_config","def strip_variables(*args):\n return [arg.strip(\" '\\\"\") if arg is not None else arg for arg in args]","def remove_im_params(model, im):\n for param in model.parameters:\n # If the node doesn't exist e.g., it may have already been removed),\n # skip over the parameter without error\n try:\n im.remove_node(param.name)\n except:\n pass","def _remove_build(dep):\n m = re.match(r\"([^=]+=[^=]+)=([^=]+)$\", dep, re.IGNORECASE)\n return m.group(1) if m else dep","def _param_computation_memory_cleanup(self, param, keep_backpack_buffers):\n if keep_backpack_buffers:\n savefields = []\n else:\n savefields = {\n self._savefield_directions,\n self._savefield_first,\n self._savefield_second,\n }\n\n if not self._compute_gammas:\n savefields.remove(self._savefield_first)\n\n for savefield in savefields:\n delattr(param, savefield)\n\n if self._verbose:\n print(f\"Param {id(param)}: Delete '{savefield}'\")","def test_find_unused_parameters_when_unused_parameters_empty(self):\n\n class FindUnusedParamModule(nn.Module):\n def __init__(self):\n super().__init__()\n self.t0 = Task()\n self.t1 = Task()\n\n def task_parameters(self):\n return (self.t0.p, self.t1.p)\n\n def forward(self, x, rank):\n return self.t1(self.t0(x)) if rank == 0 else self.t1(x)\n\n def run_and_verify_grad(model):\n # Run forward\n output = model(8, self.rank)\n\n # The grads of all parameters should be None at this point.\n [self.assertIsNone(t_p.grad) for t_p in model.module.task_parameters()]\n\n # Run backward\n output.mean().backward()\n\n # Now locally unused parameter should have grad updated on all ranks.\n [self.assertIsNotNone(t_p.grad) for t_p in model.module.task_parameters()]\n\n process_group = self._get_process_group()\n\n # Test on CPU\n cpu_model = DistributedDataParallel(\n FindUnusedParamModule().cpu(),\n process_group=process_group,\n find_unused_parameters=True,\n )\n run_and_verify_grad(cpu_model)\n\n # Test on GPU\n device_id = gpus_for_rank(self.world_size)[self.rank][0]\n gpu_model = DistributedDataParallel(\n FindUnusedParamModule().to(device_id),\n device_ids=[device_id],\n process_group=process_group,\n find_unused_parameters=True,\n )\n run_and_verify_grad(gpu_model)","def removeFilenameValidate(call, args=(), kwargs={}, nodeClass='Write'):","def remove_empty_sources(self):\n for source in [\"dxf\", \"edilizia\", \"easyroom\", \"merged\"]:\n if source in self and not self[source]:\n del self[source]","def removeParameter(cTag, name): #@NoSelf","def remove(self, task):\n pass","def clean_data(self, path, exclude_msgtypes=None):","def remove_task(self, task: Task):\n self.tasks.remove(task)\n self.assignments = [\n assignment for assignment in self.assignments if assignment.task != task\n ]\n self.solution = [\n assignment for assignment in self.solution if assignment.task != task\n ]\n\n new_combinations = []\n for combination in self.combinations:\n try:\n combination.tasks.remove(task)\n except ValueError:\n pass\n else:\n if not combination.tasks: # became empty\n continue\n new_combinations.append(combination)\n self.combinations = new_combinations","def _RemoveUnusedFlags( flags, filename ):\n\n new_flags = []\n\n # When flags come from the compile_commands.json file, the first flag is\n # usually the path to the compiler that should be invoked. We want to strip\n # that.\n if not flags[ 0 ].startswith( '-' ):\n flags = flags[ 1: ]\n\n skip = False\n for flag in flags:\n if skip:\n skip = False\n continue\n\n if flag == '-c':\n continue\n\n if flag == '-o':\n skip = True;\n continue\n\n if flag == filename or os.path.realpath( flag ) == filename:\n continue\n\n new_flags.append( flag )\n return new_flags","def param_remove(params, arg):\n d = params.copy()\n if arg in d:\n del d[arg]\n return d.urlencode()","def cleanup(modpath):\n yield\n\n # Remove dummy modules from sys.modules\n pathlist = [p for p in sys.modules\n if p != modpath and p.startswith(modpath)]\n\n for p in pathlist:\n del sys.modules[p]\n\n if modpath in sys.modules:\n del sys.modules[modpath]\n\n # Remove TaskImporter\n index = [i for i, obj in enumerate(sys.meta_path)\n if isinstance(obj, TaskImporter)]\n\n for i in reversed(index):\n assert isinstance(sys.meta_path[i], TaskImporter)\n sys.meta_path.pop(i)","def _remove_swarm_keys(self):\n for key in SWARM_PROPERTIES:\n self.spec.pop(key, None)","def test_remove_workflow_definitions_in_job(self):\n pass","def strip_file_top_level(self, file_node: MypyFile) -> None:\n self.names = file_node.names\n self.file_node = file_node\n self.recurse_into_functions = False\n file_node.accept(self)","def _clear_tasks(self):\n listOfTasks = self.model.find(xmlns + 'ListOfTasks') \n assert listOfTasks != None\n \n for task in listOfTasks:\n task.attrib['scheduled'] = 'false'","def postCompile(root, excluded):\n # If you need to delete specific -parsed but not wanted- files, do it here\n pass","def _clean_up_optimization():\n for (root, dirs, files) in walk(TEMP_MODULES_DIR_PATH, topdown=False):\n for file in files:\n if file.startswith(\"__temp_\"):\n remove(f\"{root}/{file}\")\n try:\n rmdir(root)\n except OSError:\n G.warn_(f\"Unidentified file found in temporary directory: {root}\")","def clear_includepatterns(self):\n self._excludepatterns = []","def purge_tasks(self, filter_not_match):\n self._rf_worker.purge_tasks(filter_not_match)","def test_check_exclude_none(self):\n\n self.assertTrue(PostfixExclude([]).check(self.file_gitignore))\n self.assertTrue(PostfixExclude([]).check(self.file_py))\n self.assertTrue(PostfixExclude([]).check(self.file_authors))\n self.assertTrue(PostfixExclude([]).check(self.file__init__))\n self.assertTrue(PostfixExclude([]).check(self.file_bin))","def remove_abs_vars(self):\n self.m.remove(self.bp_abs)\n self.m.remove(self.bn_abs)\n self.m.remove(self.gp_abs)\n self.m.remove(self.gn_abs)\n self.m.remove(self.beta_p)\n self.m.remove(self.beta_n)\n self.m.remove(self.gamma_p)\n self.m.remove(self.gamma_n)","def delModule(name):","def cleanup(options=None):\n if options is None:\n for f in glob.glob(\"*.grmpy.*\"):\n os.remove(f)\n elif options == 'regression':\n for f in glob.glob(\"*.grmpy.*\"):\n if f.startswith('regression'):\n pass\n else:\n os.remove(f)","def dev_clean():\n clean_files(\"csv\", True)\n clean_files(\"jsontxt\", True)","def teardown_module(module):\n for datafile in datafiles:\n os.remove(datafile)","def clean_condor(c):\n for fname in glob.glob(os.path.join(ROOT_DIR, '*.dag')):\n _delete_pattern(fname + '.*')\n for fname in glob.glob(os.path.join(ROOT_DIR, '*.sub')):\n temps = []\n with open(fname, 'r') as f:\n for line in f.readlines():\n for w in ('log', 'error', 'output'):\n if line.startswith(w):\n temp = line.split('=')[-1].strip()\n if os.path.isfile(temp):\n temps.append(temp)\n if temps:\n print(f'Found submit script {fname}:')\n for temp in temps:\n _delete_file(temp, indent=' ')\n _delete_file(f'{ROOT_DIR}/docker_stderror')\n _delete_file(f'{ROOT_DIR}/parsetab.py')","def remove_all_fields(self):\n self.fields = None","def teardown_module(module):\n import os\n import time\n\n # temp file will be removed soon\n time.sleep(1.0)\n\n for p in [data_pk, data_pk_gz, data_js, data_js_gz]:\n try:\n os.remove(p)\n except:\n pass","def clear_quantities(shared):\n\n del_list = [i for i, fm in enumerate(shared.field_mappings)\n if fm.extra is not None]\n for index in reversed(del_list):\n del shared.field_mappings[index]\n\n shared.config.remove_section('extra')\n shared.config.add_section('extra')","def keep_params(self, base_key, *params):\n self.params[base_key + '.' + '|'.join(params)] = keep_params(\n self.params[base_key], *params)","def clean(self):\n os.remove(\"temp.py\") # Delete the file \"temp.py\", to free up disk space","def clear_time_position_specific_params(self):\n for param_name in self._time_position_params:\n setattr(self, param_name, None)","def zero_module(module):\n for p in module.parameters():\n p.detach().zero_()\n return module","def cleanUp(self):\n import evoware.fileutil as F\n F.tryRemove(self.f_project, verbose=(self.VERBOSITY>1), tree=1)","def discard_config(self):\n raise NotImplementedError","def test_findtasks_none(monkeypatch, modpath):\n monkeypatch.setattr(loadlimit.importhook, 'lstaskfiles', fake_lstaskfiles)\n # monkeypatch.setattr(loadlimit.importhook, 'SourceFileLoader',\n # FakeModuleWithTasks)\n monkeypatch.setattr(loadlimit.importhook, 'SourceFileLoader',\n FakeSourceFileLoader)\n\n taskfile = 'a_0.py'\n\n sys.meta_path.append(TaskImporter(taskfile))\n taskmod = import_module(modpath)\n\n assert hasattr(taskmod, '__tasks__')\n assert taskmod.__tasks__ == []","def clear_specific(self):\n self.specific_file = None\n self.specific_parser = None\n\n self.specific_box.delete(0, END)","def _clean_files(self):\n if self.delfiles & 1:\n ProcUtils.remove(self.okm)\n if self.delfiles & 2:\n ProcUtils.remove(self.hkm)\n if self.delfiles & 4:\n ProcUtils.remove(self.qkm)\n if self.delfiles & 8:\n ProcUtils.remove(self.obc)\n\n if self.log is False:\n ProcUtils.remove(self.pcf_file)\n base = os.path.basename(self.okm)\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogReport', base])))\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogStatus', base])))\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogUser', base])))","def keep_params(self, base_key, *params):\n self.params[base_key + \".\" + \"|\".join(params)] = self.keep_params_s(self.params[base_key], params)","def clean(_context):","def remove_extra(self, entry):\n\n config_spec = vim.vm.ConfigSpec()\n self.logger.info(\"Removing extra config {0}\".format(entry))\n opt = vim.option.OptionValue()\n opt.key = entry\n opt.value = \"\"\n config_spec.extraConfig = [opt]\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)","def remove_useless_file(rootPath):\n all_file_paddle_list = get_all_paddle_file(rootPath)\n ut_file_map_new = {}\n ut_file_map = \"%s/build/ut_file_map.json\" % rootPath\n with open(ut_file_map, 'r') as load_f:\n load_dict = json.load(load_f)\n for key in load_dict:\n if key in all_file_paddle_list:\n ut_file_map_new[key] = load_dict[key]\n\n with open(\"%s/build/ut_file_map.json\" % rootPath, \"w\") as f:\n json.dump(ut_file_map_new, f, indent=4)\n print(\"remove_useless_file ut_file_map success!!\")","def _filter_unused_argument_dash_m(self, arguments):\n return [argument for argument in arguments if argument != '-m']","def delete_many_preprocessor(search_params=None, **kw):\n pass","def _unset_pipeline_cfg(self, field):","def test_ignore(self):\n parser = hhsuite.FastaParser(ignore={\"foo\"})\n results = parser.run(self.pipeline)\n self.assertNotIn(\"foo\", results[\"templates\"][1][\"sequence_alignments\"])","def clear_lists(self): \n self.fp_config_files = []\n self.txt_files = []\n self.fr_config_files = []","def test_do_not_ignore_empty_files(self):\n\n node_mock = MagicMock()\n node_mock.stream.return_value.__enter__.return_value.read.return_value.decode.return_value = ''\n with self.assertAddsMessages(pylint.testutils.Message(\n msg_id='invalid-file-header',\n line=1,\n args=self.EXPECTED_HEADER)):\n self.checker.process_module(node_mock)","def reset(self):\n def remove_auxiliary_dir():\n egg_info_dir = self.project_name_sc + \".egg-info\"\n remove_directories([\n egg_info_dir,\n \".env\",\n \".eggs\",\n \".pytest_cache\",\n \"build\",\n \"dist\",\n \".cache\",\n \".benchmark\",\n \".tox\",\n \".vagrant\",\n \".tox\"])\n remove_files([\n \".coverage\",\n \".doit.db\",\n \".doit.bak\",\n \".doit.dat\",\n \".doit.dir\",\n ])\n\n # TODO(lschneider): Remove unnecessary files without command lines.\n # This code could be run directly from this function. However\n # the pathlib library is not part of the standard python 2.\n prefix = \"python -c \\\"import pathlib; \"\n delete_pyfiles = prefix + \"import pathlib; [p.unlink() for p in pathlib.Path('.').rglob('*.py[co]')]\\\"\"\n delete_dirs = prefix + \"import pathlib; [p.rmdir() for p in pathlib.Path('.').rglob('__pycache__')]\\\"\"\n\n return {\n \"actions\": [\n delete_pyfiles,\n delete_dirs,\n remove_auxiliary_dir,\n ],\n \"verbosity\": 2\n }","def remove_part(self, part_name: str, ignore_if_absent: bool = False):\n super.remove_part(\n part_name,\n ignore_if_absent,\n lambda p: self.build_order.remove(p) if p is not None else None,\n )","def filter_allowed_fields(self):\n allowed_fields = super().filter_allowed_fields\n # Remove assignment_id\n allowed_fields.remove('assignment_id')\n return allowed_fields","def remove_param(self, step_id, name):\n if step_id in self._params:\n del self._params[step_id][name]","def prepare_cleaning(self, task):\n node = task.node\n\n inband_steps = [step for step\n in node.driver_internal_info.get('clean_steps', [])\n if {'interface': step['interface'],\n 'step': step['step']} not in _OOB_CLEAN_STEPS]\n\n if ('agent_cached_clean_steps' not in node.driver_internal_info or\n inband_steps):\n return deploy_utils.prepare_inband_cleaning(task,\n manage_boot=True)","def removeAllTasks(self):\n for taskName in self.runningTasks:\n taskMgr.remove(taskName)","def remove_all(self):\n self._options.clear()\n self._programs.clear()","def remove_module_files(self, base_dir):\n for file, pkg in self.files:\n package_path = os.path.join(base_dir, 'gocat', pkg)\n # Check if entire package is to be deleted\n if file == '*':\n self._unstage_folder(package_path)\n else:\n file_path = os.path.join(package_path, file)\n if os.path.exists(file_path):\n os.remove(file_path)","def remove(self):\n mod_file = self.layout.filename\n if os.path.exists(mod_file):\n try:\n os.remove(mod_file) # Remove the module file\n os.removedirs(\n os.path.dirname(mod_file)\n ) # Remove all the empty directories from the leaf up\n except OSError:\n # removedirs throws OSError on first non-empty directory found\n pass","def _clean_input_dir():\n for existing_file in os.listdir(join(input_dir, 'fitting')):\n if existing_file != '.hold':\n os.remove(join(input_dir, 'fitting', existing_file))","def remDependency(self, mods):\n for mod in mods:\n try:\n self.reqmodules.remove(mod)\n except:\n print \"\\n*** WARNING: \" + mod + \" not found in the list of dependencies from \" + self.name + \"!!\"\n print \"please recheck your config file: names are case-sensitive!!\"","def _removeSpecs(self):\n self.specGenerator.removeSpecs()","def do_remove(self, loglevel=logging.DEBUG):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tcfg = self.cfg\n\t\t# Now get the run_order keys in order and go.\n\t\tself.log('PHASE: remove', level=loglevel)\n\t\tself.pause_point('\\nNow removing any modules that need removing', print_input=False, level=3)\n\t\t# Login at least once to get the exports.\n\t\tfor module_id in self.module_ids():\n\t\t\tmodule = self.shutit_map[module_id]\n\t\t\tself.log('considering whether to remove: ' + module_id, level=logging.DEBUG)\n\t\t\tif cfg[module_id]['shutit.core.module.remove']:\n\t\t\t\tself.log('removing: ' + module_id, level=logging.DEBUG)\n\t\t\t\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\n\t\t\t\tif not module.remove(self):\n\t\t\t\t\tself.log(self.print_modules(), level=logging.DEBUG)\n\t\t\t\t\tself.fail(module_id + ' failed on remove', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\n\t\t\t\telse:\n\t\t\t\t\tif self.build['delivery'] in ('docker','dockerfile'):\n\t\t\t\t\t\t# Create a directory and files to indicate this has been removed.\n\t\t\t\t\t\tself.send(' command mkdir -p ' + shutit_global.shutit_global_object.shutit_state_dir_build_db_dir + '/module_record/' + module.module_id + ' && command rm -f ' + shutit_global.shutit_global_object.shutit_state_dir_build_db_dir + '/module_record/' + module.module_id + '/built && command touch ' + shutit_global.shutit_global_object.shutit_state_dir_build_db_dir + '/module_record/' + module.module_id + '/removed', loglevel=loglevel, echo=False)\n\t\t\t\t\t\t# Remove from \"installed\" cache\n\t\t\t\t\t\tif module.module_id in self.get_current_shutit_pexpect_session_environment().modules_installed:\n\t\t\t\t\t\t\tself.get_current_shutit_pexpect_session_environment().modules_installed.remove(module.module_id)\n\t\t\t\t\t\t# Add to \"not installed\" cache\n\t\t\t\t\t\tself.get_current_shutit_pexpect_session_environment().modules_not_installed.append(module.module_id)\n\t\t\t\tself.logout(echo=False)","def CleanEpi(self):\n for entry in self.info.keys():\n info = self.info[entry]\n if info['psdname'] == 'epi':\n for tag in ('imgfile', 'imgfile_m', 'imgfile_mf', 'imgfile_t'):\n if info.has_key(tag) and info[tag] is not None and \\\n os.path.exists(info[tag]):\n print 'Deleting %s*' % (info[tag], info['suffix'])\n cmd = '/bin/rm %s%s*' % (info[tag], info['suffix'])\n self.ExecCmd(cmd)\n if '.BRIK' in info['suffix']:\n cmd = '/bin/rm %s%s*' % (info[tag], \\\n info['suffix'].replace('.BRIK','.HEAD'))\n self.ExecCmd(cmd)","def get_unpinned_params(opts, params):\n return params - set([p for p, v in opts.__dict__.items() if p in LIKELIHOOD_PINNABLE_PARAMS and v is not None])","def _clear_variables( self ):\r\n self.navigation = None\r\n self.resPath = None\r\n self.resolutions = None\r\n self.currentResolution = None\r\n self.resolution = None\r\n for doc in self.include_doc:\r\n try: doc.unlink()\r\n except: pass","def remove_parameter_check(self, parameter):\n if isinstance(parameter, list):\n [self.remove_parameter_check(p) for p in parameter]\n elif parameter in self.parameter_checks:\n self.parameter_checks.pop(parameter)\n else:\n log.warning(f'No check was configured for parameter {parameter}.')","def setup_default_arguments(self):\n self.add_argument('--clean', action='store_true',\n help='Cleans all generated files.')","def _clean_input_dir():\n for existing_file in os.listdir(join(input_dir, 'analysis')):\n if existing_file != '.hold':\n os.remove(join(input_dir, 'analysis', existing_file))","def remove_aliases(self):\n for k, v in iteritems(self.argspec):\n if 'aliases' in v:\n for alias in v['aliases']:\n if alias in self.params:\n self.params.pop(alias)","def clear(self):\n self.vars = []"],"string":"[\n \"def remove_parameters(self):\\n self.parameters = []\",\n \"def purge(root=os.path.join(\\\"~\\\", \\\".daisykit\\\", \\\"assets\\\")):\\n root = os.path.expanduser(root)\\n files = os.listdir(root)\\n for f in files:\\n if f.endswith(\\\".params\\\"):\\n os.remove(os.path.join(root, f))\",\n \"def buildWithout(self, mods):\\n for modname in mods:\\n try:\\n self.optmodules.remove(modname)\\n except:\\n print \\\"\\\\n*** WARNING: \\\" + modname + \\\" not found in the list of modules from \\\" + self.name + \\\"!!\\\"\\n print \\\"please recheck your config file: names are case-sensitive!!\\\"\",\n \"def scrub():\\n\\n\\tlocal(\\\"rm -fr dist build\\\")\\n\\tlocal(\\\"find . -name \\\\\\\"*.pyc\\\\\\\" -exec rm '{}' ';'\\\")\",\n \"def test_module():\\n print(\\\"-------Testing Module-------\\\")\\n \\n test_string = [\\\"//Remove Me\\\", \\\"(Me Too)\\\", \\\"But leave me\\\", \\n \\\"and most of me //just this bit\\\",\\n \\\"Don't forget //this aswell\\\", \\\" \\\"]\\n print(\\\"--Original--\\\")\\n print(test_string)\\n print(\\\"--Cleaned--\\\")\\n print(remove_psuedoinstructions(clean_file(test_string)))\",\n \"def clean_modules(gallery_conf, fname):\\n for reset_module in gallery_conf['reset_modules']:\\n reset_module(gallery_conf, fname)\",\n \"def remove_unused_args(args, thnn_args):\\n def clean_name(name):\\n name = name[:name.index('[')] if '[' in name else name\\n if name.endswith('_'):\\n name = name[:-1]\\n return name\\n uses = set([clean_name(arg['name']) for arg in thnn_args])\\n uses.add('output_mask')\\n args = [arg for arg in args if arg['name'] in uses]\\n for arg in args:\\n if 'default' in arg:\\n del arg['default']\\n return args\",\n \"def pop_non_relevant_module_fields(data: Dict):\\n keys_to_keep = [\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"content_type\\\",\\n \\\"published_at\\\",\\n \\\"references\\\",\\n \\\"architectures\\\",\\n \\\"authors\\\",\\n \\\"rank\\\",\\n \\\"reliability\\\",\\n ]\\n for key in list(data):\\n if key not in keys_to_keep:\\n data.pop(key)\",\n \"def exclude_params(params_expanded, exclude):\\n if exclude is None:\\n exclude = {}\\n cut_params(params=params_expanded, exclude=exclude)\\n print_grid_params(params_expanded)\\n\\n params_full = grid_tools.enumerate_params(params_expanded)\\n return params_full\",\n \"def delete_params(self, base_key, *params):\\n self.params[\\n base_key + '.no_' + '|'.join(params)] = delete_params(\\n self.params[base_key], *params)\",\n \"def removeModulesNotOnAPathExcluding( process, keepList=() ):\\n allMods=set((x for x in process.producers_().iterkeys()))\\n allMods.update((x for x in process.filters_().iterkeys()))\\n allMods.update((x for x in process.analyzers_().iterkeys()))\\n allMods.update((x for x in process.outputModules_().iterkeys()))\\n \\n modulesOnPaths = set()\\n for p in process.paths_():\\n modulesOnPaths.update( (x for x in getattr(process,p).moduleNames())) \\n for p in process.endpaths_():\\n modulesOnPaths.update( (x for x in getattr(process,p).moduleNames()))\\n\\n notOnPaths = allMods.difference(modulesOnPaths)\\n \\n keepModuleNames = set( (x.label_() for x in keepList) )\\n \\n getRidOf = notOnPaths.difference(keepModuleNames)\\n \\n for n in getRidOf:\\n delattr(process,n)\",\n \"def clean(pattern=default, *, module=None):\\n pattern = default.unwrap(pattern, current_config[\\\"clean\\\"])\\n\\n if pattern is False:\\n return\\n\\n if module is None:\\n import __main__ as module\\n\\n items = vars(module)\\n to_delete = [key for key in items if fnmatch.fnmatchcase(key, pattern)]\\n\\n for key in to_delete:\\n del items[key]\",\n \"def _remove_node_address_from_params(cls, params: dict):\\n if ConstantKeys.NODE_ADDRESS in params:\\n del params[ConstantKeys.NODE_ADDRESS]\",\n \"def test_ignore_empty_files(self):\\n\\n self.checker.config.file_header_ignore_empty_files = True\\n node_mock = MagicMock()\\n node_mock.stream.return_value.__enter__.return_value.read.return_value.decode.return_value = ''\\n with self.assertNoMessages():\\n self.checker.process_module(node_mock)\",\n \"def remove_variables(self):\\n self.variables = []\",\n \"def delete_params(self, base_key, *params):\\n self.params[base_key + \\\".no_\\\" + \\\"|\\\".join(params)] = self.delete_params_s(self.params[base_key], params)\",\n \"def unusedFromKDOTDataPreparation():\",\n \"def _finalize_parameters(fsdp_module: XlaFullyShardedDataParallel) -> None:\\n for p in fsdp_module.full_params:\\n if not p.requires_grad:\\n continue\\n if hasattr(p, \\\"_shard_bwd_hook\\\"):\\n assert len(p._shard_bwd_hook) == 2, len(p._shard_bwd_hook)\\n p._shard_bwd_hook[1].remove()\\n delattr(p, \\\"_shard_bwd_hook\\\")\",\n \"def clean(params):\\n if tf.gfile.IsDirectory(params.ckpt_path):\\n tf.gfile.DeleteRecursively(params.ckpt_path)\\n\\n if tf.gfile.IsDirectory(params.summary_path):\\n tf.gfile.DeleteRecursively(params.summary_path)\\n\\n if tf.gfile.IsDirectory(params.result_path):\\n tf.gfile.DeleteRecursively(params.result_path)\\n\\n if tf.gfile.IsDirectory(params.log_path):\\n tf.gfile.DeleteRecursively(params.log_path)\\n\\n if tf.gfile.IsDirectory(\\\"__pycache__\\\"):\\n tf.gfile.DeleteRecursively(\\\"__pycache__\\\")\\n\\n if tf.gfile.IsDirectory(params.config_path):\\n tf.gfile.DeleteRecursively(params.config_path)\",\n \"def remBuildOnlyDependency(self, mods):\\n for mod in mods:\\n try:\\n self.reqmodules_buildonly.remove(mod)\\n except:\\n print \\\"\\\\n*** WARNING: \\\" + mod + \\\" not found in the list of build only dependencies from \\\" + self.name + \\\"!!\\\"\\n print \\\"please recheck your config file: names are case-sensitive!!\\\"\",\n \"def removeFilenameFilter(call, args=(), kwargs={}, nodeClass='*'):\",\n \"def cleanTask(self, blTaskName):\\n return self._genericCommand('clean', blTaskName, 'all')\",\n \"def clear_empty_values(args):\\n result = {}\\n for param in args:\\n if args[param] is not None:\\n result[param] = args[param]\\n return result\",\n \"def remove_var(b, exclude):\\n return dict((k, v) for k, v in b.items() if param_name(k) not in exclude)\",\n \"def exclude_opts(cls) -> Tuple[str, ...]:\\n return \\\"required\\\", \\\"print_config\\\", \\\"config\\\", \\\"ngpu\\\"\",\n \"def emptyModule():\\n return(Module(\\\"\\\",[]))\",\n \"def _clear(self):\\n self._argparse_group = None\",\n \"def remove_info(config):\\n clean_config = copy.deepcopy(config)\\n\\n if 'parameters' in clean_config:\\n params = clean_config['parameters']\\n for name in params:\\n val = params[name]\\n if isinstance(val, dict):\\n # This should not generally happen since we deal with it in update_param_info, but just in case\\n if 'val' not in val:\\n raise ConfigurationError(\\n \\\"Parameter info remove error.\\\"\\n \\\" Parameter that is defined by a dictionary must contain 'val' field that\\\"\\n \\\" defines its default value. Found this definition: %s=%s\\\" % (name, val)\\n )\\n params[name] = val['val']\\n\\n return clean_config\",\n \"def strip_variables(*args):\\n return [arg.strip(\\\" '\\\\\\\"\\\") if arg is not None else arg for arg in args]\",\n \"def remove_im_params(model, im):\\n for param in model.parameters:\\n # If the node doesn't exist e.g., it may have already been removed),\\n # skip over the parameter without error\\n try:\\n im.remove_node(param.name)\\n except:\\n pass\",\n \"def _remove_build(dep):\\n m = re.match(r\\\"([^=]+=[^=]+)=([^=]+)$\\\", dep, re.IGNORECASE)\\n return m.group(1) if m else dep\",\n \"def _param_computation_memory_cleanup(self, param, keep_backpack_buffers):\\n if keep_backpack_buffers:\\n savefields = []\\n else:\\n savefields = {\\n self._savefield_directions,\\n self._savefield_first,\\n self._savefield_second,\\n }\\n\\n if not self._compute_gammas:\\n savefields.remove(self._savefield_first)\\n\\n for savefield in savefields:\\n delattr(param, savefield)\\n\\n if self._verbose:\\n print(f\\\"Param {id(param)}: Delete '{savefield}'\\\")\",\n \"def test_find_unused_parameters_when_unused_parameters_empty(self):\\n\\n class FindUnusedParamModule(nn.Module):\\n def __init__(self):\\n super().__init__()\\n self.t0 = Task()\\n self.t1 = Task()\\n\\n def task_parameters(self):\\n return (self.t0.p, self.t1.p)\\n\\n def forward(self, x, rank):\\n return self.t1(self.t0(x)) if rank == 0 else self.t1(x)\\n\\n def run_and_verify_grad(model):\\n # Run forward\\n output = model(8, self.rank)\\n\\n # The grads of all parameters should be None at this point.\\n [self.assertIsNone(t_p.grad) for t_p in model.module.task_parameters()]\\n\\n # Run backward\\n output.mean().backward()\\n\\n # Now locally unused parameter should have grad updated on all ranks.\\n [self.assertIsNotNone(t_p.grad) for t_p in model.module.task_parameters()]\\n\\n process_group = self._get_process_group()\\n\\n # Test on CPU\\n cpu_model = DistributedDataParallel(\\n FindUnusedParamModule().cpu(),\\n process_group=process_group,\\n find_unused_parameters=True,\\n )\\n run_and_verify_grad(cpu_model)\\n\\n # Test on GPU\\n device_id = gpus_for_rank(self.world_size)[self.rank][0]\\n gpu_model = DistributedDataParallel(\\n FindUnusedParamModule().to(device_id),\\n device_ids=[device_id],\\n process_group=process_group,\\n find_unused_parameters=True,\\n )\\n run_and_verify_grad(gpu_model)\",\n \"def removeFilenameValidate(call, args=(), kwargs={}, nodeClass='Write'):\",\n \"def remove_empty_sources(self):\\n for source in [\\\"dxf\\\", \\\"edilizia\\\", \\\"easyroom\\\", \\\"merged\\\"]:\\n if source in self and not self[source]:\\n del self[source]\",\n \"def removeParameter(cTag, name): #@NoSelf\",\n \"def remove(self, task):\\n pass\",\n \"def clean_data(self, path, exclude_msgtypes=None):\",\n \"def remove_task(self, task: Task):\\n self.tasks.remove(task)\\n self.assignments = [\\n assignment for assignment in self.assignments if assignment.task != task\\n ]\\n self.solution = [\\n assignment for assignment in self.solution if assignment.task != task\\n ]\\n\\n new_combinations = []\\n for combination in self.combinations:\\n try:\\n combination.tasks.remove(task)\\n except ValueError:\\n pass\\n else:\\n if not combination.tasks: # became empty\\n continue\\n new_combinations.append(combination)\\n self.combinations = new_combinations\",\n \"def _RemoveUnusedFlags( flags, filename ):\\n\\n new_flags = []\\n\\n # When flags come from the compile_commands.json file, the first flag is\\n # usually the path to the compiler that should be invoked. We want to strip\\n # that.\\n if not flags[ 0 ].startswith( '-' ):\\n flags = flags[ 1: ]\\n\\n skip = False\\n for flag in flags:\\n if skip:\\n skip = False\\n continue\\n\\n if flag == '-c':\\n continue\\n\\n if flag == '-o':\\n skip = True;\\n continue\\n\\n if flag == filename or os.path.realpath( flag ) == filename:\\n continue\\n\\n new_flags.append( flag )\\n return new_flags\",\n \"def param_remove(params, arg):\\n d = params.copy()\\n if arg in d:\\n del d[arg]\\n return d.urlencode()\",\n \"def cleanup(modpath):\\n yield\\n\\n # Remove dummy modules from sys.modules\\n pathlist = [p for p in sys.modules\\n if p != modpath and p.startswith(modpath)]\\n\\n for p in pathlist:\\n del sys.modules[p]\\n\\n if modpath in sys.modules:\\n del sys.modules[modpath]\\n\\n # Remove TaskImporter\\n index = [i for i, obj in enumerate(sys.meta_path)\\n if isinstance(obj, TaskImporter)]\\n\\n for i in reversed(index):\\n assert isinstance(sys.meta_path[i], TaskImporter)\\n sys.meta_path.pop(i)\",\n \"def _remove_swarm_keys(self):\\n for key in SWARM_PROPERTIES:\\n self.spec.pop(key, None)\",\n \"def test_remove_workflow_definitions_in_job(self):\\n pass\",\n \"def strip_file_top_level(self, file_node: MypyFile) -> None:\\n self.names = file_node.names\\n self.file_node = file_node\\n self.recurse_into_functions = False\\n file_node.accept(self)\",\n \"def _clear_tasks(self):\\n listOfTasks = self.model.find(xmlns + 'ListOfTasks') \\n assert listOfTasks != None\\n \\n for task in listOfTasks:\\n task.attrib['scheduled'] = 'false'\",\n \"def postCompile(root, excluded):\\n # If you need to delete specific -parsed but not wanted- files, do it here\\n pass\",\n \"def _clean_up_optimization():\\n for (root, dirs, files) in walk(TEMP_MODULES_DIR_PATH, topdown=False):\\n for file in files:\\n if file.startswith(\\\"__temp_\\\"):\\n remove(f\\\"{root}/{file}\\\")\\n try:\\n rmdir(root)\\n except OSError:\\n G.warn_(f\\\"Unidentified file found in temporary directory: {root}\\\")\",\n \"def clear_includepatterns(self):\\n self._excludepatterns = []\",\n \"def purge_tasks(self, filter_not_match):\\n self._rf_worker.purge_tasks(filter_not_match)\",\n \"def test_check_exclude_none(self):\\n\\n self.assertTrue(PostfixExclude([]).check(self.file_gitignore))\\n self.assertTrue(PostfixExclude([]).check(self.file_py))\\n self.assertTrue(PostfixExclude([]).check(self.file_authors))\\n self.assertTrue(PostfixExclude([]).check(self.file__init__))\\n self.assertTrue(PostfixExclude([]).check(self.file_bin))\",\n \"def remove_abs_vars(self):\\n self.m.remove(self.bp_abs)\\n self.m.remove(self.bn_abs)\\n self.m.remove(self.gp_abs)\\n self.m.remove(self.gn_abs)\\n self.m.remove(self.beta_p)\\n self.m.remove(self.beta_n)\\n self.m.remove(self.gamma_p)\\n self.m.remove(self.gamma_n)\",\n \"def delModule(name):\",\n \"def cleanup(options=None):\\n if options is None:\\n for f in glob.glob(\\\"*.grmpy.*\\\"):\\n os.remove(f)\\n elif options == 'regression':\\n for f in glob.glob(\\\"*.grmpy.*\\\"):\\n if f.startswith('regression'):\\n pass\\n else:\\n os.remove(f)\",\n \"def dev_clean():\\n clean_files(\\\"csv\\\", True)\\n clean_files(\\\"jsontxt\\\", True)\",\n \"def teardown_module(module):\\n for datafile in datafiles:\\n os.remove(datafile)\",\n \"def clean_condor(c):\\n for fname in glob.glob(os.path.join(ROOT_DIR, '*.dag')):\\n _delete_pattern(fname + '.*')\\n for fname in glob.glob(os.path.join(ROOT_DIR, '*.sub')):\\n temps = []\\n with open(fname, 'r') as f:\\n for line in f.readlines():\\n for w in ('log', 'error', 'output'):\\n if line.startswith(w):\\n temp = line.split('=')[-1].strip()\\n if os.path.isfile(temp):\\n temps.append(temp)\\n if temps:\\n print(f'Found submit script {fname}:')\\n for temp in temps:\\n _delete_file(temp, indent=' ')\\n _delete_file(f'{ROOT_DIR}/docker_stderror')\\n _delete_file(f'{ROOT_DIR}/parsetab.py')\",\n \"def remove_all_fields(self):\\n self.fields = None\",\n \"def teardown_module(module):\\n import os\\n import time\\n\\n # temp file will be removed soon\\n time.sleep(1.0)\\n\\n for p in [data_pk, data_pk_gz, data_js, data_js_gz]:\\n try:\\n os.remove(p)\\n except:\\n pass\",\n \"def clear_quantities(shared):\\n\\n del_list = [i for i, fm in enumerate(shared.field_mappings)\\n if fm.extra is not None]\\n for index in reversed(del_list):\\n del shared.field_mappings[index]\\n\\n shared.config.remove_section('extra')\\n shared.config.add_section('extra')\",\n \"def keep_params(self, base_key, *params):\\n self.params[base_key + '.' + '|'.join(params)] = keep_params(\\n self.params[base_key], *params)\",\n \"def clean(self):\\n os.remove(\\\"temp.py\\\") # Delete the file \\\"temp.py\\\", to free up disk space\",\n \"def clear_time_position_specific_params(self):\\n for param_name in self._time_position_params:\\n setattr(self, param_name, None)\",\n \"def zero_module(module):\\n for p in module.parameters():\\n p.detach().zero_()\\n return module\",\n \"def cleanUp(self):\\n import evoware.fileutil as F\\n F.tryRemove(self.f_project, verbose=(self.VERBOSITY>1), tree=1)\",\n \"def discard_config(self):\\n raise NotImplementedError\",\n \"def test_findtasks_none(monkeypatch, modpath):\\n monkeypatch.setattr(loadlimit.importhook, 'lstaskfiles', fake_lstaskfiles)\\n # monkeypatch.setattr(loadlimit.importhook, 'SourceFileLoader',\\n # FakeModuleWithTasks)\\n monkeypatch.setattr(loadlimit.importhook, 'SourceFileLoader',\\n FakeSourceFileLoader)\\n\\n taskfile = 'a_0.py'\\n\\n sys.meta_path.append(TaskImporter(taskfile))\\n taskmod = import_module(modpath)\\n\\n assert hasattr(taskmod, '__tasks__')\\n assert taskmod.__tasks__ == []\",\n \"def clear_specific(self):\\n self.specific_file = None\\n self.specific_parser = None\\n\\n self.specific_box.delete(0, END)\",\n \"def _clean_files(self):\\n if self.delfiles & 1:\\n ProcUtils.remove(self.okm)\\n if self.delfiles & 2:\\n ProcUtils.remove(self.hkm)\\n if self.delfiles & 4:\\n ProcUtils.remove(self.qkm)\\n if self.delfiles & 8:\\n ProcUtils.remove(self.obc)\\n\\n if self.log is False:\\n ProcUtils.remove(self.pcf_file)\\n base = os.path.basename(self.okm)\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogReport', base])))\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogStatus', base])))\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogUser', base])))\",\n \"def keep_params(self, base_key, *params):\\n self.params[base_key + \\\".\\\" + \\\"|\\\".join(params)] = self.keep_params_s(self.params[base_key], params)\",\n \"def clean(_context):\",\n \"def remove_extra(self, entry):\\n\\n config_spec = vim.vm.ConfigSpec()\\n self.logger.info(\\\"Removing extra config {0}\\\".format(entry))\\n opt = vim.option.OptionValue()\\n opt.key = entry\\n opt.value = \\\"\\\"\\n config_spec.extraConfig = [opt]\\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)\",\n \"def remove_useless_file(rootPath):\\n all_file_paddle_list = get_all_paddle_file(rootPath)\\n ut_file_map_new = {}\\n ut_file_map = \\\"%s/build/ut_file_map.json\\\" % rootPath\\n with open(ut_file_map, 'r') as load_f:\\n load_dict = json.load(load_f)\\n for key in load_dict:\\n if key in all_file_paddle_list:\\n ut_file_map_new[key] = load_dict[key]\\n\\n with open(\\\"%s/build/ut_file_map.json\\\" % rootPath, \\\"w\\\") as f:\\n json.dump(ut_file_map_new, f, indent=4)\\n print(\\\"remove_useless_file ut_file_map success!!\\\")\",\n \"def _filter_unused_argument_dash_m(self, arguments):\\n return [argument for argument in arguments if argument != '-m']\",\n \"def delete_many_preprocessor(search_params=None, **kw):\\n pass\",\n \"def _unset_pipeline_cfg(self, field):\",\n \"def test_ignore(self):\\n parser = hhsuite.FastaParser(ignore={\\\"foo\\\"})\\n results = parser.run(self.pipeline)\\n self.assertNotIn(\\\"foo\\\", results[\\\"templates\\\"][1][\\\"sequence_alignments\\\"])\",\n \"def clear_lists(self): \\n self.fp_config_files = []\\n self.txt_files = []\\n self.fr_config_files = []\",\n \"def test_do_not_ignore_empty_files(self):\\n\\n node_mock = MagicMock()\\n node_mock.stream.return_value.__enter__.return_value.read.return_value.decode.return_value = ''\\n with self.assertAddsMessages(pylint.testutils.Message(\\n msg_id='invalid-file-header',\\n line=1,\\n args=self.EXPECTED_HEADER)):\\n self.checker.process_module(node_mock)\",\n \"def reset(self):\\n def remove_auxiliary_dir():\\n egg_info_dir = self.project_name_sc + \\\".egg-info\\\"\\n remove_directories([\\n egg_info_dir,\\n \\\".env\\\",\\n \\\".eggs\\\",\\n \\\".pytest_cache\\\",\\n \\\"build\\\",\\n \\\"dist\\\",\\n \\\".cache\\\",\\n \\\".benchmark\\\",\\n \\\".tox\\\",\\n \\\".vagrant\\\",\\n \\\".tox\\\"])\\n remove_files([\\n \\\".coverage\\\",\\n \\\".doit.db\\\",\\n \\\".doit.bak\\\",\\n \\\".doit.dat\\\",\\n \\\".doit.dir\\\",\\n ])\\n\\n # TODO(lschneider): Remove unnecessary files without command lines.\\n # This code could be run directly from this function. However\\n # the pathlib library is not part of the standard python 2.\\n prefix = \\\"python -c \\\\\\\"import pathlib; \\\"\\n delete_pyfiles = prefix + \\\"import pathlib; [p.unlink() for p in pathlib.Path('.').rglob('*.py[co]')]\\\\\\\"\\\"\\n delete_dirs = prefix + \\\"import pathlib; [p.rmdir() for p in pathlib.Path('.').rglob('__pycache__')]\\\\\\\"\\\"\\n\\n return {\\n \\\"actions\\\": [\\n delete_pyfiles,\\n delete_dirs,\\n remove_auxiliary_dir,\\n ],\\n \\\"verbosity\\\": 2\\n }\",\n \"def remove_part(self, part_name: str, ignore_if_absent: bool = False):\\n super.remove_part(\\n part_name,\\n ignore_if_absent,\\n lambda p: self.build_order.remove(p) if p is not None else None,\\n )\",\n \"def filter_allowed_fields(self):\\n allowed_fields = super().filter_allowed_fields\\n # Remove assignment_id\\n allowed_fields.remove('assignment_id')\\n return allowed_fields\",\n \"def remove_param(self, step_id, name):\\n if step_id in self._params:\\n del self._params[step_id][name]\",\n \"def prepare_cleaning(self, task):\\n node = task.node\\n\\n inband_steps = [step for step\\n in node.driver_internal_info.get('clean_steps', [])\\n if {'interface': step['interface'],\\n 'step': step['step']} not in _OOB_CLEAN_STEPS]\\n\\n if ('agent_cached_clean_steps' not in node.driver_internal_info or\\n inband_steps):\\n return deploy_utils.prepare_inband_cleaning(task,\\n manage_boot=True)\",\n \"def removeAllTasks(self):\\n for taskName in self.runningTasks:\\n taskMgr.remove(taskName)\",\n \"def remove_all(self):\\n self._options.clear()\\n self._programs.clear()\",\n \"def remove_module_files(self, base_dir):\\n for file, pkg in self.files:\\n package_path = os.path.join(base_dir, 'gocat', pkg)\\n # Check if entire package is to be deleted\\n if file == '*':\\n self._unstage_folder(package_path)\\n else:\\n file_path = os.path.join(package_path, file)\\n if os.path.exists(file_path):\\n os.remove(file_path)\",\n \"def remove(self):\\n mod_file = self.layout.filename\\n if os.path.exists(mod_file):\\n try:\\n os.remove(mod_file) # Remove the module file\\n os.removedirs(\\n os.path.dirname(mod_file)\\n ) # Remove all the empty directories from the leaf up\\n except OSError:\\n # removedirs throws OSError on first non-empty directory found\\n pass\",\n \"def _clean_input_dir():\\n for existing_file in os.listdir(join(input_dir, 'fitting')):\\n if existing_file != '.hold':\\n os.remove(join(input_dir, 'fitting', existing_file))\",\n \"def remDependency(self, mods):\\n for mod in mods:\\n try:\\n self.reqmodules.remove(mod)\\n except:\\n print \\\"\\\\n*** WARNING: \\\" + mod + \\\" not found in the list of dependencies from \\\" + self.name + \\\"!!\\\"\\n print \\\"please recheck your config file: names are case-sensitive!!\\\"\",\n \"def _removeSpecs(self):\\n self.specGenerator.removeSpecs()\",\n \"def do_remove(self, loglevel=logging.DEBUG):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tcfg = self.cfg\\n\\t\\t# Now get the run_order keys in order and go.\\n\\t\\tself.log('PHASE: remove', level=loglevel)\\n\\t\\tself.pause_point('\\\\nNow removing any modules that need removing', print_input=False, level=3)\\n\\t\\t# Login at least once to get the exports.\\n\\t\\tfor module_id in self.module_ids():\\n\\t\\t\\tmodule = self.shutit_map[module_id]\\n\\t\\t\\tself.log('considering whether to remove: ' + module_id, level=logging.DEBUG)\\n\\t\\t\\tif cfg[module_id]['shutit.core.module.remove']:\\n\\t\\t\\t\\tself.log('removing: ' + module_id, level=logging.DEBUG)\\n\\t\\t\\t\\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\\n\\t\\t\\t\\tif not module.remove(self):\\n\\t\\t\\t\\t\\tself.log(self.print_modules(), level=logging.DEBUG)\\n\\t\\t\\t\\t\\tself.fail(module_id + ' failed on remove', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif self.build['delivery'] in ('docker','dockerfile'):\\n\\t\\t\\t\\t\\t\\t# Create a directory and files to indicate this has been removed.\\n\\t\\t\\t\\t\\t\\tself.send(' command mkdir -p ' + shutit_global.shutit_global_object.shutit_state_dir_build_db_dir + '/module_record/' + module.module_id + ' && command rm -f ' + shutit_global.shutit_global_object.shutit_state_dir_build_db_dir + '/module_record/' + module.module_id + '/built && command touch ' + shutit_global.shutit_global_object.shutit_state_dir_build_db_dir + '/module_record/' + module.module_id + '/removed', loglevel=loglevel, echo=False)\\n\\t\\t\\t\\t\\t\\t# Remove from \\\"installed\\\" cache\\n\\t\\t\\t\\t\\t\\tif module.module_id in self.get_current_shutit_pexpect_session_environment().modules_installed:\\n\\t\\t\\t\\t\\t\\t\\tself.get_current_shutit_pexpect_session_environment().modules_installed.remove(module.module_id)\\n\\t\\t\\t\\t\\t\\t# Add to \\\"not installed\\\" cache\\n\\t\\t\\t\\t\\t\\tself.get_current_shutit_pexpect_session_environment().modules_not_installed.append(module.module_id)\\n\\t\\t\\t\\tself.logout(echo=False)\",\n \"def CleanEpi(self):\\n for entry in self.info.keys():\\n info = self.info[entry]\\n if info['psdname'] == 'epi':\\n for tag in ('imgfile', 'imgfile_m', 'imgfile_mf', 'imgfile_t'):\\n if info.has_key(tag) and info[tag] is not None and \\\\\\n os.path.exists(info[tag]):\\n print 'Deleting %s*' % (info[tag], info['suffix'])\\n cmd = '/bin/rm %s%s*' % (info[tag], info['suffix'])\\n self.ExecCmd(cmd)\\n if '.BRIK' in info['suffix']:\\n cmd = '/bin/rm %s%s*' % (info[tag], \\\\\\n info['suffix'].replace('.BRIK','.HEAD'))\\n self.ExecCmd(cmd)\",\n \"def get_unpinned_params(opts, params):\\n return params - set([p for p, v in opts.__dict__.items() if p in LIKELIHOOD_PINNABLE_PARAMS and v is not None])\",\n \"def _clear_variables( self ):\\r\\n self.navigation = None\\r\\n self.resPath = None\\r\\n self.resolutions = None\\r\\n self.currentResolution = None\\r\\n self.resolution = None\\r\\n for doc in self.include_doc:\\r\\n try: doc.unlink()\\r\\n except: pass\",\n \"def remove_parameter_check(self, parameter):\\n if isinstance(parameter, list):\\n [self.remove_parameter_check(p) for p in parameter]\\n elif parameter in self.parameter_checks:\\n self.parameter_checks.pop(parameter)\\n else:\\n log.warning(f'No check was configured for parameter {parameter}.')\",\n \"def setup_default_arguments(self):\\n self.add_argument('--clean', action='store_true',\\n help='Cleans all generated files.')\",\n \"def _clean_input_dir():\\n for existing_file in os.listdir(join(input_dir, 'analysis')):\\n if existing_file != '.hold':\\n os.remove(join(input_dir, 'analysis', existing_file))\",\n \"def remove_aliases(self):\\n for k, v in iteritems(self.argspec):\\n if 'aliases' in v:\\n for alias in v['aliases']:\\n if alias in self.params:\\n self.params.pop(alias)\",\n \"def clear(self):\\n self.vars = []\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.57616585","0.5588865","0.5586321","0.5513312","0.5493854","0.5449779","0.54313815","0.5419195","0.53861827","0.53727466","0.53104013","0.53044254","0.52886343","0.5246557","0.52453816","0.52363384","0.52314204","0.51972383","0.51937133","0.5189336","0.51719946","0.515026","0.5130937","0.5100845","0.50725985","0.50688773","0.5061255","0.5048983","0.5045995","0.50457597","0.503691","0.5026429","0.5026191","0.5025328","0.5021827","0.4996826","0.49924648","0.49894032","0.4975507","0.497473","0.49738243","0.49629483","0.49590838","0.49563655","0.49402547","0.4935589","0.49349022","0.49313658","0.49261358","0.492186","0.4916046","0.49136826","0.49129218","0.4909044","0.49074358","0.49054462","0.4904419","0.4904205","0.49008262","0.4892001","0.48801184","0.48689404","0.4868098","0.48661715","0.48566458","0.48557654","0.4848524","0.4834686","0.48281384","0.4824012","0.48206973","0.48134807","0.48120102","0.48106506","0.48095733","0.48086506","0.48064613","0.47991645","0.47965997","0.47943747","0.4793203","0.4791843","0.47897524","0.47884783","0.4783952","0.4783589","0.47746763","0.47745764","0.47714165","0.4771189","0.47709027","0.47549844","0.4750862","0.47467506","0.474336","0.47412854","0.47320384","0.47292346","0.47261652","0.472588"],"string":"[\n \"0.57616585\",\n \"0.5588865\",\n \"0.5586321\",\n \"0.5513312\",\n \"0.5493854\",\n \"0.5449779\",\n \"0.54313815\",\n \"0.5419195\",\n \"0.53861827\",\n \"0.53727466\",\n \"0.53104013\",\n \"0.53044254\",\n \"0.52886343\",\n \"0.5246557\",\n \"0.52453816\",\n \"0.52363384\",\n \"0.52314204\",\n \"0.51972383\",\n \"0.51937133\",\n \"0.5189336\",\n \"0.51719946\",\n \"0.515026\",\n \"0.5130937\",\n \"0.5100845\",\n \"0.50725985\",\n \"0.50688773\",\n \"0.5061255\",\n \"0.5048983\",\n \"0.5045995\",\n \"0.50457597\",\n \"0.503691\",\n \"0.5026429\",\n \"0.5026191\",\n \"0.5025328\",\n \"0.5021827\",\n \"0.4996826\",\n \"0.49924648\",\n \"0.49894032\",\n \"0.4975507\",\n \"0.497473\",\n \"0.49738243\",\n \"0.49629483\",\n \"0.49590838\",\n \"0.49563655\",\n \"0.49402547\",\n \"0.4935589\",\n \"0.49349022\",\n \"0.49313658\",\n \"0.49261358\",\n \"0.492186\",\n \"0.4916046\",\n \"0.49136826\",\n \"0.49129218\",\n \"0.4909044\",\n \"0.49074358\",\n \"0.49054462\",\n \"0.4904419\",\n \"0.4904205\",\n \"0.49008262\",\n \"0.4892001\",\n \"0.48801184\",\n \"0.48689404\",\n \"0.4868098\",\n \"0.48661715\",\n \"0.48566458\",\n \"0.48557654\",\n \"0.4848524\",\n \"0.4834686\",\n \"0.48281384\",\n \"0.4824012\",\n \"0.48206973\",\n \"0.48134807\",\n \"0.48120102\",\n \"0.48106506\",\n \"0.48095733\",\n \"0.48086506\",\n \"0.48064613\",\n \"0.47991645\",\n \"0.47965997\",\n \"0.47943747\",\n \"0.4793203\",\n \"0.4791843\",\n \"0.47897524\",\n \"0.47884783\",\n \"0.4783952\",\n \"0.4783589\",\n \"0.47746763\",\n \"0.47745764\",\n \"0.47714165\",\n \"0.4771189\",\n \"0.47709027\",\n \"0.47549844\",\n \"0.4750862\",\n \"0.47467506\",\n \"0.474336\",\n \"0.47412854\",\n \"0.47320384\",\n \"0.47292346\",\n \"0.47261652\",\n \"0.472588\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5294682"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":93773,"cells":{"query":{"kind":"string","value":"Sets up other parameters for the api call that may not be specified in the modules from tasks file."},"document":{"kind":"string","value":"def api_params(self):\n params = dict(\n (key, self.module.params.get(key))\n for key, value in RECORD_KEYS_MAP.items()\n if key != \"answers\" and self.module.params.get(key) is not None\n )\n return params"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_params():\n global module \n global ora_inst\n global response_loc\n\n module_args=dict(\n ora_inst=dict(type='str', required=True),\n response_loc=dict(type='str', required=True)\n )\n\n module=AnsibleModule(\n argument_spec=module_args\n )\n\n ora_inst = module.params['ora_inst']\n response_loc = module.params['response_loc']","def prepare_api(self):\n return None","def setup_params():\n Script.fullname = os.path.splitext(os.path.abspath(__file__))[0]\n Script.basename = os.path.basename(__file__)\n Script.name = os.path.splitext(Script.basename)[0]\n Script.service = modUtils.check_service(Script.name)","def __init__(self):\n super().__init__()\n self._api_url = API_BASE_URL\n self._api_params = API_BASE_PARAMS.copy()","def test_api_calls_parameters(self):\n quantum_program = self._get_quantum_program()\n\n # Invoke with hub, group and project parameters.\n quantum_program.set_api(QE_TOKEN, QE_URL, QE_HUB, QE_GROUP, QE_PROJECT)\n\n self.log.info(quantum_program.online_backends())\n self.log.info(quantum_program.get_backend_parameters(self.backend))\n self.log.info(quantum_program.get_backend_calibration(self.backend))","def __init__(self, **kwargs):\n esp_name = self.esp_name\n self.api_key = get_anymail_setting(\n \"api_key\",\n esp_name=esp_name,\n kwargs=kwargs,\n allow_bare=True,\n )\n api_url = get_anymail_setting(\n \"api_url\",\n esp_name=esp_name,\n kwargs=kwargs,\n default=\"https://api.brevo.com/v3/\",\n )\n if not api_url.endswith(\"/\"):\n api_url += \"/\"\n super().__init__(api_url, **kwargs)","def init_params(self):\n blah","def __init__(self, requests_api=None):\r\n self._requests = requests_api or requests","def __init__(self, api_use=False):\n self.api_use = api_use","def exec_module(self, **kwargs):\n\n for key in list(self.module_arg_spec.keys()) + ['tags']:\n if hasattr(self, key):\n setattr(self, key, kwargs[key])\n elif kwargs[key] is not None:\n if key == \"location\":\n self.parameters[\"location\"] = kwargs[key]\n elif key == \"plan\":\n self.parameters[\"plan\"] = kwargs[key]\n elif key == \"open_shift_version\":\n self.parameters[\"open_shift_version\"] = kwargs[key]\n elif key == \"public_hostname\":\n self.parameters[\"public_hostname\"] = kwargs[key]\n elif key == \"fqdn\":\n self.parameters[\"fqdn\"] = kwargs[key]\n elif key == \"network_profile\":\n self.parameters[\"network_profile\"] = kwargs[key]\n elif key == \"router_profiles\":\n self.parameters[\"router_profiles\"] = kwargs[key]\n elif key == \"master_pool_profile\":\n ev = kwargs[key]\n if 'vm_size' in ev:\n if ev['vm_size'] == 'standard_d2s_v3':\n ev['vm_size'] = 'Standard_D2s_v3'\n elif ev['vm_size'] == 'standard_d4s_v3':\n ev['vm_size'] = 'Standard_D4s_v3'\n if 'os_type' in ev:\n if ev['os_type'] == 'linux':\n ev['os_type'] = 'Linux'\n elif ev['os_type'] == 'windows':\n ev['os_type'] = 'Windows'\n self.parameters[\"master_pool_profile\"] = ev\n elif key == \"agent_pool_profiles\":\n ev = kwargs[key]\n if 'vm_size' in ev:\n if ev['vm_size'] == 'standard_d2s_v3':\n ev['vm_size'] = 'Standard_D2s_v3'\n elif ev['vm_size'] == 'standard_d4s_v3':\n ev['vm_size'] = 'Standard_D4s_v3'\n if 'os_type' in ev:\n if ev['os_type'] == 'linux':\n ev['os_type'] = 'Linux'\n elif ev['os_type'] == 'windows':\n ev['os_type'] = 'Windows'\n self.parameters[\"agent_pool_profiles\"] = ev\n elif key == \"auth_profile\":\n self.parameters[\"auth_profile\"] = kwargs[key]\n\n old_response = None\n response = None\n\n self.mgmt_client = self.get_mgmt_svc_client(ContainerServiceClient,\n base_url=self._cloud_environment.endpoints.resource_manager)\n\n resource_group = self.get_resource_group(self.resource_group)\n\n if \"location\" not in self.parameters:\n self.parameters[\"location\"] = resource_group.location\n\n old_response = self.get_openshiftmanagedcluster()\n\n if not old_response:\n self.log(\"Open Shift Managed Cluster instance doesn't exist\")\n if self.state == 'absent':\n self.log(\"Old instance didn't exist\")\n else:\n self.to_do = Actions.Create\n else:\n self.log(\"Open Shift Managed Cluster instance already exists\")\n if self.state == 'absent':\n self.to_do = Actions.Delete\n elif self.state == 'present':\n self.log(\"Need to check if Open Shift Managed Cluster instance has to be deleted or may be updated\")\n self.to_do = Actions.Update\n\n if (self.to_do == Actions.Create) or (self.to_do == Actions.Update):\n self.log(\"Need to Create / Update the Open Shift Managed Cluster instance\")\n\n if self.check_mode:\n self.results['changed'] = True\n return self.results\n\n response = self.create_update_openshiftmanagedcluster()\n\n if not old_response:\n self.results['changed'] = True\n else:\n self.results['changed'] = old_response.__ne__(response)\n self.log(\"Creation / Update done\")\n elif self.to_do == Actions.Delete:\n self.log(\"Open Shift Managed Cluster instance deleted\")\n self.results['changed'] = True\n\n if self.check_mode:\n return self.results\n\n self.delete_openshiftmanagedcluster()\n # make sure instance is actually deleted, for some Azure resources, instance is hanging around\n # for some time after deletion -- this should be really fixed in Azure.\n while self.get_openshiftmanagedcluster():\n time.sleep(20)\n else:\n self.log(\"Open Shift Managed Cluster instance unchanged\")\n self.results['changed'] = False\n response = old_response\n\n if self.state == 'present':\n self.results.update(self.format_item(response))\n return self.results","def set_params(self):\r\n pass","def setup(self):\r\n \r\n if self.requestedAction == admin.ACTION_EDIT or self.requestedAction == admin.ACTION_CREATE:\r\n \r\n # Set the required parameters\r\n for arg in RadiusAuthRestHandler.REQUIRED_PARAMS:\r\n self.supportedArgs.addReqArg(arg)\r\n \r\n # Set up the valid parameters\r\n for arg in RadiusAuthRestHandler.VALID_PARAMS:\r\n if arg not in RadiusAuthRestHandler.REQUIRED_PARAMS:\r\n self.supportedArgs.addOptArg(arg)","def __init__(self, *args, **kwargs):\n super(CBCloudAPI, self).__init__(*args, **kwargs)\n self._thread_pool_count = kwargs.pop('thread_pool_count', 1)\n self._lr_scheduler = None\n self._async_executor = None\n\n if not self.credentials.org_key:\n raise CredentialError(\"No organization key specified\")","def setUp(self):\n global access_token\n global accountID\n global account_cur\n global api\n # self.maxDiff = None\n try:\n accountID, account_cur, access_token = unittestsetup.auth()\n setattr(sys.modules[\"oandapyV20.oandapyV20\"],\n \"TRADING_ENVIRONMENTS\",\n {\"practice\": {\n \"stream\": \"https://test.com\",\n \"api\": \"https://test.com\",\n }})\n api = API(environment=environment,\n access_token=access_token,\n headers={\"Content-Type\": \"application/json\"})\n api.api_url = 'https://test.com'\n except Exception as e:\n print(\"%s\" % e)\n exit(0)","def Params(cls):\n p = super().Params()\n p.Define('train_task', None, 'Underlying task')\n p.Define('decode_task', None, 'Underlying task')\n p.Define('train_dataset_name', None, '')\n p.Define('decode_dataset_name', None, '')\n p.Define('train_steps_per_loop', 0, '')\n p.Define('decode_steps_per_loop', 0, '')\n return p","def __init__(__self__, *,\n api_groups: Optional[Sequence[str]] = None,\n api_versions: Optional[Sequence[str]] = None,\n operations: Optional[Sequence[str]] = None,\n resources: Optional[Sequence[str]] = None,\n scope: Optional[str] = None):\n if api_groups is not None:\n pulumi.set(__self__, \"api_groups\", api_groups)\n if api_versions is not None:\n pulumi.set(__self__, \"api_versions\", api_versions)\n if operations is not None:\n pulumi.set(__self__, \"operations\", operations)\n if resources is not None:\n pulumi.set(__self__, \"resources\", resources)\n if scope is not None:\n pulumi.set(__self__, \"scope\", scope)","def __init__(__self__, *,\n api_groups: Optional[Sequence[str]] = None,\n api_versions: Optional[Sequence[str]] = None,\n operations: Optional[Sequence[str]] = None,\n resources: Optional[Sequence[str]] = None,\n scope: Optional[str] = None):\n if api_groups is not None:\n pulumi.set(__self__, \"api_groups\", api_groups)\n if api_versions is not None:\n pulumi.set(__self__, \"api_versions\", api_versions)\n if operations is not None:\n pulumi.set(__self__, \"operations\", operations)\n if resources is not None:\n pulumi.set(__self__, \"resources\", resources)\n if scope is not None:\n pulumi.set(__self__, \"scope\", scope)","def __init__(self, apiToken=None):\r\n self.requestHelper = RequestHelper(apiToken)","def __init__(self, api_key=\"3d34a9a9b0e544269a3ddbb97ec89ba7\", api_current=\"https://api.weatherbit.io/v2.0/current\", api_forecast = \"https://api.weatherbit.io/v2.0/forecast/hourly?\"): \n # constants\n self.API_KEY = api_key\n\n # necessary data for request\n self.API_CURRENT = api_current\n self.API_FORECAST = api_forecast","def as_api_parameters(self):\n raise NotImplementedError(\n 'as_api_parameters not implemented on ' + self.__class__)","def setup_task(self, *args, **kwargs):\n pass","def set_params(self, **kwargs):\n ...","def setup(api_token, endpoint_url=None):\n global DEFAULT_API_TOKEN, DEFAULT_ENDPOINT_URL\n DEFAULT_API_TOKEN = api_token\n\n if endpoint_url is not None:\n DEFAULT_ENDPOINT_URL = endpoint_url","def set_params(self, **kwargs) -> NoReturn:\n pass","def __init__(__self__, *,\n api_groups: Optional[Sequence[str]] = None,\n api_versions: Optional[Sequence[str]] = None,\n operations: Optional[Sequence[str]] = None,\n resource_names: Optional[Sequence[str]] = None,\n resources: Optional[Sequence[str]] = None,\n scope: Optional[str] = None):\n if api_groups is not None:\n pulumi.set(__self__, \"api_groups\", api_groups)\n if api_versions is not None:\n pulumi.set(__self__, \"api_versions\", api_versions)\n if operations is not None:\n pulumi.set(__self__, \"operations\", operations)\n if resource_names is not None:\n pulumi.set(__self__, \"resource_names\", resource_names)\n if resources is not None:\n pulumi.set(__self__, \"resources\", resources)\n if scope is not None:\n pulumi.set(__self__, \"scope\", scope)","def __init__(__self__, *,\n api_groups: Optional[Sequence[str]] = None,\n api_versions: Optional[Sequence[str]] = None,\n operations: Optional[Sequence[str]] = None,\n resource_names: Optional[Sequence[str]] = None,\n resources: Optional[Sequence[str]] = None,\n scope: Optional[str] = None):\n if api_groups is not None:\n pulumi.set(__self__, \"api_groups\", api_groups)\n if api_versions is not None:\n pulumi.set(__self__, \"api_versions\", api_versions)\n if operations is not None:\n pulumi.set(__self__, \"operations\", operations)\n if resource_names is not None:\n pulumi.set(__self__, \"resource_names\", resource_names)\n if resources is not None:\n pulumi.set(__self__, \"resources\", resources)\n if scope is not None:\n pulumi.set(__self__, \"scope\", scope)","def define_parameters(self):","def __init__(__self__, *,\n api_id: pulumi.Input[str],\n api_service: pulumi.Input[str],\n location: pulumi.Input[str],\n properties: pulumi.Input['APIMgmtAPISpecPropertiesArgs'],\n resource_group: pulumi.Input[str]):\n pulumi.set(__self__, \"api_id\", api_id)\n pulumi.set(__self__, \"api_service\", api_service)\n pulumi.set(__self__, \"location\", location)\n pulumi.set(__self__, \"properties\", properties)\n pulumi.set(__self__, \"resource_group\", resource_group)","def setup(self, request_params):\n raise NotImplementedError(\n u\"%s: Method not implemented\", self.__class__.__name__)","def init_cloud_api(self, args=None):\n pass","def __init__(self):\n load_dotenv()\n self.api_url_base = os.getenv('API_URL_BASE')\n self.token = os.getenv('API_TOKEN')\n self.title_map = defaultdict(list)","def _setup(self) -> None:\n self._api = get_api(\n self._password,\n self._host,\n self._username,\n self._port,\n self._ssl,\n )\n\n self._info = self._api.get_info()\n self.device_name = self._info.get(\"DeviceName\", DEFAULT_NAME)\n self.model = self._info.get(\"ModelName\")\n self.firmware_version = self._info.get(\"Firmwareversion\")\n\n for model in MODELS_V2:\n if self.model.startswith(model):\n self._method_version = 2","def _setup_requests(self, url, user=None, key=None, rateLimit=3600, api_suffix=''):\n self.url = url\n self.rateLimit = rateLimit\n self.api_suffix = ''\n self.request_url = ''\n self.status = False\n self._auth = requests.auth.HTTPBasicAuth(user, key) if user and key else None","def __init__(self, apikey, providerkey = None):\n self.apikey = apikey\n \n # Set User-Agent\n self.headers = {'User-Agent': \"Prowlpy/%s\" % str(__version__),\n 'Content-type': \"application/x-www-form-urlencoded\"}\n\n # Aliasing\n self.add = self.post","def set_parameters(api_name='',\r\n targeted_flag='true',\r\n tv_flag='false',\r\n hinge_flag='true',\r\n cos_flag='false',\r\n interpolation='bilinear',\r\n model_type='large',\r\n loss_type='triplet',\r\n dataset_type='vgg',\r\n target_model='large',\r\n target_loss='center',\r\n target_dataset='VGG',\r\n attack='CW',\r\n norm='2',\r\n epsilon=0.1,\r\n iterations=20,\r\n binary_steps=5,\r\n learning_rate=0.01,\r\n epsilon_steps=0.01,\r\n init_const=0.3,\r\n mean_loss='embeddingmean',\r\n batch_size=-1,\r\n margin=15.0,\r\n amplification=6.0,\r\n granularity='normal',\r\n whitebox_target=False,\r\n pair_flag='false'):\r\n \r\n params = {}\r\n params['model_type'] = model_type\r\n params['loss_type'] = loss_type\r\n params['dataset_type'] = dataset_type\r\n params['target_model'] = target_model\r\n params['target_loss'] = target_loss\r\n params['target_dataset'] = target_dataset\r\n params['attack'] = attack\r\n params['norm'] = norm\r\n params['epsilon'] = epsilon\r\n params['iterations'] = iterations\r\n params['binary_steps'] = binary_steps\r\n params['learning_rate'] = learning_rate\r\n params['epsilon_steps'] = epsilon_steps\r\n params['init_const'] = init_const\r\n params['mean_loss'] = mean_loss\r\n params['batch_size'] = batch_size\r\n params['test_dir'] = TEST_DIR\r\n params['full_dir'] = FULL_DIR\r\n params['whitebox_target'] = whitebox_target\r\n params['targeted_flag'] = string_to_bool(targeted_flag)\r\n params['tv_flag'] = string_to_bool(tv_flag)\r\n params['hinge_flag'] = string_to_bool(hinge_flag)\r\n params['cos_flag'] = string_to_bool(cos_flag)\r\n params['pair_flag'] = string_to_bool(pair_flag)\r\n params['api_name'] = api_name\r\n\r\n if model_type == 'small' and loss_type == 'center':\r\n params['pixel_max'] = 1.0\r\n params['pixel_min'] = -1.0\r\n else:\r\n params['pixel_max'] = 1.0\r\n params['pixel_min'] = 0.0\r\n\r\n if dataset_type == 'vggsmall' and not whitebox_target:\r\n params['align_dir'] = VGG_ALIGN_160_DIR\r\n params['test_dir'] = VGG_TEST_DIR\r\n elif model_type == 'large' or dataset_type == 'casia':\r\n params['align_dir'] = ALIGN_160_DIR\r\n elif model_type == 'small':\r\n params['align_dir'] = ALIGN_96_DIR\r\n else:\r\n ValueError('ValueError: Argument must be either \"small\" or \"large\".')\r\n \r\n if interpolation == 'nearest':\r\n params['interpolation'] = cv2.INTER_NEAREST\r\n elif interpolation == 'bilinear':\r\n params['interpolation'] = cv2.INTER_LINEAR\r\n elif interpolation == 'bicubic':\r\n params['interpolation'] = cv2.INTER_CUBIC\r\n elif interpolation == 'lanczos':\r\n params['interpolation'] = cv2.INTER_LANCZOS4\r\n elif interpolation == 'super':\r\n ValueError('ValueError: Super interpolation not yet implemented.')\r\n else:\r\n raise ValueError('ValueError: Argument must be of the following, [nearest, bilinear, bicubic, lanczos, super].')\r\n\r\n if granularity == 'fine':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 20.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\r\n elif granularity == 'normal':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 10.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.5)\r\n elif granularity == 'coarse':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 5.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 1.0)\r\n elif granularity == 'coarser':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\r\n elif granularity == 'coarsest':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 1.0)\r\n elif granularity == 'single':\r\n params['margin_list'] = np.array([margin])\r\n params['amp_list'] = np.array([amplification])\r\n elif granularity == 'fine-tuned':\r\n params['margin_list'] = np.arange(10.0, margin, 1.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\r\n elif granularity == 'coarse-single':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\r\n params['amp_list'] = np.array([1.0])\r\n elif granularity == 'api-eval':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.8)\r\n else:\r\n raise ValueError('ValueError: Argument must be of the following, [fine, normal, coarse, coarser, single].')\r\n\r\n if params['hinge_flag']:\r\n params['attack_loss'] = 'hinge'\r\n else:\r\n params['attack_loss'] = 'target'\r\n if not params['targeted_flag']:\r\n params['attack_loss'] = 'target'\r\n if norm == 'inf':\r\n norm_name = 'i'\r\n else:\r\n norm_name = '2'\r\n if params['tv_flag']:\r\n tv_name = '_tv'\r\n else:\r\n tv_name = ''\r\n if params['cos_flag']:\r\n cos_name = '_cos'\r\n else:\r\n cos_name = ''\r\n\r\n params['model_name'] = '{}_{}'.format(model_type, loss_type)\r\n if dataset_type == 'casia' or dataset_type == 'vggsmall':\r\n params['model_name'] = dataset_type\r\n params['target_model_name'] = '{}_{}_{}'.format(target_model, target_loss, target_dataset)\r\n params['attack_name'] = '{}_l{}{}{}'.format(attack.lower(), norm_name, tv_name, cos_name)\r\n params['directory_path'] = os.path.join(ROOT,\r\n OUT_DIR,\r\n params['attack_name'],\r\n params['model_name'],\r\n '{}_loss/full'.format(params['attack_loss']))\r\n params['directory_path_crop'] = os.path.join(ROOT,\r\n OUT_DIR,\r\n params['attack_name'],\r\n params['model_name'],\r\n '{}_loss/crop'.format(params['attack_loss']))\r\n params['directory_path_npz'] = os.path.join(ROOT,\r\n OUT_DIR,\r\n params['attack_name'],\r\n params['model_name'],\r\n '{}_loss/npz'.format(params['attack_loss']))\r\n params['api_path'] = os.path.join(ROOT,\r\n API_DIR,\r\n params['attack_name'],\r\n params['model_name'],\r\n '{}_loss/npz'.format(params['attack_loss']))\r\n if params['mean_loss'] == 'embedding':\r\n params['directory_path'] += '_mean'\r\n params['directory_path_crop'] += '_mean'\r\n params['directory_path_npz'] += '_mean'\r\n params['api_path'] += '_mean'\r\n\r\n return params","def manage_params(args):\n # Socrata API\n with open(\"secret/builtby-socrata.yaml\", 'r') as f:\n try:\n socrata_api_credentials = yaml.load(f)\n except yaml.YAMLError as exc:\n print(exc)\n\n socrata_app_token = socrata_api_credentials['app_token']\n\n # base params\n params = {\n '$$app_token': socrata_app_token\n }\n # remove null attributes\n args = {k: v for k, v in args.items() if v is not None}\n # add args to params\n params.update(args) # inplace\n\n return params","def _update_params(self):\n _load = not self.san_interface.runmode\n params={}\n if ('iosched' in self._updatedattr or _load) and self.iosched<>IoSchedType.default:\n params['iosched']=str(self.iosched)\n if ('readahead' in self._updatedattr or _load) and self.readahead :\n params['readahead']=self.readahead\n if params:\n for pt in self.paths():\n pt.provider.set_dev_params(pt,params)","def __init__(**params):","def _update_params(self):\n pass","def _setup_api_properties(self):\n self.implicit_api_logical_id = GeneratedLogicalId.implicit_http_api()\n self.implicit_api_condition = \"ServerlessHttpApiCondition\"\n self.api_event_type = \"HttpApi\"\n self.api_type = SamResourceType.HttpApi.value\n self.api_id_property = \"ApiId\"\n self.editor = OpenApiEditor","def __init__(self, base_url, client_id, client_secret, client_scope, api_json = None):\n # type: (str, str, str, str, str) -> None\n\n self.base_url = base_url\n self.client_id = client_id\n self.client_secret = client_secret\n self.client_scope = client_scope\n\n # If the user doesn't pass an alternate API file use the included one\n if not api_json:\n api_json = pkg_resources.resource_filename(__name__, 'apis.json')\n\n with open(api_json, encoding='utf-8') as api_file:\n apis = json.loads(api_file.read())\n\n if client_scope in apis: \n api = apis.get(client_scope)\n self.token_url = api.get('token_url')\n self.api_call = sleep_and_retry(limits(calls=api.get('limits_calls'), period=api.get('limits_period'))(self._api_call))\n self.access_token = self.get_access_token(self.token_url)\n else: \n raise Exception(f\"Scope {client_scope} not in known API dict\")","def task_init(self, param1):\n raise NotImplementedError","def build_task_args(self, test_name):\n task_args = {'service_list': [test_name]}\n task_args['image_name'] = str(self.image.name)\n task_args['flavor_name'] = str(self.flavor.name)\n task_args['flavor_alt_name'] = str(self.flavor_alt.name)\n task_args['glance_image_location'] = str(self.filename)\n task_args['glance_image_format'] = str(self.image_format)\n task_args['tmpl_dir'] = str(self.template_dir)\n task_args['sup_dir'] = str(self.support_dir)\n task_args['users_amount'] = self.users_amount\n task_args['tenants_amount'] = self.tenants_amount\n task_args['use_existing_users'] = False\n task_args['iterations'] = self.iterations_amount\n task_args['concurrency'] = self.concurrency\n task_args['smoke'] = self.smoke\n task_args['volume_version'] = self.volume_version\n task_args['volume_service_type'] = self.volume_service_type\n task_args['block_migration'] = env.get(\"BLOCK_MIGRATION\").lower()\n task_args['username'] = self.username\n\n if self.ext_net:\n task_args['floating_network'] = str(self.ext_net.name)\n else:\n task_args['floating_network'] = ''\n\n if self.network:\n task_args['netid'] = str(self.network.id)\n else:\n LOGGER.warning(\n 'No tenant network created. '\n 'Trying EXTERNAL_NETWORK as a fallback')\n if env.get(\"EXTERNAL_NETWORK\"):\n network = self.cloud.get_network(env.get(\"EXTERNAL_NETWORK\"))\n task_args['netid'] = str(network.id) if network else ''\n else:\n task_args['netid'] = ''\n\n return task_args","def __init__(self, rc: RequestsCommon, package_name: str, app_configs: dict) -> None:\n self.rc = rc\n self.package_name = package_name\n\n # required configs\n self.api_token = app_configs.get(\"api_token\")\n self.endpoint_url = app_configs.get(\"endpoint_url\")\n self.api_version = app_configs.get(\"api_version\")\n self.organization_name = app_configs.get(\"organization_name\")\n self.verify = _get_verify_ssl(app_configs)\n self.polling_filters = eval_mapping(app_configs.get('polling_filters', ''), wrapper='[{}]')\n\n self.header = self._make_header(self.api_token)","def _apply_params(self):\n config = self.get_startup_config()\n # Pass true to _set_params so we know these are startup values\n self._set_params(config, True)","def update_params(self):\n pass","def base_params(self):\n return {\"key\": self.api_key, \"contentCheckOk\": True, \"racyCheckOk\": True}","def __init__(self):\n self.api = Api(consumer_key=credentials[\"consumer_key\"],\n consumer_secret=credentials[\"consumer_secret\"],\n access_token_key=credentials[\"access_token_key\"],\n access_token_secret=credentials[\"access_token_secret\"])","def _init_vars_():\n\n global ADMIN_USERNAME\n ADMIN_USERNAME = _fetch_ssm_parameter(\n '/app/MDL/{}/{}/LDAP/User/HerdAdminUsername'\n .format(INSTANCE_NAME, ENVIRONMENT), False)\n\n global ADMIN_PASS\n ADMIN_PASS = _fetch_ssm_parameter(\n '/app/MDL/{}/{}/LDAP/Password/HerdAdminPassword'.format(INSTANCE_NAME,\n ENVIRONMENT),\n True)\n\n global HERD_REST_BASE_PATH\n HERD_REST_BASE_PATH = 'herd-app/rest'\n\n global HERD_BASE_URL\n HERD_BASE_URL = '{}-herd.dev.aws.cloudfjord.com'.format(INSTANCE_NAME)\n\n global HERD_HEADERS\n HERD_HEADERS = {\n 'Accept': 'application/json',\n 'Content-type': 'application/json'\n }\n\n global S3_HOME_BUCKET\n S3_HOME_BUCKET = _fetch_ssm_parameter('/app/MDL/{}/{}/S3/MDL'\n .format(INSTANCE_NAME, ENVIRONMENT),\n False)\n\n global BDSQL_MASTER\n BDSQL_MASTER = _fetch_ssm_parameter('/app/MDL/{}/{}/Bdsql/MasterIp'\n .format(INSTANCE_NAME, ENVIRONMENT),\n False)","def handle_task(self, request):\n \"\"\"\n @api {get} /tasks/:id Get a task\n @apiName GetTask\n @apiGroup Tasks\n @apiVersion 1.0.0\n\n @apiDescription Returns the configuration of a task.\n\n @apiParam {String} :id Task ID.\n\n @apiSuccess {String} name Name.\n @apiSuccess {String} description Description.\n @apiSuccess {String[]} tags Tags.\n @apiSuccess {Boolean} enabled Task is enabled.\n @apiSuccess {String} mode Task mode (\"any\" or \"all\").\n @apiSuccess {String[]} pools Pools on which the task should run.\n @apiSuccess {Object[]} schedules Schedules at which the task should run.\n @apiSuccess {String} command Command to run.\n @apiSuccess {String} workdir Working directory.\n @apiSuccess {String} user User which the task will be run.\n @apiSuccess {String} group Group which the task will be run.\n @apiSuccess {Object} env Environment variables to set.\n @apiSuccess {String} mailreport If the mailer plugin is enabled, condition to send a report (\"error\", \"stdout\", \"stderr\", \"output\", \"always\").\n @apiSuccess {String[]} mailto If the mailer plugin is enabled, email addresses to send the reports to.\n\n @apiSuccessExample {json} Example response:\n {\n \"name\": \"My task\",\n \"description\": \"Task description\",\n \"tags\": [\"tasg1\", \"tag2\"],\n \"enabled\": true,\n \"mode\": \"all\",\n \"pools\": [\"web\"],\n \"schedules\": [\n {\"minute\": [\"*/1\"]}\n ],\n \"command\": \"/bin/true\",\n \"workdir\": \"/tmp/\",\n \"user\": \"www-data\",\n \"group\": \"www-data\",\n \"env\": {\n \"MYENVVAR\": \"myvalue\"\n },\n \"mailreport\": \"output\",\n \"mailto\": [\"user@domain.org\"]\n }\n \"\"\"\n \"\"\"\n @api {put} /task/:id Update a task\n @apiName PutTask\n @apiGroup Tasks\n @apiVersion 1.0.0\n\n @apiDescription Update a task. Can also be used to create a task with a specific ID.\n\n @apiParam {String} :id Task ID.\n\n @apiParam {String} name Name.\n @apiParam {String} description Description.\n @apiParam {String[]} tags Tags.\n @apiParam {Boolean} enabled Task is enabled.\n @apiParam {String} mode Task mode (\"any\" or \"all\").\n @apiParam {String[]} pools Pools on which the task should run.\n @apiParam {Object[]} schedules Schedules at which the task should run.\n @apiParam {String} command Command to run.\n @apiParam {String} workdir Working directory.\n @apiParam {String} user User which the task will be run.\n @apiParam {String} group Group which the task will be run.\n @apiParam {Object} env Environment variables to set.\n @apiParam {String} mailreport If the mailer plugin is enabled, condition to send a report (\"error\", \"stdout\", \"stderr\", \"output\", \"always\").\n @apiParam {String[]} mailto If the mailer plugin is enabled, email addresses to send the reports to.\n\n @apiParamExample {json} Example parameters:\n {\n \"name\": \"My task\",\n \"description\": \"Task description\",\n \"tags\": [\"tasg1\", \"tag2\"],\n \"enabled\": true,\n \"mode\": \"all\",\n \"pools\": [\"web\"],\n \"schedules\": [\n {\"minute\": [\"*/1\"]}\n ],\n \"command\": \"/bin/true\",\n \"workdir\": \"/tmp/\",\n \"user\": \"www-data\",\n \"group\": \"www-data\",\n \"env\": {\n \"MYENVVAR\": \"myvalue\"\n },\n \"mailreport\": \"output\",\n \"mailto\": [\"user@domain.org\"]\n }\n\n @apiSuccess {Boolean} updated The task has been updated.\n @apiSuccess {String} id ID of the task.\n\n @apiSuccessExample {json} Example response:\n {\n \"updated\": true,\n \"id\": \"021b2092ef4111e481a852540064e600\"\n }\n \"\"\"\n \"\"\"\n @api {delete} /task/:id Delete a task\n @apiName DeleteTask\n @apiGroup Tasks\n @apiVersion 1.0.0\n\n @apiDescription Delete a task.\n\n @apiParam {String} :id Task ID.\n\n @apiSuccess {Boolean} deleted The task has been deleted.\n @apiSuccess {String} id ID of the task.\n\n @apiSuccessExample {json} Example response:\n {\n \"deleted\": true,\n \"id\": \"021b2092ef4111e481a852540064e600\"\n }\n \"\"\"\n \"\"\"\n @api {execute} /task/:id Execute a task\n @apiName ExecuteTask\n @apiGroup Tasks\n @apiVersion 1.1.0\n\n @apiDescription Execute a task.\n\n @apiParam {String} :id Task ID.\n @apiParam {String} :target Target for task execution (\"local\" to execute on the local node, otherwise execute on the nodes on which the task is configured to run).\n @apiParam {Boolean} :force Force the execution even if the concurrency limit is reached.\n\n @apiSuccess {Boolean} Executed The task has been executed.\n @apiSuccess {String} id ID of the task.\n\n @apiSuccessExample {json} Example response:\n {\n \"deleted\": true,\n \"id\": \"021b2092ef4111e481a852540064e600\"\n }\n \"\"\"\n\n\n headers = {\n 'Content-Type': 'application/javascript',\n 'Access-Control-Allow-Origin': '*'\n }\n\n match = re.match('/tasks/([0-9a-z]+)', request.uri_path)\n task = match.group(1)\n\n tasks = self.cluster.config.get('tasks')\n\n if request.method == \"GET\":\n if task in tasks:\n return HTTPReply(code = 200, body = json.dumps(tasks[task]), headers = headers)\n else:\n return HTTPReply(code = 404, headers = {'Access-Control-Allow-Origin': '*'})\n\n elif request.method == \"PUT\":\n new = json.loads(request.body)\n if task in tasks:\n old = tasks[task]\n else:\n old = None\n\n tasks[task] = new\n self.cluster.config.set('tasks', tasks)\n\n if old:\n code = 200\n body = json.dumps({\"id\": task, \"updated\": True})\n get_plugin_registry().call_hook('TaskUpdated', task, old, new)\n else:\n code = 201\n body = json.dumps({\"id\": task, \"created\": True})\n get_plugin_registry().call_hook('TaskCreated', task, new)\n\n return HTTPReply(code = code, body = body, headers = headers)\n\n elif request.method == \"DELETE\":\n if task in tasks:\n old = tasks[task]\n del tasks[task]\n self.cluster.config.set('tasks', tasks)\n\n get_plugin_registry().call_hook('TaskDeleted', task, old)\n\n return HTTPReply(code = 200, body = json.dumps({\"id\": task, \"deleted\": True}), headers = headers)\n else:\n return HTTPReply(code = 404, headers = {'Access-Control-Allow-Origin': '*'})\n\n if request.method == \"EXECUTE\":\n try:\n if 'target' in request.args and request.args['target'] == 'local':\n self.manager.execute_task(task)\n else:\n self.cluster.scheduler.run_task(task, ignore_concurrency = 'force' in request.args)\n\n return HTTPReply(code = 200, body = json.dumps({\"id\": task, \"executed\": True}), headers = headers)\n except ExecutionDisabled:\n return HTTPReply(code = 503, body = json.dumps({\"id\": task, \"executed\": False}), headers = headers)","def __init__(self, api_key):\n self.api_key = api_key\n self.base_url = 'https://studio.spotflock.com/api/v1'","def _invocation_params(self) -> Dict[str, Any]:\n return self._default_params","def __init__(self, *args, **kwargs):\n\n super(TestGoogleBooksTelescope, self).__init__(*args, **kwargs)\n self.host = \"localhost\"\n self.api_port = 5000\n self.sftp_port = 3373\n self.project_id = os.getenv(\"TEST_GCP_PROJECT_ID\")\n self.data_location = os.getenv(\"TEST_GCP_DATA_LOCATION\")\n self.organisation_name = \"anu-press\"\n self.organisation_folder = \"anu-press\"","def __init__(self, *args, **kwargs):\n super(FakeWeatherAPI, self).__init__(*args, **kwargs)","def handle_tasks(self, request):\n \"\"\"\n @api {get} /tasks List tasks\n @apiName GetTasks\n @apiGroup Tasks\n @apiVersion 1.0.0\n\n @apiDescription Return a list of all configured tasks, along with their configuration.\n\n @apiSuccessExample {json} Example response:\n {\n \"021b2092ef4111e481a852540064e600\": {\n \"name\": \"task 1\",\n \"enabled\": true,\n \"mode\": \"all\",\n \"pools\": [\"web\"],\n \"schedules\": [\n {\"minute\": [\"*/5\"]}\n ],\n \"command\": \"/bin/task1\",\n },\n \"508b4b72e44611e49e76c81f66cd0cca\": {\n \"name\": \"task 2\",\n \"enabled\": false,\n \"mode\": \"all\",\n \"pools\": [\"pool2\"],\n \"schedules\": [\n {\"hours\": [15], \"minutes\": [0]}\n ],\n \"command\": \"/bin/task2\",\n }\n }\n \"\"\"\n \"\"\"\n @api {post} /tasks Create a new task\n @apiName PostTasks\n @apiGroup Tasks\n @apiVersion 1.0.0\n\n @apiDescription Add a new task, providing its configuration.\n\n @apiParam {String} name Name.\n @apiParam {String} description Description.\n @apiParam {String[]} tags Tags.\n @apiParam {Boolean} enabled Task is enabled.\n @apiParam {String} mode Task mode (\"any\" or \"all\").\n @apiParam {String[]} pools Pools on which the task should run.\n @apiParam {Object[]} schedules Schedules at which the task should run.\n @apiParam {String} command Command to run.\n @apiParam {String} workdir Working directory.\n @apiParam {String} user User which the task will be run.\n @apiParam {String} group Group which the task will be run.\n @apiParam {Object} env Environment variables to set.\n @apiParam {String} mailreport If the mailer plugin is enabled, condition to send a report (\"error\", \"stdout\", \"stderr\", \"output\", \"always\").\n @apiParam {String[]} mailto If the mailer plugin is enabled, email addresses to send the reports to.\n\n @apiParamExample {json} Example parameters:\n {\n \"name\": \"My task\",\n \"description\": \"Task description\",\n \"tags\": [\"tasg1\", \"tag2\"],\n \"enabled\": true,\n \"mode\": \"all\",\n \"pools\": [\"web\"],\n \"schedules\": [\n {\"minute\": [\"*/1\"]}\n ],\n \"command\": \"/bin/true\",\n \"workdir\": \"/tmp/\",\n \"user\": \"www-data\",\n \"group\": \"www-data\",\n \"env\": {\n \"MYENVVAR\": \"myvalue\"\n },\n \"mailreport\": \"output\",\n \"mailto\": [\"user@domain.org\"]\n }\n\n @apiSuccess {Boolean} created The task has been created.\n @apiSuccess {String} id ID of the newly created task.\n\n @apiSuccessExample {json} Example response:\n {\n \"created\": true,\n \"id\": \"021b2092ef4111e481a852540064e600\"\n }\n \"\"\"\n \"\"\"\n @api {delete} /tasks Delete all tasks\n @apiName DeleteTasks\n @apiGroup Tasks\n @apiVersion 1.0.0\n\n @apiDescription Delete all tasks. Use with caution.\n\n @apiSuccess {Boolean} deleted The tasks have been deleted.\n\n @apiSuccessExample {json} Example response:\n {\n \"deleted\": true\n }\n \"\"\"\n\n headers = {\n 'Content-Type': 'application/javascript',\n 'Access-Control-Allow-Origin': '*'\n }\n\n if request.method == \"GET\":\n tasks = self.cluster.config.get('tasks')\n\n if 'tag' in request.args and request.args['tag']:\n tasks = dict( (taskid, task) for taskid, task in tasks.items() if 'tags' in task and request.args['tag'] in task['tags'] )\n\n return HTTPReply(code = 200, body = json.dumps(tasks), headers = headers)\n\n elif request.method == \"DELETE\":\n oldtasks = self.cluster.config.get('tasks')\n self.cluster.config.set('tasks', {})\n\n for (task, taskconfig) in oldtasks.items():\n get_plugin_registry().call_hook('TaskDeleted', task, taskconfig)\n\n return HTTPReply(code = 200, body = json.dumps({\"deleted\": True}), headers = headers)\n\n elif request.method == \"POST\":\n task = uuid.uuid1().hex\n tasks = self.cluster.config.get('tasks')\n tasks[task] = json.loads(request.body)\n self.cluster.config.set('tasks', tasks)\n\n get_plugin_registry().call_hook('TaskCreated', task, tasks[task])\n\n return HTTPReply(code = 201, body = json.dumps({\"id\": task, \"created\": True}), headers = headers)","def set_params(self):\n raise NotImplementedError","def get_params():\n\n parser = get_params_parser()\n args = parser.parse_args()\n\n tasks = [args.raw, args.enrich, args.identities_load, args.identities_merge, args.panels]\n\n if not any(tasks):\n print(\"No tasks enabled\")\n sys.exit(1)\n\n return args","def _set_defaults(self):\n self.api_protocol = 'https'\n self.api_host = 'nhl-score-api.herokuapp.com'\n self.current_score = 0\n self.sleep_seconds = 30 # Time to sleep after calling the API\n self.desired_game_state = 'LIVE' # Desired game state is LIVE","def setup_class(cls):\n initialize(api_key=API_KEY, app_key=APP_KEY, api_host=API_HOST)","def main():\n\n\n parser = argparse.ArgumentParser(prog='pyapi', description='PYthon API caller utilty')\n parser.add_argument('-l', '--list', action=\"store_true\", help='print list of saved names and exit')\n parser.add_argument('-s', '--save', action=\"store_true\", help='saves URL to NAME in config')\n parser.add_argument('-r', '--remove', action=\"store_true\", help='removes NAME from the config')\n parser.add_argument('-a', '--action', help='HTTP action - post get put delete')\n parser.add_argument('-n', '--name', help='name to reference the url in ~/.pyapi')\n parser.add_argument('-u', '--url', help='URL to post to (overrides NAME)')\n parser.add_argument('-d', '--data', help='JSON formatted string to pass in')\n parser.add_argument('-v', '--verbose', action=\"store_true\", help='shows full output')\n args = vars(parser.parse_args())\n\n #print args\n #exit()\n\n\n if args['list'] is not False:\n dump_config()\n return(0)\n\n if args['remove'] is not False:\n if args['name'] is None:\n print \"--name must be present with the --remove option\"\n return(-6)\n remove_config(args['name'])\n return(0)\n\n action = args['action']\n if action is not None and action not in methods.keys():\n print \"unknown action '%s'\" % action\n return(-3)\n\n if args['url'] is None and args['name'] is None:\n print \"you must include either a url or name\"\n return(-2)\n\n if action is None and args['save'] is False:\n print \"you must include either a action or save\"\n return(-5)\n\n if action is not None:\n # execute the action now\n target = args['url'] if args['url'] is not None else get_config(args['name'])\n data = None if args['data'] is None else ast.literal_eval(args['data'])\n response = methods[action](target, data)\n\n if args['verbose'] is not False:\n print response.text\n print response.status_code\n\n\n if args['save'] is not False:\n if args['url'] is None or args['name'] is None:\n print \"both --url and --name must be present with the --save option\"\n return(-4)\n save_config(args['name'], args['url'])","def define_parameters(self):\n\n self.add_argument('--input1',dest='input1',type=str,optional=False,\n help='What file do you want to upload?')\n self.add_argument('--input2',dest='input2',type=str,optional=False,\n help='What file do you want to upload?')","def setup(self,**kwargs):\n pass","def test_execute_api_modified_parameters(self):\n quantum_program = self._get_quantum_program()\n\n # Invoke with hub, group and project parameters.\n FAKE_QE_HUB = 'HUB'\n FAKE_QE_GROUP = 'GROUP'\n FAKE_QE_PROJECT = 'PROJECT'\n quantum_program.set_api(QE_TOKEN, QE_URL,\n FAKE_QE_HUB, FAKE_QE_GROUP, FAKE_QE_PROJECT)\n\n # Modify the api parameters.\n self.assertEqual(quantum_program._QuantumProgram__api.config['hub'],\n FAKE_QE_HUB)\n quantum_program.set_api_hubs_config(QE_HUB, QE_GROUP, QE_PROJECT)\n self.assertEqual(quantum_program._QuantumProgram__api.config['hub'],\n QE_HUB)\n\n # Store the original post() method.\n post_original = quantum_program._QuantumProgram__api.req.post\n with patch.object(_Request, 'post',\n wraps=post_original) as mocked_post:\n _ = quantum_program.execute(\n ['qc'], backend=self.backend, shots=1, max_credits=3)\n\n # Get the first parameter of the `run_job` POST call.\n url = mocked_post.call_args_list[-1][0][0]\n self.assertEqual('/Network/%s/Groups/%s/Projects/%s/jobs' %\n (QE_HUB, QE_GROUP, QE_PROJECT),\n url)","def __init__(self, api_wrapper):\n\n self.api_wrapper = api_wrapper","def as_api_parameters(self):\n return {\n 'return_token': self.token,\n 'return_url': self.url,\n 'client_http_user_agent': self.user_agent,\n 'client_http_accept': self.accept,\n 'remote_site': self.remote_site,\n }","def __init__(self, api_key=None):\n self.api_key = api_key","def __init__(self, apiKey, apiSecret):\n self.apiKey = apiKey\n self.apiSecret = apiSecret","def test_api_calls_no_parameters(self):\n quantum_program = self._get_quantum_program()\n\n # Invoke with no hub, group or project parameters.\n quantum_program.set_api(QE_TOKEN, QE_URL)\n\n self.log.info(quantum_program.online_backends())\n self.log.info(quantum_program.get_backend_parameters(self.backend))\n self.log.info(quantum_program.get_backend_calibration(self.backend))","def set_api_access_keys(**kwargs):\n API_BASE_PARAMS['key'] = kwargs['key']","def __init__( self, parameters={} ):\n self.params = {}","def __init__(__self__, *,\n api: Optional[pulumi.Input['ApplicationApiArgs']] = None,\n app_role_ids: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n app_roles: Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationAppRoleArgs']]]] = None,\n application_id: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n device_only_auth_enabled: Optional[pulumi.Input[bool]] = None,\n disabled_by_microsoft: Optional[pulumi.Input[str]] = None,\n display_name: Optional[pulumi.Input[str]] = None,\n fallback_public_client_enabled: Optional[pulumi.Input[bool]] = None,\n feature_tags: Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationFeatureTagArgs']]]] = None,\n group_membership_claims: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n identifier_uris: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n logo_image: Optional[pulumi.Input[str]] = None,\n logo_url: Optional[pulumi.Input[str]] = None,\n marketing_url: Optional[pulumi.Input[str]] = None,\n notes: Optional[pulumi.Input[str]] = None,\n oauth2_permission_scope_ids: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n oauth2_post_response_required: Optional[pulumi.Input[bool]] = None,\n object_id: Optional[pulumi.Input[str]] = None,\n optional_claims: Optional[pulumi.Input['ApplicationOptionalClaimsArgs']] = None,\n owners: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n prevent_duplicate_names: Optional[pulumi.Input[bool]] = None,\n privacy_statement_url: Optional[pulumi.Input[str]] = None,\n public_client: Optional[pulumi.Input['ApplicationPublicClientArgs']] = None,\n publisher_domain: Optional[pulumi.Input[str]] = None,\n required_resource_accesses: Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationRequiredResourceAccessArgs']]]] = None,\n service_management_reference: Optional[pulumi.Input[str]] = None,\n sign_in_audience: Optional[pulumi.Input[str]] = None,\n single_page_application: Optional[pulumi.Input['ApplicationSinglePageApplicationArgs']] = None,\n support_url: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n template_id: Optional[pulumi.Input[str]] = None,\n terms_of_service_url: Optional[pulumi.Input[str]] = None,\n web: Optional[pulumi.Input['ApplicationWebArgs']] = None):\n if api is not None:\n pulumi.set(__self__, \"api\", api)\n if app_role_ids is not None:\n pulumi.set(__self__, \"app_role_ids\", app_role_ids)\n if app_roles is not None:\n pulumi.set(__self__, \"app_roles\", app_roles)\n if application_id is not None:\n pulumi.set(__self__, \"application_id\", application_id)\n if description is not None:\n pulumi.set(__self__, \"description\", description)\n if device_only_auth_enabled is not None:\n pulumi.set(__self__, \"device_only_auth_enabled\", device_only_auth_enabled)\n if disabled_by_microsoft is not None:\n pulumi.set(__self__, \"disabled_by_microsoft\", disabled_by_microsoft)\n if display_name is not None:\n pulumi.set(__self__, \"display_name\", display_name)\n if fallback_public_client_enabled is not None:\n pulumi.set(__self__, \"fallback_public_client_enabled\", fallback_public_client_enabled)\n if feature_tags is not None:\n pulumi.set(__self__, \"feature_tags\", feature_tags)\n if group_membership_claims is not None:\n pulumi.set(__self__, \"group_membership_claims\", group_membership_claims)\n if identifier_uris is not None:\n pulumi.set(__self__, \"identifier_uris\", identifier_uris)\n if logo_image is not None:\n pulumi.set(__self__, \"logo_image\", logo_image)\n if logo_url is not None:\n pulumi.set(__self__, \"logo_url\", logo_url)\n if marketing_url is not None:\n pulumi.set(__self__, \"marketing_url\", marketing_url)\n if notes is not None:\n pulumi.set(__self__, \"notes\", notes)\n if oauth2_permission_scope_ids is not None:\n pulumi.set(__self__, \"oauth2_permission_scope_ids\", oauth2_permission_scope_ids)\n if oauth2_post_response_required is not None:\n pulumi.set(__self__, \"oauth2_post_response_required\", oauth2_post_response_required)\n if object_id is not None:\n pulumi.set(__self__, \"object_id\", object_id)\n if optional_claims is not None:\n pulumi.set(__self__, \"optional_claims\", optional_claims)\n if owners is not None:\n pulumi.set(__self__, \"owners\", owners)\n if prevent_duplicate_names is not None:\n pulumi.set(__self__, \"prevent_duplicate_names\", prevent_duplicate_names)\n if privacy_statement_url is not None:\n pulumi.set(__self__, \"privacy_statement_url\", privacy_statement_url)\n if public_client is not None:\n pulumi.set(__self__, \"public_client\", public_client)\n if publisher_domain is not None:\n pulumi.set(__self__, \"publisher_domain\", publisher_domain)\n if required_resource_accesses is not None:\n pulumi.set(__self__, \"required_resource_accesses\", required_resource_accesses)\n if service_management_reference is not None:\n pulumi.set(__self__, \"service_management_reference\", service_management_reference)\n if sign_in_audience is not None:\n pulumi.set(__self__, \"sign_in_audience\", sign_in_audience)\n if single_page_application is not None:\n pulumi.set(__self__, \"single_page_application\", single_page_application)\n if support_url is not None:\n pulumi.set(__self__, \"support_url\", support_url)\n if tags is not None:\n pulumi.set(__self__, \"tags\", tags)\n if template_id is not None:\n pulumi.set(__self__, \"template_id\", template_id)\n if terms_of_service_url is not None:\n pulumi.set(__self__, \"terms_of_service_url\", terms_of_service_url)\n if web is not None:\n pulumi.set(__self__, \"web\", web)","def __init__(self, *args, **kwargs):\n self.logger = logging.getLogger(__file__)\n kwargs.setdefault('api_major', 2)\n kwargs.setdefault('api_minor', 0)\n baseclient_2_0.DataONEBaseClient_2_0.__init__(self, *args, **kwargs)\n mnclient_1_1.MemberNodeClient_1_1.__init__(self, *args, **kwargs)","def __init__(self, api_key, api_name):\n self.api_key = api_key\n self.api_name = api_name\n self.raven_client = get_raven_client()\n self.stats_client = get_stats_client()","def __init__(self):\n super(Nasbench201, self).__init__()\n self.args.data_path = FileOps.download_dataset(self.args.data_path)\n self.nasbench201_api = API('self.args.data_path')","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict['base'] = self.base\n return paramDict","def _default_params(self) -> Dict[str, Any]:\n normal_params = {\n \"temperature\": self.temperature,\n \"max_tokens\": self.max_tokens,\n \"top_p\": self.top_p,\n \"frequency_penalty\": self.frequency_penalty,\n \"presence_penalty\": self.presence_penalty,\n \"n\": self.n,\n # \"best_of\": self.best_of,\n \"request_timeout\": self.request_timeout,\n \"logit_bias\": self.logit_bias,\n }\n return {**normal_params, **self.model_kwargs}","def api_settings(settings):\n settings.REST_FRAMEWORK['PAGINATE_BY_PARAM'] = 'limit'\n settings.REST_FRAMEWORK['PAGINATE_PARAM'] = 'offset'\n settings.REST_FRAMEWORK['SEARCH_PARAM'] = 'search'\n settings.REST_FRAMEWORK['SEARCHTYPE_PARAM'] = 'searchtype'\n settings.REST_FRAMEWORK['MAX_PAGINATE_BY'] = 500\n settings.REST_FRAMEWORK['PAGINATE_BY'] = 500\n return settings","def __init__(self, api_url):\n self.api_url = api_url","def setup_params(self, response):\n self.login_url = self.login_handler.login_url\n ((self.region_id, self.region),) = response[\"region\"].items()\n self._host = \"{}.{}\".format(self.region_id, BLINK_URL)\n self._token = response[\"authtoken\"][\"authtoken\"]\n self._auth_header = {\"Host\": self._host, \"TOKEN_AUTH\": self._token}\n self.urls = BlinkURLHandler(self.region_id, legacy=self.legacy)\n self.networks = self.get_networks()\n self.client_id = response[\"client\"][\"id\"]\n self.account_id = response[\"account\"][\"id\"]","def get_dynamic_setup_params():\n return {\n # Retrieve the long description from the README\n # 'long_description': read_file('README.rst'),\n 'install_requires': substitute_crypto_to_req(\n read_requirements('requirements.txt'),\n ),\n # 'extras_require': read_extras(),\n }","def __init__(self, api):\n self.api = api\n self.data = None","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict['functionType'] = self.functionType\n paramDict['dataFilename'] = self.dataFilename\n paramDict['workingDir'] = self.workingDir\n return paramDict","def __init__(self, token,\n base_url=r'https://developer.api.autodesk.com/data/v1/projects/',\n timeout=1):\n super().__init__(token=token, base_url=base_url, timeout=timeout)","def init_config_values():\n global HEADER, PROCESS_PROGRAM_NAME, METADATA_HANDLE_EVENT_NAME\n global APPINSIGHTS_INSTRUMENTATIONKEY\n global DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL, DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN\n global ADX_INGEST_QUEUE_URL_LIST, ADX_INGEST_QUEUE_SAS_TOKEN\n global CONCURRENT_ENQUEUE_TASKS\n global MAX_COMPACT_FILE_RECORDS\n APPINSIGHTS_INSTRUMENTATIONKEY = os.getenv(\"APPINSIGHTS_INSTRUMENTATIONKEY\",\n APPINSIGHTS_INSTRUMENTATIONKEY)\n DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL = os.getenv(\"DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL\",\n DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL)\n DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN = os.getenv(\"DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN\",\n DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN)\n ADX_INGEST_QUEUE_URL_LIST = os.getenv(\"ADX_INGEST_QUEUE_URL_LIST\", ADX_INGEST_QUEUE_URL_LIST)\n ADX_INGEST_QUEUE_SAS_TOKEN = os.getenv(\"ADX_INGEST_QUEUE_SAS_TOKEN\", ADX_INGEST_QUEUE_SAS_TOKEN)\n CONCURRENT_ENQUEUE_TASKS = int(os.getenv(\"CONCURRENT_ENQUEUE_TASKS\", CONCURRENT_ENQUEUE_TASKS))\n ADX_INGEST_QUEUE_URL_LIST = ADX_INGEST_QUEUE_URL_LIST.replace(' ', '').split(',')\n logging.info(f\"ADX_INGEST_QUEUE_URL_LIST: {ADX_INGEST_QUEUE_URL_LIST}\")\n\n\n HEADER = os.getenv(\"LOG_MESSAGE_HEADER\", HEADER)\n PROCESS_PROGRAM_NAME = os.getenv(\"PROCESS_PROGRAM_NAME\", PROCESS_PROGRAM_NAME)\n METADATA_HANDLE_EVENT_NAME = os.getenv(\"METADATA_HANDLE_EVENT_NAME\", METADATA_HANDLE_EVENT_NAME)\n MAX_COMPACT_FILE_RECORDS = int(os.getenv(\"MAX_COMPACT_FILE_RECORDS\", str(MAX_COMPACT_FILE_RECORDS)))","def define_parameters(self):\n self.add_argument('--prefix', dest='prefix', type=str, optional=False,\n help='prefix for file names')\n self.add_argument('--sleepLength',\n dest = 'sleepLength',\n type = str,\n optional = True,\n help ='time to sleep before performing plugin action',\n default = '0')","def build_task_args(self, test_name):\n task_args = {}\n if self.ext_net:\n task_args['floating_network'] = str(self.ext_net.name)\n else:\n task_args['floating_network'] = ''\n task_args['image_name'] = str(self.image.name)\n task_args['flavor_name'] = str(self.flavor.name)\n return task_args","def __init__(self, api_key):\n SteamAPI.__init__(self, \"\", api_key)\n self.tf2_items = None\n self.dota2_items = None","def __init__(self, app_id, api_key):\r\n self.apiroot = 'https://api.intercom.io/v1'\r\n\r\n self.add_filter(auth.BasicAuth(app_id, api_key))\r\n self.add_filter(self.use_json)","def __init__(self, api_key='YOUR_API_KEY'):\n self.api_key = api_key","def __init__(self):\n self._params = None","def configure_news_api(self):\n self.news_api = newsapi.NewsApiClient(self.api_key)","async def _setup(self):","def __init__(self, **kwargs):\n\n builder_kwargs = {}\n\n if \"token\" in kwargs and str(kwargs[\"token\"]) != \"None\":\n\n # If there is a token use it along with the specified proxy details if specified\n config = ApiConfiguration(\n api_url=kwargs.get(\"api_url\", None),\n certificate_filename=kwargs.get(\"certificate_filename\", None),\n proxy_config=ProxyConfig(\n address=kwargs.get(\"proxy_url\", None),\n username=kwargs.get(\"proxy_username\", None),\n password=kwargs.get(\"proxy_password\", None),\n ) if kwargs.get(\"proxy_url\", None) is not None else None,\n app_name=kwargs.get(\"app_name\", None)\n )\n\n builder_kwargs[\"api_configuration\"] = config\n builder_kwargs[\"token\"] = kwargs[\"token\"]\n\n # Otherwise use a secrets file if it exists\n builder_kwargs[\"api_secrets_filename\"] = kwargs.get(\"api_secrets_filename\", None)\n\n # add the correlation id if specified\n builder_kwargs[\"correlation_id\"] = kwargs.get(\"correlation_id\", None)\n\n # add the id provider response handler if specified\n builder_kwargs[\"id_provider_response_handler\"] = kwargs.get(\"id_provider_response_handler\", None)\n\n builder_kwargs[\"tcp_keep_alive\"] = kwargs.get(\"tcp_keep_alive\", False)\n\n # Call the client builder, this will result in using either a token, secrets file or environment variables\n self.api_client = ApiClientBuilder.build(**builder_kwargs)","def api_params(self):\n return self._api_params","def get_dynamic_setup_params():\n\n return {\n # Retrieve the long description from the README\n \"long_description\": read_file(\"README.md\")\n }","def rest_api_config(self):\n with self.resource_lock:\n pass"],"string":"[\n \"def set_params():\\n global module \\n global ora_inst\\n global response_loc\\n\\n module_args=dict(\\n ora_inst=dict(type='str', required=True),\\n response_loc=dict(type='str', required=True)\\n )\\n\\n module=AnsibleModule(\\n argument_spec=module_args\\n )\\n\\n ora_inst = module.params['ora_inst']\\n response_loc = module.params['response_loc']\",\n \"def prepare_api(self):\\n return None\",\n \"def setup_params():\\n Script.fullname = os.path.splitext(os.path.abspath(__file__))[0]\\n Script.basename = os.path.basename(__file__)\\n Script.name = os.path.splitext(Script.basename)[0]\\n Script.service = modUtils.check_service(Script.name)\",\n \"def __init__(self):\\n super().__init__()\\n self._api_url = API_BASE_URL\\n self._api_params = API_BASE_PARAMS.copy()\",\n \"def test_api_calls_parameters(self):\\n quantum_program = self._get_quantum_program()\\n\\n # Invoke with hub, group and project parameters.\\n quantum_program.set_api(QE_TOKEN, QE_URL, QE_HUB, QE_GROUP, QE_PROJECT)\\n\\n self.log.info(quantum_program.online_backends())\\n self.log.info(quantum_program.get_backend_parameters(self.backend))\\n self.log.info(quantum_program.get_backend_calibration(self.backend))\",\n \"def __init__(self, **kwargs):\\n esp_name = self.esp_name\\n self.api_key = get_anymail_setting(\\n \\\"api_key\\\",\\n esp_name=esp_name,\\n kwargs=kwargs,\\n allow_bare=True,\\n )\\n api_url = get_anymail_setting(\\n \\\"api_url\\\",\\n esp_name=esp_name,\\n kwargs=kwargs,\\n default=\\\"https://api.brevo.com/v3/\\\",\\n )\\n if not api_url.endswith(\\\"/\\\"):\\n api_url += \\\"/\\\"\\n super().__init__(api_url, **kwargs)\",\n \"def init_params(self):\\n blah\",\n \"def __init__(self, requests_api=None):\\r\\n self._requests = requests_api or requests\",\n \"def __init__(self, api_use=False):\\n self.api_use = api_use\",\n \"def exec_module(self, **kwargs):\\n\\n for key in list(self.module_arg_spec.keys()) + ['tags']:\\n if hasattr(self, key):\\n setattr(self, key, kwargs[key])\\n elif kwargs[key] is not None:\\n if key == \\\"location\\\":\\n self.parameters[\\\"location\\\"] = kwargs[key]\\n elif key == \\\"plan\\\":\\n self.parameters[\\\"plan\\\"] = kwargs[key]\\n elif key == \\\"open_shift_version\\\":\\n self.parameters[\\\"open_shift_version\\\"] = kwargs[key]\\n elif key == \\\"public_hostname\\\":\\n self.parameters[\\\"public_hostname\\\"] = kwargs[key]\\n elif key == \\\"fqdn\\\":\\n self.parameters[\\\"fqdn\\\"] = kwargs[key]\\n elif key == \\\"network_profile\\\":\\n self.parameters[\\\"network_profile\\\"] = kwargs[key]\\n elif key == \\\"router_profiles\\\":\\n self.parameters[\\\"router_profiles\\\"] = kwargs[key]\\n elif key == \\\"master_pool_profile\\\":\\n ev = kwargs[key]\\n if 'vm_size' in ev:\\n if ev['vm_size'] == 'standard_d2s_v3':\\n ev['vm_size'] = 'Standard_D2s_v3'\\n elif ev['vm_size'] == 'standard_d4s_v3':\\n ev['vm_size'] = 'Standard_D4s_v3'\\n if 'os_type' in ev:\\n if ev['os_type'] == 'linux':\\n ev['os_type'] = 'Linux'\\n elif ev['os_type'] == 'windows':\\n ev['os_type'] = 'Windows'\\n self.parameters[\\\"master_pool_profile\\\"] = ev\\n elif key == \\\"agent_pool_profiles\\\":\\n ev = kwargs[key]\\n if 'vm_size' in ev:\\n if ev['vm_size'] == 'standard_d2s_v3':\\n ev['vm_size'] = 'Standard_D2s_v3'\\n elif ev['vm_size'] == 'standard_d4s_v3':\\n ev['vm_size'] = 'Standard_D4s_v3'\\n if 'os_type' in ev:\\n if ev['os_type'] == 'linux':\\n ev['os_type'] = 'Linux'\\n elif ev['os_type'] == 'windows':\\n ev['os_type'] = 'Windows'\\n self.parameters[\\\"agent_pool_profiles\\\"] = ev\\n elif key == \\\"auth_profile\\\":\\n self.parameters[\\\"auth_profile\\\"] = kwargs[key]\\n\\n old_response = None\\n response = None\\n\\n self.mgmt_client = self.get_mgmt_svc_client(ContainerServiceClient,\\n base_url=self._cloud_environment.endpoints.resource_manager)\\n\\n resource_group = self.get_resource_group(self.resource_group)\\n\\n if \\\"location\\\" not in self.parameters:\\n self.parameters[\\\"location\\\"] = resource_group.location\\n\\n old_response = self.get_openshiftmanagedcluster()\\n\\n if not old_response:\\n self.log(\\\"Open Shift Managed Cluster instance doesn't exist\\\")\\n if self.state == 'absent':\\n self.log(\\\"Old instance didn't exist\\\")\\n else:\\n self.to_do = Actions.Create\\n else:\\n self.log(\\\"Open Shift Managed Cluster instance already exists\\\")\\n if self.state == 'absent':\\n self.to_do = Actions.Delete\\n elif self.state == 'present':\\n self.log(\\\"Need to check if Open Shift Managed Cluster instance has to be deleted or may be updated\\\")\\n self.to_do = Actions.Update\\n\\n if (self.to_do == Actions.Create) or (self.to_do == Actions.Update):\\n self.log(\\\"Need to Create / Update the Open Shift Managed Cluster instance\\\")\\n\\n if self.check_mode:\\n self.results['changed'] = True\\n return self.results\\n\\n response = self.create_update_openshiftmanagedcluster()\\n\\n if not old_response:\\n self.results['changed'] = True\\n else:\\n self.results['changed'] = old_response.__ne__(response)\\n self.log(\\\"Creation / Update done\\\")\\n elif self.to_do == Actions.Delete:\\n self.log(\\\"Open Shift Managed Cluster instance deleted\\\")\\n self.results['changed'] = True\\n\\n if self.check_mode:\\n return self.results\\n\\n self.delete_openshiftmanagedcluster()\\n # make sure instance is actually deleted, for some Azure resources, instance is hanging around\\n # for some time after deletion -- this should be really fixed in Azure.\\n while self.get_openshiftmanagedcluster():\\n time.sleep(20)\\n else:\\n self.log(\\\"Open Shift Managed Cluster instance unchanged\\\")\\n self.results['changed'] = False\\n response = old_response\\n\\n if self.state == 'present':\\n self.results.update(self.format_item(response))\\n return self.results\",\n \"def set_params(self):\\r\\n pass\",\n \"def setup(self):\\r\\n \\r\\n if self.requestedAction == admin.ACTION_EDIT or self.requestedAction == admin.ACTION_CREATE:\\r\\n \\r\\n # Set the required parameters\\r\\n for arg in RadiusAuthRestHandler.REQUIRED_PARAMS:\\r\\n self.supportedArgs.addReqArg(arg)\\r\\n \\r\\n # Set up the valid parameters\\r\\n for arg in RadiusAuthRestHandler.VALID_PARAMS:\\r\\n if arg not in RadiusAuthRestHandler.REQUIRED_PARAMS:\\r\\n self.supportedArgs.addOptArg(arg)\",\n \"def __init__(self, *args, **kwargs):\\n super(CBCloudAPI, self).__init__(*args, **kwargs)\\n self._thread_pool_count = kwargs.pop('thread_pool_count', 1)\\n self._lr_scheduler = None\\n self._async_executor = None\\n\\n if not self.credentials.org_key:\\n raise CredentialError(\\\"No organization key specified\\\")\",\n \"def setUp(self):\\n global access_token\\n global accountID\\n global account_cur\\n global api\\n # self.maxDiff = None\\n try:\\n accountID, account_cur, access_token = unittestsetup.auth()\\n setattr(sys.modules[\\\"oandapyV20.oandapyV20\\\"],\\n \\\"TRADING_ENVIRONMENTS\\\",\\n {\\\"practice\\\": {\\n \\\"stream\\\": \\\"https://test.com\\\",\\n \\\"api\\\": \\\"https://test.com\\\",\\n }})\\n api = API(environment=environment,\\n access_token=access_token,\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"})\\n api.api_url = 'https://test.com'\\n except Exception as e:\\n print(\\\"%s\\\" % e)\\n exit(0)\",\n \"def Params(cls):\\n p = super().Params()\\n p.Define('train_task', None, 'Underlying task')\\n p.Define('decode_task', None, 'Underlying task')\\n p.Define('train_dataset_name', None, '')\\n p.Define('decode_dataset_name', None, '')\\n p.Define('train_steps_per_loop', 0, '')\\n p.Define('decode_steps_per_loop', 0, '')\\n return p\",\n \"def __init__(__self__, *,\\n api_groups: Optional[Sequence[str]] = None,\\n api_versions: Optional[Sequence[str]] = None,\\n operations: Optional[Sequence[str]] = None,\\n resources: Optional[Sequence[str]] = None,\\n scope: Optional[str] = None):\\n if api_groups is not None:\\n pulumi.set(__self__, \\\"api_groups\\\", api_groups)\\n if api_versions is not None:\\n pulumi.set(__self__, \\\"api_versions\\\", api_versions)\\n if operations is not None:\\n pulumi.set(__self__, \\\"operations\\\", operations)\\n if resources is not None:\\n pulumi.set(__self__, \\\"resources\\\", resources)\\n if scope is not None:\\n pulumi.set(__self__, \\\"scope\\\", scope)\",\n \"def __init__(__self__, *,\\n api_groups: Optional[Sequence[str]] = None,\\n api_versions: Optional[Sequence[str]] = None,\\n operations: Optional[Sequence[str]] = None,\\n resources: Optional[Sequence[str]] = None,\\n scope: Optional[str] = None):\\n if api_groups is not None:\\n pulumi.set(__self__, \\\"api_groups\\\", api_groups)\\n if api_versions is not None:\\n pulumi.set(__self__, \\\"api_versions\\\", api_versions)\\n if operations is not None:\\n pulumi.set(__self__, \\\"operations\\\", operations)\\n if resources is not None:\\n pulumi.set(__self__, \\\"resources\\\", resources)\\n if scope is not None:\\n pulumi.set(__self__, \\\"scope\\\", scope)\",\n \"def __init__(self, apiToken=None):\\r\\n self.requestHelper = RequestHelper(apiToken)\",\n \"def __init__(self, api_key=\\\"3d34a9a9b0e544269a3ddbb97ec89ba7\\\", api_current=\\\"https://api.weatherbit.io/v2.0/current\\\", api_forecast = \\\"https://api.weatherbit.io/v2.0/forecast/hourly?\\\"): \\n # constants\\n self.API_KEY = api_key\\n\\n # necessary data for request\\n self.API_CURRENT = api_current\\n self.API_FORECAST = api_forecast\",\n \"def as_api_parameters(self):\\n raise NotImplementedError(\\n 'as_api_parameters not implemented on ' + self.__class__)\",\n \"def setup_task(self, *args, **kwargs):\\n pass\",\n \"def set_params(self, **kwargs):\\n ...\",\n \"def setup(api_token, endpoint_url=None):\\n global DEFAULT_API_TOKEN, DEFAULT_ENDPOINT_URL\\n DEFAULT_API_TOKEN = api_token\\n\\n if endpoint_url is not None:\\n DEFAULT_ENDPOINT_URL = endpoint_url\",\n \"def set_params(self, **kwargs) -> NoReturn:\\n pass\",\n \"def __init__(__self__, *,\\n api_groups: Optional[Sequence[str]] = None,\\n api_versions: Optional[Sequence[str]] = None,\\n operations: Optional[Sequence[str]] = None,\\n resource_names: Optional[Sequence[str]] = None,\\n resources: Optional[Sequence[str]] = None,\\n scope: Optional[str] = None):\\n if api_groups is not None:\\n pulumi.set(__self__, \\\"api_groups\\\", api_groups)\\n if api_versions is not None:\\n pulumi.set(__self__, \\\"api_versions\\\", api_versions)\\n if operations is not None:\\n pulumi.set(__self__, \\\"operations\\\", operations)\\n if resource_names is not None:\\n pulumi.set(__self__, \\\"resource_names\\\", resource_names)\\n if resources is not None:\\n pulumi.set(__self__, \\\"resources\\\", resources)\\n if scope is not None:\\n pulumi.set(__self__, \\\"scope\\\", scope)\",\n \"def __init__(__self__, *,\\n api_groups: Optional[Sequence[str]] = None,\\n api_versions: Optional[Sequence[str]] = None,\\n operations: Optional[Sequence[str]] = None,\\n resource_names: Optional[Sequence[str]] = None,\\n resources: Optional[Sequence[str]] = None,\\n scope: Optional[str] = None):\\n if api_groups is not None:\\n pulumi.set(__self__, \\\"api_groups\\\", api_groups)\\n if api_versions is not None:\\n pulumi.set(__self__, \\\"api_versions\\\", api_versions)\\n if operations is not None:\\n pulumi.set(__self__, \\\"operations\\\", operations)\\n if resource_names is not None:\\n pulumi.set(__self__, \\\"resource_names\\\", resource_names)\\n if resources is not None:\\n pulumi.set(__self__, \\\"resources\\\", resources)\\n if scope is not None:\\n pulumi.set(__self__, \\\"scope\\\", scope)\",\n \"def define_parameters(self):\",\n \"def __init__(__self__, *,\\n api_id: pulumi.Input[str],\\n api_service: pulumi.Input[str],\\n location: pulumi.Input[str],\\n properties: pulumi.Input['APIMgmtAPISpecPropertiesArgs'],\\n resource_group: pulumi.Input[str]):\\n pulumi.set(__self__, \\\"api_id\\\", api_id)\\n pulumi.set(__self__, \\\"api_service\\\", api_service)\\n pulumi.set(__self__, \\\"location\\\", location)\\n pulumi.set(__self__, \\\"properties\\\", properties)\\n pulumi.set(__self__, \\\"resource_group\\\", resource_group)\",\n \"def setup(self, request_params):\\n raise NotImplementedError(\\n u\\\"%s: Method not implemented\\\", self.__class__.__name__)\",\n \"def init_cloud_api(self, args=None):\\n pass\",\n \"def __init__(self):\\n load_dotenv()\\n self.api_url_base = os.getenv('API_URL_BASE')\\n self.token = os.getenv('API_TOKEN')\\n self.title_map = defaultdict(list)\",\n \"def _setup(self) -> None:\\n self._api = get_api(\\n self._password,\\n self._host,\\n self._username,\\n self._port,\\n self._ssl,\\n )\\n\\n self._info = self._api.get_info()\\n self.device_name = self._info.get(\\\"DeviceName\\\", DEFAULT_NAME)\\n self.model = self._info.get(\\\"ModelName\\\")\\n self.firmware_version = self._info.get(\\\"Firmwareversion\\\")\\n\\n for model in MODELS_V2:\\n if self.model.startswith(model):\\n self._method_version = 2\",\n \"def _setup_requests(self, url, user=None, key=None, rateLimit=3600, api_suffix=''):\\n self.url = url\\n self.rateLimit = rateLimit\\n self.api_suffix = ''\\n self.request_url = ''\\n self.status = False\\n self._auth = requests.auth.HTTPBasicAuth(user, key) if user and key else None\",\n \"def __init__(self, apikey, providerkey = None):\\n self.apikey = apikey\\n \\n # Set User-Agent\\n self.headers = {'User-Agent': \\\"Prowlpy/%s\\\" % str(__version__),\\n 'Content-type': \\\"application/x-www-form-urlencoded\\\"}\\n\\n # Aliasing\\n self.add = self.post\",\n \"def set_parameters(api_name='',\\r\\n targeted_flag='true',\\r\\n tv_flag='false',\\r\\n hinge_flag='true',\\r\\n cos_flag='false',\\r\\n interpolation='bilinear',\\r\\n model_type='large',\\r\\n loss_type='triplet',\\r\\n dataset_type='vgg',\\r\\n target_model='large',\\r\\n target_loss='center',\\r\\n target_dataset='VGG',\\r\\n attack='CW',\\r\\n norm='2',\\r\\n epsilon=0.1,\\r\\n iterations=20,\\r\\n binary_steps=5,\\r\\n learning_rate=0.01,\\r\\n epsilon_steps=0.01,\\r\\n init_const=0.3,\\r\\n mean_loss='embeddingmean',\\r\\n batch_size=-1,\\r\\n margin=15.0,\\r\\n amplification=6.0,\\r\\n granularity='normal',\\r\\n whitebox_target=False,\\r\\n pair_flag='false'):\\r\\n \\r\\n params = {}\\r\\n params['model_type'] = model_type\\r\\n params['loss_type'] = loss_type\\r\\n params['dataset_type'] = dataset_type\\r\\n params['target_model'] = target_model\\r\\n params['target_loss'] = target_loss\\r\\n params['target_dataset'] = target_dataset\\r\\n params['attack'] = attack\\r\\n params['norm'] = norm\\r\\n params['epsilon'] = epsilon\\r\\n params['iterations'] = iterations\\r\\n params['binary_steps'] = binary_steps\\r\\n params['learning_rate'] = learning_rate\\r\\n params['epsilon_steps'] = epsilon_steps\\r\\n params['init_const'] = init_const\\r\\n params['mean_loss'] = mean_loss\\r\\n params['batch_size'] = batch_size\\r\\n params['test_dir'] = TEST_DIR\\r\\n params['full_dir'] = FULL_DIR\\r\\n params['whitebox_target'] = whitebox_target\\r\\n params['targeted_flag'] = string_to_bool(targeted_flag)\\r\\n params['tv_flag'] = string_to_bool(tv_flag)\\r\\n params['hinge_flag'] = string_to_bool(hinge_flag)\\r\\n params['cos_flag'] = string_to_bool(cos_flag)\\r\\n params['pair_flag'] = string_to_bool(pair_flag)\\r\\n params['api_name'] = api_name\\r\\n\\r\\n if model_type == 'small' and loss_type == 'center':\\r\\n params['pixel_max'] = 1.0\\r\\n params['pixel_min'] = -1.0\\r\\n else:\\r\\n params['pixel_max'] = 1.0\\r\\n params['pixel_min'] = 0.0\\r\\n\\r\\n if dataset_type == 'vggsmall' and not whitebox_target:\\r\\n params['align_dir'] = VGG_ALIGN_160_DIR\\r\\n params['test_dir'] = VGG_TEST_DIR\\r\\n elif model_type == 'large' or dataset_type == 'casia':\\r\\n params['align_dir'] = ALIGN_160_DIR\\r\\n elif model_type == 'small':\\r\\n params['align_dir'] = ALIGN_96_DIR\\r\\n else:\\r\\n ValueError('ValueError: Argument must be either \\\"small\\\" or \\\"large\\\".')\\r\\n \\r\\n if interpolation == 'nearest':\\r\\n params['interpolation'] = cv2.INTER_NEAREST\\r\\n elif interpolation == 'bilinear':\\r\\n params['interpolation'] = cv2.INTER_LINEAR\\r\\n elif interpolation == 'bicubic':\\r\\n params['interpolation'] = cv2.INTER_CUBIC\\r\\n elif interpolation == 'lanczos':\\r\\n params['interpolation'] = cv2.INTER_LANCZOS4\\r\\n elif interpolation == 'super':\\r\\n ValueError('ValueError: Super interpolation not yet implemented.')\\r\\n else:\\r\\n raise ValueError('ValueError: Argument must be of the following, [nearest, bilinear, bicubic, lanczos, super].')\\r\\n\\r\\n if granularity == 'fine':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 20.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\\r\\n elif granularity == 'normal':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 10.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.5)\\r\\n elif granularity == 'coarse':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 5.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 1.0)\\r\\n elif granularity == 'coarser':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\\r\\n elif granularity == 'coarsest':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 1.0)\\r\\n elif granularity == 'single':\\r\\n params['margin_list'] = np.array([margin])\\r\\n params['amp_list'] = np.array([amplification])\\r\\n elif granularity == 'fine-tuned':\\r\\n params['margin_list'] = np.arange(10.0, margin, 1.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\\r\\n elif granularity == 'coarse-single':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\\r\\n params['amp_list'] = np.array([1.0])\\r\\n elif granularity == 'api-eval':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.8)\\r\\n else:\\r\\n raise ValueError('ValueError: Argument must be of the following, [fine, normal, coarse, coarser, single].')\\r\\n\\r\\n if params['hinge_flag']:\\r\\n params['attack_loss'] = 'hinge'\\r\\n else:\\r\\n params['attack_loss'] = 'target'\\r\\n if not params['targeted_flag']:\\r\\n params['attack_loss'] = 'target'\\r\\n if norm == 'inf':\\r\\n norm_name = 'i'\\r\\n else:\\r\\n norm_name = '2'\\r\\n if params['tv_flag']:\\r\\n tv_name = '_tv'\\r\\n else:\\r\\n tv_name = ''\\r\\n if params['cos_flag']:\\r\\n cos_name = '_cos'\\r\\n else:\\r\\n cos_name = ''\\r\\n\\r\\n params['model_name'] = '{}_{}'.format(model_type, loss_type)\\r\\n if dataset_type == 'casia' or dataset_type == 'vggsmall':\\r\\n params['model_name'] = dataset_type\\r\\n params['target_model_name'] = '{}_{}_{}'.format(target_model, target_loss, target_dataset)\\r\\n params['attack_name'] = '{}_l{}{}{}'.format(attack.lower(), norm_name, tv_name, cos_name)\\r\\n params['directory_path'] = os.path.join(ROOT,\\r\\n OUT_DIR,\\r\\n params['attack_name'],\\r\\n params['model_name'],\\r\\n '{}_loss/full'.format(params['attack_loss']))\\r\\n params['directory_path_crop'] = os.path.join(ROOT,\\r\\n OUT_DIR,\\r\\n params['attack_name'],\\r\\n params['model_name'],\\r\\n '{}_loss/crop'.format(params['attack_loss']))\\r\\n params['directory_path_npz'] = os.path.join(ROOT,\\r\\n OUT_DIR,\\r\\n params['attack_name'],\\r\\n params['model_name'],\\r\\n '{}_loss/npz'.format(params['attack_loss']))\\r\\n params['api_path'] = os.path.join(ROOT,\\r\\n API_DIR,\\r\\n params['attack_name'],\\r\\n params['model_name'],\\r\\n '{}_loss/npz'.format(params['attack_loss']))\\r\\n if params['mean_loss'] == 'embedding':\\r\\n params['directory_path'] += '_mean'\\r\\n params['directory_path_crop'] += '_mean'\\r\\n params['directory_path_npz'] += '_mean'\\r\\n params['api_path'] += '_mean'\\r\\n\\r\\n return params\",\n \"def manage_params(args):\\n # Socrata API\\n with open(\\\"secret/builtby-socrata.yaml\\\", 'r') as f:\\n try:\\n socrata_api_credentials = yaml.load(f)\\n except yaml.YAMLError as exc:\\n print(exc)\\n\\n socrata_app_token = socrata_api_credentials['app_token']\\n\\n # base params\\n params = {\\n '$$app_token': socrata_app_token\\n }\\n # remove null attributes\\n args = {k: v for k, v in args.items() if v is not None}\\n # add args to params\\n params.update(args) # inplace\\n\\n return params\",\n \"def _update_params(self):\\n _load = not self.san_interface.runmode\\n params={}\\n if ('iosched' in self._updatedattr or _load) and self.iosched<>IoSchedType.default:\\n params['iosched']=str(self.iosched)\\n if ('readahead' in self._updatedattr or _load) and self.readahead :\\n params['readahead']=self.readahead\\n if params:\\n for pt in self.paths():\\n pt.provider.set_dev_params(pt,params)\",\n \"def __init__(**params):\",\n \"def _update_params(self):\\n pass\",\n \"def _setup_api_properties(self):\\n self.implicit_api_logical_id = GeneratedLogicalId.implicit_http_api()\\n self.implicit_api_condition = \\\"ServerlessHttpApiCondition\\\"\\n self.api_event_type = \\\"HttpApi\\\"\\n self.api_type = SamResourceType.HttpApi.value\\n self.api_id_property = \\\"ApiId\\\"\\n self.editor = OpenApiEditor\",\n \"def __init__(self, base_url, client_id, client_secret, client_scope, api_json = None):\\n # type: (str, str, str, str, str) -> None\\n\\n self.base_url = base_url\\n self.client_id = client_id\\n self.client_secret = client_secret\\n self.client_scope = client_scope\\n\\n # If the user doesn't pass an alternate API file use the included one\\n if not api_json:\\n api_json = pkg_resources.resource_filename(__name__, 'apis.json')\\n\\n with open(api_json, encoding='utf-8') as api_file:\\n apis = json.loads(api_file.read())\\n\\n if client_scope in apis: \\n api = apis.get(client_scope)\\n self.token_url = api.get('token_url')\\n self.api_call = sleep_and_retry(limits(calls=api.get('limits_calls'), period=api.get('limits_period'))(self._api_call))\\n self.access_token = self.get_access_token(self.token_url)\\n else: \\n raise Exception(f\\\"Scope {client_scope} not in known API dict\\\")\",\n \"def task_init(self, param1):\\n raise NotImplementedError\",\n \"def build_task_args(self, test_name):\\n task_args = {'service_list': [test_name]}\\n task_args['image_name'] = str(self.image.name)\\n task_args['flavor_name'] = str(self.flavor.name)\\n task_args['flavor_alt_name'] = str(self.flavor_alt.name)\\n task_args['glance_image_location'] = str(self.filename)\\n task_args['glance_image_format'] = str(self.image_format)\\n task_args['tmpl_dir'] = str(self.template_dir)\\n task_args['sup_dir'] = str(self.support_dir)\\n task_args['users_amount'] = self.users_amount\\n task_args['tenants_amount'] = self.tenants_amount\\n task_args['use_existing_users'] = False\\n task_args['iterations'] = self.iterations_amount\\n task_args['concurrency'] = self.concurrency\\n task_args['smoke'] = self.smoke\\n task_args['volume_version'] = self.volume_version\\n task_args['volume_service_type'] = self.volume_service_type\\n task_args['block_migration'] = env.get(\\\"BLOCK_MIGRATION\\\").lower()\\n task_args['username'] = self.username\\n\\n if self.ext_net:\\n task_args['floating_network'] = str(self.ext_net.name)\\n else:\\n task_args['floating_network'] = ''\\n\\n if self.network:\\n task_args['netid'] = str(self.network.id)\\n else:\\n LOGGER.warning(\\n 'No tenant network created. '\\n 'Trying EXTERNAL_NETWORK as a fallback')\\n if env.get(\\\"EXTERNAL_NETWORK\\\"):\\n network = self.cloud.get_network(env.get(\\\"EXTERNAL_NETWORK\\\"))\\n task_args['netid'] = str(network.id) if network else ''\\n else:\\n task_args['netid'] = ''\\n\\n return task_args\",\n \"def __init__(self, rc: RequestsCommon, package_name: str, app_configs: dict) -> None:\\n self.rc = rc\\n self.package_name = package_name\\n\\n # required configs\\n self.api_token = app_configs.get(\\\"api_token\\\")\\n self.endpoint_url = app_configs.get(\\\"endpoint_url\\\")\\n self.api_version = app_configs.get(\\\"api_version\\\")\\n self.organization_name = app_configs.get(\\\"organization_name\\\")\\n self.verify = _get_verify_ssl(app_configs)\\n self.polling_filters = eval_mapping(app_configs.get('polling_filters', ''), wrapper='[{}]')\\n\\n self.header = self._make_header(self.api_token)\",\n \"def _apply_params(self):\\n config = self.get_startup_config()\\n # Pass true to _set_params so we know these are startup values\\n self._set_params(config, True)\",\n \"def update_params(self):\\n pass\",\n \"def base_params(self):\\n return {\\\"key\\\": self.api_key, \\\"contentCheckOk\\\": True, \\\"racyCheckOk\\\": True}\",\n \"def __init__(self):\\n self.api = Api(consumer_key=credentials[\\\"consumer_key\\\"],\\n consumer_secret=credentials[\\\"consumer_secret\\\"],\\n access_token_key=credentials[\\\"access_token_key\\\"],\\n access_token_secret=credentials[\\\"access_token_secret\\\"])\",\n \"def _init_vars_():\\n\\n global ADMIN_USERNAME\\n ADMIN_USERNAME = _fetch_ssm_parameter(\\n '/app/MDL/{}/{}/LDAP/User/HerdAdminUsername'\\n .format(INSTANCE_NAME, ENVIRONMENT), False)\\n\\n global ADMIN_PASS\\n ADMIN_PASS = _fetch_ssm_parameter(\\n '/app/MDL/{}/{}/LDAP/Password/HerdAdminPassword'.format(INSTANCE_NAME,\\n ENVIRONMENT),\\n True)\\n\\n global HERD_REST_BASE_PATH\\n HERD_REST_BASE_PATH = 'herd-app/rest'\\n\\n global HERD_BASE_URL\\n HERD_BASE_URL = '{}-herd.dev.aws.cloudfjord.com'.format(INSTANCE_NAME)\\n\\n global HERD_HEADERS\\n HERD_HEADERS = {\\n 'Accept': 'application/json',\\n 'Content-type': 'application/json'\\n }\\n\\n global S3_HOME_BUCKET\\n S3_HOME_BUCKET = _fetch_ssm_parameter('/app/MDL/{}/{}/S3/MDL'\\n .format(INSTANCE_NAME, ENVIRONMENT),\\n False)\\n\\n global BDSQL_MASTER\\n BDSQL_MASTER = _fetch_ssm_parameter('/app/MDL/{}/{}/Bdsql/MasterIp'\\n .format(INSTANCE_NAME, ENVIRONMENT),\\n False)\",\n \"def handle_task(self, request):\\n \\\"\\\"\\\"\\n @api {get} /tasks/:id Get a task\\n @apiName GetTask\\n @apiGroup Tasks\\n @apiVersion 1.0.0\\n\\n @apiDescription Returns the configuration of a task.\\n\\n @apiParam {String} :id Task ID.\\n\\n @apiSuccess {String} name Name.\\n @apiSuccess {String} description Description.\\n @apiSuccess {String[]} tags Tags.\\n @apiSuccess {Boolean} enabled Task is enabled.\\n @apiSuccess {String} mode Task mode (\\\"any\\\" or \\\"all\\\").\\n @apiSuccess {String[]} pools Pools on which the task should run.\\n @apiSuccess {Object[]} schedules Schedules at which the task should run.\\n @apiSuccess {String} command Command to run.\\n @apiSuccess {String} workdir Working directory.\\n @apiSuccess {String} user User which the task will be run.\\n @apiSuccess {String} group Group which the task will be run.\\n @apiSuccess {Object} env Environment variables to set.\\n @apiSuccess {String} mailreport If the mailer plugin is enabled, condition to send a report (\\\"error\\\", \\\"stdout\\\", \\\"stderr\\\", \\\"output\\\", \\\"always\\\").\\n @apiSuccess {String[]} mailto If the mailer plugin is enabled, email addresses to send the reports to.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"name\\\": \\\"My task\\\",\\n \\\"description\\\": \\\"Task description\\\",\\n \\\"tags\\\": [\\\"tasg1\\\", \\\"tag2\\\"],\\n \\\"enabled\\\": true,\\n \\\"mode\\\": \\\"all\\\",\\n \\\"pools\\\": [\\\"web\\\"],\\n \\\"schedules\\\": [\\n {\\\"minute\\\": [\\\"*/1\\\"]}\\n ],\\n \\\"command\\\": \\\"/bin/true\\\",\\n \\\"workdir\\\": \\\"/tmp/\\\",\\n \\\"user\\\": \\\"www-data\\\",\\n \\\"group\\\": \\\"www-data\\\",\\n \\\"env\\\": {\\n \\\"MYENVVAR\\\": \\\"myvalue\\\"\\n },\\n \\\"mailreport\\\": \\\"output\\\",\\n \\\"mailto\\\": [\\\"user@domain.org\\\"]\\n }\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n @api {put} /task/:id Update a task\\n @apiName PutTask\\n @apiGroup Tasks\\n @apiVersion 1.0.0\\n\\n @apiDescription Update a task. Can also be used to create a task with a specific ID.\\n\\n @apiParam {String} :id Task ID.\\n\\n @apiParam {String} name Name.\\n @apiParam {String} description Description.\\n @apiParam {String[]} tags Tags.\\n @apiParam {Boolean} enabled Task is enabled.\\n @apiParam {String} mode Task mode (\\\"any\\\" or \\\"all\\\").\\n @apiParam {String[]} pools Pools on which the task should run.\\n @apiParam {Object[]} schedules Schedules at which the task should run.\\n @apiParam {String} command Command to run.\\n @apiParam {String} workdir Working directory.\\n @apiParam {String} user User which the task will be run.\\n @apiParam {String} group Group which the task will be run.\\n @apiParam {Object} env Environment variables to set.\\n @apiParam {String} mailreport If the mailer plugin is enabled, condition to send a report (\\\"error\\\", \\\"stdout\\\", \\\"stderr\\\", \\\"output\\\", \\\"always\\\").\\n @apiParam {String[]} mailto If the mailer plugin is enabled, email addresses to send the reports to.\\n\\n @apiParamExample {json} Example parameters:\\n {\\n \\\"name\\\": \\\"My task\\\",\\n \\\"description\\\": \\\"Task description\\\",\\n \\\"tags\\\": [\\\"tasg1\\\", \\\"tag2\\\"],\\n \\\"enabled\\\": true,\\n \\\"mode\\\": \\\"all\\\",\\n \\\"pools\\\": [\\\"web\\\"],\\n \\\"schedules\\\": [\\n {\\\"minute\\\": [\\\"*/1\\\"]}\\n ],\\n \\\"command\\\": \\\"/bin/true\\\",\\n \\\"workdir\\\": \\\"/tmp/\\\",\\n \\\"user\\\": \\\"www-data\\\",\\n \\\"group\\\": \\\"www-data\\\",\\n \\\"env\\\": {\\n \\\"MYENVVAR\\\": \\\"myvalue\\\"\\n },\\n \\\"mailreport\\\": \\\"output\\\",\\n \\\"mailto\\\": [\\\"user@domain.org\\\"]\\n }\\n\\n @apiSuccess {Boolean} updated The task has been updated.\\n @apiSuccess {String} id ID of the task.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"updated\\\": true,\\n \\\"id\\\": \\\"021b2092ef4111e481a852540064e600\\\"\\n }\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n @api {delete} /task/:id Delete a task\\n @apiName DeleteTask\\n @apiGroup Tasks\\n @apiVersion 1.0.0\\n\\n @apiDescription Delete a task.\\n\\n @apiParam {String} :id Task ID.\\n\\n @apiSuccess {Boolean} deleted The task has been deleted.\\n @apiSuccess {String} id ID of the task.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"deleted\\\": true,\\n \\\"id\\\": \\\"021b2092ef4111e481a852540064e600\\\"\\n }\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n @api {execute} /task/:id Execute a task\\n @apiName ExecuteTask\\n @apiGroup Tasks\\n @apiVersion 1.1.0\\n\\n @apiDescription Execute a task.\\n\\n @apiParam {String} :id Task ID.\\n @apiParam {String} :target Target for task execution (\\\"local\\\" to execute on the local node, otherwise execute on the nodes on which the task is configured to run).\\n @apiParam {Boolean} :force Force the execution even if the concurrency limit is reached.\\n\\n @apiSuccess {Boolean} Executed The task has been executed.\\n @apiSuccess {String} id ID of the task.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"deleted\\\": true,\\n \\\"id\\\": \\\"021b2092ef4111e481a852540064e600\\\"\\n }\\n \\\"\\\"\\\"\\n\\n\\n headers = {\\n 'Content-Type': 'application/javascript',\\n 'Access-Control-Allow-Origin': '*'\\n }\\n\\n match = re.match('/tasks/([0-9a-z]+)', request.uri_path)\\n task = match.group(1)\\n\\n tasks = self.cluster.config.get('tasks')\\n\\n if request.method == \\\"GET\\\":\\n if task in tasks:\\n return HTTPReply(code = 200, body = json.dumps(tasks[task]), headers = headers)\\n else:\\n return HTTPReply(code = 404, headers = {'Access-Control-Allow-Origin': '*'})\\n\\n elif request.method == \\\"PUT\\\":\\n new = json.loads(request.body)\\n if task in tasks:\\n old = tasks[task]\\n else:\\n old = None\\n\\n tasks[task] = new\\n self.cluster.config.set('tasks', tasks)\\n\\n if old:\\n code = 200\\n body = json.dumps({\\\"id\\\": task, \\\"updated\\\": True})\\n get_plugin_registry().call_hook('TaskUpdated', task, old, new)\\n else:\\n code = 201\\n body = json.dumps({\\\"id\\\": task, \\\"created\\\": True})\\n get_plugin_registry().call_hook('TaskCreated', task, new)\\n\\n return HTTPReply(code = code, body = body, headers = headers)\\n\\n elif request.method == \\\"DELETE\\\":\\n if task in tasks:\\n old = tasks[task]\\n del tasks[task]\\n self.cluster.config.set('tasks', tasks)\\n\\n get_plugin_registry().call_hook('TaskDeleted', task, old)\\n\\n return HTTPReply(code = 200, body = json.dumps({\\\"id\\\": task, \\\"deleted\\\": True}), headers = headers)\\n else:\\n return HTTPReply(code = 404, headers = {'Access-Control-Allow-Origin': '*'})\\n\\n if request.method == \\\"EXECUTE\\\":\\n try:\\n if 'target' in request.args and request.args['target'] == 'local':\\n self.manager.execute_task(task)\\n else:\\n self.cluster.scheduler.run_task(task, ignore_concurrency = 'force' in request.args)\\n\\n return HTTPReply(code = 200, body = json.dumps({\\\"id\\\": task, \\\"executed\\\": True}), headers = headers)\\n except ExecutionDisabled:\\n return HTTPReply(code = 503, body = json.dumps({\\\"id\\\": task, \\\"executed\\\": False}), headers = headers)\",\n \"def __init__(self, api_key):\\n self.api_key = api_key\\n self.base_url = 'https://studio.spotflock.com/api/v1'\",\n \"def _invocation_params(self) -> Dict[str, Any]:\\n return self._default_params\",\n \"def __init__(self, *args, **kwargs):\\n\\n super(TestGoogleBooksTelescope, self).__init__(*args, **kwargs)\\n self.host = \\\"localhost\\\"\\n self.api_port = 5000\\n self.sftp_port = 3373\\n self.project_id = os.getenv(\\\"TEST_GCP_PROJECT_ID\\\")\\n self.data_location = os.getenv(\\\"TEST_GCP_DATA_LOCATION\\\")\\n self.organisation_name = \\\"anu-press\\\"\\n self.organisation_folder = \\\"anu-press\\\"\",\n \"def __init__(self, *args, **kwargs):\\n super(FakeWeatherAPI, self).__init__(*args, **kwargs)\",\n \"def handle_tasks(self, request):\\n \\\"\\\"\\\"\\n @api {get} /tasks List tasks\\n @apiName GetTasks\\n @apiGroup Tasks\\n @apiVersion 1.0.0\\n\\n @apiDescription Return a list of all configured tasks, along with their configuration.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"021b2092ef4111e481a852540064e600\\\": {\\n \\\"name\\\": \\\"task 1\\\",\\n \\\"enabled\\\": true,\\n \\\"mode\\\": \\\"all\\\",\\n \\\"pools\\\": [\\\"web\\\"],\\n \\\"schedules\\\": [\\n {\\\"minute\\\": [\\\"*/5\\\"]}\\n ],\\n \\\"command\\\": \\\"/bin/task1\\\",\\n },\\n \\\"508b4b72e44611e49e76c81f66cd0cca\\\": {\\n \\\"name\\\": \\\"task 2\\\",\\n \\\"enabled\\\": false,\\n \\\"mode\\\": \\\"all\\\",\\n \\\"pools\\\": [\\\"pool2\\\"],\\n \\\"schedules\\\": [\\n {\\\"hours\\\": [15], \\\"minutes\\\": [0]}\\n ],\\n \\\"command\\\": \\\"/bin/task2\\\",\\n }\\n }\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n @api {post} /tasks Create a new task\\n @apiName PostTasks\\n @apiGroup Tasks\\n @apiVersion 1.0.0\\n\\n @apiDescription Add a new task, providing its configuration.\\n\\n @apiParam {String} name Name.\\n @apiParam {String} description Description.\\n @apiParam {String[]} tags Tags.\\n @apiParam {Boolean} enabled Task is enabled.\\n @apiParam {String} mode Task mode (\\\"any\\\" or \\\"all\\\").\\n @apiParam {String[]} pools Pools on which the task should run.\\n @apiParam {Object[]} schedules Schedules at which the task should run.\\n @apiParam {String} command Command to run.\\n @apiParam {String} workdir Working directory.\\n @apiParam {String} user User which the task will be run.\\n @apiParam {String} group Group which the task will be run.\\n @apiParam {Object} env Environment variables to set.\\n @apiParam {String} mailreport If the mailer plugin is enabled, condition to send a report (\\\"error\\\", \\\"stdout\\\", \\\"stderr\\\", \\\"output\\\", \\\"always\\\").\\n @apiParam {String[]} mailto If the mailer plugin is enabled, email addresses to send the reports to.\\n\\n @apiParamExample {json} Example parameters:\\n {\\n \\\"name\\\": \\\"My task\\\",\\n \\\"description\\\": \\\"Task description\\\",\\n \\\"tags\\\": [\\\"tasg1\\\", \\\"tag2\\\"],\\n \\\"enabled\\\": true,\\n \\\"mode\\\": \\\"all\\\",\\n \\\"pools\\\": [\\\"web\\\"],\\n \\\"schedules\\\": [\\n {\\\"minute\\\": [\\\"*/1\\\"]}\\n ],\\n \\\"command\\\": \\\"/bin/true\\\",\\n \\\"workdir\\\": \\\"/tmp/\\\",\\n \\\"user\\\": \\\"www-data\\\",\\n \\\"group\\\": \\\"www-data\\\",\\n \\\"env\\\": {\\n \\\"MYENVVAR\\\": \\\"myvalue\\\"\\n },\\n \\\"mailreport\\\": \\\"output\\\",\\n \\\"mailto\\\": [\\\"user@domain.org\\\"]\\n }\\n\\n @apiSuccess {Boolean} created The task has been created.\\n @apiSuccess {String} id ID of the newly created task.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"created\\\": true,\\n \\\"id\\\": \\\"021b2092ef4111e481a852540064e600\\\"\\n }\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n @api {delete} /tasks Delete all tasks\\n @apiName DeleteTasks\\n @apiGroup Tasks\\n @apiVersion 1.0.0\\n\\n @apiDescription Delete all tasks. Use with caution.\\n\\n @apiSuccess {Boolean} deleted The tasks have been deleted.\\n\\n @apiSuccessExample {json} Example response:\\n {\\n \\\"deleted\\\": true\\n }\\n \\\"\\\"\\\"\\n\\n headers = {\\n 'Content-Type': 'application/javascript',\\n 'Access-Control-Allow-Origin': '*'\\n }\\n\\n if request.method == \\\"GET\\\":\\n tasks = self.cluster.config.get('tasks')\\n\\n if 'tag' in request.args and request.args['tag']:\\n tasks = dict( (taskid, task) for taskid, task in tasks.items() if 'tags' in task and request.args['tag'] in task['tags'] )\\n\\n return HTTPReply(code = 200, body = json.dumps(tasks), headers = headers)\\n\\n elif request.method == \\\"DELETE\\\":\\n oldtasks = self.cluster.config.get('tasks')\\n self.cluster.config.set('tasks', {})\\n\\n for (task, taskconfig) in oldtasks.items():\\n get_plugin_registry().call_hook('TaskDeleted', task, taskconfig)\\n\\n return HTTPReply(code = 200, body = json.dumps({\\\"deleted\\\": True}), headers = headers)\\n\\n elif request.method == \\\"POST\\\":\\n task = uuid.uuid1().hex\\n tasks = self.cluster.config.get('tasks')\\n tasks[task] = json.loads(request.body)\\n self.cluster.config.set('tasks', tasks)\\n\\n get_plugin_registry().call_hook('TaskCreated', task, tasks[task])\\n\\n return HTTPReply(code = 201, body = json.dumps({\\\"id\\\": task, \\\"created\\\": True}), headers = headers)\",\n \"def set_params(self):\\n raise NotImplementedError\",\n \"def get_params():\\n\\n parser = get_params_parser()\\n args = parser.parse_args()\\n\\n tasks = [args.raw, args.enrich, args.identities_load, args.identities_merge, args.panels]\\n\\n if not any(tasks):\\n print(\\\"No tasks enabled\\\")\\n sys.exit(1)\\n\\n return args\",\n \"def _set_defaults(self):\\n self.api_protocol = 'https'\\n self.api_host = 'nhl-score-api.herokuapp.com'\\n self.current_score = 0\\n self.sleep_seconds = 30 # Time to sleep after calling the API\\n self.desired_game_state = 'LIVE' # Desired game state is LIVE\",\n \"def setup_class(cls):\\n initialize(api_key=API_KEY, app_key=APP_KEY, api_host=API_HOST)\",\n \"def main():\\n\\n\\n parser = argparse.ArgumentParser(prog='pyapi', description='PYthon API caller utilty')\\n parser.add_argument('-l', '--list', action=\\\"store_true\\\", help='print list of saved names and exit')\\n parser.add_argument('-s', '--save', action=\\\"store_true\\\", help='saves URL to NAME in config')\\n parser.add_argument('-r', '--remove', action=\\\"store_true\\\", help='removes NAME from the config')\\n parser.add_argument('-a', '--action', help='HTTP action - post get put delete')\\n parser.add_argument('-n', '--name', help='name to reference the url in ~/.pyapi')\\n parser.add_argument('-u', '--url', help='URL to post to (overrides NAME)')\\n parser.add_argument('-d', '--data', help='JSON formatted string to pass in')\\n parser.add_argument('-v', '--verbose', action=\\\"store_true\\\", help='shows full output')\\n args = vars(parser.parse_args())\\n\\n #print args\\n #exit()\\n\\n\\n if args['list'] is not False:\\n dump_config()\\n return(0)\\n\\n if args['remove'] is not False:\\n if args['name'] is None:\\n print \\\"--name must be present with the --remove option\\\"\\n return(-6)\\n remove_config(args['name'])\\n return(0)\\n\\n action = args['action']\\n if action is not None and action not in methods.keys():\\n print \\\"unknown action '%s'\\\" % action\\n return(-3)\\n\\n if args['url'] is None and args['name'] is None:\\n print \\\"you must include either a url or name\\\"\\n return(-2)\\n\\n if action is None and args['save'] is False:\\n print \\\"you must include either a action or save\\\"\\n return(-5)\\n\\n if action is not None:\\n # execute the action now\\n target = args['url'] if args['url'] is not None else get_config(args['name'])\\n data = None if args['data'] is None else ast.literal_eval(args['data'])\\n response = methods[action](target, data)\\n\\n if args['verbose'] is not False:\\n print response.text\\n print response.status_code\\n\\n\\n if args['save'] is not False:\\n if args['url'] is None or args['name'] is None:\\n print \\\"both --url and --name must be present with the --save option\\\"\\n return(-4)\\n save_config(args['name'], args['url'])\",\n \"def define_parameters(self):\\n\\n self.add_argument('--input1',dest='input1',type=str,optional=False,\\n help='What file do you want to upload?')\\n self.add_argument('--input2',dest='input2',type=str,optional=False,\\n help='What file do you want to upload?')\",\n \"def setup(self,**kwargs):\\n pass\",\n \"def test_execute_api_modified_parameters(self):\\n quantum_program = self._get_quantum_program()\\n\\n # Invoke with hub, group and project parameters.\\n FAKE_QE_HUB = 'HUB'\\n FAKE_QE_GROUP = 'GROUP'\\n FAKE_QE_PROJECT = 'PROJECT'\\n quantum_program.set_api(QE_TOKEN, QE_URL,\\n FAKE_QE_HUB, FAKE_QE_GROUP, FAKE_QE_PROJECT)\\n\\n # Modify the api parameters.\\n self.assertEqual(quantum_program._QuantumProgram__api.config['hub'],\\n FAKE_QE_HUB)\\n quantum_program.set_api_hubs_config(QE_HUB, QE_GROUP, QE_PROJECT)\\n self.assertEqual(quantum_program._QuantumProgram__api.config['hub'],\\n QE_HUB)\\n\\n # Store the original post() method.\\n post_original = quantum_program._QuantumProgram__api.req.post\\n with patch.object(_Request, 'post',\\n wraps=post_original) as mocked_post:\\n _ = quantum_program.execute(\\n ['qc'], backend=self.backend, shots=1, max_credits=3)\\n\\n # Get the first parameter of the `run_job` POST call.\\n url = mocked_post.call_args_list[-1][0][0]\\n self.assertEqual('/Network/%s/Groups/%s/Projects/%s/jobs' %\\n (QE_HUB, QE_GROUP, QE_PROJECT),\\n url)\",\n \"def __init__(self, api_wrapper):\\n\\n self.api_wrapper = api_wrapper\",\n \"def as_api_parameters(self):\\n return {\\n 'return_token': self.token,\\n 'return_url': self.url,\\n 'client_http_user_agent': self.user_agent,\\n 'client_http_accept': self.accept,\\n 'remote_site': self.remote_site,\\n }\",\n \"def __init__(self, api_key=None):\\n self.api_key = api_key\",\n \"def __init__(self, apiKey, apiSecret):\\n self.apiKey = apiKey\\n self.apiSecret = apiSecret\",\n \"def test_api_calls_no_parameters(self):\\n quantum_program = self._get_quantum_program()\\n\\n # Invoke with no hub, group or project parameters.\\n quantum_program.set_api(QE_TOKEN, QE_URL)\\n\\n self.log.info(quantum_program.online_backends())\\n self.log.info(quantum_program.get_backend_parameters(self.backend))\\n self.log.info(quantum_program.get_backend_calibration(self.backend))\",\n \"def set_api_access_keys(**kwargs):\\n API_BASE_PARAMS['key'] = kwargs['key']\",\n \"def __init__( self, parameters={} ):\\n self.params = {}\",\n \"def __init__(__self__, *,\\n api: Optional[pulumi.Input['ApplicationApiArgs']] = None,\\n app_role_ids: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n app_roles: Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationAppRoleArgs']]]] = None,\\n application_id: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n device_only_auth_enabled: Optional[pulumi.Input[bool]] = None,\\n disabled_by_microsoft: Optional[pulumi.Input[str]] = None,\\n display_name: Optional[pulumi.Input[str]] = None,\\n fallback_public_client_enabled: Optional[pulumi.Input[bool]] = None,\\n feature_tags: Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationFeatureTagArgs']]]] = None,\\n group_membership_claims: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n identifier_uris: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n logo_image: Optional[pulumi.Input[str]] = None,\\n logo_url: Optional[pulumi.Input[str]] = None,\\n marketing_url: Optional[pulumi.Input[str]] = None,\\n notes: Optional[pulumi.Input[str]] = None,\\n oauth2_permission_scope_ids: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n oauth2_post_response_required: Optional[pulumi.Input[bool]] = None,\\n object_id: Optional[pulumi.Input[str]] = None,\\n optional_claims: Optional[pulumi.Input['ApplicationOptionalClaimsArgs']] = None,\\n owners: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n prevent_duplicate_names: Optional[pulumi.Input[bool]] = None,\\n privacy_statement_url: Optional[pulumi.Input[str]] = None,\\n public_client: Optional[pulumi.Input['ApplicationPublicClientArgs']] = None,\\n publisher_domain: Optional[pulumi.Input[str]] = None,\\n required_resource_accesses: Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationRequiredResourceAccessArgs']]]] = None,\\n service_management_reference: Optional[pulumi.Input[str]] = None,\\n sign_in_audience: Optional[pulumi.Input[str]] = None,\\n single_page_application: Optional[pulumi.Input['ApplicationSinglePageApplicationArgs']] = None,\\n support_url: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n template_id: Optional[pulumi.Input[str]] = None,\\n terms_of_service_url: Optional[pulumi.Input[str]] = None,\\n web: Optional[pulumi.Input['ApplicationWebArgs']] = None):\\n if api is not None:\\n pulumi.set(__self__, \\\"api\\\", api)\\n if app_role_ids is not None:\\n pulumi.set(__self__, \\\"app_role_ids\\\", app_role_ids)\\n if app_roles is not None:\\n pulumi.set(__self__, \\\"app_roles\\\", app_roles)\\n if application_id is not None:\\n pulumi.set(__self__, \\\"application_id\\\", application_id)\\n if description is not None:\\n pulumi.set(__self__, \\\"description\\\", description)\\n if device_only_auth_enabled is not None:\\n pulumi.set(__self__, \\\"device_only_auth_enabled\\\", device_only_auth_enabled)\\n if disabled_by_microsoft is not None:\\n pulumi.set(__self__, \\\"disabled_by_microsoft\\\", disabled_by_microsoft)\\n if display_name is not None:\\n pulumi.set(__self__, \\\"display_name\\\", display_name)\\n if fallback_public_client_enabled is not None:\\n pulumi.set(__self__, \\\"fallback_public_client_enabled\\\", fallback_public_client_enabled)\\n if feature_tags is not None:\\n pulumi.set(__self__, \\\"feature_tags\\\", feature_tags)\\n if group_membership_claims is not None:\\n pulumi.set(__self__, \\\"group_membership_claims\\\", group_membership_claims)\\n if identifier_uris is not None:\\n pulumi.set(__self__, \\\"identifier_uris\\\", identifier_uris)\\n if logo_image is not None:\\n pulumi.set(__self__, \\\"logo_image\\\", logo_image)\\n if logo_url is not None:\\n pulumi.set(__self__, \\\"logo_url\\\", logo_url)\\n if marketing_url is not None:\\n pulumi.set(__self__, \\\"marketing_url\\\", marketing_url)\\n if notes is not None:\\n pulumi.set(__self__, \\\"notes\\\", notes)\\n if oauth2_permission_scope_ids is not None:\\n pulumi.set(__self__, \\\"oauth2_permission_scope_ids\\\", oauth2_permission_scope_ids)\\n if oauth2_post_response_required is not None:\\n pulumi.set(__self__, \\\"oauth2_post_response_required\\\", oauth2_post_response_required)\\n if object_id is not None:\\n pulumi.set(__self__, \\\"object_id\\\", object_id)\\n if optional_claims is not None:\\n pulumi.set(__self__, \\\"optional_claims\\\", optional_claims)\\n if owners is not None:\\n pulumi.set(__self__, \\\"owners\\\", owners)\\n if prevent_duplicate_names is not None:\\n pulumi.set(__self__, \\\"prevent_duplicate_names\\\", prevent_duplicate_names)\\n if privacy_statement_url is not None:\\n pulumi.set(__self__, \\\"privacy_statement_url\\\", privacy_statement_url)\\n if public_client is not None:\\n pulumi.set(__self__, \\\"public_client\\\", public_client)\\n if publisher_domain is not None:\\n pulumi.set(__self__, \\\"publisher_domain\\\", publisher_domain)\\n if required_resource_accesses is not None:\\n pulumi.set(__self__, \\\"required_resource_accesses\\\", required_resource_accesses)\\n if service_management_reference is not None:\\n pulumi.set(__self__, \\\"service_management_reference\\\", service_management_reference)\\n if sign_in_audience is not None:\\n pulumi.set(__self__, \\\"sign_in_audience\\\", sign_in_audience)\\n if single_page_application is not None:\\n pulumi.set(__self__, \\\"single_page_application\\\", single_page_application)\\n if support_url is not None:\\n pulumi.set(__self__, \\\"support_url\\\", support_url)\\n if tags is not None:\\n pulumi.set(__self__, \\\"tags\\\", tags)\\n if template_id is not None:\\n pulumi.set(__self__, \\\"template_id\\\", template_id)\\n if terms_of_service_url is not None:\\n pulumi.set(__self__, \\\"terms_of_service_url\\\", terms_of_service_url)\\n if web is not None:\\n pulumi.set(__self__, \\\"web\\\", web)\",\n \"def __init__(self, *args, **kwargs):\\n self.logger = logging.getLogger(__file__)\\n kwargs.setdefault('api_major', 2)\\n kwargs.setdefault('api_minor', 0)\\n baseclient_2_0.DataONEBaseClient_2_0.__init__(self, *args, **kwargs)\\n mnclient_1_1.MemberNodeClient_1_1.__init__(self, *args, **kwargs)\",\n \"def __init__(self, api_key, api_name):\\n self.api_key = api_key\\n self.api_name = api_name\\n self.raven_client = get_raven_client()\\n self.stats_client = get_stats_client()\",\n \"def __init__(self):\\n super(Nasbench201, self).__init__()\\n self.args.data_path = FileOps.download_dataset(self.args.data_path)\\n self.nasbench201_api = API('self.args.data_path')\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict['base'] = self.base\\n return paramDict\",\n \"def _default_params(self) -> Dict[str, Any]:\\n normal_params = {\\n \\\"temperature\\\": self.temperature,\\n \\\"max_tokens\\\": self.max_tokens,\\n \\\"top_p\\\": self.top_p,\\n \\\"frequency_penalty\\\": self.frequency_penalty,\\n \\\"presence_penalty\\\": self.presence_penalty,\\n \\\"n\\\": self.n,\\n # \\\"best_of\\\": self.best_of,\\n \\\"request_timeout\\\": self.request_timeout,\\n \\\"logit_bias\\\": self.logit_bias,\\n }\\n return {**normal_params, **self.model_kwargs}\",\n \"def api_settings(settings):\\n settings.REST_FRAMEWORK['PAGINATE_BY_PARAM'] = 'limit'\\n settings.REST_FRAMEWORK['PAGINATE_PARAM'] = 'offset'\\n settings.REST_FRAMEWORK['SEARCH_PARAM'] = 'search'\\n settings.REST_FRAMEWORK['SEARCHTYPE_PARAM'] = 'searchtype'\\n settings.REST_FRAMEWORK['MAX_PAGINATE_BY'] = 500\\n settings.REST_FRAMEWORK['PAGINATE_BY'] = 500\\n return settings\",\n \"def __init__(self, api_url):\\n self.api_url = api_url\",\n \"def setup_params(self, response):\\n self.login_url = self.login_handler.login_url\\n ((self.region_id, self.region),) = response[\\\"region\\\"].items()\\n self._host = \\\"{}.{}\\\".format(self.region_id, BLINK_URL)\\n self._token = response[\\\"authtoken\\\"][\\\"authtoken\\\"]\\n self._auth_header = {\\\"Host\\\": self._host, \\\"TOKEN_AUTH\\\": self._token}\\n self.urls = BlinkURLHandler(self.region_id, legacy=self.legacy)\\n self.networks = self.get_networks()\\n self.client_id = response[\\\"client\\\"][\\\"id\\\"]\\n self.account_id = response[\\\"account\\\"][\\\"id\\\"]\",\n \"def get_dynamic_setup_params():\\n return {\\n # Retrieve the long description from the README\\n # 'long_description': read_file('README.rst'),\\n 'install_requires': substitute_crypto_to_req(\\n read_requirements('requirements.txt'),\\n ),\\n # 'extras_require': read_extras(),\\n }\",\n \"def __init__(self, api):\\n self.api = api\\n self.data = None\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict['functionType'] = self.functionType\\n paramDict['dataFilename'] = self.dataFilename\\n paramDict['workingDir'] = self.workingDir\\n return paramDict\",\n \"def __init__(self, token,\\n base_url=r'https://developer.api.autodesk.com/data/v1/projects/',\\n timeout=1):\\n super().__init__(token=token, base_url=base_url, timeout=timeout)\",\n \"def init_config_values():\\n global HEADER, PROCESS_PROGRAM_NAME, METADATA_HANDLE_EVENT_NAME\\n global APPINSIGHTS_INSTRUMENTATIONKEY\\n global DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL, DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN\\n global ADX_INGEST_QUEUE_URL_LIST, ADX_INGEST_QUEUE_SAS_TOKEN\\n global CONCURRENT_ENQUEUE_TASKS\\n global MAX_COMPACT_FILE_RECORDS\\n APPINSIGHTS_INSTRUMENTATIONKEY = os.getenv(\\\"APPINSIGHTS_INSTRUMENTATIONKEY\\\",\\n APPINSIGHTS_INSTRUMENTATIONKEY)\\n DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL = os.getenv(\\\"DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL\\\",\\n DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL)\\n DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN = os.getenv(\\\"DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN\\\",\\n DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN)\\n ADX_INGEST_QUEUE_URL_LIST = os.getenv(\\\"ADX_INGEST_QUEUE_URL_LIST\\\", ADX_INGEST_QUEUE_URL_LIST)\\n ADX_INGEST_QUEUE_SAS_TOKEN = os.getenv(\\\"ADX_INGEST_QUEUE_SAS_TOKEN\\\", ADX_INGEST_QUEUE_SAS_TOKEN)\\n CONCURRENT_ENQUEUE_TASKS = int(os.getenv(\\\"CONCURRENT_ENQUEUE_TASKS\\\", CONCURRENT_ENQUEUE_TASKS))\\n ADX_INGEST_QUEUE_URL_LIST = ADX_INGEST_QUEUE_URL_LIST.replace(' ', '').split(',')\\n logging.info(f\\\"ADX_INGEST_QUEUE_URL_LIST: {ADX_INGEST_QUEUE_URL_LIST}\\\")\\n\\n\\n HEADER = os.getenv(\\\"LOG_MESSAGE_HEADER\\\", HEADER)\\n PROCESS_PROGRAM_NAME = os.getenv(\\\"PROCESS_PROGRAM_NAME\\\", PROCESS_PROGRAM_NAME)\\n METADATA_HANDLE_EVENT_NAME = os.getenv(\\\"METADATA_HANDLE_EVENT_NAME\\\", METADATA_HANDLE_EVENT_NAME)\\n MAX_COMPACT_FILE_RECORDS = int(os.getenv(\\\"MAX_COMPACT_FILE_RECORDS\\\", str(MAX_COMPACT_FILE_RECORDS)))\",\n \"def define_parameters(self):\\n self.add_argument('--prefix', dest='prefix', type=str, optional=False,\\n help='prefix for file names')\\n self.add_argument('--sleepLength',\\n dest = 'sleepLength',\\n type = str,\\n optional = True,\\n help ='time to sleep before performing plugin action',\\n default = '0')\",\n \"def build_task_args(self, test_name):\\n task_args = {}\\n if self.ext_net:\\n task_args['floating_network'] = str(self.ext_net.name)\\n else:\\n task_args['floating_network'] = ''\\n task_args['image_name'] = str(self.image.name)\\n task_args['flavor_name'] = str(self.flavor.name)\\n return task_args\",\n \"def __init__(self, api_key):\\n SteamAPI.__init__(self, \\\"\\\", api_key)\\n self.tf2_items = None\\n self.dota2_items = None\",\n \"def __init__(self, app_id, api_key):\\r\\n self.apiroot = 'https://api.intercom.io/v1'\\r\\n\\r\\n self.add_filter(auth.BasicAuth(app_id, api_key))\\r\\n self.add_filter(self.use_json)\",\n \"def __init__(self, api_key='YOUR_API_KEY'):\\n self.api_key = api_key\",\n \"def __init__(self):\\n self._params = None\",\n \"def configure_news_api(self):\\n self.news_api = newsapi.NewsApiClient(self.api_key)\",\n \"async def _setup(self):\",\n \"def __init__(self, **kwargs):\\n\\n builder_kwargs = {}\\n\\n if \\\"token\\\" in kwargs and str(kwargs[\\\"token\\\"]) != \\\"None\\\":\\n\\n # If there is a token use it along with the specified proxy details if specified\\n config = ApiConfiguration(\\n api_url=kwargs.get(\\\"api_url\\\", None),\\n certificate_filename=kwargs.get(\\\"certificate_filename\\\", None),\\n proxy_config=ProxyConfig(\\n address=kwargs.get(\\\"proxy_url\\\", None),\\n username=kwargs.get(\\\"proxy_username\\\", None),\\n password=kwargs.get(\\\"proxy_password\\\", None),\\n ) if kwargs.get(\\\"proxy_url\\\", None) is not None else None,\\n app_name=kwargs.get(\\\"app_name\\\", None)\\n )\\n\\n builder_kwargs[\\\"api_configuration\\\"] = config\\n builder_kwargs[\\\"token\\\"] = kwargs[\\\"token\\\"]\\n\\n # Otherwise use a secrets file if it exists\\n builder_kwargs[\\\"api_secrets_filename\\\"] = kwargs.get(\\\"api_secrets_filename\\\", None)\\n\\n # add the correlation id if specified\\n builder_kwargs[\\\"correlation_id\\\"] = kwargs.get(\\\"correlation_id\\\", None)\\n\\n # add the id provider response handler if specified\\n builder_kwargs[\\\"id_provider_response_handler\\\"] = kwargs.get(\\\"id_provider_response_handler\\\", None)\\n\\n builder_kwargs[\\\"tcp_keep_alive\\\"] = kwargs.get(\\\"tcp_keep_alive\\\", False)\\n\\n # Call the client builder, this will result in using either a token, secrets file or environment variables\\n self.api_client = ApiClientBuilder.build(**builder_kwargs)\",\n \"def api_params(self):\\n return self._api_params\",\n \"def get_dynamic_setup_params():\\n\\n return {\\n # Retrieve the long description from the README\\n \\\"long_description\\\": read_file(\\\"README.md\\\")\\n }\",\n \"def rest_api_config(self):\\n with self.resource_lock:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6986048","0.64589137","0.63455117","0.62272775","0.60726595","0.6069171","0.5985259","0.5889219","0.5888519","0.5883398","0.58747536","0.58570075","0.5845327","0.5829871","0.5793331","0.5773644","0.5773644","0.57736164","0.5762372","0.5761211","0.57322764","0.571662","0.56985426","0.56902546","0.56761616","0.56761616","0.5675189","0.5661315","0.56598705","0.5651039","0.56471807","0.56332624","0.56142634","0.56070966","0.5605237","0.55872864","0.5583642","0.5583407","0.5551203","0.5541243","0.55396396","0.55200326","0.5518813","0.5518249","0.5513725","0.5509544","0.55048865","0.54987586","0.5488588","0.5468199","0.5453393","0.5450284","0.54494625","0.5444302","0.54367363","0.5436559","0.5433904","0.5433481","0.5426777","0.5422065","0.5400583","0.53976256","0.53957605","0.53902656","0.5388672","0.5387663","0.5385161","0.53850615","0.5382027","0.5382015","0.5368637","0.536581","0.5365705","0.5360539","0.5357219","0.53570765","0.5351898","0.5350786","0.53484666","0.53473693","0.5346627","0.5346477","0.5346477","0.5346477","0.5346477","0.5346477","0.5338599","0.53382623","0.5335217","0.53312653","0.53303856","0.53301144","0.532886","0.5324223","0.53180826","0.53178126","0.53162855","0.5315616","0.53125185","0.531241","0.53119576"],"string":"[\n \"0.6986048\",\n \"0.64589137\",\n \"0.63455117\",\n \"0.62272775\",\n \"0.60726595\",\n \"0.6069171\",\n \"0.5985259\",\n \"0.5889219\",\n \"0.5888519\",\n \"0.5883398\",\n \"0.58747536\",\n \"0.58570075\",\n \"0.5845327\",\n \"0.5829871\",\n \"0.5793331\",\n \"0.5773644\",\n \"0.5773644\",\n \"0.57736164\",\n \"0.5762372\",\n \"0.5761211\",\n \"0.57322764\",\n \"0.571662\",\n \"0.56985426\",\n \"0.56902546\",\n \"0.56761616\",\n \"0.56761616\",\n \"0.5675189\",\n \"0.5661315\",\n \"0.56598705\",\n \"0.5651039\",\n \"0.56471807\",\n \"0.56332624\",\n \"0.56142634\",\n \"0.56070966\",\n \"0.5605237\",\n \"0.55872864\",\n \"0.5583642\",\n \"0.5583407\",\n \"0.5551203\",\n \"0.5541243\",\n \"0.55396396\",\n \"0.55200326\",\n \"0.5518813\",\n \"0.5518249\",\n \"0.5513725\",\n \"0.5509544\",\n \"0.55048865\",\n \"0.54987586\",\n \"0.5488588\",\n \"0.5468199\",\n \"0.5453393\",\n \"0.5450284\",\n \"0.54494625\",\n \"0.5444302\",\n \"0.54367363\",\n \"0.5436559\",\n \"0.5433904\",\n \"0.5433481\",\n \"0.5426777\",\n \"0.5422065\",\n \"0.5400583\",\n \"0.53976256\",\n \"0.53957605\",\n \"0.53902656\",\n \"0.5388672\",\n \"0.5387663\",\n \"0.5385161\",\n \"0.53850615\",\n \"0.5382027\",\n \"0.5382015\",\n \"0.5368637\",\n \"0.536581\",\n \"0.5365705\",\n \"0.5360539\",\n \"0.5357219\",\n \"0.53570765\",\n \"0.5351898\",\n \"0.5350786\",\n \"0.53484666\",\n \"0.53473693\",\n \"0.5346627\",\n \"0.5346477\",\n \"0.5346477\",\n \"0.5346477\",\n \"0.5346477\",\n \"0.5346477\",\n \"0.5338599\",\n \"0.53382623\",\n \"0.5335217\",\n \"0.53312653\",\n \"0.53303856\",\n \"0.53301144\",\n \"0.532886\",\n \"0.5324223\",\n \"0.53180826\",\n \"0.53178126\",\n \"0.53162855\",\n \"0.5315616\",\n \"0.53125185\",\n \"0.531241\",\n \"0.53119576\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93774,"cells":{"query":{"kind":"string","value":"Remove fields from the APIreturned record that we don't want to pass back, or consider when diffing."},"document":{"kind":"string","value":"def sanitize_record(self, record):\n def remove_ids(d):\n if isinstance(d, dict):\n if 'id' in d:\n del d['id']\n for key, val in d.items():\n if isinstance(val, (dict, list)):\n remove_ids(val)\n if isinstance(d, list):\n for i in d:\n if isinstance(i, (dict, list)):\n remove_ids(i)\n return d\n\n record = remove_ids(record)\n for answer in record['answers']:\n answer.pop('feeds', None)\n return record"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def strip_unwanted_fields(self, data, many, **kwargs):\n unwanted_fields = [\"resource_type\"]\n for field in unwanted_fields:\n if field in data:\n data.pop(field)\n return data","def prune(record: cfg.OpenAPI, outformat: cfg.Format = None) -> NoReturn:\n content = record.oas\n # Cleanup\n content, _, _ = clean.remove_unused_components(content)\n content = clean.remove_empty_objects(content)\n # Output\n out(content, outformat or record.oastype)","def data_without(self, fields):\n without = {}\n data = json.loads(self.data())\n for field, value in data.items():\n if field not in fields:\n without[field] = value\n return json.dumps(without)","def cleanup(self, data):\n tmp = copy.copy(data)\n for field in ('log_entries', 'instances',\n 'picked_instances', 'saved_instances',\n 'terminated_instances', 'skipped_instances'):\n if field in tmp:\n del tmp[field]\n return tmp","def pop_non_relevant_vuln_fields(data: Dict):\n keys_to_keep = [\n \"title\",\n \"description\",\n \"content_type\",\n \"published_at\",\n \"references\",\n \"severity\",\n \"solutions\",\n \"alternate_ids\",\n ]\n for key in list(data):\n if key not in keys_to_keep:\n data.pop(key)","def clean_record(self):\n _dict = {\n key: value for (key, value) in self.record.items() if not key in\n BAMBOO_RESERVED_KEYS\n }\n return remove_mongo_reserved_keys(_dict)","def assertEqualExceptMeta(self, original, response, msg=None, ignore=()):\n self.assertTrue(response.pop('id')) # Sanity check that ID > 0\n response.pop('url', None)\n response.pop('created_at', None)\n response.pop('updated_at', None)\n response.pop('slug', None)\n for field in ignore:\n response.pop(field, None)\n self.assertEqual(original, dict(response), msg)","def _handle_dump_unknown(self, data, original):\n for key, val in original.items():\n if key not in self.fields:\n data[key] = val\n return data","def hide_confidential_fields(record, fields=_CONFIDENTIAL_FIELDS):\n if not(isinstance(record, dict) and fields):\n return record\n\n keys = list(record.keys())\n keys = (k for k in keys for f in fields if k == f or k.endswith('.'+f))\n\n return merge_dicts(record, {k: '********' for k in keys if record[k]})","def revive(self):\n field_name = self.get_delete_flag_field_name()\n return self.update(**{field_name: None})","def preprocess_record(record):\n automatic_fields = ['created_at', 'modified_at']\n record = serialize_fields(filter_out_dict_keys(record, automatic_fields))\n\n return record","def hide_fields_in_newer_versions(obj):\n if not api_utils.allow_start_end_audit_time():\n obj.start_time = wtypes.Unset\n obj.end_time = wtypes.Unset\n if not api_utils.allow_force():\n obj.force = wtypes.Unset","def remove_all_fields(self):\n self.fields = None","def remove_record_failure():\n\t\tpass","def test_unchanged_handle_removals(self):\n original_fields = self.form.fields\n fields = original_fields.copy()\n self.form.removed_fields = {}\n self.form.remove_field_names = []\n result = self.form.handle_removals(fields)\n\n self.assertEqual(len(original_fields), len(result))\n self.assertEqual(0, len(self.form.removed_fields))\n self.assertEqual(0, len(self.form.remove_field_names))\n self.assertDictEqual(original_fields, result)\n self.assertIs(fields, result)","def test_handle_removals_missing_removed_fields(self):\n original_fields = self.form.fields\n fields = original_fields.copy()\n self.form.remove_field_names = []\n if hasattr(self.form, 'removed_fields'):\n del self.form.removed_fields\n result = self.form.handle_removals(fields)\n\n self.assertTrue(hasattr(self.form, 'removed_fields'))\n self.assertEqual(len(original_fields), len(result))\n self.assertEqual(0, len(self.form.removed_fields))\n self.assertEqual(0, len(self.form.remove_field_names))\n self.assertDictEqual(original_fields, result)\n self.assertIs(fields, result)","def remove_field_from_every_item_in_response_copy(context, name):\n items = context.response_copy['items']\n for item in items:\n print(item)\n if item['owner']['user_type'] == 'does_not_exist':\n continue\n if name in item:\n del(item[name])\n logging.debug(\n 'Successfully removed field \"%s\" from item: %s', name,\n item['question_id']) \n else:\n logging.debug(\n 'Item %s does not contain field \"%s', name, item) \n logging.debug(\n 'Response copy after removing \"%s\" field:\\n%s', name,\n pformat(context.response_copy))","def _postprocess_record(record, hide=_CONFIDENTIAL_FIELDS):\n record = hide_confidential_fields(record, hide)\n record = unserialize_fields(record, hide)\n\n convert_float_timestamp2str(record)\n\n return record","def drop(self, fields, inplace=True):\n if inplace:\n obj = self\n else:\n obj = copy.deepcopy(self)\n\n for field in list(fields):\n del obj[field]\n\n return obj","def _trimRecords(self):\n self.highpassrecords.resize(self.nhighpassrecords, refcheck=False)\n self.lowpassrecords.resize(self.nlowpassrecords, refcheck=False)\n self.digitalsvalrecords.resize(self.ndigitalsvalrecords, refcheck=False)\n # cleanup by deleting any struct arrays of len 0\n for recname in ('highpassrecords', 'lowpassrecords', 'digitalsvalrecords'):\n if len(self.__getattribute__(recname)) == 0:\n self.__delattr__(recname)","def pop_non_relevant_search_fields(data: Dict):\n keys_to_keep = [\"title\", \"published_at\", \"identifier\"]\n for key in list(data):\n if key not in keys_to_keep:\n data.pop(key)","def clean_none_response(self):\n\n print(\"# Rows before non response are removed: {} \".format(len(self.data)))\n self.data = self.data[self.data['names'].map(lambda d: len(d) > 0)]\n print(\"# Rows after non response are removed: {} \".format(len(self.data)))","def clear_field_values(self):\n\t\tlogging.info(\"Clearing values in the field[] dictionary of the object\")\n\t\tlogging.debug(\"Before = \" + str(self.field))\n\t\tfor key, value in self.fields.items():\n\t\t\tself.field[str(key)] = None\n\t\tlogging.debug(\"After = \" + str(self.field))\n\t\treturn","def test_handle_removals_named_fields_not_in_data(self):\n original_fields = self.form.fields\n fields = original_fields.copy()\n remove_names = ['second', 'last']\n original_data = self.form.data\n data = original_data.copy()\n data.appendlist(remove_names[1], 'test_data_last')\n data._mutable = False\n self.form.data = data\n expected_fields = {name: field for name, field in fields.items() if name != remove_names[0]}\n self.form.removed_fields = {}\n self.form.remove_field_names = remove_names\n result = self.form.handle_removals(fields)\n\n self.assertEqual(len(original_fields), len(result) + len(remove_names) - 1)\n self.assertEqual(len(remove_names) - 1, len(self.form.removed_fields))\n self.assertEqual(1, len(self.form.remove_field_names))\n self.assertDictEqual(expected_fields, result)\n self.assertIs(fields, result)\n\n self.form.data = original_data","def ignore_field(self, record_contents, field, alignments=None):\n return (\"\", [])","def _fetch_fields(self, old_or_new, target_fields):\r\n \r\n returned_fields = []\r\n for target_field in target_fields:\r\n if target_field in [\",\", \"\\t\"]:\r\n returned_fields.append(target_field)\r\n else:\r\n try:\r\n returned_fields.append(str(getattr(old_or_new, target_field)))\r\n except:\r\n returned_fields.append(\"-\") \r\n return returned_fields","def pop_non_relevant_module_fields(data: Dict):\n keys_to_keep = [\n \"title\",\n \"description\",\n \"content_type\",\n \"published_at\",\n \"references\",\n \"architectures\",\n \"authors\",\n \"rank\",\n \"reliability\",\n ]\n for key in list(data):\n if key not in keys_to_keep:\n data.pop(key)","def unserialize_fields(record, hide=_CONFIDENTIAL_FIELDS,\n fields=_SERIALIZED_FIELDS):\n keys = list(record.keys())\n keys = (k for k in keys for f in fields if k == f or k.endswith('.'+f))\n\n return merge_dicts(record, {\n key: hide_confidential_fields(_unserialize(record[key]), hide)\n for key in keys if record[key]\n })","def clean_up(exc):\n\n FORBIDDEN_FIELDS_TECH = [\n \"categories\",\n ]\n\n FORBIDDEN_FIELDS_BIO = [\"location\", \"product\"]\n\n for field in list(exc.keys()):\n if exc[field] is None or exc[field] == \"None\":\n del exc[field]\n continue\n\n if exc[\"type\"] == \"biosphere\" and field in FORBIDDEN_FIELDS_BIO:\n del exc[field]\n if exc[\"type\"] == \"technosphere\" and field in FORBIDDEN_FIELDS_TECH:\n del exc[field]\n\n return exc","def reset_modified(self):\n self.modified_fields = set()\n\n # compensate for us not having knowledge of certain fields changing\n for field_name, field in self.schema.normal_fields.items():\n if isinstance(field, ObjectField):\n self.modified_fields.add(field_name)","def task_2_remove_dict_fields(data: DT, redundant_keys: List[str]) -> DT:\n dict2 = copy.deepcopy(data)\n for item in dict2:\n for key in redundant_keys:\n item.pop(key)\n return dict2","def test_handle_removals_remove_field_names(self):\n original_fields = self.form.fields\n fields = original_fields.copy()\n remove_names = ['second', 'last']\n expected_fields = {name: field for name, field in fields.items() if name not in remove_names}\n self.form.removed_fields = {}\n self.form.remove_field_names = remove_names\n result = self.form.handle_removals(fields)\n\n self.assertEqual(len(original_fields), len(result) + len(remove_names))\n self.assertEqual(len(remove_names), len(self.form.removed_fields))\n self.assertEqual(0, len(self.form.remove_field_names))\n self.assertDictEqual(expected_fields, result)\n self.assertIs(fields, result)","def remove_unuseful(remove_fields: np.ndarray, remove_values: np.ndarray):\n remove_fields = remove_fields[[0, 1, 2, 3, 4, 6]]\n remove_values = remove_values[:, [0, 1, 2, 3, 4, 6]]\n return remove_fields, remove_values","def _fix_items(items):\n for _, item in items.iteritems():\n if 'url' in item['fields']:\n del item['fields']['url']\n return items","def _handle_load_unknown(self, data, original):\n for key, val in original.items():\n if key not in self.fields:\n data[key] = val\n return data","def purgeOmittedFields(self, rsrcType, rsrcs):\n myRsrcs = copy.deepcopy(rsrcs)\n [[a.pop(k, None) for k in util.resourceTypeFromString(\n rsrcType)._omittedFields] for a in myRsrcs]\n return myRsrcs","def full_clean():\n response_to_df_csv()\n dirty_data = pd.read_csv(\"./data/dirty_data.csv\")\n cleaned_data = dirty_data\n cleaned_data = drop_cols(cleaned_data)\n cleaned_data = lowercase_columns(cleaned_data)\n cleaned_data = make_numeric(cleaned_data)\n cleaned_data = drop_zero_pay(cleaned_data)\n cleaned_data = add_log_col(cleaned_data)\n cleaned_data.to_csv('./data/cleaned_for_testing.csv')\n\n return cleaned_data","def task_2_remove_dict_fields(data: DT, redundant_keys: List[str]) -> DT:\n return [{key: value for key, value in dic.items() if key not in redundant_keys} for dic in data]","def test_handle_removals_add_if_named_in_data(self):\n original_data = self.form.data\n original_fields = self.form.fields\n fields = original_fields.copy()\n remove_names = ['second', 'last']\n self.form.removed_fields = {name: fields.pop(name) for name in remove_names if name in fields}\n self.form.remove_field_names = []\n expected_fields = dict(**fields, **self.form.removed_fields)\n test_data = original_data.copy()\n test_data.update({name: f\"value_{name}\" for name in remove_names})\n test_data._mutable = False\n self.form.data = test_data\n result = self.form.handle_removals(fields)\n\n self.assertEqual(len(original_fields), len(result))\n self.assertEqual(0, len(self.form.removed_fields))\n self.assertEqual(0, len(self.form.remove_field_names))\n self.assertDictEqual(expected_fields, result)\n self.assertDictEqual(original_fields, result)\n self.assertIs(fields, result)\n\n self.data = original_data","def depopulate(self, is_update):\n fields = {}\n schema = self.schema\n for k, field in schema.fields.items():\n is_modified = k in self.modified_fields\n orig_v = getattr(self, k)\n v = field.iset(\n self,\n orig_v,\n is_update=is_update,\n is_modified=is_modified\n )\n\n if is_modified or v is not None:\n if is_update and field.is_pk() and v == orig_v:\n continue\n\n else:\n fields[k] = v\n\n if not is_update:\n for field_name in schema.required_fields.keys():\n if field_name not in fields:\n raise KeyError(\"Missing required field {}\".format(field_name))\n\n return fields","def _rec_only_updated(cls, rec):\n return rec.get('uplinked', None) \\\n and not rec.get('queued', None) \\\n and not rec.get('announced', None) \\\n and not rec.get('blocked', None) \\\n and not rec.get('finished', None) \\\n and not rec.get('aborted', None)","def _sanitise_fields(self, record):\n sanitised = {}\n for k, v in record.items():\n new_key = k.replace('(', '_').replace(')', '_')\n sanitised[new_key] = v\n return sanitised","def _modify(self, fields):\n return fields","def test_fetchSpecificHeaderFieldsNotWithoutHeaders(self):\n self.assertFetchSpecificFieldsWithEmptyList(\"HEADER.FIELDS.NOT\")","def filter_allowed_fields(self):\n allowed_fields = super().filter_allowed_fields\n # Remove assignment_id\n allowed_fields.remove('assignment_id')\n return allowed_fields","def drop_info(batch):\n if 'id' in batch:\n batch.pop('id')\n if 'rng' in batch:\n batch.pop('rng')\n return batch","def check_returnable_fields(fields, result):\n returnable_fields = get_returnable_fields(result, verbose=False)\n for field in fields:\n if field not in returnable_fields:\n err_str = \"The field %s is not a returnable field for \" % (field)\n err_str += \"result %s\" % (result)\n raise ValueError(err_str)","def _remove_none(self, data):\r\n for key, value in data.items():\r\n if value is None or isinstance(value, forms.Field):\r\n del data[key]\r\n if isinstance(value, dict):\r\n self._remove_none(data[key])","def sweep_record(r):\n return dict_sweep(r, vals=[None])","def filter_excluded_fields(fields, Meta, exclude_dump_only):\n exclude = getattr(Meta, \"exclude\", [])\n if exclude_dump_only:\n exclude += getattr(Meta, \"dump_only\", [])\n\n filtered_fields = OrderedDict(\n (key, value) for key, value in fields.items() if key not in exclude\n )\n\n return filtered_fields","def prepare_duplication(self):\n for field in self.fields:\n ofield = self.fields[field]\n\n if self.duplicate:\n if ofield.primary_key:\n self.exclude_field(field)\n continue\n\n if not self.auto_fields:\n # add others if needed\n if hasattr(ofield, 'auto_now') or \\\n hasattr(ofield, 'auto_now_add'):\n if ofield.auto_now or ofield.auto_now_add:\n self.exclude_field(field)\n continue","def filter_unsupported_fields_from_config(self, configs, existing_endpoints, fields):\n for field in fields:\n if not any(field in e for e in existing_endpoints):\n for c in configs:\n endpoint = c['endpoint']\n if field in endpoint and endpoint[field] is None:\n del endpoint[field]","def remove_ei(remove_fields: np.ndarray, remove_values: np.ndarray):\n remove_fields = remove_fields[2:10]\n remove_values = remove_values[:, 2:10]\n return remove_fields, remove_values","def conform_output_data(rowdict,fields_to_show=''):\n rowdict['TimeStamp'] = str(rowdict['TimeStamp'])\n if fields_to_show:\n rowdict= removed_fields(fields_to_show, rowdict)\n return rowdict","def clean(self, value):\n return [f.clean(v) for v,f in zip(value, self.fields)]","def clean_metadata(metaobj):\n if len(metaobj) == 1 and 'href' in metaobj[0] and '/api/programs/' in metaobj[0]['href']:\n metaobj = metaobj # Keep lastUpdated for program\n else:\n metaobj = remove_subset_from_set(metaobj, 'lastUpdated')\n metaobj = remove_subset_from_set(metaobj, 'lastUpdatedBy')\n metaobj = remove_subset_from_set(metaobj, 'created')\n metaobj = remove_subset_from_set(metaobj, 'createdBy')\n metaobj = remove_subset_from_set(metaobj, 'href')\n metaobj = remove_subset_from_set(metaobj, 'access')\n metaobj = remove_subset_from_set(metaobj, 'favorites')\n metaobj = remove_subset_from_set(metaobj, 'allItems')\n metaobj = remove_subset_from_set(metaobj, 'displayName')\n metaobj = remove_subset_from_set(metaobj, 'displayFormName')\n metaobj = remove_subset_from_set(metaobj, 'displayShortName')\n metaobj = remove_subset_from_set(metaobj, 'displayDenominatorDescription')\n metaobj = remove_subset_from_set(metaobj, 'displayNumeratorDescription')\n metaobj = remove_subset_from_set(metaobj, 'displayDescription')\n metaobj = remove_subset_from_set(metaobj, 'interpretations')\n if len(metaobj) > 0:\n for subtag in ['dashboardItems', 'analyticsPeriodBoundaries', 'mapViews', 'user', 'userGroupAccesses',\n 'programStageDataElements', 'programTrackedEntityAttributes',\n 'trackedEntityTypeAttributes', 'userCredentials', 'legends', 'greyedFields']:\n for i in range(0, len(metaobj)):\n if subtag in metaobj[i]:\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'lastUpdated')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'lastUpdatedBy')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'created')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'createdBy')\n # There is access : { read: true, delete: false ... } dictionary\n # and there is access : \"rw----\"... Make sure we only delete the dictionary version\n if subtag not in ['user', 'userGroupAccesses']:\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'access')\n\n if subtag == 'programTrackedEntityAttributes':\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'name')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayName')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayFormName')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayShortName')\n\n return metaobj","def cleaning (data):","def _cleanse_dict(original):\n return {k: v for k, v in original.items() if \"_pass\" not in k}","def _revert(self):\n self.kwargs[\"collect\"].reset_scan_form_data()","def assert_nonfield_entry_attributes_unchanged(original_copy, transformed_entry):\n assert transformed_entry.start_line == original_copy.start_line\n assert transformed_entry.raw == original_copy.raw\n assert transformed_entry.entry_type == original_copy.entry_type\n assert transformed_entry.key == original_copy.key","def _clean_results(self):\n\t\tif self.file_type == \"Automobile\":\n\t\t\tcols = [\"Year\", \"Mileage\", \"Price\"]\n\t\t\tself.data.Mileage.replace([',', 'mi.', 'nan', ' '], '', regex=True, inplace=True) # Fix mileage column\n\t\t\tself.data.Price.replace([',', '\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\n\t\t\tself.data[cols] = self.data[cols].apply(pd.to_numeric, errors='coerce') # Coerces errors into NaN values\n\t\t\tself.data.drop(self.data[self.data.Year < 2000].index, inplace=True) # Remove cars made before 2000\n\t\t\tself.data.drop(self.data[self.data.Price > 30000].index, inplace=True) # Remove cars over $30,000\n\t\t\tself.data.drop(self.data[(self.data.Mileage < 1000) | (self.data.Mileage > 300000)].index, inplace=True) # Remove cars with over 300,000 miles\n\t\t\tself.data['Age'] = 2018 - self.data['Year'] # Change years to Age\n\t\telif self.file_type == \"Apartment\":\n\t\t\tself.data.Area.replace(['ft2'], '', regex=True, inplace=True) # Remove ft2 from square footage column\n\t\t\tself.data.Price.replace([',', '\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\n\t\telse:\n\t\t\tself.data['Street'], self.data['City'], self.data['State'] = self.data['Address'].str.split(',', 2).str\n\t\t\tdel self.data.Address\n\t\t\tself.data.drop(self.data[self.data.Price > 1000000].index, inplace=True) # Remove houses worth more than $1 million\n\n\t\tself.data.replace('^\\s*$', np.nan, regex=True, inplace=True) # Replace all empty values with np.NaN\n\t\tself.data = self.data.dropna(axis=1, how='all') # Remove Null Columns\n\t\tself.data = self.data.apply(pd.to_numeric, errors='ignore') # Coerces errors into NaN values","def test_api_fields(self) -> None:\n expected_fields = set(Stream.API_FIELDS) | {\"stream_id\"}\n expected_fields -= {\"id\", \"can_remove_subscribers_group_id\"}\n expected_fields |= {\"can_remove_subscribers_group\"}\n\n stream_dict_fields = set(APIStreamDict.__annotations__.keys())\n computed_fields = {\"is_announcement_only\", \"is_default\", \"stream_weekly_traffic\"}\n\n self.assertEqual(stream_dict_fields - computed_fields, expected_fields)\n\n expected_fields = set(Subscription.API_FIELDS)\n\n subscription_dict_fields = set(APISubscriptionDict.__annotations__.keys())\n computed_fields = {\"in_home_view\", \"email_address\", \"stream_weekly_traffic\", \"subscribers\"}\n # `APISubscriptionDict` is a subclass of `APIStreamDict`, therefore having all the\n # fields in addition to the computed fields and `Subscription.API_FIELDS` that\n # need to be excluded here.\n self.assertEqual(\n subscription_dict_fields - computed_fields - stream_dict_fields,\n expected_fields,\n )","def unmigrated(self):\n return self.exclude(\n Q(diff_hash__isnull=False) &\n (Q(parent_diff_hash__isnull=False) | Q(parent_diff64='')))","def drop_fields(key):\n return scom.drop_fields(key)","def _cleanse_dict(original):\n return dict((k, v) for k, v in original.items() if \"_pass\" not in k)","def filter_out_non_helpful_records(data_records):\n for r in data_records:\n if r.get_cruciality() is None:\n continue\n # We only want records that got exactly 3 votes\n if len(r.cruciality) != 3:\n continue\n else:\n yield r","def clean(self):\n cleaned_data = super().clean()\n cleaned_data = {key: field for key, field in cleaned_data.items()\n if field is not None}\n return cleaned_data","def _test_bad_request_omit_field(self, user, fields, omit_field, zendesk_mock_class, datadog_mock):\r\n filtered_fields = {k: v for (k, v) in fields.items() if k != omit_field}\r\n resp = self._build_and_run_request(user, filtered_fields)\r\n self._assert_bad_request(resp, omit_field, zendesk_mock_class, datadog_mock)","def dict_cleanup(self, data):\n for data_key, data_value in list(data.items()):\n # TODO: Add DEBUG logging (?)\n for filter_key, filter_value in self.required.items():\n if filter_key in data_value.keys():\n if isinstance(filter_value, str) and self.exact_match:\n if data_value[filter_key] != filter_value:\n del data[data_key]\n break\n elif isinstance(filter_value, str) and (not self.exact_match):\n if data_value[filter_key] is None:\n del data[data_key]\n break\n if filter_value not in data_value[filter_key]:\n del data[data_key]\n break\n elif isinstance(filter_value, list) and self.exact_match:\n if data_value[filter_key] not in filter_value:\n del data[data_key]\n break\n elif isinstance(filter_value, list) and (not self.exact_match):\n if data_value[filter_key] is None:\n del data[data_key]\n break\n found_match = False\n for filter_value_item in filter_value:\n if filter_value_item in data_value[filter_key]:\n found_match = True\n if not found_match:\n del data[data_key]\n break\n else:\n self.logger.warning(msg=\"Dict_Cleanup: None of the cases matched. Data: %s Filter: %s\" % (data_value, self.filter))\n # TODO: Handle other possible cases\n else:\n self.logger.warning(msg=\"Dict_Cleanup: Filter key: %s not present in Data: %s\" % (filter_key, data_value))\n continue\n\n for data_key, data_value in list(data.items()):\n # TODO: Add DEBUG logging (?)\n for filter_key, filter_value in self.excluded.items():\n if filter_key in data_value.keys():\n if isinstance(filter_value, str) and self.exact_match:\n if data_value[filter_key] == filter_value:\n del data[data_key]\n break\n elif isinstance(filter_value, str) and (not self.exact_match):\n if data_value[filter_key] is None:\n continue\n if filter_value in data_value[filter_key]:\n del data[data_key]\n break\n elif isinstance(filter_value, list) and self.exact_match:\n if data_value[filter_key] in filter_value:\n del data[data_key]\n break\n elif isinstance(filter_value, list) and (not self.exact_match):\n if data_value[filter_key] is None:\n continue\n found_match = False\n for filter_value_item in filter_value:\n if filter_value_item in data_value[filter_key]:\n found_match = True\n if found_match:\n del data[data_key]\n break\n else:\n self.logger.warning(msg=\"Dict_Cleanup: None of the cases matched. Data: %s Filter: %s\" % (data_value, self.filter))\n # TODO: Handle other possible cases\n else:\n self.logger.warning(msg=\"Dict_Cleanup: Filter key: %s not present in Data: %s\" % (filter_key, data_value))\n continue\n return data","def print_untouched(self, input_file: TextIO, output_file: TextIO) -> None:\n reader = csv.DictReader(input_file)\n columns = reader.fieldnames\n\n touched_columns = set(self.config.columns.keys())\n untouched_columns = [\n column for column in columns\n if column not in touched_columns\n ]\n\n if not untouched_columns:\n return\n\n writer = csv.DictWriter(output_file, fieldnames=untouched_columns)\n writer.writeheader()\n\n untouched_columns_set = set(untouched_columns)\n for row in reader:\n sliced_row = {\n column_name: column_value\n for column_name, column_value\n in row.items()\n if column_name in untouched_columns_set\n }\n writer.writerow(sliced_row)","def _clean(self):\n map(self.__delitem__, self.keys())\n self._original = []\n self._columns = {}\n self._modified, self._deleted = {}, {}","def clean_fields(self, exclude=None):\n obj = self._obj\n if obj is None:\n return None\n\n self.event = self.clean_event(self.event)\n self.resource_name = self.clean_resource_name(obj.__class__.__name__)\n self.resource_id = obj.id\n self.site = self.clean_site(obj)\n\n serializer_class = self.get_serializer_for_resource(self.resource_name)\n serializer = serializer_class(obj)\n self._resource = serializer.data","def attributes(self):\n attr = super(BaseDataRecord, self).attributes()\n return [a for a in attr if a not in self.excluded()]","def test_fetchSpecificHeaderFieldsWithoutHeaders(self):\n self.assertFetchSpecificFieldsWithEmptyList(\"HEADER.FIELDS\")","def _remove_data(things, lst_remove=None):\n\n for data in things:\n data.pop(\"_sa_instance_state\", None)\n data.pop(\"user_id\", None)\n\n if lst_remove is not None:\n for str_remove in lst_remove:\n if str_remove in data:\n data.pop(str_remove, None)\n\n return things","def inflateRecord(self, record):\n # Dirty bit is turned off by default\n return [record.recordID, record.recordData, 0, record.partitionFacility, record.partitionUser]","def remove_data():\n # Removing the existing data\n col_answer_given.remove()\n col_answer_not_given.remove()\n col_q_not_given.remove()\n col_to_summarize.remove()","def undo_format_field_names(obj):\n if json_api_settings.FORMAT_FIELD_NAMES:\n return format_field_names(obj, \"underscore\")\n\n return obj","def remove_access_request_field(self):\n self.fields = XML_List(Elements.FIELDS, [field for field in self.fields if\n field.FIELD_CONTENT_ATTRIBUTES != Elements.ACCESS_REQUESTS])","def _remove_empty(self, data, many):\n if not many:\n for key in list(data):\n if key == 'versions':\n data.pop(key)\n\n return {\n key: value for key, value in data.items()\n if value is not None\n }\n for item in data:\n for key in list(item):\n if (key == 'versions') or (item[key] is None):\n item.pop(key)\n\n return data","def customization(record):\n for popkey in pop_list:\n if popkey in record:\n record.pop(popkey)\n return record","def _remove_none_fields(klass, d):\n\n return dict((k, v) for k, v in d.iteritems() if v is not None)","def fake_clean_row(row):\n\treturn row","def _get_meta_only_fields(self, table_name, data_fields):\n meta_only_fields = {}\n for field in self.meta[table_name]['fields']:\n if field['source_name'] not in data_fields:\n # adds 'sql_name',None as key,value pairs in dict\n meta_only_fields[field['sql_name']] = None\n return meta_only_fields","def _revert(self):\n if self.kwargs.get(\"collect\"):\n remove_exported_collect_data(self.kwargs[\"collect\"])","def remove_read_only_fields(self):\n self.fields = XML_List(Elements.FIELDS, [field for field in self.fields if\n not field.read_only or not str_to_bool(field.read_only)])","def clean_attrs(cls, diffsync: DiffSync, attrs):\n return cls.clean_ids_or_attrs(diffsync, attrs)","def cleanup_record(schema, record):\n if not isinstance(record, dict) and not isinstance(record, list):\n return record\n\n elif isinstance(record, list):\n nr = []\n for item in record:\n nr.append(cleanup_record(schema, item))\n return nr\n\n elif isinstance(record, dict):\n nr = {}\n for key, value in record.items():\n nkey = bigquery_transformed_key(key)\n nr[nkey] = cleanup_record(schema, value)\n return nr\n\n else:\n raise Exception(f\"unhandled instance of record: {record}\")","def _trim_query_result(query_result):\n good_stuff = ['name', 'cmc', 'colors', 'set', 'usd']\n\n # UGHHHH I hate this, but I don't feel like refactoring into map or comprehensions right now.\n final = []\n for card in query_result['data']:\n card_dict = {}\n for k, v in card.items():\n if k in good_stuff:\n card_dict[k] = v\n card_dict['png'] = card['image_uris'].get('png')\n if not card['image_uris'].get('png'):\n raise IndexError('WTF: ', card['image_uris'], card['name'])\n final.append(card_dict)\n\n return final","def _export(self, filled_only=False, without_id=False):\n response = dict()\n for field in self.fields:\n if filled_only and not self.fields.get(field).get():\n continue\n if field == '_id' and without_id:\n continue\n response[field] = self.fields.get(field).get()\n return response","def _remove_dot_notation(cls, document: dict):\n field_names = [field.name for field in cls.__fields__]\n unknown_fields = [\n field_name for field_name in document if field_name not in field_names\n ]\n for unknown_field in unknown_fields:\n known_field, field_value = cls._to_known_field(\n unknown_field, document[unknown_field]\n )\n del document[unknown_field]\n if known_field:\n document.setdefault(known_field.name, {}).update(field_value)\n elif unknown_field not in cls._skip_log_for_unknown_fields:\n cls.logger.warning(f\"Skipping unknown field {unknown_field}.\")","def get_empty_fields(self):\n return [f for f in self.__dict__ if not self.__dict__[f]]","def copy_fields(self, model):\n fields = super(HistoricalRecords, self).copy_fields(model)\n for name, field in self.additional_fields.items():\n assert name not in fields\n assert hasattr(self, 'get_%s_value' % name)\n fields[name] = field\n return fields","def test_patch_record(self):\n pass","def test_trim_row(self):\n row = {'PROJ_NO': 'A'*500, 'LOCATION': 'B'*500, 'CHANGE_DATE': 'C'*500,\n 'PROJ_NAME1': 'D'*500, 'PCV_NAME': 'E'*500, 'SUMMARY': 'F'*500,\n 'STATE': 'G'*500, 'OVERS_PART': 'H'*500, 'OVERS_PCT': 'I'*500,\n 'PROJ_REQ': 'J'*500, 'UNIDENT_BAL': 'K'*500,\n 'ATTRIBUTE4': 'L'*500, 'SECTOR': 'M'*500, 'SUB_SECTOR': 'N'*500,\n 'LAST_UPDATED_FROM_PAYGOV': 'O'*500}\n should_trim = ('PROJ_NO', 'LOCATION', 'PROJ_NAME1', 'PCV_NAME',\n 'STATE', 'SECTOR')\n no_trim = [key for key in row if key not in should_trim]\n logger = Mock()\n row = sync.trim_row(row, logger)\n for key in should_trim:\n self.assertTrue(len(row[key]) < 500)\n for key in no_trim:\n self.assertEqual(len(row[key]), 500)\n self.assertEqual(logger.warning.call_count, len(should_trim))","def uncleanable():\n data = attrdict.AttrDict()\n data.backup_ids = set()\n data.image_ids = set()\n data.keypair_ids = set()\n data.server_ids = set()\n data.nodes_ids = set()\n data.chassis_ids = set()\n data.snapshot_ids = set()\n data.transfer_ids = set()\n data.volume_ids = set()\n return data","def _strip_successful_records(cls, messages, response):\n success_ids = {\n item['Id'] for item in response['Successful']\n }\n\n LOGGER.info('Removing sucessful message indices from batch: %s', success_ids)\n\n for success_id in success_ids:\n # Get the successful message by ID and remove it\n message = cls._extract_message_by_id(messages, success_id)\n if not message:\n continue\n messages.remove(message)","def test_record_without_values(self):\n zone = Zone('test.example.com')\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\n self.assertListEqual(record.values, [])","def test_entities__Entity__removeField__1(entities, entity_with_field, field):\n assert field in entities.values()\n entity_with_field.removeField(field)\n assert field not in entities.values()","def _check_process_resp(self, resp, expected_fields=None):\n\n if resp.status_code != LenovoRestClient.RESP_CODE_OK:\n raise cexc.NOSRestHTTPError(http_code=resp.status_code,\n http_reason=resp.reason, http_op=resp.request.method,\n url=resp.url, http_response=resp.text)\n\n rj = resp.json()\n\n if not expected_fields:\n return rj\n\n for field in expected_fields:\n try:\n val = rj[field]\n except KeyError:\n raise cexc.NOSJsonFieldNotFound(field=field, url=resp.url, json=rj)\n\n return rj"],"string":"[\n \"def strip_unwanted_fields(self, data, many, **kwargs):\\n unwanted_fields = [\\\"resource_type\\\"]\\n for field in unwanted_fields:\\n if field in data:\\n data.pop(field)\\n return data\",\n \"def prune(record: cfg.OpenAPI, outformat: cfg.Format = None) -> NoReturn:\\n content = record.oas\\n # Cleanup\\n content, _, _ = clean.remove_unused_components(content)\\n content = clean.remove_empty_objects(content)\\n # Output\\n out(content, outformat or record.oastype)\",\n \"def data_without(self, fields):\\n without = {}\\n data = json.loads(self.data())\\n for field, value in data.items():\\n if field not in fields:\\n without[field] = value\\n return json.dumps(without)\",\n \"def cleanup(self, data):\\n tmp = copy.copy(data)\\n for field in ('log_entries', 'instances',\\n 'picked_instances', 'saved_instances',\\n 'terminated_instances', 'skipped_instances'):\\n if field in tmp:\\n del tmp[field]\\n return tmp\",\n \"def pop_non_relevant_vuln_fields(data: Dict):\\n keys_to_keep = [\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"content_type\\\",\\n \\\"published_at\\\",\\n \\\"references\\\",\\n \\\"severity\\\",\\n \\\"solutions\\\",\\n \\\"alternate_ids\\\",\\n ]\\n for key in list(data):\\n if key not in keys_to_keep:\\n data.pop(key)\",\n \"def clean_record(self):\\n _dict = {\\n key: value for (key, value) in self.record.items() if not key in\\n BAMBOO_RESERVED_KEYS\\n }\\n return remove_mongo_reserved_keys(_dict)\",\n \"def assertEqualExceptMeta(self, original, response, msg=None, ignore=()):\\n self.assertTrue(response.pop('id')) # Sanity check that ID > 0\\n response.pop('url', None)\\n response.pop('created_at', None)\\n response.pop('updated_at', None)\\n response.pop('slug', None)\\n for field in ignore:\\n response.pop(field, None)\\n self.assertEqual(original, dict(response), msg)\",\n \"def _handle_dump_unknown(self, data, original):\\n for key, val in original.items():\\n if key not in self.fields:\\n data[key] = val\\n return data\",\n \"def hide_confidential_fields(record, fields=_CONFIDENTIAL_FIELDS):\\n if not(isinstance(record, dict) and fields):\\n return record\\n\\n keys = list(record.keys())\\n keys = (k for k in keys for f in fields if k == f or k.endswith('.'+f))\\n\\n return merge_dicts(record, {k: '********' for k in keys if record[k]})\",\n \"def revive(self):\\n field_name = self.get_delete_flag_field_name()\\n return self.update(**{field_name: None})\",\n \"def preprocess_record(record):\\n automatic_fields = ['created_at', 'modified_at']\\n record = serialize_fields(filter_out_dict_keys(record, automatic_fields))\\n\\n return record\",\n \"def hide_fields_in_newer_versions(obj):\\n if not api_utils.allow_start_end_audit_time():\\n obj.start_time = wtypes.Unset\\n obj.end_time = wtypes.Unset\\n if not api_utils.allow_force():\\n obj.force = wtypes.Unset\",\n \"def remove_all_fields(self):\\n self.fields = None\",\n \"def remove_record_failure():\\n\\t\\tpass\",\n \"def test_unchanged_handle_removals(self):\\n original_fields = self.form.fields\\n fields = original_fields.copy()\\n self.form.removed_fields = {}\\n self.form.remove_field_names = []\\n result = self.form.handle_removals(fields)\\n\\n self.assertEqual(len(original_fields), len(result))\\n self.assertEqual(0, len(self.form.removed_fields))\\n self.assertEqual(0, len(self.form.remove_field_names))\\n self.assertDictEqual(original_fields, result)\\n self.assertIs(fields, result)\",\n \"def test_handle_removals_missing_removed_fields(self):\\n original_fields = self.form.fields\\n fields = original_fields.copy()\\n self.form.remove_field_names = []\\n if hasattr(self.form, 'removed_fields'):\\n del self.form.removed_fields\\n result = self.form.handle_removals(fields)\\n\\n self.assertTrue(hasattr(self.form, 'removed_fields'))\\n self.assertEqual(len(original_fields), len(result))\\n self.assertEqual(0, len(self.form.removed_fields))\\n self.assertEqual(0, len(self.form.remove_field_names))\\n self.assertDictEqual(original_fields, result)\\n self.assertIs(fields, result)\",\n \"def remove_field_from_every_item_in_response_copy(context, name):\\n items = context.response_copy['items']\\n for item in items:\\n print(item)\\n if item['owner']['user_type'] == 'does_not_exist':\\n continue\\n if name in item:\\n del(item[name])\\n logging.debug(\\n 'Successfully removed field \\\"%s\\\" from item: %s', name,\\n item['question_id']) \\n else:\\n logging.debug(\\n 'Item %s does not contain field \\\"%s', name, item) \\n logging.debug(\\n 'Response copy after removing \\\"%s\\\" field:\\\\n%s', name,\\n pformat(context.response_copy))\",\n \"def _postprocess_record(record, hide=_CONFIDENTIAL_FIELDS):\\n record = hide_confidential_fields(record, hide)\\n record = unserialize_fields(record, hide)\\n\\n convert_float_timestamp2str(record)\\n\\n return record\",\n \"def drop(self, fields, inplace=True):\\n if inplace:\\n obj = self\\n else:\\n obj = copy.deepcopy(self)\\n\\n for field in list(fields):\\n del obj[field]\\n\\n return obj\",\n \"def _trimRecords(self):\\n self.highpassrecords.resize(self.nhighpassrecords, refcheck=False)\\n self.lowpassrecords.resize(self.nlowpassrecords, refcheck=False)\\n self.digitalsvalrecords.resize(self.ndigitalsvalrecords, refcheck=False)\\n # cleanup by deleting any struct arrays of len 0\\n for recname in ('highpassrecords', 'lowpassrecords', 'digitalsvalrecords'):\\n if len(self.__getattribute__(recname)) == 0:\\n self.__delattr__(recname)\",\n \"def pop_non_relevant_search_fields(data: Dict):\\n keys_to_keep = [\\\"title\\\", \\\"published_at\\\", \\\"identifier\\\"]\\n for key in list(data):\\n if key not in keys_to_keep:\\n data.pop(key)\",\n \"def clean_none_response(self):\\n\\n print(\\\"# Rows before non response are removed: {} \\\".format(len(self.data)))\\n self.data = self.data[self.data['names'].map(lambda d: len(d) > 0)]\\n print(\\\"# Rows after non response are removed: {} \\\".format(len(self.data)))\",\n \"def clear_field_values(self):\\n\\t\\tlogging.info(\\\"Clearing values in the field[] dictionary of the object\\\")\\n\\t\\tlogging.debug(\\\"Before = \\\" + str(self.field))\\n\\t\\tfor key, value in self.fields.items():\\n\\t\\t\\tself.field[str(key)] = None\\n\\t\\tlogging.debug(\\\"After = \\\" + str(self.field))\\n\\t\\treturn\",\n \"def test_handle_removals_named_fields_not_in_data(self):\\n original_fields = self.form.fields\\n fields = original_fields.copy()\\n remove_names = ['second', 'last']\\n original_data = self.form.data\\n data = original_data.copy()\\n data.appendlist(remove_names[1], 'test_data_last')\\n data._mutable = False\\n self.form.data = data\\n expected_fields = {name: field for name, field in fields.items() if name != remove_names[0]}\\n self.form.removed_fields = {}\\n self.form.remove_field_names = remove_names\\n result = self.form.handle_removals(fields)\\n\\n self.assertEqual(len(original_fields), len(result) + len(remove_names) - 1)\\n self.assertEqual(len(remove_names) - 1, len(self.form.removed_fields))\\n self.assertEqual(1, len(self.form.remove_field_names))\\n self.assertDictEqual(expected_fields, result)\\n self.assertIs(fields, result)\\n\\n self.form.data = original_data\",\n \"def ignore_field(self, record_contents, field, alignments=None):\\n return (\\\"\\\", [])\",\n \"def _fetch_fields(self, old_or_new, target_fields):\\r\\n \\r\\n returned_fields = []\\r\\n for target_field in target_fields:\\r\\n if target_field in [\\\",\\\", \\\"\\\\t\\\"]:\\r\\n returned_fields.append(target_field)\\r\\n else:\\r\\n try:\\r\\n returned_fields.append(str(getattr(old_or_new, target_field)))\\r\\n except:\\r\\n returned_fields.append(\\\"-\\\") \\r\\n return returned_fields\",\n \"def pop_non_relevant_module_fields(data: Dict):\\n keys_to_keep = [\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"content_type\\\",\\n \\\"published_at\\\",\\n \\\"references\\\",\\n \\\"architectures\\\",\\n \\\"authors\\\",\\n \\\"rank\\\",\\n \\\"reliability\\\",\\n ]\\n for key in list(data):\\n if key not in keys_to_keep:\\n data.pop(key)\",\n \"def unserialize_fields(record, hide=_CONFIDENTIAL_FIELDS,\\n fields=_SERIALIZED_FIELDS):\\n keys = list(record.keys())\\n keys = (k for k in keys for f in fields if k == f or k.endswith('.'+f))\\n\\n return merge_dicts(record, {\\n key: hide_confidential_fields(_unserialize(record[key]), hide)\\n for key in keys if record[key]\\n })\",\n \"def clean_up(exc):\\n\\n FORBIDDEN_FIELDS_TECH = [\\n \\\"categories\\\",\\n ]\\n\\n FORBIDDEN_FIELDS_BIO = [\\\"location\\\", \\\"product\\\"]\\n\\n for field in list(exc.keys()):\\n if exc[field] is None or exc[field] == \\\"None\\\":\\n del exc[field]\\n continue\\n\\n if exc[\\\"type\\\"] == \\\"biosphere\\\" and field in FORBIDDEN_FIELDS_BIO:\\n del exc[field]\\n if exc[\\\"type\\\"] == \\\"technosphere\\\" and field in FORBIDDEN_FIELDS_TECH:\\n del exc[field]\\n\\n return exc\",\n \"def reset_modified(self):\\n self.modified_fields = set()\\n\\n # compensate for us not having knowledge of certain fields changing\\n for field_name, field in self.schema.normal_fields.items():\\n if isinstance(field, ObjectField):\\n self.modified_fields.add(field_name)\",\n \"def task_2_remove_dict_fields(data: DT, redundant_keys: List[str]) -> DT:\\n dict2 = copy.deepcopy(data)\\n for item in dict2:\\n for key in redundant_keys:\\n item.pop(key)\\n return dict2\",\n \"def test_handle_removals_remove_field_names(self):\\n original_fields = self.form.fields\\n fields = original_fields.copy()\\n remove_names = ['second', 'last']\\n expected_fields = {name: field for name, field in fields.items() if name not in remove_names}\\n self.form.removed_fields = {}\\n self.form.remove_field_names = remove_names\\n result = self.form.handle_removals(fields)\\n\\n self.assertEqual(len(original_fields), len(result) + len(remove_names))\\n self.assertEqual(len(remove_names), len(self.form.removed_fields))\\n self.assertEqual(0, len(self.form.remove_field_names))\\n self.assertDictEqual(expected_fields, result)\\n self.assertIs(fields, result)\",\n \"def remove_unuseful(remove_fields: np.ndarray, remove_values: np.ndarray):\\n remove_fields = remove_fields[[0, 1, 2, 3, 4, 6]]\\n remove_values = remove_values[:, [0, 1, 2, 3, 4, 6]]\\n return remove_fields, remove_values\",\n \"def _fix_items(items):\\n for _, item in items.iteritems():\\n if 'url' in item['fields']:\\n del item['fields']['url']\\n return items\",\n \"def _handle_load_unknown(self, data, original):\\n for key, val in original.items():\\n if key not in self.fields:\\n data[key] = val\\n return data\",\n \"def purgeOmittedFields(self, rsrcType, rsrcs):\\n myRsrcs = copy.deepcopy(rsrcs)\\n [[a.pop(k, None) for k in util.resourceTypeFromString(\\n rsrcType)._omittedFields] for a in myRsrcs]\\n return myRsrcs\",\n \"def full_clean():\\n response_to_df_csv()\\n dirty_data = pd.read_csv(\\\"./data/dirty_data.csv\\\")\\n cleaned_data = dirty_data\\n cleaned_data = drop_cols(cleaned_data)\\n cleaned_data = lowercase_columns(cleaned_data)\\n cleaned_data = make_numeric(cleaned_data)\\n cleaned_data = drop_zero_pay(cleaned_data)\\n cleaned_data = add_log_col(cleaned_data)\\n cleaned_data.to_csv('./data/cleaned_for_testing.csv')\\n\\n return cleaned_data\",\n \"def task_2_remove_dict_fields(data: DT, redundant_keys: List[str]) -> DT:\\n return [{key: value for key, value in dic.items() if key not in redundant_keys} for dic in data]\",\n \"def test_handle_removals_add_if_named_in_data(self):\\n original_data = self.form.data\\n original_fields = self.form.fields\\n fields = original_fields.copy()\\n remove_names = ['second', 'last']\\n self.form.removed_fields = {name: fields.pop(name) for name in remove_names if name in fields}\\n self.form.remove_field_names = []\\n expected_fields = dict(**fields, **self.form.removed_fields)\\n test_data = original_data.copy()\\n test_data.update({name: f\\\"value_{name}\\\" for name in remove_names})\\n test_data._mutable = False\\n self.form.data = test_data\\n result = self.form.handle_removals(fields)\\n\\n self.assertEqual(len(original_fields), len(result))\\n self.assertEqual(0, len(self.form.removed_fields))\\n self.assertEqual(0, len(self.form.remove_field_names))\\n self.assertDictEqual(expected_fields, result)\\n self.assertDictEqual(original_fields, result)\\n self.assertIs(fields, result)\\n\\n self.data = original_data\",\n \"def depopulate(self, is_update):\\n fields = {}\\n schema = self.schema\\n for k, field in schema.fields.items():\\n is_modified = k in self.modified_fields\\n orig_v = getattr(self, k)\\n v = field.iset(\\n self,\\n orig_v,\\n is_update=is_update,\\n is_modified=is_modified\\n )\\n\\n if is_modified or v is not None:\\n if is_update and field.is_pk() and v == orig_v:\\n continue\\n\\n else:\\n fields[k] = v\\n\\n if not is_update:\\n for field_name in schema.required_fields.keys():\\n if field_name not in fields:\\n raise KeyError(\\\"Missing required field {}\\\".format(field_name))\\n\\n return fields\",\n \"def _rec_only_updated(cls, rec):\\n return rec.get('uplinked', None) \\\\\\n and not rec.get('queued', None) \\\\\\n and not rec.get('announced', None) \\\\\\n and not rec.get('blocked', None) \\\\\\n and not rec.get('finished', None) \\\\\\n and not rec.get('aborted', None)\",\n \"def _sanitise_fields(self, record):\\n sanitised = {}\\n for k, v in record.items():\\n new_key = k.replace('(', '_').replace(')', '_')\\n sanitised[new_key] = v\\n return sanitised\",\n \"def _modify(self, fields):\\n return fields\",\n \"def test_fetchSpecificHeaderFieldsNotWithoutHeaders(self):\\n self.assertFetchSpecificFieldsWithEmptyList(\\\"HEADER.FIELDS.NOT\\\")\",\n \"def filter_allowed_fields(self):\\n allowed_fields = super().filter_allowed_fields\\n # Remove assignment_id\\n allowed_fields.remove('assignment_id')\\n return allowed_fields\",\n \"def drop_info(batch):\\n if 'id' in batch:\\n batch.pop('id')\\n if 'rng' in batch:\\n batch.pop('rng')\\n return batch\",\n \"def check_returnable_fields(fields, result):\\n returnable_fields = get_returnable_fields(result, verbose=False)\\n for field in fields:\\n if field not in returnable_fields:\\n err_str = \\\"The field %s is not a returnable field for \\\" % (field)\\n err_str += \\\"result %s\\\" % (result)\\n raise ValueError(err_str)\",\n \"def _remove_none(self, data):\\r\\n for key, value in data.items():\\r\\n if value is None or isinstance(value, forms.Field):\\r\\n del data[key]\\r\\n if isinstance(value, dict):\\r\\n self._remove_none(data[key])\",\n \"def sweep_record(r):\\n return dict_sweep(r, vals=[None])\",\n \"def filter_excluded_fields(fields, Meta, exclude_dump_only):\\n exclude = getattr(Meta, \\\"exclude\\\", [])\\n if exclude_dump_only:\\n exclude += getattr(Meta, \\\"dump_only\\\", [])\\n\\n filtered_fields = OrderedDict(\\n (key, value) for key, value in fields.items() if key not in exclude\\n )\\n\\n return filtered_fields\",\n \"def prepare_duplication(self):\\n for field in self.fields:\\n ofield = self.fields[field]\\n\\n if self.duplicate:\\n if ofield.primary_key:\\n self.exclude_field(field)\\n continue\\n\\n if not self.auto_fields:\\n # add others if needed\\n if hasattr(ofield, 'auto_now') or \\\\\\n hasattr(ofield, 'auto_now_add'):\\n if ofield.auto_now or ofield.auto_now_add:\\n self.exclude_field(field)\\n continue\",\n \"def filter_unsupported_fields_from_config(self, configs, existing_endpoints, fields):\\n for field in fields:\\n if not any(field in e for e in existing_endpoints):\\n for c in configs:\\n endpoint = c['endpoint']\\n if field in endpoint and endpoint[field] is None:\\n del endpoint[field]\",\n \"def remove_ei(remove_fields: np.ndarray, remove_values: np.ndarray):\\n remove_fields = remove_fields[2:10]\\n remove_values = remove_values[:, 2:10]\\n return remove_fields, remove_values\",\n \"def conform_output_data(rowdict,fields_to_show=''):\\n rowdict['TimeStamp'] = str(rowdict['TimeStamp'])\\n if fields_to_show:\\n rowdict= removed_fields(fields_to_show, rowdict)\\n return rowdict\",\n \"def clean(self, value):\\n return [f.clean(v) for v,f in zip(value, self.fields)]\",\n \"def clean_metadata(metaobj):\\n if len(metaobj) == 1 and 'href' in metaobj[0] and '/api/programs/' in metaobj[0]['href']:\\n metaobj = metaobj # Keep lastUpdated for program\\n else:\\n metaobj = remove_subset_from_set(metaobj, 'lastUpdated')\\n metaobj = remove_subset_from_set(metaobj, 'lastUpdatedBy')\\n metaobj = remove_subset_from_set(metaobj, 'created')\\n metaobj = remove_subset_from_set(metaobj, 'createdBy')\\n metaobj = remove_subset_from_set(metaobj, 'href')\\n metaobj = remove_subset_from_set(metaobj, 'access')\\n metaobj = remove_subset_from_set(metaobj, 'favorites')\\n metaobj = remove_subset_from_set(metaobj, 'allItems')\\n metaobj = remove_subset_from_set(metaobj, 'displayName')\\n metaobj = remove_subset_from_set(metaobj, 'displayFormName')\\n metaobj = remove_subset_from_set(metaobj, 'displayShortName')\\n metaobj = remove_subset_from_set(metaobj, 'displayDenominatorDescription')\\n metaobj = remove_subset_from_set(metaobj, 'displayNumeratorDescription')\\n metaobj = remove_subset_from_set(metaobj, 'displayDescription')\\n metaobj = remove_subset_from_set(metaobj, 'interpretations')\\n if len(metaobj) > 0:\\n for subtag in ['dashboardItems', 'analyticsPeriodBoundaries', 'mapViews', 'user', 'userGroupAccesses',\\n 'programStageDataElements', 'programTrackedEntityAttributes',\\n 'trackedEntityTypeAttributes', 'userCredentials', 'legends', 'greyedFields']:\\n for i in range(0, len(metaobj)):\\n if subtag in metaobj[i]:\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'lastUpdated')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'lastUpdatedBy')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'created')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'createdBy')\\n # There is access : { read: true, delete: false ... } dictionary\\n # and there is access : \\\"rw----\\\"... Make sure we only delete the dictionary version\\n if subtag not in ['user', 'userGroupAccesses']:\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'access')\\n\\n if subtag == 'programTrackedEntityAttributes':\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'name')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayName')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayFormName')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayShortName')\\n\\n return metaobj\",\n \"def cleaning (data):\",\n \"def _cleanse_dict(original):\\n return {k: v for k, v in original.items() if \\\"_pass\\\" not in k}\",\n \"def _revert(self):\\n self.kwargs[\\\"collect\\\"].reset_scan_form_data()\",\n \"def assert_nonfield_entry_attributes_unchanged(original_copy, transformed_entry):\\n assert transformed_entry.start_line == original_copy.start_line\\n assert transformed_entry.raw == original_copy.raw\\n assert transformed_entry.entry_type == original_copy.entry_type\\n assert transformed_entry.key == original_copy.key\",\n \"def _clean_results(self):\\n\\t\\tif self.file_type == \\\"Automobile\\\":\\n\\t\\t\\tcols = [\\\"Year\\\", \\\"Mileage\\\", \\\"Price\\\"]\\n\\t\\t\\tself.data.Mileage.replace([',', 'mi.', 'nan', ' '], '', regex=True, inplace=True) # Fix mileage column\\n\\t\\t\\tself.data.Price.replace([',', '\\\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\\n\\t\\t\\tself.data[cols] = self.data[cols].apply(pd.to_numeric, errors='coerce') # Coerces errors into NaN values\\n\\t\\t\\tself.data.drop(self.data[self.data.Year < 2000].index, inplace=True) # Remove cars made before 2000\\n\\t\\t\\tself.data.drop(self.data[self.data.Price > 30000].index, inplace=True) # Remove cars over $30,000\\n\\t\\t\\tself.data.drop(self.data[(self.data.Mileage < 1000) | (self.data.Mileage > 300000)].index, inplace=True) # Remove cars with over 300,000 miles\\n\\t\\t\\tself.data['Age'] = 2018 - self.data['Year'] # Change years to Age\\n\\t\\telif self.file_type == \\\"Apartment\\\":\\n\\t\\t\\tself.data.Area.replace(['ft2'], '', regex=True, inplace=True) # Remove ft2 from square footage column\\n\\t\\t\\tself.data.Price.replace([',', '\\\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\\n\\t\\telse:\\n\\t\\t\\tself.data['Street'], self.data['City'], self.data['State'] = self.data['Address'].str.split(',', 2).str\\n\\t\\t\\tdel self.data.Address\\n\\t\\t\\tself.data.drop(self.data[self.data.Price > 1000000].index, inplace=True) # Remove houses worth more than $1 million\\n\\n\\t\\tself.data.replace('^\\\\s*$', np.nan, regex=True, inplace=True) # Replace all empty values with np.NaN\\n\\t\\tself.data = self.data.dropna(axis=1, how='all') # Remove Null Columns\\n\\t\\tself.data = self.data.apply(pd.to_numeric, errors='ignore') # Coerces errors into NaN values\",\n \"def test_api_fields(self) -> None:\\n expected_fields = set(Stream.API_FIELDS) | {\\\"stream_id\\\"}\\n expected_fields -= {\\\"id\\\", \\\"can_remove_subscribers_group_id\\\"}\\n expected_fields |= {\\\"can_remove_subscribers_group\\\"}\\n\\n stream_dict_fields = set(APIStreamDict.__annotations__.keys())\\n computed_fields = {\\\"is_announcement_only\\\", \\\"is_default\\\", \\\"stream_weekly_traffic\\\"}\\n\\n self.assertEqual(stream_dict_fields - computed_fields, expected_fields)\\n\\n expected_fields = set(Subscription.API_FIELDS)\\n\\n subscription_dict_fields = set(APISubscriptionDict.__annotations__.keys())\\n computed_fields = {\\\"in_home_view\\\", \\\"email_address\\\", \\\"stream_weekly_traffic\\\", \\\"subscribers\\\"}\\n # `APISubscriptionDict` is a subclass of `APIStreamDict`, therefore having all the\\n # fields in addition to the computed fields and `Subscription.API_FIELDS` that\\n # need to be excluded here.\\n self.assertEqual(\\n subscription_dict_fields - computed_fields - stream_dict_fields,\\n expected_fields,\\n )\",\n \"def unmigrated(self):\\n return self.exclude(\\n Q(diff_hash__isnull=False) &\\n (Q(parent_diff_hash__isnull=False) | Q(parent_diff64='')))\",\n \"def drop_fields(key):\\n return scom.drop_fields(key)\",\n \"def _cleanse_dict(original):\\n return dict((k, v) for k, v in original.items() if \\\"_pass\\\" not in k)\",\n \"def filter_out_non_helpful_records(data_records):\\n for r in data_records:\\n if r.get_cruciality() is None:\\n continue\\n # We only want records that got exactly 3 votes\\n if len(r.cruciality) != 3:\\n continue\\n else:\\n yield r\",\n \"def clean(self):\\n cleaned_data = super().clean()\\n cleaned_data = {key: field for key, field in cleaned_data.items()\\n if field is not None}\\n return cleaned_data\",\n \"def _test_bad_request_omit_field(self, user, fields, omit_field, zendesk_mock_class, datadog_mock):\\r\\n filtered_fields = {k: v for (k, v) in fields.items() if k != omit_field}\\r\\n resp = self._build_and_run_request(user, filtered_fields)\\r\\n self._assert_bad_request(resp, omit_field, zendesk_mock_class, datadog_mock)\",\n \"def dict_cleanup(self, data):\\n for data_key, data_value in list(data.items()):\\n # TODO: Add DEBUG logging (?)\\n for filter_key, filter_value in self.required.items():\\n if filter_key in data_value.keys():\\n if isinstance(filter_value, str) and self.exact_match:\\n if data_value[filter_key] != filter_value:\\n del data[data_key]\\n break\\n elif isinstance(filter_value, str) and (not self.exact_match):\\n if data_value[filter_key] is None:\\n del data[data_key]\\n break\\n if filter_value not in data_value[filter_key]:\\n del data[data_key]\\n break\\n elif isinstance(filter_value, list) and self.exact_match:\\n if data_value[filter_key] not in filter_value:\\n del data[data_key]\\n break\\n elif isinstance(filter_value, list) and (not self.exact_match):\\n if data_value[filter_key] is None:\\n del data[data_key]\\n break\\n found_match = False\\n for filter_value_item in filter_value:\\n if filter_value_item in data_value[filter_key]:\\n found_match = True\\n if not found_match:\\n del data[data_key]\\n break\\n else:\\n self.logger.warning(msg=\\\"Dict_Cleanup: None of the cases matched. Data: %s Filter: %s\\\" % (data_value, self.filter))\\n # TODO: Handle other possible cases\\n else:\\n self.logger.warning(msg=\\\"Dict_Cleanup: Filter key: %s not present in Data: %s\\\" % (filter_key, data_value))\\n continue\\n\\n for data_key, data_value in list(data.items()):\\n # TODO: Add DEBUG logging (?)\\n for filter_key, filter_value in self.excluded.items():\\n if filter_key in data_value.keys():\\n if isinstance(filter_value, str) and self.exact_match:\\n if data_value[filter_key] == filter_value:\\n del data[data_key]\\n break\\n elif isinstance(filter_value, str) and (not self.exact_match):\\n if data_value[filter_key] is None:\\n continue\\n if filter_value in data_value[filter_key]:\\n del data[data_key]\\n break\\n elif isinstance(filter_value, list) and self.exact_match:\\n if data_value[filter_key] in filter_value:\\n del data[data_key]\\n break\\n elif isinstance(filter_value, list) and (not self.exact_match):\\n if data_value[filter_key] is None:\\n continue\\n found_match = False\\n for filter_value_item in filter_value:\\n if filter_value_item in data_value[filter_key]:\\n found_match = True\\n if found_match:\\n del data[data_key]\\n break\\n else:\\n self.logger.warning(msg=\\\"Dict_Cleanup: None of the cases matched. Data: %s Filter: %s\\\" % (data_value, self.filter))\\n # TODO: Handle other possible cases\\n else:\\n self.logger.warning(msg=\\\"Dict_Cleanup: Filter key: %s not present in Data: %s\\\" % (filter_key, data_value))\\n continue\\n return data\",\n \"def print_untouched(self, input_file: TextIO, output_file: TextIO) -> None:\\n reader = csv.DictReader(input_file)\\n columns = reader.fieldnames\\n\\n touched_columns = set(self.config.columns.keys())\\n untouched_columns = [\\n column for column in columns\\n if column not in touched_columns\\n ]\\n\\n if not untouched_columns:\\n return\\n\\n writer = csv.DictWriter(output_file, fieldnames=untouched_columns)\\n writer.writeheader()\\n\\n untouched_columns_set = set(untouched_columns)\\n for row in reader:\\n sliced_row = {\\n column_name: column_value\\n for column_name, column_value\\n in row.items()\\n if column_name in untouched_columns_set\\n }\\n writer.writerow(sliced_row)\",\n \"def _clean(self):\\n map(self.__delitem__, self.keys())\\n self._original = []\\n self._columns = {}\\n self._modified, self._deleted = {}, {}\",\n \"def clean_fields(self, exclude=None):\\n obj = self._obj\\n if obj is None:\\n return None\\n\\n self.event = self.clean_event(self.event)\\n self.resource_name = self.clean_resource_name(obj.__class__.__name__)\\n self.resource_id = obj.id\\n self.site = self.clean_site(obj)\\n\\n serializer_class = self.get_serializer_for_resource(self.resource_name)\\n serializer = serializer_class(obj)\\n self._resource = serializer.data\",\n \"def attributes(self):\\n attr = super(BaseDataRecord, self).attributes()\\n return [a for a in attr if a not in self.excluded()]\",\n \"def test_fetchSpecificHeaderFieldsWithoutHeaders(self):\\n self.assertFetchSpecificFieldsWithEmptyList(\\\"HEADER.FIELDS\\\")\",\n \"def _remove_data(things, lst_remove=None):\\n\\n for data in things:\\n data.pop(\\\"_sa_instance_state\\\", None)\\n data.pop(\\\"user_id\\\", None)\\n\\n if lst_remove is not None:\\n for str_remove in lst_remove:\\n if str_remove in data:\\n data.pop(str_remove, None)\\n\\n return things\",\n \"def inflateRecord(self, record):\\n # Dirty bit is turned off by default\\n return [record.recordID, record.recordData, 0, record.partitionFacility, record.partitionUser]\",\n \"def remove_data():\\n # Removing the existing data\\n col_answer_given.remove()\\n col_answer_not_given.remove()\\n col_q_not_given.remove()\\n col_to_summarize.remove()\",\n \"def undo_format_field_names(obj):\\n if json_api_settings.FORMAT_FIELD_NAMES:\\n return format_field_names(obj, \\\"underscore\\\")\\n\\n return obj\",\n \"def remove_access_request_field(self):\\n self.fields = XML_List(Elements.FIELDS, [field for field in self.fields if\\n field.FIELD_CONTENT_ATTRIBUTES != Elements.ACCESS_REQUESTS])\",\n \"def _remove_empty(self, data, many):\\n if not many:\\n for key in list(data):\\n if key == 'versions':\\n data.pop(key)\\n\\n return {\\n key: value for key, value in data.items()\\n if value is not None\\n }\\n for item in data:\\n for key in list(item):\\n if (key == 'versions') or (item[key] is None):\\n item.pop(key)\\n\\n return data\",\n \"def customization(record):\\n for popkey in pop_list:\\n if popkey in record:\\n record.pop(popkey)\\n return record\",\n \"def _remove_none_fields(klass, d):\\n\\n return dict((k, v) for k, v in d.iteritems() if v is not None)\",\n \"def fake_clean_row(row):\\n\\treturn row\",\n \"def _get_meta_only_fields(self, table_name, data_fields):\\n meta_only_fields = {}\\n for field in self.meta[table_name]['fields']:\\n if field['source_name'] not in data_fields:\\n # adds 'sql_name',None as key,value pairs in dict\\n meta_only_fields[field['sql_name']] = None\\n return meta_only_fields\",\n \"def _revert(self):\\n if self.kwargs.get(\\\"collect\\\"):\\n remove_exported_collect_data(self.kwargs[\\\"collect\\\"])\",\n \"def remove_read_only_fields(self):\\n self.fields = XML_List(Elements.FIELDS, [field for field in self.fields if\\n not field.read_only or not str_to_bool(field.read_only)])\",\n \"def clean_attrs(cls, diffsync: DiffSync, attrs):\\n return cls.clean_ids_or_attrs(diffsync, attrs)\",\n \"def cleanup_record(schema, record):\\n if not isinstance(record, dict) and not isinstance(record, list):\\n return record\\n\\n elif isinstance(record, list):\\n nr = []\\n for item in record:\\n nr.append(cleanup_record(schema, item))\\n return nr\\n\\n elif isinstance(record, dict):\\n nr = {}\\n for key, value in record.items():\\n nkey = bigquery_transformed_key(key)\\n nr[nkey] = cleanup_record(schema, value)\\n return nr\\n\\n else:\\n raise Exception(f\\\"unhandled instance of record: {record}\\\")\",\n \"def _trim_query_result(query_result):\\n good_stuff = ['name', 'cmc', 'colors', 'set', 'usd']\\n\\n # UGHHHH I hate this, but I don't feel like refactoring into map or comprehensions right now.\\n final = []\\n for card in query_result['data']:\\n card_dict = {}\\n for k, v in card.items():\\n if k in good_stuff:\\n card_dict[k] = v\\n card_dict['png'] = card['image_uris'].get('png')\\n if not card['image_uris'].get('png'):\\n raise IndexError('WTF: ', card['image_uris'], card['name'])\\n final.append(card_dict)\\n\\n return final\",\n \"def _export(self, filled_only=False, without_id=False):\\n response = dict()\\n for field in self.fields:\\n if filled_only and not self.fields.get(field).get():\\n continue\\n if field == '_id' and without_id:\\n continue\\n response[field] = self.fields.get(field).get()\\n return response\",\n \"def _remove_dot_notation(cls, document: dict):\\n field_names = [field.name for field in cls.__fields__]\\n unknown_fields = [\\n field_name for field_name in document if field_name not in field_names\\n ]\\n for unknown_field in unknown_fields:\\n known_field, field_value = cls._to_known_field(\\n unknown_field, document[unknown_field]\\n )\\n del document[unknown_field]\\n if known_field:\\n document.setdefault(known_field.name, {}).update(field_value)\\n elif unknown_field not in cls._skip_log_for_unknown_fields:\\n cls.logger.warning(f\\\"Skipping unknown field {unknown_field}.\\\")\",\n \"def get_empty_fields(self):\\n return [f for f in self.__dict__ if not self.__dict__[f]]\",\n \"def copy_fields(self, model):\\n fields = super(HistoricalRecords, self).copy_fields(model)\\n for name, field in self.additional_fields.items():\\n assert name not in fields\\n assert hasattr(self, 'get_%s_value' % name)\\n fields[name] = field\\n return fields\",\n \"def test_patch_record(self):\\n pass\",\n \"def test_trim_row(self):\\n row = {'PROJ_NO': 'A'*500, 'LOCATION': 'B'*500, 'CHANGE_DATE': 'C'*500,\\n 'PROJ_NAME1': 'D'*500, 'PCV_NAME': 'E'*500, 'SUMMARY': 'F'*500,\\n 'STATE': 'G'*500, 'OVERS_PART': 'H'*500, 'OVERS_PCT': 'I'*500,\\n 'PROJ_REQ': 'J'*500, 'UNIDENT_BAL': 'K'*500,\\n 'ATTRIBUTE4': 'L'*500, 'SECTOR': 'M'*500, 'SUB_SECTOR': 'N'*500,\\n 'LAST_UPDATED_FROM_PAYGOV': 'O'*500}\\n should_trim = ('PROJ_NO', 'LOCATION', 'PROJ_NAME1', 'PCV_NAME',\\n 'STATE', 'SECTOR')\\n no_trim = [key for key in row if key not in should_trim]\\n logger = Mock()\\n row = sync.trim_row(row, logger)\\n for key in should_trim:\\n self.assertTrue(len(row[key]) < 500)\\n for key in no_trim:\\n self.assertEqual(len(row[key]), 500)\\n self.assertEqual(logger.warning.call_count, len(should_trim))\",\n \"def uncleanable():\\n data = attrdict.AttrDict()\\n data.backup_ids = set()\\n data.image_ids = set()\\n data.keypair_ids = set()\\n data.server_ids = set()\\n data.nodes_ids = set()\\n data.chassis_ids = set()\\n data.snapshot_ids = set()\\n data.transfer_ids = set()\\n data.volume_ids = set()\\n return data\",\n \"def _strip_successful_records(cls, messages, response):\\n success_ids = {\\n item['Id'] for item in response['Successful']\\n }\\n\\n LOGGER.info('Removing sucessful message indices from batch: %s', success_ids)\\n\\n for success_id in success_ids:\\n # Get the successful message by ID and remove it\\n message = cls._extract_message_by_id(messages, success_id)\\n if not message:\\n continue\\n messages.remove(message)\",\n \"def test_record_without_values(self):\\n zone = Zone('test.example.com')\\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\\n self.assertListEqual(record.values, [])\",\n \"def test_entities__Entity__removeField__1(entities, entity_with_field, field):\\n assert field in entities.values()\\n entity_with_field.removeField(field)\\n assert field not in entities.values()\",\n \"def _check_process_resp(self, resp, expected_fields=None):\\n\\n if resp.status_code != LenovoRestClient.RESP_CODE_OK:\\n raise cexc.NOSRestHTTPError(http_code=resp.status_code,\\n http_reason=resp.reason, http_op=resp.request.method,\\n url=resp.url, http_response=resp.text)\\n\\n rj = resp.json()\\n\\n if not expected_fields:\\n return rj\\n\\n for field in expected_fields:\\n try:\\n val = rj[field]\\n except KeyError:\\n raise cexc.NOSJsonFieldNotFound(field=field, url=resp.url, json=rj)\\n\\n return rj\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6754392","0.66556805","0.62742776","0.62539446","0.6249638","0.6215634","0.6152936","0.60918176","0.60912836","0.6078683","0.602921","0.59926665","0.59876364","0.59813493","0.5973602","0.5944134","0.5934237","0.5881215","0.58473516","0.5843913","0.58421016","0.5796429","0.57958066","0.5783038","0.57799137","0.5715526","0.5700336","0.569984","0.5691415","0.5681901","0.56572366","0.56544715","0.56383735","0.5606199","0.55748725","0.5543447","0.5520286","0.5517068","0.5513111","0.5490309","0.54705834","0.54696834","0.5444105","0.5430919","0.54227203","0.5420909","0.5403444","0.54005086","0.53968114","0.538521","0.5382858","0.53755665","0.53544176","0.53505564","0.5348944","0.53436816","0.53406376","0.53249395","0.531807","0.53161794","0.53140265","0.5299228","0.529206","0.5289396","0.52838564","0.5280231","0.52763534","0.5259777","0.5237489","0.5235669","0.5225841","0.52218366","0.52163047","0.5205978","0.52049893","0.52026767","0.5200372","0.51997215","0.51972663","0.51941746","0.51875764","0.51846635","0.51795167","0.51696235","0.51614505","0.51453125","0.51309013","0.5124421","0.51135826","0.51005644","0.5100387","0.50945204","0.509346","0.5085237","0.5083171","0.5075376","0.50721216","0.5070755","0.5070041","0.5066802"],"string":"[\n \"0.6754392\",\n \"0.66556805\",\n \"0.62742776\",\n \"0.62539446\",\n \"0.6249638\",\n \"0.6215634\",\n \"0.6152936\",\n \"0.60918176\",\n \"0.60912836\",\n \"0.6078683\",\n \"0.602921\",\n \"0.59926665\",\n \"0.59876364\",\n \"0.59813493\",\n \"0.5973602\",\n \"0.5944134\",\n \"0.5934237\",\n \"0.5881215\",\n \"0.58473516\",\n \"0.5843913\",\n \"0.58421016\",\n \"0.5796429\",\n \"0.57958066\",\n \"0.5783038\",\n \"0.57799137\",\n \"0.5715526\",\n \"0.5700336\",\n \"0.569984\",\n \"0.5691415\",\n \"0.5681901\",\n \"0.56572366\",\n \"0.56544715\",\n \"0.56383735\",\n \"0.5606199\",\n \"0.55748725\",\n \"0.5543447\",\n \"0.5520286\",\n \"0.5517068\",\n \"0.5513111\",\n \"0.5490309\",\n \"0.54705834\",\n \"0.54696834\",\n \"0.5444105\",\n \"0.5430919\",\n \"0.54227203\",\n \"0.5420909\",\n \"0.5403444\",\n \"0.54005086\",\n \"0.53968114\",\n \"0.538521\",\n \"0.5382858\",\n \"0.53755665\",\n \"0.53544176\",\n \"0.53505564\",\n \"0.5348944\",\n \"0.53436816\",\n \"0.53406376\",\n \"0.53249395\",\n \"0.531807\",\n \"0.53161794\",\n \"0.53140265\",\n \"0.5299228\",\n \"0.529206\",\n \"0.5289396\",\n \"0.52838564\",\n \"0.5280231\",\n \"0.52763534\",\n \"0.5259777\",\n \"0.5237489\",\n \"0.5235669\",\n \"0.5225841\",\n \"0.52218366\",\n \"0.52163047\",\n \"0.5205978\",\n \"0.52049893\",\n \"0.52026767\",\n \"0.5200372\",\n \"0.51997215\",\n \"0.51972663\",\n \"0.51941746\",\n \"0.51875764\",\n \"0.51846635\",\n \"0.51795167\",\n \"0.51696235\",\n \"0.51614505\",\n \"0.51453125\",\n \"0.51309013\",\n \"0.5124421\",\n \"0.51135826\",\n \"0.51005644\",\n \"0.5100387\",\n \"0.50945204\",\n \"0.509346\",\n \"0.5085237\",\n \"0.5083171\",\n \"0.5075376\",\n \"0.50721216\",\n \"0.5070755\",\n \"0.5070041\",\n \"0.5066802\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5617131"},"document_rank":{"kind":"string","value":"33"}}},{"rowIdx":93775,"cells":{"query":{"kind":"string","value":"Used to get the zone associated with the record being worked on."},"document":{"kind":"string","value":"def get_zone(self):\n to_return = None\n try:\n to_return = self.ns1.loadZone(self.module.params.get('zone'))\n except ResourceException as re:\n if re.response.code == 404:\n if (\n self.module.params.get('ignore_missing_zone')\n and self.module.params.get('state') == \"absent\"\n ):\n # zone not found but we are in the absent state\n # and the user doesn't care that the zone doesn't exist\n # nothing to do and no change\n self.module.exit_json(changed=False)\n else:\n # generic error or user cares about missing zone\n self.module.fail_json(\n msg=\"error code %s - %s \" % (re.response.code, re.message)\n )\n return to_return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def zone(self):\n return self._zone","def get_zone(self):\n return self.project.get_flow().get_zone_of_object(self)","def zone(self) -> str:\n return pulumi.get(self, \"zone\")","def zone(self) -> str:\n return pulumi.get(self, \"zone\")","def zone(self) -> str:\n return self._zone","def zone(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone\")","def zone(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone\")","def get_zone(self, zone_id, records=True):\r\n mask = None\r\n if records:\r\n mask = 'resourceRecords'\r\n return self.service.getObject(id=zone_id, mask=mask)","def zone_name(self):\n return self._zone_name","def get_current_zone() -> Zone:\n return services.current_zone()","def access_zone(self):\n return self._access_zone","def getZoneId(self):\n return self.zoneId","def _get_zone():\n r = get('http://metadata.google.internal/'\n 'computeMetadata/v1/instance/zone',\n headers={'Metadata-Flavor': 'Google'})\n if r.status_code == 200:\n return sub(r'.+zones/(.+)', r'\\1', r.text)\n else:\n return ''","def zone_id(self) -> str:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> str:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> str:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> str:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> str:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[str]:\n return pulumi.get(self, \"zone_id\")","def local_zone():\n return get_localzone()","def getZoneId(self):\n #self.zoneId\n if self.zoneId:\n return self.zoneId\n else:\n self.notify.warning(\"no zone id available\")","def zone_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"zone_id\")","def sc_dns_zone(self):\n return self._sc_dns_zone","def get_zone(self, kwargs):\n dns_zone = kwargs[\"dns_zone\"]\n try:\n results = self.engine.query(\n self.engine.ZONE_FILTER(),\n base=','.join([f\"DC={dns_zone}\", \"CN=MicrosoftDNS,DC=DomainDNSZones\", self.engine.base_dn])\n )\n except LdapActiveDirectoryView.ActiveDirectoryLdapException as e:\n error(e)\n else:\n self.display(results)","def get_zone_db_details(self, context, id):\n zone_obj = self.dns_manager.get_zone_db_details(context, id)\n return zone_obj","def get_zone(self, zone_id):\n data = self.connection.request(\"/v2/domains/%s\" % (zone_id)).object[\"domain\"]\n\n return self._to_zone(data)","def get_zone(self, conn, host):\n fl = 'name=\"%s\"' % host\n request = conn.instances().aggregatedList(project=PROJECT, filter=fl)\n \twhile request is not None:\n \t\tresponse = request.execute()\n \t\tzones = response.get('items', {})\n \t\tfor zone in zones.values():\n \t\t\tfor inst in zone.get('instances', []):\n \t\t\t\tif inst['name'] == host:\n \t\t\t\t\treturn inst['zone'].split(\"/\")[-1]\n \t\trequest = conn.instances().aggregatedList_next(previous_request=request, previous_response=response)\n \traise Exception(\"Unable to determin the zone for instance %s\" % (host))","def get_zone_connection(self):\n return self.m_connection.zones","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"zone_id\")","def get_zone(zone_id: int, allow_unloaded_zones: bool=False) -> Zone:\n return services.get_zone(zone_id, allow_uninstantiated_zones=allow_unloaded_zones)","def get_zone(self, zone_id):\n zones = self.list_zones()\n zone = [zone for zone in zones if zone.id == zone_id]\n if len(zone) == 0:\n raise ZoneDoesNotExistError(\n driver=self, value=\"The zone doesn't exists\", zone_id=zone_id\n )\n return zone[0]","def time_zone(self):\n # type: () -> string_types\n return self._time_zone","def get_zone(self, zone_id):\n\n if zone_id not in self._zones:\n raise ZoneDoesNotExistError(driver=self, value=None, zone_id=zone_id)\n\n return self._zones[zone_id][\"zone\"]","def availability_zone(self) -> str:\n return pulumi.get(self, \"availability_zone\")","def time_zone(self) -> str:\n return pulumi.get(self, \"time_zone\")","def get_zone(cls, name):\n\n def get_closest(n):\n \"\"\"\n Return closest matching zone\n \"\"\"\n while n:\n try:\n return DNSZone.objects.get(name=n)\n except DNSZone.DoesNotExist:\n pass\n n = \".\".join(n.split(\".\")[1:])\n return None\n\n if not name:\n return None\n if is_ipv4(name):\n # IPv4 zone\n n = name.split(\".\")\n n.reverse()\n return get_closest(\"%s.in-addr.arpa\" % (\".\".join(n[1:])))\n elif is_ipv6(name):\n # IPv6 zone\n d = IPv6(name).digits\n d.reverse()\n c = \".\".join(d)\n return get_closest(\"%s.ip6.arpa\" % c) or get_closest(\"%s.ip6.int\" % c)\n else:\n return get_closest(name)","def get_single_zone(self, region):\n # TODO: Implement zones list\n # Hardcoded to us-west2\n z = {\n \"us-west2\": ['us-west2-a', 'us-west2-b', 'us-west2-c'],\n \"us-west1\": ['us-west1-a', 'us-west1-b', 'us-west1-c']\n }\n return z[region][:1]","def get_current_zone_id() -> int:\n return services.current_zone_id()","def availability_zone(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"availability_zone\")","def recurrence_time_zone(self):\n return self.__recurrence_time_zone","def getTaskZoneId(self):\n return self.getZoneId()","def _find_managed_zone(self, domain, record_name):\n\n zone_dns_name_guesses = [record_name] + dns_common.base_domain_name_guesses(domain)\n\n logger.debug(\"Guesses: \")\n for zone_name in zone_dns_name_guesses:\n logger.debug(\" - %s\", zone_name)\n\n for zone_name in zone_dns_name_guesses:\n # get the zone id\n try:\n logger.debug(\"looking for zone: %s\", zone_name)\n try:\n response = self.dns_client.get_zone(zone_name)\n if response.status == 200:\n logger.debug(\"Response data %s\", response.data)\n logger.debug(\"Found zone: %s\", zone_name)\n logger.debug(\"OCID: %s\", response.data.id)\n logger.debug(\"Compartment: %s\", response.data.compartment_id)\n return response.data.id, zone_name\n except oci.exceptions.ServiceError as e:\n logger.debug(\"Zone '%s' not found\", zone_name)\n except errors.PluginError as e:\n pass\n return None, None","def get_zone_id(ctx, param, zone_name):\n del ctx #unused\n del param #unused\n cf = CloudFlare.CloudFlare()\n zones = cf.zones.get(params={'name': zone_name})\n if len(zones) != 1:\n raise click.ClickException('Invalid zone name: {}'.format(zone_name))\n return (zones[0]['id'], zones[0]['name'])","def time_zone(self):\n\n\t\tg = geocoders.GoogleV3()\n\n\t\t#Gives the name of the timezone, ex: Africa/Luanda\n\t\ttimezone_name = str(g.timezone((self.latitude_value(), self.longitude_value())))\n\n\t\t#Returns the numeric value of the timezone, ex: +0100\n\t\treturn int(pytz.timezone(timezone_name).localize(datetime.datetime(2011,1,1)).strftime('%z'))/100","def availability_zone(self) -> Optional[str]:\n return pulumi.get(self, \"availability_zone\")","def show(self, req, id):\n zone_id = int(id)\n zone = api.zone_get(req.environ['nova.context'], zone_id)\n return dict(zone=_scrub_zone(zone))","async def get_zone_id(self) -> str:\n try:\n data: list[dict[str, str]] = await self.api.get(\n url=self._endpoint(query={\"name\": self.zone})\n )\n return data[0][\"id\"]\n except Exception as error:\n raise CloudflareZoneException(\"Could not get zone ID\") from error","def get_region(zone):\n return '-'.join(zone.split('-')[:2])","def get_region_db_detail(self, context, id):\n zone_obj = self.dns_manager.get_region_db_detail(context, id)\n return zone_obj","def time_zone(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"time_zone\")","def get_athlete_zones(self):\n pass","def __zone_object_address(self):\n\t\treturn self.__c_version.object_address()","def availability_zone(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"availability_zone\")","def get_zone_id(zone: Zone) -> int:\n return zone.id","def get_record(self, zone):\n to_return = None\n try:\n to_return = zone.loadRecord(self.module.params.get('name'),\n self.module.params.get('type').upper())\n except ResourceException as re:\n if re.response.code != 404:\n self.module.fail_json(\n msg=\"error code %s - %s \" % (re.response.code, re.message)\n )\n to_return = None\n return to_return","def get_record(self, zone_id, record_id):\n\n self.get_zone(zone_id=zone_id)\n zone_records = self._zones[zone_id][\"records\"]\n\n if record_id not in zone_records:\n raise RecordDoesNotExistError(record_id=record_id, value=None, driver=self)\n\n return zone_records[record_id]","def fetch_zone(self, level, code=None, before=None, after=None):\n zone = list(self.fetch_zones(level, code, before, after)\n .sort('-validity.start')\n .limit(1))\n return zone and zone[0] or None","def get_zone(zone=\"\", vsys=\"1\"):\n query = {\n \"type\": \"config\",\n \"action\": \"get\",\n \"xpath\": (\n \"/config/devices/entry[@name='localhost.localdomain']/vsys/entry[@name='vsys{}']/\"\n \"zone/entry[@name='{}']\".format(vsys, zone)\n ),\n }\n\n return __proxy__[\"panos.call\"](query)","def availability_zone(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"availability_zone\")","def availability_zone(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"availability_zone\")","def get_timezone(self) -> str:\n return self.AD.time_zone","def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"zones\")","def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"zones\")","def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"zones\")","def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"zones\")","def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"zones\")","def get_proximity_db_detail(self, context, id):\n zone_obj = self.dns_manager.get_proximity_db_detail(context, id)\n return zone_obj","def test_get_zone_from_account(self):\n account = Account('test-account')\n zone = Zone('test.example.com')\n account.add_zone(zone)\n self.assertEqual(account.get_zone('test.example.com'), zone)","def zones(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"zones\")","def zones(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"zones\")","def timezone(self):\n tz_data = self._router_request(\n self._make_request_data(\n 'getTimeZone',\n data=dict()\n )\n )\n\n return tz_data['data']","def zone_depth(self):\n return self._zone_depth","def original_start_time_zone(self):\n if \"originalStartTimeZone\" in self._prop_dict:\n return self._prop_dict[\"originalStartTimeZone\"]\n else:\n return None","def zones(self) -> pulumi.Output[Optional[Sequence[str]]]:\n return pulumi.get(self, \"zones\")","def zones(self) -> pulumi.Output[Optional[Sequence[str]]]:\n return pulumi.get(self, \"zones\")","def secondary_gce_zone(self) -> str:\n return pulumi.get(self, \"secondary_gce_zone\")","def get_zone(name: Optional[str] = None,\n private_zone: Optional[bool] = None,\n resource_record_set_count: Optional[int] = None,\n tags: Optional[Mapping[str, str]] = None,\n vpc_id: Optional[str] = None,\n zone_id: Optional[str] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetZoneResult:\n __args__ = dict()\n __args__['name'] = name\n __args__['privateZone'] = private_zone\n __args__['resourceRecordSetCount'] = resource_record_set_count\n __args__['tags'] = tags\n __args__['vpcId'] = vpc_id\n __args__['zoneId'] = zone_id\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\n __ret__ = pulumi.runtime.invoke('aws:route53/getZone:getZone', __args__, opts=opts, typ=GetZoneResult).value\n\n return AwaitableGetZoneResult(\n arn=pulumi.get(__ret__, 'arn'),\n caller_reference=pulumi.get(__ret__, 'caller_reference'),\n comment=pulumi.get(__ret__, 'comment'),\n id=pulumi.get(__ret__, 'id'),\n linked_service_description=pulumi.get(__ret__, 'linked_service_description'),\n linked_service_principal=pulumi.get(__ret__, 'linked_service_principal'),\n name=pulumi.get(__ret__, 'name'),\n name_servers=pulumi.get(__ret__, 'name_servers'),\n primary_name_server=pulumi.get(__ret__, 'primary_name_server'),\n private_zone=pulumi.get(__ret__, 'private_zone'),\n resource_record_set_count=pulumi.get(__ret__, 'resource_record_set_count'),\n tags=pulumi.get(__ret__, 'tags'),\n vpc_id=pulumi.get(__ret__, 'vpc_id'),\n zone_id=pulumi.get(__ret__, 'zone_id'))","def get_ipzone_connection(self):\n return self.m_connection.ip_zones","def create_zone(self, zone, serial=None):\r\n return self.service.createObject({\r\n 'name': zone,\r\n 'serial': serial or strftime('%Y%m%d01'),\r\n \"resourceRecords\": {}})","def zoning(self):\n return self._zoning","def zones(self) -> pulumi.Output[Sequence[str]]:\n return pulumi.get(self, \"zones\")","def ozone(self) -> float | None:\n return self._ozone","def GetZoneResourceSpec():\n return concepts.ResourceSpec(\n 'dataplex.projects.locations.lakes.zones',\n resource_name='zones',\n projectsId=concepts.DEFAULT_PROJECT_ATTRIBUTE_CONFIG,\n locationsId=LocationAttributeConfig(),\n lakesId=LakeAttributeConfig(),\n zonesId=ZoneAttributeConfig())","def get_private_dns_zone(self) -> Union[str, None]:\n return self._get_private_dns_zone(enable_validation=True)","def compute_zones(self):\n path = '/os-availability-zone/detail'\n res = self.compute.call(path, 'GET', data='', \n token=self.manager.identity.token)\n self.logger.debug('Get openstack availability zone: %s' % truncate(res))\n return res[0]['availabilityZoneInfo']","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None,\n name: Optional[pulumi.Input[str]] = None,\n priority: Optional[pulumi.Input[str]] = None,\n qualified_name: Optional[pulumi.Input[str]] = None,\n ttl: Optional[pulumi.Input[str]] = None,\n type: Optional[pulumi.Input[str]] = None,\n value: Optional[pulumi.Input[str]] = None,\n zone_id: Optional[pulumi.Input[str]] = None,\n zone_name: Optional[pulumi.Input[str]] = None) -> 'ZoneRecord':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = _ZoneRecordState.__new__(_ZoneRecordState)\n\n __props__.__dict__[\"name\"] = name\n __props__.__dict__[\"priority\"] = priority\n __props__.__dict__[\"qualified_name\"] = qualified_name\n __props__.__dict__[\"ttl\"] = ttl\n __props__.__dict__[\"type\"] = type\n __props__.__dict__[\"value\"] = value\n __props__.__dict__[\"zone_id\"] = zone_id\n __props__.__dict__[\"zone_name\"] = zone_name\n return ZoneRecord(resource_name, opts=opts, __props__=__props__)","def get_zonepath(self, refresh=False):\n return self.get_attr(ZONE_ENTRY['ZROOT'], refresh)","def time_zone_name(self):\n return icemac.addressbook.preferences.utils.get_time_zone_name()","def secondary_gce_zone(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"secondary_gce_zone\")","def time_zone():\n return timezone('Etc/GMT-10')"],"string":"[\n \"def zone(self):\\n return self._zone\",\n \"def get_zone(self):\\n return self.project.get_flow().get_zone_of_object(self)\",\n \"def zone(self) -> str:\\n return pulumi.get(self, \\\"zone\\\")\",\n \"def zone(self) -> str:\\n return pulumi.get(self, \\\"zone\\\")\",\n \"def zone(self) -> str:\\n return self._zone\",\n \"def zone(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone\\\")\",\n \"def zone(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone\\\")\",\n \"def get_zone(self, zone_id, records=True):\\r\\n mask = None\\r\\n if records:\\r\\n mask = 'resourceRecords'\\r\\n return self.service.getObject(id=zone_id, mask=mask)\",\n \"def zone_name(self):\\n return self._zone_name\",\n \"def get_current_zone() -> Zone:\\n return services.current_zone()\",\n \"def access_zone(self):\\n return self._access_zone\",\n \"def getZoneId(self):\\n return self.zoneId\",\n \"def _get_zone():\\n r = get('http://metadata.google.internal/'\\n 'computeMetadata/v1/instance/zone',\\n headers={'Metadata-Flavor': 'Google'})\\n if r.status_code == 200:\\n return sub(r'.+zones/(.+)', r'\\\\1', r.text)\\n else:\\n return ''\",\n \"def zone_id(self) -> str:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> str:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> str:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> str:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> str:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def local_zone():\\n return get_localzone()\",\n \"def getZoneId(self):\\n #self.zoneId\\n if self.zoneId:\\n return self.zoneId\\n else:\\n self.notify.warning(\\\"no zone id available\\\")\",\n \"def zone_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def sc_dns_zone(self):\\n return self._sc_dns_zone\",\n \"def get_zone(self, kwargs):\\n dns_zone = kwargs[\\\"dns_zone\\\"]\\n try:\\n results = self.engine.query(\\n self.engine.ZONE_FILTER(),\\n base=','.join([f\\\"DC={dns_zone}\\\", \\\"CN=MicrosoftDNS,DC=DomainDNSZones\\\", self.engine.base_dn])\\n )\\n except LdapActiveDirectoryView.ActiveDirectoryLdapException as e:\\n error(e)\\n else:\\n self.display(results)\",\n \"def get_zone_db_details(self, context, id):\\n zone_obj = self.dns_manager.get_zone_db_details(context, id)\\n return zone_obj\",\n \"def get_zone(self, zone_id):\\n data = self.connection.request(\\\"/v2/domains/%s\\\" % (zone_id)).object[\\\"domain\\\"]\\n\\n return self._to_zone(data)\",\n \"def get_zone(self, conn, host):\\n fl = 'name=\\\"%s\\\"' % host\\n request = conn.instances().aggregatedList(project=PROJECT, filter=fl)\\n \\twhile request is not None:\\n \\t\\tresponse = request.execute()\\n \\t\\tzones = response.get('items', {})\\n \\t\\tfor zone in zones.values():\\n \\t\\t\\tfor inst in zone.get('instances', []):\\n \\t\\t\\t\\tif inst['name'] == host:\\n \\t\\t\\t\\t\\treturn inst['zone'].split(\\\"/\\\")[-1]\\n \\t\\trequest = conn.instances().aggregatedList_next(previous_request=request, previous_response=response)\\n \\traise Exception(\\\"Unable to determin the zone for instance %s\\\" % (host))\",\n \"def get_zone_connection(self):\\n return self.m_connection.zones\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def get_zone(zone_id: int, allow_unloaded_zones: bool=False) -> Zone:\\n return services.get_zone(zone_id, allow_uninstantiated_zones=allow_unloaded_zones)\",\n \"def get_zone(self, zone_id):\\n zones = self.list_zones()\\n zone = [zone for zone in zones if zone.id == zone_id]\\n if len(zone) == 0:\\n raise ZoneDoesNotExistError(\\n driver=self, value=\\\"The zone doesn't exists\\\", zone_id=zone_id\\n )\\n return zone[0]\",\n \"def time_zone(self):\\n # type: () -> string_types\\n return self._time_zone\",\n \"def get_zone(self, zone_id):\\n\\n if zone_id not in self._zones:\\n raise ZoneDoesNotExistError(driver=self, value=None, zone_id=zone_id)\\n\\n return self._zones[zone_id][\\\"zone\\\"]\",\n \"def availability_zone(self) -> str:\\n return pulumi.get(self, \\\"availability_zone\\\")\",\n \"def time_zone(self) -> str:\\n return pulumi.get(self, \\\"time_zone\\\")\",\n \"def get_zone(cls, name):\\n\\n def get_closest(n):\\n \\\"\\\"\\\"\\n Return closest matching zone\\n \\\"\\\"\\\"\\n while n:\\n try:\\n return DNSZone.objects.get(name=n)\\n except DNSZone.DoesNotExist:\\n pass\\n n = \\\".\\\".join(n.split(\\\".\\\")[1:])\\n return None\\n\\n if not name:\\n return None\\n if is_ipv4(name):\\n # IPv4 zone\\n n = name.split(\\\".\\\")\\n n.reverse()\\n return get_closest(\\\"%s.in-addr.arpa\\\" % (\\\".\\\".join(n[1:])))\\n elif is_ipv6(name):\\n # IPv6 zone\\n d = IPv6(name).digits\\n d.reverse()\\n c = \\\".\\\".join(d)\\n return get_closest(\\\"%s.ip6.arpa\\\" % c) or get_closest(\\\"%s.ip6.int\\\" % c)\\n else:\\n return get_closest(name)\",\n \"def get_single_zone(self, region):\\n # TODO: Implement zones list\\n # Hardcoded to us-west2\\n z = {\\n \\\"us-west2\\\": ['us-west2-a', 'us-west2-b', 'us-west2-c'],\\n \\\"us-west1\\\": ['us-west1-a', 'us-west1-b', 'us-west1-c']\\n }\\n return z[region][:1]\",\n \"def get_current_zone_id() -> int:\\n return services.current_zone_id()\",\n \"def availability_zone(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"availability_zone\\\")\",\n \"def recurrence_time_zone(self):\\n return self.__recurrence_time_zone\",\n \"def getTaskZoneId(self):\\n return self.getZoneId()\",\n \"def _find_managed_zone(self, domain, record_name):\\n\\n zone_dns_name_guesses = [record_name] + dns_common.base_domain_name_guesses(domain)\\n\\n logger.debug(\\\"Guesses: \\\")\\n for zone_name in zone_dns_name_guesses:\\n logger.debug(\\\" - %s\\\", zone_name)\\n\\n for zone_name in zone_dns_name_guesses:\\n # get the zone id\\n try:\\n logger.debug(\\\"looking for zone: %s\\\", zone_name)\\n try:\\n response = self.dns_client.get_zone(zone_name)\\n if response.status == 200:\\n logger.debug(\\\"Response data %s\\\", response.data)\\n logger.debug(\\\"Found zone: %s\\\", zone_name)\\n logger.debug(\\\"OCID: %s\\\", response.data.id)\\n logger.debug(\\\"Compartment: %s\\\", response.data.compartment_id)\\n return response.data.id, zone_name\\n except oci.exceptions.ServiceError as e:\\n logger.debug(\\\"Zone '%s' not found\\\", zone_name)\\n except errors.PluginError as e:\\n pass\\n return None, None\",\n \"def get_zone_id(ctx, param, zone_name):\\n del ctx #unused\\n del param #unused\\n cf = CloudFlare.CloudFlare()\\n zones = cf.zones.get(params={'name': zone_name})\\n if len(zones) != 1:\\n raise click.ClickException('Invalid zone name: {}'.format(zone_name))\\n return (zones[0]['id'], zones[0]['name'])\",\n \"def time_zone(self):\\n\\n\\t\\tg = geocoders.GoogleV3()\\n\\n\\t\\t#Gives the name of the timezone, ex: Africa/Luanda\\n\\t\\ttimezone_name = str(g.timezone((self.latitude_value(), self.longitude_value())))\\n\\n\\t\\t#Returns the numeric value of the timezone, ex: +0100\\n\\t\\treturn int(pytz.timezone(timezone_name).localize(datetime.datetime(2011,1,1)).strftime('%z'))/100\",\n \"def availability_zone(self) -> Optional[str]:\\n return pulumi.get(self, \\\"availability_zone\\\")\",\n \"def show(self, req, id):\\n zone_id = int(id)\\n zone = api.zone_get(req.environ['nova.context'], zone_id)\\n return dict(zone=_scrub_zone(zone))\",\n \"async def get_zone_id(self) -> str:\\n try:\\n data: list[dict[str, str]] = await self.api.get(\\n url=self._endpoint(query={\\\"name\\\": self.zone})\\n )\\n return data[0][\\\"id\\\"]\\n except Exception as error:\\n raise CloudflareZoneException(\\\"Could not get zone ID\\\") from error\",\n \"def get_region(zone):\\n return '-'.join(zone.split('-')[:2])\",\n \"def get_region_db_detail(self, context, id):\\n zone_obj = self.dns_manager.get_region_db_detail(context, id)\\n return zone_obj\",\n \"def time_zone(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"time_zone\\\")\",\n \"def get_athlete_zones(self):\\n pass\",\n \"def __zone_object_address(self):\\n\\t\\treturn self.__c_version.object_address()\",\n \"def availability_zone(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"availability_zone\\\")\",\n \"def get_zone_id(zone: Zone) -> int:\\n return zone.id\",\n \"def get_record(self, zone):\\n to_return = None\\n try:\\n to_return = zone.loadRecord(self.module.params.get('name'),\\n self.module.params.get('type').upper())\\n except ResourceException as re:\\n if re.response.code != 404:\\n self.module.fail_json(\\n msg=\\\"error code %s - %s \\\" % (re.response.code, re.message)\\n )\\n to_return = None\\n return to_return\",\n \"def get_record(self, zone_id, record_id):\\n\\n self.get_zone(zone_id=zone_id)\\n zone_records = self._zones[zone_id][\\\"records\\\"]\\n\\n if record_id not in zone_records:\\n raise RecordDoesNotExistError(record_id=record_id, value=None, driver=self)\\n\\n return zone_records[record_id]\",\n \"def fetch_zone(self, level, code=None, before=None, after=None):\\n zone = list(self.fetch_zones(level, code, before, after)\\n .sort('-validity.start')\\n .limit(1))\\n return zone and zone[0] or None\",\n \"def get_zone(zone=\\\"\\\", vsys=\\\"1\\\"):\\n query = {\\n \\\"type\\\": \\\"config\\\",\\n \\\"action\\\": \\\"get\\\",\\n \\\"xpath\\\": (\\n \\\"/config/devices/entry[@name='localhost.localdomain']/vsys/entry[@name='vsys{}']/\\\"\\n \\\"zone/entry[@name='{}']\\\".format(vsys, zone)\\n ),\\n }\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"def availability_zone(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"availability_zone\\\")\",\n \"def availability_zone(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"availability_zone\\\")\",\n \"def get_timezone(self) -> str:\\n return self.AD.time_zone\",\n \"def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def get_proximity_db_detail(self, context, id):\\n zone_obj = self.dns_manager.get_proximity_db_detail(context, id)\\n return zone_obj\",\n \"def test_get_zone_from_account(self):\\n account = Account('test-account')\\n zone = Zone('test.example.com')\\n account.add_zone(zone)\\n self.assertEqual(account.get_zone('test.example.com'), zone)\",\n \"def zones(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def zones(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def timezone(self):\\n tz_data = self._router_request(\\n self._make_request_data(\\n 'getTimeZone',\\n data=dict()\\n )\\n )\\n\\n return tz_data['data']\",\n \"def zone_depth(self):\\n return self._zone_depth\",\n \"def original_start_time_zone(self):\\n if \\\"originalStartTimeZone\\\" in self._prop_dict:\\n return self._prop_dict[\\\"originalStartTimeZone\\\"]\\n else:\\n return None\",\n \"def zones(self) -> pulumi.Output[Optional[Sequence[str]]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def zones(self) -> pulumi.Output[Optional[Sequence[str]]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def secondary_gce_zone(self) -> str:\\n return pulumi.get(self, \\\"secondary_gce_zone\\\")\",\n \"def get_zone(name: Optional[str] = None,\\n private_zone: Optional[bool] = None,\\n resource_record_set_count: Optional[int] = None,\\n tags: Optional[Mapping[str, str]] = None,\\n vpc_id: Optional[str] = None,\\n zone_id: Optional[str] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetZoneResult:\\n __args__ = dict()\\n __args__['name'] = name\\n __args__['privateZone'] = private_zone\\n __args__['resourceRecordSetCount'] = resource_record_set_count\\n __args__['tags'] = tags\\n __args__['vpcId'] = vpc_id\\n __args__['zoneId'] = zone_id\\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\\n __ret__ = pulumi.runtime.invoke('aws:route53/getZone:getZone', __args__, opts=opts, typ=GetZoneResult).value\\n\\n return AwaitableGetZoneResult(\\n arn=pulumi.get(__ret__, 'arn'),\\n caller_reference=pulumi.get(__ret__, 'caller_reference'),\\n comment=pulumi.get(__ret__, 'comment'),\\n id=pulumi.get(__ret__, 'id'),\\n linked_service_description=pulumi.get(__ret__, 'linked_service_description'),\\n linked_service_principal=pulumi.get(__ret__, 'linked_service_principal'),\\n name=pulumi.get(__ret__, 'name'),\\n name_servers=pulumi.get(__ret__, 'name_servers'),\\n primary_name_server=pulumi.get(__ret__, 'primary_name_server'),\\n private_zone=pulumi.get(__ret__, 'private_zone'),\\n resource_record_set_count=pulumi.get(__ret__, 'resource_record_set_count'),\\n tags=pulumi.get(__ret__, 'tags'),\\n vpc_id=pulumi.get(__ret__, 'vpc_id'),\\n zone_id=pulumi.get(__ret__, 'zone_id'))\",\n \"def get_ipzone_connection(self):\\n return self.m_connection.ip_zones\",\n \"def create_zone(self, zone, serial=None):\\r\\n return self.service.createObject({\\r\\n 'name': zone,\\r\\n 'serial': serial or strftime('%Y%m%d01'),\\r\\n \\\"resourceRecords\\\": {}})\",\n \"def zoning(self):\\n return self._zoning\",\n \"def zones(self) -> pulumi.Output[Sequence[str]]:\\n return pulumi.get(self, \\\"zones\\\")\",\n \"def ozone(self) -> float | None:\\n return self._ozone\",\n \"def GetZoneResourceSpec():\\n return concepts.ResourceSpec(\\n 'dataplex.projects.locations.lakes.zones',\\n resource_name='zones',\\n projectsId=concepts.DEFAULT_PROJECT_ATTRIBUTE_CONFIG,\\n locationsId=LocationAttributeConfig(),\\n lakesId=LakeAttributeConfig(),\\n zonesId=ZoneAttributeConfig())\",\n \"def get_private_dns_zone(self) -> Union[str, None]:\\n return self._get_private_dns_zone(enable_validation=True)\",\n \"def compute_zones(self):\\n path = '/os-availability-zone/detail'\\n res = self.compute.call(path, 'GET', data='', \\n token=self.manager.identity.token)\\n self.logger.debug('Get openstack availability zone: %s' % truncate(res))\\n return res[0]['availabilityZoneInfo']\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n priority: Optional[pulumi.Input[str]] = None,\\n qualified_name: Optional[pulumi.Input[str]] = None,\\n ttl: Optional[pulumi.Input[str]] = None,\\n type: Optional[pulumi.Input[str]] = None,\\n value: Optional[pulumi.Input[str]] = None,\\n zone_id: Optional[pulumi.Input[str]] = None,\\n zone_name: Optional[pulumi.Input[str]] = None) -> 'ZoneRecord':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = _ZoneRecordState.__new__(_ZoneRecordState)\\n\\n __props__.__dict__[\\\"name\\\"] = name\\n __props__.__dict__[\\\"priority\\\"] = priority\\n __props__.__dict__[\\\"qualified_name\\\"] = qualified_name\\n __props__.__dict__[\\\"ttl\\\"] = ttl\\n __props__.__dict__[\\\"type\\\"] = type\\n __props__.__dict__[\\\"value\\\"] = value\\n __props__.__dict__[\\\"zone_id\\\"] = zone_id\\n __props__.__dict__[\\\"zone_name\\\"] = zone_name\\n return ZoneRecord(resource_name, opts=opts, __props__=__props__)\",\n \"def get_zonepath(self, refresh=False):\\n return self.get_attr(ZONE_ENTRY['ZROOT'], refresh)\",\n \"def time_zone_name(self):\\n return icemac.addressbook.preferences.utils.get_time_zone_name()\",\n \"def secondary_gce_zone(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"secondary_gce_zone\\\")\",\n \"def time_zone():\\n return timezone('Etc/GMT-10')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80801183","0.80179244","0.78354365","0.78354365","0.7710645","0.7473004","0.7473004","0.7236832","0.7219368","0.7201566","0.7194352","0.71658826","0.7155139","0.7087766","0.7087766","0.7087766","0.7087766","0.7087766","0.7074198","0.70047337","0.69664335","0.6922261","0.6922261","0.6922261","0.6922261","0.6909246","0.68641204","0.67307156","0.67055184","0.67014766","0.6664958","0.66637504","0.66637504","0.66637504","0.66637504","0.66637504","0.66637504","0.65948564","0.65948564","0.65725416","0.6558105","0.6510091","0.64893085","0.6462459","0.6459386","0.6445796","0.643298","0.64162153","0.637435","0.6321455","0.63124126","0.630595","0.63015807","0.6300784","0.62926716","0.62844276","0.6262941","0.62497824","0.6218445","0.61566716","0.61550194","0.6146055","0.61209667","0.60974526","0.6094525","0.6087934","0.6070217","0.6040557","0.6033958","0.6033958","0.601175","0.60006964","0.60006964","0.60006964","0.60006964","0.60006964","0.5984668","0.59796494","0.5968209","0.5968209","0.59540635","0.593846","0.59264773","0.59261215","0.59261215","0.59213907","0.5914421","0.5904294","0.5902851","0.58890885","0.5877776","0.58712953","0.58461136","0.5843507","0.5825854","0.58232486","0.5819667","0.5819563","0.58180267","0.58016276"],"string":"[\n \"0.80801183\",\n \"0.80179244\",\n \"0.78354365\",\n \"0.78354365\",\n \"0.7710645\",\n \"0.7473004\",\n \"0.7473004\",\n \"0.7236832\",\n \"0.7219368\",\n \"0.7201566\",\n \"0.7194352\",\n \"0.71658826\",\n \"0.7155139\",\n \"0.7087766\",\n \"0.7087766\",\n \"0.7087766\",\n \"0.7087766\",\n \"0.7087766\",\n \"0.7074198\",\n \"0.70047337\",\n \"0.69664335\",\n \"0.6922261\",\n \"0.6922261\",\n \"0.6922261\",\n \"0.6922261\",\n \"0.6909246\",\n \"0.68641204\",\n \"0.67307156\",\n \"0.67055184\",\n \"0.67014766\",\n \"0.6664958\",\n \"0.66637504\",\n \"0.66637504\",\n \"0.66637504\",\n \"0.66637504\",\n \"0.66637504\",\n \"0.66637504\",\n \"0.65948564\",\n \"0.65948564\",\n \"0.65725416\",\n \"0.6558105\",\n \"0.6510091\",\n \"0.64893085\",\n \"0.6462459\",\n \"0.6459386\",\n \"0.6445796\",\n \"0.643298\",\n \"0.64162153\",\n \"0.637435\",\n \"0.6321455\",\n \"0.63124126\",\n \"0.630595\",\n \"0.63015807\",\n \"0.6300784\",\n \"0.62926716\",\n \"0.62844276\",\n \"0.6262941\",\n \"0.62497824\",\n \"0.6218445\",\n \"0.61566716\",\n \"0.61550194\",\n \"0.6146055\",\n \"0.61209667\",\n \"0.60974526\",\n \"0.6094525\",\n \"0.6087934\",\n \"0.6070217\",\n \"0.6040557\",\n \"0.6033958\",\n \"0.6033958\",\n \"0.601175\",\n \"0.60006964\",\n \"0.60006964\",\n \"0.60006964\",\n \"0.60006964\",\n \"0.60006964\",\n \"0.5984668\",\n \"0.59796494\",\n \"0.5968209\",\n \"0.5968209\",\n \"0.59540635\",\n \"0.593846\",\n \"0.59264773\",\n \"0.59261215\",\n \"0.59261215\",\n \"0.59213907\",\n \"0.5914421\",\n \"0.5904294\",\n \"0.5902851\",\n \"0.58890885\",\n \"0.5877776\",\n \"0.58712953\",\n \"0.58461136\",\n \"0.5843507\",\n \"0.5825854\",\n \"0.58232486\",\n \"0.5819667\",\n \"0.5819563\",\n \"0.58180267\",\n \"0.58016276\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.710795"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":93776,"cells":{"query":{"kind":"string","value":"Used to look up the record name and type from the specified zone."},"document":{"kind":"string","value":"def get_record(self, zone):\n to_return = None\n try:\n to_return = zone.loadRecord(self.module.params.get('name'),\n self.module.params.get('type').upper())\n except ResourceException as re:\n if re.response.code != 404:\n self.module.fail_json(\n msg=\"error code %s - %s \" % (re.response.code, re.message)\n )\n to_return = None\n return to_return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_r53_record_by_name ( route53_conn, dns_name, record_type = 'A' ) :\n dns_name = dns_name.lower( )\n zone_info = route53_conn.get_hosted_zone( route_53_hosted_zoneid )\n zone_name = zone_info[ 'GetHostedZoneResponse' ][ 'HostedZone' ][ 'Name' ]\n zone = route53_conn.get_zone( zone_name )\n return zone.find_records( name = dns_name, type = record_type )","def get_zone(self, zone_id, records=True):\r\n mask = None\r\n if records:\r\n mask = 'resourceRecords'\r\n return self.service.getObject(id=zone_id, mask=mask)","async def get_zone_records(\n self,\n zone_id: str,\n *,\n record_type: str | None = None,\n ) -> list[str] | None:\n data: list[dict[str, str]] | None = await self.api.get(\n self._endpoint(\n path=f\"{zone_id}/dns_records\",\n query={\"per_page\": \"100\", \"type\": record_type},\n )\n )\n\n if data is None:\n return None\n\n return [record[\"name\"] for record in data]","def _get_available_record_entry(self, zone):\n entries = zone.extra\n for entry in range(1, MAX_RECORD_ENTRIES + 1):\n subdomain = entries.get(\"S%s\" % entry)\n _type = entries.get(\"T%s\" % entry)\n data = entries.get(\"D%s\" % entry)\n if not any([subdomain, _type, data]):\n return entry\n return None","def _get_zone_specifics_for_zone_type(self, zone_type):\n with self.engine.connect() as conn:\n\n if zone_type == 'ward':\n table = 'project'\n elif zone_type == 'neighborhood_cluster':\n table = 'census_tract_to_neighborhood_cluster'\n else:\n table = 'census'\n\n query_result = conn.execute(\n 'select distinct {zone} from {table};'.format(zone=zone_type,\n table=table))\n zone_specifics = [row[0] for row in query_result.fetchall()]\n # zones.append(zone)\n\n return zone_specifics","def get_record(self, zone_id, record_id):\n\n self.get_zone(zone_id=zone_id)\n zone_records = self._zones[zone_id][\"records\"]\n\n if record_id not in zone_records:\n raise RecordDoesNotExistError(record_id=record_id, value=None, driver=self)\n\n return zone_records[record_id]","def get_zone(self, kwargs):\n dns_zone = kwargs[\"dns_zone\"]\n try:\n results = self.engine.query(\n self.engine.ZONE_FILTER(),\n base=','.join([f\"DC={dns_zone}\", \"CN=MicrosoftDNS,DC=DomainDNSZones\", self.engine.base_dn])\n )\n except LdapActiveDirectoryView.ActiveDirectoryLdapException as e:\n error(e)\n else:\n self.display(results)","def get_record(self, zone_id, record_id):\n zone = self.get_zone(zone_id)\n try:\n if int(record_id) not in range(1, MAX_RECORD_ENTRIES + 1):\n raise RecordDoesNotExistError(\n value=\"Record doesn't exists\",\n driver=zone.driver,\n record_id=record_id,\n )\n except ValueError:\n raise WorldWideDNSError(value=\"Record id should be a string number\", driver=self)\n subdomain = zone.extra.get(\"S%s\" % record_id)\n type = zone.extra.get(\"T%s\" % record_id)\n data = zone.extra.get(\"D%s\" % record_id)\n record = self._to_record(record_id, subdomain, type, data, zone)\n return record","async def get_record_info(self, zone_id: str) -> list[CloudflareDNSRecord]:\n record_information: list[CloudflareDNSRecord] = []\n if self.records is None:\n self.records = []\n data = await self.get_zone_records(zone_id)\n\n if data is None:\n raise CloudflareException(f\"No records found for {zone_id}\")\n\n self.records = data\n\n if not self.records:\n return record_information\n\n for record in self.records:\n if self.zone not in record:\n record = f\"{record}.{self.zone}\"\n\n recorddata: list[dict[str, Any]] = await self.api.get(\n self._endpoint(\n path=f\"{zone_id}/dns_records\",\n query={\"name\": record},\n )\n )\n\n first_record = recorddata[0]\n record_information.append(\n CloudflareDNSRecord(\n content=first_record[\"content\"],\n id=first_record[\"id\"],\n name=first_record[\"name\"],\n proxied=first_record[\"proxied\"],\n type=first_record[\"type\"],\n )\n )\n return record_information","def get_record(self, zone_id, record_id):\n data = self.connection.request(\n \"/v2/domains/{}/records/{}\".format(zone_id, record_id)\n ).object[\"domain_record\"]\n\n # TODO: Any way of not using get_zone which polls the API again\n # without breaking the DNSDriver.get_record parameters?\n return self._to_record(data, self.get_zone(zone_id))","def _find_managed_zone(self, domain, record_name):\n\n zone_dns_name_guesses = [record_name] + dns_common.base_domain_name_guesses(domain)\n\n logger.debug(\"Guesses: \")\n for zone_name in zone_dns_name_guesses:\n logger.debug(\" - %s\", zone_name)\n\n for zone_name in zone_dns_name_guesses:\n # get the zone id\n try:\n logger.debug(\"looking for zone: %s\", zone_name)\n try:\n response = self.dns_client.get_zone(zone_name)\n if response.status == 200:\n logger.debug(\"Response data %s\", response.data)\n logger.debug(\"Found zone: %s\", zone_name)\n logger.debug(\"OCID: %s\", response.data.id)\n logger.debug(\"Compartment: %s\", response.data.compartment_id)\n return response.data.id, zone_name\n except oci.exceptions.ServiceError as e:\n logger.debug(\"Zone '%s' not found\", zone_name)\n except errors.PluginError as e:\n pass\n return None, None","def test_record_name(self):\n zone = Zone('test.example.com')\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\n self.assertEqual(record.name, 'test-record')","def get_dns_A_record(self, zone_name):\n a_record = None\n\n a_record = self._get_cache(zone_name) # check memcached server\n if a_record is not None:\n return a_record\n\n r = self.api.get_records(zone_name) # Retrive A record from Dozens\n a_record = self.extract_A_records(r[\"record\"])\n if a_record is not None:\n self._set_cache(zone_name, a_record)\n return a_record","def MakeRecordsFromZone(self):\n return self.core_helper_instance.AddFormattedRecords(\n self.zone_name, self.zone_file_string, self.view)","def get_record_type(rr, offset=0):\n (generic_type, _) = RR.fromData(rr,offset)\n return {\n RR.TYPE_A : RR_A,\n RR.TYPE_AAAA : RR_AAAA,\n RR.TYPE_NS : RR_NS,\n RR.TYPE_CNAME : RR_CNAME\n }[generic_type._type]","def get_zone(self, conn, host):\n fl = 'name=\"%s\"' % host\n request = conn.instances().aggregatedList(project=PROJECT, filter=fl)\n \twhile request is not None:\n \t\tresponse = request.execute()\n \t\tzones = response.get('items', {})\n \t\tfor zone in zones.values():\n \t\t\tfor inst in zone.get('instances', []):\n \t\t\t\tif inst['name'] == host:\n \t\t\t\t\treturn inst['zone'].split(\"/\")[-1]\n \t\trequest = conn.instances().aggregatedList_next(previous_request=request, previous_response=response)\n \traise Exception(\"Unable to determin the zone for instance %s\" % (host))","def get_zone_id(ctx, param, zone_name):\n del ctx #unused\n del param #unused\n cf = CloudFlare.CloudFlare()\n zones = cf.zones.get(params={'name': zone_name})\n if len(zones) != 1:\n raise click.ClickException('Invalid zone name: {}'.format(zone_name))\n return (zones[0]['id'], zones[0]['name'])","def list_zone(self, args):\r\n manager = DNSManager(self.client)\r\n table = Table(['id', 'record', 'type', 'ttl', 'value'])\r\n\r\n table.align['ttl'] = 'l'\r\n table.align['record'] = 'r'\r\n table.align['value'] = 'l'\r\n\r\n zone_id = resolve_id(manager.resolve_ids, args['<zone>'], name='zone')\r\n\r\n records = manager.get_records(\r\n zone_id,\r\n record_type=args.get('--type'),\r\n host=args.get('--record'),\r\n ttl=args.get('--ttl'),\r\n data=args.get('--data'),\r\n )\r\n\r\n for record in records:\r\n table.add_row([\r\n record['id'],\r\n record['host'],\r\n record['type'].upper(),\r\n record['ttl'],\r\n record['data']\r\n ])\r\n\r\n return table","def list_records(self, zone):\n return self._zones[zone.id][\"records\"].values()","def create_record(self, name, zone, type, data, extra=None):\n id = \"id-%s\" % (name)\n\n zone = self.get_zone(zone_id=zone.id)\n\n if id in self._zones[zone.id][\"records\"]:\n raise RecordAlreadyExistsError(record_id=id, value=None, driver=self)\n\n record = Record(id=id, name=name, type=type, data=data, extra=extra, zone=zone, driver=self)\n self._zones[zone.id][\"records\"][id] = record\n return record","def do_info_record_type(self, arg):\n try:\n record_types = {}\n for key, val in self.phil.mem_map.items():\n if arg in val:\n if val[arg]:\n record_types[key] = val[arg]\n print(dumps(record_types, sort_keys=True, indent=4))\n except KeyError as exc:\n print(\"Cannot parse {}\".format(exc))","def find_zone_by_name(self, zone_name):\n zones = self.client.collections.zones\n return next((z.id for z in zones if z.name == zone_name), None)","def get_zone_db_details(self, context, id):\n zone_obj = self.dns_manager.get_zone_db_details(context, id)\n return zone_obj","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None,\n name: Optional[pulumi.Input[str]] = None,\n priority: Optional[pulumi.Input[str]] = None,\n qualified_name: Optional[pulumi.Input[str]] = None,\n ttl: Optional[pulumi.Input[str]] = None,\n type: Optional[pulumi.Input[str]] = None,\n value: Optional[pulumi.Input[str]] = None,\n zone_id: Optional[pulumi.Input[str]] = None,\n zone_name: Optional[pulumi.Input[str]] = None) -> 'ZoneRecord':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = _ZoneRecordState.__new__(_ZoneRecordState)\n\n __props__.__dict__[\"name\"] = name\n __props__.__dict__[\"priority\"] = priority\n __props__.__dict__[\"qualified_name\"] = qualified_name\n __props__.__dict__[\"ttl\"] = ttl\n __props__.__dict__[\"type\"] = type\n __props__.__dict__[\"value\"] = value\n __props__.__dict__[\"zone_id\"] = zone_id\n __props__.__dict__[\"zone_name\"] = zone_name\n return ZoneRecord(resource_name, opts=opts, __props__=__props__)","def get_zone(self, zone_id):\n zones = self.list_zones()\n zone = [zone for zone in zones if zone.id == zone_id]\n if len(zone) == 0:\n raise ZoneDoesNotExistError(\n driver=self, value=\"The zone doesn't exists\", zone_id=zone_id\n )\n return zone[0]","def get_zone(self):\n to_return = None\n try:\n to_return = self.ns1.loadZone(self.module.params.get('zone'))\n except ResourceException as re:\n if re.response.code == 404:\n if (\n self.module.params.get('ignore_missing_zone')\n and self.module.params.get('state') == \"absent\"\n ):\n # zone not found but we are in the absent state\n # and the user doesn't care that the zone doesn't exist\n # nothing to do and no change\n self.module.exit_json(changed=False)\n else:\n # generic error or user cares about missing zone\n self.module.fail_json(\n msg=\"error code %s - %s \" % (re.response.code, re.message)\n )\n return to_return","def show(self, req, id):\n zone_id = int(id)\n zone = api.zone_get(req.environ['nova.context'], zone_id)\n return dict(zone=_scrub_zone(zone))","def create_record(self, name, zone, type, data, extra=None):\n if (extra is None) or (\"entry\" not in extra):\n # If no entry is specified, we look for an available one. If all\n # are full, raise error.\n record_id = self._get_available_record_entry(zone)\n if not record_id:\n raise WorldWideDNSError(value=\"All record entries are full\", driver=zone.driver)\n else:\n record_id = extra.get(\"entry\")\n if name == \"\":\n name = \"@\"\n if type not in self.RECORD_TYPE_MAP:\n raise RecordError(\n value=\"Record type is not allowed\",\n driver=zone.driver,\n record_id=record_id,\n )\n extra = {\n \"S%s\" % record_id: name,\n \"T%s\" % record_id: type,\n \"D%s\" % record_id: data,\n }\n zone = self.update_zone(zone, zone.domain, extra=extra)\n record = self.get_record(zone.id, record_id)\n return record","def get_zone(zone=\"\", vsys=\"1\"):\n query = {\n \"type\": \"config\",\n \"action\": \"get\",\n \"xpath\": (\n \"/config/devices/entry[@name='localhost.localdomain']/vsys/entry[@name='vsys{}']/\"\n \"zone/entry[@name='{}']\".format(vsys, zone)\n ),\n }\n\n return __proxy__[\"panos.call\"](query)","def getZoneRecords(self, filters=[]):\n return self._getRecords('zone', filters)","def get_zone(self, zone_id):\n data = self.connection.request(\"/v2/domains/%s\" % (zone_id)).object[\"domain\"]\n\n return self._to_zone(data)","def cli(ctx, domain, ip_address, hostname):\n zone = getzone(domain)\n #print('.%s:%s:%s' % (domain, ip_address, hostname))\n for r in zone:\n if r['type'] == 'CNAME':\n print('C%s:%s' %( r['name'], r['content']))\n elif r['type'] == 'TXT':\n print('\\'%s:%s' %( r['name'], r['content']))\n elif r['type'] == 'MX':\n pass\n elif r['type'] == 'A':\n print('=%s:%s' %( r['name'], r['content']))\n else:\n exit('unknown DNS record type: %s' % r['type'])","def select_zone(\n zone_name: str,\n zones: Dict[str, List[List[List[Tray]]]] = {}\n ) -> Union[List[List[List[Tray]]], None]:\n return zones.get(zone_name)","def get_infoblox_base_url(self, zone, record_type):\n zone_regex = self.__convert_zone_to_regex(zone)\n url = (\n \"https://\"\n + self.IBLOX_HOST\n + \"/wapi/v\"\n + self.IBLOX_VERSION\n + \"/record:\"\n + record_type\n + \"?view=External&name~=\"\n + zone_regex.pattern\n + \"{return_fields}{paging_info}\"\n )\n\n if record_type == \"zone\":\n url = (\n \"https://\"\n + self.IBLOX_HOST\n + \"/wapi/v\"\n + self.IBLOX_VERSION\n + \"/zone_auth?view=External&fqdn~=\"\n + zone_regex.pattern\n + \"{return_fields}{paging_info}\"\n )\n\n return url","def get_record_from_db(self, context, record_id):\n record = self.dns_manager.get_record_from_db(context, record_id)\n return record","def zone_name(self):\n return self._zone_name","def sa_read_zone_schedule_by_zonejob(self, zone_name, zone_job):\n with engine.begin() as conn:\n return (conn.execute(select([scheduled_jobs]).where(and_(scheduled_jobs.c.zone == (zone_name),\n scheduled_jobs.c.zone_job == (zone_job))))).fetchall()","def get_zone(cls, name):\n\n def get_closest(n):\n \"\"\"\n Return closest matching zone\n \"\"\"\n while n:\n try:\n return DNSZone.objects.get(name=n)\n except DNSZone.DoesNotExist:\n pass\n n = \".\".join(n.split(\".\")[1:])\n return None\n\n if not name:\n return None\n if is_ipv4(name):\n # IPv4 zone\n n = name.split(\".\")\n n.reverse()\n return get_closest(\"%s.in-addr.arpa\" % (\".\".join(n[1:])))\n elif is_ipv6(name):\n # IPv6 zone\n d = IPv6(name).digits\n d.reverse()\n c = \".\".join(d)\n return get_closest(\"%s.ip6.arpa\" % c) or get_closest(\"%s.ip6.int\" % c)\n else:\n return get_closest(name)","def fromZone(cls, zone, origin=\"\", ttl=0):\n return list(ZoneParser(zone, origin=origin, ttl=ttl))","def test_repr(self):\n zone = Zone('test.example.com')\n record = Record(zone, \"test-record\", {'type': 'A', 'ttl': 300})\n self.assertEqual(f'{record}', 'Record<A, test-record>')","def dig(zone_name, nameserver, rdatatype):\n host = nameserver\n port = 53\n\n if ':' in nameserver:\n host, port = nameserver.split(':')\n port = int(port)\n\n if isinstance(rdatatype, basestring):\n rdatatype = dns.rdatatype.from_text(rdatatype)\n\n query = prepare_query(zone_name, rdatatype)\n return dns.query.udp(query, host, timeout=1, port=port)","def decode_zone(obj, name):\n try:\n zone_obj = Zone([Card(c) for c in obj], name)\n except TypeError as e:\n raise GTREncodingError('Error decoding Zone: ' + e.message)\n\n return zone_obj","def get_db_records(self, context, zone_id):\n records = self.dns_manager.get_db_records(context, zone_id)\n return records","def getRecord(self,type,id,Class=None):\n idLower = id.lower()\n typeIds = self.indexed[type]\n if idLower in typeIds:\n return typeIds[idLower]\n elif Class:\n record = Class()\n record.id = id\n self.records.append(record)\n typeIds[idLower] = record\n return record\n else:\n return None","def find_zone_serial(self, zone_name):\n zone_name = zone_name.rstrip(\".\")\n LOG.debug(\"Finding zone: %s\", zone_name)\n try:\n return self._dnsutils.get_zone_serial(zone_name)\n except os_win_exc.DNSZoneNotFound:\n # Return None if the zone was not found\n return None","def test_repr_apex(self):\n zone = Zone('test.example.com')\n record = Record(zone, '', {'type': 'A', 'ttl': 300})\n self.assertEqual(f'{record}', 'Record<A>')","def verify_zone_exists(self, zone):\n for x in self._aggregates:\n if x.availability_zone == zone:\n agg = x\n break\n else:\n print 'No such zone %s' % zone\n return False\n print \"Found zone %s in host-agg %s\" % (zone, agg.name)\n return agg","def get_zone_ip(config, section):\n\n current_zone = api.domain.zone.record.list(config.get(section, \"apikey\"),\n get_zone_id(config, section), 0)\n ip = '0.0.0.0'\n\n # There may be more than one A record - we're interested in one with\n # the specific name (typically @ but could be sub domain)\n for d in current_zone:\n if d['type'] == 'A'and d['name'] == config.get(section, \"a_name\"):\n ip = d['value']\n\n return ip","def list_records(self, zone):\n data = self._paginated_request(\"/v2/domains/%s/records\" % (zone.id), \"domain_records\")\n # TODO: Not use list comprehension to add zone to record for proper data map\n # functionality? This passes a reference to zone for each data currently\n # to _to_record which returns a Record. map() does not take keywords\n return list(map(self._to_record, data, [zone for z in data]))","def _get_zone():\n r = get('http://metadata.google.internal/'\n 'computeMetadata/v1/instance/zone',\n headers={'Metadata-Flavor': 'Google'})\n if r.status_code == 200:\n return sub(r'.+zones/(.+)', r'\\1', r.text)\n else:\n return ''","def get_zone(self, zone_id):\n\n if zone_id not in self._zones:\n raise ZoneDoesNotExistError(driver=self, value=None, zone_id=zone_id)\n\n return self._zones[zone_id][\"zone\"]","def getRecord(self, rr):\n\t\tif rr.type == 1: return {'type': 'a', 'ipv4': socket.inet_ntoa(rr.rdata), 'hostname': rr.name}\n\t\telif rr.type == 28: return {'type': 'aaaa', 'ipv6': socket.inet_ntop(socket.AF_INET6, rr.rdata), 'hostname': rr.name}\n\t\telif rr.type == 5: return {'type': 'cname', 'hostname': rr.name, 'cname': rr.cname}\n\t\telif rr.type == 13: return {'type': 'hostinfo', 'hostname': rr.name, 'info': rr.rdata}\n\t\telif rr.type == 33: return {'type': 'srv', 'hostname': rr.srvname, 'port': rr.port, 'srv': rr.name.split('.')[-3], 'proto': rr.name.split('.')[-2]}\n\t\telif rr.type == 12: return {'type': 'ptr'}\n\t\telif rr.type == 16: return {'type': 'txt'}\n\t\telif rr.type == 10: return {'type': 'wtf'}","def __init__(__self__,\n resource_name: str,\n args: ZoneRecordArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def get_region(zone):\n return '-'.join(zone.split('-')[:2])","def zone(self, zone: str):\n\n self._zone = zone","def test_record_fqdn(self):\n zone = Zone('test.example.com')\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\n self.assertEqual(record.fqdn, 'test-record.test.example.com')","def getRR(self, name):\n reply = self.rpc.getZoneRecords(self.username,\n self.password,\n self.domain,\n name)\n if reply in (['BAD_INDATA'],\n ['RATE_LIMITED'],\n ['AUTH_ERROR']):\n raise Exception(\"RPC returned error: \" + reply[0])\n return reply","def check_OTHERS_records(zonefile_list,zonename):\n others_type_list = set([record for line in zonefile_list for record in DNS_RECORD_TYPE if record in line[1]])\n for other_type in others_type_list:\n print (SPACE_CHAR * 2) + \"-\"\n print (SPACE_CHAR * 4) + \"namevar : '\" + str(other_type) + \" for \" + zonename + \"'\"\n print (SPACE_CHAR * 4) + \"zone : '\" + zonename + \"'\"\n print (SPACE_CHAR * 4) + \"record_type : '\" + str(other_type) + \"'\"\n print (SPACE_CHAR * 4) + \"hash_data :\"\n for line in zonefile_list:\n if other_type in line[1].split():\n idx_after_other_type = line[1].split().index(other_type) + 1\n print (SPACE_CHAR * 6) + \"'\" + line[1].split()[0] + \"' :\"\n print (SPACE_CHAR * 8) + \"owner :\"\n print (SPACE_CHAR * 10) + \"'\" + ' '.join(line[1].split()[idx_after_other_type:]) + \"'\"","def get_region_db_detail(self, context, id):\n zone_obj = self.dns_manager.get_region_db_detail(context, id)\n return zone_obj","def record_type(values):\n field = basic.lookup(values, name='Record Type (one of %s)' % values)\n\n return field.setResultsName('record_type')","def test_get_zone_from_account(self):\n account = Account('test-account')\n zone = Zone('test.example.com')\n account.add_zone(zone)\n self.assertEqual(account.get_zone('test.example.com'), zone)","def _lookup(self, name, cls, qtype, timeout):\n if name == 'getHostByNameTest':\n rr = dns.RRHeader(name=name, type=dns.A, cls=cls, ttl=60,\n payload=dns.Record_A(address='127.0.0.1', ttl=60))\n else:\n rr = dns.RRHeader(name=name, type=qtype, cls=cls, ttl=60)\n\n results = [rr]\n authority = []\n addtional = []\n return defer.succeed((results, authority, addtional))","def get_zone(zone_id: int, allow_unloaded_zones: bool=False) -> Zone:\n return services.get_zone(zone_id, allow_uninstantiated_zones=allow_unloaded_zones)","def getPlayersForZone(players, zone):\n player = filter(lambda p: p['zone'] != u'Unknown' and p['name'] != u'Dead', players)\n if zone is None: return players\n return filter(lambda p: p['zone'] == zone, players)","def get_zone(name: Optional[str] = None,\n private_zone: Optional[bool] = None,\n resource_record_set_count: Optional[int] = None,\n tags: Optional[Mapping[str, str]] = None,\n vpc_id: Optional[str] = None,\n zone_id: Optional[str] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetZoneResult:\n __args__ = dict()\n __args__['name'] = name\n __args__['privateZone'] = private_zone\n __args__['resourceRecordSetCount'] = resource_record_set_count\n __args__['tags'] = tags\n __args__['vpcId'] = vpc_id\n __args__['zoneId'] = zone_id\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\n __ret__ = pulumi.runtime.invoke('aws:route53/getZone:getZone', __args__, opts=opts, typ=GetZoneResult).value\n\n return AwaitableGetZoneResult(\n arn=pulumi.get(__ret__, 'arn'),\n caller_reference=pulumi.get(__ret__, 'caller_reference'),\n comment=pulumi.get(__ret__, 'comment'),\n id=pulumi.get(__ret__, 'id'),\n linked_service_description=pulumi.get(__ret__, 'linked_service_description'),\n linked_service_principal=pulumi.get(__ret__, 'linked_service_principal'),\n name=pulumi.get(__ret__, 'name'),\n name_servers=pulumi.get(__ret__, 'name_servers'),\n primary_name_server=pulumi.get(__ret__, 'primary_name_server'),\n private_zone=pulumi.get(__ret__, 'private_zone'),\n resource_record_set_count=pulumi.get(__ret__, 'resource_record_set_count'),\n tags=pulumi.get(__ret__, 'tags'),\n vpc_id=pulumi.get(__ret__, 'vpc_id'),\n zone_id=pulumi.get(__ret__, 'zone_id'))","def test_zone_name(self):\n zone = Zone('test.example.com')\n self.assertEqual(zone.name, 'test.example.com')","def get_zone_output(name: Optional[pulumi.Input[Optional[str]]] = None,\n private_zone: Optional[pulumi.Input[Optional[bool]]] = None,\n resource_record_set_count: Optional[pulumi.Input[Optional[int]]] = None,\n tags: Optional[pulumi.Input[Optional[Mapping[str, str]]]] = None,\n vpc_id: Optional[pulumi.Input[Optional[str]]] = None,\n zone_id: Optional[pulumi.Input[Optional[str]]] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetZoneResult]:\n ...","def get_records(self, zone_id, ttl=None, data=None, host=None,\r\n record_type=None):\r\n _filter = NestedDict()\r\n\r\n if ttl:\r\n _filter['resourceRecords']['ttl'] = query_filter(ttl)\r\n\r\n if host:\r\n _filter['resourceRecords']['host'] = query_filter(host)\r\n\r\n if data:\r\n _filter['resourceRecords']['data'] = query_filter(data)\r\n\r\n if record_type:\r\n _filter['resourceRecords']['type'] = query_filter(\r\n record_type.lower())\r\n\r\n results = self.service.getResourceRecords(\r\n id=zone_id,\r\n mask='id,expire,domainId,host,minimum,refresh,retry,'\r\n 'mxPriority,ttl,type,data,responsiblePerson',\r\n filter=_filter.to_dict(),\r\n )\r\n\r\n return results","def extract_zone(zone_url):\n zone = zone_url[zone_url.rfind('/')+1:]\n assert is_valid_zone(zone), zone\n return zone","def test_zone_repr(self):\n zone = Zone('example.com')\n self.assertEqual(f'{zone}', 'Zone<example.com>')","def zone(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone\")","def zone(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone\")","def test_add_record_to_zone(self):\n zone = Zone('test.example.com')\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\n zone.add_record(record)\n self.assertEqual(zone.records.get('test-record'), record)","def get_resource_record(self, resource_record_type):\n return self._get_record(resource_record_type)","def _get_sensor_name(self, zone=0, sensor_type=None):\n zone = int(zone)\n if zone == 0:\n return self._name + \" Last heartbeat\"\n else:\n zone_name = self._get_zone_name(zone)\n if sensor_type:\n return (\n self._name\n + (\" \" + zone_name + \" \" if zone_name else \" \")\n + sensor_type\n )\n else:\n _LOGGER.error(\n \"Hub: Get Sensor Name: Not allowed to create an entity_id without type, unless zone == 0.\"\n )\n return None","def create_zone(self, zone, serial=None):\r\n return self.service.createObject({\r\n 'name': zone,\r\n 'serial': serial or strftime('%Y%m%d01'),\r\n \"resourceRecords\": {}})","def get_zone_id(zone: Zone) -> int:\n return zone.id","def get_proximity_db_detail(self, context, id):\n zone_obj = self.dns_manager.get_proximity_db_detail(context, id)\n return zone_obj","def get_zone_from_flag_values(region, zone):\n zones = subprocess.check_output(\n ['gcloud', 'compute', 'zones', 'list', '--format', 'value(name)'])\n filtered_zones = [z.strip() for z in zones.splitlines()\n if z.strip().startswith(region)]\n if zone and zone not in filtered_zones:\n raise ValueError('zone: \"%s\" not in region %s' % (zone, region))\n if zone:\n return zone\n return random.choice(filtered_zones)","def get_dev_records(self, context, zone_id):\n records = self.dns_manager.get_dev_records(context, zone_id)\n return records","def zone(self) -> str:\n return pulumi.get(self, \"zone\")","def zone(self) -> str:\n return pulumi.get(self, \"zone\")","def dump_zone(self, zone_id):\r\n return self.service.getZoneFileContents(id=zone_id)","def test_record_without_type_should_fail(self):\n zone = Zone('test.example.com')\n with self.assertRaises(InvalidRecordData) as e:\n Record(zone, 'test-record', {'ttl': 300})\n self.assertEqual('missing key \\'type\\' in Record data',\n str(e.exception))","def create_record(self, name, zone, type, data, extra=None):\n params = {\"type\": self.RECORD_TYPE_MAP[type], \"name\": name, \"data\": data}\n if extra:\n try:\n params[\"priority\"] = extra[\"priority\"]\n except KeyError:\n params[\"priority\"] = None\n try:\n params[\"port\"] = extra[\"port\"]\n except KeyError:\n params[\"port\"] = None\n try:\n params[\"weight\"] = extra[\"weight\"]\n except KeyError:\n params[\"weight\"] = None\n\n if \"ttl\" in extra:\n params[\"ttl\"] = extra[\"ttl\"]\n\n res = self.connection.request(\n \"/v2/domains/%s/records\" % zone.id, data=json.dumps(params), method=\"POST\"\n )\n\n return Record(\n id=res.object[\"domain_record\"][\"id\"],\n name=res.object[\"domain_record\"][\"name\"],\n type=type,\n data=data,\n zone=zone,\n ttl=res.object[\"domain_record\"].get(\"ttl\", None),\n driver=self,\n extra=extra,\n )","def zone_id(self) -> Optional[str]:\n return pulumi.get(self, \"zone_id\")","async def update_dns_record(\n self,\n *,\n zone_id: str,\n record: CloudflareDNSRecord,\n ) -> dict[str, Any]:\n result: dict[str, Any] = await self.api.put(\n url=self._endpoint(path=f\"{zone_id}/dns_records/{record['id']}\"),\n json_data={\n \"type\": record[\"type\"],\n \"name\": record[\"name\"],\n \"content\": record[\"content\"],\n \"proxied\": record[\"proxied\"],\n },\n )\n return result","def zone(self) -> str:\n return self._zone","def get_instance(self, zone, instance, fields=None):\n assert is_valid_zone(zone), zone\n assert is_valid_instance(instance), instance\n try:\n return self.call_api(\n '/zones/%s/instances/%s' % (zone, instance),\n params={'fields': ','.join(fields)} if fields else None)\n except net.NotFoundError: # pragma: no cover\n return None","def get_members(self, context):\n zone_obj = self.dns_manager.get_members(context)\n return zone_obj","def __get_names(record: TNSRecord) -> Dict[str, str]:\n aliases = {'iau': record.name}\n internal_names = record.internal_names.split(',')\n for provider, pattern in Object.name_patterns.items():\n for name in internal_names:\n if pattern.match(name):\n aliases[provider] = name\n return aliases","def _get_records(self, domain, domain_id, record):\n for needed in [\"type\", \"source\", \"target\"]:\n if needed not in record:\n raise ValueError(\"{} not provided in record dict\".format(needed))\n\n if record[\"source\"] == \".\":\n fqdn = domain\n else:\n fqdn = \"{source}.{domain}\".format(source=record[\"source\"], domain=domain)\n return list(\n filter(\n lambda x: (\n x[\"source_idn\"] == fqdn\n and x[\"type\"] == record[\"type\"]\n and x[\"target\"] == record[\"target\"]\n ),\n self._get_request(\"/1/domain/{domain_id}/dns/record\".format(domain_id=domain_id)),\n )\n )","def create_record(self, zone_id, record, record_type, data, ttl=60):\r\n self.record.createObject({\r\n 'domainId': zone_id,\r\n 'ttl': ttl,\r\n 'host': record,\r\n 'type': record_type,\r\n 'data': data})","def zone_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"zone_id\")","def zone(self):\n return self._zone","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")","def zone_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"zone_id\")"],"string":"[\n \"def get_r53_record_by_name ( route53_conn, dns_name, record_type = 'A' ) :\\n dns_name = dns_name.lower( )\\n zone_info = route53_conn.get_hosted_zone( route_53_hosted_zoneid )\\n zone_name = zone_info[ 'GetHostedZoneResponse' ][ 'HostedZone' ][ 'Name' ]\\n zone = route53_conn.get_zone( zone_name )\\n return zone.find_records( name = dns_name, type = record_type )\",\n \"def get_zone(self, zone_id, records=True):\\r\\n mask = None\\r\\n if records:\\r\\n mask = 'resourceRecords'\\r\\n return self.service.getObject(id=zone_id, mask=mask)\",\n \"async def get_zone_records(\\n self,\\n zone_id: str,\\n *,\\n record_type: str | None = None,\\n ) -> list[str] | None:\\n data: list[dict[str, str]] | None = await self.api.get(\\n self._endpoint(\\n path=f\\\"{zone_id}/dns_records\\\",\\n query={\\\"per_page\\\": \\\"100\\\", \\\"type\\\": record_type},\\n )\\n )\\n\\n if data is None:\\n return None\\n\\n return [record[\\\"name\\\"] for record in data]\",\n \"def _get_available_record_entry(self, zone):\\n entries = zone.extra\\n for entry in range(1, MAX_RECORD_ENTRIES + 1):\\n subdomain = entries.get(\\\"S%s\\\" % entry)\\n _type = entries.get(\\\"T%s\\\" % entry)\\n data = entries.get(\\\"D%s\\\" % entry)\\n if not any([subdomain, _type, data]):\\n return entry\\n return None\",\n \"def _get_zone_specifics_for_zone_type(self, zone_type):\\n with self.engine.connect() as conn:\\n\\n if zone_type == 'ward':\\n table = 'project'\\n elif zone_type == 'neighborhood_cluster':\\n table = 'census_tract_to_neighborhood_cluster'\\n else:\\n table = 'census'\\n\\n query_result = conn.execute(\\n 'select distinct {zone} from {table};'.format(zone=zone_type,\\n table=table))\\n zone_specifics = [row[0] for row in query_result.fetchall()]\\n # zones.append(zone)\\n\\n return zone_specifics\",\n \"def get_record(self, zone_id, record_id):\\n\\n self.get_zone(zone_id=zone_id)\\n zone_records = self._zones[zone_id][\\\"records\\\"]\\n\\n if record_id not in zone_records:\\n raise RecordDoesNotExistError(record_id=record_id, value=None, driver=self)\\n\\n return zone_records[record_id]\",\n \"def get_zone(self, kwargs):\\n dns_zone = kwargs[\\\"dns_zone\\\"]\\n try:\\n results = self.engine.query(\\n self.engine.ZONE_FILTER(),\\n base=','.join([f\\\"DC={dns_zone}\\\", \\\"CN=MicrosoftDNS,DC=DomainDNSZones\\\", self.engine.base_dn])\\n )\\n except LdapActiveDirectoryView.ActiveDirectoryLdapException as e:\\n error(e)\\n else:\\n self.display(results)\",\n \"def get_record(self, zone_id, record_id):\\n zone = self.get_zone(zone_id)\\n try:\\n if int(record_id) not in range(1, MAX_RECORD_ENTRIES + 1):\\n raise RecordDoesNotExistError(\\n value=\\\"Record doesn't exists\\\",\\n driver=zone.driver,\\n record_id=record_id,\\n )\\n except ValueError:\\n raise WorldWideDNSError(value=\\\"Record id should be a string number\\\", driver=self)\\n subdomain = zone.extra.get(\\\"S%s\\\" % record_id)\\n type = zone.extra.get(\\\"T%s\\\" % record_id)\\n data = zone.extra.get(\\\"D%s\\\" % record_id)\\n record = self._to_record(record_id, subdomain, type, data, zone)\\n return record\",\n \"async def get_record_info(self, zone_id: str) -> list[CloudflareDNSRecord]:\\n record_information: list[CloudflareDNSRecord] = []\\n if self.records is None:\\n self.records = []\\n data = await self.get_zone_records(zone_id)\\n\\n if data is None:\\n raise CloudflareException(f\\\"No records found for {zone_id}\\\")\\n\\n self.records = data\\n\\n if not self.records:\\n return record_information\\n\\n for record in self.records:\\n if self.zone not in record:\\n record = f\\\"{record}.{self.zone}\\\"\\n\\n recorddata: list[dict[str, Any]] = await self.api.get(\\n self._endpoint(\\n path=f\\\"{zone_id}/dns_records\\\",\\n query={\\\"name\\\": record},\\n )\\n )\\n\\n first_record = recorddata[0]\\n record_information.append(\\n CloudflareDNSRecord(\\n content=first_record[\\\"content\\\"],\\n id=first_record[\\\"id\\\"],\\n name=first_record[\\\"name\\\"],\\n proxied=first_record[\\\"proxied\\\"],\\n type=first_record[\\\"type\\\"],\\n )\\n )\\n return record_information\",\n \"def get_record(self, zone_id, record_id):\\n data = self.connection.request(\\n \\\"/v2/domains/{}/records/{}\\\".format(zone_id, record_id)\\n ).object[\\\"domain_record\\\"]\\n\\n # TODO: Any way of not using get_zone which polls the API again\\n # without breaking the DNSDriver.get_record parameters?\\n return self._to_record(data, self.get_zone(zone_id))\",\n \"def _find_managed_zone(self, domain, record_name):\\n\\n zone_dns_name_guesses = [record_name] + dns_common.base_domain_name_guesses(domain)\\n\\n logger.debug(\\\"Guesses: \\\")\\n for zone_name in zone_dns_name_guesses:\\n logger.debug(\\\" - %s\\\", zone_name)\\n\\n for zone_name in zone_dns_name_guesses:\\n # get the zone id\\n try:\\n logger.debug(\\\"looking for zone: %s\\\", zone_name)\\n try:\\n response = self.dns_client.get_zone(zone_name)\\n if response.status == 200:\\n logger.debug(\\\"Response data %s\\\", response.data)\\n logger.debug(\\\"Found zone: %s\\\", zone_name)\\n logger.debug(\\\"OCID: %s\\\", response.data.id)\\n logger.debug(\\\"Compartment: %s\\\", response.data.compartment_id)\\n return response.data.id, zone_name\\n except oci.exceptions.ServiceError as e:\\n logger.debug(\\\"Zone '%s' not found\\\", zone_name)\\n except errors.PluginError as e:\\n pass\\n return None, None\",\n \"def test_record_name(self):\\n zone = Zone('test.example.com')\\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\\n self.assertEqual(record.name, 'test-record')\",\n \"def get_dns_A_record(self, zone_name):\\n a_record = None\\n\\n a_record = self._get_cache(zone_name) # check memcached server\\n if a_record is not None:\\n return a_record\\n\\n r = self.api.get_records(zone_name) # Retrive A record from Dozens\\n a_record = self.extract_A_records(r[\\\"record\\\"])\\n if a_record is not None:\\n self._set_cache(zone_name, a_record)\\n return a_record\",\n \"def MakeRecordsFromZone(self):\\n return self.core_helper_instance.AddFormattedRecords(\\n self.zone_name, self.zone_file_string, self.view)\",\n \"def get_record_type(rr, offset=0):\\n (generic_type, _) = RR.fromData(rr,offset)\\n return {\\n RR.TYPE_A : RR_A,\\n RR.TYPE_AAAA : RR_AAAA,\\n RR.TYPE_NS : RR_NS,\\n RR.TYPE_CNAME : RR_CNAME\\n }[generic_type._type]\",\n \"def get_zone(self, conn, host):\\n fl = 'name=\\\"%s\\\"' % host\\n request = conn.instances().aggregatedList(project=PROJECT, filter=fl)\\n \\twhile request is not None:\\n \\t\\tresponse = request.execute()\\n \\t\\tzones = response.get('items', {})\\n \\t\\tfor zone in zones.values():\\n \\t\\t\\tfor inst in zone.get('instances', []):\\n \\t\\t\\t\\tif inst['name'] == host:\\n \\t\\t\\t\\t\\treturn inst['zone'].split(\\\"/\\\")[-1]\\n \\t\\trequest = conn.instances().aggregatedList_next(previous_request=request, previous_response=response)\\n \\traise Exception(\\\"Unable to determin the zone for instance %s\\\" % (host))\",\n \"def get_zone_id(ctx, param, zone_name):\\n del ctx #unused\\n del param #unused\\n cf = CloudFlare.CloudFlare()\\n zones = cf.zones.get(params={'name': zone_name})\\n if len(zones) != 1:\\n raise click.ClickException('Invalid zone name: {}'.format(zone_name))\\n return (zones[0]['id'], zones[0]['name'])\",\n \"def list_zone(self, args):\\r\\n manager = DNSManager(self.client)\\r\\n table = Table(['id', 'record', 'type', 'ttl', 'value'])\\r\\n\\r\\n table.align['ttl'] = 'l'\\r\\n table.align['record'] = 'r'\\r\\n table.align['value'] = 'l'\\r\\n\\r\\n zone_id = resolve_id(manager.resolve_ids, args['<zone>'], name='zone')\\r\\n\\r\\n records = manager.get_records(\\r\\n zone_id,\\r\\n record_type=args.get('--type'),\\r\\n host=args.get('--record'),\\r\\n ttl=args.get('--ttl'),\\r\\n data=args.get('--data'),\\r\\n )\\r\\n\\r\\n for record in records:\\r\\n table.add_row([\\r\\n record['id'],\\r\\n record['host'],\\r\\n record['type'].upper(),\\r\\n record['ttl'],\\r\\n record['data']\\r\\n ])\\r\\n\\r\\n return table\",\n \"def list_records(self, zone):\\n return self._zones[zone.id][\\\"records\\\"].values()\",\n \"def create_record(self, name, zone, type, data, extra=None):\\n id = \\\"id-%s\\\" % (name)\\n\\n zone = self.get_zone(zone_id=zone.id)\\n\\n if id in self._zones[zone.id][\\\"records\\\"]:\\n raise RecordAlreadyExistsError(record_id=id, value=None, driver=self)\\n\\n record = Record(id=id, name=name, type=type, data=data, extra=extra, zone=zone, driver=self)\\n self._zones[zone.id][\\\"records\\\"][id] = record\\n return record\",\n \"def do_info_record_type(self, arg):\\n try:\\n record_types = {}\\n for key, val in self.phil.mem_map.items():\\n if arg in val:\\n if val[arg]:\\n record_types[key] = val[arg]\\n print(dumps(record_types, sort_keys=True, indent=4))\\n except KeyError as exc:\\n print(\\\"Cannot parse {}\\\".format(exc))\",\n \"def find_zone_by_name(self, zone_name):\\n zones = self.client.collections.zones\\n return next((z.id for z in zones if z.name == zone_name), None)\",\n \"def get_zone_db_details(self, context, id):\\n zone_obj = self.dns_manager.get_zone_db_details(context, id)\\n return zone_obj\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n priority: Optional[pulumi.Input[str]] = None,\\n qualified_name: Optional[pulumi.Input[str]] = None,\\n ttl: Optional[pulumi.Input[str]] = None,\\n type: Optional[pulumi.Input[str]] = None,\\n value: Optional[pulumi.Input[str]] = None,\\n zone_id: Optional[pulumi.Input[str]] = None,\\n zone_name: Optional[pulumi.Input[str]] = None) -> 'ZoneRecord':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = _ZoneRecordState.__new__(_ZoneRecordState)\\n\\n __props__.__dict__[\\\"name\\\"] = name\\n __props__.__dict__[\\\"priority\\\"] = priority\\n __props__.__dict__[\\\"qualified_name\\\"] = qualified_name\\n __props__.__dict__[\\\"ttl\\\"] = ttl\\n __props__.__dict__[\\\"type\\\"] = type\\n __props__.__dict__[\\\"value\\\"] = value\\n __props__.__dict__[\\\"zone_id\\\"] = zone_id\\n __props__.__dict__[\\\"zone_name\\\"] = zone_name\\n return ZoneRecord(resource_name, opts=opts, __props__=__props__)\",\n \"def get_zone(self, zone_id):\\n zones = self.list_zones()\\n zone = [zone for zone in zones if zone.id == zone_id]\\n if len(zone) == 0:\\n raise ZoneDoesNotExistError(\\n driver=self, value=\\\"The zone doesn't exists\\\", zone_id=zone_id\\n )\\n return zone[0]\",\n \"def get_zone(self):\\n to_return = None\\n try:\\n to_return = self.ns1.loadZone(self.module.params.get('zone'))\\n except ResourceException as re:\\n if re.response.code == 404:\\n if (\\n self.module.params.get('ignore_missing_zone')\\n and self.module.params.get('state') == \\\"absent\\\"\\n ):\\n # zone not found but we are in the absent state\\n # and the user doesn't care that the zone doesn't exist\\n # nothing to do and no change\\n self.module.exit_json(changed=False)\\n else:\\n # generic error or user cares about missing zone\\n self.module.fail_json(\\n msg=\\\"error code %s - %s \\\" % (re.response.code, re.message)\\n )\\n return to_return\",\n \"def show(self, req, id):\\n zone_id = int(id)\\n zone = api.zone_get(req.environ['nova.context'], zone_id)\\n return dict(zone=_scrub_zone(zone))\",\n \"def create_record(self, name, zone, type, data, extra=None):\\n if (extra is None) or (\\\"entry\\\" not in extra):\\n # If no entry is specified, we look for an available one. If all\\n # are full, raise error.\\n record_id = self._get_available_record_entry(zone)\\n if not record_id:\\n raise WorldWideDNSError(value=\\\"All record entries are full\\\", driver=zone.driver)\\n else:\\n record_id = extra.get(\\\"entry\\\")\\n if name == \\\"\\\":\\n name = \\\"@\\\"\\n if type not in self.RECORD_TYPE_MAP:\\n raise RecordError(\\n value=\\\"Record type is not allowed\\\",\\n driver=zone.driver,\\n record_id=record_id,\\n )\\n extra = {\\n \\\"S%s\\\" % record_id: name,\\n \\\"T%s\\\" % record_id: type,\\n \\\"D%s\\\" % record_id: data,\\n }\\n zone = self.update_zone(zone, zone.domain, extra=extra)\\n record = self.get_record(zone.id, record_id)\\n return record\",\n \"def get_zone(zone=\\\"\\\", vsys=\\\"1\\\"):\\n query = {\\n \\\"type\\\": \\\"config\\\",\\n \\\"action\\\": \\\"get\\\",\\n \\\"xpath\\\": (\\n \\\"/config/devices/entry[@name='localhost.localdomain']/vsys/entry[@name='vsys{}']/\\\"\\n \\\"zone/entry[@name='{}']\\\".format(vsys, zone)\\n ),\\n }\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"def getZoneRecords(self, filters=[]):\\n return self._getRecords('zone', filters)\",\n \"def get_zone(self, zone_id):\\n data = self.connection.request(\\\"/v2/domains/%s\\\" % (zone_id)).object[\\\"domain\\\"]\\n\\n return self._to_zone(data)\",\n \"def cli(ctx, domain, ip_address, hostname):\\n zone = getzone(domain)\\n #print('.%s:%s:%s' % (domain, ip_address, hostname))\\n for r in zone:\\n if r['type'] == 'CNAME':\\n print('C%s:%s' %( r['name'], r['content']))\\n elif r['type'] == 'TXT':\\n print('\\\\'%s:%s' %( r['name'], r['content']))\\n elif r['type'] == 'MX':\\n pass\\n elif r['type'] == 'A':\\n print('=%s:%s' %( r['name'], r['content']))\\n else:\\n exit('unknown DNS record type: %s' % r['type'])\",\n \"def select_zone(\\n zone_name: str,\\n zones: Dict[str, List[List[List[Tray]]]] = {}\\n ) -> Union[List[List[List[Tray]]], None]:\\n return zones.get(zone_name)\",\n \"def get_infoblox_base_url(self, zone, record_type):\\n zone_regex = self.__convert_zone_to_regex(zone)\\n url = (\\n \\\"https://\\\"\\n + self.IBLOX_HOST\\n + \\\"/wapi/v\\\"\\n + self.IBLOX_VERSION\\n + \\\"/record:\\\"\\n + record_type\\n + \\\"?view=External&name~=\\\"\\n + zone_regex.pattern\\n + \\\"{return_fields}{paging_info}\\\"\\n )\\n\\n if record_type == \\\"zone\\\":\\n url = (\\n \\\"https://\\\"\\n + self.IBLOX_HOST\\n + \\\"/wapi/v\\\"\\n + self.IBLOX_VERSION\\n + \\\"/zone_auth?view=External&fqdn~=\\\"\\n + zone_regex.pattern\\n + \\\"{return_fields}{paging_info}\\\"\\n )\\n\\n return url\",\n \"def get_record_from_db(self, context, record_id):\\n record = self.dns_manager.get_record_from_db(context, record_id)\\n return record\",\n \"def zone_name(self):\\n return self._zone_name\",\n \"def sa_read_zone_schedule_by_zonejob(self, zone_name, zone_job):\\n with engine.begin() as conn:\\n return (conn.execute(select([scheduled_jobs]).where(and_(scheduled_jobs.c.zone == (zone_name),\\n scheduled_jobs.c.zone_job == (zone_job))))).fetchall()\",\n \"def get_zone(cls, name):\\n\\n def get_closest(n):\\n \\\"\\\"\\\"\\n Return closest matching zone\\n \\\"\\\"\\\"\\n while n:\\n try:\\n return DNSZone.objects.get(name=n)\\n except DNSZone.DoesNotExist:\\n pass\\n n = \\\".\\\".join(n.split(\\\".\\\")[1:])\\n return None\\n\\n if not name:\\n return None\\n if is_ipv4(name):\\n # IPv4 zone\\n n = name.split(\\\".\\\")\\n n.reverse()\\n return get_closest(\\\"%s.in-addr.arpa\\\" % (\\\".\\\".join(n[1:])))\\n elif is_ipv6(name):\\n # IPv6 zone\\n d = IPv6(name).digits\\n d.reverse()\\n c = \\\".\\\".join(d)\\n return get_closest(\\\"%s.ip6.arpa\\\" % c) or get_closest(\\\"%s.ip6.int\\\" % c)\\n else:\\n return get_closest(name)\",\n \"def fromZone(cls, zone, origin=\\\"\\\", ttl=0):\\n return list(ZoneParser(zone, origin=origin, ttl=ttl))\",\n \"def test_repr(self):\\n zone = Zone('test.example.com')\\n record = Record(zone, \\\"test-record\\\", {'type': 'A', 'ttl': 300})\\n self.assertEqual(f'{record}', 'Record<A, test-record>')\",\n \"def dig(zone_name, nameserver, rdatatype):\\n host = nameserver\\n port = 53\\n\\n if ':' in nameserver:\\n host, port = nameserver.split(':')\\n port = int(port)\\n\\n if isinstance(rdatatype, basestring):\\n rdatatype = dns.rdatatype.from_text(rdatatype)\\n\\n query = prepare_query(zone_name, rdatatype)\\n return dns.query.udp(query, host, timeout=1, port=port)\",\n \"def decode_zone(obj, name):\\n try:\\n zone_obj = Zone([Card(c) for c in obj], name)\\n except TypeError as e:\\n raise GTREncodingError('Error decoding Zone: ' + e.message)\\n\\n return zone_obj\",\n \"def get_db_records(self, context, zone_id):\\n records = self.dns_manager.get_db_records(context, zone_id)\\n return records\",\n \"def getRecord(self,type,id,Class=None):\\n idLower = id.lower()\\n typeIds = self.indexed[type]\\n if idLower in typeIds:\\n return typeIds[idLower]\\n elif Class:\\n record = Class()\\n record.id = id\\n self.records.append(record)\\n typeIds[idLower] = record\\n return record\\n else:\\n return None\",\n \"def find_zone_serial(self, zone_name):\\n zone_name = zone_name.rstrip(\\\".\\\")\\n LOG.debug(\\\"Finding zone: %s\\\", zone_name)\\n try:\\n return self._dnsutils.get_zone_serial(zone_name)\\n except os_win_exc.DNSZoneNotFound:\\n # Return None if the zone was not found\\n return None\",\n \"def test_repr_apex(self):\\n zone = Zone('test.example.com')\\n record = Record(zone, '', {'type': 'A', 'ttl': 300})\\n self.assertEqual(f'{record}', 'Record<A>')\",\n \"def verify_zone_exists(self, zone):\\n for x in self._aggregates:\\n if x.availability_zone == zone:\\n agg = x\\n break\\n else:\\n print 'No such zone %s' % zone\\n return False\\n print \\\"Found zone %s in host-agg %s\\\" % (zone, agg.name)\\n return agg\",\n \"def get_zone_ip(config, section):\\n\\n current_zone = api.domain.zone.record.list(config.get(section, \\\"apikey\\\"),\\n get_zone_id(config, section), 0)\\n ip = '0.0.0.0'\\n\\n # There may be more than one A record - we're interested in one with\\n # the specific name (typically @ but could be sub domain)\\n for d in current_zone:\\n if d['type'] == 'A'and d['name'] == config.get(section, \\\"a_name\\\"):\\n ip = d['value']\\n\\n return ip\",\n \"def list_records(self, zone):\\n data = self._paginated_request(\\\"/v2/domains/%s/records\\\" % (zone.id), \\\"domain_records\\\")\\n # TODO: Not use list comprehension to add zone to record for proper data map\\n # functionality? This passes a reference to zone for each data currently\\n # to _to_record which returns a Record. map() does not take keywords\\n return list(map(self._to_record, data, [zone for z in data]))\",\n \"def _get_zone():\\n r = get('http://metadata.google.internal/'\\n 'computeMetadata/v1/instance/zone',\\n headers={'Metadata-Flavor': 'Google'})\\n if r.status_code == 200:\\n return sub(r'.+zones/(.+)', r'\\\\1', r.text)\\n else:\\n return ''\",\n \"def get_zone(self, zone_id):\\n\\n if zone_id not in self._zones:\\n raise ZoneDoesNotExistError(driver=self, value=None, zone_id=zone_id)\\n\\n return self._zones[zone_id][\\\"zone\\\"]\",\n \"def getRecord(self, rr):\\n\\t\\tif rr.type == 1: return {'type': 'a', 'ipv4': socket.inet_ntoa(rr.rdata), 'hostname': rr.name}\\n\\t\\telif rr.type == 28: return {'type': 'aaaa', 'ipv6': socket.inet_ntop(socket.AF_INET6, rr.rdata), 'hostname': rr.name}\\n\\t\\telif rr.type == 5: return {'type': 'cname', 'hostname': rr.name, 'cname': rr.cname}\\n\\t\\telif rr.type == 13: return {'type': 'hostinfo', 'hostname': rr.name, 'info': rr.rdata}\\n\\t\\telif rr.type == 33: return {'type': 'srv', 'hostname': rr.srvname, 'port': rr.port, 'srv': rr.name.split('.')[-3], 'proto': rr.name.split('.')[-2]}\\n\\t\\telif rr.type == 12: return {'type': 'ptr'}\\n\\t\\telif rr.type == 16: return {'type': 'txt'}\\n\\t\\telif rr.type == 10: return {'type': 'wtf'}\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ZoneRecordArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def get_region(zone):\\n return '-'.join(zone.split('-')[:2])\",\n \"def zone(self, zone: str):\\n\\n self._zone = zone\",\n \"def test_record_fqdn(self):\\n zone = Zone('test.example.com')\\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\\n self.assertEqual(record.fqdn, 'test-record.test.example.com')\",\n \"def getRR(self, name):\\n reply = self.rpc.getZoneRecords(self.username,\\n self.password,\\n self.domain,\\n name)\\n if reply in (['BAD_INDATA'],\\n ['RATE_LIMITED'],\\n ['AUTH_ERROR']):\\n raise Exception(\\\"RPC returned error: \\\" + reply[0])\\n return reply\",\n \"def check_OTHERS_records(zonefile_list,zonename):\\n others_type_list = set([record for line in zonefile_list for record in DNS_RECORD_TYPE if record in line[1]])\\n for other_type in others_type_list:\\n print (SPACE_CHAR * 2) + \\\"-\\\"\\n print (SPACE_CHAR * 4) + \\\"namevar : '\\\" + str(other_type) + \\\" for \\\" + zonename + \\\"'\\\"\\n print (SPACE_CHAR * 4) + \\\"zone : '\\\" + zonename + \\\"'\\\"\\n print (SPACE_CHAR * 4) + \\\"record_type : '\\\" + str(other_type) + \\\"'\\\"\\n print (SPACE_CHAR * 4) + \\\"hash_data :\\\"\\n for line in zonefile_list:\\n if other_type in line[1].split():\\n idx_after_other_type = line[1].split().index(other_type) + 1\\n print (SPACE_CHAR * 6) + \\\"'\\\" + line[1].split()[0] + \\\"' :\\\"\\n print (SPACE_CHAR * 8) + \\\"owner :\\\"\\n print (SPACE_CHAR * 10) + \\\"'\\\" + ' '.join(line[1].split()[idx_after_other_type:]) + \\\"'\\\"\",\n \"def get_region_db_detail(self, context, id):\\n zone_obj = self.dns_manager.get_region_db_detail(context, id)\\n return zone_obj\",\n \"def record_type(values):\\n field = basic.lookup(values, name='Record Type (one of %s)' % values)\\n\\n return field.setResultsName('record_type')\",\n \"def test_get_zone_from_account(self):\\n account = Account('test-account')\\n zone = Zone('test.example.com')\\n account.add_zone(zone)\\n self.assertEqual(account.get_zone('test.example.com'), zone)\",\n \"def _lookup(self, name, cls, qtype, timeout):\\n if name == 'getHostByNameTest':\\n rr = dns.RRHeader(name=name, type=dns.A, cls=cls, ttl=60,\\n payload=dns.Record_A(address='127.0.0.1', ttl=60))\\n else:\\n rr = dns.RRHeader(name=name, type=qtype, cls=cls, ttl=60)\\n\\n results = [rr]\\n authority = []\\n addtional = []\\n return defer.succeed((results, authority, addtional))\",\n \"def get_zone(zone_id: int, allow_unloaded_zones: bool=False) -> Zone:\\n return services.get_zone(zone_id, allow_uninstantiated_zones=allow_unloaded_zones)\",\n \"def getPlayersForZone(players, zone):\\n player = filter(lambda p: p['zone'] != u'Unknown' and p['name'] != u'Dead', players)\\n if zone is None: return players\\n return filter(lambda p: p['zone'] == zone, players)\",\n \"def get_zone(name: Optional[str] = None,\\n private_zone: Optional[bool] = None,\\n resource_record_set_count: Optional[int] = None,\\n tags: Optional[Mapping[str, str]] = None,\\n vpc_id: Optional[str] = None,\\n zone_id: Optional[str] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetZoneResult:\\n __args__ = dict()\\n __args__['name'] = name\\n __args__['privateZone'] = private_zone\\n __args__['resourceRecordSetCount'] = resource_record_set_count\\n __args__['tags'] = tags\\n __args__['vpcId'] = vpc_id\\n __args__['zoneId'] = zone_id\\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\\n __ret__ = pulumi.runtime.invoke('aws:route53/getZone:getZone', __args__, opts=opts, typ=GetZoneResult).value\\n\\n return AwaitableGetZoneResult(\\n arn=pulumi.get(__ret__, 'arn'),\\n caller_reference=pulumi.get(__ret__, 'caller_reference'),\\n comment=pulumi.get(__ret__, 'comment'),\\n id=pulumi.get(__ret__, 'id'),\\n linked_service_description=pulumi.get(__ret__, 'linked_service_description'),\\n linked_service_principal=pulumi.get(__ret__, 'linked_service_principal'),\\n name=pulumi.get(__ret__, 'name'),\\n name_servers=pulumi.get(__ret__, 'name_servers'),\\n primary_name_server=pulumi.get(__ret__, 'primary_name_server'),\\n private_zone=pulumi.get(__ret__, 'private_zone'),\\n resource_record_set_count=pulumi.get(__ret__, 'resource_record_set_count'),\\n tags=pulumi.get(__ret__, 'tags'),\\n vpc_id=pulumi.get(__ret__, 'vpc_id'),\\n zone_id=pulumi.get(__ret__, 'zone_id'))\",\n \"def test_zone_name(self):\\n zone = Zone('test.example.com')\\n self.assertEqual(zone.name, 'test.example.com')\",\n \"def get_zone_output(name: Optional[pulumi.Input[Optional[str]]] = None,\\n private_zone: Optional[pulumi.Input[Optional[bool]]] = None,\\n resource_record_set_count: Optional[pulumi.Input[Optional[int]]] = None,\\n tags: Optional[pulumi.Input[Optional[Mapping[str, str]]]] = None,\\n vpc_id: Optional[pulumi.Input[Optional[str]]] = None,\\n zone_id: Optional[pulumi.Input[Optional[str]]] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetZoneResult]:\\n ...\",\n \"def get_records(self, zone_id, ttl=None, data=None, host=None,\\r\\n record_type=None):\\r\\n _filter = NestedDict()\\r\\n\\r\\n if ttl:\\r\\n _filter['resourceRecords']['ttl'] = query_filter(ttl)\\r\\n\\r\\n if host:\\r\\n _filter['resourceRecords']['host'] = query_filter(host)\\r\\n\\r\\n if data:\\r\\n _filter['resourceRecords']['data'] = query_filter(data)\\r\\n\\r\\n if record_type:\\r\\n _filter['resourceRecords']['type'] = query_filter(\\r\\n record_type.lower())\\r\\n\\r\\n results = self.service.getResourceRecords(\\r\\n id=zone_id,\\r\\n mask='id,expire,domainId,host,minimum,refresh,retry,'\\r\\n 'mxPriority,ttl,type,data,responsiblePerson',\\r\\n filter=_filter.to_dict(),\\r\\n )\\r\\n\\r\\n return results\",\n \"def extract_zone(zone_url):\\n zone = zone_url[zone_url.rfind('/')+1:]\\n assert is_valid_zone(zone), zone\\n return zone\",\n \"def test_zone_repr(self):\\n zone = Zone('example.com')\\n self.assertEqual(f'{zone}', 'Zone<example.com>')\",\n \"def zone(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone\\\")\",\n \"def zone(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone\\\")\",\n \"def test_add_record_to_zone(self):\\n zone = Zone('test.example.com')\\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\\n zone.add_record(record)\\n self.assertEqual(zone.records.get('test-record'), record)\",\n \"def get_resource_record(self, resource_record_type):\\n return self._get_record(resource_record_type)\",\n \"def _get_sensor_name(self, zone=0, sensor_type=None):\\n zone = int(zone)\\n if zone == 0:\\n return self._name + \\\" Last heartbeat\\\"\\n else:\\n zone_name = self._get_zone_name(zone)\\n if sensor_type:\\n return (\\n self._name\\n + (\\\" \\\" + zone_name + \\\" \\\" if zone_name else \\\" \\\")\\n + sensor_type\\n )\\n else:\\n _LOGGER.error(\\n \\\"Hub: Get Sensor Name: Not allowed to create an entity_id without type, unless zone == 0.\\\"\\n )\\n return None\",\n \"def create_zone(self, zone, serial=None):\\r\\n return self.service.createObject({\\r\\n 'name': zone,\\r\\n 'serial': serial or strftime('%Y%m%d01'),\\r\\n \\\"resourceRecords\\\": {}})\",\n \"def get_zone_id(zone: Zone) -> int:\\n return zone.id\",\n \"def get_proximity_db_detail(self, context, id):\\n zone_obj = self.dns_manager.get_proximity_db_detail(context, id)\\n return zone_obj\",\n \"def get_zone_from_flag_values(region, zone):\\n zones = subprocess.check_output(\\n ['gcloud', 'compute', 'zones', 'list', '--format', 'value(name)'])\\n filtered_zones = [z.strip() for z in zones.splitlines()\\n if z.strip().startswith(region)]\\n if zone and zone not in filtered_zones:\\n raise ValueError('zone: \\\"%s\\\" not in region %s' % (zone, region))\\n if zone:\\n return zone\\n return random.choice(filtered_zones)\",\n \"def get_dev_records(self, context, zone_id):\\n records = self.dns_manager.get_dev_records(context, zone_id)\\n return records\",\n \"def zone(self) -> str:\\n return pulumi.get(self, \\\"zone\\\")\",\n \"def zone(self) -> str:\\n return pulumi.get(self, \\\"zone\\\")\",\n \"def dump_zone(self, zone_id):\\r\\n return self.service.getZoneFileContents(id=zone_id)\",\n \"def test_record_without_type_should_fail(self):\\n zone = Zone('test.example.com')\\n with self.assertRaises(InvalidRecordData) as e:\\n Record(zone, 'test-record', {'ttl': 300})\\n self.assertEqual('missing key \\\\'type\\\\' in Record data',\\n str(e.exception))\",\n \"def create_record(self, name, zone, type, data, extra=None):\\n params = {\\\"type\\\": self.RECORD_TYPE_MAP[type], \\\"name\\\": name, \\\"data\\\": data}\\n if extra:\\n try:\\n params[\\\"priority\\\"] = extra[\\\"priority\\\"]\\n except KeyError:\\n params[\\\"priority\\\"] = None\\n try:\\n params[\\\"port\\\"] = extra[\\\"port\\\"]\\n except KeyError:\\n params[\\\"port\\\"] = None\\n try:\\n params[\\\"weight\\\"] = extra[\\\"weight\\\"]\\n except KeyError:\\n params[\\\"weight\\\"] = None\\n\\n if \\\"ttl\\\" in extra:\\n params[\\\"ttl\\\"] = extra[\\\"ttl\\\"]\\n\\n res = self.connection.request(\\n \\\"/v2/domains/%s/records\\\" % zone.id, data=json.dumps(params), method=\\\"POST\\\"\\n )\\n\\n return Record(\\n id=res.object[\\\"domain_record\\\"][\\\"id\\\"],\\n name=res.object[\\\"domain_record\\\"][\\\"name\\\"],\\n type=type,\\n data=data,\\n zone=zone,\\n ttl=res.object[\\\"domain_record\\\"].get(\\\"ttl\\\", None),\\n driver=self,\\n extra=extra,\\n )\",\n \"def zone_id(self) -> Optional[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"async def update_dns_record(\\n self,\\n *,\\n zone_id: str,\\n record: CloudflareDNSRecord,\\n ) -> dict[str, Any]:\\n result: dict[str, Any] = await self.api.put(\\n url=self._endpoint(path=f\\\"{zone_id}/dns_records/{record['id']}\\\"),\\n json_data={\\n \\\"type\\\": record[\\\"type\\\"],\\n \\\"name\\\": record[\\\"name\\\"],\\n \\\"content\\\": record[\\\"content\\\"],\\n \\\"proxied\\\": record[\\\"proxied\\\"],\\n },\\n )\\n return result\",\n \"def zone(self) -> str:\\n return self._zone\",\n \"def get_instance(self, zone, instance, fields=None):\\n assert is_valid_zone(zone), zone\\n assert is_valid_instance(instance), instance\\n try:\\n return self.call_api(\\n '/zones/%s/instances/%s' % (zone, instance),\\n params={'fields': ','.join(fields)} if fields else None)\\n except net.NotFoundError: # pragma: no cover\\n return None\",\n \"def get_members(self, context):\\n zone_obj = self.dns_manager.get_members(context)\\n return zone_obj\",\n \"def __get_names(record: TNSRecord) -> Dict[str, str]:\\n aliases = {'iau': record.name}\\n internal_names = record.internal_names.split(',')\\n for provider, pattern in Object.name_patterns.items():\\n for name in internal_names:\\n if pattern.match(name):\\n aliases[provider] = name\\n return aliases\",\n \"def _get_records(self, domain, domain_id, record):\\n for needed in [\\\"type\\\", \\\"source\\\", \\\"target\\\"]:\\n if needed not in record:\\n raise ValueError(\\\"{} not provided in record dict\\\".format(needed))\\n\\n if record[\\\"source\\\"] == \\\".\\\":\\n fqdn = domain\\n else:\\n fqdn = \\\"{source}.{domain}\\\".format(source=record[\\\"source\\\"], domain=domain)\\n return list(\\n filter(\\n lambda x: (\\n x[\\\"source_idn\\\"] == fqdn\\n and x[\\\"type\\\"] == record[\\\"type\\\"]\\n and x[\\\"target\\\"] == record[\\\"target\\\"]\\n ),\\n self._get_request(\\\"/1/domain/{domain_id}/dns/record\\\".format(domain_id=domain_id)),\\n )\\n )\",\n \"def create_record(self, zone_id, record, record_type, data, ttl=60):\\r\\n self.record.createObject({\\r\\n 'domainId': zone_id,\\r\\n 'ttl': ttl,\\r\\n 'host': record,\\r\\n 'type': record_type,\\r\\n 'data': data})\",\n \"def zone_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone(self):\\n return self._zone\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\",\n \"def zone_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"zone_id\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.684966","0.6636891","0.6613396","0.6532525","0.64089847","0.6375922","0.6265335","0.6205971","0.61214995","0.6064319","0.6040061","0.60173184","0.60097045","0.5822029","0.5814325","0.5788017","0.5685655","0.56776536","0.56634295","0.56493497","0.5640705","0.56006163","0.5595947","0.5556687","0.55421185","0.55071634","0.5492643","0.5492123","0.53952914","0.5380821","0.53782064","0.5377613","0.5363911","0.53386515","0.53053534","0.52872145","0.5285054","0.5274584","0.52531636","0.5251632","0.5245302","0.52444047","0.5232543","0.52320933","0.5231435","0.52302986","0.5224378","0.52081174","0.52069515","0.5193955","0.51880527","0.51848865","0.5182897","0.517735","0.5159295","0.5150786","0.5147898","0.5146119","0.5138514","0.5133315","0.51316","0.5108673","0.5099389","0.5097415","0.50812733","0.50810665","0.50768167","0.5068353","0.5065747","0.50592077","0.5058773","0.5058773","0.50438786","0.5034038","0.5033824","0.50323135","0.50216603","0.50050837","0.5004638","0.49944717","0.49885666","0.49885666","0.49760428","0.49689442","0.4965089","0.49634096","0.49555612","0.4952017","0.49075213","0.48932436","0.48928902","0.48880002","0.48863333","0.48806936","0.48806936","0.4853301","0.48495856","0.48495856","0.48495856","0.48495856"],"string":"[\n \"0.684966\",\n \"0.6636891\",\n \"0.6613396\",\n \"0.6532525\",\n \"0.64089847\",\n \"0.6375922\",\n \"0.6265335\",\n \"0.6205971\",\n \"0.61214995\",\n \"0.6064319\",\n \"0.6040061\",\n \"0.60173184\",\n \"0.60097045\",\n \"0.5822029\",\n \"0.5814325\",\n \"0.5788017\",\n \"0.5685655\",\n \"0.56776536\",\n \"0.56634295\",\n \"0.56493497\",\n \"0.5640705\",\n \"0.56006163\",\n \"0.5595947\",\n \"0.5556687\",\n \"0.55421185\",\n \"0.55071634\",\n \"0.5492643\",\n \"0.5492123\",\n \"0.53952914\",\n \"0.5380821\",\n \"0.53782064\",\n \"0.5377613\",\n \"0.5363911\",\n \"0.53386515\",\n \"0.53053534\",\n \"0.52872145\",\n \"0.5285054\",\n \"0.5274584\",\n \"0.52531636\",\n \"0.5251632\",\n \"0.5245302\",\n \"0.52444047\",\n \"0.5232543\",\n \"0.52320933\",\n \"0.5231435\",\n \"0.52302986\",\n \"0.5224378\",\n \"0.52081174\",\n \"0.52069515\",\n \"0.5193955\",\n \"0.51880527\",\n \"0.51848865\",\n \"0.5182897\",\n \"0.517735\",\n \"0.5159295\",\n \"0.5150786\",\n \"0.5147898\",\n \"0.5146119\",\n \"0.5138514\",\n \"0.5133315\",\n \"0.51316\",\n \"0.5108673\",\n \"0.5099389\",\n \"0.5097415\",\n \"0.50812733\",\n \"0.50810665\",\n \"0.50768167\",\n \"0.5068353\",\n \"0.5065747\",\n \"0.50592077\",\n \"0.5058773\",\n \"0.5058773\",\n \"0.50438786\",\n \"0.5034038\",\n \"0.5033824\",\n \"0.50323135\",\n \"0.50216603\",\n \"0.50050837\",\n \"0.5004638\",\n \"0.49944717\",\n \"0.49885666\",\n \"0.49885666\",\n \"0.49760428\",\n \"0.49689442\",\n \"0.4965089\",\n \"0.49634096\",\n \"0.49555612\",\n \"0.4952017\",\n \"0.49075213\",\n \"0.48932436\",\n \"0.48928902\",\n \"0.48880002\",\n \"0.48863333\",\n \"0.48806936\",\n \"0.48806936\",\n \"0.4853301\",\n \"0.48495856\",\n \"0.48495856\",\n \"0.48495856\",\n \"0.48495856\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74746597"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93777,"cells":{"query":{"kind":"string","value":"Used to handle records with values changing."},"document":{"kind":"string","value":"def update(self, record):\n # clean copy of record to preserve IDs for response if no update required\n record_data = self.sanitize_record(copy.deepcopy(record.data))\n changed = False\n args = {}\n\n for key in RECORD_KEYS_MAP:\n input_data = self.filter_empty_subparams(key)\n\n if input_data is not None:\n if (\n RECORD_KEYS_MAP[key]['appendable']\n and self.module.params.get('record_mode') == 'append'\n ):\n # create union of input and existing record data,\n # preserving existing order\n input_data = record_data[key] + [\n input_obj\n for input_obj in input_data\n if input_obj not in record_data[key]\n ]\n\n if input_data != record_data[key]:\n changed = True\n args[key] = input_data\n\n # create a new copy of the previously sanitized dict that will be\n # updated with changing args to support --diff\n after_changes = copy.deepcopy(record_data)\n for k, v in args.items():\n if after_changes[k] != v:\n after_changes[k] = v\n\n # check mode short circuit before update\n if self.module.check_mode:\n self.record_exit(before=record_data,\n changed=changed,\n after=after_changes,\n record=record)\n\n # update only if some changed data\n if changed:\n record.update(errback=self.errback_generator(),\n **args)\n\n self.record_exit(before=record_data,\n changed=changed,\n after=after_changes,\n record=record)\n\n # catch exit if not running in check mode and no changes are to be made.\n self.record_exit(changed=False, record=record)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def handle_record(self, record):\n raise NotImplementedError","def process_record(self, record):\n raise NotImplementedError('Process record needs to be customized')","def visit_record(self, syrecord):\n for other_key, other_value in syrecord.items():\n try:\n getattr(self.current, other_key).update(other_value)\n except KeyError:\n setattr(self.current, other_key, other_value)","def metric_recorded(self, record):\n if record.name in self.headers and self.current_row is not None:\n if record.name == \"learning_rate\" and not record.is_scalar:\n # record is a list of scalars\n value = \",\".join([f\"{lr:.4f}\" for lr in record.value])\n elif record.is_scalar and isinstance(record.value, int):\n value = str(record.value)\n else:\n assert record.is_scalar\n\n value = f\"{record.value:.4f}\"\n\n self.current_row[record.name] = value","def exogenous_change(self):\n pass","def exogenous_change(self):\n pass","def exogenous_change(self):\n pass","def test_record_eq_record_different_values(self):\n zone = Zone('test.example.com')\n data = {'type': 'A', 'ttl': 30, 'values': ['1.1.1.1', '2.2.2.2']}\n record_current = Record(zone, 'test-record', data)\n data = {'type': 'A', 'ttl': 30, 'values': ['1.1.1.1', '3.3.3.3']}\n record_desired = Record(zone, 'test-record', data)\n self.assertTrue(record_current != record_desired)","def _UpdateDataSetValues( self ):\n pass","def product_record_values(self, _data):\n return self.no_record_values()","def _reload_values(self):\r\n raise NotImplementedError","def data_changed(self):\n return","def test_record_eq_record_different_values_order(self):\n zone = Zone('test.example.com')\n data = {'type': 'A', 'ttl': 30, 'values': ['1.1.1.1', '2.2.2.2']}\n record_current = Record(zone, 'test-record', data)\n data = {'type': 'A', 'ttl': 30, 'values': ['2.2.2.2', '1.1.1.1']}\n record_desired = Record(zone, 'test-record', data)\n self.assertTrue(record_current == record_desired)","def test_patch_record(self):\n pass","def _valueRemoved(self):\n self.__changed = 1\n if self.size() == 0:\n raise ConsistencyFailure()","def _process_change(self):\n self._sort_records()\n self._store_writer.to_file(self.records)\n self._store_writer.to_csv_file(self.records)","def _db(self, value):","def on_put_field(self, ins, const, obj, value):\n pass","def db_values(self, db):","def _data_process(self, v):\n pass","def on_change(self, value):","def filter_record(self, record):\n raise NotImplementedError(\"Override in subclass\")","def handle_nval(self):\n pass","def record_updater(records, observations):\n for record in records:\n try:\n record = ast.literal_eval(records[record])\n except Exception:\n record = record\n try:\n if type(records[record]) is dict:\n records[record] = Common.record_updater(\n records[record], observations\n )\n elif type(records[record]) is list:\n list_records = []\n for list_record in records[record]:\n for observation in observations:\n if observation != \"_id\":\n try:\n if re.search(observation, f\"{list_record}\"):\n if not re.search(\n observations[observation],\n f\"{records[record]}\",\n ):\n if not re.search(\"-->\", f\"{list_record}\"):\n list_records.append(\n f\"{list_record}\"\n + \" --> \"\n + observations[observation]\n )\n else:\n list_records.append(list_record)\n else:\n list_records.append(list_record)\n else:\n list_records.append(list_record)\n except Exception as ex:\n Common.logger.warning(\n f\"Exception happened in observation comparison {ex}\"\n )\n records[record] = list_records\n else:\n records = Common.data_comparison(observations, records, record)\n except Exception:\n records = Common.data_comparison(observations, records, record)\n return records","def parse_record(self, record):\n raise NotImplementedError()","def process_sample_val(self):\n raise NotImplementedError","def _update_val_ids(self, change=None):\n\n val_min, val_max = self.val_range\n self.val_ids = list(\n self.data_frame[\n (self.data_frame[self.col] >= val_min)\n & (self.data_frame[self.col] <= val_max)\n ].index\n )\n # pylint: enable=unused-argument","def _analyze(self):\r\n if self.value is None or self.value == self.previous:\r\n pass\r\n elif self._operation == \"add\":\r\n self._additions = self.value\r\n elif self._operation == \"remove\":\r\n self._removals = self.value\r\n elif self.previous is None:\r\n self._assignments = self.value\r\n else:\r\n # partial update time\r\n self._additions = (self.value - self.previous) or None\r\n self._removals = (self.previous - self.value) or None\r\n self._analyzed = True","def test_update_record(self):\n pass","def processRecord(self, record):\n\n current_status = record.status\n\n if current_status == 'pre-accepted':\n new_status = 'accepted'\n elif current_status == 'pre-rejected':\n new_status = 'rejected'\n else:\n # no work to be done\n return record\n\n fields = {'status': new_status}\n return self.updateEntityProperties(record, fields)","async def _update_values(self, model: Model):\n\n raise NotImplementedError","def ignore_value_changes(self):\n return self._parent.ignore_value_changes","def set_field( self, data ):\n super( UnsteadyField1D, self ).set_field( data )\n self.history[:] = self.val[:]\n return","def data_dict_update(self, change):\n self.data_dict = change['value']","def emit(self, record):\n pass","def XPLMDataChanged_f(inRefcon):","def reloadData(self):\n self.dto.readFromData()\n print(\"Record reloaded.\")","def list_value_changes(self, field_name):\n\n t = self.data['timestamp']\n x = self.data[field_name]\n indices = t != 0 # filter out 0 values\n t = t[indices]\n x = x[indices]\n if len(t) == 0: return []\n ret = [(t[0], x[0])]\n indices = np.where(x[:-1] != x[1:])[0] + 1\n ret.extend(zip(t[indices], x[indices]))\n return ret","def write(self):\n if not self._table: raise ValueError ( \"_table is Null\" )\n if self._isnew:\n for m in self._modified_values:\n self._modified_values[m] = self._table[m].val_py2sql(self._modified_values[m])\n \n try:\n rec = CFG.CX.insert ( CFG.DB.SCHEMA + \".\" + self._table.name,\n self._modified_values )\n #this will automatically re-read the data from the db, to take all changes\n #done by triggers and default values into account.\n self._objectid = rec['objectid']\n\n #print \"Record # {0} inserted into {1}.\".format(self._objectid, self._table.name)\n self.raiseEvent ( \"record_added\", self )\n \n except pg.DatabaseError, e:\n print \"Error inserting record.\"\n raise Record.DataManipulationError ( \"Inserting a new record into '{0}'\".format(self._table.name),\n str(self._modified_values),\n e)\n elif self._ismodified:\n \n for m in self._modified_values: \n self._modified_values[m] = self._table[m].val_py2sql(self._modified_values[m])\n \n self._modified_values['objectid'] = self._objectid\n del self.TextCache[self._objectid]\n try:\n rec = CFG.CX.update ( CFG.DB.SCHEMA + \".\" + self._table.name,\n self._modified_values )\n self.read() \n self.raiseEvent ( \"record_saved\", self )\n except pg.DatabaseError, e:\n print \"Error updating record\"\n raise Record.DataManipulationError ( \"Updating record {1} of '{0}'\".format(self._table.name, self._objectid),\n str(self._modified_values),\n e)","def _postprocess_record(record, hide=_CONFIDENTIAL_FIELDS):\n record = hide_confidential_fields(record, hide)\n record = unserialize_fields(record, hide)\n\n convert_float_timestamp2str(record)\n\n return record","def processLine(self, col, line):\n col.linesRecv += 1\n # BUGBUG : Limit should be fixed\n if len(line) >= DATA_LINE_MAX_SIZE:\n LOG.warning('%s line too long: %s', col.name, line)\n col.linesInvalid += 1\n return\n parsed = re.match('^([-_./a-zA-Z0-9]+)\\s+' # Metric name\n '(\\d+)\\s+' # Timestamp\n '(\\S+?)' # Value (int or float)\n '((?:\\s+[-_./a-zA-Z0-9]+=[-_./a-zA-Z0-9]+)*)$', # Tags\n line)\n if parsed is None:\n LOG.warning('%s sent invalid data: %s', col.name, line)\n col.linesInvalid += 1\n return\n metric, timestamp, value, tags = parsed.groups()\n timestamp = int(timestamp)\n\n # eliminating repeated value logic\n key = (metric, tags)\n if key in col.values:\n if timestamp <= col.values[key][3]:\n LOG.error(\"Timestamp out of order: metric=%s%s,\"\n \" old_ts=%d >= new_ts=%d - ignoring data point\"\n \" (value=%r, collector=%s)\", metric, tags,\n col.values[key][3], timestamp, value, col.name)\n col.linesInvalid += 1\n return\n\n if (col.values[key][0] == value and\n (timestamp - col.values[key][3] < self.repeatinterval)):\n # don't update the old timestamp\n col.values[key] = (value, True, line, col.values[key][3])\n return\n\n if ((col.values[key][1] or\n # BUGBUG : this may always False\n (timestamp - col.values[key][3] >= self.repeatinterval))\n and\n col.values[key][0] != value):\n col.linesSent += 1\n if not self.readerq.nput(col.values[key][2]):\n self.linesDropped += 1\n\n # case 1 : (old_val == new_val) and (>=repeatinterval)\n # case 2 : (old_val != new_val) and (<repeatinterval) and values[][1]== False\n col.values[key] = (value, False, line, timestamp)\n col.linesSent += 1\n if not self.readerq.nput(line):\n self.linesDropped += 1","def save_vals (self):\n raise NotImplementedError","def _data_updated_callback(self, attr, old, new):\n pass","def __call__(self, record):\n fs = record.fs\n signal = record.p_signal\n filtered_signal = self.filter_signal(signal, fs)\n record.p_signal = filtered_signal\n\n return record","def put_record(self, record):\r\n row = [record.get(field) for field in self.fields.names()]\r\n\r\n self.put(row)","def __call__(self, record):\n filtered_signal = self.filter_signal(record.p_signal, record.fs)\n record.p_signal = filtered_signal\n return record","def on_columnvalue_modified( self, *data ):\n\t\tif (len(data) == 4):\t( cell, path, model, user_data ) = data\n\t\telse:\t\t\t( cell, path, new_text, model, user_data ) = data\n\t\t(datatype,) = user_data\n\t\tcolid = self.window2.type2colid[datatype]\n\t\tif \t(datatype == \"combo\"):\n\t\t\tmodel[path][colid] = new_text\n\t\telif \t(datatype == \"spin\"):\n\t\t\tmodel[path][colid] = long(new_text)\n\t\telif \t(datatype == \"text\"):\n\t\t\tmodel[path][colid] = new_text\n\t\telif \t(datatype == \"check\"):\n\t\t\tmodel[path][colid] = not model[path][colid]","def update(self) -> \"Sample\":\n self.is_savable(do_raise=True)\n for fv in self.field_values:\n fv.reload(fv.save())\n\n new_fvs = self.field_values\n server_fvs = self.session.FieldValue.where(\n dict(parent_id=self.id, parent_class=\"Sample\")\n )\n\n to_remove = [\n fv for fv in server_fvs if fv.id not in [_fv.id for _fv in new_fvs]\n ]\n if to_remove:\n warn(\n \"Trident tried to save a Sample, but it required FieldValues to be deleted.\"\n )\n for fv in to_remove:\n fv.parent_id = None\n fv.save()\n self.reload(self.session.utils.json_save(\"Sample\", self.dump()))\n return self","def update_record():\n global data_base, table, choice, res, output1, result, column_names, choice_row, number_str\n a = output1.get('1.0', END)\n a = a[0:-1]\n changed_string = a.split('\\n')\n changed_string = changed_string[0:-2]\n\n number_str = int(number_str) - 1\n source_string = []\n for i in result:\n for j in i:\n source_string.append(j)\n\n for i in range(0, 4):\n try:\n if changed_string[i] == source_string[i]:\n pass\n except IndexError:\n pass\n else:\n param_value = changed_string[i]\n step = i\n param_column = column_names[step]\n sqlite3_update_record(data_base, table, param_column, param_value, choice_row, res)\n output1.delete(1.0, END)","def Transform(self, record):\n pass","def update_records(self, something):\n print(\"Some logic (not shown) to update database of units\")","def test_entries_maintains_values_per_transaction(self):\n\n self.assertEquals(self.data_manager.variables[\"x6\"].value, \"60\")\n self.data_manager.write_new_data(2, \"x6\", 999, \"T1\")\n\n # Confirm the latest Committed Value wasn't changed by a transaction update to variable\n self.assertEquals(self.data_manager.variables[\"x6\"].value, \"60\")\n \n # Confirm that the new write just adds to the log\n self.assertEquals(self.data_manager.entries[\"T1\"][\"x6\"].written_values[1], \"60\")\n self.assertEquals(self.data_manager.entries[\"T1\"][\"x6\"].written_values[2], \"999\")","def changed(self):\n\t\tpass","def val_acc(self):\n raise Exception(\" not implemented in base model\")","def _get_line_vals(self, record, line_field, fields):\n line_field, max_row = get_line_max(line_field)\n lines = record[line_field]\n if max_row > 0 and len(lines) > 3800 :#and len(lines) > max_row:\n raise Exception(\n _('Records in %s exceed max record allowed!') % line_field)\n vals = dict([(field, []) for field in fields])\n # Get field condition & aggre function\n field_cond_dict = {}\n aggre_func_dict = {}\n field_format_dict = {}\n pair_fields = [] # I.e., ('debit${value and . or .}@{sum}', 'debit')\n for field in fields:\n temp_field, eval_cond = get_field_condition(field)\n temp_field, field_format = get_field_format(temp_field)\n raw_field, aggre_func = get_field_aggregation(temp_field)\n # Dict of all special conditions\n field_cond_dict.update({field: eval_cond})\n aggre_func_dict.update({field: aggre_func})\n field_format_dict.update({field: field_format})\n # --\n pair_fields.append((field, raw_field))\n # --\n for line in lines:\n for field in pair_fields: # (field, raw_field)\n value = self._get_field_data(field[1], line)\n if type(value) == type(''):\n value = re.sub(r\"[\u001e\u0006]\", '', value)\n # Case Eval\n eval_cond = field_cond_dict[field[0]]\n if eval_cond: # Get eval_cond of a raw field\n eval_context = {'float_compare': float_compare,\n 'time': time,\n 'datetime': dt,\n 'date': date,\n 'value': value,\n 'object': line,\n 'model': self.env[record._name],\n 'env': self.env,\n 'context': self._context,\n }\n # value = str(eval(eval_cond, eval_context))\n # Test removing str(), coz some case, need resulting number\n value = eval(eval_cond, eval_context)\n # --\n vals[field[0]].append(value)\n return (vals, aggre_func_dict, field_format_dict)","def update_record(self):\n new_record = self.create_record()\n for record in self.records:\n if self.date_str == record[\"date\"] and not record == new_record:\n record.update(new_record)\n self.write_json_file(self.records_file, self.records)\n return True\n return False","def _validate_fields(self, change_fields):\n pass","def transform(self, record):\n if not record:\n return None\n\n if not type(record) is DASRecord:\n logging.warning('Improper format record: %s',record)\n return None\n\n update = False\n for field_name in record.fields:\n if field_name in self.course_fields:\n self.course_val = record.fields[field_name]\n elif field_name in self.speed_fields:\n self.speed_val = record.fields[field_name] * self.convert_speed_factor\n elif field_name in self.heading_fields:\n self.heading_val = record.fields[field_name]\n elif field_name in self.wind_dir_fields:\n self.wind_dir_val = record.fields[field_name]\n elif field_name in self.wind_speed_fields:\n self.wind_speed_val = record.fields[field_name] * self.convert_wind_factor\n \n if field_name in self.update_on_fields:\n update = True\n\n # If we've not seen anything that updates fields that would\n # trigger a new true winds value, return None.\n if not update:\n return None\n\n if self.course_val is None:\n logging.info('Still missing course_val')\n return None\n if self.speed_val is None:\n logging.info('Still missing speed_val')\n return None\n if self.heading_val is None:\n logging.info('Still missing heading_val')\n return None\n if self.wind_dir_val is None:\n logging.info('Still missing wind_dir_val')\n return None\n if self.wind_speed_val is None:\n logging.info('Still missing wind_speed_val')\n return None\n\n logging.info('Computing new true winds')\n (true_dir, true_speed, app_dir) = truew(crse=self.course_val,\n cspd=self.speed_val,\n hd=self.heading_val,\n wdir=self.wind_dir_val,\n zlr=self.zero_line_reference,\n wspd=self.wind_speed_val)\n\n logging.info('Got true winds: dir: %s, speed: %s, app_dir: %s',\n true_dir, true_speed, app_dir)\n if true_dir is None or true_speed is None or app_dir is None:\n logging.info('Got invalid true winds')\n return None\n\n # If here, we've got a valid new true wind result\n if self.output_nmea:\n new_record = '%s %s %g,%g,%g' % (self.data_id,\n time_str(record.timestamp),\n true_dir, true_speed, app_dir)\n else:\n new_record = DASRecord(data_id=self.data_id,\n timestamp=record.timestamp,\n fields={'TrueWindDir': true_dir,\n 'TrueWindSpeed': true_speed,\n 'ApparentWindDir': app_dir})\n return new_record","def setRecord(self,record):\n idLower = record.getId().lower()\n type = record.name\n typeIds = self.indexed[type]\n if idLower in typeIds:\n oldRecord = typeIds[idLower]\n index = self.records.index(oldRecord)\n self.records[index] = record\n else:\n self.records.append(record)\n typeIds[idLower] = record","def update_original_data(self):\n pass","def getChanges():","def case_records(self, case_records):\n self._case_records = case_records","def new_archive_record(self, event):\n for obs_type in self.min_max_dict:\n if event.record.has_key(obs_type) and event.record[obs_type] is not None:\n if not self.min_max_dict[obs_type][0] <= event.record[obs_type] <= self.min_max_dict[obs_type][1]:\n syslog.syslog(syslog.LOG_NOTICE, \"engine: %s Archive value '%s' %s outside limits (%s, %s)\" % \n (weeutil.weeutil.timestamp_to_string(event.record['dateTime']),\n obs_type, event.record[obs_type], \n self.min_max_dict[obs_type][0], self.min_max_dict[obs_type][1]))\n event.record[obs_type] = None","def process_original_data():\n audioSamples = AudioDataOriginal.query.filter().all()\n\n currentSampleDatetime = None\n maxValueRecord = None\n for sample in audioSamples:\n if currentSampleDatetime is None:\n currentSampleDatetime = sample.datetime\n if maxValueRecord is None:\n maxValueRecord = sample\n # Check if we've reached a new sample batch.\n if currentSampleDatetime != sample.datetime:\n # If we reach a new sample batch, update the `processedValue` of the\n # record held in `maxValueRecord` to be the `audio` value.\n maxValueRecord.processedValue = maxValueRecord.audio\n # Update to the first value of the new sample batch.\n currentSampleDatetime = sample.datetime\n maxValueRecord = sample\n elif sample.audio > maxValueRecord.audio:\n # If not, then check if the value is higher than the current one\n # and overwrite if so.\n maxValueRecord = sample\n # Update the last record, since this won't be caught in the if statement\n # inside the loop (it compares the current.datetime vs a new.datetime after\n # all).\n maxValueRecord.processedValue = maxValueRecord.audio\n db.session.commit()","def update(self, values):\n pass","def visit_record(self, syrecord):\n for key, value in syrecord.items():\n child = type(value)(value.container_type)\n setattr(self.current, key, child)\n value.visit(SpineCopyVisitor(child))","def test_fields_updated_with_computed(self):\n pass","def process_data(self, value):\n try:\n self.data = value.value\n except AttributeError:\n self.data = value","def _rec_only_updated(cls, rec):\n return rec.get('uplinked', None) \\\n and not rec.get('queued', None) \\\n and not rec.get('announced', None) \\\n and not rec.get('blocked', None) \\\n and not rec.get('finished', None) \\\n and not rec.get('aborted', None)","def __add_one_day_values__(self):\n values = self.values()\n for value in values:\n ls = []\n if value.label in self.values_dict:\n ls = self.values_dict[value.label]\n ls.append(value)\n else:\n ls = [value]\n self.values_dict[value.label] = ls","def pre_update(self, **values):\r\n pass","def _check_for_change(self, sender, instance, created, **kwargs):\n previous = []\n current = []\n for i, f in enumerate(self.fields_to_track):\n previous.append(getattr(instance, self.tracker_attnames[i], None))\n current.append(self.get_tracked_value(instance, i))\n if created:\n previous = None\n if previous != current: # short circuit if nothing has changed\n sources, sinks = self.states_to_stocks_func(previous, current)\n for source, sink in zip(sources, sinks):\n if source is not sink: # short circuit if no change in state/stock\n self.create_flow_event(source, sink, instance)","def _GetFieldValues(\n self, output_mediator, event, event_data, event_data_stream, event_tag):","def compare_ao3_rec(db_rec, current_rec, report_gen):\r\n tests = [\r\n 'hits', 'kudos',\r\n 'comments', 'bookmarks', 'ref']\r\n current_dict = current_rec\r\n db_dict = db_rec.__dict__\r\n changed = []\r\n for value_key in tests:\r\n if report_gen.compare_and_print(\r\n current_rec[\"title\"],\r\n value_key,\r\n current_dict,\r\n db_dict) > 0:\r\n setattr(db_rec, value_key, current_dict[value_key])\r\n changed.append(value_key)\r\n return changed","def _handle_load_unknown(self, data, original):\n for key, val in original.items():\n if key not in self.fields:\n data[key] = val\n return data","def handle_actual_updated(self):\n self._actual_updated()","def process_IN_MODIFY(self, event):","def UpdateValues( self ):\r\n msg =wx.grid.GridTableMessage(self, wx.grid.GRIDTABLE_REQUEST_VIEW_GET_VALUES)\r\n self.GetView().ProcessTableMessage(msg)","def process_records(records):\n changes = defaultdict(int)\n cascaded_create_records = set()\n cascaded_publish_records = set()\n cascaded_unpublish_records = set()\n cascaded_undelete_records = set()\n cascaded_update_records = set()\n cascaded_delete_records = set()\n cascaded_location_changes = set()\n\n for record in records:\n if record.change != ChangeType.deleted and record.object is None:\n # Skip entries which are not deletions but have no corresponding objects.\n # Probably they are updates for objects that got deleted afterwards.\n continue\n if record.change == ChangeType.created:\n assert record.type != EntryType.category\n cascaded_create_records.add(record)\n elif record.change == ChangeType.published:\n cascaded_publish_records.add(record)\n elif record.change == ChangeType.unpublished:\n cascaded_unpublish_records.add(record)\n elif record.change == ChangeType.undeleted:\n assert record.type != EntryType.category\n cascaded_undelete_records.add(record)\n elif record.change == ChangeType.deleted:\n assert record.type != EntryType.category\n cascaded_delete_records.add(record)\n elif record.change in {ChangeType.moved, ChangeType.protection_changed}:\n cascaded_update_records.add(record)\n elif record.change == ChangeType.data_changed:\n assert record.type != EntryType.category\n changes[record.object] |= SimpleChange.updated\n # subcontributions have their parent's time information, so we need to\n # cascade contribution updates to them\n if record.type == EntryType.contribution:\n for subcontrib in record.object.subcontributions:\n changes[subcontrib] |= SimpleChange.updated\n elif record.change == ChangeType.location_changed:\n assert record.type in (EntryType.event, EntryType.contribution, EntryType.session)\n cascaded_location_changes.add(record)\n\n for obj in _process_cascaded_category_contents(cascaded_update_records):\n changes[obj] |= SimpleChange.updated\n\n for obj in _process_cascaded_category_contents(cascaded_unpublish_records):\n changes[obj] |= SimpleChange.deleted\n\n for obj in _process_cascaded_category_contents(cascaded_publish_records):\n changes[obj] |= SimpleChange.created\n\n for obj in _process_cascaded_event_contents(cascaded_delete_records):\n changes[obj] |= SimpleChange.deleted\n\n for obj in _process_cascaded_event_contents(cascaded_create_records, include_deleted=True):\n changes[obj] |= SimpleChange.created\n\n for obj in _process_cascaded_locations(cascaded_location_changes):\n changes[obj] |= SimpleChange.updated\n\n for obj in _process_cascaded_event_contents(cascaded_undelete_records, skip_all_deleted=True):\n # This may result in a create for an object which is already created - in the (somewhat rare)\n # case of a deletion being followed by a restore in the same set of records.\n # However, since we expect backends to either convert those operations to an update or skip\n # them altogether this shouldn't be a problem\n changes[obj] |= SimpleChange.created\n changes[obj] &= ~SimpleChange.deleted\n\n created_and_deleted = {obj for obj, flags in changes.items() if (flags & CREATED_DELETED) == CREATED_DELETED}\n for obj in created_and_deleted:\n # discard any change where the object was both created and deleted\n del changes[obj]\n\n return {obj: _get_final_change(flags) for obj, flags in changes.items()}","def apply_ruling(self, ruling, record):\r\n record.update(ruling)\r\n return record","def process_event(self, event):\n if not self.frozen:\n if event[\"event\"] in [self.event, self.devent]:\n if self.what is None or event[\"target\"].startswith(self.what):\n self.lastone = None\n if event[\"event\"] == self.event:\n val = event\n for key in self.subval:\n val = val[key]\n if val in self.addV:\n if event[\"target\"] not in self.entities:\n self.entities.add(event[\"target\"])\n self.lastone = event[\"target\"]\n if event[\"event\"] == self.devent:\n val = event\n for key in self.dsubval:\n val = val[key]\n if val in self.subV:\n try:\n self.entities.remove(event[\"target\"])\n self.lastone = event[\"target\"]\n except:\n pass\n if self.lastone is not None and bridgectl.log:\n bridgectl.log.debug(\n \"New value for {} is {}\".format(\n self.name,\n self.entities))\n if event['event'] == 'time tick':\n if self.period in event[\"starts\"]:\n self.reset()","def process_duplicate_rows(self):\n pass","def on_get_field(self, ins, const, obj):\n pass","def test_edit_record_edits_the_correct_record(self):\n # create some db records\n dataset = self.create_mixed_test_data()\n test_log_entries = dataset['test_log_entries']\n # set the menu instance's `records` property\n self.menu.records = test_log_entries\n record_index = 1\n\n # handle the user input to select the record\n # handle the user input to specify the new values for the record\n log_entry_for_input = OrderedDict([\n ('name', \"New Test Employee\"),\n ('date', \"2017-10-05\"),\n ('task_name', \"New Test Task\"),\n ('duration', 55),\n ('notes', \"New Note\")\n ])\n\n user_inputs = [record_index + 1] + list(log_entry_for_input.values())\n # get the unedited requested record from the db\n old_query = self.base_query(test_log_entries[record_index])\n self.assertEqual(len(old_query), 1)\n\n # execute the method\n with patch('builtins.input', side_effect=user_inputs):\n self.menu.edit_record()\n\n # verify the record that was changed is the one selected by the user\n # (make sure we can get the record with the new details and we can't\n # get the record with the old details)\n new_query = self.base_query(log_entry_for_input)\n repeat_old_query = self.base_query(test_log_entries[record_index])\n\n self.assertEqual(len(new_query), 1) # new_query shd return one result\n self.assertEqual(len(repeat_old_query), 0) # query should be empty","def UpdateValues(self, grid):\n # This sends an event to the grid table to update all of the values\n msg = wx.grid.GridTableMessage(self, wx.grid.GRIDTABLE_REQUEST_VIEW_GET_VALUES)\n grid.ProcessTableMessage(msg)","def UpdateValues( self ):\r\n msg =wx.grid.GridTableMessage(self, wx.grid.GRIDTABLE_REQUEST_VIEW_GET_VALUES)\r\n self.GetView().ProcessTableMessage(msg)","def records(self):\r\n raise NotImplementedError()","def __value_set(self, new_value):\n self.db_value = to_pickle(new_value)\n # print(\"value_set, self.db_value:\", repr(self.db_value)) # DEBUG\n self.save(update_fields=[\"db_value\"])","def _metrics_update(orig, new):\n revsd = orig\n for k, v in orig.items():\n if not v:\n revsd[k] = new[k]\n elif new[k]:\n if new[k] != v:\n # LOG ME, do something\n print(orig)\n print(new)\n elif not new[k] or v:\n pass\n else:\n raise Exception(\"_metrics_update error\")\n return revsd","def changeAdded(change):","def _update_data(self):\n for attribute in [\"flow_rate\"]:\n self._data[attribute] = self._connection.measure","def mutate_json_record(self, json_record):\n for attr_name in json_record:\n attr = json_record[attr_name]\n if isinstance(attr, datetime):\n json_record[attr_name] = attr.isoformat()\n return json_record","def change_data(self):\n\n if self.changed is not True:\n self.changed = True\n print('True')","def __setitem__(self, name, value):\r\n if self.values.has_key(name) and self.values[name] == value:\r\n return\r\n if not self._old.has_key(name): # Changed field\r\n self._old[name] = self.values.get(name)\r\n elif self._old[name] == value: # Change of field reverted\r\n del self._old[name]\r\n if value:\r\n field = [field for field in self.fields if field['name'] == name]\r\n if field and field[0].get('type') != 'textarea':\r\n value = value.strip()\r\n self.values[name] = value","def updateItem(self, value):\n self.value = value\n self.age = 0\n self.freq += 1","def handleRowDataChange(self, changedItem ):\n with Tracer(traceLogger):\n # Figure out which row this widget is in\n row = changedItem.row()\n column = changedItem.column()\n\n # Can't update until the row is fully initialized\n needUpdate = True\n needUpdate &= column == Column.Name or column == Column.InternalID\n needUpdate &= self.fileInfoTableWidget.item(row, Column.Name) != None\n needUpdate &= self.fileInfoTableWidget.cellWidget(row, Column.InternalID) != None\n needUpdate &= self.fileInfoTableWidget.cellWidget(row, column) is not None\n\n if needUpdate:\n self.updateFilePath(row)","def _measurement_update(self):\n pass","def updated_full_record(full_record):\n full_record[\"access\"][\"status\"] = \"embargoed\"\n full_record[\"created\"] = \"2023-03-23T00:00:00.000000+00:00\"\n full_record[\"id\"] = \"abcde-fghij\"\n full_record[\"metadata\"][\"resource_type\"][\"id\"] = \"other\"\n\n return full_record","def onChanged(self, fp, prop):\n if prop == \"Length\" or prop == \"Breadth\" or prop == \"Draft\":\n pass","def records(self, records):\n\n self._records = records"],"string":"[\n \"def handle_record(self, record):\\n raise NotImplementedError\",\n \"def process_record(self, record):\\n raise NotImplementedError('Process record needs to be customized')\",\n \"def visit_record(self, syrecord):\\n for other_key, other_value in syrecord.items():\\n try:\\n getattr(self.current, other_key).update(other_value)\\n except KeyError:\\n setattr(self.current, other_key, other_value)\",\n \"def metric_recorded(self, record):\\n if record.name in self.headers and self.current_row is not None:\\n if record.name == \\\"learning_rate\\\" and not record.is_scalar:\\n # record is a list of scalars\\n value = \\\",\\\".join([f\\\"{lr:.4f}\\\" for lr in record.value])\\n elif record.is_scalar and isinstance(record.value, int):\\n value = str(record.value)\\n else:\\n assert record.is_scalar\\n\\n value = f\\\"{record.value:.4f}\\\"\\n\\n self.current_row[record.name] = value\",\n \"def exogenous_change(self):\\n pass\",\n \"def exogenous_change(self):\\n pass\",\n \"def exogenous_change(self):\\n pass\",\n \"def test_record_eq_record_different_values(self):\\n zone = Zone('test.example.com')\\n data = {'type': 'A', 'ttl': 30, 'values': ['1.1.1.1', '2.2.2.2']}\\n record_current = Record(zone, 'test-record', data)\\n data = {'type': 'A', 'ttl': 30, 'values': ['1.1.1.1', '3.3.3.3']}\\n record_desired = Record(zone, 'test-record', data)\\n self.assertTrue(record_current != record_desired)\",\n \"def _UpdateDataSetValues( self ):\\n pass\",\n \"def product_record_values(self, _data):\\n return self.no_record_values()\",\n \"def _reload_values(self):\\r\\n raise NotImplementedError\",\n \"def data_changed(self):\\n return\",\n \"def test_record_eq_record_different_values_order(self):\\n zone = Zone('test.example.com')\\n data = {'type': 'A', 'ttl': 30, 'values': ['1.1.1.1', '2.2.2.2']}\\n record_current = Record(zone, 'test-record', data)\\n data = {'type': 'A', 'ttl': 30, 'values': ['2.2.2.2', '1.1.1.1']}\\n record_desired = Record(zone, 'test-record', data)\\n self.assertTrue(record_current == record_desired)\",\n \"def test_patch_record(self):\\n pass\",\n \"def _valueRemoved(self):\\n self.__changed = 1\\n if self.size() == 0:\\n raise ConsistencyFailure()\",\n \"def _process_change(self):\\n self._sort_records()\\n self._store_writer.to_file(self.records)\\n self._store_writer.to_csv_file(self.records)\",\n \"def _db(self, value):\",\n \"def on_put_field(self, ins, const, obj, value):\\n pass\",\n \"def db_values(self, db):\",\n \"def _data_process(self, v):\\n pass\",\n \"def on_change(self, value):\",\n \"def filter_record(self, record):\\n raise NotImplementedError(\\\"Override in subclass\\\")\",\n \"def handle_nval(self):\\n pass\",\n \"def record_updater(records, observations):\\n for record in records:\\n try:\\n record = ast.literal_eval(records[record])\\n except Exception:\\n record = record\\n try:\\n if type(records[record]) is dict:\\n records[record] = Common.record_updater(\\n records[record], observations\\n )\\n elif type(records[record]) is list:\\n list_records = []\\n for list_record in records[record]:\\n for observation in observations:\\n if observation != \\\"_id\\\":\\n try:\\n if re.search(observation, f\\\"{list_record}\\\"):\\n if not re.search(\\n observations[observation],\\n f\\\"{records[record]}\\\",\\n ):\\n if not re.search(\\\"-->\\\", f\\\"{list_record}\\\"):\\n list_records.append(\\n f\\\"{list_record}\\\"\\n + \\\" --> \\\"\\n + observations[observation]\\n )\\n else:\\n list_records.append(list_record)\\n else:\\n list_records.append(list_record)\\n else:\\n list_records.append(list_record)\\n except Exception as ex:\\n Common.logger.warning(\\n f\\\"Exception happened in observation comparison {ex}\\\"\\n )\\n records[record] = list_records\\n else:\\n records = Common.data_comparison(observations, records, record)\\n except Exception:\\n records = Common.data_comparison(observations, records, record)\\n return records\",\n \"def parse_record(self, record):\\n raise NotImplementedError()\",\n \"def process_sample_val(self):\\n raise NotImplementedError\",\n \"def _update_val_ids(self, change=None):\\n\\n val_min, val_max = self.val_range\\n self.val_ids = list(\\n self.data_frame[\\n (self.data_frame[self.col] >= val_min)\\n & (self.data_frame[self.col] <= val_max)\\n ].index\\n )\\n # pylint: enable=unused-argument\",\n \"def _analyze(self):\\r\\n if self.value is None or self.value == self.previous:\\r\\n pass\\r\\n elif self._operation == \\\"add\\\":\\r\\n self._additions = self.value\\r\\n elif self._operation == \\\"remove\\\":\\r\\n self._removals = self.value\\r\\n elif self.previous is None:\\r\\n self._assignments = self.value\\r\\n else:\\r\\n # partial update time\\r\\n self._additions = (self.value - self.previous) or None\\r\\n self._removals = (self.previous - self.value) or None\\r\\n self._analyzed = True\",\n \"def test_update_record(self):\\n pass\",\n \"def processRecord(self, record):\\n\\n current_status = record.status\\n\\n if current_status == 'pre-accepted':\\n new_status = 'accepted'\\n elif current_status == 'pre-rejected':\\n new_status = 'rejected'\\n else:\\n # no work to be done\\n return record\\n\\n fields = {'status': new_status}\\n return self.updateEntityProperties(record, fields)\",\n \"async def _update_values(self, model: Model):\\n\\n raise NotImplementedError\",\n \"def ignore_value_changes(self):\\n return self._parent.ignore_value_changes\",\n \"def set_field( self, data ):\\n super( UnsteadyField1D, self ).set_field( data )\\n self.history[:] = self.val[:]\\n return\",\n \"def data_dict_update(self, change):\\n self.data_dict = change['value']\",\n \"def emit(self, record):\\n pass\",\n \"def XPLMDataChanged_f(inRefcon):\",\n \"def reloadData(self):\\n self.dto.readFromData()\\n print(\\\"Record reloaded.\\\")\",\n \"def list_value_changes(self, field_name):\\n\\n t = self.data['timestamp']\\n x = self.data[field_name]\\n indices = t != 0 # filter out 0 values\\n t = t[indices]\\n x = x[indices]\\n if len(t) == 0: return []\\n ret = [(t[0], x[0])]\\n indices = np.where(x[:-1] != x[1:])[0] + 1\\n ret.extend(zip(t[indices], x[indices]))\\n return ret\",\n \"def write(self):\\n if not self._table: raise ValueError ( \\\"_table is Null\\\" )\\n if self._isnew:\\n for m in self._modified_values:\\n self._modified_values[m] = self._table[m].val_py2sql(self._modified_values[m])\\n \\n try:\\n rec = CFG.CX.insert ( CFG.DB.SCHEMA + \\\".\\\" + self._table.name,\\n self._modified_values )\\n #this will automatically re-read the data from the db, to take all changes\\n #done by triggers and default values into account.\\n self._objectid = rec['objectid']\\n\\n #print \\\"Record # {0} inserted into {1}.\\\".format(self._objectid, self._table.name)\\n self.raiseEvent ( \\\"record_added\\\", self )\\n \\n except pg.DatabaseError, e:\\n print \\\"Error inserting record.\\\"\\n raise Record.DataManipulationError ( \\\"Inserting a new record into '{0}'\\\".format(self._table.name),\\n str(self._modified_values),\\n e)\\n elif self._ismodified:\\n \\n for m in self._modified_values: \\n self._modified_values[m] = self._table[m].val_py2sql(self._modified_values[m])\\n \\n self._modified_values['objectid'] = self._objectid\\n del self.TextCache[self._objectid]\\n try:\\n rec = CFG.CX.update ( CFG.DB.SCHEMA + \\\".\\\" + self._table.name,\\n self._modified_values )\\n self.read() \\n self.raiseEvent ( \\\"record_saved\\\", self )\\n except pg.DatabaseError, e:\\n print \\\"Error updating record\\\"\\n raise Record.DataManipulationError ( \\\"Updating record {1} of '{0}'\\\".format(self._table.name, self._objectid),\\n str(self._modified_values),\\n e)\",\n \"def _postprocess_record(record, hide=_CONFIDENTIAL_FIELDS):\\n record = hide_confidential_fields(record, hide)\\n record = unserialize_fields(record, hide)\\n\\n convert_float_timestamp2str(record)\\n\\n return record\",\n \"def processLine(self, col, line):\\n col.linesRecv += 1\\n # BUGBUG : Limit should be fixed\\n if len(line) >= DATA_LINE_MAX_SIZE:\\n LOG.warning('%s line too long: %s', col.name, line)\\n col.linesInvalid += 1\\n return\\n parsed = re.match('^([-_./a-zA-Z0-9]+)\\\\s+' # Metric name\\n '(\\\\d+)\\\\s+' # Timestamp\\n '(\\\\S+?)' # Value (int or float)\\n '((?:\\\\s+[-_./a-zA-Z0-9]+=[-_./a-zA-Z0-9]+)*)$', # Tags\\n line)\\n if parsed is None:\\n LOG.warning('%s sent invalid data: %s', col.name, line)\\n col.linesInvalid += 1\\n return\\n metric, timestamp, value, tags = parsed.groups()\\n timestamp = int(timestamp)\\n\\n # eliminating repeated value logic\\n key = (metric, tags)\\n if key in col.values:\\n if timestamp <= col.values[key][3]:\\n LOG.error(\\\"Timestamp out of order: metric=%s%s,\\\"\\n \\\" old_ts=%d >= new_ts=%d - ignoring data point\\\"\\n \\\" (value=%r, collector=%s)\\\", metric, tags,\\n col.values[key][3], timestamp, value, col.name)\\n col.linesInvalid += 1\\n return\\n\\n if (col.values[key][0] == value and\\n (timestamp - col.values[key][3] < self.repeatinterval)):\\n # don't update the old timestamp\\n col.values[key] = (value, True, line, col.values[key][3])\\n return\\n\\n if ((col.values[key][1] or\\n # BUGBUG : this may always False\\n (timestamp - col.values[key][3] >= self.repeatinterval))\\n and\\n col.values[key][0] != value):\\n col.linesSent += 1\\n if not self.readerq.nput(col.values[key][2]):\\n self.linesDropped += 1\\n\\n # case 1 : (old_val == new_val) and (>=repeatinterval)\\n # case 2 : (old_val != new_val) and (<repeatinterval) and values[][1]== False\\n col.values[key] = (value, False, line, timestamp)\\n col.linesSent += 1\\n if not self.readerq.nput(line):\\n self.linesDropped += 1\",\n \"def save_vals (self):\\n raise NotImplementedError\",\n \"def _data_updated_callback(self, attr, old, new):\\n pass\",\n \"def __call__(self, record):\\n fs = record.fs\\n signal = record.p_signal\\n filtered_signal = self.filter_signal(signal, fs)\\n record.p_signal = filtered_signal\\n\\n return record\",\n \"def put_record(self, record):\\r\\n row = [record.get(field) for field in self.fields.names()]\\r\\n\\r\\n self.put(row)\",\n \"def __call__(self, record):\\n filtered_signal = self.filter_signal(record.p_signal, record.fs)\\n record.p_signal = filtered_signal\\n return record\",\n \"def on_columnvalue_modified( self, *data ):\\n\\t\\tif (len(data) == 4):\\t( cell, path, model, user_data ) = data\\n\\t\\telse:\\t\\t\\t( cell, path, new_text, model, user_data ) = data\\n\\t\\t(datatype,) = user_data\\n\\t\\tcolid = self.window2.type2colid[datatype]\\n\\t\\tif \\t(datatype == \\\"combo\\\"):\\n\\t\\t\\tmodel[path][colid] = new_text\\n\\t\\telif \\t(datatype == \\\"spin\\\"):\\n\\t\\t\\tmodel[path][colid] = long(new_text)\\n\\t\\telif \\t(datatype == \\\"text\\\"):\\n\\t\\t\\tmodel[path][colid] = new_text\\n\\t\\telif \\t(datatype == \\\"check\\\"):\\n\\t\\t\\tmodel[path][colid] = not model[path][colid]\",\n \"def update(self) -> \\\"Sample\\\":\\n self.is_savable(do_raise=True)\\n for fv in self.field_values:\\n fv.reload(fv.save())\\n\\n new_fvs = self.field_values\\n server_fvs = self.session.FieldValue.where(\\n dict(parent_id=self.id, parent_class=\\\"Sample\\\")\\n )\\n\\n to_remove = [\\n fv for fv in server_fvs if fv.id not in [_fv.id for _fv in new_fvs]\\n ]\\n if to_remove:\\n warn(\\n \\\"Trident tried to save a Sample, but it required FieldValues to be deleted.\\\"\\n )\\n for fv in to_remove:\\n fv.parent_id = None\\n fv.save()\\n self.reload(self.session.utils.json_save(\\\"Sample\\\", self.dump()))\\n return self\",\n \"def update_record():\\n global data_base, table, choice, res, output1, result, column_names, choice_row, number_str\\n a = output1.get('1.0', END)\\n a = a[0:-1]\\n changed_string = a.split('\\\\n')\\n changed_string = changed_string[0:-2]\\n\\n number_str = int(number_str) - 1\\n source_string = []\\n for i in result:\\n for j in i:\\n source_string.append(j)\\n\\n for i in range(0, 4):\\n try:\\n if changed_string[i] == source_string[i]:\\n pass\\n except IndexError:\\n pass\\n else:\\n param_value = changed_string[i]\\n step = i\\n param_column = column_names[step]\\n sqlite3_update_record(data_base, table, param_column, param_value, choice_row, res)\\n output1.delete(1.0, END)\",\n \"def Transform(self, record):\\n pass\",\n \"def update_records(self, something):\\n print(\\\"Some logic (not shown) to update database of units\\\")\",\n \"def test_entries_maintains_values_per_transaction(self):\\n\\n self.assertEquals(self.data_manager.variables[\\\"x6\\\"].value, \\\"60\\\")\\n self.data_manager.write_new_data(2, \\\"x6\\\", 999, \\\"T1\\\")\\n\\n # Confirm the latest Committed Value wasn't changed by a transaction update to variable\\n self.assertEquals(self.data_manager.variables[\\\"x6\\\"].value, \\\"60\\\")\\n \\n # Confirm that the new write just adds to the log\\n self.assertEquals(self.data_manager.entries[\\\"T1\\\"][\\\"x6\\\"].written_values[1], \\\"60\\\")\\n self.assertEquals(self.data_manager.entries[\\\"T1\\\"][\\\"x6\\\"].written_values[2], \\\"999\\\")\",\n \"def changed(self):\\n\\t\\tpass\",\n \"def val_acc(self):\\n raise Exception(\\\" not implemented in base model\\\")\",\n \"def _get_line_vals(self, record, line_field, fields):\\n line_field, max_row = get_line_max(line_field)\\n lines = record[line_field]\\n if max_row > 0 and len(lines) > 3800 :#and len(lines) > max_row:\\n raise Exception(\\n _('Records in %s exceed max record allowed!') % line_field)\\n vals = dict([(field, []) for field in fields])\\n # Get field condition & aggre function\\n field_cond_dict = {}\\n aggre_func_dict = {}\\n field_format_dict = {}\\n pair_fields = [] # I.e., ('debit${value and . or .}@{sum}', 'debit')\\n for field in fields:\\n temp_field, eval_cond = get_field_condition(field)\\n temp_field, field_format = get_field_format(temp_field)\\n raw_field, aggre_func = get_field_aggregation(temp_field)\\n # Dict of all special conditions\\n field_cond_dict.update({field: eval_cond})\\n aggre_func_dict.update({field: aggre_func})\\n field_format_dict.update({field: field_format})\\n # --\\n pair_fields.append((field, raw_field))\\n # --\\n for line in lines:\\n for field in pair_fields: # (field, raw_field)\\n value = self._get_field_data(field[1], line)\\n if type(value) == type(''):\\n value = re.sub(r\\\"[\\u001e\\u0006]\\\", '', value)\\n # Case Eval\\n eval_cond = field_cond_dict[field[0]]\\n if eval_cond: # Get eval_cond of a raw field\\n eval_context = {'float_compare': float_compare,\\n 'time': time,\\n 'datetime': dt,\\n 'date': date,\\n 'value': value,\\n 'object': line,\\n 'model': self.env[record._name],\\n 'env': self.env,\\n 'context': self._context,\\n }\\n # value = str(eval(eval_cond, eval_context))\\n # Test removing str(), coz some case, need resulting number\\n value = eval(eval_cond, eval_context)\\n # --\\n vals[field[0]].append(value)\\n return (vals, aggre_func_dict, field_format_dict)\",\n \"def update_record(self):\\n new_record = self.create_record()\\n for record in self.records:\\n if self.date_str == record[\\\"date\\\"] and not record == new_record:\\n record.update(new_record)\\n self.write_json_file(self.records_file, self.records)\\n return True\\n return False\",\n \"def _validate_fields(self, change_fields):\\n pass\",\n \"def transform(self, record):\\n if not record:\\n return None\\n\\n if not type(record) is DASRecord:\\n logging.warning('Improper format record: %s',record)\\n return None\\n\\n update = False\\n for field_name in record.fields:\\n if field_name in self.course_fields:\\n self.course_val = record.fields[field_name]\\n elif field_name in self.speed_fields:\\n self.speed_val = record.fields[field_name] * self.convert_speed_factor\\n elif field_name in self.heading_fields:\\n self.heading_val = record.fields[field_name]\\n elif field_name in self.wind_dir_fields:\\n self.wind_dir_val = record.fields[field_name]\\n elif field_name in self.wind_speed_fields:\\n self.wind_speed_val = record.fields[field_name] * self.convert_wind_factor\\n \\n if field_name in self.update_on_fields:\\n update = True\\n\\n # If we've not seen anything that updates fields that would\\n # trigger a new true winds value, return None.\\n if not update:\\n return None\\n\\n if self.course_val is None:\\n logging.info('Still missing course_val')\\n return None\\n if self.speed_val is None:\\n logging.info('Still missing speed_val')\\n return None\\n if self.heading_val is None:\\n logging.info('Still missing heading_val')\\n return None\\n if self.wind_dir_val is None:\\n logging.info('Still missing wind_dir_val')\\n return None\\n if self.wind_speed_val is None:\\n logging.info('Still missing wind_speed_val')\\n return None\\n\\n logging.info('Computing new true winds')\\n (true_dir, true_speed, app_dir) = truew(crse=self.course_val,\\n cspd=self.speed_val,\\n hd=self.heading_val,\\n wdir=self.wind_dir_val,\\n zlr=self.zero_line_reference,\\n wspd=self.wind_speed_val)\\n\\n logging.info('Got true winds: dir: %s, speed: %s, app_dir: %s',\\n true_dir, true_speed, app_dir)\\n if true_dir is None or true_speed is None or app_dir is None:\\n logging.info('Got invalid true winds')\\n return None\\n\\n # If here, we've got a valid new true wind result\\n if self.output_nmea:\\n new_record = '%s %s %g,%g,%g' % (self.data_id,\\n time_str(record.timestamp),\\n true_dir, true_speed, app_dir)\\n else:\\n new_record = DASRecord(data_id=self.data_id,\\n timestamp=record.timestamp,\\n fields={'TrueWindDir': true_dir,\\n 'TrueWindSpeed': true_speed,\\n 'ApparentWindDir': app_dir})\\n return new_record\",\n \"def setRecord(self,record):\\n idLower = record.getId().lower()\\n type = record.name\\n typeIds = self.indexed[type]\\n if idLower in typeIds:\\n oldRecord = typeIds[idLower]\\n index = self.records.index(oldRecord)\\n self.records[index] = record\\n else:\\n self.records.append(record)\\n typeIds[idLower] = record\",\n \"def update_original_data(self):\\n pass\",\n \"def getChanges():\",\n \"def case_records(self, case_records):\\n self._case_records = case_records\",\n \"def new_archive_record(self, event):\\n for obs_type in self.min_max_dict:\\n if event.record.has_key(obs_type) and event.record[obs_type] is not None:\\n if not self.min_max_dict[obs_type][0] <= event.record[obs_type] <= self.min_max_dict[obs_type][1]:\\n syslog.syslog(syslog.LOG_NOTICE, \\\"engine: %s Archive value '%s' %s outside limits (%s, %s)\\\" % \\n (weeutil.weeutil.timestamp_to_string(event.record['dateTime']),\\n obs_type, event.record[obs_type], \\n self.min_max_dict[obs_type][0], self.min_max_dict[obs_type][1]))\\n event.record[obs_type] = None\",\n \"def process_original_data():\\n audioSamples = AudioDataOriginal.query.filter().all()\\n\\n currentSampleDatetime = None\\n maxValueRecord = None\\n for sample in audioSamples:\\n if currentSampleDatetime is None:\\n currentSampleDatetime = sample.datetime\\n if maxValueRecord is None:\\n maxValueRecord = sample\\n # Check if we've reached a new sample batch.\\n if currentSampleDatetime != sample.datetime:\\n # If we reach a new sample batch, update the `processedValue` of the\\n # record held in `maxValueRecord` to be the `audio` value.\\n maxValueRecord.processedValue = maxValueRecord.audio\\n # Update to the first value of the new sample batch.\\n currentSampleDatetime = sample.datetime\\n maxValueRecord = sample\\n elif sample.audio > maxValueRecord.audio:\\n # If not, then check if the value is higher than the current one\\n # and overwrite if so.\\n maxValueRecord = sample\\n # Update the last record, since this won't be caught in the if statement\\n # inside the loop (it compares the current.datetime vs a new.datetime after\\n # all).\\n maxValueRecord.processedValue = maxValueRecord.audio\\n db.session.commit()\",\n \"def update(self, values):\\n pass\",\n \"def visit_record(self, syrecord):\\n for key, value in syrecord.items():\\n child = type(value)(value.container_type)\\n setattr(self.current, key, child)\\n value.visit(SpineCopyVisitor(child))\",\n \"def test_fields_updated_with_computed(self):\\n pass\",\n \"def process_data(self, value):\\n try:\\n self.data = value.value\\n except AttributeError:\\n self.data = value\",\n \"def _rec_only_updated(cls, rec):\\n return rec.get('uplinked', None) \\\\\\n and not rec.get('queued', None) \\\\\\n and not rec.get('announced', None) \\\\\\n and not rec.get('blocked', None) \\\\\\n and not rec.get('finished', None) \\\\\\n and not rec.get('aborted', None)\",\n \"def __add_one_day_values__(self):\\n values = self.values()\\n for value in values:\\n ls = []\\n if value.label in self.values_dict:\\n ls = self.values_dict[value.label]\\n ls.append(value)\\n else:\\n ls = [value]\\n self.values_dict[value.label] = ls\",\n \"def pre_update(self, **values):\\r\\n pass\",\n \"def _check_for_change(self, sender, instance, created, **kwargs):\\n previous = []\\n current = []\\n for i, f in enumerate(self.fields_to_track):\\n previous.append(getattr(instance, self.tracker_attnames[i], None))\\n current.append(self.get_tracked_value(instance, i))\\n if created:\\n previous = None\\n if previous != current: # short circuit if nothing has changed\\n sources, sinks = self.states_to_stocks_func(previous, current)\\n for source, sink in zip(sources, sinks):\\n if source is not sink: # short circuit if no change in state/stock\\n self.create_flow_event(source, sink, instance)\",\n \"def _GetFieldValues(\\n self, output_mediator, event, event_data, event_data_stream, event_tag):\",\n \"def compare_ao3_rec(db_rec, current_rec, report_gen):\\r\\n tests = [\\r\\n 'hits', 'kudos',\\r\\n 'comments', 'bookmarks', 'ref']\\r\\n current_dict = current_rec\\r\\n db_dict = db_rec.__dict__\\r\\n changed = []\\r\\n for value_key in tests:\\r\\n if report_gen.compare_and_print(\\r\\n current_rec[\\\"title\\\"],\\r\\n value_key,\\r\\n current_dict,\\r\\n db_dict) > 0:\\r\\n setattr(db_rec, value_key, current_dict[value_key])\\r\\n changed.append(value_key)\\r\\n return changed\",\n \"def _handle_load_unknown(self, data, original):\\n for key, val in original.items():\\n if key not in self.fields:\\n data[key] = val\\n return data\",\n \"def handle_actual_updated(self):\\n self._actual_updated()\",\n \"def process_IN_MODIFY(self, event):\",\n \"def UpdateValues( self ):\\r\\n msg =wx.grid.GridTableMessage(self, wx.grid.GRIDTABLE_REQUEST_VIEW_GET_VALUES)\\r\\n self.GetView().ProcessTableMessage(msg)\",\n \"def process_records(records):\\n changes = defaultdict(int)\\n cascaded_create_records = set()\\n cascaded_publish_records = set()\\n cascaded_unpublish_records = set()\\n cascaded_undelete_records = set()\\n cascaded_update_records = set()\\n cascaded_delete_records = set()\\n cascaded_location_changes = set()\\n\\n for record in records:\\n if record.change != ChangeType.deleted and record.object is None:\\n # Skip entries which are not deletions but have no corresponding objects.\\n # Probably they are updates for objects that got deleted afterwards.\\n continue\\n if record.change == ChangeType.created:\\n assert record.type != EntryType.category\\n cascaded_create_records.add(record)\\n elif record.change == ChangeType.published:\\n cascaded_publish_records.add(record)\\n elif record.change == ChangeType.unpublished:\\n cascaded_unpublish_records.add(record)\\n elif record.change == ChangeType.undeleted:\\n assert record.type != EntryType.category\\n cascaded_undelete_records.add(record)\\n elif record.change == ChangeType.deleted:\\n assert record.type != EntryType.category\\n cascaded_delete_records.add(record)\\n elif record.change in {ChangeType.moved, ChangeType.protection_changed}:\\n cascaded_update_records.add(record)\\n elif record.change == ChangeType.data_changed:\\n assert record.type != EntryType.category\\n changes[record.object] |= SimpleChange.updated\\n # subcontributions have their parent's time information, so we need to\\n # cascade contribution updates to them\\n if record.type == EntryType.contribution:\\n for subcontrib in record.object.subcontributions:\\n changes[subcontrib] |= SimpleChange.updated\\n elif record.change == ChangeType.location_changed:\\n assert record.type in (EntryType.event, EntryType.contribution, EntryType.session)\\n cascaded_location_changes.add(record)\\n\\n for obj in _process_cascaded_category_contents(cascaded_update_records):\\n changes[obj] |= SimpleChange.updated\\n\\n for obj in _process_cascaded_category_contents(cascaded_unpublish_records):\\n changes[obj] |= SimpleChange.deleted\\n\\n for obj in _process_cascaded_category_contents(cascaded_publish_records):\\n changes[obj] |= SimpleChange.created\\n\\n for obj in _process_cascaded_event_contents(cascaded_delete_records):\\n changes[obj] |= SimpleChange.deleted\\n\\n for obj in _process_cascaded_event_contents(cascaded_create_records, include_deleted=True):\\n changes[obj] |= SimpleChange.created\\n\\n for obj in _process_cascaded_locations(cascaded_location_changes):\\n changes[obj] |= SimpleChange.updated\\n\\n for obj in _process_cascaded_event_contents(cascaded_undelete_records, skip_all_deleted=True):\\n # This may result in a create for an object which is already created - in the (somewhat rare)\\n # case of a deletion being followed by a restore in the same set of records.\\n # However, since we expect backends to either convert those operations to an update or skip\\n # them altogether this shouldn't be a problem\\n changes[obj] |= SimpleChange.created\\n changes[obj] &= ~SimpleChange.deleted\\n\\n created_and_deleted = {obj for obj, flags in changes.items() if (flags & CREATED_DELETED) == CREATED_DELETED}\\n for obj in created_and_deleted:\\n # discard any change where the object was both created and deleted\\n del changes[obj]\\n\\n return {obj: _get_final_change(flags) for obj, flags in changes.items()}\",\n \"def apply_ruling(self, ruling, record):\\r\\n record.update(ruling)\\r\\n return record\",\n \"def process_event(self, event):\\n if not self.frozen:\\n if event[\\\"event\\\"] in [self.event, self.devent]:\\n if self.what is None or event[\\\"target\\\"].startswith(self.what):\\n self.lastone = None\\n if event[\\\"event\\\"] == self.event:\\n val = event\\n for key in self.subval:\\n val = val[key]\\n if val in self.addV:\\n if event[\\\"target\\\"] not in self.entities:\\n self.entities.add(event[\\\"target\\\"])\\n self.lastone = event[\\\"target\\\"]\\n if event[\\\"event\\\"] == self.devent:\\n val = event\\n for key in self.dsubval:\\n val = val[key]\\n if val in self.subV:\\n try:\\n self.entities.remove(event[\\\"target\\\"])\\n self.lastone = event[\\\"target\\\"]\\n except:\\n pass\\n if self.lastone is not None and bridgectl.log:\\n bridgectl.log.debug(\\n \\\"New value for {} is {}\\\".format(\\n self.name,\\n self.entities))\\n if event['event'] == 'time tick':\\n if self.period in event[\\\"starts\\\"]:\\n self.reset()\",\n \"def process_duplicate_rows(self):\\n pass\",\n \"def on_get_field(self, ins, const, obj):\\n pass\",\n \"def test_edit_record_edits_the_correct_record(self):\\n # create some db records\\n dataset = self.create_mixed_test_data()\\n test_log_entries = dataset['test_log_entries']\\n # set the menu instance's `records` property\\n self.menu.records = test_log_entries\\n record_index = 1\\n\\n # handle the user input to select the record\\n # handle the user input to specify the new values for the record\\n log_entry_for_input = OrderedDict([\\n ('name', \\\"New Test Employee\\\"),\\n ('date', \\\"2017-10-05\\\"),\\n ('task_name', \\\"New Test Task\\\"),\\n ('duration', 55),\\n ('notes', \\\"New Note\\\")\\n ])\\n\\n user_inputs = [record_index + 1] + list(log_entry_for_input.values())\\n # get the unedited requested record from the db\\n old_query = self.base_query(test_log_entries[record_index])\\n self.assertEqual(len(old_query), 1)\\n\\n # execute the method\\n with patch('builtins.input', side_effect=user_inputs):\\n self.menu.edit_record()\\n\\n # verify the record that was changed is the one selected by the user\\n # (make sure we can get the record with the new details and we can't\\n # get the record with the old details)\\n new_query = self.base_query(log_entry_for_input)\\n repeat_old_query = self.base_query(test_log_entries[record_index])\\n\\n self.assertEqual(len(new_query), 1) # new_query shd return one result\\n self.assertEqual(len(repeat_old_query), 0) # query should be empty\",\n \"def UpdateValues(self, grid):\\n # This sends an event to the grid table to update all of the values\\n msg = wx.grid.GridTableMessage(self, wx.grid.GRIDTABLE_REQUEST_VIEW_GET_VALUES)\\n grid.ProcessTableMessage(msg)\",\n \"def UpdateValues( self ):\\r\\n msg =wx.grid.GridTableMessage(self, wx.grid.GRIDTABLE_REQUEST_VIEW_GET_VALUES)\\r\\n self.GetView().ProcessTableMessage(msg)\",\n \"def records(self):\\r\\n raise NotImplementedError()\",\n \"def __value_set(self, new_value):\\n self.db_value = to_pickle(new_value)\\n # print(\\\"value_set, self.db_value:\\\", repr(self.db_value)) # DEBUG\\n self.save(update_fields=[\\\"db_value\\\"])\",\n \"def _metrics_update(orig, new):\\n revsd = orig\\n for k, v in orig.items():\\n if not v:\\n revsd[k] = new[k]\\n elif new[k]:\\n if new[k] != v:\\n # LOG ME, do something\\n print(orig)\\n print(new)\\n elif not new[k] or v:\\n pass\\n else:\\n raise Exception(\\\"_metrics_update error\\\")\\n return revsd\",\n \"def changeAdded(change):\",\n \"def _update_data(self):\\n for attribute in [\\\"flow_rate\\\"]:\\n self._data[attribute] = self._connection.measure\",\n \"def mutate_json_record(self, json_record):\\n for attr_name in json_record:\\n attr = json_record[attr_name]\\n if isinstance(attr, datetime):\\n json_record[attr_name] = attr.isoformat()\\n return json_record\",\n \"def change_data(self):\\n\\n if self.changed is not True:\\n self.changed = True\\n print('True')\",\n \"def __setitem__(self, name, value):\\r\\n if self.values.has_key(name) and self.values[name] == value:\\r\\n return\\r\\n if not self._old.has_key(name): # Changed field\\r\\n self._old[name] = self.values.get(name)\\r\\n elif self._old[name] == value: # Change of field reverted\\r\\n del self._old[name]\\r\\n if value:\\r\\n field = [field for field in self.fields if field['name'] == name]\\r\\n if field and field[0].get('type') != 'textarea':\\r\\n value = value.strip()\\r\\n self.values[name] = value\",\n \"def updateItem(self, value):\\n self.value = value\\n self.age = 0\\n self.freq += 1\",\n \"def handleRowDataChange(self, changedItem ):\\n with Tracer(traceLogger):\\n # Figure out which row this widget is in\\n row = changedItem.row()\\n column = changedItem.column()\\n\\n # Can't update until the row is fully initialized\\n needUpdate = True\\n needUpdate &= column == Column.Name or column == Column.InternalID\\n needUpdate &= self.fileInfoTableWidget.item(row, Column.Name) != None\\n needUpdate &= self.fileInfoTableWidget.cellWidget(row, Column.InternalID) != None\\n needUpdate &= self.fileInfoTableWidget.cellWidget(row, column) is not None\\n\\n if needUpdate:\\n self.updateFilePath(row)\",\n \"def _measurement_update(self):\\n pass\",\n \"def updated_full_record(full_record):\\n full_record[\\\"access\\\"][\\\"status\\\"] = \\\"embargoed\\\"\\n full_record[\\\"created\\\"] = \\\"2023-03-23T00:00:00.000000+00:00\\\"\\n full_record[\\\"id\\\"] = \\\"abcde-fghij\\\"\\n full_record[\\\"metadata\\\"][\\\"resource_type\\\"][\\\"id\\\"] = \\\"other\\\"\\n\\n return full_record\",\n \"def onChanged(self, fp, prop):\\n if prop == \\\"Length\\\" or prop == \\\"Breadth\\\" or prop == \\\"Draft\\\":\\n pass\",\n \"def records(self, records):\\n\\n self._records = records\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6425481","0.60396576","0.5966757","0.5945433","0.58991665","0.58991665","0.58991665","0.5884155","0.58602935","0.58449084","0.57962346","0.57869387","0.5782773","0.57684565","0.57654285","0.5758463","0.57228583","0.57219404","0.5706814","0.5650283","0.56483305","0.564831","0.5625027","0.56052846","0.5586178","0.5569665","0.5538233","0.55294585","0.5516665","0.54866695","0.5478916","0.5470494","0.54694456","0.54681957","0.54532945","0.5448365","0.54401565","0.54371864","0.54348713","0.5419797","0.5404171","0.54034853","0.53928816","0.53861654","0.53648585","0.5360101","0.5355687","0.5341889","0.5326912","0.53263175","0.5325353","0.53124595","0.5305881","0.530393","0.5299745","0.52935106","0.52889824","0.52782595","0.52726066","0.52700174","0.526953","0.52600205","0.52422726","0.52369606","0.52364206","0.5232309","0.52228427","0.52126855","0.5208011","0.52071416","0.5196994","0.5195751","0.5193792","0.51811713","0.51793283","0.51770574","0.5165923","0.5162808","0.51541215","0.5151816","0.5151606","0.5145646","0.5141846","0.51333326","0.5120348","0.5116742","0.5114713","0.51145726","0.51132387","0.5110759","0.5105294","0.5103957","0.5090112","0.5088964","0.5088016","0.5084591","0.508414","0.50781614","0.50778085","0.50733083"],"string":"[\n \"0.6425481\",\n \"0.60396576\",\n \"0.5966757\",\n \"0.5945433\",\n \"0.58991665\",\n \"0.58991665\",\n \"0.58991665\",\n \"0.5884155\",\n \"0.58602935\",\n \"0.58449084\",\n \"0.57962346\",\n \"0.57869387\",\n \"0.5782773\",\n \"0.57684565\",\n \"0.57654285\",\n \"0.5758463\",\n \"0.57228583\",\n \"0.57219404\",\n \"0.5706814\",\n \"0.5650283\",\n \"0.56483305\",\n \"0.564831\",\n \"0.5625027\",\n \"0.56052846\",\n \"0.5586178\",\n \"0.5569665\",\n \"0.5538233\",\n \"0.55294585\",\n \"0.5516665\",\n \"0.54866695\",\n \"0.5478916\",\n \"0.5470494\",\n \"0.54694456\",\n \"0.54681957\",\n \"0.54532945\",\n \"0.5448365\",\n \"0.54401565\",\n \"0.54371864\",\n \"0.54348713\",\n \"0.5419797\",\n \"0.5404171\",\n \"0.54034853\",\n \"0.53928816\",\n \"0.53861654\",\n \"0.53648585\",\n \"0.5360101\",\n \"0.5355687\",\n \"0.5341889\",\n \"0.5326912\",\n \"0.53263175\",\n \"0.5325353\",\n \"0.53124595\",\n \"0.5305881\",\n \"0.530393\",\n \"0.5299745\",\n \"0.52935106\",\n \"0.52889824\",\n \"0.52782595\",\n \"0.52726066\",\n \"0.52700174\",\n \"0.526953\",\n \"0.52600205\",\n \"0.52422726\",\n \"0.52369606\",\n \"0.52364206\",\n \"0.5232309\",\n \"0.52228427\",\n \"0.52126855\",\n \"0.5208011\",\n \"0.52071416\",\n \"0.5196994\",\n \"0.5195751\",\n \"0.5193792\",\n \"0.51811713\",\n \"0.51793283\",\n \"0.51770574\",\n \"0.5165923\",\n \"0.5162808\",\n \"0.51541215\",\n \"0.5151816\",\n \"0.5151606\",\n \"0.5145646\",\n \"0.5141846\",\n \"0.51333326\",\n \"0.5120348\",\n \"0.5116742\",\n \"0.5114713\",\n \"0.51145726\",\n \"0.51132387\",\n \"0.5110759\",\n \"0.5105294\",\n \"0.5103957\",\n \"0.5090112\",\n \"0.5088964\",\n \"0.5088016\",\n \"0.5084591\",\n \"0.508414\",\n \"0.50781614\",\n \"0.50778085\",\n \"0.50733083\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.54137516"},"document_rank":{"kind":"string","value":"40"}}},{"rowIdx":93778,"cells":{"query":{"kind":"string","value":"Central exit point for the module."},"document":{"kind":"string","value":"def record_exit(self, after=None, before=None, changed=None, record=None):\n # convert dictionaries to yaml txt dump\n before_yaml = yaml.safe_dump(before, default_flow_style=False)\n after_yaml = yaml.safe_dump(after, default_flow_style=False)\n\n # build the final dict to pass into exit_json\n if self.module._diff:\n exec_result = dict(\n diff={'before': {}, 'after': {}})\n if after is not None:\n exec_result['diff']['after'] = after_yaml\n if before is not None:\n exec_result['diff']['before'] = before_yaml\n if changed is not None:\n exec_result['changed'] = changed\n if record is not None:\n try:\n exec_result['record'] = record.data\n except AttributeError:\n exec_result['record'] = record\n self.module.exit_json(**exec_result)\n\n # catch if the module is not being run with --diff\n self.module.exit_json(changed=changed)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def exit(self):\n pass","def __exit(self, *args):\n sys.exit(0)","def exit(self):\n super(Agent, self).exit()\n\n if self._module is not None:\n self._module.exit()","def __exit__(self, *args):\n pass","def exit(self):\n logger.debug(\"EXIT\")","def __exit__(self, *args, **kwargs):\n\n pass","def on_exit(self):\n pass","def exit():\n sys.exit(1)","def exit(self):\n self.runtime.halted = True","def exit(self) -> None:\n self.on_exit(None)","def _exit(self, save_vars):\n raise NotImplementedError()","def exit(self):\n print(\"\\n***************************** Exit Metafor *****************************\")","def exit(self):\n self.exit_flag = True","async def __aexit__(self, exc_type, exc_val, exc_tb):\n pass","def exit(self) -> None:\n logger.info(messages.general(\"Thank You for using FooTools.\\n\"))\n exit()","def _exit() -> None:\n\n print(\n \"Thanks for using TbSET. \"\n \"See you next time!\\n\"\n )","def _PExit(self, m):\n pass","def __exit__(self):\n self._stop_all()","def do_exit(self,*args):\r\n return True","def __exit__(self, exc_type, exc_val, exc_tb):\n\n self.quit()","def exit(self, *args):\n self.stop('all')\n sys.exit(1)","def exit_program():\n quit()","def main_thread_exit(self):\n ...","def __exit__(self, exc_type, exc_value, traceback):\r\n pass","def __exit__(self, exc_type, exc_value, traceback):\r\n pass","def __exit__(self, exc_type, exc_value, traceback):\r\n pass","def __my_exit__(self, arg=0):\n self.services.error('Called sys.exit() from component code')\n raise Exception('Called sys.exit() from component code')","def on_exit(self, userdata):\n pass","def run(self):\n sys.exit(-1)","def exitprogram():\n sys.exit()","def exit(self):\n return self.__exit","def __exit__(self, exc_type, exc_value, traceback):\n nvmlShutdown()","def __exit__(self, *excinfo):\n pass","def __exit__(self, exc_type, exc_val, exc_tb):\r\n pass","def __exit__(self, *args):\n self.stop()","def finish(self) -> None:\n self.__exit__(None, None, None)","def __exit__(self, exc_type, exc_value, traceback):\n pass","def do_exit(self, args):\n return -1","def __exit__(self, exc_type, exc_val, exc_tb):\n pass","def exit(self):\n self.current.exit()","def program_exit(self, button_object):\n\t\tsys.exit(0)","def __del__(self):\n self.exit()","def exit_client(self):\r\n\r\n sys.exit()","def quit(self):\n sys.exit(0)","def do_exit(self, _):\n return True","def exit_engine(self):\n self.stop_flag = True","def do_exit(self, args):\n sys.exit(1)","def __exit__(self, *args):\n logging.debug(\"In the FMC __exit__() class method.\")\n\n if self.autodeploy:\n self.deploy_changes()\n else:\n logging.info(\"Auto deploy changes set to False. \"\n \"Use the Deploy button in FMC to push changes to FTDs.\\n\\n\")","def __exit__(self, exec_type, exec_value, traceback):\n #TODO: probably should shut down the visualization server component gracefully here","def exit_handler(self):\n self.logger.debug(\n \"Starting script shutdown in the class \" +\n self.__class__.__name__\n )\n\n # Clean up dead processes before exiting\n self.cleanup_dead_processes()\n \"\"\"\n print(\"FAKELOG: [\" + time.strftime(\"%c\") + \"] [UnisonCTRL] Exiting\\n\")\n \"\"\"\n self.logger.debug(\n \"Script shutdown complete in class \" +\n self.__class__.__name__\n )\n\n self.logger.info(\"Exiting UnisonCTRL\")","def _exit(msg):\n __exit(msg)","def exit_handler():\n logger.debug(\"Application exit caught.\")\n save_state()","def call_exit(self, _) -> None:\n self.save_class()\n for _ in range(self.PATH.count(\"/\") + 1):\n self.queue.insert(0, \"quit\")","def __exit__(self):\n self.host = 0\n self.port = 0\n #print \"Exiting TvModel\"","def __exit__(self, exc_type, exc_value, traceback):\n self._cleanup()","def __exit__(self, exception, value, trace):\n self.manual_exit()","def __exit__(self, *args):\n if self.teardown:\n super().__exit__(*args)","def exit(self):\n if self.debug:\n print(\"%s exit\" % self.name)\n self.stop()","def quit(): # pylint: disable=redefined-builtin\n if _module_init():\n # TODO: find all Input and Output classes and close them first?\n _pypm.Terminate()\n _module_init(False)","def force_exit(self):\n self.exit = 1","def exit_program():\n print(\"Good bye\")\n sys.exit()","def exit(self, _):\n try:\n self.execution_manager.close()\n except QMapError as e:\n print(e)\n raise urwid.ExitMainLoop()","def quit():\n\tsys.exit()","def exit(status=None): # real signature unknown; restored from __doc__\n pass","def do_exit(self, args):\n return True","def __exit__(self, exc_type, exc_value, traceback): \n self.shutdown()","def exit(self):\n self.close()","def shutdown(self):\n self.exit_app()","def _handler_direct_access_exit(self, *args, **kwargs):","def exit(self):\n QtCore.QCoreApplication.exit()","def shutdown() -> None: # TODO Better place for this code\n # TODO Safe landing\n pass","def exit(context):\n return _nfc.exit(context)","def do_exit(self, line): \n sys.exit(0)","def _on_exit(cls, error=None):\n # type: (Exception) -> None\n pass","def server_exit():\n return","def user_exit(cls):\n cls.exit_program(ErrorCodes.E_USER_EXIT)","def __exit__(self, exc_type, exc_value, traceback):\n return None","def click_Exit(self, event):\n exit()","def do_exit(self, args):\n return sys.exit(1)","def do_exit(self, args):\n logger.debug(\"do_exit() was called.\")\n \n raise Exception(\"Shutting server down.\")","def close(self):\n self.__exit__(None, None, None)","def close(self):\n self.__exit__(None, None, None)","def _Exit(__s, __f):\n print(\"_Exit\")\n Exit()","def close():\n sys.exit()","def __exit__(self, exception_type, exception_value, traceback):\n self._file.source.__exit__(exception_type, exception_value, traceback)","def OnExit(self, event):\n \n print 'Cleaning up...'\n self.Destroy()","def quit_program():\n sys.exit()","def exit_program(cls, return_code):\n print(\"Exiting due to: %s\" % (ErrorMessages.verbose(return_code)))\n sys.exit(return_code)","def __exit__(self, ex_type, ex_value, traceback):\n self._distrubtor.stop()","def __exit__(self, *args, **kwargs):\n\t\tfcntl.flock(self.file, fcntl.LOCK_UN)\n\t\tself.file.close()\n\t\tfor function in self.exit_functions:\n\t\t\tfunction()","def exit_loop(self):\n self.loop.exit()","def __exit(self, exit_code=0):\r\n self._workspace_manager.stopAutoLoader()\r\n self._workspace_manager.stopReportManager()\r\n\r\n self._main_window.hide()\r\n print \"Closing Faraday...\"\r\n self._workspace_manager.saveWorkspaces()\r\n envs = [env for env in self._shell_envs.itervalues()]\r\n for env in envs:\r\n env.terminate() \r\n \r\n print \"stopping model controller thread...\"\r\n self._model_controller.stop()\r\n print \"stopping model controller thread...\"\r\n self.qapp.quit()\r\n print \"Waiting for controller threads to end...\"\r\n self._model_controller.join()\r\n \r\n return exit_code","def do_exit(_arg=None):\n return True","def state_finish_exit(cfg, app, win):","def analyze_on_exit(self):\n atexit.register(exit_handler, self)","def __exit__(self, exec_type, value, traceback): # suppress(no-self-use)\n IndentedLogger.__exit__(exec_type, value, traceback)\n Task.nest_level -= 1","def __exit__(self, exc_type, exc_val, exc_tb) -> None: # type: ignore\n self.shutdown()","def close(self):\n self.exit()","def dummy_exit():\r\n def dummy_exit(_exitcode=0):\r\n raise DummyExitException(exitcode=_exitcode)\r\n return dummy_exit","def do_finalize(self):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tdef _finalize(self):\n\t\t\t# Stop all the modules\n\t\t\tself.stop_all()\n\t\t\t# Finalize in reverse order\n\t\t\tself.log('PHASE: finalizing object ' + str(self), level=logging.DEBUG)\n\t\t\t# Login at least once to get the exports.\n\t\t\tfor module_id in self.module_ids(rev=True):\n\t\t\t\t# Only finalize if it's thought to be installed.\n\t\t\t\tif self.is_installed(self.shutit_map[module_id]):\n\t\t\t\t\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\n\t\t\t\t\tif not self.shutit_map[module_id].finalize(self):\n\t\t\t\t\t\tself.fail(module_id + ' failed on finalize', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\n\t\t\t\t\tself.logout(echo=False)\n\t\t_finalize(self)","def do_exit(self, arg):\n self.db.close_db()\n print(\" \\\\o_ Bye-bye...\")\n print(\" / \")\n print(\"<\\\\\")\n sys.exit()"],"string":"[\n \"def exit(self):\\n pass\",\n \"def __exit(self, *args):\\n sys.exit(0)\",\n \"def exit(self):\\n super(Agent, self).exit()\\n\\n if self._module is not None:\\n self._module.exit()\",\n \"def __exit__(self, *args):\\n pass\",\n \"def exit(self):\\n logger.debug(\\\"EXIT\\\")\",\n \"def __exit__(self, *args, **kwargs):\\n\\n pass\",\n \"def on_exit(self):\\n pass\",\n \"def exit():\\n sys.exit(1)\",\n \"def exit(self):\\n self.runtime.halted = True\",\n \"def exit(self) -> None:\\n self.on_exit(None)\",\n \"def _exit(self, save_vars):\\n raise NotImplementedError()\",\n \"def exit(self):\\n print(\\\"\\\\n***************************** Exit Metafor *****************************\\\")\",\n \"def exit(self):\\n self.exit_flag = True\",\n \"async def __aexit__(self, exc_type, exc_val, exc_tb):\\n pass\",\n \"def exit(self) -> None:\\n logger.info(messages.general(\\\"Thank You for using FooTools.\\\\n\\\"))\\n exit()\",\n \"def _exit() -> None:\\n\\n print(\\n \\\"Thanks for using TbSET. \\\"\\n \\\"See you next time!\\\\n\\\"\\n )\",\n \"def _PExit(self, m):\\n pass\",\n \"def __exit__(self):\\n self._stop_all()\",\n \"def do_exit(self,*args):\\r\\n return True\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n\\n self.quit()\",\n \"def exit(self, *args):\\n self.stop('all')\\n sys.exit(1)\",\n \"def exit_program():\\n quit()\",\n \"def main_thread_exit(self):\\n ...\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\r\\n pass\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\r\\n pass\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\r\\n pass\",\n \"def __my_exit__(self, arg=0):\\n self.services.error('Called sys.exit() from component code')\\n raise Exception('Called sys.exit() from component code')\",\n \"def on_exit(self, userdata):\\n pass\",\n \"def run(self):\\n sys.exit(-1)\",\n \"def exitprogram():\\n sys.exit()\",\n \"def exit(self):\\n return self.__exit\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n nvmlShutdown()\",\n \"def __exit__(self, *excinfo):\\n pass\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\r\\n pass\",\n \"def __exit__(self, *args):\\n self.stop()\",\n \"def finish(self) -> None:\\n self.__exit__(None, None, None)\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n pass\",\n \"def do_exit(self, args):\\n return -1\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n pass\",\n \"def exit(self):\\n self.current.exit()\",\n \"def program_exit(self, button_object):\\n\\t\\tsys.exit(0)\",\n \"def __del__(self):\\n self.exit()\",\n \"def exit_client(self):\\r\\n\\r\\n sys.exit()\",\n \"def quit(self):\\n sys.exit(0)\",\n \"def do_exit(self, _):\\n return True\",\n \"def exit_engine(self):\\n self.stop_flag = True\",\n \"def do_exit(self, args):\\n sys.exit(1)\",\n \"def __exit__(self, *args):\\n logging.debug(\\\"In the FMC __exit__() class method.\\\")\\n\\n if self.autodeploy:\\n self.deploy_changes()\\n else:\\n logging.info(\\\"Auto deploy changes set to False. \\\"\\n \\\"Use the Deploy button in FMC to push changes to FTDs.\\\\n\\\\n\\\")\",\n \"def __exit__(self, exec_type, exec_value, traceback):\\n #TODO: probably should shut down the visualization server component gracefully here\",\n \"def exit_handler(self):\\n self.logger.debug(\\n \\\"Starting script shutdown in the class \\\" +\\n self.__class__.__name__\\n )\\n\\n # Clean up dead processes before exiting\\n self.cleanup_dead_processes()\\n \\\"\\\"\\\"\\n print(\\\"FAKELOG: [\\\" + time.strftime(\\\"%c\\\") + \\\"] [UnisonCTRL] Exiting\\\\n\\\")\\n \\\"\\\"\\\"\\n self.logger.debug(\\n \\\"Script shutdown complete in class \\\" +\\n self.__class__.__name__\\n )\\n\\n self.logger.info(\\\"Exiting UnisonCTRL\\\")\",\n \"def _exit(msg):\\n __exit(msg)\",\n \"def exit_handler():\\n logger.debug(\\\"Application exit caught.\\\")\\n save_state()\",\n \"def call_exit(self, _) -> None:\\n self.save_class()\\n for _ in range(self.PATH.count(\\\"/\\\") + 1):\\n self.queue.insert(0, \\\"quit\\\")\",\n \"def __exit__(self):\\n self.host = 0\\n self.port = 0\\n #print \\\"Exiting TvModel\\\"\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n self._cleanup()\",\n \"def __exit__(self, exception, value, trace):\\n self.manual_exit()\",\n \"def __exit__(self, *args):\\n if self.teardown:\\n super().__exit__(*args)\",\n \"def exit(self):\\n if self.debug:\\n print(\\\"%s exit\\\" % self.name)\\n self.stop()\",\n \"def quit(): # pylint: disable=redefined-builtin\\n if _module_init():\\n # TODO: find all Input and Output classes and close them first?\\n _pypm.Terminate()\\n _module_init(False)\",\n \"def force_exit(self):\\n self.exit = 1\",\n \"def exit_program():\\n print(\\\"Good bye\\\")\\n sys.exit()\",\n \"def exit(self, _):\\n try:\\n self.execution_manager.close()\\n except QMapError as e:\\n print(e)\\n raise urwid.ExitMainLoop()\",\n \"def quit():\\n\\tsys.exit()\",\n \"def exit(status=None): # real signature unknown; restored from __doc__\\n pass\",\n \"def do_exit(self, args):\\n return True\",\n \"def __exit__(self, exc_type, exc_value, traceback): \\n self.shutdown()\",\n \"def exit(self):\\n self.close()\",\n \"def shutdown(self):\\n self.exit_app()\",\n \"def _handler_direct_access_exit(self, *args, **kwargs):\",\n \"def exit(self):\\n QtCore.QCoreApplication.exit()\",\n \"def shutdown() -> None: # TODO Better place for this code\\n # TODO Safe landing\\n pass\",\n \"def exit(context):\\n return _nfc.exit(context)\",\n \"def do_exit(self, line): \\n sys.exit(0)\",\n \"def _on_exit(cls, error=None):\\n # type: (Exception) -> None\\n pass\",\n \"def server_exit():\\n return\",\n \"def user_exit(cls):\\n cls.exit_program(ErrorCodes.E_USER_EXIT)\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n return None\",\n \"def click_Exit(self, event):\\n exit()\",\n \"def do_exit(self, args):\\n return sys.exit(1)\",\n \"def do_exit(self, args):\\n logger.debug(\\\"do_exit() was called.\\\")\\n \\n raise Exception(\\\"Shutting server down.\\\")\",\n \"def close(self):\\n self.__exit__(None, None, None)\",\n \"def close(self):\\n self.__exit__(None, None, None)\",\n \"def _Exit(__s, __f):\\n print(\\\"_Exit\\\")\\n Exit()\",\n \"def close():\\n sys.exit()\",\n \"def __exit__(self, exception_type, exception_value, traceback):\\n self._file.source.__exit__(exception_type, exception_value, traceback)\",\n \"def OnExit(self, event):\\n \\n print 'Cleaning up...'\\n self.Destroy()\",\n \"def quit_program():\\n sys.exit()\",\n \"def exit_program(cls, return_code):\\n print(\\\"Exiting due to: %s\\\" % (ErrorMessages.verbose(return_code)))\\n sys.exit(return_code)\",\n \"def __exit__(self, ex_type, ex_value, traceback):\\n self._distrubtor.stop()\",\n \"def __exit__(self, *args, **kwargs):\\n\\t\\tfcntl.flock(self.file, fcntl.LOCK_UN)\\n\\t\\tself.file.close()\\n\\t\\tfor function in self.exit_functions:\\n\\t\\t\\tfunction()\",\n \"def exit_loop(self):\\n self.loop.exit()\",\n \"def __exit(self, exit_code=0):\\r\\n self._workspace_manager.stopAutoLoader()\\r\\n self._workspace_manager.stopReportManager()\\r\\n\\r\\n self._main_window.hide()\\r\\n print \\\"Closing Faraday...\\\"\\r\\n self._workspace_manager.saveWorkspaces()\\r\\n envs = [env for env in self._shell_envs.itervalues()]\\r\\n for env in envs:\\r\\n env.terminate() \\r\\n \\r\\n print \\\"stopping model controller thread...\\\"\\r\\n self._model_controller.stop()\\r\\n print \\\"stopping model controller thread...\\\"\\r\\n self.qapp.quit()\\r\\n print \\\"Waiting for controller threads to end...\\\"\\r\\n self._model_controller.join()\\r\\n \\r\\n return exit_code\",\n \"def do_exit(_arg=None):\\n return True\",\n \"def state_finish_exit(cfg, app, win):\",\n \"def analyze_on_exit(self):\\n atexit.register(exit_handler, self)\",\n \"def __exit__(self, exec_type, value, traceback): # suppress(no-self-use)\\n IndentedLogger.__exit__(exec_type, value, traceback)\\n Task.nest_level -= 1\",\n \"def __exit__(self, exc_type, exc_val, exc_tb) -> None: # type: ignore\\n self.shutdown()\",\n \"def close(self):\\n self.exit()\",\n \"def dummy_exit():\\r\\n def dummy_exit(_exitcode=0):\\r\\n raise DummyExitException(exitcode=_exitcode)\\r\\n return dummy_exit\",\n \"def do_finalize(self):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tdef _finalize(self):\\n\\t\\t\\t# Stop all the modules\\n\\t\\t\\tself.stop_all()\\n\\t\\t\\t# Finalize in reverse order\\n\\t\\t\\tself.log('PHASE: finalizing object ' + str(self), level=logging.DEBUG)\\n\\t\\t\\t# Login at least once to get the exports.\\n\\t\\t\\tfor module_id in self.module_ids(rev=True):\\n\\t\\t\\t\\t# Only finalize if it's thought to be installed.\\n\\t\\t\\t\\tif self.is_installed(self.shutit_map[module_id]):\\n\\t\\t\\t\\t\\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\\n\\t\\t\\t\\t\\tif not self.shutit_map[module_id].finalize(self):\\n\\t\\t\\t\\t\\t\\tself.fail(module_id + ' failed on finalize', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\\n\\t\\t\\t\\t\\tself.logout(echo=False)\\n\\t\\t_finalize(self)\",\n \"def do_exit(self, arg):\\n self.db.close_db()\\n print(\\\" \\\\\\\\o_ Bye-bye...\\\")\\n print(\\\" / \\\")\\n print(\\\"<\\\\\\\\\\\")\\n sys.exit()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7896138","0.78592885","0.77071196","0.7696922","0.7685185","0.756464","0.7539602","0.7297987","0.7291299","0.7278677","0.7160537","0.714784","0.71462065","0.7087686","0.7087392","0.70799416","0.70055544","0.69983804","0.6993124","0.6984299","0.69821894","0.6977655","0.69728667","0.6946843","0.6946843","0.6946843","0.6932189","0.6929626","0.6911495","0.6909656","0.6900167","0.6898431","0.68947816","0.6893472","0.68878615","0.68811315","0.6872714","0.6848435","0.6840699","0.68190575","0.6811614","0.68113494","0.6809404","0.68058366","0.67984015","0.67897296","0.6787145","0.6776886","0.6765157","0.67336196","0.66911817","0.6683514","0.6666604","0.665446","0.66396064","0.6637294","0.6635507","0.66322625","0.6625496","0.66242605","0.6621704","0.6617582","0.6616221","0.66142625","0.66122526","0.6602513","0.6599776","0.6597386","0.65924037","0.6590503","0.6579565","0.6575272","0.6562866","0.6555711","0.65501755","0.6547762","0.65441215","0.6542436","0.6541627","0.6541405","0.6537381","0.6537381","0.65157324","0.6507426","0.65072197","0.65063953","0.6495949","0.6487087","0.6484993","0.6481809","0.6476853","0.6471665","0.6460545","0.64599866","0.64511496","0.6440615","0.64285225","0.64279646","0.64168674","0.64155734","0.6395026"],"string":"[\n \"0.7896138\",\n \"0.78592885\",\n \"0.77071196\",\n \"0.7696922\",\n \"0.7685185\",\n \"0.756464\",\n \"0.7539602\",\n \"0.7297987\",\n \"0.7291299\",\n \"0.7278677\",\n \"0.7160537\",\n \"0.714784\",\n \"0.71462065\",\n \"0.7087686\",\n \"0.7087392\",\n \"0.70799416\",\n \"0.70055544\",\n \"0.69983804\",\n \"0.6993124\",\n \"0.6984299\",\n \"0.69821894\",\n \"0.6977655\",\n \"0.69728667\",\n \"0.6946843\",\n \"0.6946843\",\n \"0.6946843\",\n \"0.6932189\",\n \"0.6929626\",\n \"0.6911495\",\n \"0.6909656\",\n \"0.6900167\",\n \"0.6898431\",\n \"0.68947816\",\n \"0.6893472\",\n \"0.68878615\",\n \"0.68811315\",\n \"0.6872714\",\n \"0.6848435\",\n \"0.6840699\",\n \"0.68190575\",\n \"0.6811614\",\n \"0.68113494\",\n \"0.6809404\",\n \"0.68058366\",\n \"0.67984015\",\n \"0.67897296\",\n \"0.6787145\",\n \"0.6776886\",\n \"0.6765157\",\n \"0.67336196\",\n \"0.66911817\",\n \"0.6683514\",\n \"0.6666604\",\n \"0.665446\",\n \"0.66396064\",\n \"0.6637294\",\n \"0.6635507\",\n \"0.66322625\",\n \"0.6625496\",\n \"0.66242605\",\n \"0.6621704\",\n \"0.6617582\",\n \"0.6616221\",\n \"0.66142625\",\n \"0.66122526\",\n \"0.6602513\",\n \"0.6599776\",\n \"0.6597386\",\n \"0.65924037\",\n \"0.6590503\",\n \"0.6579565\",\n \"0.6575272\",\n \"0.6562866\",\n \"0.6555711\",\n \"0.65501755\",\n \"0.6547762\",\n \"0.65441215\",\n \"0.6542436\",\n \"0.6541627\",\n \"0.6541405\",\n \"0.6537381\",\n \"0.6537381\",\n \"0.65157324\",\n \"0.6507426\",\n \"0.65072197\",\n \"0.65063953\",\n \"0.6495949\",\n \"0.6487087\",\n \"0.6484993\",\n \"0.6481809\",\n \"0.6476853\",\n \"0.6471665\",\n \"0.6460545\",\n \"0.64599866\",\n \"0.64511496\",\n \"0.6440615\",\n \"0.64285225\",\n \"0.64279646\",\n \"0.64168674\",\n \"0.64155734\",\n \"0.6395026\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93779,"cells":{"query":{"kind":"string","value":"Method called by main to handle record state logic handling."},"document":{"kind":"string","value":"def exec_module(self):\n state = self.module.params.get('state')\n zone = self.get_zone()\n if zone is None:\n self.module.fail_json(msg='zone \"{}\" not found'.format(\n self.module.params.get('zone')\n ))\n record = self.get_record(zone)\n\n # record found\n if record:\n # absent param handling\n if state == \"absent\":\n if self.module.check_mode:\n # short circut in check mode\n self.record_exit(before=record.data,\n changed=True, record=record)\n record.delete(errback=self.errback_generator())\n self.record_exit(before=record.data,\n changed=True, record=record)\n # present param handling - Create and update go down the same\n # path when record is found.\n else:\n self.update(record)\n\n # record not found\n else:\n # absent param handling\n if state == \"absent\":\n self.module.exit_json(changed=False)\n # present param handling - Create tasks go down this path.\n else:\n # short circuit in check mode\n if self.module.check_mode:\n # setting record to module params as nothing has been\n # returned from the API to use for --diff\n record = self.module.params\n self.record_exit(changed=True, after=record, record=record)\n\n method_to_call = getattr(\n zone, 'add_%s' % (self.module.params.get('type').upper())\n )\n record = method_to_call(\n self.module.params.get('name'),\n self.filter_empty_subparams('answers'),\n errback=self.errback_generator(),\n **self.api_params()\n )\n self.record_exit(\n changed=True, after=record.data, record=record)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def handle_record(self, record):\n raise NotImplementedError","def process_record(self, record):\n raise NotImplementedError('Process record needs to be customized')","def _post_init(self):\n self.set_new_record_state(False)","def record(self, state):\r\n\r\n if state: # Start Recording.\r\n logo_rec = QtGui.QPixmap(\":/freeseer/logo_rec.png\")\r\n sysIcon2 = QtGui.QIcon(logo_rec)\r\n self.systray.setIcon(sysIcon2)\r\n self.controller.record()\r\n self.mainWidget.recordPushButton.setText(self.stopString)\r\n self.recordAction.setText(self.stopString)\r\n\r\n # Hide if auto-hide is set.\r\n if self.config.auto_hide:\r\n self.hide_window()\r\n self.visibilityAction.setText(self.showWindowString)\r\n log.debug('auto-hide is enabled, main window is now hidden in systray.')\r\n\r\n # Start timer.\r\n self.timer.start(1000)\r\n\r\n else: # Stop Recording.\r\n logo_rec = QtGui.QPixmap(\":/freeseer/logo.png\")\r\n sysIcon = QtGui.QIcon(logo_rec)\r\n self.systray.setIcon(sysIcon)\r\n self.controller.stop()\r\n self.mainWidget.pauseToolButton.setChecked(False)\r\n self.mainWidget.recordPushButton.setText(self.recordString)\r\n self.recordAction.setText(self.recordString)\r\n self.mainWidget.audioSlider.setValue(0)\r\n self.mainWidget.statusLabel.setText(\"{} {} --- {} \".format(self.freeSpaceString,\r\n get_free_space(self.config.videodir),\r\n self.idleString))\r\n\r\n # Finally set the standby button back to unchecked position.\r\n self.standby(False)\r\n\r\n # Stop and reset timer.\r\n self.timer.stop()\r\n self.reset_timer()\r\n\r\n #Show playback button\r\n self.mainWidget.playPushButton.setVisible(True)\r\n self.mainWidget.playPushButton.setEnabled(True)\r\n\r\n # Select next talk if there is one within 15 minutes.\r\n if self.current_event and self.current_room:\r\n starttime = QtCore.QDateTime().currentDateTime()\r\n stoptime = starttime.addSecs(900)\r\n talkid = self.db.get_talk_between_time(self.current_event, self.current_room,\r\n starttime.toString(), stoptime.toString())\r\n\r\n if talkid is not None:\r\n for i in range(self.mainWidget.talkComboBox.count()):\r\n if talkid == self.mainWidget.talkComboBox.model().index(i, 1).data(QtCore.Qt.DisplayRole).toString():\r\n self.mainWidget.talkComboBox.setCurrentIndex(i)","def _post_training(self):\n self._write_state_key()","def record(self, step):","def readState(self, saveState: ghidra.framework.options.SaveState) -> None:\n ...","def final(self, state):\n \"*** YOUR CODE HERE ***\"\n return\n util.raiseNotDefined()","def make_record(self):\n answer = self.input('enter your name: ')\n if answer:\n print('hi ' + answer + '! Your answer has been recorded.')\n self.record_score(answer, len(self.board.tiles.blacks))\n self.game_on = False\n else:\n self.game_on = False # Canceled dialog will turn off","def state_processing_do(cfg, app, win, events):","def enter_state(self):\r\n self.__log__(logging.debug)\r\n return","def state_capture_do(cfg, app, win, events):","def _get_state(self):","def record_status(self, record_status):\n\n self._record_status = record_status","def state_processing_enter(cfg, app, win):","def record(self):\n # TODO: record the data","def save_state(self):\n pass","def state_capture_enter(cfg, app, win):","def run(engine, Session):\n session = Session()\n\n trx_type: str = st.selectbox(\"Transaction Type\", trx_types)\n\n # Purchase Workflow\n if trx_type == \"Purchase\":\n st.write(\"\")\n with st.form(key=\"record_data_form\"):\n record_data = display_record_purchase_form_return_data(trx_type)\n add: bool = st.form_submit_button(\"Add Record\")\n if add and isinstance(record_data, dict):\n record_data = prepare_record_data_for_DB_insert(record_data)\n db_functions.add_new_record(session, record_data)\n st.write(\"Record inserted.\")\n record_data_from_DB = get_record_data_from_queried_record(\n session, record_data\n )\n st.write(record_data_from_DB)\n # TODO Output if artist, label etc. has been created/updated\n\n # Update Workflow\n elif trx_type == \"Update\":\n st.write(\"---\")\n artist, title = display_record_select_form(session)\n if artist != \"-\" and title != \"-\":\n record = db_functions.fetch_a_record_from_the_shelf(\n session, artist, title\n )\n if record is not None:\n st.write(\"\")\n with st.form(key=\"record_data_form\"):\n record_data = display_record_update_form_and_return_data(\n record, trx_type\n )\n update: bool = st.form_submit_button(\"Update Record\")\n if update and isinstance(record_data, dict):\n record_data = prepare_record_data_for_DB_insert(record_data)\n db_functions.update_record(session, record_data)\n st.write(\"Record updated.\")\n record_data_from_DB = get_record_data_from_queried_record(\n session, record_data\n )\n st.write(record_data_from_DB)\n # TODO Output if artist, label etc. has been created/updated\n\n # Removal (Inactivation) Workflow - no real deletion from DB!\n elif trx_type == \"Remove\":\n st.write(\"---\")\n artist, title = display_record_select_form(session)\n if artist != \"-\" and title != \"-\":\n record = db_functions.fetch_a_record_from_the_shelf(\n session, artist, title\n )\n if record is not None:\n if record.is_active == 0:\n st.write(\n \"Record is already inactive. You cannot remove it twice.\"\n )\n elif record.is_active == 1:\n st.write(\"---\")\n (\n removal_date,\n credit_value,\n ) = display_record_removal_form_and_return_data()\n remove: bool = st.checkbox(\"Inactivate Record\")\n if remove:\n record_data = create_record_data_for_removal(\n record, trx_type, removal_date, credit_value\n )\n db_functions.set_record_to_inactive(\n session, record_data\n )\n st.write(\"Record set to inactive.\")\n record_data_from_DB = get_record_data_from_queried_record(\n session, record_data\n )\n st.write(record_data)\n\n session.close()","def _handle_record(self, record: Record):\n\n if self.tru is None or self.feedback_mode is None:\n return\n\n record_id = self.tru.add_record(record=record)\n\n if len(self.feedbacks) == 0:\n return\n\n # Add empty (to run) feedback to db.\n if self.feedback_mode == FeedbackMode.DEFERRED:\n for f in self.feedbacks:\n self.db.insert_feedback(\n FeedbackResult(\n name=f.name,\n record_id=record_id,\n feedback_definition_id=f.feedback_definition_id\n )\n )\n\n elif self.feedback_mode in [FeedbackMode.WITH_APP,\n FeedbackMode.WITH_APP_THREAD]:\n\n results = self.tru.run_feedback_functions(\n record=record, feedback_functions=self.feedbacks, app=self\n )\n\n for result in results:\n self.tru.add_feedback(result)","def _parseRecords(self):\n # dict of parse methods for most common records that will be stored in structured arrays\n FLAG2METHOD = {'PS' : self.parseHighPassRecord,\n 'PC' : self.parseLowPassRecord,\n 'VD' : self.parseDigitalSValRecord}\n # dict of (record type, listname to store it in) tuples\n FLAG2REC = {'L' : (LayoutRecord, 'layoutrecords'),\n 'MS' : (SurfMessageRecord, 'messagerecords'),\n 'MU' : (UserMessageRecord, 'messagerecords'),\n 'PE' : (EpochRecord, 'epochrecords'),\n 'D' : (DisplayRecord, 'displayrecords'),\n 'VA' : (AnalogSValRecord, 'analogsvalrecords')}\n f = self.f\n while True:\n # returns an empty string when EOF is reached\n flag = f.read(2).rstrip(NULL).decode() # TODO: should this strip NULL?\n if flag == '':\n break\n # put file pointer back to start of flag\n f.seek(-2, 1) # TODO: unnecessary - doesn't this slow down parsing quite a bit?\n if flag in FLAG2METHOD: # these are the most common\n FLAG2METHOD[flag](f) # call the method\n elif flag in FLAG2REC:\n rectype, reclistname = FLAG2REC[flag]\n rec = rectype()\n rec.parse(f)\n #wx.Yield() # allow wx GUI event processing during parsing\n self._appendRecord(rec, reclistname)\n else:\n raise ValueError('Unexpected flag %r at offset %d' % (flag, f.tell()))\n #self.percentParsed = f.tell() / self.filesize * 100","def triage_record(self, record):\n # Filter out any garbage commands/responses\n # Record any changes to the reader state\n # Pass OBD2 records on for formatting\n \n # We need to figure out whether this record is :\n # - line noise / garbage \"?\"\n # - the result of an \"AT\" command \n # - the result of an OBD2 command \n \n # skip over garbage \n if record == [] \\\n or record[0] == [] \\\n or record[0][0] == '' \\\n or record[0][0] == '?' :\n #print \"Garbage record. Skipping.\"\n return []\n\n # handle ELM327 errors\n # \"?\" - unrecognized command\n # \"NO DATA\" - reader timed out waiting for response from vehicle\n # \"BUFFER FULL\" - need to read data from reader faster, ie. increase baud rate on serial connection\n # many more...\n if len(record) > 1 :\n if record[1][0] == '?' \\\n or record[1][0] == 'NO':\n #print \"Garbage record. Skipping.\"\n return []\n\n # record the changes made by AT commands\n cmd = str.upper(record[0][0])\n if cmd[0:2] == 'AT':\n self.interpret_at_cmd(record)\n return []\n \n # remove \"SEARCHING...\" from response\n # example:\n # >0100\n # SEARCHING...\n # 41 00 BE 3E A8 11 \n if len(record) > 1 :\n if record[1][0] == 'SEARCHING...':\n record.pop(1)\n\n # BUFFER FULL - ugh, need to speed up the serial connection\n rl = len(record)\n rec = 0\n while rec < rl:\n if record[rec][0] == 'BUFFER' and record[rec][1] == 'FULL':\n record.pop(rec)\n print \" ERROR - BUFFER FULL - Increase speed of serial connection\"\n #return []\n rec += 1\n # \"BUS BUSY\", \"CAN ERROR\", ???\n\n # if we get a 7F, that means there was an error\n # 10 - general reject\n # 11 - service not supported\n # 12 - subfunction not supported OR invalid format\n # 21 - busy repeat\n # 22 - conditions or sequence not correct \n # 78 - response pending\n if record[1][0] == '7F':\n mode = record[1][1]\n err = record[1][2]\n if err == 10:\n print \"General Error -- Mode:\", mode\n elif err == 11:\n print \"Service Not Supported Error -- Mode:\", mode\n elif err == 12:\n print \"Subfunction Not Supported or Invalid Format Error -- Mode:\", mode\n elif err == 21:\n print \"BUSY, Repeat -- Mode:\", mode\n elif err == 22:\n print \"Conditions or Sequence Not Correct -- Mode:\", mode\n elif err == 78:\n print \"Unknown Error -- Mode:\", mode, \" -- Error code:\", err\n return []\n\n\n # format an OBD 2 command for further processing at a higher layer\n try:\n obd2_record = self.format_obd2_record(record)\n except self.ErrorIncompleteRecord:\n print \"Garbage record. Skipping.\"\n return []\n \n return obd2_record","def processRecord(self, record):\n\n current_status = record.status\n\n if current_status == 'pre-accepted':\n new_status = 'accepted'\n elif current_status == 'pre-rejected':\n new_status = 'rejected'\n else:\n # no work to be done\n return record\n\n fields = {'status': new_status}\n return self.updateEntityProperties(record, fields)","def _post_record(\n self, ret_record_args, error, cost, start_time, end_time, record\n ):\n\n ret_record_args['main_error'] = str(error)\n ret_record_args['calls'] = record\n ret_record_args['cost'] = cost\n ret_record_args['perf'] = Perf(start_time=start_time, end_time=end_time)\n ret_record_args['app_id'] = self.app_id\n ret_record_args['tags'] = self.tags\n\n ret_record = Record(**ret_record_args)\n\n if error is not None:\n if self.feedback_mode == FeedbackMode.WITH_APP:\n self._handle_error(record=ret_record, error=error)\n\n elif self.feedback_mode in [FeedbackMode.DEFERRED,\n FeedbackMode.WITH_APP_THREAD]:\n TP().runlater(\n self._handle_error, record=ret_record, error=error\n )\n\n raise error\n\n if self.feedback_mode == FeedbackMode.WITH_APP:\n self._handle_record(record=ret_record)\n\n elif self.feedback_mode in [FeedbackMode.DEFERRED,\n FeedbackMode.WITH_APP_THREAD]:\n TP().runlater(self._handle_record, record=ret_record)\n\n return ret_record","def store_state(self):\n if self.is_main:\n super(AbstractFlowComponent, self).store_state()","def enter_state(self):\r\n self._logger_.enter_state()\r\n return","def main():\n data = []\n\n menu_text = \"\"\"\n 1. Display all records\n 2. Add new record\n 3. Edit existing record\n 4. Delete record \n 5. Quit\n \"\"\"\n\n while True:\n print(menu_text)\n choice = input('Enter your choice: ')\n if choice == '1':\n display_all_records()\n elif choice == '2':\n add_new_record(data)\n elif choice == '3':\n results = find_player()\n\n if results:\n edit_existing_record(results)\n else:\n print(f'Player is not on our records\\n')\n elif choice == '4':\n delete_record()\n elif choice == '5':\n break\n else:\n print('Not a valid selection, please try again')","def _stateHandler(self, state):\n try:\n if state.getType() == TSMessage.STATUS and self.parent:\n _params = state.getParams()\n if _params.get('main'):\n _descr = _params['main'].split(';')\n\n if _descr[0] == 'starting':\n self.msg_params['prebuf'] = 0\n\n elif _descr[0] == 'prebuf':\n if _descr[1] != self.msg_params.get('prebuf', 0):\n self.msg_params['last_update'] = state.getTime()\n self.msg_params['prebuf'] = _descr[1]\n log.d('_stateHandler: Пытаюсь показать состояние')\n self.parent.showStatus('Пребуферизация {0}'.format(self.msg_params['prebuf']))\n self.parent.player.showStatus('Пребуферизация {0}'.format(self.msg_params['prebuf']))\n\n if time.time() - self.msg_params['last_update'] >= AcePlayer.TIMEOUT_FREEZE:\n log.w('AceEngine is freeze')\n self.autoStop()\n\n elif _descr[0] == 'check':\n log.d('_stateHandler: Проверка {0}'.format(_descr[1]))\n self.parent.showStatus('Проверка {0}'.format(_descr[1]))\n # elif _descr[0] == 'dl':\n # self.parent.showInfoStatus('Total:%s DL:%s UL:%s' % (_descr[1], _descr[3], _descr[5]))\n elif _descr[0] == 'buf':\n # self.parent.showStatus('Буферизация: %s DL: %s UL: %s' % (_descr[1],\n # _descr[5], _descr[7])) @IgnorePep8\n\n if _descr[1] != self.msg_params.get('buf', 0):\n self.msg_params['last_update'] = state.getTime()\n self.msg_params['buf'] = _descr[1]\n # self.parent.player.showStatus('Буферизация {0}'.format(self.msg_params['value']))\n if time.time() - self.msg_params['last_update'] >= AcePlayer.TIMEOUT_FREEZE:\n self.parent.player.showStatus('Пребуферизация {0}'.format(self.msg_params['buf']))\n log.w('AceEngine is freeze')\n self.autoStop()\n # elif _descr[0] == 'dl':\n # if _descr[8] != self.msg_params.get('downloaded', 0):\n # self.msg_params['last_update'] = state.getTime()\n # self.msg_params['downloaded'] = _descr[8]\n # if time.time() - self.msg_params['last_update'] >= 10:\n # log.w('AceEngine is freeze')\n # self.autoStop()\n\n # self.parent.showInfoStatus('Buf:%s DL:%s UL:%s' % (_descr[1], _descr[5], _descr[7]))\n # else:\n # self.parent.showInfoStatus('%s' % _params)\n elif state.getType() in (TSMessage.RESUME, TSMessage.PAUSE, TSMessage.START):\n self.msg_params['value'] = 0\n # self.msg_params['downloaded'] = 0\n\n elif state.getType() == TSMessage.EVENT:\n if state.getParams() == 'getuserdata':\n self._send_command('USERDATA [{{\"gender\": {0}}} {{\"age\": {1}}}]'.format(\n utils.str2int(defines.GENDER) + 1,\n utils.str2int(defines.AGE) + 1))\n elif state.getParams().startswith('showdialog'):\n _parts = state.getParams().split()\n self.parent.showStatus('{0}: {1}'.format(unquote(_parts[2].split('=')[1]),\n unquote(_parts[1].split('=')[1])))\n elif state.getType() == TSMessage.ERROR:\n self.parent.showStatus(state.getParams())\n\n elif state.getType() == TSMessage.STOP:\n self.waiting.abort.set()\n\n elif state.getType() == TSMessage.STATE:\n _params = state.getParams()\n _param = utils.str2int(_params)\n if _param == 0:\n self.waiting.abort.set()\n\n except Exception as e:\n log.e('_stateHandler error: \"{0}\"'.format(uni(e)))\n finally:\n try:\n if self.waiting.msg == state.getType():\n self.waiting.msg = state\n self.waiting.event.set()\n\n except Exception as e:\n log.e('_stateHandler error: \"{0}\"'.format(uni(e)))","def __call__(self, recording, **kwargs):\n raise NotImplementedError()","def handle_record_begin():\n LOG.info(\"Begin Recording...\")\n context = {'client_name': 'mycroft_listener',\n 'source': 'audio',\n 'destination': [\"skills\"]}\n bus.emit(Message('recognizer_loop:record_begin', context=context))","def state(self):\n pass","def integrateRecord(self, record):\n\n inflatedIncomingBufferRecord = self.inflateRecord(record)\n # Checking if record exists in the application\n recordIndex = self.searchRecordInApp(record.recordID)\n\n # If record exists in store\n if record.recordID in self.store:\n\n # Record exists in the application\n if recordIndex >= 0:\n\n # Dirty bit in the application is not set\n if self.appData[recordIndex][2] == 0:\n\n storeRecordHistory = self.store[record.recordID].lastSavedByHistory\n storeRecord = self.store[record.recordID]\n versionComparison = self.compareVersions(storeRecordHistory, record.lastSavedByHistory, \\\n (storeRecord.lastSavedByInstance,\n storeRecord.lastSavedByCounter), \\\n (record.lastSavedByInstance, record.lastSavedByCounter))\n\n # Merge conflict between incoming buffer record and store record\n if versionComparison == 2:\n\n self.updateCounter()\n if self.resolveMergeConflict(inflatedIncomingBufferRecord, recordIndex):\n # Merge conflict resolution did not choose the app data\n self.bufferDataChosen(record, {self.instanceID: self.counter})\n\n else:\n # Merge conflict resolution algorithm chose app data\n self.appDataChosen(record, {self.instanceID: self.counter})\n\n # Chooses incoming buffer record\n elif versionComparison == 0:\n self.bufferDataChosen(record, {})\n\n # Application record is updated\n elif versionComparison == 1 or versionComparison == 3:\n return\n\n else:\n raise ValueError(\"Invalid return from compare versions method!\")\n\n # Dirty bit for the record is set\n else:\n self.updateCounter()\n self.appData[recordIndex][2] = 2\n # Merge conflict resolution did not choose the app Data\n if self.resolveMergeConflict(inflatedIncomingBufferRecord, recordIndex):\n self.bufferDataChosen(record, {self.instanceID: self.counter})\n\n # Merge conflict resolution chose the app Data\n else:\n self.appDataChosen(record, {self.instanceID: self.counter})\n\n # Record does not exist in the application\n else:\n raise ValueError('Record exists in store but not in application!')\n\n # Record does not exist in the store\n else:\n # Record exists in the application\n if recordIndex >= 0:\n\n if self.appData[i][2] == 0:\n raise ValueError('Data not present in Store but present in Application!')\n\n else:\n self.updateCounter()\n # Does not choose app Data\n if self.resolveMergeConflict(inflatedIncomingBufferRecord, recordIndex):\n self.store[record.recordID] = deepcopy(record)\n self.bufferDataChosen(record, {self.instanceID: self.counter})\n\n # Chooses app Data\n else:\n self.appData[recordIndex][2] = 0\n self.store[record.recordID] = deepcopy(record)\n self.appDataChosen(record, {self.instanceID: self.counter})\n\n # Record does not exist in the application\n else:\n self.appData.append(self.inflateRecord(record))\n self.store[str(record.recordID)] = deepcopy(record)","def after_record(self):\n self.record_label.set(\"Re-Record\")\n self.button_2['state'] = \"normal\"","def set_recording(self, recording):\n self.record_states = recording","def doAdd(self):\n\n # Prompt for each thing we need in the record\n record = XMLRecord()\n record.recordType = self.recordType\n print \"Enter the fields for the record (ctrl-D to stop at any time)\"\n try:\n # uid\n while True:\n record.uid = raw_input(\"Id: \")\n if not record.uid:\n print \"A valid uid is required. Please try again.\"\n elif self.directory.containsRecord(self.recordType, record.uid):\n print \"Record uid: '%s' of type: '%s' does already exists in the directory.\" % (record.uid, self.recordType,)\n else:\n break\n\n # guid\n while True:\n record.guid = raw_input(\"GUID [leave empty to auto-generate]: \")\n if record.guid and self.directory.containsGUID(record.guid):\n print \"GUID: '%s' already used in the directory\" % (record.guid,)\n else:\n break\n\n # password\n record.password = self.promptPassword()\n\n # name\n record.name = raw_input(\"Name: \")\n\n # members\n if self.recordType in (recordtypes.recordType_groups,):\n record.members = self.getMemberList(\"Enter members of this group\", \"Member\", \"members\")\n\n # cuaddr\n while True:\n cuaddr = raw_input(\"Calendar user address [leave empty to stop adding addresses]: \")\n if cuaddr:\n record.calendarUserAddresses.add(cuaddr)\n else:\n break\n\n # auto-schedule\n if self.recordType in (recordtypes.recordType_locations, recordtypes.recordType_resources,):\n auto_schedule = raw_input(\"Turn on automatic scheduling [y/n]?: \")\n if auto_schedule == \"y\":\n record.autoSchedule = True\n\n # enabled for calendaring\n if self.recordType in (recordtypes.recordType_users, recordtypes.recordType_groups,):\n enable_calendars = raw_input(\"Create calendar account rather than access-only account [y/n]?: \")\n if enable_calendars == \"n\":\n record.enabledForCalendaring = False\n\n # proxies\n if self.recordType in (recordtypes.recordType_locations, recordtypes.recordType_resources,):\n record.proxies = self.getMemberList(\"Enter proxies of this location or resource\", \"Proxy\", \"proxies\")\n\n except EOFError:\n return 0\n\n # Now validate the record and save it\n if not record.guid:\n record.guid = str(uuid4())\n\n self.directory.addRecord(record)\n\n return 1","def _during_execute(self, db):\n pass","def parse_record(self, record):\n raise NotImplementedError()","def handle_state(self):\r\n if self.state == 'walk':\r\n self.walking()\r\n elif self.state == 'fall':\r\n self.falling()\r\n elif self.state == 'jumped on':\r\n self.jumped_on()\r\n elif self.state == 'shell slide':\r\n self.shell_sliding()\r\n elif self.state == 'death jump':\r\n self.death_jumping()","def __init__(self, record=None):\n self.record = record","def run_record(self, state, pipeline, record_path):\n e1 = cv2.getTickCount()\n while state.record_btn:\n # Wait for a coherent pair of frames: depth and color\n frames = pipeline.wait_for_frames()\n depth_frame = frames.get_depth_frame()\n color_frame = frames.get_color_frame()\n if not depth_frame or not color_frame:\n continue\n \n # filter\n depth_frame = self.th_filter.process(depth_frame)\n depth_frame = self.sp_filter.process(depth_frame)\n depth_frame = self.tmp_filter.process(depth_frame)\n\n depth_colormap = self.colorizer.colorize(depth_frame)\n # Convert images to numpy arrays\n depth_image = np.asanyarray(depth_colormap.get_data())\n color_image = np.asanyarray(color_frame.get_data())\n\n lane_masked = self.lane_detector.detect(color_image)\n # Show images\n stacked_imgs = (color_image, depth_image, lane_masked)\n images = np.hstack(stacked_imgs)\n cv2.resizeWindow(state.WIN_NAME, \n self.width*len(stacked_imgs), \n self.height)\n cv2.imshow(state.WIN_NAME, images)\n cv2.setMouseCallback(state.WIN_NAME, state.mouse_controll)\n key = cv2.waitKey(1)\n if key == 27:\n state.app_btn = False\n state.record_btn = False\n break\n # Calculate Runtime Tick to quit\n e2 = cv2.getTickCount()\n tick = int((e2 - e1) / cv2.getTickFrequency())\n # Save images per tick\n if self.saveimg:\n color_file = record_path / f\"color-{tick}.npy\"\n depth_file = record_path / f\"depth-{tick}.npy\"\n ps_file = record_path / f\"ps-{tick}.ply\"\n if not ps_file.exists():\n np.save(color_file, color_image)\n np.save(depth_file, depth_image)\n \n # Create point cloud\n if self.savepc and (not ps_file.exists()):\n points = self.pc.calculate(depth_frame)\n self.pc.map_to(depth_frame)\n points.export_to_ply(str(ps_file), color_frame)\n\n if tick > self.record_time:\n print(\"Finish Record\")\n state.app_btn = False\n state.record_btn = False\n cv2.destroyAllWindows()\n break\n\n if not state.app_btn:\n break","def assess_status(self):\n\n # Update the PEATDB_record information\n if self.data.has_key('PEAT_DBrecord'):\n self.PEAT_DBrecord.set(self.data['PEAT_DBrecord'])\n\n # Do we have an open project?\n if self.data['Project filename']:\n self.file_menu.entryconfigure(0,state=DISABLED) # Open project\n self.file_menu.entryconfigure(1,state=NORMAL) # Close project\n self.file_menu.entryconfigure(3,state=NORMAL) # Save project as\n\n # Is the current project saved?\n if not self.data['Project saved']:\n self.file_menu.entryconfigure(2,state=NORMAL) # Save Project\n else:\n self.file_menu.entryconfigure(2,state=DISABLED) # Save Project\n else:\n\n # No project\n self.file_menu.entryconfigure(1,state=DISABLED) # Close project\n self.file_menu.entryconfigure(2,state=DISABLED) # Save Project\n self.file_menu.entryconfigure(3,state=DISABLED) # Save project as\n\n # Do we have a DNA sequence?\n if self.data['DNAseq']:\n self.ORFbutton.configure(state=NORMAL) # ORF button\n # Sequence menu\n states={'Open':None,\n 'Save':1,\n 'Save As':1,\n 'Complementary':1,\n 'Inverse':1,\n 'Find':1}\n self.set_pulldown_and_shortcut_states(states,self.seqmenu)\n # Primer menu\n self.primer_menu.entryconfigure(0,state=ACTIVE) # PCR primer\n # .\n self.file_menu.entryconfigure(0,state=DISABLED) # Open project\n self.file_menu.entryconfigure(3,state=NORMAL) # Save project as\n else:\n # No DNA\n states={'Open':1,\n 'Save':None,\n 'Save As':None,\n 'Complementary':None,\n 'Inverse':None,\n 'Find':None,\n 'Cut':None,\n 'Copy':None,\n 'Paste':None,\n 'Delete':None}\n self.set_pulldown_and_shortcut_states(states,self.seqmenu)\n\n # Primer menu\n self.primer_menu.entryconfigure(0,state=DISABLED) # PCR primers\n self.primer_menu.entryconfigure(1,state=DISABLED) # Mutagenic primer\n\n # Has an ORF been selected?\n if self.data.has_key('ORF_selected'):\n self.primer_menu.entryconfigure(1,state=ACTIVE) # Mutagenic primer\n\n start=self.data['ORF_selected']['start']\n stop=self.data['ORF_selected']['stop']\n return","def execute(self) -> None:\n self.state()","def take_action(self, state):","def _load(self) -> None:\n self.record = self._saved_record\n self.counter = self._saved_counter\n self.current_objects = self._saved_objects","def commit_record( self, update_photo_state=True ):\n\n # update the record based on what's currently visible if requested.\n if update_photo_state:\n self.record[\"modified_time\"] = time.mktime( time.gmtime() )\n self.record[\"state\"] = self.photoProcessingStateComboBox.currentText()\n self.record[\"tags\"] = list( map( lambda x: x.strip(),\n self.photoTagsLineEdit.text().split( \", \" ) ) )\n\n self.db.mark_data_dirty()\n\n super().commit_record()","def handle(cls, record):\n print(datetime.datetime.now(), record, flush=True)","def add_record(self, record):\n pass","def testRecord(self):\n\t\tfca = FunctionCallAction(('key',), 'c', 'd', 'ret')\n\t\tself.failUnless(fca.record() == 'ret')","def run_states(self):\n if (self.state == \"off\"):\n if (self.in_power.value == 1):\n self.off_to_on()\n \n elif self.state == \"on\":\n if (self.in_power.value == 0):\n self.any_to_off()\n elif (self.in_alert.value == 1):\n self.on_to_alert()\n \n elif self.state == \"alert\":\n if (self.in_power.value == 0):\n self.any_to_off()\n elif (self.in_alert.value == 0):\n self.alert_to_was_alert()\n\n elif self.state == \"was_alert\":\n if (self.in_power.value == 0):\n self.any_to_off()","def on_start(self): # noqa: D401\n logger.debug(\"on_start()\")\n\n state = self.read_state(self.statefile)\n if state:\n self.restore_state(state)","def run(self, state: State) -> State:","def run(self):\n if self.next_state == \"initialize_rexarm\":\n self.initialize_rexarm()\n\n if self.next_state == \"idle\":\n self.idle()\n\n if self.next_state == \"estop\":\n self.estop()\n\n if self.next_state == \"execute_tp\":\n self.execute_tp()\n\n if self.next_state == \"execute\":\n self.execute()\n\n if self.next_state == \"calibrate\":\n self.calibrate()\n\n if self.next_state == \"manual\":\n self.manual()\n\n if self.next_state == \"learn\":\n self.learn()\n\n if self.next_state == \"remember\":\n self.remember()\n\n if self.next_state == \"write\":\n self.write()\n\n if self.next_state == \"get_color\":\n self.get_color()\n\n if self.next_state == \"find_blocks\":\n self.find_blocks()\n\n # if self.next_state == \"dance\":\n # self.execute_dance()","def run_state_machine(self):\n\n # Initial check for startup state\n if self.status == 'STARTUP':\n self.update_action(self.status, None, None, None)\n # Get apriltag data from the detector\n\n return_tag_data = self.apriltag_detector.get_apriltag_data()\n\n # Check if it saw an apriltag\n if len(return_tag_data) != 0:\n\n # Iterate over the data from each of the apriltags seen\n for i in range(len(return_tag_data)):\n\n # Temp apriltag data\n temp_tag = return_tag_data[i]\n\n print(\"current state: \", self.status)\n\n print(\"Statemachine x VAL: \", temp_tag[1])\n\n print(\"Statemachine y VAL: \", temp_tag[2])\n\n # If the smallbot is currently in the CREEP_FORWARD state\n # handle cases for when the camera sees the side tags while it is driving forward\n if self.status == 'CREEP_FORWARD' and (temp_tag[0] == self.back_tag):\n self.current_tag = temp_tag[0]\n print(\"ATTEMPTING TO UPDATE STATUS 1\")\n self.update_action(self.status, temp_tag[1], temp_tag[2], temp_tag[3])\n # Ignore cases when the camera sees the side apriltags\n elif self.status == 'CREEP_FORWARD' and (temp_tag[0] == self.right_tag):\n print(\"ATTEMPTING TO UPDATE STATUS 2\")\n pass\n elif self.status == 'CREEP_FORWARD' and (temp_tag[0] == self.left_tag):\n print(\"ATTEMPTING TO UPDATE STATUS 2\")\n pass\n else:\n self.current_tag = temp_tag[0]\n print(\"ATTEMPTING TO UPDATE STATUS 3\")\n self.update_action(self.status, temp_tag[1], temp_tag[2], temp_tag[3])\n\n # If the camera did not see any apriltags\n else:\n self.current_tag = None\n self.update_action(None, None, None, None)","def emit(self, record):\n self.eng.dispose()\n trys = 3\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\"__\")}\n for t in range(trys):\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\n failed = False\n try:\n with self.eng.connect() as con:\n # formating values to convert from python or parsl to db standards\n info['task_fail_history'] = str(info.get('task_fail_history', None))\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\n run_id = info['run_id']\n\n # if workflow or task has completed, update their entries with the time.\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\n con.execute(up)\n return\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\n workflow = self.meta.tables['task']\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\n .where(workflow.c.run_id == run_id)\n con.execute(up)\n\n # create workflows table if this is a new database without one\n if 'workflows' not in self.meta.tables.keys():\n workflows = create_workflows_table(self.meta)\n self.meta.create_all(con)\n # if this is the first sight of the workflow, add it to the workflows table\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\n workflows = self.meta.tables['workflows']\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\n con.execute(ins)\n\n # if log has task counts, update the workflow entry in the workflows table\n if 'tasks_completed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n if 'tasks_failed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n\n # create task table if this is a new run without one\n if 'task' not in self.meta.tables.keys():\n workflow = create_task_table(self.meta)\n self.meta.create_all(con)\n\n # check to make sure it is a task log and not just a workflow overview log\n if info.get('task_id', None) is not None:\n # if this is the first sight of the task in the workflow, add it to the workflow table\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\n if 'psutil_process_pid' in info.keys():\n # this is the race condition that a resource log is before a status log so ignore this resource update\n return\n\n workflow = self.meta.tables['task']\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\n con.execute(ins)\n\n if 'task_status' in info.keys():\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\n if 'task_status' not in self.meta.tables.keys():\n task_status_table = create_task_status_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n # if this status table already exists, just insert the update\n else:\n task_status_table = self.meta.tables['task_status']\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n return\n\n if 'psutil_process_pid' in info.keys():\n # if this is a task resource update then handle that, if the resource table DNE then create it\n if 'task_resources' not in self.meta.tables.keys():\n task_resource_table = create_task_resource_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n # if this resource table already exists, just insert the update\n else:\n task_resource_table = self.meta.tables['task_resources']\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n return\n\n except Exception as e:\n logger.error(\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\".format(t, str(e)))\n failed = True\n time.sleep(5)\n if not failed:\n return","def saveState(self) -> None:\n # TODO: Saves State\n pass","def save_record(record):\n record. save_details()","def on_incoming_records(self, connection: ConnectionInterface) -> None:\n self.generate_metadata()\n\n df = connection.record_containers[0].build_dataframe()\n df[\"optional_value\"] = self.workflow_config[\"Value\"]\n\n self.output_anchor.push_records(\n generate_records_from_df(df, self.output_anchor.record_info)\n )\n\n connection.clear_records()","def my_record_vars(context, data):\n pass","def load_state(self, dictionary):\n self.log_formatstr = dictionary['log_formatstr']\n self.backend_interval = dictionary['backend_interval']","def save(self, record):\n pass","def savestate(self, state):\n pass","def reloadData(self):\n self.dto.readFromData()\n print(\"Record reloaded.\")","def read(self):\n self.record_d = {}\n if self.__read_file():\n self.__print_report()","def _stage(self):\n\n pass","def main():\n \n option_parser = optparse.OptionParser(usage='usage: %prog [options] filename')\n option_parser.add_option('-d', '--debug',\n dest='debug',\n default=False,\n action='store_true', \n help='Enable debugging messages')\n option_parser.add_option('-n', '--no-store', \n dest='store', \n default=True, \n action='store_false', \n help='Test but do not store the data') \n option_parser.add_option('-v', '--verbose', \n dest='verbose', \n default=False, \n action='store_true', \n help='Produce verbose output') \n (options, args) = option_parser.parse_args()\n \n if not args:\n raise RuntimeError('Please specify at least one filename to load.')\n\n if options.debug:\n level = logging.DEBUG\n else:\n level = logging.INFO\n logging.basicConfig(level=level,\n format='%(levelname)-8s %(message)s',\n )\n\n # Establish our django set-up so we can import models\n # to work with the database.\n import settings\n setup_environ(settings)\n\n # Now we can import the other modules we need that depend on django\n from vtr_loader import load_data_from_stream, ErrorKeeper\n \n errors = ErrorKeeper()\n num_new = 0\n num_existing = 0\n for filename in args:\n logging.info('Processing %s', filename)\n f = open(filename, 'rt')\n try:\n counts = load_data_from_stream(\n f, \n store=options.store, \n verbose=options.verbose, \n message_stream=sys.stdout, \n errorcb=errors,\n )\n finally:\n f.close()\n \n num_existing += counts[0]\n num_new += counts[1]\n\n for error in errors.errors:\n error['file'] = filename\n\n if options.verbose:\n print\n print 'Found %d new records' % num_new\n print 'Found %d existing records' % num_existing\n print 'Found %d errors' % len(errors.errors)\n\n for error in errors.errors:\n print\n error['file'] = filename\n print 'ERROR in %(file)s: %(start)d - %(end)d (%(error)s)' % error\n for line in error['lines']:\n print '\\t%s' % line\n \n return","def _process_change(self):\n self._sort_records()\n self._store_writer.to_file(self.records)\n self._store_writer.to_csv_file(self.records)","def dbtrace_filter_on_change(new_state):\n\n pass","def _record(self):\r\n self._plot()\r\n self._csvWriter()\r\n self._logger()","def stage(self):\n pass","def __call__(self):\r\n AddNewRecords()","def set_state( self ):","def __exit__(self, _type, value, traceback):\n try:\n if not self.record_finish:\n return\n print >>sys.stderr, 'record', self.record\n dutset = {'last_finish_time':time()}\n if not self.record:\n return\n upd = {'end_time': time(), 'modification_time':time()}\n\n if value: # i.e. , if test failed:\n upd['failure'] = repr(value)\n upd['exception'] = value.__class__.__name__\n if not isinstance(value, KeyboardInterrupt):\n print 'HEADLINE: exception', upd['exception'], value\n for clause in format_exception(_type, value, traceback):\n for line in clause.split('\\n'):\n print 'CRASH:', line\n else:\n upd['infrastructure_problem'] = True\n upd['whiteboard'] = '[infrastructure] test interrupted'\n if self.reinstall_on_failure:\n dutset['test_failed'] = True\n tnext = time() + 300\n print 'INFO: test failed, so will reinstall machine at', \\\n asctime(localtime(tnext))\n\n if self.failed: #some test suite failed\n upd['failure'] = 'test failed'\n\n self.mdb.results.update({'_id':self.result_id}, {'$set':upd})\n classify = process_result(self.mdb.results.find_one({'_id':self.result_id}))\n print 'HEADLINE:', classify, self.full_description()\n\n get_track().updates.save({'result_id':self.result_id,\n 'action':'experiment finished'})\n\n if self.dut_id:\n self.mdb.duts.update({'_id':self.dut_id}, \n {'$unset': {'control_pid':1, 'result_id':1,\n 'control_command_line':1},\n '$set': dutset})\n if self.build:\n recount(self.build)\n if classify == 'infrastructure_problems':\n pass\n else:\n col = 'green' if classify == 'passes' else 'red'\n finally:\n if self.record_queue:\n self.record_queue.put('finish')\n self.record_queue.close()\n self.record_queue.join_thread()\n if self.stream_process:\n self.stream_process.join()\n if self.stdout_filter:\n self.stdout_filter.del_callback(self)","def _ParseRecord(self, parser_mediator, key, structure):\n if key == 'record_start':\n self._ParseRecordStart(structure)\n\n elif key == 'record_body':\n self._ParseRecordBody(structure)\n\n elif key == 'record_end':\n self._ParseRecordEnd(parser_mediator, structure)","def _process(self):\n self.kwargs[\"collect\"].change_status(self.kwargs[\"collect\"].FINALIZED)","def before_model_run(self, data_handle=None):\n pass","def on_record(self):\n self.record_label.set(\"Recording...\")\n\n recorder = RecorderThread(self.after_record)\n recorder.start()","def __enter__(self):\n try:\n run(['logger', 'BVT', 'starting', self.full_description()], \n host=self.dut, timeout=10)\n except SubprocessError:\n print 'INFO: unable to mark test log'\n if not self.record:\n return self\n if self.result_id is None:\n self.mdb = get_autotest()\n terms = {'test_case':self.description or 'to be determined',\n 'automation_user': getpwuid(getuid()).pw_gecos.split(',')[0],\n 'control_pid' : getpid(), 'start_time' : time(),\n 'development_mode' : 0,\n 'command_line':abbreviate(' '.join(sys.argv))}\n if self.dut:\n dutdoc = self.mdb.duts.find_one({'name':self.dut})\n self.dut_id = terms['dut'] = dutdoc['_id']\n terms['dut_name'] = dutdoc['name']\n if 'development_mode' in dutdoc:\n terms['development_mode'] = dutdoc['development_mode']\n self.result_id = self.mdb.results.save(terms)\n if self.job_id is not None:\n self.mdb.jobs.update({'_id':objectid.ObjectId(self.job_id)}, {'$set':{'results_id':self.result_id}})\n if self.build is None and self.dut:\n self.build = get_build(self.dut, timeout=10)\n self.mdb.results.update({'_id':self.result_id}, \n {'$set':{'build':self.build}})\n if self.dut:\n self.mdb.duts.update({'_id':terms['dut']}, {'$set': {\n 'build':self.build,\n 'control_command_line': abbreviate(' '.join(sys.argv)),\n 'result_id' : self.result_id}})\n if self.stdout_filter:\n self.record_queue = Queue()\n self.stream_process = Process(\n target=service_queue, \n args=[self.record_queue, self.result_id, \n self.dut, self.dut_id])\n self.stream_process.start()\n self.stdout_filter.add_callback(self, \n lambda *x: self.record_queue.put(x))\n\n if self.description:\n print 'HEADLINE: starting', self.full_description()\n get_track().updates.save({'result_id':self.result_id,\n 'action':'new result record'})\n return self","def main(self):\n\n while True:\n print('Main Menu:')\n user_input = input('What would you like to do? (C)reate new record or (L)ookup existing? ').lower().strip()\n self.check_input(user_input)\n\n if user_input == 'c':\n print('Great! Let\\'s create a new log entry!\\n')\n self.create_entry()\n elif user_input == 'l':\n print('Awesome! Let\\'s look up some entries!\\n')\n self.lookup_entry()","def do_post_action_processing(self, i_state, low_level_actions):\n pass","def addRecord(self):\n\n ## Saving recorded entries to the CRM and Mailings Database\n print(\"Saving entries to the CRM and Mailings database...\")\n db_connection.executeQuery(\"INSERT INTO dbo.CRM (f_name, l_name, company, address, city, county, state, zip, primary_phone, secondary_phone, email_address) VALUES ('\" + self.first_name.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.last_name.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.crm_company_name.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.address.title() + \"', '\" + self.city.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.county.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.state_code.upper() + \"', '\" + str(self.zip_code) + \"', '\" + self.phone_number + \"', '\" + self.phone_number_2 + \"' , '\" + self.email_address + \"'); COMMIT\")\n db_connection.executeQuery(\"INSERT INTO dbo.Mailings (name, company, address) VALUES ('\" + self.first_name.replace(\"\\'\", \"\\'\\'\").title() + \" \" + self.last_name.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.company_name.replace(\"\\'\", \"\\'\\'\").title() + \"','\" + self.address + \" \" + self.city.title() + \" \" + self.county.title() + \" \" + self.state_code.upper() + \" \" + str(self.zip_code) + \"'); COMMIT\")","def __call__(self, state, action):\n pass","def __getstate__(self):\n d = self.__dict__.copy() # copy it cuz we'll be making changes\n if 'f' in d:\n del d['f'] # exclude open .srf file handle, if any\n if not self._pickle_all_records:\n # these are hogs:\n keys = ['lowpassrecords', 'highpassrecords', 'lowpassmultichanrecords',\n #'digitalsvalrecords' # also a hog, but useful to keep\n ]\n for key in keys:\n if key in d:\n del d[key]\n # else self was probably restored from a sort, so all of these attribs have\n # probably already been stripped\n # leave the streams be, since you need them for their enabled chans info\n return d","def run():\n\n options = [\"Add new record to table\",\n \"Remove a record with a given id from the table\",\n \"Update specified record in the table\",\n \"Number of different kinds of game that are available of each manufacturer\",\n \"Average amount of games in stock of a given manufacturer\",\n \"Print table\"]\n\n title_list = [\"*id\",\n \"* title\",\n \"* manufacturer\",\n \"* price\",\n \"* in_stock\"]\n os.system('clear')\n choice = None\n while choice != \"0\":\n terminal_view.print_menu(\"What do you want to do:\", options, \"Back to main menu\")\n choice = terminal_view.get_choice(options)\n os.system('clear')\n if choice == \"1\":\n # to jest dzialajacy plik model/store/games.csv\n file_name = common.get_input(\"Choose a file: \")\n if file_name == \"\":\n file_name = \"model/store/games.csv\"\n table = common.get_table_from_file(file_name)\n terminal_view.print_table(table, title_list)\n record = terminal_view.get_inputs(title_list, \"Enter data: \")\n table = store.add(table, record)\n common.write_table_to_file(file_name, table)\n os.system(\"clear\")\n terminal_view.gprint('*** Record has been added ***')\n common.waiting()\n os.system(\"clear\")\n elif choice == \"2\":\n file_name = common.get_input(\"Choose a file: \")\n if file_name == \"\":\n file_name = \"model/store/games.csv\"\n table = common.get_table_from_file(file_name)\n terminal_view.print_table(table, title_list)\n id_ = common.get_input(\"Get id to removed: \")\n table = store.remove(table, id_)\n common.write_table_to_file(file_name, table)\n os.system(\"clear\")\n terminal_view.gprint('*** Record has been removed ***')\n common.waiting()\n os.system(\"clear\")\n elif choice == \"3\":\n file_name = common.get_input(\"Choose a file: \")\n if file_name == \"\":\n file_name = \"model/store/games.csv\"\n table = common.get_table_from_file(file_name)\n terminal_view.print_table(table, title_list)\n id_ = common.get_input(\"Enter id to update: \")\n record = terminal_view.get_inputs(title_list, \"Enter data: \")\n table = store.update(table, id_, record)\n common.write_table_to_file(file_name, table)\n os.system(\"clear\")\n terminal_view.gprint('*** Record has been updated ***')\n common.waiting()\n os.system(\"clear\")\n elif choice == \"4\":\n file_name = common.get_input(\"Choose a file: \")\n if file_name == \"\":\n file_name = \"model/store/games.csv\"\n table = common.get_table_from_file(file_name)\n dictionary = store.get_counts_by_manufacturers(table)\n terminal_view.print_dictionary(\"Number of different kinds of game that are\" +\n \"available of each manufacturer:\", dictionary)\n common.waiting()\n os.system(\"clear\")\n elif choice == \"5\":\n file_name = common.get_input(\"Choose a file: \")\n if file_name == \"\":\n file_name = \"model/store/games.csv\"\n table = common.get_table_from_file(file_name)\n terminal_view.print_table(table, title_list)\n manufacturer = common.get_input(\"Enter a manufacturer: \")\n print(store.get_average_by_manufacturer(table, manufacturer))\n common.waiting()\n os.system(\"clear\")\n elif choice == \"6\":\n file_name = common.get_input(\"Choose a file: \")\n if file_name == \"\":\n file_name = \"model/store/games.csv\"\n table = common.get_table_from_file(file_name)\n terminal_view.print_table(table, title_list)\n common.waiting()\n os.system(\"clear\")\n\n else:\n if choice != \"0\":\n terminal_view.print_error_message(\"There is no such choice.\")","def save_state(self) -> None:\n raise NotImplementedError(\"Save state is is not implemented.\")","def main(self):\n\n self.setRecordAudio(True)\n self.general()\n sleep(1)\n self.introduction()\n sleep(1)\n self.get_name()\n sleep(1)\n self.get_age()\n sleep(1)\n self.get_origin()\n sleep(1)\n self.get_company()\n sleep(1)\n self.get_travel_route()\n sleep(1)\n self.get_entrance()\n sleep(1)\n self.get_documentation()\n sleep(1)\n self.sayAnimated(\n 'OK ' + self.name + '. We would like to know why you came to The Netherlands. Can you please answer the following '\n 'questions with yes, or, no?')\n self.speechLock.acquire()\n self.stopListening()\n self.get_exclusion()\n sleep(1)\n self.get_conflict()\n sleep(1)\n self.get_inhumanity()\n sleep(1)\n self.get_family()\n sleep(1)\n\n # end interview and save answers\n self.wrapup()\n self.store_story()","def emit(self, record):\n pass","def state_print_do(cfg, app, win, events):","def handle(self):","def _run_state(self):\n main_log.debug(\"Running state \" + self.state)\n\n if not self.get_state_info(\"condition\"):\n self._run_next_state()\n return\n\n try:\n self._pre()\n except StateSwitchException as e:\n self.state = e.next_state\n self._run_state()\n return\n\n if self.get_state_info(\"wake_players\"):\n self._waiting_for_players = True\n self._wake_players()\n else:\n self._players_are_done()","def state(self, state: str) -> None:","def commit_record( self ):\n\n print( \"Committing art record #{:d}.\".format( self.record[\"id\"] ) )\n\n # update the record based on what's currently visible.\n self.record[\"type\"] = self.artTypeComboBox.currentText()\n self.record[\"size\"] = self.artSizeComboBox.currentText()\n self.record[\"quality\"] = self.artQualityComboBox.currentText()\n self.record[\"date\"] = self.artDateLineEdit.text()\n self.record[\"state\"] = self.artProcessingStateComboBox.currentText()\n self.record[\"modified_time\"] = time.mktime( time.gmtime() )\n self.record[\"tags\"] = list( map( lambda x: x.strip(),\n self.artTagsLineEdit.text().split( \", \" ) ) )\n\n self.record[\"artists\"] = [artist.data() for artist in self.artArtistsListView.selectedIndexes()]\n self.record[\"associates\"] = [associate.data() for associate in self.artAssociatesListView.selectedIndexes()]\n self.record[\"vandals\"] = [vandal.data() for vandal in self.artVandalsListView.selectedIndexes()]\n\n # update the region.\n normalized_geometry = self.photoPreview.get_region_geometry( True )\n\n self.record[\"region\"] = normalized_geometry.getRect()\n\n self.db.mark_data_dirty()\n\n super().commit_record()","def __init__(self, store_writer, records):\n self._store_writer = store_writer\n self.records = records","def start():\r\n acts = ['add', 'a', 'remove', 'r', 'search', 's', 'update', 'u', 'exit', 'e'] # list of allowed user defined inputs\r\n act = re.sub(r'\\s', '', str(input('Would you like to ADD entry, REMOVE entry, SEARCH, UPDATE, or EXIT?\\n'))\r\n .lower()) # lower all letters, then remove whitespaces\r\n\r\n act = check(act, acts) # perform error check\r\n\r\n \"\"\" take user to next stages according to their input \"\"\"\r\n if act == 'add' or act == 'a':\r\n add()\r\n elif act == 'remove' or act == 'r':\r\n remove()\r\n elif act == 'search' or act == 's':\r\n search()\r\n elif act == 'update' or act == 'u':\r\n update()\r\n else:\r\n print('Exiting database...')\r\n conn.close() # close operations\r\n sys.exit(0)","def storeState(self):\n\n self.action_history[self.trial] = self.action\n self.ball_history[self.trial] = self.ballcolor","def run(self, event, db):\n pass","def post_process(self):\n pass","def post_process(self):\n pass","def post_process(self):\n pass","def post_process(self):\n pass","def post_process(self):\n pass"],"string":"[\n \"def handle_record(self, record):\\n raise NotImplementedError\",\n \"def process_record(self, record):\\n raise NotImplementedError('Process record needs to be customized')\",\n \"def _post_init(self):\\n self.set_new_record_state(False)\",\n \"def record(self, state):\\r\\n\\r\\n if state: # Start Recording.\\r\\n logo_rec = QtGui.QPixmap(\\\":/freeseer/logo_rec.png\\\")\\r\\n sysIcon2 = QtGui.QIcon(logo_rec)\\r\\n self.systray.setIcon(sysIcon2)\\r\\n self.controller.record()\\r\\n self.mainWidget.recordPushButton.setText(self.stopString)\\r\\n self.recordAction.setText(self.stopString)\\r\\n\\r\\n # Hide if auto-hide is set.\\r\\n if self.config.auto_hide:\\r\\n self.hide_window()\\r\\n self.visibilityAction.setText(self.showWindowString)\\r\\n log.debug('auto-hide is enabled, main window is now hidden in systray.')\\r\\n\\r\\n # Start timer.\\r\\n self.timer.start(1000)\\r\\n\\r\\n else: # Stop Recording.\\r\\n logo_rec = QtGui.QPixmap(\\\":/freeseer/logo.png\\\")\\r\\n sysIcon = QtGui.QIcon(logo_rec)\\r\\n self.systray.setIcon(sysIcon)\\r\\n self.controller.stop()\\r\\n self.mainWidget.pauseToolButton.setChecked(False)\\r\\n self.mainWidget.recordPushButton.setText(self.recordString)\\r\\n self.recordAction.setText(self.recordString)\\r\\n self.mainWidget.audioSlider.setValue(0)\\r\\n self.mainWidget.statusLabel.setText(\\\"{} {} --- {} \\\".format(self.freeSpaceString,\\r\\n get_free_space(self.config.videodir),\\r\\n self.idleString))\\r\\n\\r\\n # Finally set the standby button back to unchecked position.\\r\\n self.standby(False)\\r\\n\\r\\n # Stop and reset timer.\\r\\n self.timer.stop()\\r\\n self.reset_timer()\\r\\n\\r\\n #Show playback button\\r\\n self.mainWidget.playPushButton.setVisible(True)\\r\\n self.mainWidget.playPushButton.setEnabled(True)\\r\\n\\r\\n # Select next talk if there is one within 15 minutes.\\r\\n if self.current_event and self.current_room:\\r\\n starttime = QtCore.QDateTime().currentDateTime()\\r\\n stoptime = starttime.addSecs(900)\\r\\n talkid = self.db.get_talk_between_time(self.current_event, self.current_room,\\r\\n starttime.toString(), stoptime.toString())\\r\\n\\r\\n if talkid is not None:\\r\\n for i in range(self.mainWidget.talkComboBox.count()):\\r\\n if talkid == self.mainWidget.talkComboBox.model().index(i, 1).data(QtCore.Qt.DisplayRole).toString():\\r\\n self.mainWidget.talkComboBox.setCurrentIndex(i)\",\n \"def _post_training(self):\\n self._write_state_key()\",\n \"def record(self, step):\",\n \"def readState(self, saveState: ghidra.framework.options.SaveState) -> None:\\n ...\",\n \"def final(self, state):\\n \\\"*** YOUR CODE HERE ***\\\"\\n return\\n util.raiseNotDefined()\",\n \"def make_record(self):\\n answer = self.input('enter your name: ')\\n if answer:\\n print('hi ' + answer + '! Your answer has been recorded.')\\n self.record_score(answer, len(self.board.tiles.blacks))\\n self.game_on = False\\n else:\\n self.game_on = False # Canceled dialog will turn off\",\n \"def state_processing_do(cfg, app, win, events):\",\n \"def enter_state(self):\\r\\n self.__log__(logging.debug)\\r\\n return\",\n \"def state_capture_do(cfg, app, win, events):\",\n \"def _get_state(self):\",\n \"def record_status(self, record_status):\\n\\n self._record_status = record_status\",\n \"def state_processing_enter(cfg, app, win):\",\n \"def record(self):\\n # TODO: record the data\",\n \"def save_state(self):\\n pass\",\n \"def state_capture_enter(cfg, app, win):\",\n \"def run(engine, Session):\\n session = Session()\\n\\n trx_type: str = st.selectbox(\\\"Transaction Type\\\", trx_types)\\n\\n # Purchase Workflow\\n if trx_type == \\\"Purchase\\\":\\n st.write(\\\"\\\")\\n with st.form(key=\\\"record_data_form\\\"):\\n record_data = display_record_purchase_form_return_data(trx_type)\\n add: bool = st.form_submit_button(\\\"Add Record\\\")\\n if add and isinstance(record_data, dict):\\n record_data = prepare_record_data_for_DB_insert(record_data)\\n db_functions.add_new_record(session, record_data)\\n st.write(\\\"Record inserted.\\\")\\n record_data_from_DB = get_record_data_from_queried_record(\\n session, record_data\\n )\\n st.write(record_data_from_DB)\\n # TODO Output if artist, label etc. has been created/updated\\n\\n # Update Workflow\\n elif trx_type == \\\"Update\\\":\\n st.write(\\\"---\\\")\\n artist, title = display_record_select_form(session)\\n if artist != \\\"-\\\" and title != \\\"-\\\":\\n record = db_functions.fetch_a_record_from_the_shelf(\\n session, artist, title\\n )\\n if record is not None:\\n st.write(\\\"\\\")\\n with st.form(key=\\\"record_data_form\\\"):\\n record_data = display_record_update_form_and_return_data(\\n record, trx_type\\n )\\n update: bool = st.form_submit_button(\\\"Update Record\\\")\\n if update and isinstance(record_data, dict):\\n record_data = prepare_record_data_for_DB_insert(record_data)\\n db_functions.update_record(session, record_data)\\n st.write(\\\"Record updated.\\\")\\n record_data_from_DB = get_record_data_from_queried_record(\\n session, record_data\\n )\\n st.write(record_data_from_DB)\\n # TODO Output if artist, label etc. has been created/updated\\n\\n # Removal (Inactivation) Workflow - no real deletion from DB!\\n elif trx_type == \\\"Remove\\\":\\n st.write(\\\"---\\\")\\n artist, title = display_record_select_form(session)\\n if artist != \\\"-\\\" and title != \\\"-\\\":\\n record = db_functions.fetch_a_record_from_the_shelf(\\n session, artist, title\\n )\\n if record is not None:\\n if record.is_active == 0:\\n st.write(\\n \\\"Record is already inactive. You cannot remove it twice.\\\"\\n )\\n elif record.is_active == 1:\\n st.write(\\\"---\\\")\\n (\\n removal_date,\\n credit_value,\\n ) = display_record_removal_form_and_return_data()\\n remove: bool = st.checkbox(\\\"Inactivate Record\\\")\\n if remove:\\n record_data = create_record_data_for_removal(\\n record, trx_type, removal_date, credit_value\\n )\\n db_functions.set_record_to_inactive(\\n session, record_data\\n )\\n st.write(\\\"Record set to inactive.\\\")\\n record_data_from_DB = get_record_data_from_queried_record(\\n session, record_data\\n )\\n st.write(record_data)\\n\\n session.close()\",\n \"def _handle_record(self, record: Record):\\n\\n if self.tru is None or self.feedback_mode is None:\\n return\\n\\n record_id = self.tru.add_record(record=record)\\n\\n if len(self.feedbacks) == 0:\\n return\\n\\n # Add empty (to run) feedback to db.\\n if self.feedback_mode == FeedbackMode.DEFERRED:\\n for f in self.feedbacks:\\n self.db.insert_feedback(\\n FeedbackResult(\\n name=f.name,\\n record_id=record_id,\\n feedback_definition_id=f.feedback_definition_id\\n )\\n )\\n\\n elif self.feedback_mode in [FeedbackMode.WITH_APP,\\n FeedbackMode.WITH_APP_THREAD]:\\n\\n results = self.tru.run_feedback_functions(\\n record=record, feedback_functions=self.feedbacks, app=self\\n )\\n\\n for result in results:\\n self.tru.add_feedback(result)\",\n \"def _parseRecords(self):\\n # dict of parse methods for most common records that will be stored in structured arrays\\n FLAG2METHOD = {'PS' : self.parseHighPassRecord,\\n 'PC' : self.parseLowPassRecord,\\n 'VD' : self.parseDigitalSValRecord}\\n # dict of (record type, listname to store it in) tuples\\n FLAG2REC = {'L' : (LayoutRecord, 'layoutrecords'),\\n 'MS' : (SurfMessageRecord, 'messagerecords'),\\n 'MU' : (UserMessageRecord, 'messagerecords'),\\n 'PE' : (EpochRecord, 'epochrecords'),\\n 'D' : (DisplayRecord, 'displayrecords'),\\n 'VA' : (AnalogSValRecord, 'analogsvalrecords')}\\n f = self.f\\n while True:\\n # returns an empty string when EOF is reached\\n flag = f.read(2).rstrip(NULL).decode() # TODO: should this strip NULL?\\n if flag == '':\\n break\\n # put file pointer back to start of flag\\n f.seek(-2, 1) # TODO: unnecessary - doesn't this slow down parsing quite a bit?\\n if flag in FLAG2METHOD: # these are the most common\\n FLAG2METHOD[flag](f) # call the method\\n elif flag in FLAG2REC:\\n rectype, reclistname = FLAG2REC[flag]\\n rec = rectype()\\n rec.parse(f)\\n #wx.Yield() # allow wx GUI event processing during parsing\\n self._appendRecord(rec, reclistname)\\n else:\\n raise ValueError('Unexpected flag %r at offset %d' % (flag, f.tell()))\\n #self.percentParsed = f.tell() / self.filesize * 100\",\n \"def triage_record(self, record):\\n # Filter out any garbage commands/responses\\n # Record any changes to the reader state\\n # Pass OBD2 records on for formatting\\n \\n # We need to figure out whether this record is :\\n # - line noise / garbage \\\"?\\\"\\n # - the result of an \\\"AT\\\" command \\n # - the result of an OBD2 command \\n \\n # skip over garbage \\n if record == [] \\\\\\n or record[0] == [] \\\\\\n or record[0][0] == '' \\\\\\n or record[0][0] == '?' :\\n #print \\\"Garbage record. Skipping.\\\"\\n return []\\n\\n # handle ELM327 errors\\n # \\\"?\\\" - unrecognized command\\n # \\\"NO DATA\\\" - reader timed out waiting for response from vehicle\\n # \\\"BUFFER FULL\\\" - need to read data from reader faster, ie. increase baud rate on serial connection\\n # many more...\\n if len(record) > 1 :\\n if record[1][0] == '?' \\\\\\n or record[1][0] == 'NO':\\n #print \\\"Garbage record. Skipping.\\\"\\n return []\\n\\n # record the changes made by AT commands\\n cmd = str.upper(record[0][0])\\n if cmd[0:2] == 'AT':\\n self.interpret_at_cmd(record)\\n return []\\n \\n # remove \\\"SEARCHING...\\\" from response\\n # example:\\n # >0100\\n # SEARCHING...\\n # 41 00 BE 3E A8 11 \\n if len(record) > 1 :\\n if record[1][0] == 'SEARCHING...':\\n record.pop(1)\\n\\n # BUFFER FULL - ugh, need to speed up the serial connection\\n rl = len(record)\\n rec = 0\\n while rec < rl:\\n if record[rec][0] == 'BUFFER' and record[rec][1] == 'FULL':\\n record.pop(rec)\\n print \\\" ERROR - BUFFER FULL - Increase speed of serial connection\\\"\\n #return []\\n rec += 1\\n # \\\"BUS BUSY\\\", \\\"CAN ERROR\\\", ???\\n\\n # if we get a 7F, that means there was an error\\n # 10 - general reject\\n # 11 - service not supported\\n # 12 - subfunction not supported OR invalid format\\n # 21 - busy repeat\\n # 22 - conditions or sequence not correct \\n # 78 - response pending\\n if record[1][0] == '7F':\\n mode = record[1][1]\\n err = record[1][2]\\n if err == 10:\\n print \\\"General Error -- Mode:\\\", mode\\n elif err == 11:\\n print \\\"Service Not Supported Error -- Mode:\\\", mode\\n elif err == 12:\\n print \\\"Subfunction Not Supported or Invalid Format Error -- Mode:\\\", mode\\n elif err == 21:\\n print \\\"BUSY, Repeat -- Mode:\\\", mode\\n elif err == 22:\\n print \\\"Conditions or Sequence Not Correct -- Mode:\\\", mode\\n elif err == 78:\\n print \\\"Unknown Error -- Mode:\\\", mode, \\\" -- Error code:\\\", err\\n return []\\n\\n\\n # format an OBD 2 command for further processing at a higher layer\\n try:\\n obd2_record = self.format_obd2_record(record)\\n except self.ErrorIncompleteRecord:\\n print \\\"Garbage record. Skipping.\\\"\\n return []\\n \\n return obd2_record\",\n \"def processRecord(self, record):\\n\\n current_status = record.status\\n\\n if current_status == 'pre-accepted':\\n new_status = 'accepted'\\n elif current_status == 'pre-rejected':\\n new_status = 'rejected'\\n else:\\n # no work to be done\\n return record\\n\\n fields = {'status': new_status}\\n return self.updateEntityProperties(record, fields)\",\n \"def _post_record(\\n self, ret_record_args, error, cost, start_time, end_time, record\\n ):\\n\\n ret_record_args['main_error'] = str(error)\\n ret_record_args['calls'] = record\\n ret_record_args['cost'] = cost\\n ret_record_args['perf'] = Perf(start_time=start_time, end_time=end_time)\\n ret_record_args['app_id'] = self.app_id\\n ret_record_args['tags'] = self.tags\\n\\n ret_record = Record(**ret_record_args)\\n\\n if error is not None:\\n if self.feedback_mode == FeedbackMode.WITH_APP:\\n self._handle_error(record=ret_record, error=error)\\n\\n elif self.feedback_mode in [FeedbackMode.DEFERRED,\\n FeedbackMode.WITH_APP_THREAD]:\\n TP().runlater(\\n self._handle_error, record=ret_record, error=error\\n )\\n\\n raise error\\n\\n if self.feedback_mode == FeedbackMode.WITH_APP:\\n self._handle_record(record=ret_record)\\n\\n elif self.feedback_mode in [FeedbackMode.DEFERRED,\\n FeedbackMode.WITH_APP_THREAD]:\\n TP().runlater(self._handle_record, record=ret_record)\\n\\n return ret_record\",\n \"def store_state(self):\\n if self.is_main:\\n super(AbstractFlowComponent, self).store_state()\",\n \"def enter_state(self):\\r\\n self._logger_.enter_state()\\r\\n return\",\n \"def main():\\n data = []\\n\\n menu_text = \\\"\\\"\\\"\\n 1. Display all records\\n 2. Add new record\\n 3. Edit existing record\\n 4. Delete record \\n 5. Quit\\n \\\"\\\"\\\"\\n\\n while True:\\n print(menu_text)\\n choice = input('Enter your choice: ')\\n if choice == '1':\\n display_all_records()\\n elif choice == '2':\\n add_new_record(data)\\n elif choice == '3':\\n results = find_player()\\n\\n if results:\\n edit_existing_record(results)\\n else:\\n print(f'Player is not on our records\\\\n')\\n elif choice == '4':\\n delete_record()\\n elif choice == '5':\\n break\\n else:\\n print('Not a valid selection, please try again')\",\n \"def _stateHandler(self, state):\\n try:\\n if state.getType() == TSMessage.STATUS and self.parent:\\n _params = state.getParams()\\n if _params.get('main'):\\n _descr = _params['main'].split(';')\\n\\n if _descr[0] == 'starting':\\n self.msg_params['prebuf'] = 0\\n\\n elif _descr[0] == 'prebuf':\\n if _descr[1] != self.msg_params.get('prebuf', 0):\\n self.msg_params['last_update'] = state.getTime()\\n self.msg_params['prebuf'] = _descr[1]\\n log.d('_stateHandler: Пытаюсь показать состояние')\\n self.parent.showStatus('Пребуферизация {0}'.format(self.msg_params['prebuf']))\\n self.parent.player.showStatus('Пребуферизация {0}'.format(self.msg_params['prebuf']))\\n\\n if time.time() - self.msg_params['last_update'] >= AcePlayer.TIMEOUT_FREEZE:\\n log.w('AceEngine is freeze')\\n self.autoStop()\\n\\n elif _descr[0] == 'check':\\n log.d('_stateHandler: Проверка {0}'.format(_descr[1]))\\n self.parent.showStatus('Проверка {0}'.format(_descr[1]))\\n # elif _descr[0] == 'dl':\\n # self.parent.showInfoStatus('Total:%s DL:%s UL:%s' % (_descr[1], _descr[3], _descr[5]))\\n elif _descr[0] == 'buf':\\n # self.parent.showStatus('Буферизация: %s DL: %s UL: %s' % (_descr[1],\\n # _descr[5], _descr[7])) @IgnorePep8\\n\\n if _descr[1] != self.msg_params.get('buf', 0):\\n self.msg_params['last_update'] = state.getTime()\\n self.msg_params['buf'] = _descr[1]\\n # self.parent.player.showStatus('Буферизация {0}'.format(self.msg_params['value']))\\n if time.time() - self.msg_params['last_update'] >= AcePlayer.TIMEOUT_FREEZE:\\n self.parent.player.showStatus('Пребуферизация {0}'.format(self.msg_params['buf']))\\n log.w('AceEngine is freeze')\\n self.autoStop()\\n # elif _descr[0] == 'dl':\\n # if _descr[8] != self.msg_params.get('downloaded', 0):\\n # self.msg_params['last_update'] = state.getTime()\\n # self.msg_params['downloaded'] = _descr[8]\\n # if time.time() - self.msg_params['last_update'] >= 10:\\n # log.w('AceEngine is freeze')\\n # self.autoStop()\\n\\n # self.parent.showInfoStatus('Buf:%s DL:%s UL:%s' % (_descr[1], _descr[5], _descr[7]))\\n # else:\\n # self.parent.showInfoStatus('%s' % _params)\\n elif state.getType() in (TSMessage.RESUME, TSMessage.PAUSE, TSMessage.START):\\n self.msg_params['value'] = 0\\n # self.msg_params['downloaded'] = 0\\n\\n elif state.getType() == TSMessage.EVENT:\\n if state.getParams() == 'getuserdata':\\n self._send_command('USERDATA [{{\\\"gender\\\": {0}}} {{\\\"age\\\": {1}}}]'.format(\\n utils.str2int(defines.GENDER) + 1,\\n utils.str2int(defines.AGE) + 1))\\n elif state.getParams().startswith('showdialog'):\\n _parts = state.getParams().split()\\n self.parent.showStatus('{0}: {1}'.format(unquote(_parts[2].split('=')[1]),\\n unquote(_parts[1].split('=')[1])))\\n elif state.getType() == TSMessage.ERROR:\\n self.parent.showStatus(state.getParams())\\n\\n elif state.getType() == TSMessage.STOP:\\n self.waiting.abort.set()\\n\\n elif state.getType() == TSMessage.STATE:\\n _params = state.getParams()\\n _param = utils.str2int(_params)\\n if _param == 0:\\n self.waiting.abort.set()\\n\\n except Exception as e:\\n log.e('_stateHandler error: \\\"{0}\\\"'.format(uni(e)))\\n finally:\\n try:\\n if self.waiting.msg == state.getType():\\n self.waiting.msg = state\\n self.waiting.event.set()\\n\\n except Exception as e:\\n log.e('_stateHandler error: \\\"{0}\\\"'.format(uni(e)))\",\n \"def __call__(self, recording, **kwargs):\\n raise NotImplementedError()\",\n \"def handle_record_begin():\\n LOG.info(\\\"Begin Recording...\\\")\\n context = {'client_name': 'mycroft_listener',\\n 'source': 'audio',\\n 'destination': [\\\"skills\\\"]}\\n bus.emit(Message('recognizer_loop:record_begin', context=context))\",\n \"def state(self):\\n pass\",\n \"def integrateRecord(self, record):\\n\\n inflatedIncomingBufferRecord = self.inflateRecord(record)\\n # Checking if record exists in the application\\n recordIndex = self.searchRecordInApp(record.recordID)\\n\\n # If record exists in store\\n if record.recordID in self.store:\\n\\n # Record exists in the application\\n if recordIndex >= 0:\\n\\n # Dirty bit in the application is not set\\n if self.appData[recordIndex][2] == 0:\\n\\n storeRecordHistory = self.store[record.recordID].lastSavedByHistory\\n storeRecord = self.store[record.recordID]\\n versionComparison = self.compareVersions(storeRecordHistory, record.lastSavedByHistory, \\\\\\n (storeRecord.lastSavedByInstance,\\n storeRecord.lastSavedByCounter), \\\\\\n (record.lastSavedByInstance, record.lastSavedByCounter))\\n\\n # Merge conflict between incoming buffer record and store record\\n if versionComparison == 2:\\n\\n self.updateCounter()\\n if self.resolveMergeConflict(inflatedIncomingBufferRecord, recordIndex):\\n # Merge conflict resolution did not choose the app data\\n self.bufferDataChosen(record, {self.instanceID: self.counter})\\n\\n else:\\n # Merge conflict resolution algorithm chose app data\\n self.appDataChosen(record, {self.instanceID: self.counter})\\n\\n # Chooses incoming buffer record\\n elif versionComparison == 0:\\n self.bufferDataChosen(record, {})\\n\\n # Application record is updated\\n elif versionComparison == 1 or versionComparison == 3:\\n return\\n\\n else:\\n raise ValueError(\\\"Invalid return from compare versions method!\\\")\\n\\n # Dirty bit for the record is set\\n else:\\n self.updateCounter()\\n self.appData[recordIndex][2] = 2\\n # Merge conflict resolution did not choose the app Data\\n if self.resolveMergeConflict(inflatedIncomingBufferRecord, recordIndex):\\n self.bufferDataChosen(record, {self.instanceID: self.counter})\\n\\n # Merge conflict resolution chose the app Data\\n else:\\n self.appDataChosen(record, {self.instanceID: self.counter})\\n\\n # Record does not exist in the application\\n else:\\n raise ValueError('Record exists in store but not in application!')\\n\\n # Record does not exist in the store\\n else:\\n # Record exists in the application\\n if recordIndex >= 0:\\n\\n if self.appData[i][2] == 0:\\n raise ValueError('Data not present in Store but present in Application!')\\n\\n else:\\n self.updateCounter()\\n # Does not choose app Data\\n if self.resolveMergeConflict(inflatedIncomingBufferRecord, recordIndex):\\n self.store[record.recordID] = deepcopy(record)\\n self.bufferDataChosen(record, {self.instanceID: self.counter})\\n\\n # Chooses app Data\\n else:\\n self.appData[recordIndex][2] = 0\\n self.store[record.recordID] = deepcopy(record)\\n self.appDataChosen(record, {self.instanceID: self.counter})\\n\\n # Record does not exist in the application\\n else:\\n self.appData.append(self.inflateRecord(record))\\n self.store[str(record.recordID)] = deepcopy(record)\",\n \"def after_record(self):\\n self.record_label.set(\\\"Re-Record\\\")\\n self.button_2['state'] = \\\"normal\\\"\",\n \"def set_recording(self, recording):\\n self.record_states = recording\",\n \"def doAdd(self):\\n\\n # Prompt for each thing we need in the record\\n record = XMLRecord()\\n record.recordType = self.recordType\\n print \\\"Enter the fields for the record (ctrl-D to stop at any time)\\\"\\n try:\\n # uid\\n while True:\\n record.uid = raw_input(\\\"Id: \\\")\\n if not record.uid:\\n print \\\"A valid uid is required. Please try again.\\\"\\n elif self.directory.containsRecord(self.recordType, record.uid):\\n print \\\"Record uid: '%s' of type: '%s' does already exists in the directory.\\\" % (record.uid, self.recordType,)\\n else:\\n break\\n\\n # guid\\n while True:\\n record.guid = raw_input(\\\"GUID [leave empty to auto-generate]: \\\")\\n if record.guid and self.directory.containsGUID(record.guid):\\n print \\\"GUID: '%s' already used in the directory\\\" % (record.guid,)\\n else:\\n break\\n\\n # password\\n record.password = self.promptPassword()\\n\\n # name\\n record.name = raw_input(\\\"Name: \\\")\\n\\n # members\\n if self.recordType in (recordtypes.recordType_groups,):\\n record.members = self.getMemberList(\\\"Enter members of this group\\\", \\\"Member\\\", \\\"members\\\")\\n\\n # cuaddr\\n while True:\\n cuaddr = raw_input(\\\"Calendar user address [leave empty to stop adding addresses]: \\\")\\n if cuaddr:\\n record.calendarUserAddresses.add(cuaddr)\\n else:\\n break\\n\\n # auto-schedule\\n if self.recordType in (recordtypes.recordType_locations, recordtypes.recordType_resources,):\\n auto_schedule = raw_input(\\\"Turn on automatic scheduling [y/n]?: \\\")\\n if auto_schedule == \\\"y\\\":\\n record.autoSchedule = True\\n\\n # enabled for calendaring\\n if self.recordType in (recordtypes.recordType_users, recordtypes.recordType_groups,):\\n enable_calendars = raw_input(\\\"Create calendar account rather than access-only account [y/n]?: \\\")\\n if enable_calendars == \\\"n\\\":\\n record.enabledForCalendaring = False\\n\\n # proxies\\n if self.recordType in (recordtypes.recordType_locations, recordtypes.recordType_resources,):\\n record.proxies = self.getMemberList(\\\"Enter proxies of this location or resource\\\", \\\"Proxy\\\", \\\"proxies\\\")\\n\\n except EOFError:\\n return 0\\n\\n # Now validate the record and save it\\n if not record.guid:\\n record.guid = str(uuid4())\\n\\n self.directory.addRecord(record)\\n\\n return 1\",\n \"def _during_execute(self, db):\\n pass\",\n \"def parse_record(self, record):\\n raise NotImplementedError()\",\n \"def handle_state(self):\\r\\n if self.state == 'walk':\\r\\n self.walking()\\r\\n elif self.state == 'fall':\\r\\n self.falling()\\r\\n elif self.state == 'jumped on':\\r\\n self.jumped_on()\\r\\n elif self.state == 'shell slide':\\r\\n self.shell_sliding()\\r\\n elif self.state == 'death jump':\\r\\n self.death_jumping()\",\n \"def __init__(self, record=None):\\n self.record = record\",\n \"def run_record(self, state, pipeline, record_path):\\n e1 = cv2.getTickCount()\\n while state.record_btn:\\n # Wait for a coherent pair of frames: depth and color\\n frames = pipeline.wait_for_frames()\\n depth_frame = frames.get_depth_frame()\\n color_frame = frames.get_color_frame()\\n if not depth_frame or not color_frame:\\n continue\\n \\n # filter\\n depth_frame = self.th_filter.process(depth_frame)\\n depth_frame = self.sp_filter.process(depth_frame)\\n depth_frame = self.tmp_filter.process(depth_frame)\\n\\n depth_colormap = self.colorizer.colorize(depth_frame)\\n # Convert images to numpy arrays\\n depth_image = np.asanyarray(depth_colormap.get_data())\\n color_image = np.asanyarray(color_frame.get_data())\\n\\n lane_masked = self.lane_detector.detect(color_image)\\n # Show images\\n stacked_imgs = (color_image, depth_image, lane_masked)\\n images = np.hstack(stacked_imgs)\\n cv2.resizeWindow(state.WIN_NAME, \\n self.width*len(stacked_imgs), \\n self.height)\\n cv2.imshow(state.WIN_NAME, images)\\n cv2.setMouseCallback(state.WIN_NAME, state.mouse_controll)\\n key = cv2.waitKey(1)\\n if key == 27:\\n state.app_btn = False\\n state.record_btn = False\\n break\\n # Calculate Runtime Tick to quit\\n e2 = cv2.getTickCount()\\n tick = int((e2 - e1) / cv2.getTickFrequency())\\n # Save images per tick\\n if self.saveimg:\\n color_file = record_path / f\\\"color-{tick}.npy\\\"\\n depth_file = record_path / f\\\"depth-{tick}.npy\\\"\\n ps_file = record_path / f\\\"ps-{tick}.ply\\\"\\n if not ps_file.exists():\\n np.save(color_file, color_image)\\n np.save(depth_file, depth_image)\\n \\n # Create point cloud\\n if self.savepc and (not ps_file.exists()):\\n points = self.pc.calculate(depth_frame)\\n self.pc.map_to(depth_frame)\\n points.export_to_ply(str(ps_file), color_frame)\\n\\n if tick > self.record_time:\\n print(\\\"Finish Record\\\")\\n state.app_btn = False\\n state.record_btn = False\\n cv2.destroyAllWindows()\\n break\\n\\n if not state.app_btn:\\n break\",\n \"def assess_status(self):\\n\\n # Update the PEATDB_record information\\n if self.data.has_key('PEAT_DBrecord'):\\n self.PEAT_DBrecord.set(self.data['PEAT_DBrecord'])\\n\\n # Do we have an open project?\\n if self.data['Project filename']:\\n self.file_menu.entryconfigure(0,state=DISABLED) # Open project\\n self.file_menu.entryconfigure(1,state=NORMAL) # Close project\\n self.file_menu.entryconfigure(3,state=NORMAL) # Save project as\\n\\n # Is the current project saved?\\n if not self.data['Project saved']:\\n self.file_menu.entryconfigure(2,state=NORMAL) # Save Project\\n else:\\n self.file_menu.entryconfigure(2,state=DISABLED) # Save Project\\n else:\\n\\n # No project\\n self.file_menu.entryconfigure(1,state=DISABLED) # Close project\\n self.file_menu.entryconfigure(2,state=DISABLED) # Save Project\\n self.file_menu.entryconfigure(3,state=DISABLED) # Save project as\\n\\n # Do we have a DNA sequence?\\n if self.data['DNAseq']:\\n self.ORFbutton.configure(state=NORMAL) # ORF button\\n # Sequence menu\\n states={'Open':None,\\n 'Save':1,\\n 'Save As':1,\\n 'Complementary':1,\\n 'Inverse':1,\\n 'Find':1}\\n self.set_pulldown_and_shortcut_states(states,self.seqmenu)\\n # Primer menu\\n self.primer_menu.entryconfigure(0,state=ACTIVE) # PCR primer\\n # .\\n self.file_menu.entryconfigure(0,state=DISABLED) # Open project\\n self.file_menu.entryconfigure(3,state=NORMAL) # Save project as\\n else:\\n # No DNA\\n states={'Open':1,\\n 'Save':None,\\n 'Save As':None,\\n 'Complementary':None,\\n 'Inverse':None,\\n 'Find':None,\\n 'Cut':None,\\n 'Copy':None,\\n 'Paste':None,\\n 'Delete':None}\\n self.set_pulldown_and_shortcut_states(states,self.seqmenu)\\n\\n # Primer menu\\n self.primer_menu.entryconfigure(0,state=DISABLED) # PCR primers\\n self.primer_menu.entryconfigure(1,state=DISABLED) # Mutagenic primer\\n\\n # Has an ORF been selected?\\n if self.data.has_key('ORF_selected'):\\n self.primer_menu.entryconfigure(1,state=ACTIVE) # Mutagenic primer\\n\\n start=self.data['ORF_selected']['start']\\n stop=self.data['ORF_selected']['stop']\\n return\",\n \"def execute(self) -> None:\\n self.state()\",\n \"def take_action(self, state):\",\n \"def _load(self) -> None:\\n self.record = self._saved_record\\n self.counter = self._saved_counter\\n self.current_objects = self._saved_objects\",\n \"def commit_record( self, update_photo_state=True ):\\n\\n # update the record based on what's currently visible if requested.\\n if update_photo_state:\\n self.record[\\\"modified_time\\\"] = time.mktime( time.gmtime() )\\n self.record[\\\"state\\\"] = self.photoProcessingStateComboBox.currentText()\\n self.record[\\\"tags\\\"] = list( map( lambda x: x.strip(),\\n self.photoTagsLineEdit.text().split( \\\", \\\" ) ) )\\n\\n self.db.mark_data_dirty()\\n\\n super().commit_record()\",\n \"def handle(cls, record):\\n print(datetime.datetime.now(), record, flush=True)\",\n \"def add_record(self, record):\\n pass\",\n \"def testRecord(self):\\n\\t\\tfca = FunctionCallAction(('key',), 'c', 'd', 'ret')\\n\\t\\tself.failUnless(fca.record() == 'ret')\",\n \"def run_states(self):\\n if (self.state == \\\"off\\\"):\\n if (self.in_power.value == 1):\\n self.off_to_on()\\n \\n elif self.state == \\\"on\\\":\\n if (self.in_power.value == 0):\\n self.any_to_off()\\n elif (self.in_alert.value == 1):\\n self.on_to_alert()\\n \\n elif self.state == \\\"alert\\\":\\n if (self.in_power.value == 0):\\n self.any_to_off()\\n elif (self.in_alert.value == 0):\\n self.alert_to_was_alert()\\n\\n elif self.state == \\\"was_alert\\\":\\n if (self.in_power.value == 0):\\n self.any_to_off()\",\n \"def on_start(self): # noqa: D401\\n logger.debug(\\\"on_start()\\\")\\n\\n state = self.read_state(self.statefile)\\n if state:\\n self.restore_state(state)\",\n \"def run(self, state: State) -> State:\",\n \"def run(self):\\n if self.next_state == \\\"initialize_rexarm\\\":\\n self.initialize_rexarm()\\n\\n if self.next_state == \\\"idle\\\":\\n self.idle()\\n\\n if self.next_state == \\\"estop\\\":\\n self.estop()\\n\\n if self.next_state == \\\"execute_tp\\\":\\n self.execute_tp()\\n\\n if self.next_state == \\\"execute\\\":\\n self.execute()\\n\\n if self.next_state == \\\"calibrate\\\":\\n self.calibrate()\\n\\n if self.next_state == \\\"manual\\\":\\n self.manual()\\n\\n if self.next_state == \\\"learn\\\":\\n self.learn()\\n\\n if self.next_state == \\\"remember\\\":\\n self.remember()\\n\\n if self.next_state == \\\"write\\\":\\n self.write()\\n\\n if self.next_state == \\\"get_color\\\":\\n self.get_color()\\n\\n if self.next_state == \\\"find_blocks\\\":\\n self.find_blocks()\\n\\n # if self.next_state == \\\"dance\\\":\\n # self.execute_dance()\",\n \"def run_state_machine(self):\\n\\n # Initial check for startup state\\n if self.status == 'STARTUP':\\n self.update_action(self.status, None, None, None)\\n # Get apriltag data from the detector\\n\\n return_tag_data = self.apriltag_detector.get_apriltag_data()\\n\\n # Check if it saw an apriltag\\n if len(return_tag_data) != 0:\\n\\n # Iterate over the data from each of the apriltags seen\\n for i in range(len(return_tag_data)):\\n\\n # Temp apriltag data\\n temp_tag = return_tag_data[i]\\n\\n print(\\\"current state: \\\", self.status)\\n\\n print(\\\"Statemachine x VAL: \\\", temp_tag[1])\\n\\n print(\\\"Statemachine y VAL: \\\", temp_tag[2])\\n\\n # If the smallbot is currently in the CREEP_FORWARD state\\n # handle cases for when the camera sees the side tags while it is driving forward\\n if self.status == 'CREEP_FORWARD' and (temp_tag[0] == self.back_tag):\\n self.current_tag = temp_tag[0]\\n print(\\\"ATTEMPTING TO UPDATE STATUS 1\\\")\\n self.update_action(self.status, temp_tag[1], temp_tag[2], temp_tag[3])\\n # Ignore cases when the camera sees the side apriltags\\n elif self.status == 'CREEP_FORWARD' and (temp_tag[0] == self.right_tag):\\n print(\\\"ATTEMPTING TO UPDATE STATUS 2\\\")\\n pass\\n elif self.status == 'CREEP_FORWARD' and (temp_tag[0] == self.left_tag):\\n print(\\\"ATTEMPTING TO UPDATE STATUS 2\\\")\\n pass\\n else:\\n self.current_tag = temp_tag[0]\\n print(\\\"ATTEMPTING TO UPDATE STATUS 3\\\")\\n self.update_action(self.status, temp_tag[1], temp_tag[2], temp_tag[3])\\n\\n # If the camera did not see any apriltags\\n else:\\n self.current_tag = None\\n self.update_action(None, None, None, None)\",\n \"def emit(self, record):\\n self.eng.dispose()\\n trys = 3\\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\\\"__\\\")}\\n for t in range(trys):\\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\\n failed = False\\n try:\\n with self.eng.connect() as con:\\n # formating values to convert from python or parsl to db standards\\n info['task_fail_history'] = str(info.get('task_fail_history', None))\\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\\n run_id = info['run_id']\\n\\n # if workflow or task has completed, update their entries with the time.\\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n return\\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\\n workflow = self.meta.tables['task']\\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\\\\n .where(workflow.c.run_id == run_id)\\n con.execute(up)\\n\\n # create workflows table if this is a new database without one\\n if 'workflows' not in self.meta.tables.keys():\\n workflows = create_workflows_table(self.meta)\\n self.meta.create_all(con)\\n # if this is the first sight of the workflow, add it to the workflows table\\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\\n workflows = self.meta.tables['workflows']\\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\\n con.execute(ins)\\n\\n # if log has task counts, update the workflow entry in the workflows table\\n if 'tasks_completed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n if 'tasks_failed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n\\n # create task table if this is a new run without one\\n if 'task' not in self.meta.tables.keys():\\n workflow = create_task_table(self.meta)\\n self.meta.create_all(con)\\n\\n # check to make sure it is a task log and not just a workflow overview log\\n if info.get('task_id', None) is not None:\\n # if this is the first sight of the task in the workflow, add it to the workflow table\\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\\n if 'psutil_process_pid' in info.keys():\\n # this is the race condition that a resource log is before a status log so ignore this resource update\\n return\\n\\n workflow = self.meta.tables['task']\\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\\n con.execute(ins)\\n\\n if 'task_status' in info.keys():\\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\\n if 'task_status' not in self.meta.tables.keys():\\n task_status_table = create_task_status_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n # if this status table already exists, just insert the update\\n else:\\n task_status_table = self.meta.tables['task_status']\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n return\\n\\n if 'psutil_process_pid' in info.keys():\\n # if this is a task resource update then handle that, if the resource table DNE then create it\\n if 'task_resources' not in self.meta.tables.keys():\\n task_resource_table = create_task_resource_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n # if this resource table already exists, just insert the update\\n else:\\n task_resource_table = self.meta.tables['task_resources']\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n return\\n\\n except Exception as e:\\n logger.error(\\\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\\\".format(t, str(e)))\\n failed = True\\n time.sleep(5)\\n if not failed:\\n return\",\n \"def saveState(self) -> None:\\n # TODO: Saves State\\n pass\",\n \"def save_record(record):\\n record. save_details()\",\n \"def on_incoming_records(self, connection: ConnectionInterface) -> None:\\n self.generate_metadata()\\n\\n df = connection.record_containers[0].build_dataframe()\\n df[\\\"optional_value\\\"] = self.workflow_config[\\\"Value\\\"]\\n\\n self.output_anchor.push_records(\\n generate_records_from_df(df, self.output_anchor.record_info)\\n )\\n\\n connection.clear_records()\",\n \"def my_record_vars(context, data):\\n pass\",\n \"def load_state(self, dictionary):\\n self.log_formatstr = dictionary['log_formatstr']\\n self.backend_interval = dictionary['backend_interval']\",\n \"def save(self, record):\\n pass\",\n \"def savestate(self, state):\\n pass\",\n \"def reloadData(self):\\n self.dto.readFromData()\\n print(\\\"Record reloaded.\\\")\",\n \"def read(self):\\n self.record_d = {}\\n if self.__read_file():\\n self.__print_report()\",\n \"def _stage(self):\\n\\n pass\",\n \"def main():\\n \\n option_parser = optparse.OptionParser(usage='usage: %prog [options] filename')\\n option_parser.add_option('-d', '--debug',\\n dest='debug',\\n default=False,\\n action='store_true', \\n help='Enable debugging messages')\\n option_parser.add_option('-n', '--no-store', \\n dest='store', \\n default=True, \\n action='store_false', \\n help='Test but do not store the data') \\n option_parser.add_option('-v', '--verbose', \\n dest='verbose', \\n default=False, \\n action='store_true', \\n help='Produce verbose output') \\n (options, args) = option_parser.parse_args()\\n \\n if not args:\\n raise RuntimeError('Please specify at least one filename to load.')\\n\\n if options.debug:\\n level = logging.DEBUG\\n else:\\n level = logging.INFO\\n logging.basicConfig(level=level,\\n format='%(levelname)-8s %(message)s',\\n )\\n\\n # Establish our django set-up so we can import models\\n # to work with the database.\\n import settings\\n setup_environ(settings)\\n\\n # Now we can import the other modules we need that depend on django\\n from vtr_loader import load_data_from_stream, ErrorKeeper\\n \\n errors = ErrorKeeper()\\n num_new = 0\\n num_existing = 0\\n for filename in args:\\n logging.info('Processing %s', filename)\\n f = open(filename, 'rt')\\n try:\\n counts = load_data_from_stream(\\n f, \\n store=options.store, \\n verbose=options.verbose, \\n message_stream=sys.stdout, \\n errorcb=errors,\\n )\\n finally:\\n f.close()\\n \\n num_existing += counts[0]\\n num_new += counts[1]\\n\\n for error in errors.errors:\\n error['file'] = filename\\n\\n if options.verbose:\\n print\\n print 'Found %d new records' % num_new\\n print 'Found %d existing records' % num_existing\\n print 'Found %d errors' % len(errors.errors)\\n\\n for error in errors.errors:\\n print\\n error['file'] = filename\\n print 'ERROR in %(file)s: %(start)d - %(end)d (%(error)s)' % error\\n for line in error['lines']:\\n print '\\\\t%s' % line\\n \\n return\",\n \"def _process_change(self):\\n self._sort_records()\\n self._store_writer.to_file(self.records)\\n self._store_writer.to_csv_file(self.records)\",\n \"def dbtrace_filter_on_change(new_state):\\n\\n pass\",\n \"def _record(self):\\r\\n self._plot()\\r\\n self._csvWriter()\\r\\n self._logger()\",\n \"def stage(self):\\n pass\",\n \"def __call__(self):\\r\\n AddNewRecords()\",\n \"def set_state( self ):\",\n \"def __exit__(self, _type, value, traceback):\\n try:\\n if not self.record_finish:\\n return\\n print >>sys.stderr, 'record', self.record\\n dutset = {'last_finish_time':time()}\\n if not self.record:\\n return\\n upd = {'end_time': time(), 'modification_time':time()}\\n\\n if value: # i.e. , if test failed:\\n upd['failure'] = repr(value)\\n upd['exception'] = value.__class__.__name__\\n if not isinstance(value, KeyboardInterrupt):\\n print 'HEADLINE: exception', upd['exception'], value\\n for clause in format_exception(_type, value, traceback):\\n for line in clause.split('\\\\n'):\\n print 'CRASH:', line\\n else:\\n upd['infrastructure_problem'] = True\\n upd['whiteboard'] = '[infrastructure] test interrupted'\\n if self.reinstall_on_failure:\\n dutset['test_failed'] = True\\n tnext = time() + 300\\n print 'INFO: test failed, so will reinstall machine at', \\\\\\n asctime(localtime(tnext))\\n\\n if self.failed: #some test suite failed\\n upd['failure'] = 'test failed'\\n\\n self.mdb.results.update({'_id':self.result_id}, {'$set':upd})\\n classify = process_result(self.mdb.results.find_one({'_id':self.result_id}))\\n print 'HEADLINE:', classify, self.full_description()\\n\\n get_track().updates.save({'result_id':self.result_id,\\n 'action':'experiment finished'})\\n\\n if self.dut_id:\\n self.mdb.duts.update({'_id':self.dut_id}, \\n {'$unset': {'control_pid':1, 'result_id':1,\\n 'control_command_line':1},\\n '$set': dutset})\\n if self.build:\\n recount(self.build)\\n if classify == 'infrastructure_problems':\\n pass\\n else:\\n col = 'green' if classify == 'passes' else 'red'\\n finally:\\n if self.record_queue:\\n self.record_queue.put('finish')\\n self.record_queue.close()\\n self.record_queue.join_thread()\\n if self.stream_process:\\n self.stream_process.join()\\n if self.stdout_filter:\\n self.stdout_filter.del_callback(self)\",\n \"def _ParseRecord(self, parser_mediator, key, structure):\\n if key == 'record_start':\\n self._ParseRecordStart(structure)\\n\\n elif key == 'record_body':\\n self._ParseRecordBody(structure)\\n\\n elif key == 'record_end':\\n self._ParseRecordEnd(parser_mediator, structure)\",\n \"def _process(self):\\n self.kwargs[\\\"collect\\\"].change_status(self.kwargs[\\\"collect\\\"].FINALIZED)\",\n \"def before_model_run(self, data_handle=None):\\n pass\",\n \"def on_record(self):\\n self.record_label.set(\\\"Recording...\\\")\\n\\n recorder = RecorderThread(self.after_record)\\n recorder.start()\",\n \"def __enter__(self):\\n try:\\n run(['logger', 'BVT', 'starting', self.full_description()], \\n host=self.dut, timeout=10)\\n except SubprocessError:\\n print 'INFO: unable to mark test log'\\n if not self.record:\\n return self\\n if self.result_id is None:\\n self.mdb = get_autotest()\\n terms = {'test_case':self.description or 'to be determined',\\n 'automation_user': getpwuid(getuid()).pw_gecos.split(',')[0],\\n 'control_pid' : getpid(), 'start_time' : time(),\\n 'development_mode' : 0,\\n 'command_line':abbreviate(' '.join(sys.argv))}\\n if self.dut:\\n dutdoc = self.mdb.duts.find_one({'name':self.dut})\\n self.dut_id = terms['dut'] = dutdoc['_id']\\n terms['dut_name'] = dutdoc['name']\\n if 'development_mode' in dutdoc:\\n terms['development_mode'] = dutdoc['development_mode']\\n self.result_id = self.mdb.results.save(terms)\\n if self.job_id is not None:\\n self.mdb.jobs.update({'_id':objectid.ObjectId(self.job_id)}, {'$set':{'results_id':self.result_id}})\\n if self.build is None and self.dut:\\n self.build = get_build(self.dut, timeout=10)\\n self.mdb.results.update({'_id':self.result_id}, \\n {'$set':{'build':self.build}})\\n if self.dut:\\n self.mdb.duts.update({'_id':terms['dut']}, {'$set': {\\n 'build':self.build,\\n 'control_command_line': abbreviate(' '.join(sys.argv)),\\n 'result_id' : self.result_id}})\\n if self.stdout_filter:\\n self.record_queue = Queue()\\n self.stream_process = Process(\\n target=service_queue, \\n args=[self.record_queue, self.result_id, \\n self.dut, self.dut_id])\\n self.stream_process.start()\\n self.stdout_filter.add_callback(self, \\n lambda *x: self.record_queue.put(x))\\n\\n if self.description:\\n print 'HEADLINE: starting', self.full_description()\\n get_track().updates.save({'result_id':self.result_id,\\n 'action':'new result record'})\\n return self\",\n \"def main(self):\\n\\n while True:\\n print('Main Menu:')\\n user_input = input('What would you like to do? (C)reate new record or (L)ookup existing? ').lower().strip()\\n self.check_input(user_input)\\n\\n if user_input == 'c':\\n print('Great! Let\\\\'s create a new log entry!\\\\n')\\n self.create_entry()\\n elif user_input == 'l':\\n print('Awesome! Let\\\\'s look up some entries!\\\\n')\\n self.lookup_entry()\",\n \"def do_post_action_processing(self, i_state, low_level_actions):\\n pass\",\n \"def addRecord(self):\\n\\n ## Saving recorded entries to the CRM and Mailings Database\\n print(\\\"Saving entries to the CRM and Mailings database...\\\")\\n db_connection.executeQuery(\\\"INSERT INTO dbo.CRM (f_name, l_name, company, address, city, county, state, zip, primary_phone, secondary_phone, email_address) VALUES ('\\\" + self.first_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.last_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.crm_company_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.address.title() + \\\"', '\\\" + self.city.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.county.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.state_code.upper() + \\\"', '\\\" + str(self.zip_code) + \\\"', '\\\" + self.phone_number + \\\"', '\\\" + self.phone_number_2 + \\\"' , '\\\" + self.email_address + \\\"'); COMMIT\\\")\\n db_connection.executeQuery(\\\"INSERT INTO dbo.Mailings (name, company, address) VALUES ('\\\" + self.first_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\" \\\" + self.last_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.company_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"','\\\" + self.address + \\\" \\\" + self.city.title() + \\\" \\\" + self.county.title() + \\\" \\\" + self.state_code.upper() + \\\" \\\" + str(self.zip_code) + \\\"'); COMMIT\\\")\",\n \"def __call__(self, state, action):\\n pass\",\n \"def __getstate__(self):\\n d = self.__dict__.copy() # copy it cuz we'll be making changes\\n if 'f' in d:\\n del d['f'] # exclude open .srf file handle, if any\\n if not self._pickle_all_records:\\n # these are hogs:\\n keys = ['lowpassrecords', 'highpassrecords', 'lowpassmultichanrecords',\\n #'digitalsvalrecords' # also a hog, but useful to keep\\n ]\\n for key in keys:\\n if key in d:\\n del d[key]\\n # else self was probably restored from a sort, so all of these attribs have\\n # probably already been stripped\\n # leave the streams be, since you need them for their enabled chans info\\n return d\",\n \"def run():\\n\\n options = [\\\"Add new record to table\\\",\\n \\\"Remove a record with a given id from the table\\\",\\n \\\"Update specified record in the table\\\",\\n \\\"Number of different kinds of game that are available of each manufacturer\\\",\\n \\\"Average amount of games in stock of a given manufacturer\\\",\\n \\\"Print table\\\"]\\n\\n title_list = [\\\"*id\\\",\\n \\\"* title\\\",\\n \\\"* manufacturer\\\",\\n \\\"* price\\\",\\n \\\"* in_stock\\\"]\\n os.system('clear')\\n choice = None\\n while choice != \\\"0\\\":\\n terminal_view.print_menu(\\\"What do you want to do:\\\", options, \\\"Back to main menu\\\")\\n choice = terminal_view.get_choice(options)\\n os.system('clear')\\n if choice == \\\"1\\\":\\n # to jest dzialajacy plik model/store/games.csv\\n file_name = common.get_input(\\\"Choose a file: \\\")\\n if file_name == \\\"\\\":\\n file_name = \\\"model/store/games.csv\\\"\\n table = common.get_table_from_file(file_name)\\n terminal_view.print_table(table, title_list)\\n record = terminal_view.get_inputs(title_list, \\\"Enter data: \\\")\\n table = store.add(table, record)\\n common.write_table_to_file(file_name, table)\\n os.system(\\\"clear\\\")\\n terminal_view.gprint('*** Record has been added ***')\\n common.waiting()\\n os.system(\\\"clear\\\")\\n elif choice == \\\"2\\\":\\n file_name = common.get_input(\\\"Choose a file: \\\")\\n if file_name == \\\"\\\":\\n file_name = \\\"model/store/games.csv\\\"\\n table = common.get_table_from_file(file_name)\\n terminal_view.print_table(table, title_list)\\n id_ = common.get_input(\\\"Get id to removed: \\\")\\n table = store.remove(table, id_)\\n common.write_table_to_file(file_name, table)\\n os.system(\\\"clear\\\")\\n terminal_view.gprint('*** Record has been removed ***')\\n common.waiting()\\n os.system(\\\"clear\\\")\\n elif choice == \\\"3\\\":\\n file_name = common.get_input(\\\"Choose a file: \\\")\\n if file_name == \\\"\\\":\\n file_name = \\\"model/store/games.csv\\\"\\n table = common.get_table_from_file(file_name)\\n terminal_view.print_table(table, title_list)\\n id_ = common.get_input(\\\"Enter id to update: \\\")\\n record = terminal_view.get_inputs(title_list, \\\"Enter data: \\\")\\n table = store.update(table, id_, record)\\n common.write_table_to_file(file_name, table)\\n os.system(\\\"clear\\\")\\n terminal_view.gprint('*** Record has been updated ***')\\n common.waiting()\\n os.system(\\\"clear\\\")\\n elif choice == \\\"4\\\":\\n file_name = common.get_input(\\\"Choose a file: \\\")\\n if file_name == \\\"\\\":\\n file_name = \\\"model/store/games.csv\\\"\\n table = common.get_table_from_file(file_name)\\n dictionary = store.get_counts_by_manufacturers(table)\\n terminal_view.print_dictionary(\\\"Number of different kinds of game that are\\\" +\\n \\\"available of each manufacturer:\\\", dictionary)\\n common.waiting()\\n os.system(\\\"clear\\\")\\n elif choice == \\\"5\\\":\\n file_name = common.get_input(\\\"Choose a file: \\\")\\n if file_name == \\\"\\\":\\n file_name = \\\"model/store/games.csv\\\"\\n table = common.get_table_from_file(file_name)\\n terminal_view.print_table(table, title_list)\\n manufacturer = common.get_input(\\\"Enter a manufacturer: \\\")\\n print(store.get_average_by_manufacturer(table, manufacturer))\\n common.waiting()\\n os.system(\\\"clear\\\")\\n elif choice == \\\"6\\\":\\n file_name = common.get_input(\\\"Choose a file: \\\")\\n if file_name == \\\"\\\":\\n file_name = \\\"model/store/games.csv\\\"\\n table = common.get_table_from_file(file_name)\\n terminal_view.print_table(table, title_list)\\n common.waiting()\\n os.system(\\\"clear\\\")\\n\\n else:\\n if choice != \\\"0\\\":\\n terminal_view.print_error_message(\\\"There is no such choice.\\\")\",\n \"def save_state(self) -> None:\\n raise NotImplementedError(\\\"Save state is is not implemented.\\\")\",\n \"def main(self):\\n\\n self.setRecordAudio(True)\\n self.general()\\n sleep(1)\\n self.introduction()\\n sleep(1)\\n self.get_name()\\n sleep(1)\\n self.get_age()\\n sleep(1)\\n self.get_origin()\\n sleep(1)\\n self.get_company()\\n sleep(1)\\n self.get_travel_route()\\n sleep(1)\\n self.get_entrance()\\n sleep(1)\\n self.get_documentation()\\n sleep(1)\\n self.sayAnimated(\\n 'OK ' + self.name + '. We would like to know why you came to The Netherlands. Can you please answer the following '\\n 'questions with yes, or, no?')\\n self.speechLock.acquire()\\n self.stopListening()\\n self.get_exclusion()\\n sleep(1)\\n self.get_conflict()\\n sleep(1)\\n self.get_inhumanity()\\n sleep(1)\\n self.get_family()\\n sleep(1)\\n\\n # end interview and save answers\\n self.wrapup()\\n self.store_story()\",\n \"def emit(self, record):\\n pass\",\n \"def state_print_do(cfg, app, win, events):\",\n \"def handle(self):\",\n \"def _run_state(self):\\n main_log.debug(\\\"Running state \\\" + self.state)\\n\\n if not self.get_state_info(\\\"condition\\\"):\\n self._run_next_state()\\n return\\n\\n try:\\n self._pre()\\n except StateSwitchException as e:\\n self.state = e.next_state\\n self._run_state()\\n return\\n\\n if self.get_state_info(\\\"wake_players\\\"):\\n self._waiting_for_players = True\\n self._wake_players()\\n else:\\n self._players_are_done()\",\n \"def state(self, state: str) -> None:\",\n \"def commit_record( self ):\\n\\n print( \\\"Committing art record #{:d}.\\\".format( self.record[\\\"id\\\"] ) )\\n\\n # update the record based on what's currently visible.\\n self.record[\\\"type\\\"] = self.artTypeComboBox.currentText()\\n self.record[\\\"size\\\"] = self.artSizeComboBox.currentText()\\n self.record[\\\"quality\\\"] = self.artQualityComboBox.currentText()\\n self.record[\\\"date\\\"] = self.artDateLineEdit.text()\\n self.record[\\\"state\\\"] = self.artProcessingStateComboBox.currentText()\\n self.record[\\\"modified_time\\\"] = time.mktime( time.gmtime() )\\n self.record[\\\"tags\\\"] = list( map( lambda x: x.strip(),\\n self.artTagsLineEdit.text().split( \\\", \\\" ) ) )\\n\\n self.record[\\\"artists\\\"] = [artist.data() for artist in self.artArtistsListView.selectedIndexes()]\\n self.record[\\\"associates\\\"] = [associate.data() for associate in self.artAssociatesListView.selectedIndexes()]\\n self.record[\\\"vandals\\\"] = [vandal.data() for vandal in self.artVandalsListView.selectedIndexes()]\\n\\n # update the region.\\n normalized_geometry = self.photoPreview.get_region_geometry( True )\\n\\n self.record[\\\"region\\\"] = normalized_geometry.getRect()\\n\\n self.db.mark_data_dirty()\\n\\n super().commit_record()\",\n \"def __init__(self, store_writer, records):\\n self._store_writer = store_writer\\n self.records = records\",\n \"def start():\\r\\n acts = ['add', 'a', 'remove', 'r', 'search', 's', 'update', 'u', 'exit', 'e'] # list of allowed user defined inputs\\r\\n act = re.sub(r'\\\\s', '', str(input('Would you like to ADD entry, REMOVE entry, SEARCH, UPDATE, or EXIT?\\\\n'))\\r\\n .lower()) # lower all letters, then remove whitespaces\\r\\n\\r\\n act = check(act, acts) # perform error check\\r\\n\\r\\n \\\"\\\"\\\" take user to next stages according to their input \\\"\\\"\\\"\\r\\n if act == 'add' or act == 'a':\\r\\n add()\\r\\n elif act == 'remove' or act == 'r':\\r\\n remove()\\r\\n elif act == 'search' or act == 's':\\r\\n search()\\r\\n elif act == 'update' or act == 'u':\\r\\n update()\\r\\n else:\\r\\n print('Exiting database...')\\r\\n conn.close() # close operations\\r\\n sys.exit(0)\",\n \"def storeState(self):\\n\\n self.action_history[self.trial] = self.action\\n self.ball_history[self.trial] = self.ballcolor\",\n \"def run(self, event, db):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_process(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6171262","0.6028878","0.5895539","0.5820173","0.57114506","0.5640522","0.56325686","0.5569406","0.55691856","0.5568817","0.555975","0.5550883","0.55424047","0.5522525","0.5502867","0.54969925","0.5492289","0.5486894","0.5483148","0.54682165","0.54612875","0.5433475","0.54082227","0.53731835","0.5354079","0.53438157","0.5340608","0.5322497","0.5311018","0.529938","0.52891535","0.5281435","0.5280451","0.5280428","0.5263956","0.5249377","0.52480537","0.52305114","0.5229307","0.52239704","0.5218732","0.521302","0.52074903","0.52059007","0.5189535","0.5180023","0.51616144","0.5155818","0.5149034","0.5146983","0.5146183","0.51313394","0.5129398","0.51274306","0.5122327","0.5106087","0.5106005","0.5103434","0.5093836","0.50900173","0.5082055","0.50790256","0.50755674","0.50655305","0.50613815","0.5061273","0.5060316","0.5057855","0.50562525","0.5051514","0.5038337","0.50362664","0.50261486","0.5020314","0.50131184","0.5012325","0.5011798","0.50101316","0.500815","0.50005245","0.49956554","0.49925303","0.49893966","0.49879506","0.49861592","0.4978614","0.4978571","0.49696547","0.49682274","0.4966777","0.496336","0.49610466","0.49583498","0.4956338","0.49505347","0.49461573","0.49461573","0.49461573","0.49461573","0.49461573"],"string":"[\n \"0.6171262\",\n \"0.6028878\",\n \"0.5895539\",\n \"0.5820173\",\n \"0.57114506\",\n \"0.5640522\",\n \"0.56325686\",\n \"0.5569406\",\n \"0.55691856\",\n \"0.5568817\",\n \"0.555975\",\n \"0.5550883\",\n \"0.55424047\",\n \"0.5522525\",\n \"0.5502867\",\n \"0.54969925\",\n \"0.5492289\",\n \"0.5486894\",\n \"0.5483148\",\n \"0.54682165\",\n \"0.54612875\",\n \"0.5433475\",\n \"0.54082227\",\n \"0.53731835\",\n \"0.5354079\",\n \"0.53438157\",\n \"0.5340608\",\n \"0.5322497\",\n \"0.5311018\",\n \"0.529938\",\n \"0.52891535\",\n \"0.5281435\",\n \"0.5280451\",\n \"0.5280428\",\n \"0.5263956\",\n \"0.5249377\",\n \"0.52480537\",\n \"0.52305114\",\n \"0.5229307\",\n \"0.52239704\",\n \"0.5218732\",\n \"0.521302\",\n \"0.52074903\",\n \"0.52059007\",\n \"0.5189535\",\n \"0.5180023\",\n \"0.51616144\",\n \"0.5155818\",\n \"0.5149034\",\n \"0.5146983\",\n \"0.5146183\",\n \"0.51313394\",\n \"0.5129398\",\n \"0.51274306\",\n \"0.5122327\",\n \"0.5106087\",\n \"0.5106005\",\n \"0.5103434\",\n \"0.5093836\",\n \"0.50900173\",\n \"0.5082055\",\n \"0.50790256\",\n \"0.50755674\",\n \"0.50655305\",\n \"0.50613815\",\n \"0.5061273\",\n \"0.5060316\",\n \"0.5057855\",\n \"0.50562525\",\n \"0.5051514\",\n \"0.5038337\",\n \"0.50362664\",\n \"0.50261486\",\n \"0.5020314\",\n \"0.50131184\",\n \"0.5012325\",\n \"0.5011798\",\n \"0.50101316\",\n \"0.500815\",\n \"0.50005245\",\n \"0.49956554\",\n \"0.49925303\",\n \"0.49893966\",\n \"0.49879506\",\n \"0.49861592\",\n \"0.4978614\",\n \"0.4978571\",\n \"0.49696547\",\n \"0.49682274\",\n \"0.4966777\",\n \"0.496336\",\n \"0.49610466\",\n \"0.49583498\",\n \"0.4956338\",\n \"0.49505347\",\n \"0.49461573\",\n \"0.49461573\",\n \"0.49461573\",\n \"0.49461573\",\n \"0.49461573\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.51843923"},"document_rank":{"kind":"string","value":"45"}}},{"rowIdx":93780,"cells":{"query":{"kind":"string","value":"write an NPZ file (Numpy binary files) containing only the preprocessed data needed for running Component Contribution estimations"},"document":{"kind":"string","value":"def save_equilibrator_bin_data(self, npz_file_name):\n preprocess_dict = {'cids': self.params['cids']}\n for k, v in self.params.items():\n if k.find('preprocess_') != -1:\n preprocess_dict[k.replace('preprocess_', '')] = v\n np.savez_compressed(npz_file_name, **preprocess_dict)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate(self):\n super().generate()\n records = random.random((self._dimension, self._dimension, self.num_samples))\n record_labels = [0] * self.num_samples\n prev_out_spec =\"\"\n count = 0\n for i in range(0, int(self.num_files)):\n if i % self.comm_size == self.my_rank:\n progress(i+1, self.num_files, \"Generating NPZ Data\")\n out_path_spec = \"{}_{}_of_{}.npz\".format(self._file_prefix, i, self.num_files)\n if count == 0:\n prev_out_spec = out_path_spec\n if self.compression != Compression.ZIP:\n np.savez(out_path_spec, x=records, y=record_labels)\n else:\n np.savez_compressed(out_path_spec, x=records, y=record_labels)\n count += 1\n else:\n copyfile(prev_out_spec, out_path_spec)","def make_npz_file(data_type):\n\n\tdata_folder = data_type + \"_images\"\n\tlabel_file = os.path.join(dataset_params.data_path, data_type + \"_lables.csv\")\n\toutput_file = os.path.join(dataset_params.data_path, \"synthetic_\" + data_type + \"_data\")\n\tline_reader = csv.DictReader(open(label_file,\"r\"))\n\n\tdata = []\n\tlabels = []\n\tdata_points = 0\n\tfor row in line_reader:\n\t\timage_name = os.path.join(dataset_params.data_path,data_folder,row[\"figNum\"] + \".png\")\n\t\timage_data = cv2.imread(image_name, cv2.IMREAD_COLOR)\n\t\timage_data = cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB)\n\t\timage_label = [int(dataset_params.shapes[row[\"shape\"]]), int(dataset_params.colors[row[\"color\"]]), int(dataset_params.sizes[row[\"size\"]]), int(row[\"quadrant\"]), int(dataset_params.backgrounds[row[\"background\"]]) ]\n\t\tdata.append(image_data)\n\t\tlabels.append(image_label)\n\t\tdata_points += 1\n\n\t# Converting list to data to np array\n\tdata = np.asarray(data)\n\tlabels = np.asarray(labels)\n\n\t# Printing log information\n\tprint(data_type, \"statistics being saved: \")\n\tprint(data_type, \"data shape\", data.shape)\n\tprint(data_type, \"label shape\", labels.shape)\n\n\t# saveing the file as npz file\n\tnp.savez_compressed(output_file, data=data, lables=labels)","def test_save_npz_file(save_npz) -> None:\n save_npz","def outputPulses(self,filename):\n np.save(filename,self.getData())\n return","def write_file(_data, _label, _clinical, _contour, _type):\n pickle.dump(np.array(_data), open(_type + '_data.pxl', 'wb'))\n pickle.dump(np.array(_label), open(_type + '_label.pxl', 'wb'))\n pickle.dump(np.array(_clinical), open(_type + '_clinical.pxl', 'wb'))\n pickle.dump(np.array(_contour), open(_type + '_contour.pxl', 'wb'))","def export_npz(self, filename, dtype=np.float32):\n np.savez(\n filename, impulse_response=self.in_time.astype(dtype), samplerate=self.fs\n )","def save(self, filename):\n np.savez(temp_dir + '/' + filename + '.npz', chip_ids=self.chip_ids, core_ids=self.core_ids, cx_ids=self.cx_ids)","def main():\n train_src = read_file(SRC_TRAIN)\n train_tgt = read_file(TRGT_TRAIN)\n val_src = read_file(SRC_VAL)\n val_tgt = read_file(TRGT_VAL)\n # val = read_files(VAL_FILES)\n np.savez(\n DATA_NPZ_NAME, train_src=train_src, train_tgt=train_tgt, val_src=val_src, val_tgt=val_tgt)","def write_to_file_z(path):\n path1 = path + \"/z_Macros\"\n if not os.path.exists(path1):\n os.mkdir(path1)\n for e in range(int(e_steps)+1):\n filename = \"x0y0z%ske%s.mac\" %(dz*z + z_min, e*de + e_min)\n path = path1\n fullpath = os.path.join(path, filename)\n f = open(fullpath, \"w\")\n f.write('/rat/physics_list/OmitMuonicProcesses true\\n')\n f.write(\"/rat/physics_list/OmitHadronicProcesses true \\n\")\n f.write(\"\\n\")\n f.write(\"\\n\")\n f.write('/rat/db/set DETECTOR geo_file \"geo/snoplus.geo\"\\n')\n f.write('/rat/db/set GEO[scint] material \"labppo_scintillator\"\\n')\n f.write('/rat/db/set DAQ dqxx_info 0 \\n')\n f.write(\"/run/initialize \\n\")\n f.write(\"\\n\")\n f.write(\"\\n\")\n f.write(\"/rat/proc frontend\\n\")\n f.write(\"/rat/proc trigger\\n\")\n f.write(\"/rat/proc eventbuilder\\n\")\n f.write(\"/rat/proc count\\n\")\n f.write(\"/rat/procset update 100\\n\")\n f.write(\"/rat/proc calibratePMT\\n\")\n f.write(\"/rat/proc scintFitter\\n\")\n f.write(\"/rat/proclast outroot\\n\")\n f.write('/rat/procset file \"x0y0z%ske%s.root\"\\n' %(dz*z + z_min, e*de + e_min))\n f.write(\"\\n\")\n f.write(\"\\n\")\n f.write(\"/generator/add combo gun:point:poisson\\n\")\n f.write(\"# want random, isotropic momentum distribution; energy given in MeV\\n\")\n f.write(\"/generator/vtx/set e- 0 0 0 %s\\n\" %(e*de + e_min))\n f.write(\"# position given in Cartesians, relative to detector center, in mm\\n\")\n f.write(\"/generator/pos/set 0 0 %s\\n\" % (dz*z + z_min))\n f.write(\"/generator/rate/set 1\\n\")\n f.write(\"\\n\")\n f.write(\"\\n\")\n f.write(\"/rat/run/start %s\\n\" %(n))\n f.write(\"exit\")","def dump_npz(filename: str, obj, **kwargs):\n return np.savez(filename, obj)","def save(self, filename):\n np.savez(temp_dir + '/' + filename + '.npz', core_ids=self.core_ids, cx_ids=self.cx_ids)","def write_psts(self,prefix,existing_jco=None,noptmax=None):\n self.log(\"writing realized pest control files\")\n # get a copy of the pest control file\n pst = self.pst.get(par_names=self.pst.par_names,obs_names=self.pst.obs_names)\n\n if noptmax is not None:\n pst.control_data.noptmax = noptmax\n pst.control_data.noptmax = noptmax\n\n if existing_jco is not None:\n pst.pestpp_options[\"BASE_JACOBIAN\"] = existing_jco\n\n # set the indices\n pst.parameter_data.index = pst.parameter_data.parnme\n pst.observation_data.index = pst.observation_data.obsnme\n\n if self.parensemble.istransformed:\n par_en = self.parensemble._back_transform(inplace=False)\n else:\n par_en = self.parensemble\n\n for i in range(self.num_reals):\n pst_name = prefix + \"{0:d}.pst\".format(i)\n self.log(\"writing realized pest control file \" + pst_name)\n pst.parameter_data.loc[par_en.columns,\"parval1\"] = par_en.iloc[i, :].T\n\n # reset the regularization\n #if pst.control_data.pestmode == \"regularization\":\n #pst.zero_order_tikhonov(parbounds=True)\n #zero_order_tikhonov(pst,parbounds=True)\n # add the obs noise realization if needed\n if self.obsensemble.shape[0] == self.num_reals:\n pst.observation_data.loc[self.obsensemble.columns,\"obsval\"] = \\\n self.obsensemble.iloc[i, :].T\n\n # write\n pst.write(pst_name)\n self.log(\"writing realized pest control file \" + pst_name)\n self.log(\"writing realized pest control files\")","def _cmd_export_nbo(args):\n cnarr = read_cna(args.filename)\n varr = load_het_snps(\n args.vcf,\n args.sample_id,\n args.normal_id,\n args.min_variant_depth,\n args.zygosity_freq,\n )\n table = export.export_nexus_ogt(cnarr, varr, args.min_weight)\n write_dataframe(args.output, table)","def write_to_file(output, data, datafields):\n if (len(data) != len(datafields)):\n print \"Error! number of data fields != number of headers!\"\n print 'len: ', len(data), len(datafields)\n print 'shape: ', np.shape(data), np.shape(datafields)\n\n ofile = open(output,'w')\n ofile.write(\"# g(r) in the xy-plane from 2Drdf.py\\n\")\n header = \"# chunk \"\n for element in datafields:\n header += element + \" \"\n\n header = header + '\\n'\n ofile.write(header)\n \n it = 0\n for i in xrange(len(data[0])):\n line = str(it) + \" \"\n it += 1\n for j in xrange(len(data)):\n line += str(float(data[j][i])) + \" \"\n line += \"\\n\"\n ofile.write(line)\n \n ofile.close()\n print \"Finished writing file: \", output","def generate_POSCAR(formu,mat_list,my_ordered_elements,my_ordered_numbers,revise_dos):\n out_name='POSCAR.'+formu\n out_name='POSCAR_files/'+out_name.replace(' ','')\n openfile = open(out_name,'wt')\n openfile.write(formu+'\\n')\n openfile.write(str(1.0)+'\\n')\n for str_lines in mat_list:\n openfile.write(str_lines+'\\n')\n openfile.write(my_ordered_elements+'\\n')\n openfile.write(my_ordered_numbers+'\\n')\n openfile.write('direct'+'\\n')\n for string_lines in revise_dos:\n openfile.write(string_lines+'\\n')\n openfile.close()","def save(uNetwork, uDir):\n ## first of all, save the network spec\n out = open(uDir + \"net_spec.py\", \"w\")\n out.write(\"spec = \" + str(uNetwork.spec))\n out.close()\n \n ## then, save the state of each layer\n layers = uNetwork.layers\n\n for l in range(len(layers) - 1):\n if layers[l].node_sharing:\n ## save just one node\n layers[l].pipes[0][0].send(\"clone_state\")\n state = layers[l].pipes[0][0].recv()\n \n ## save coincidences and PCG\n np.save(uDir + str(l) + \".0.0.coincidences\", state['coincidences'])\n np.save(uDir + str(l) + \".0.0.PCG\", state['PCG'])\n\n else:\n for i in range(len(layers[l].nodes)): \n for j in range(len(layers[l].nodes[i])):\n layers[l].pipes[i][j].send(\"clone_state\")\n state = layers[l].pipes[i][j].recv()\n\n np.save(uDir + str(l) + \".\" + str(i) + \".\" + str(j) + \".coincidences\", state['coincidences'])\n np.save(uDir + str(l) + \".\" + str(i) + \".\" + str(j) + \".PCG\", state['PCG'])\n\n ## then, save also the output layer\n layers[-1].pipes[0][0].send(\"clone_state\")\n state = layers[-1].pipes[0][0].recv()\n np.save(uDir + str(len(layers) - 1) + \".0.0.coincidences\", state['coincidences'])\n np.save(uDir + str(len(layers) - 1) + \".0.0.cls_prior_prob\", state['cls_prior_prob'])\n np.save(uDir + str(len(layers) - 1) + \".0.0.PCW\", state['PCW'])","def generate(inputFilename, outputFilename = defaultFileName, \n sizeOfReducedSample = DEFSIZEOFREDUCEDSAMPLE, \n centerEta = DEFCENTERETA, centerPhi = DEFCENTERPHI): \n listOfSignals = convert(inputFilename)\n arrayOfSignals = np.array(listOfSignals)\n arrayOfSignals.shape\n np.save(outputFilename, arrayOfSignals, allow_pickle=False)\n print(\"npy array name: \",outputFilename)","def preprocess_data(data_dir, export_dir, npz_name=\"smp_all.npz\", overwrite=False,**params):\n print(\"Starting to export and/or convert data\")\n # export, unite and label smp data\n start = time.time()\n # export data from pnt to csv or npz\n export_pnt(pnt_dir=data_dir, target_dir=export_dir, export_as=\"npz\", overwrite=overwrite, **params)\n # load pd.DataFrame from all npz files and save this pd as united DataFrame in npz\n smp_first = npz_to_pd(export_dir, is_dir=True)\n dict = smp_first.to_dict(orient=\"list\")\n np.savez_compressed(npz_name, **dict)\n\n end = time.time()\n print(\"Elapsed time for export and dataframe creation: \", end-start)\n print(\"\\nUnited smp data was stored in {}.\".format(npz_name))","def write_compressed(self, filename):\n\n # Define which molecules to use \n # (counting indices of processed data set)\n indices = np.arange(len(self))\n # All charges and position arrays have the same size\n # (the one of the biggest molecule)\n size = np.max( self.num_atoms )\n # Initialize arrays\n num_atoms = np.zeros(len(indices))\n labels = np.zeros(len(indices))\n charges = np.zeros([len(indices),size])\n positions = np.zeros([len(indices),size,3])\n # For each molecule ...\n for j,idx in enumerate(indices):\n # load the data\n sample = self[idx]\n # assign per-molecule data\n labels[j] = sample['data']\n num_atoms[j] = sample['num_atoms']\n # ... and for each atom:\n for ia in range(sample['num_atoms']):\n charges[j,ia] = sample['charges'][ia]\n positions[j,ia,0] = sample['positions'][ia][0] \n positions[j,ia,1] = sample['positions'][ia][1] \n positions[j,ia,2] = sample['positions'][ia][2]\n\n # Merge pairs\n print(labels.shape,charges.shape,positions.shape)\n labels = labels[0::2]\n charges = np.array([np.concatenate((charges[i],charges[i+1])) for i in indices[0::2]])\n positions = np.array([np.concatenate((positions[i],positions[i+1])) for i in indices[0::2]])\n print(labels.shape,charges.shape,positions.shape)\n \n # Create a dictionary with all the values to save\n save_dict = {}\n save_dict['label'] = labels\n save_dict['charges'] = charges\n save_dict['positions'] = positions\n\n # Save as a compressed array \n np.savez_compressed(filename,**save_dict)\n \n return","def serialize(self, data):\n buffer = io.BytesIO()\n scipy.sparse.save_npz(buffer, data)\n return buffer.getvalue()","def _write(self):\n f = FortranFile(self.filename,mode='w')\n # Default omnivor binary header\n f.writeInts ( self.data['MK'] , 'i' ) \n f.writeInts ( self.data['itime'] , 'i' ) \n f.writeString ( self.data['version'] ) \n f.writeInts ( self.data['file_id'] , 'i' ) \n f.writeString ( self.data['sversion'] ) \n # Velocity field\n f.writeString ( self.data['stype'] ) \n f.writeInts ( self.data['is_grid'] , 'i' ) \n f.writeInts ( self.data['nCPs'] , 'i' ) \n if self.data['MK'] == 8:\n real_char='d'\n else:\n real_char='f'\n if self.data['is_grid']:\n f.writeInts ( self.data['n1'] , 'i' ) \n f.writeInts ( self.data['n2'] , 'i' ) \n f.writeInts ( self.data['n3'] , 'i' ) \n f.writeInts ( self.data['is_straight'] , 'i' ) \n f.writeReals ( self.data['v1'] , real_char ) \n f.writeReals ( self.data['v2'] , real_char ) \n f.writeReals ( self.data['v3'] , real_char ) \n\n CPs = self.data['CPs'].flatten(order = 'F')\n Utot = self.data['Utot'].flatten(order = 'F')\n f.writeReals(CPs,real_char)\n f.writeReals(Utot,real_char)","def to_file(self, filename):\n self.header['n'] = self.n\n save_gyre(filename, self.header, self.data)","def write_prb_file(\n nchans=64, radius=1, y=1, num_tetrodes=16, \n tetrodes_to_use=[], out_loc=\"channel_map.prb\",\n num_chans_for_clust=4):\n if tetrodes_to_use == []:\n tetrodes_to_use = [j+1 for j in range(num_tetrodes+1)]\n\n # Actual code here\n channel_groups = {}\n for i in range(num_tetrodes):\n # Skip tetrodes which should not be clustered\n if not i + 1 in tetrodes_to_use:\n continue\n start = i*4\n chans = [j for j in range(start, start + num_chans_for_clust)]\n geometry = {}\n label_letters = [\"a\", \"b\", \"c\", \"d\"]\n label = [str(i+1) + label_letters[k] for k in range(num_chans_for_clust)]\n graph = []\n for j, c in enumerate(chans):\n geometry[c] = [i * 2 * radius, j*y]\n for j in range(len(chans)):\n for k in range(j+1, len(chans)):\n graph.append((chans[j], chans[k]))\n channel_groups[i] = {\n 'channels': chans,\n 'geometry': geometry,\n 'label': label,\n 'graph': graph}\n\n with open(out_loc, \"w\") as f:\n f.write(\"channel_groups = {\\n\")\n for k, v in channel_groups.items():\n f.write(\"\\t{}:\\n\".format(k))\n f.write(\"\\t\\t{\\n\")\n for k2, v2 in v.items():\n f.write(\"\\t\\t \\'{}\\': {},\\n\".format(k2, v2))\n f.write(\"\\t\\t},\\n\")\n f.write(\"\\n\\t}\")","def _cmd_export_nb(args):\n cnarr = read_cna(args.filename)\n table = export.export_nexus_basic(cnarr)\n write_dataframe(args.output, table)","def save_npz(tmpdir_factory: _pytest.tmpdir.TempdirFactory):\n filename = \"test.npz\"\n fn1 = tmpdir_factory.mktemp(\"data\").join(filename)\n data = {\"test1\": np.arange(0, 50), \"test2\": np.arange(50, 100)}\n\n loader.save_npz_file(str(fn1), data)\n return str(fn1), data","def export_wells(self, w, title):\r\n self._check_out(title)\r\n np.savez_compressed(os.path.join(self.out_dir, title, title), w)","def write_input_file(y,z,fname):\n file = open('c:/4nec2/out/' + fname + '.nec', 'w')\n file.write('CM Seeddesign \\n')\n file.write('CM Zigzag Antenna \\n')\n file.write('CE File generated by python \\n')\n seg = 1\n\n #write the antenna\n for i in range(0,len(y)-1):\n file.write('GW %3i %3i %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\\n' % (1,seg,0,y[i],z[i],0,y[i+1],z[i+1],1))\n\n file.write('GE 0 \\n')\n file.write('EK \\n')\n file.write('EX %3i %3i %3i %3i %3i %3i %3i\\n' % (0,1,1,1,1,0,0))\n file.write('GN -1 \\n')\n \n file.write('FR %3i %3i %3i %3i %8.4f %8.4f\\n' % (0,1,0,0,900,0))\n file.write('FR %3i %3i %3i %3i %8.4f %8.4f\\n' % (0,11,0,0,850,10))\n\n file.write('LD %3i %3i %3i %3i %8.4f %8.4f\\n' % (5,1,0,0,58000000,2))\n file.write('RP %3i %3i %3i %3i %8.4f %8.4f %8.4f %8.4f\\n' % (0,1,1,1000,90,0,0,0))\n\n file.write('EN \\n')\n file.close()","def processfile(file, out, me):\n if out.endswith('.npz'):\n out = out[:-4]\n out = out + ('-%03d' % me) + '.npz'\n if os.path.exists(out):\n return\n f = QPM.QPMSubsampleFile(file)\n xis = []\n mybf = QPM.LogWindowBiasFunction(edges[me], edges[me + 1])\n mymock = QPM.halomock(f, 1e6, mybf, continuous=False)\n for i in range(me + 1):\n bf = QPM.LogWindowBiasFunction(edges[i], edges[i + 1])\n mock = QPM.halomock(f, 1e6, bf, continuous=False)\n # the error is nonsense since we have just one sample\n # deal with it in combine\n xi = QPM.xi([mock], mocks2=[mymock])\n r = xi[0]\n xis.append(xi[1])\n print edges[i], edges[i + 1], len(mock[0]), mock[1].sum()\n numpy.savez(out, r=r, xi=xis, edges=edges, N=1e6, me=me)","def write_program(file_prefix=None, point_file=None, dir=None, inc=None, N=None, num_points=None, labels=None, tick_locations=None, tick_values=None, date=None, chi_surface=[7.0, 20.0, 100.0, 500.0]):\n\n # Print out.\n print(\"\\nCreating the OpenDX .net program file.\")\n\n # Open the file.\n file = open_write_file(file_name=file_prefix+\".net\", dir=dir, force=True)\n\n # Corners.\n corners = \"{[0\"\n for i in range(N - 1):\n corners += \" 0\"\n corners += \"] [%s\" % inc\n for i in range(N - 1):\n corners += \" %s\" % inc\n corners += \"]}\"\n\n # Sphere size.\n sphere_size = repr(0.025 * (inc + 1.0))\n\n # Image setup.\n image_array1 = \"[%s %s %s]\" % (0.6 * (inc + 1.0), 0.3 * (inc + 1.0), 0.6 * (inc + 1.0))\n image_array2 = \"[%s %s %s]\" % (0.6 * (inc + 1.0), 0.3 * (inc + 1.0), 6.0 * (inc + 1.0))\n image_val = repr(3.0 * (inc + 1.0))\n\n # Unpacking the values for the 4 isosurfaces.\n innermost_isosurface_value, inner_isosurface_value, middle_isosurface_value, outer_isosurface_value = chi_surface\n\n # Generate the text of the program.\n ###################################\n\n file.write(\"//\\n\")\n file.write(\"// time: %s\\n\" % date)\n file.write(\"//\\n\")\n file.write(\"// version: 3.2.0 (format), 4.3.2 (DX)\\n\")\n file.write(\"//\\n\")\n file.write(\"//\\n\")\n file.write(\"// MODULE main\\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\"// page assignment: Colour Space\\torder=3, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: ColourScene\\t\\torder=5, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: Glyph\\t\\torder=2, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: Grey Space\\t\\torder=4, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: GreyScene\\t\\torder=6, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: Image\\t\\t\\torder=7, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: Isosurfaces\\t\\torder=1, windowed=0, showing=1\\n\")\n\n # No sphere.\n else:\n file.write(\"\\n\")\n file.write(\"// page assignment: Colour Space\\torder=4, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: ColourScene\\t\\torder=6, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: Grey Space\\t\\torder=5, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: GreyScene\\t\\torder=7, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: Image\\t\\t\\torder=8, windowed=0, showing=0\\n\")\n file.write(\"// page assignment: Isosurfaces\\t\\torder=2, windowed=0, showing=1\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\"// workspace: width = 474, height = 354\\n\")\n file.write(\"// layout: snap = 0, width = 50, height = 50, align = NN\\n\")\n file.write(\"//\\n\")\n file.write(\"macro main(\\n\")\n file.write(\") -> (\\n\")\n file.write(\") {\\n\")\n file.write(\" // \\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\" // node Import[4]: x = 177, y = 62, inputs = 6, label = %s\\n\" % point_file)\n file.write(\" // input[1]: defaulting = 0, visible = 1, type = 32, value = \\\"%s.general\\\"\\n\" % point_file)\n file.write(\" // input[3]: defaulting = 1, visible = 1, type = 32, value = \\\"general\\\"\\n\")\n file.write(\" // page group: Glyph\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Import_4_out_1 = \\n\")\n file.write(\" Import(\\n\")\n file.write(\" main_Import_4_in_1,\\n\")\n file.write(\" main_Import_4_in_2,\\n\")\n file.write(\" main_Import_4_in_3,\\n\")\n file.write(\" main_Import_4_in_4,\\n\")\n file.write(\" main_Import_4_in_5,\\n\")\n file.write(\" main_Import_4_in_6\\n\")\n file.write(\" ) [instance: 4, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Glyph[2]: x = 201, y = 182, inputs = 7, label = Glyph\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 32, value = \\\"sphere\\\"\\n\")\n file.write(\" // input[3]: defaulting = 1, visible = 1, type = 5, value = 10.0\\n\")\n file.write(\" // input[4]: defaulting = 0, visible = 1, type = 5, value = %s\\n\" % sphere_size)\n file.write(\" // input[5]: defaulting = 0, visible = 1, type = 5, value = 0.0\\n\")\n file.write(\" // page group: Glyph\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Glyph_2_out_1 = \\n\")\n file.write(\" Glyph(\\n\")\n file.write(\" main_Import_4_out_1,\\n\")\n file.write(\" main_Glyph_2_in_2,\\n\")\n file.write(\" main_Glyph_2_in_3,\\n\")\n file.write(\" main_Glyph_2_in_4,\\n\")\n file.write(\" main_Glyph_2_in_5,\\n\")\n file.write(\" main_Glyph_2_in_6,\\n\")\n file.write(\" main_Glyph_2_in_7\\n\")\n file.write(\" ) [instance: 2, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[10]: x = 357, y = 278, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0 0 0]\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 1.0\\n\")\n file.write(\" // page group: Glyph\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_10_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Glyph_2_out_1,\\n\")\n file.write(\" main_Color_10_in_2,\\n\")\n file.write(\" main_Color_10_in_3,\\n\")\n file.write(\" main_Color_10_in_4,\\n\")\n file.write(\" main_Color_10_in_5\\n\")\n file.write(\" ) [instance: 10, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[1]: x = 352, y = 386, inputs = 1, label = GreySphere\\n\")\n file.write(\" // page group: Glyph\\n\")\n file.write(\" //\\n\")\n file.write(\"GreySphere = main_Color_10_out_1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[2]: x = 190, y = 350, inputs = 1, label = GreySphere\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_2_out_1[cache: 0] = GreySphere;\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Import[3]: x = 225, y = 84, inputs = 6, label = %s\\n\" % file_prefix)\n file.write(\" // input[1]: defaulting = 0, visible = 1, type = 32, value = \\\"%s.general\\\"\\n\" % file_prefix)\n file.write(\" // input[3]: defaulting = 1, visible = 1, type = 32, value = \\\"general\\\"\\n\")\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Import_3_out_1 = \\n\")\n file.write(\" Import(\\n\")\n file.write(\" main_Import_3_in_1,\\n\")\n file.write(\" main_Import_3_in_2,\\n\")\n file.write(\" main_Import_3_in_3,\\n\")\n file.write(\" main_Import_3_in_4,\\n\")\n file.write(\" main_Import_3_in_5,\\n\")\n file.write(\" main_Import_3_in_6\\n\")\n file.write(\" ) [instance: 3, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Isosurface[5]: x = 102, y = 191, inputs = 6, label = Outer Isosurface\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 5, value = %.1f\\n\"%(outer_isosurface_value))\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Isosurface_5_out_1 = \\n\")\n file.write(\" Isosurface(\\n\")\n file.write(\" main_Import_3_out_1,\\n\")\n file.write(\" main_Isosurface_5_in_2,\\n\")\n file.write(\" main_Isosurface_5_in_3,\\n\")\n file.write(\" main_Isosurface_5_in_4,\\n\")\n file.write(\" main_Isosurface_5_in_5,\\n\")\n file.write(\" main_Isosurface_5_in_6\\n\")\n file.write(\" ) [instance: 5, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[7]: x = 110, y = 292, inputs = 1, label = Surface4\\n\")\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"Surface4 = main_Isosurface_5_out_1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[14]: x = 123, y = 51, inputs = 1, label = Surface4\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_14_out_1[cache: 0] = Surface4;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[6]: x = 142, y = 145, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0 0 0]\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.2\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_6_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Receiver_14_out_1,\\n\")\n file.write(\" main_Color_6_in_2,\\n\")\n file.write(\" main_Color_6_in_3,\\n\")\n file.write(\" main_Color_6_in_4,\\n\")\n file.write(\" main_Color_6_in_5\\n\")\n file.write(\" ) [instance: 6, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Isosurface[6]: x = 200, y = 191, inputs = 6, label = Middle Isosurface\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 5, value = %.1f\\n\"%(middle_isosurface_value))\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Isosurface_6_out_1 = \\n\")\n file.write(\" Isosurface(\\n\")\n file.write(\" main_Import_3_out_1,\\n\")\n file.write(\" main_Isosurface_6_in_2,\\n\")\n file.write(\" main_Isosurface_6_in_3,\\n\")\n file.write(\" main_Isosurface_6_in_4,\\n\")\n file.write(\" main_Isosurface_6_in_5,\\n\")\n file.write(\" main_Isosurface_6_in_6\\n\")\n file.write(\" ) [instance: 6, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[8]: x = 208, y = 292, inputs = 1, label = Surface3\\n\")\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"Surface3 = main_Isosurface_6_out_1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[13]: x = 227, y = 51, inputs = 1, label = Surface3\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_13_out_1[cache: 0] = Surface3;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[7]: x = 246, y = 145, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0.2 0.2 0.2]\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.4\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_7_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Receiver_13_out_1,\\n\")\n file.write(\" main_Color_7_in_2,\\n\")\n file.write(\" main_Color_7_in_3,\\n\")\n file.write(\" main_Color_7_in_4,\\n\")\n file.write(\" main_Color_7_in_5\\n\")\n file.write(\" ) [instance: 7, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Collect[5]: x = 203, y = 236, inputs = 2, label = Collect\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Collect_5_out_1 = \\n\")\n file.write(\" Collect(\\n\")\n file.write(\" main_Color_6_out_1,\\n\")\n file.write(\" main_Color_7_out_1\\n\")\n file.write(\" ) [instance: 5, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Isosurface[7]: x = 298, y = 191, inputs = 6, label = Inner Isosurface\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 5, value = %.1f\\n\"%(inner_isosurface_value))\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Isosurface_7_out_1 = \\n\")\n file.write(\" Isosurface(\\n\")\n file.write(\" main_Import_3_out_1,\\n\")\n file.write(\" main_Isosurface_7_in_2,\\n\")\n file.write(\" main_Isosurface_7_in_3,\\n\")\n file.write(\" main_Isosurface_7_in_4,\\n\")\n file.write(\" main_Isosurface_7_in_5,\\n\")\n file.write(\" main_Isosurface_7_in_6\\n\")\n file.write(\" ) [instance: 7, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[9]: x = 306, y = 292, inputs = 1, label = Surface2\\n\")\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"Surface2 = main_Isosurface_7_out_1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[12]: x = 331, y = 51, inputs = 1, label = Surface2\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_12_out_1[cache: 0] = Surface2;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[8]: x = 350, y = 145, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0.5 0.5 0.5]\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.5\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_8_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Receiver_12_out_1,\\n\")\n file.write(\" main_Color_8_in_2,\\n\")\n file.write(\" main_Color_8_in_3,\\n\")\n file.write(\" main_Color_8_in_4,\\n\")\n file.write(\" main_Color_8_in_5\\n\")\n file.write(\" ) [instance: 8, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Isosurface[8]: x = 396, y = 191, inputs = 6, label = Innermost Isosurface\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 5, value = %.1f\\n\"%(innermost_isosurface_value))\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Isosurface_8_out_1 = \\n\")\n file.write(\" Isosurface(\\n\")\n file.write(\" main_Import_3_out_1,\\n\")\n file.write(\" main_Isosurface_8_in_2,\\n\")\n file.write(\" main_Isosurface_8_in_3,\\n\")\n file.write(\" main_Isosurface_8_in_4,\\n\")\n file.write(\" main_Isosurface_8_in_5,\\n\")\n file.write(\" main_Isosurface_8_in_6\\n\")\n file.write(\" ) [instance: 8, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[10]: x = 404, y = 292, inputs = 1, label = Surface1\\n\")\n file.write(\" // page group: Isosurfaces\\n\")\n file.write(\" //\\n\")\n file.write(\"Surface1 = main_Isosurface_8_out_1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[11]: x = 434, y = 51, inputs = 1, label = Surface1\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_11_out_1[cache: 0] = Surface1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[9]: x = 453, y = 145, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 32, value = \\\"white\\\"\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.7\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_9_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Receiver_11_out_1,\\n\")\n file.write(\" main_Color_9_in_2,\\n\")\n file.write(\" main_Color_9_in_3,\\n\")\n file.write(\" main_Color_9_in_4,\\n\")\n file.write(\" main_Color_9_in_5\\n\")\n file.write(\" ) [instance: 9, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Collect[6]: x = 409, y = 236, inputs = 2, label = Collect\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Collect_6_out_1 = \\n\")\n file.write(\" Collect(\\n\")\n file.write(\" main_Color_8_out_1,\\n\")\n file.write(\" main_Color_9_out_1\\n\")\n file.write(\" ) [instance: 6, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Collect[7]: x = 307, y = 327, inputs = 2, label = Collect\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Collect_7_out_1 = \\n\")\n file.write(\" Collect(\\n\")\n file.write(\" main_Collect_5_out_1,\\n\")\n file.write(\" main_Collect_6_out_1\\n\")\n file.write(\" ) [instance: 7, cache: 1];\\n\")\n file.write(\" // \\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\" // node Collect[8]: x = 293, y = 431, inputs = 2, label = Collect\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Collect_8_out_1 = \\n\")\n file.write(\" Collect(\\n\")\n file.write(\" main_Receiver_2_out_1,\\n\")\n file.write(\" main_Collect_7_out_1\\n\")\n file.write(\" ) [instance: 8, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[4]: x = 282, y = 517, inputs = 1, label = GreySpace\\n\")\n\n # No sphere.\n else:\n file.write(\"\\n\")\n file.write(\" // node Transmitter[4]: x = 296, y = 439, inputs = 1, label = GreySpace\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\" // page group: Grey Space\\n\")\n file.write(\" // \\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\"GreySpace = main_Collect_8_out_1;\\n\")\n\n # No sphere.\n else:\n file.write(\"\\n\")\n file.write(\"GreySpace = main_Collect_7_out_1;\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[3]: x = 137, y = 57, inputs = 1, label = GreySpace\\n\")\n file.write(\" // page group: GreyScene\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_3_out_1[cache: 0] = GreySpace;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Scale[3]: x = 163, y = 159, inputs = 2, label = Scale\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 1 1]\\n\")\n file.write(\" // page group: GreyScene\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Scale_3_out_1 = \\n\")\n file.write(\" Scale(\\n\")\n file.write(\" main_Receiver_3_out_1,\\n\")\n file.write(\" main_Scale_3_in_2\\n\")\n file.write(\" ) [instance: 3, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node AutoCamera[2]: x = 273, y = 264, inputs = 9, label = AutoCamera\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 -1 1]\\n\")\n file.write(\" // input[3]: defaulting = 1, visible = 1, type = 5, value = 500.0\\n\")\n file.write(\" // input[4]: defaulting = 0, visible = 0, type = 1, value = 640\\n\")\n file.write(\" // input[5]: defaulting = 0, visible = 0, type = 5, value = .75\\n\")\n file.write(\" // input[6]: defaulting = 0, visible = 0, type = 8, value = [-1 1 0 ]\\n\")\n file.write(\" // input[7]: defaulting = 0, visible = 0, type = 3, value = 0\\n\")\n file.write(\" // input[8]: defaulting = 0, visible = 0, type = 5, value = 30.0\\n\")\n file.write(\" // input[9]: defaulting = 0, visible = 1, type = 32, value = \\\"white\\\"\\n\")\n file.write(\" // page group: GreyScene\\n\")\n file.write(\" //\\n\")\n file.write(\"main_AutoCamera_2_out_1 = \\n\")\n file.write(\" AutoCamera(\\n\")\n file.write(\" main_Scale_3_out_1,\\n\")\n file.write(\" main_AutoCamera_2_in_2,\\n\")\n file.write(\" main_AutoCamera_2_in_3,\\n\")\n file.write(\" main_AutoCamera_2_in_4,\\n\")\n file.write(\" main_AutoCamera_2_in_5,\\n\")\n file.write(\" main_AutoCamera_2_in_6,\\n\")\n file.write(\" main_AutoCamera_2_in_7,\\n\")\n file.write(\" main_AutoCamera_2_in_8,\\n\")\n file.write(\" main_AutoCamera_2_in_9\\n\")\n file.write(\" ) [instance: 2, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node AutoAxes[2]: x = 175, y = 379, inputs = 19, label = AutoAxes\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 16777248, value = %s\\n\" % labels)\n file.write(\" // input[4]: defaulting = 0, visible = 0, type = 1, value = 30\\n\")\n file.write(\" // input[5]: defaulting = 0, visible = 1, type = 16777224, value = %s\\n\" % corners)\n file.write(\" // input[6]: defaulting = 0, visible = 1, type = 3, value = 1\\n\")\n file.write(\" // input[7]: defaulting = 1, visible = 0, type = 3, value = 1\\n\")\n file.write(\" // input[9]: defaulting = 0, visible = 1, type = 3, value = 1\\n\")\n file.write(\" // input[10]: defaulting = 0, visible = 1, type = 16777224, value = {[1 1 1] [0.1 0.1 0.1] [0 0 0] [0 0 0]}\\n\")\n file.write(\" // input[11]: defaulting = 0, visible = 1, type = 16777248, value = {\\\"background\\\" \\\"grid\\\" \\\"labels\\\" \\\"ticks\\\"}\\n\")\n file.write(\" // input[12]: defaulting = 1, visible = 0, type = 5, value = 0.4\\n\")\n file.write(\" // input[13]: defaulting = 0, visible = 0, type = 32, value = \\\"area\\\"\\n\")\n file.write(\" // input[14]: defaulting = 0, visible = 1, type = 16777221, value = %s\\n\" % tick_locations[0])\n file.write(\" // input[15]: defaulting = 0, visible = 1, type = 16777221, value = %s\\n\" % tick_locations[1])\n file.write(\" // input[16]: defaulting = 0, visible = 1, type = 16777221, value = %s\\n\" % tick_locations[2])\n file.write(\" // input[17]: defaulting = 0, visible = 1, type = 16777248, value = %s\\n\" % tick_values[0])\n file.write(\" // input[18]: defaulting = 0, visible = 1, type = 16777248, value = %s\\n\" % tick_values[1])\n file.write(\" // input[19]: defaulting = 0, visible = 1, type = 16777248, value = %s\\n\" % tick_values[2])\n file.write(\" // page group: GreyScene\\n\")\n file.write(\" //\\n\")\n file.write(\"main_AutoAxes_2_out_1 = \\n\")\n file.write(\" AutoAxes(\\n\")\n file.write(\" main_Scale_3_out_1,\\n\")\n file.write(\" main_AutoCamera_2_out_1,\\n\")\n file.write(\" main_AutoAxes_2_in_3,\\n\")\n file.write(\" main_AutoAxes_2_in_4,\\n\")\n file.write(\" main_AutoAxes_2_in_5,\\n\")\n file.write(\" main_AutoAxes_2_in_6,\\n\")\n file.write(\" main_AutoAxes_2_in_7,\\n\")\n file.write(\" main_AutoAxes_2_in_8,\\n\")\n file.write(\" main_AutoAxes_2_in_9,\\n\")\n file.write(\" main_AutoAxes_2_in_10,\\n\")\n file.write(\" main_AutoAxes_2_in_11,\\n\")\n file.write(\" main_AutoAxes_2_in_12,\\n\")\n file.write(\" main_AutoAxes_2_in_13,\\n\")\n file.write(\" main_AutoAxes_2_in_14,\\n\")\n file.write(\" main_AutoAxes_2_in_15,\\n\")\n file.write(\" main_AutoAxes_2_in_16,\\n\")\n file.write(\" main_AutoAxes_2_in_17,\\n\")\n file.write(\" main_AutoAxes_2_in_18,\\n\")\n file.write(\" main_AutoAxes_2_in_19\\n\")\n file.write(\" ) [instance: 2, cache: 1];\\n\")\n file.write(\" // \\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\" // node Color[11]: x = 133, y = 278, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 0 0]\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 1.0\\n\")\n file.write(\" // page group: Glyph\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_11_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Glyph_2_out_1,\\n\")\n file.write(\" main_Color_11_in_2,\\n\")\n file.write(\" main_Color_11_in_3,\\n\")\n file.write(\" main_Color_11_in_4,\\n\")\n file.write(\" main_Color_11_in_5\\n\")\n file.write(\" ) [instance: 11, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[2]: x = 122, y = 386, inputs = 1, label = ColourSphere\\n\")\n file.write(\" // page group: Glyph\\n\")\n file.write(\" //\\n\")\n file.write(\"ColourSphere = main_Color_11_out_1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[1]: x = 179, y = 349, inputs = 1, label = ColourSphere\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_1_out_1[cache: 0] = ColourSphere;\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[10]: x = 123, y = 51, inputs = 1, label = Surface4\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_10_out_1[cache: 0] = Surface4;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[12]: x = 142, y = 145, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0 0 0.2]\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.3\\n\")\n file.write(\" // input[4]: defaulting = 1, visible = 0, type = 32, value = NULL\\n\")\n file.write(\" // input[5]: defaulting = 1, visible = 0, type = 3, value = NULL\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_12_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Receiver_10_out_1,\\n\")\n file.write(\" main_Color_12_in_2,\\n\")\n file.write(\" main_Color_12_in_3,\\n\")\n file.write(\" main_Color_12_in_4,\\n\")\n file.write(\" main_Color_12_in_5\\n\")\n file.write(\" ) [instance: 12, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[9]: x = 227, y = 51, inputs = 1, label = Surface3\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_9_out_1[cache: 0] = Surface3;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[13]: x = 246, y = 145, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 32, value = \\\"blue\\\"\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.3\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_13_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Receiver_9_out_1,\\n\")\n file.write(\" main_Color_13_in_2,\\n\")\n file.write(\" main_Color_13_in_3,\\n\")\n file.write(\" main_Color_13_in_4,\\n\")\n file.write(\" main_Color_13_in_5\\n\")\n file.write(\" ) [instance: 13, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Collect[9]: x = 203, y = 236, inputs = 2, label = Collect\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Collect_9_out_1 = \\n\")\n file.write(\" Collect(\\n\")\n file.write(\" main_Color_12_out_1,\\n\")\n file.write(\" main_Color_13_out_1\\n\")\n file.write(\" ) [instance: 9, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[8]: x = 331, y = 51, inputs = 1, label = Surface2\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_8_out_1[cache: 0] = Surface2;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[14]: x = 350, y = 145, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0.5 0.5 1]\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.2\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_14_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Receiver_8_out_1,\\n\")\n file.write(\" main_Color_14_in_2,\\n\")\n file.write(\" main_Color_14_in_3,\\n\")\n file.write(\" main_Color_14_in_4,\\n\")\n file.write(\" main_Color_14_in_5\\n\")\n file.write(\" ) [instance: 14, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[7]: x = 434, y = 51, inputs = 1, label = Surface1\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_7_out_1[cache: 0] = Surface1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Color[15]: x = 453, y = 145, inputs = 5, label = Color\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 32, value = \\\"white\\\"\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.55\\n\")\n file.write(\" // input[4]: defaulting = 1, visible = 0, type = 32, value = \\\"positions\\\"\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Color_15_out_1 = \\n\")\n file.write(\" Color(\\n\")\n file.write(\" main_Receiver_7_out_1,\\n\")\n file.write(\" main_Color_15_in_2,\\n\")\n file.write(\" main_Color_15_in_3,\\n\")\n file.write(\" main_Color_15_in_4,\\n\")\n file.write(\" main_Color_15_in_5\\n\")\n file.write(\" ) [instance: 15, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Collect[10]: x = 409, y = 236, inputs = 2, label = Collect\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Collect_10_out_1 = \\n\")\n file.write(\" Collect(\\n\")\n file.write(\" main_Color_14_out_1,\\n\")\n file.write(\" main_Color_15_out_1\\n\")\n file.write(\" ) [instance: 10, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Collect[11]: x = 307, y = 327, inputs = 2, label = Collect\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Collect_11_out_1 = \\n\")\n file.write(\" Collect(\\n\")\n file.write(\" main_Collect_9_out_1,\\n\")\n file.write(\" main_Collect_10_out_1\\n\")\n file.write(\" ) [instance: 11, cache: 1];\\n\")\n file.write(\" // \\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\" // node Collect[12]: x = 293, y = 431, inputs = 2, label = Collect\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Collect_12_out_1 = \\n\")\n file.write(\" Collect(\\n\")\n file.write(\" main_Receiver_1_out_1,\\n\")\n file.write(\" main_Collect_11_out_1\\n\")\n file.write(\" ) [instance: 12, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[3]: x = 276, y = 517, inputs = 1, label = ColourSpace\\n\")\n\n # No sphere.\n else:\n file.write(\"\\n\")\n file.write(\" // node Transmitter[3]: x = 290, y = 440, inputs = 1, label = ColourSpace\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\" // page group: Colour Space\\n\")\n file.write(\" // \\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\"ColourSpace = main_Collect_12_out_1;\\n\")\n\n # No sphere.\n else:\n file.write(\"\\n\")\n file.write(\"ColourSpace = main_Collect_11_out_1;\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[4]: x = 131, y = 58, inputs = 1, label = ColourSpace\\n\")\n file.write(\" // page group: ColourScene\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_4_out_1[cache: 0] = ColourSpace;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Scale[5]: x = 163, y = 159, inputs = 2, label = Scale\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 1 1]\\n\")\n file.write(\" // page group: ColourScene\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Scale_5_out_1 = \\n\")\n file.write(\" Scale(\\n\")\n file.write(\" main_Receiver_4_out_1,\\n\")\n file.write(\" main_Scale_5_in_2\\n\")\n file.write(\" ) [instance: 5, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node AutoCamera[4]: x = 273, y = 264, inputs = 9, label = AutoCamera\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 -1 1]\\n\")\n file.write(\" // input[3]: defaulting = 1, visible = 1, type = 5, value = 500.0\\n\")\n file.write(\" // input[5]: defaulting = 0, visible = 0, type = 5, value = .75\\n\")\n file.write(\" // input[6]: defaulting = 0, visible = 0, type = 8, value = [-1 1 0 ]\\n\")\n file.write(\" // input[7]: defaulting = 0, visible = 0, type = 3, value = 0\\n\")\n file.write(\" // input[8]: defaulting = 0, visible = 0, type = 5, value = 30.0\\n\")\n file.write(\" // input[9]: defaulting = 0, visible = 1, type = 32, value = \\\"black\\\"\\n\")\n file.write(\" // page group: ColourScene\\n\")\n file.write(\" //\\n\")\n file.write(\"main_AutoCamera_4_out_1 = \\n\")\n file.write(\" AutoCamera(\\n\")\n file.write(\" main_Scale_5_out_1,\\n\")\n file.write(\" main_AutoCamera_4_in_2,\\n\")\n file.write(\" main_AutoCamera_4_in_3,\\n\")\n file.write(\" main_AutoCamera_4_in_4,\\n\")\n file.write(\" main_AutoCamera_4_in_5,\\n\")\n file.write(\" main_AutoCamera_4_in_6,\\n\")\n file.write(\" main_AutoCamera_4_in_7,\\n\")\n file.write(\" main_AutoCamera_4_in_8,\\n\")\n file.write(\" main_AutoCamera_4_in_9\\n\")\n file.write(\" ) [instance: 4, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node AutoAxes[4]: x = 175, y = 379, inputs = 19, label = AutoAxes\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 16777248, value = %s\\n\" % labels)\n file.write(\" // input[4]: defaulting = 0, visible = 0, type = 1, value = 30\\n\")\n file.write(\" // input[5]: defaulting = 0, visible = 1, type = 16777224, value = %s\\n\" % corners)\n file.write(\" // input[6]: defaulting = 0, visible = 1, type = 3, value = 1\\n\")\n file.write(\" // input[7]: defaulting = 1, visible = 0, type = 3, value = 1\\n\")\n file.write(\" // input[9]: defaulting = 0, visible = 1, type = 3, value = 1\\n\")\n file.write(\" // input[10]: defaulting = 0, visible = 1, type = 16777224, value = {[0.05 0.05 0.05] [0.3 0.3 0.3] [1 1 1] [1 1 0]}\\n\")\n file.write(\" // input[11]: defaulting = 0, visible = 1, type = 16777248, value = {\\\"background\\\" \\\"grid\\\" \\\"labels\\\" \\\"ticks\\\"}\\n\")\n file.write(\" // input[12]: defaulting = 1, visible = 0, type = 5, value = 0.4\\n\")\n file.write(\" // input[13]: defaulting = 0, visible = 0, type = 32, value = \\\"area\\\"\\n\")\n file.write(\" // input[14]: defaulting = 0, visible = 1, type = 16777221, value = %s\\n\" % tick_locations[0])\n file.write(\" // input[15]: defaulting = 0, visible = 1, type = 16777221, value = %s\\n\" % tick_locations[1])\n file.write(\" // input[16]: defaulting = 0, visible = 1, type = 16777221, value = %s\\n\" % tick_locations[2])\n file.write(\" // input[17]: defaulting = 0, visible = 1, type = 16777248, value = %s\\n\" % tick_values[0])\n file.write(\" // input[18]: defaulting = 0, visible = 1, type = 16777248, value = %s\\n\" % tick_values[1])\n file.write(\" // input[19]: defaulting = 0, visible = 1, type = 16777248, value = %s\\n\" % tick_values[2])\n file.write(\" // page group: ColourScene\\n\")\n file.write(\" //\\n\")\n file.write(\"main_AutoAxes_4_out_1 = \\n\")\n file.write(\" AutoAxes(\\n\")\n file.write(\" main_Scale_5_out_1,\\n\")\n file.write(\" main_AutoCamera_4_out_1,\\n\")\n file.write(\" main_AutoAxes_4_in_3,\\n\")\n file.write(\" main_AutoAxes_4_in_4,\\n\")\n file.write(\" main_AutoAxes_4_in_5,\\n\")\n file.write(\" main_AutoAxes_4_in_6,\\n\")\n file.write(\" main_AutoAxes_4_in_7,\\n\")\n file.write(\" main_AutoAxes_4_in_8,\\n\")\n file.write(\" main_AutoAxes_4_in_9,\\n\")\n file.write(\" main_AutoAxes_4_in_10,\\n\")\n file.write(\" main_AutoAxes_4_in_11,\\n\")\n file.write(\" main_AutoAxes_4_in_12,\\n\")\n file.write(\" main_AutoAxes_4_in_13,\\n\")\n file.write(\" main_AutoAxes_4_in_14,\\n\")\n file.write(\" main_AutoAxes_4_in_15,\\n\")\n file.write(\" main_AutoAxes_4_in_16,\\n\")\n file.write(\" main_AutoAxes_4_in_17,\\n\")\n file.write(\" main_AutoAxes_4_in_18,\\n\")\n file.write(\" main_AutoAxes_4_in_19\\n\")\n file.write(\" ) [instance: 4, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Selector[1]: x = 245, y = 66, inputs = 7, label = Selector\\n\")\n file.write(\" // input[1]: defaulting = 0, visible = 0, type = 32, value = \\\"Selector_1\\\"\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 0, type = 32, value = \\\"Colour\\\" \\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 0, type = 29, value = 1 \\n\")\n file.write(\" // input[4]: defaulting = 1, visible = 1, type = 16777248, value = { \\\"Colour\\\" \\\"Grey\\\" }\\n\")\n file.write(\" // input[5]: defaulting = 1, visible = 0, type = 16777245, value = { 1 2 }\\n\")\n file.write(\" // output[1]: visible = 1, type = 29, value = 1 \\n\")\n file.write(\" // output[2]: visible = 1, type = 32, value = \\\"Colour\\\" \\n\")\n file.write(\" // page group: Image\\n\")\n file.write(\" //\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[6]: x = 299, y = 487, inputs = 1, label = ColourImage\\n\")\n file.write(\" // page group: ColourScene\\n\")\n file.write(\" //\\n\")\n file.write(\"ColourImage = main_AutoAxes_4_out_1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[5]: x = 76, y = 190, inputs = 1, label = ColourImage\\n\")\n file.write(\" // page group: Image\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_5_out_1[cache: 0] = ColourImage;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Transmitter[5]: x = 305, y = 489, inputs = 1, label = GreyImage\\n\")\n file.write(\" // page group: GreyScene\\n\")\n file.write(\" //\\n\")\n file.write(\"GreyImage = main_AutoAxes_2_out_1;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Receiver[6]: x = 199, y = 190, inputs = 1, label = GreyImage\\n\")\n file.write(\" // page group: Image\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Receiver_6_out_1[cache: 0] = GreyImage;\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Switch[1]: x = 177, y = 293, inputs = 3, label = Switch\\n\")\n file.write(\" // page group: Image\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Switch_1_out_1 = \\n\")\n file.write(\" Switch(\\n\")\n file.write(\" main_Selector_1_out_1,\\n\")\n file.write(\" main_Receiver_5_out_1,\\n\")\n file.write(\" main_Receiver_6_out_1\\n\")\n file.write(\" ) [instance: 1, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Switch[14]: x = 325, y = 293, inputs = 3, label = Switch\\n\")\n file.write(\" // input[2]: defaulting = 0, visible = 1, type = 67108863, value = \\\"black\\\"\\n\")\n file.write(\" // input[3]: defaulting = 0, visible = 1, type = 67108863, value = \\\"white\\\"\\n\")\n file.write(\" // page group: Image\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Switch_14_out_1 = \\n\")\n file.write(\" Switch(\\n\")\n file.write(\" main_Selector_1_out_1,\\n\")\n file.write(\" main_Switch_14_in_2,\\n\")\n file.write(\" main_Switch_14_in_3\\n\")\n file.write(\" ) [instance: 14, cache: 1];\\n\")\n file.write(\" // \\n\")\n file.write(\" // node Image[3]: x = 252, y = 424, inputs = 49, label = Image\\n\")\n file.write(\" // input[1]: defaulting = 0, visible = 0, type = 67108863, value = \\\"Image_3\\\"\\n\")\n file.write(\" // input[4]: defaulting = 0, visible = 0, type = 1, value = 1\\n\")\n file.write(\" // input[5]: defaulting = 0, visible = 0, type = 8, value = %s\\n\" % image_array1)\n file.write(\" // input[6]: defaulting = 0, visible = 0, type = 8, value = %s\\n\" % image_array2)\n file.write(\" // input[7]: defaulting = 0, visible = 0, type = 5, value = %s\\n\" % image_val)\n file.write(\" // input[8]: defaulting = 0, visible = 0, type = 1, value = 1376\\n\")\n file.write(\" // input[9]: defaulting = 0, visible = 0, type = 5, value = 0.678\\n\")\n file.write(\" // input[10]: defaulting = 0, visible = 0, type = 8, value = [-0.109685 0.243133 0.963772]\\n\")\n file.write(\" // input[11]: defaulting = 1, visible = 0, type = 5, value = 30.9877\\n\")\n file.write(\" // input[12]: defaulting = 0, visible = 0, type = 1, value = 0\\n\")\n file.write(\" // input[14]: defaulting = 0, visible = 0, type = 1, value = 1\\n\")\n file.write(\" // input[15]: defaulting = 0, visible = 0, type = 32, value = \\\"none\\\"\\n\")\n file.write(\" // input[16]: defaulting = 0, visible = 0, type = 32, value = \\\"none\\\"\\n\")\n file.write(\" // input[17]: defaulting = 1, visible = 0, type = 1, value = 1\\n\")\n file.write(\" // input[18]: defaulting = 1, visible = 0, type = 1, value = 1\\n\")\n file.write(\" // input[19]: defaulting = 0, visible = 0, type = 1, value = 0\\n\")\n file.write(\" // input[22]: defaulting = 1, visible = 1, type = 32, value = \\\"black\\\"\\n\")\n file.write(\" // input[25]: defaulting = 0, visible = 0, type = 32, value = \\\"iso\\\"\\n\")\n file.write(\" // input[26]: defaulting = 0, visible = 0, type = 32, value = \\\"tiff\\\"\\n\")\n file.write(\" // input[29]: defaulting = 0, visible = 0, type = 3, value = 0\\n\")\n file.write(\" // input[30]: defaulting = 1, visible = 0, type = 16777248, value = %s\\n\" % labels)\n file.write(\" // input[32]: defaulting = 1, visible = 0, type = 16777224, value = %s\\n\" % corners)\n file.write(\" // input[33]: defaulting = 0, visible = 0, type = 3, value = 1\\n\")\n file.write(\" // input[34]: defaulting = 0, visible = 0, type = 3, value = 0\\n\")\n file.write(\" // input[36]: defaulting = 0, visible = 0, type = 3, value = 1\\n\")\n file.write(\" // input[41]: defaulting = 0, visible = 0, type = 32, value = \\\"rotate\\\"\\n\")\n file.write(\" // input[42]: defaulting = 0, visible = 0, type = 32, value = \\\"Surface\\\"\\n\")\n file.write(\" // page group: Image\\n\")\n file.write(\" // title: value = Surface\\n\")\n file.write(\" // depth: value = 24\\n\")\n file.write(\" // window: position = (0.0000,0.0400), size = 0.9929x0.9276\\n\")\n file.write(\" // internal caching: 1\\n\")\n file.write(\" //\\n\")\n file.write(\"main_Image_3_out_1,\\n\")\n file.write(\"main_Image_3_out_2,\\n\")\n file.write(\"main_Image_3_out_3 = \\n\")\n file.write(\" Image(\\n\")\n file.write(\" main_Image_3_in_1,\\n\")\n file.write(\" main_Switch_1_out_1,\\n\")\n file.write(\" main_Image_3_in_3,\\n\")\n file.write(\" main_Image_3_in_4,\\n\")\n file.write(\" main_Image_3_in_5,\\n\")\n file.write(\" main_Image_3_in_6,\\n\")\n file.write(\" main_Image_3_in_7,\\n\")\n file.write(\" main_Image_3_in_8,\\n\")\n file.write(\" main_Image_3_in_9,\\n\")\n file.write(\" main_Image_3_in_10,\\n\")\n file.write(\" main_Image_3_in_11,\\n\")\n file.write(\" main_Image_3_in_12,\\n\")\n file.write(\" main_Image_3_in_13,\\n\")\n file.write(\" main_Image_3_in_14,\\n\")\n file.write(\" main_Image_3_in_15,\\n\")\n file.write(\" main_Image_3_in_16,\\n\")\n file.write(\" main_Image_3_in_17,\\n\")\n file.write(\" main_Image_3_in_18,\\n\")\n file.write(\" main_Image_3_in_19,\\n\")\n file.write(\" main_Image_3_in_20,\\n\")\n file.write(\" main_Image_3_in_21,\\n\")\n file.write(\" main_Switch_14_out_1,\\n\")\n file.write(\" main_Image_3_in_23,\\n\")\n file.write(\" main_Image_3_in_24,\\n\")\n file.write(\" main_Image_3_in_25,\\n\")\n file.write(\" main_Image_3_in_26,\\n\")\n file.write(\" main_Image_3_in_27,\\n\")\n file.write(\" main_Image_3_in_28,\\n\")\n file.write(\" main_Image_3_in_29,\\n\")\n file.write(\" main_Image_3_in_30,\\n\")\n file.write(\" main_Image_3_in_31,\\n\")\n file.write(\" main_Image_3_in_32,\\n\")\n file.write(\" main_Image_3_in_33,\\n\")\n file.write(\" main_Image_3_in_34,\\n\")\n file.write(\" main_Image_3_in_35,\\n\")\n file.write(\" main_Image_3_in_36,\\n\")\n file.write(\" main_Image_3_in_37,\\n\")\n file.write(\" main_Image_3_in_38,\\n\")\n file.write(\" main_Image_3_in_39,\\n\")\n file.write(\" main_Image_3_in_40,\\n\")\n file.write(\" main_Image_3_in_41,\\n\")\n file.write(\" main_Image_3_in_42,\\n\")\n file.write(\" main_Image_3_in_43,\\n\")\n file.write(\" main_Image_3_in_44,\\n\")\n file.write(\" main_Image_3_in_45,\\n\")\n file.write(\" main_Image_3_in_46,\\n\")\n file.write(\" main_Image_3_in_47,\\n\")\n file.write(\" main_Image_3_in_48,\\n\")\n file.write(\" main_Image_3_in_49\\n\")\n file.write(\" ) [instance: 3, cache: 1];\\n\")\n file.write(\"// network: end of macro body\\n\")\n file.write(\"CacheScene(main_Image_3_in_1, main_Image_3_out_1, main_Image_3_out_2);\\n\")\n file.write(\"}\\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\"main_Import_4_in_1 = \\\"%s.general\\\";\\n\" % point_file)\n file.write(\"main_Import_4_in_2 = NULL;\\n\")\n file.write(\"main_Import_4_in_3 = NULL;\\n\")\n file.write(\"main_Import_4_in_4 = NULL;\\n\")\n file.write(\"main_Import_4_in_5 = NULL;\\n\")\n file.write(\"main_Import_4_in_6 = NULL;\\n\")\n file.write(\"main_Import_4_out_1 = NULL;\\n\")\n file.write(\"main_Glyph_2_in_2 = \\\"sphere\\\";\\n\")\n file.write(\"main_Glyph_2_in_3 = NULL;\\n\")\n file.write(\"main_Glyph_2_in_4 = %s;\\n\" % sphere_size)\n file.write(\"main_Glyph_2_in_5 = 0.0;\\n\")\n file.write(\"main_Glyph_2_in_6 = NULL;\\n\")\n file.write(\"main_Glyph_2_in_7 = NULL;\\n\")\n file.write(\"main_Glyph_2_out_1 = NULL;\\n\")\n file.write(\"main_Color_10_in_2 = [0 0 0];\\n\")\n file.write(\"main_Color_10_in_3 = 1.0;\\n\")\n file.write(\"main_Color_10_in_4 = NULL;\\n\")\n file.write(\"main_Color_10_in_5 = NULL;\\n\")\n file.write(\"main_Color_10_out_1 = NULL;\\n\")\n file.write(\"main_Transmitter_1_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_2_out_1 = NULL;\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\"main_Import_3_in_1 = \\\"%s.general\\\";\\n\" % file_prefix)\n file.write(\"main_Import_3_in_2 = NULL;\\n\")\n file.write(\"main_Import_3_in_3 = NULL;\\n\")\n file.write(\"main_Import_3_in_4 = NULL;\\n\")\n file.write(\"main_Import_3_in_5 = NULL;\\n\")\n file.write(\"main_Import_3_in_6 = NULL;\\n\")\n file.write(\"main_Import_3_out_1 = NULL;\\n\")\n file.write(\"main_Isosurface_5_in_2 = 500.0;\\n\")\n file.write(\"main_Isosurface_5_in_3 = NULL;\\n\")\n file.write(\"main_Isosurface_5_in_4 = NULL;\\n\")\n file.write(\"main_Isosurface_5_in_5 = NULL;\\n\")\n file.write(\"main_Isosurface_5_in_6 = NULL;\\n\")\n file.write(\"main_Isosurface_5_out_1 = NULL;\\n\")\n file.write(\"main_Transmitter_7_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_14_out_1 = NULL;\\n\")\n file.write(\"main_Color_6_in_2 = [0 0 0];\\n\")\n file.write(\"main_Color_6_in_3 = 0.2;\\n\")\n file.write(\"main_Color_6_in_4 = NULL;\\n\")\n file.write(\"main_Color_6_in_5 = NULL;\\n\")\n file.write(\"main_Color_6_out_1 = NULL;\\n\")\n file.write(\"main_Isosurface_6_in_2 = 100.0;\\n\")\n file.write(\"main_Isosurface_6_in_3 = NULL;\\n\")\n file.write(\"main_Isosurface_6_in_4 = NULL;\\n\")\n file.write(\"main_Isosurface_6_in_5 = NULL;\\n\")\n file.write(\"main_Isosurface_6_in_6 = NULL;\\n\")\n file.write(\"main_Isosurface_6_out_1 = NULL;\\n\")\n file.write(\"main_Transmitter_8_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_13_out_1 = NULL;\\n\")\n file.write(\"main_Color_7_in_2 = [0.2 0.2 0.2];\\n\")\n file.write(\"main_Color_7_in_3 = 0.4;\\n\")\n file.write(\"main_Color_7_in_4 = NULL;\\n\")\n file.write(\"main_Color_7_in_5 = NULL;\\n\")\n file.write(\"main_Color_7_out_1 = NULL;\\n\")\n file.write(\"main_Collect_5_out_1 = NULL;\\n\")\n file.write(\"main_Isosurface_7_in_2 = 20.0;\\n\")\n file.write(\"main_Isosurface_7_in_3 = NULL;\\n\")\n file.write(\"main_Isosurface_7_in_4 = NULL;\\n\")\n file.write(\"main_Isosurface_7_in_5 = NULL;\\n\")\n file.write(\"main_Isosurface_7_in_6 = NULL;\\n\")\n file.write(\"main_Isosurface_7_out_1 = NULL;\\n\")\n file.write(\"main_Transmitter_9_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_12_out_1 = NULL;\\n\")\n file.write(\"main_Color_8_in_2 = [0.5 0.5 0.5];\\n\")\n file.write(\"main_Color_8_in_3 = 0.5;\\n\")\n file.write(\"main_Color_8_in_4 = NULL;\\n\")\n file.write(\"main_Color_8_in_5 = NULL;\\n\")\n file.write(\"main_Color_8_out_1 = NULL;\\n\")\n file.write(\"main_Isosurface_8_in_2 = 7.0;\\n\")\n file.write(\"main_Isosurface_8_in_3 = NULL;\\n\")\n file.write(\"main_Isosurface_8_in_4 = NULL;\\n\")\n file.write(\"main_Isosurface_8_in_5 = NULL;\\n\")\n file.write(\"main_Isosurface_8_in_6 = NULL;\\n\")\n file.write(\"main_Isosurface_8_out_1 = NULL;\\n\")\n file.write(\"main_Transmitter_10_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_11_out_1 = NULL;\\n\")\n file.write(\"main_Color_9_in_2 = \\\"white\\\";\\n\")\n file.write(\"main_Color_9_in_3 = 0.7;\\n\")\n file.write(\"main_Color_9_in_4 = NULL;\\n\")\n file.write(\"main_Color_9_in_5 = NULL;\\n\")\n file.write(\"main_Color_9_out_1 = NULL;\\n\")\n file.write(\"main_Collect_6_out_1 = NULL;\\n\")\n file.write(\"main_Collect_7_out_1 = NULL;\\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\"main_Collect_8_out_1 = NULL;\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\"main_Transmitter_4_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_3_out_1 = NULL;\\n\")\n file.write(\"main_Scale_3_in_2 = [1 1 1];\\n\")\n file.write(\"main_Scale_3_out_1 = NULL;\\n\")\n file.write(\"main_AutoCamera_2_in_2 = [1 -1 1];\\n\")\n file.write(\"main_AutoCamera_2_in_3 = NULL;\\n\")\n file.write(\"main_AutoCamera_2_in_4 = 640;\\n\")\n file.write(\"main_AutoCamera_2_in_5 = .75;\\n\")\n file.write(\"main_AutoCamera_2_in_6 = [-1 1 0 ];\\n\")\n file.write(\"main_AutoCamera_2_in_7 = 0;\\n\")\n file.write(\"main_AutoCamera_2_in_8 = 30.0;\\n\")\n file.write(\"main_AutoCamera_2_in_9 = \\\"white\\\";\\n\")\n file.write(\"main_AutoCamera_2_out_1 = NULL;\\n\")\n file.write(\"main_AutoAxes_2_in_3 = %s;\\n\" % labels)\n file.write(\"main_AutoAxes_2_in_4 = 30;\\n\")\n file.write(\"main_AutoAxes_2_in_5 = %s;\\n\" % corners)\n file.write(\"main_AutoAxes_2_in_6 = 1;\\n\")\n file.write(\"main_AutoAxes_2_in_7 = NULL;\\n\")\n file.write(\"main_AutoAxes_2_in_8 = NULL;\\n\")\n file.write(\"main_AutoAxes_2_in_9 = 1;\\n\")\n file.write(\"main_AutoAxes_2_in_10 = {[1 1 1] [0.1 0.1 0.1] [0 0 0] [0 0 0]};\\n\")\n file.write(\"main_AutoAxes_2_in_11 = {\\\"background\\\" \\\"grid\\\" \\\"labels\\\" \\\"ticks\\\"};\\n\")\n file.write(\"main_AutoAxes_2_in_12 = NULL;\\n\")\n file.write(\"main_AutoAxes_2_in_13 = \\\"area\\\";\\n\")\n file.write(\"main_AutoAxes_2_in_14 = %s;\\n\" % tick_locations[0])\n file.write(\"main_AutoAxes_2_in_15 = %s;\\n\" % tick_locations[1])\n file.write(\"main_AutoAxes_2_in_16 = %s;\\n\" % tick_locations[2])\n file.write(\"main_AutoAxes_2_in_17 = %s;\\n\" % tick_values[0])\n file.write(\"main_AutoAxes_2_in_18 = %s;\\n\" % tick_values[1])\n file.write(\"main_AutoAxes_2_in_19 = %s;\\n\" % tick_values[2])\n file.write(\"main_AutoAxes_2_out_1 = NULL;\\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\"main_Color_11_in_2 = [1 0 0];\\n\")\n file.write(\"main_Color_11_in_3 = 1.0;\\n\")\n file.write(\"main_Color_11_in_4 = NULL;\\n\")\n file.write(\"main_Color_11_in_5 = NULL;\\n\")\n file.write(\"main_Color_11_out_1 = NULL;\\n\")\n file.write(\"main_Transmitter_2_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_1_out_1 = NULL;\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\"main_Receiver_10_out_1 = NULL;\\n\")\n file.write(\"main_Color_12_in_2 = [0 0 0.2];\\n\")\n file.write(\"main_Color_12_in_3 = 0.3;\\n\")\n file.write(\"main_Color_12_in_4 = NULL;\\n\")\n file.write(\"main_Color_12_in_5 = NULL;\\n\")\n file.write(\"main_Color_12_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_9_out_1 = NULL;\\n\")\n file.write(\"main_Color_13_in_2 = \\\"blue\\\";\\n\")\n file.write(\"main_Color_13_in_3 = 0.3;\\n\")\n file.write(\"main_Color_13_in_4 = NULL;\\n\")\n file.write(\"main_Color_13_in_5 = NULL;\\n\")\n file.write(\"main_Color_13_out_1 = NULL;\\n\")\n file.write(\"main_Collect_9_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_8_out_1 = NULL;\\n\")\n file.write(\"main_Color_14_in_2 = [0.5 0.5 1];\\n\")\n file.write(\"main_Color_14_in_3 = 0.2;\\n\")\n file.write(\"main_Color_14_in_4 = NULL;\\n\")\n file.write(\"main_Color_14_in_5 = NULL;\\n\")\n file.write(\"main_Color_14_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_7_out_1 = NULL;\\n\")\n file.write(\"main_Color_15_in_2 = \\\"white\\\";\\n\")\n file.write(\"main_Color_15_in_3 = 0.55;\\n\")\n file.write(\"main_Color_15_in_4 = NULL;\\n\")\n file.write(\"main_Color_15_in_5 = NULL;\\n\")\n file.write(\"main_Color_15_out_1 = NULL;\\n\")\n file.write(\"main_Collect_10_out_1 = NULL;\\n\")\n file.write(\"main_Collect_11_out_1 = NULL;\\n\")\n\n # Include the sphere.\n if num_points > 0:\n file.write(\"\\n\")\n file.write(\"main_Collect_12_out_1 = NULL;\\n\")\n\n # Common code.\n file.write(\"\\n\")\n file.write(\"main_Transmitter_3_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_4_out_1 = NULL;\\n\")\n file.write(\"main_Scale_5_in_2 = [1 1 1];\\n\")\n file.write(\"main_Scale_5_out_1 = NULL;\\n\")\n file.write(\"main_AutoCamera_4_in_2 = [1 -1 1];\\n\")\n file.write(\"main_AutoCamera_4_in_3 = NULL;\\n\")\n file.write(\"main_AutoCamera_4_in_4 = NULL;\\n\")\n file.write(\"main_AutoCamera_4_in_5 = .75;\\n\")\n file.write(\"main_AutoCamera_4_in_6 = [-1 1 0 ];\\n\")\n file.write(\"main_AutoCamera_4_in_7 = 0;\\n\")\n file.write(\"main_AutoCamera_4_in_8 = 30.0;\\n\")\n file.write(\"main_AutoCamera_4_in_9 = \\\"black\\\";\\n\")\n file.write(\"main_AutoCamera_4_out_1 = NULL;\\n\")\n file.write(\"main_AutoAxes_4_in_3 = %s;\\n\" % labels)\n file.write(\"main_AutoAxes_4_in_4 = 30;\\n\")\n file.write(\"main_AutoAxes_4_in_5 = %s;\\n\" % corners)\n file.write(\"main_AutoAxes_4_in_6 = 1;\\n\")\n file.write(\"main_AutoAxes_4_in_7 = NULL;\\n\")\n file.write(\"main_AutoAxes_4_in_8 = NULL;\\n\")\n file.write(\"main_AutoAxes_4_in_9 = 1;\\n\")\n file.write(\"main_AutoAxes_4_in_10 = {[0.05 0.05 0.05] [0.3 0.3 0.3] [1 1 1] [1 1 0]};\\n\")\n file.write(\"main_AutoAxes_4_in_11 = {\\\"background\\\" \\\"grid\\\" \\\"labels\\\" \\\"ticks\\\"};\\n\")\n file.write(\"main_AutoAxes_4_in_12 = NULL;\\n\")\n file.write(\"main_AutoAxes_4_in_13 = \\\"area\\\";\\n\")\n file.write(\"main_AutoAxes_4_in_14 = %s;\\n\" % tick_locations[0])\n file.write(\"main_AutoAxes_4_in_15 = %s;\\n\" % tick_locations[1])\n file.write(\"main_AutoAxes_4_in_16 = %s;\\n\" % tick_locations[2])\n file.write(\"main_AutoAxes_4_in_17 = %s;\\n\" % tick_values[0])\n file.write(\"main_AutoAxes_4_in_18 = %s;\\n\" % tick_values[1])\n file.write(\"main_AutoAxes_4_in_19 = %s;\\n\" % tick_values[2])\n file.write(\"main_AutoAxes_4_out_1 = NULL;\\n\")\n file.write(\"main_Selector_1_in_1 = \\\"Selector_1\\\";\\n\")\n file.write(\"main_Selector_1_in_2 = \\\"Colour\\\" ;\\n\")\n file.write(\"main_Selector_1_in_3 = 1 ;\\n\")\n file.write(\"main_Selector_1_in_4 = NULL;\\n\")\n file.write(\"main_Selector_1_in_5 = NULL;\\n\")\n file.write(\"main_Selector_1_in_6 = NULL;\\n\")\n file.write(\"main_Selector_1_in_7 = NULL;\\n\")\n file.write(\"main_Selector_1_out_1 = 1 ;\\n\")\n file.write(\"main_Transmitter_6_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_5_out_1 = NULL;\\n\")\n file.write(\"main_Transmitter_5_out_1 = NULL;\\n\")\n file.write(\"main_Receiver_6_out_1 = NULL;\\n\")\n file.write(\"main_Switch_1_out_1 = NULL;\\n\")\n file.write(\"main_Switch_14_in_2 = \\\"black\\\";\\n\")\n file.write(\"main_Switch_14_in_3 = \\\"white\\\";\\n\")\n file.write(\"main_Switch_14_out_1 = NULL;\\n\")\n file.write(\"macro Image(\\n\")\n file.write(\" id,\\n\")\n file.write(\" object,\\n\")\n file.write(\" where,\\n\")\n file.write(\" useVector,\\n\")\n file.write(\" to,\\n\")\n file.write(\" from,\\n\")\n file.write(\" width,\\n\")\n file.write(\" resolution,\\n\")\n file.write(\" aspect,\\n\")\n file.write(\" up,\\n\")\n file.write(\" viewAngle,\\n\")\n file.write(\" perspective,\\n\")\n file.write(\" options,\\n\")\n file.write(\" buttonState = 1,\\n\")\n file.write(\" buttonUpApprox = \\\"none\\\",\\n\")\n file.write(\" buttonDownApprox = \\\"none\\\",\\n\")\n file.write(\" buttonUpDensity = 1,\\n\")\n file.write(\" buttonDownDensity = 1,\\n\")\n file.write(\" renderMode = 0,\\n\")\n file.write(\" defaultCamera,\\n\")\n file.write(\" reset,\\n\")\n file.write(\" backgroundColor,\\n\")\n file.write(\" throttle,\\n\")\n file.write(\" RECenable = 0,\\n\")\n file.write(\" RECfile,\\n\")\n file.write(\" RECformat,\\n\")\n file.write(\" RECresolution,\\n\")\n file.write(\" RECaspect,\\n\")\n file.write(\" AAenable = 0,\\n\")\n file.write(\" AAlabels,\\n\")\n file.write(\" AAticks,\\n\")\n file.write(\" AAcorners,\\n\")\n file.write(\" AAframe,\\n\")\n file.write(\" AAadjust,\\n\")\n file.write(\" AAcursor,\\n\")\n file.write(\" AAgrid,\\n\")\n file.write(\" AAcolors,\\n\")\n file.write(\" AAannotation,\\n\")\n file.write(\" AAlabelscale,\\n\")\n file.write(\" AAfont,\\n\")\n file.write(\" interactionMode,\\n\")\n file.write(\" title,\\n\")\n file.write(\" AAxTickLocs,\\n\")\n file.write(\" AAyTickLocs,\\n\")\n file.write(\" AAzTickLocs,\\n\")\n file.write(\" AAxTickLabels,\\n\")\n file.write(\" AAyTickLabels,\\n\")\n file.write(\" AAzTickLabels,\\n\")\n file.write(\" webOptions) -> (\\n\")\n file.write(\" object,\\n\")\n file.write(\" camera,\\n\")\n file.write(\" where)\\n\")\n file.write(\"{\\n\")\n file.write(\" ImageMessage(\\n\")\n file.write(\" id,\\n\")\n file.write(\" backgroundColor,\\n\")\n file.write(\" throttle,\\n\")\n file.write(\" RECenable,\\n\")\n file.write(\" RECfile,\\n\")\n file.write(\" RECformat,\\n\")\n file.write(\" RECresolution,\\n\")\n file.write(\" RECaspect,\\n\")\n file.write(\" AAenable,\\n\")\n file.write(\" AAlabels,\\n\")\n file.write(\" AAticks,\\n\")\n file.write(\" AAcorners,\\n\")\n file.write(\" AAframe,\\n\")\n file.write(\" AAadjust,\\n\")\n file.write(\" AAcursor,\\n\")\n file.write(\" AAgrid,\\n\")\n file.write(\" AAcolors,\\n\")\n file.write(\" AAannotation,\\n\")\n file.write(\" AAlabelscale,\\n\")\n file.write(\" AAfont,\\n\")\n file.write(\" AAxTickLocs,\\n\")\n file.write(\" AAyTickLocs,\\n\")\n file.write(\" AAzTickLocs,\\n\")\n file.write(\" AAxTickLabels,\\n\")\n file.write(\" AAyTickLabels,\\n\")\n file.write(\" AAzTickLabels,\\n\")\n file.write(\" interactionMode,\\n\")\n file.write(\" title,\\n\")\n file.write(\" renderMode,\\n\")\n file.write(\" buttonUpApprox,\\n\")\n file.write(\" buttonDownApprox,\\n\")\n file.write(\" buttonUpDensity,\\n\")\n file.write(\" buttonDownDensity) [instance: 1, cache: 1];\\n\")\n file.write(\" autoCamera =\\n\")\n file.write(\" AutoCamera(\\n\")\n file.write(\" object,\\n\")\n file.write(\" \\\"front\\\",\\n\")\n file.write(\" object,\\n\")\n file.write(\" resolution,\\n\")\n file.write(\" aspect,\\n\")\n file.write(\" [0,1,0],\\n\")\n file.write(\" perspective,\\n\")\n file.write(\" viewAngle,\\n\")\n file.write(\" backgroundColor) [instance: 1, cache: 1];\\n\")\n file.write(\" realCamera =\\n\")\n file.write(\" Camera(\\n\")\n file.write(\" to,\\n\")\n file.write(\" from,\\n\")\n file.write(\" width,\\n\")\n file.write(\" resolution,\\n\")\n file.write(\" aspect,\\n\")\n file.write(\" up,\\n\")\n file.write(\" perspective,\\n\")\n file.write(\" viewAngle,\\n\")\n file.write(\" backgroundColor) [instance: 1, cache: 1];\\n\")\n file.write(\" coloredDefaultCamera = \\n\")\n file.write(\" UpdateCamera(defaultCamera,\\n\")\n file.write(\" background=backgroundColor) [instance: 1, cache: 1];\\n\")\n file.write(\" nullDefaultCamera =\\n\")\n file.write(\" Inquire(defaultCamera,\\n\")\n file.write(\" \\\"is null + 1\\\") [instance: 1, cache: 1];\\n\")\n file.write(\" resetCamera =\\n\")\n file.write(\" Switch(\\n\")\n file.write(\" nullDefaultCamera,\\n\")\n file.write(\" coloredDefaultCamera,\\n\")\n file.write(\" autoCamera) [instance: 1, cache: 1];\\n\")\n file.write(\" resetNull = \\n\")\n file.write(\" Inquire(\\n\")\n file.write(\" reset,\\n\")\n file.write(\" \\\"is null + 1\\\") [instance: 2, cache: 1];\\n\")\n file.write(\" reset =\\n\")\n file.write(\" Switch(\\n\")\n file.write(\" resetNull,\\n\")\n file.write(\" reset,\\n\")\n file.write(\" 0) [instance: 2, cache: 1];\\n\")\n file.write(\" whichCamera =\\n\")\n file.write(\" Compute(\\n\")\n file.write(\" \\\"($0 != 0 || $1 == 0) ? 1 : 2\\\",\\n\")\n file.write(\" reset,\\n\")\n file.write(\" useVector) [instance: 1, cache: 1];\\n\")\n file.write(\" camera = Switch(\\n\")\n file.write(\" whichCamera,\\n\")\n file.write(\" resetCamera,\\n\")\n file.write(\" realCamera) [instance: 3, cache: 1];\\n\")\n file.write(\" AAobject =\\n\")\n file.write(\" AutoAxes(\\n\")\n file.write(\" object,\\n\")\n file.write(\" camera,\\n\")\n file.write(\" AAlabels,\\n\")\n file.write(\" AAticks,\\n\")\n file.write(\" AAcorners,\\n\")\n file.write(\" AAframe,\\n\")\n file.write(\" AAadjust,\\n\")\n file.write(\" AAcursor,\\n\")\n file.write(\" AAgrid,\\n\")\n file.write(\" AAcolors,\\n\")\n file.write(\" AAannotation,\\n\")\n file.write(\" AAlabelscale,\\n\")\n file.write(\" AAfont,\\n\")\n file.write(\" AAxTickLocs,\\n\")\n file.write(\" AAyTickLocs,\\n\")\n file.write(\" AAzTickLocs,\\n\")\n file.write(\" AAxTickLabels,\\n\")\n file.write(\" AAyTickLabels,\\n\")\n file.write(\" AAzTickLabels) [instance: 1, cache: 1];\\n\")\n file.write(\" switchAAenable = Compute(\\\"$0+1\\\",\\n\")\n file.write(\" AAenable) [instance: 2, cache: 1];\\n\")\n file.write(\" object = Switch(\\n\")\n file.write(\" switchAAenable,\\n\")\n file.write(\" object,\\n\")\n file.write(\" AAobject) [instance:4, cache: 1];\\n\")\n file.write(\" SWapproximation_options =\\n\")\n file.write(\" Switch(\\n\")\n file.write(\" buttonState,\\n\")\n file.write(\" buttonUpApprox,\\n\")\n file.write(\" buttonDownApprox) [instance: 5, cache: 1];\\n\")\n file.write(\" SWdensity_options =\\n\")\n file.write(\" Switch(\\n\")\n file.write(\" buttonState,\\n\")\n file.write(\" buttonUpDensity,\\n\")\n file.write(\" buttonDownDensity) [instance: 6, cache: 1];\\n\")\n file.write(\" HWapproximation_options =\\n\")\n file.write(\" Format(\\n\")\n file.write(\" \\\"%s,%s\\\",\\n\")\n file.write(\" buttonDownApprox,\\n\")\n file.write(\" buttonUpApprox) [instance: 1, cache: 1];\\n\")\n file.write(\" HWdensity_options =\\n\")\n file.write(\" Format(\\n\")\n file.write(\" \\\"%d,%d\\\",\\n\")\n file.write(\" buttonDownDensity,\\n\")\n file.write(\" buttonUpDensity) [instance: 2, cache: 1];\\n\")\n file.write(\" switchRenderMode = Compute(\\n\")\n file.write(\" \\\"$0+1\\\",\\n\")\n file.write(\" renderMode) [instance: 3, cache: 1];\\n\")\n file.write(\" approximation_options = Switch(\\n\")\n file.write(\" switchRenderMode,\\n\")\n file.write(\" SWapproximation_options,\\n\")\n file.write(\" HWapproximation_options) [instance: 7, cache: 1];\\n\")\n file.write(\" density_options = Switch(\\n\")\n file.write(\" switchRenderMode,\\n\")\n file.write(\" SWdensity_options,\\n\")\n file.write(\" HWdensity_options) [instance: 8, cache: 1];\\n\")\n file.write(\" renderModeString = Switch(\\n\")\n file.write(\" switchRenderMode,\\n\")\n file.write(\" \\\"software\\\",\\n\")\n file.write(\" \\\"hardware\\\")[instance: 9, cache: 1];\\n\")\n file.write(\" object_tag = Inquire(\\n\")\n file.write(\" object,\\n\")\n file.write(\" \\\"object tag\\\")[instance: 3, cache: 1];\\n\")\n file.write(\" annoted_object =\\n\")\n file.write(\" Options(\\n\")\n file.write(\" object,\\n\")\n file.write(\" \\\"send boxes\\\",\\n\")\n file.write(\" 0,\\n\")\n file.write(\" \\\"cache\\\",\\n\")\n file.write(\" 1,\\n\")\n file.write(\" \\\"object tag\\\",\\n\")\n file.write(\" object_tag,\\n\")\n file.write(\" \\\"ddcamera\\\",\\n\")\n file.write(\" whichCamera,\\n\")\n file.write(\" \\\"rendering approximation\\\",\\n\")\n file.write(\" approximation_options,\\n\")\n file.write(\" \\\"render every\\\",\\n\")\n file.write(\" density_options,\\n\")\n file.write(\" \\\"button state\\\",\\n\")\n file.write(\" buttonState,\\n\")\n file.write(\" \\\"rendering mode\\\",\\n\")\n file.write(\" renderModeString) [instance: 1, cache: 1];\\n\")\n file.write(\" RECresNull =\\n\")\n file.write(\" Inquire(\\n\")\n file.write(\" RECresolution,\\n\")\n file.write(\" \\\"is null + 1\\\") [instance: 4, cache: 1];\\n\")\n file.write(\" ImageResolution =\\n\")\n file.write(\" Inquire(\\n\")\n file.write(\" camera,\\n\")\n file.write(\" \\\"camera resolution\\\") [instance: 5, cache: 1];\\n\")\n file.write(\" RECresolution =\\n\")\n file.write(\" Switch(\\n\")\n file.write(\" RECresNull,\\n\")\n file.write(\" RECresolution,\\n\")\n file.write(\" ImageResolution) [instance: 10, cache: 1];\\n\")\n file.write(\" RECaspectNull =\\n\")\n file.write(\" Inquire(\\n\")\n file.write(\" RECaspect,\\n\")\n file.write(\" \\\"is null + 1\\\") [instance: 6, cache: 1];\\n\")\n file.write(\" ImageAspect =\\n\")\n file.write(\" Inquire(\\n\")\n file.write(\" camera,\\n\")\n file.write(\" \\\"camera aspect\\\") [instance: 7, cache: 1];\\n\")\n file.write(\" RECaspect =\\n\")\n file.write(\" Switch(\\n\")\n file.write(\" RECaspectNull,\\n\")\n file.write(\" RECaspect,\\n\")\n file.write(\" ImageAspect) [instance: 11, cache: 1];\\n\")\n file.write(\" switchRECenable = Compute(\\n\")\n file.write(\" \\\"$0 == 0 ? 1 : (($2 == $3) && ($4 == $5)) ? ($1 == 1 ? 2 : 3) : 4\\\",\\n\")\n file.write(\" RECenable,\\n\")\n file.write(\" switchRenderMode,\\n\")\n file.write(\" RECresolution,\\n\")\n file.write(\" ImageResolution,\\n\")\n file.write(\" RECaspect,\\n\")\n file.write(\" ImageAspect) [instance: 4, cache: 1];\\n\")\n file.write(\" NoRECobject, RECNoRerenderObject, RECNoRerHW, RECRerenderObject = Route(switchRECenable, annoted_object);\\n\")\n file.write(\" Display(\\n\")\n file.write(\" NoRECobject,\\n\")\n file.write(\" camera,\\n\")\n file.write(\" where,\\n\")\n file.write(\" throttle) [instance: 1, cache: 1];\\n\")\n file.write(\" image =\\n\")\n file.write(\" Render(\\n\")\n file.write(\" RECNoRerenderObject,\\n\")\n file.write(\" camera) [instance: 1, cache: 1];\\n\")\n file.write(\" Display(\\n\")\n file.write(\" image,\\n\")\n file.write(\" NULL,\\n\")\n file.write(\" where,\\n\")\n file.write(\" throttle) [instance: 2, cache: 1];\\n\")\n file.write(\" WriteImage(\\n\")\n file.write(\" image,\\n\")\n file.write(\" RECfile,\\n\")\n file.write(\" RECformat) [instance: 1, cache: 1];\\n\")\n file.write(\" rec_where = Display(\\n\")\n file.write(\" RECNoRerHW,\\n\")\n file.write(\" camera,\\n\")\n file.write(\" where,\\n\")\n file.write(\" throttle) [instance: 1, cache: 0];\\n\")\n file.write(\" rec_image = ReadImageWindow(\\n\")\n file.write(\" rec_where) [instance: 1, cache: 1];\\n\")\n file.write(\" WriteImage(\\n\")\n file.write(\" rec_image,\\n\")\n file.write(\" RECfile,\\n\")\n file.write(\" RECformat) [instance: 1, cache: 1];\\n\")\n file.write(\" RECupdateCamera =\\n\")\n file.write(\" UpdateCamera(\\n\")\n file.write(\" camera,\\n\")\n file.write(\" resolution=RECresolution,\\n\")\n file.write(\" aspect=RECaspect) [instance: 2, cache: 1];\\n\")\n file.write(\" Display(\\n\")\n file.write(\" RECRerenderObject,\\n\")\n file.write(\" camera,\\n\")\n file.write(\" where,\\n\")\n file.write(\" throttle) [instance: 1, cache: 1];\\n\")\n file.write(\" RECRerenderObject =\\n\")\n file.write(\" ScaleScreen(\\n\")\n file.write(\" RECRerenderObject,\\n\")\n file.write(\" NULL,\\n\")\n file.write(\" RECresolution,\\n\")\n file.write(\" camera) [instance: 1, cache: 1];\\n\")\n file.write(\" image =\\n\")\n file.write(\" Render(\\n\")\n file.write(\" RECRerenderObject,\\n\")\n file.write(\" RECupdateCamera) [instance: 2, cache: 1];\\n\")\n file.write(\" WriteImage(\\n\")\n file.write(\" image,\\n\")\n file.write(\" RECfile,\\n\")\n file.write(\" RECformat) [instance: 2, cache: 1];\\n\")\n file.write(\"}\\n\")\n file.write(\"main_Image_3_in_1 = \\\"Image_3\\\";\\n\")\n file.write(\"main_Image_3_in_3 = \\\"X24,,Surface\\\";\\n\")\n file.write(\"main_Image_3_in_4 = 1;\\n\")\n file.write(\"main_Image_3_in_5 = %s;\\n\" % image_array1)\n file.write(\"main_Image_3_in_6 = %s;\\n\" % image_array2)\n file.write(\"main_Image_3_in_7 = %s;\\n\" % image_val)\n file.write(\"main_Image_3_in_8 = 1376;\\n\")\n file.write(\"main_Image_3_in_9 = 0.678;\\n\")\n file.write(\"main_Image_3_in_10 = [-0.109685 0.243133 0.963772];\\n\")\n file.write(\"main_Image_3_in_11 = NULL;\\n\")\n file.write(\"main_Image_3_in_12 = 0;\\n\")\n file.write(\"main_Image_3_in_13 = NULL;\\n\")\n file.write(\"main_Image_3_in_14 = 1;\\n\")\n file.write(\"main_Image_3_in_15 = \\\"none\\\";\\n\")\n file.write(\"main_Image_3_in_16 = \\\"none\\\";\\n\")\n file.write(\"main_Image_3_in_17 = NULL;\\n\")\n file.write(\"main_Image_3_in_18 = NULL;\\n\")\n file.write(\"main_Image_3_in_19 = 0;\\n\")\n file.write(\"main_Image_3_in_20 = NULL;\\n\")\n file.write(\"main_Image_3_in_21 = NULL;\\n\")\n file.write(\"main_Image_3_in_23 = NULL;\\n\")\n file.write(\"main_Image_3_in_25 = \\\"iso\\\";\\n\")\n file.write(\"main_Image_3_in_26 = \\\"tiff\\\";\\n\")\n file.write(\"main_Image_3_in_27 = NULL;\\n\")\n file.write(\"main_Image_3_in_28 = NULL;\\n\")\n file.write(\"main_Image_3_in_29 = 0;\\n\")\n file.write(\"main_Image_3_in_30 = NULL;\\n\")\n file.write(\"main_Image_3_in_31 = NULL;\\n\")\n file.write(\"main_Image_3_in_32 = NULL;\\n\")\n file.write(\"main_Image_3_in_33 = 1;\\n\")\n file.write(\"main_Image_3_in_34 = 0;\\n\")\n file.write(\"main_Image_3_in_35 = NULL;\\n\")\n file.write(\"main_Image_3_in_36 = 1;\\n\")\n file.write(\"main_Image_3_in_37 = NULL;\\n\")\n file.write(\"main_Image_3_in_38 = NULL;\\n\")\n file.write(\"main_Image_3_in_39 = NULL;\\n\")\n file.write(\"main_Image_3_in_40 = NULL;\\n\")\n file.write(\"main_Image_3_in_41 = \\\"rotate\\\";\\n\")\n file.write(\"main_Image_3_in_42 = \\\"Surface\\\";\\n\")\n file.write(\"main_Image_3_in_43 = NULL;\\n\")\n file.write(\"main_Image_3_in_44 = NULL;\\n\")\n file.write(\"main_Image_3_in_45 = NULL;\\n\")\n file.write(\"main_Image_3_in_46 = NULL;\\n\")\n file.write(\"main_Image_3_in_47 = NULL;\\n\")\n file.write(\"main_Image_3_in_48 = NULL;\\n\")\n file.write(\"main_Image_3_in_49 = NULL;\\n\")\n file.write(\"Executive(\\\"product version 4 3 2\\\");\\n\")\n file.write(\"$sync\\n\")\n file.write(\"main();\\n\")\n\n # Close the file.\n file.close()","def write_probin(prob_param_files, cxx_prefix):\n\n params = []\n\n print(\" \")\n print(f\"write_probdata.py: creating prob_param C++ files\")\n\n # read the parameters defined in the parameter files\n\n for f in prob_param_files:\n err = parse_param_file(params, f)\n if err:\n abort(f\"Error parsing {f}\")\n\n\n # now handle the C++ -- we need to write a header and a .cpp file\n # for the parameters\n\n cxx_base = os.path.basename(cxx_prefix)\n\n ofile = f\"{cxx_prefix}_parameters.H\"\n with open(ofile, \"w\") as fout:\n fout.write(CXX_HEADER)\n\n fout.write(f\" void init_{cxx_base}_parameters();\\n\\n\")\n\n fout.write(\" namespace problem {\\n\\n\")\n\n for p in params:\n if p.dtype == \"string\":\n fout.write(f\" extern std::string {p.name};\\n\\n\")\n else:\n if p.is_array():\n if p.size == \"nspec\":\n fout.write(f\" extern AMREX_GPU_MANAGED {p.get_cxx_decl()} {p.name}[NumSpec];\\n\\n\")\n else:\n fout.write(f\" extern AMREX_GPU_MANAGED {p.get_cxx_decl()} {p.name}[{p.size}];\\n\\n\")\n else:\n fout.write(f\" extern AMREX_GPU_MANAGED {p.get_cxx_decl()} {p.name};\\n\\n\")\n\n fout.write(\" }\\n\\n\")\n\n fout.write(CXX_FOOTER)\n\n # now the C++ job_info tests\n ofile = f\"{cxx_prefix}_job_info_tests.H\"\n with open(ofile, \"w\") as fout:\n for p in params:\n if not p.is_array():\n if p.in_namelist:\n fout.write(p.get_job_info_test())\n\n # now the C++ initialization routines\n ofile = f\"{cxx_prefix}_parameters.cpp\"\n with open(ofile, \"w\") as fout:\n fout.write(f\"#include <{cxx_base}_parameters.H>\\n\")\n fout.write(\"#include <AMReX_ParmParse.H>\\n\")\n fout.write(\"#include <AMReX_REAL.H>\\n\\n\")\n for p in params:\n if p.dtype == \"string\":\n fout.write(f\" std::string problem::{p.name};\\n\\n\")\n else:\n if p.is_array():\n if p.size == \"nspec\":\n fout.write(f\" AMREX_GPU_MANAGED {p.get_cxx_decl()} problem::{p.name}[NumSpec];\\n\\n\")\n else:\n fout.write(f\" AMREX_GPU_MANAGED {p.get_cxx_decl()} problem::{p.name}[{p.size}];\\n\\n\")\n else:\n fout.write(f\" AMREX_GPU_MANAGED {p.get_cxx_decl()} problem::{p.name};\\n\\n\")\n\n fout.write(\"\\n\")\n fout.write(f\" void init_{cxx_base}_parameters() {{\\n\")\n\n\n # now write the parmparse code to get the value from the C++\n # inputs.\n\n # open namespace\n fout.write(\" {\\n\")\n\n # we need access to _rt\n fout.write(\" using namespace amrex;\\n\")\n\n fout.write(f\" amrex::ParmParse pp(\\\"problem\\\");\\n\\n\")\n for p in params:\n if p.is_array():\n size = p.size\n if (size == \"nspec\"):\n size = \"NumSpec\"\n fout.write(f\" for (int n = 0; n < {size}; n++) {{\\n\")\n fout.write(f\" problem::{p.name}[n] = {p.default_format(lang='C++')};\\n\")\n fout.write(f\" }}\\n\")\n else:\n fout.write(f\" {p.get_default_string()}\")\n\n if p.in_namelist:\n fout.write(f\" {p.get_query_string('C++')}\")\n fout.write(\"\\n\")\n fout.write(\" }\\n\")\n\n fout.write(\" }\\n\")","def compress_nparr(nparr):\n bytestream = io.BytesIO()\n np.save(bytestream, nparr)\n uncompressed = bytestream.getvalue()\n compressed = zlib.compress(uncompressed)\n return compressed, len(uncompressed), len(compressed)","def save_to_ascii(self, filename, print_progress=True):\n out=StringIO.StringIO()\n # --------------------------------\n # create string with data\n # --------------------------------\n # params_data:\n out.write('#>>params_data:\\n')\n out.write('nx=%d\\n' % self.x.size) \n out.write('np=%d\\n' % self.p.size)\n # x:\n out.write('\\n#>>x:\\n')\n np.savetxt(out, self.x, fmt='%1.8E')\n # p:\n out.write('\\n#>>p:\\n')\n np.savetxt(out, self.p, fmt='%1.8E')\n # fmci_XP:\n out.write('\\n#>>XP: (rows:%4d, columns:%4d)\\n' % (self.x.size,self.p.size))\n np.savetxt(out, self.fmci_XP, fmt='%1.8E')\n # params_physics:\n out.write('\\n#>>params_physics:\\n')\n out.write('particle=%s\\n' % self.__particle_short_names[self.name]) \n out.write('time=%1.8E\\n' % self.time)\n #\n out.write('P=%1.8E\\n' % self.PSR_P)\n out.write('B12=%1.8E\\n' % self.PSR_B12)\n out.write('Lcm=%1.8E\\n' % self.PSR_Lcm)\n #\n out.write('Theta=%1.8E\\n' % self.PSR_Theta)\n out.write('Chi=%1.8E\\n' % self.PSR_Chi)\n # params_TDC:\n out.write('\\n#>>params_TDC:\\n')\n out.write('calc_id=%s\\n' % self.calc_id) \n out.write('i_ts=%d\\n' % self.i_ts)\n # --------------------------------\n # save to file \n # --------------------------------\n f=open(filename,'w')\n f.write(out.getvalue())\n f.close()\n out.close()\n if print_progress:\n print '\\nContent saved in \"%s\" \\n' % filename","def write(self, cull=False):\n if cull:\n cull_prefixes(self).write()\n else:\n ser = self.g.serialize(format='nifttl', encoding='utf-8')\n with open(self.filename, 'wb') as f:\n f.write(ser)\n #print('yes we wrote the first version...', self.name)","def dump(self, step=None):\n if self._compressed:\n np.savez_compressed(self._file_path, **self._data)\n else:\n np.savez(self._file_path, **self._data)","def write_data(self, file_prefix, **kwargs):\n # Add a dot to separate the prefix from the population label if it\n # doesn't already have one and isn't a directory\n if (not os.path.isdir(file_prefix) and\n not file_prefix.endswith('.') and\n not file_prefix.endswith(os.path.sep)):\n file_prefix += '.'\n for comp_array in self.component_arrays.values():\n # @UndefinedVariable\n comp_array.write_data(file_prefix + comp_array.name + '.pkl',\n **kwargs)","def _multiprocessing_save_srp(in_out_path):\n vp, np = in_out_path\n if not os.path.exists(np + '.npz'):\n save_compact_fft(\n np,\n embedding_fft(load_one_srp_embedding(vp))\n )\n return np","def savez(d,file):\n np.savez(file,row=d.row,col=d.col,data=d.data,shape=d.shape)","def write_file(self):\n print 'Writing '+self.name+' binary...'\n if self.vals is not None:\n if len(self.vals) == self.size:\n stream = self.pack_mem()\n with open(self.name+'.bin','wb') as f:\n f.write(stream)\n print 'File written: '+self.name+'.bin'\n else:\n print 'Error: input array for '+self.name+'is not the right '+\\\n 'size (should be '+str(self.size)+'). Skipping.'\n else:\n print 'No array provided, skipping.'","def writexyz(self,fname):\n xyzfile = open(fname + \".xyz\",\"a+\")\n xyzfile.write(str(self.natoms) + \"\\n\\n\")\n for a in self.atoms:\n \tcxyz = a.xyz - np.array(self.pbc_correction(a.xyz))\n\t\t\txyzfile.write(str(a.type) + \"\\t\" + str(cxyz[0]) + \"\\t\" + str(cxyz[1]) + \"\\t\" + str(cxyz[2]) + \"\\n\")\n xyzfile.close()","def writeNonZeroEntry(prep_fn, tensor_folder, tensor_output_path, slab_inds, vocab_size):\n \"\"\"\n f = open(prep_fn, \"r\")\n lines = f.readlines()\n f.close()\n slab_num = len(lines)\n \"\"\"\n g = open(tensor_output_path, \"a+\")\n for slab_ind in slab_inds:\n with open(tensor_folder+str(slab_ind)+\".sav\", \"rb\") as handle:\n count_mat = joblib.load(handle)\n # only get most frequent vocab\n print \"count_mat original shape:\", count_mat.shape,\n count_mat = count_mat[:vocab_size, :vocab_size]\n print \"count_mat is reduced to:\", count_mat.shape\n\n nonzero_inds = np.nonzero(count_mat)\n nonzero_values = count_mat[nonzero_inds]\n nonzero_coords = np.transpose(nonzero_inds) + 1\n for ind in range(len(nonzero_values)):\n\t val = nonzero_values[ind]\n #log_value = np.log(nonzero_values[ind]+1)\n seq = [str(v) for v in nonzero_coords[ind]] + [str(slab_ind+1)] + [str(val)]\n print >> g, \" \".join(seq)\n g.close()\n print \"done formatting\", slab_inds","def write_po(self, outputfile):\n raise NotImplementedError(\n \"Writing to this file format is not yet implemented\")","def pcwrite(filename, xyzrgb):\n xyz = xyzrgb[:, :3]\n rgb = xyzrgb[:, 3:].astype(np.uint8)\n\n # Write header\n ply_file = open(filename, 'w')\n ply_file.write(\"ply\\n\")\n ply_file.write(\"format ascii 1.0\\n\")\n ply_file.write(\"element vertex %d\\n\" % (xyz.shape[0]))\n ply_file.write(\"property float x\\n\")\n ply_file.write(\"property float y\\n\")\n ply_file.write(\"property float z\\n\")\n ply_file.write(\"property uchar red\\n\")\n ply_file.write(\"property uchar green\\n\")\n ply_file.write(\"property uchar blue\\n\")\n ply_file.write(\"end_header\\n\")\n\n # Write vertex list\n for i in range(xyz.shape[0]):\n ply_file.write(\"%f %f %f %d %d %d\\n\" % (\n xyz[i, 0], xyz[i, 1], xyz[i, 2],\n rgb[i, 0], rgb[i, 1], rgb[i, 2],\n ))","def writeHoc(self):\n print('Writing output file %s ...' % self.outFile)\n with open(self.outFile, 'w') as fOut:\n \n def createSection(secNum):\n fOut.write('create section_%i\\n' %secNum)\n fOut.write('section_%i {\\n' %secNum)\n fOut.write('pt3dclear()\\n')\n for node in xrange(len(self.sections[secNum])):\n fOut.write('pt3dadd(%.6f, %.6f, %.6f, %.6f)\\n' \\\n % (self.sections[secNum][node][0],\n self.sections[secNum][node][1],\n self.sections[secNum][node][2],\n self.secRads[secNum][node]))\n fOut.write('}\\n')\n \n def createConnection():\n for c in xrange(len(self.connections)):\n fOut.write('connect section_%i(1), section_%i(0)\\n' \\\n % (self.connections[c][0],self.connections[c][1]))\n \n \n for sec in self.sections.keys():\n createSection(sec)\n createConnection()\n \n \n return","def save_numpy_array(self):\n np.save(\"smallest_particles.npy\", self.smallest_particles)\n np.save(\"number_counts.npy\", self.number_counts)\n np.save(\"number_counts_2.npy\", self.number_counts_2)","def to_netcdf(self, outfile):","def save_predictions_npz(fn, predictions):\n np.savez_compressed(fn, predictions=predictions)\n print(\"[save_predictions_npz] Wrote to\", fn)","def writedata(filename, inputs, outputs):#=None, noutput=1):\n\t#if outputs == None:\n\t#\toutputs = np.zeros(shape=(np.shape(inputs)[0], noutput))\n\tassert np.shape(inputs)[0] == np.shape(outputs)[0]\n\tndp = np.shape(inputs)[0]\n\tnin = np.shape(inputs)[1]\n\tnout = np.shape(outputs)[1]\n\tf = open(filename, \"w\")\n\tf.write(\"%i %i %i\\n\" % (ndp, nin, nout))\n\tfor inputline, outputline in zip(inputs, outputs):\n\t\tf.write(\" \".join([\"%.6f\" % item for item in inputline]) + \"\\n\")\n\t\tf.write(\" \".join([\"%.6f\" % item for item in outputline]) + \"\\n\")\n\tf.close()\n\tlogger.debug(\"Wrote %s\\n(datapoints : %i, inputs : %i, outputs : %i)\" % (filename, ndp,\n\t\t\t\t\t\t\t\t\t\t nin, nout))","def to_npz(self, file, compressed=False, compute=True):\n labels, nevonecut, nevcutflow, masksonecut, maskscutflow = self.result()\n\n if compressed:\n saver = numpy.savez_compressed\n else:\n saver = numpy.savez\n\n out = CutflowToNpz(\n file, labels, nevonecut, nevcutflow, masksonecut, maskscutflow, saver\n )\n if compute:\n out.compute()\n return None\n else:\n return out","def genPSFimage(filename=None):\n hdu=pf.open(filename)\n nn = len(hdu)\n for i in range(1,nn):\n img = hdu[i].data[0][4:].reshape(npix,npix)\n img = img/img.sum()\n hdu[i].data = img\n #hdu.scale('int16', '', bzero=32768)\n newfilename = filename[:-7]+'_stamp.fits'\n hdu.writeto(newfilename)\n os.system('gzip '+newfilename)","def _save_pca_matrix(self, filename):\n\n np.savetxt(filename, self.pca_matrix, delimiter=\",\")","def create_file_empty_particles( self, fullpath, iteration,\n time, dt, select_nglobal_dict=None ):\n # Create the file (can be done by one proc or in parallel)\n f = self.open_file( fullpath,\n parallel_open=self.write_metadata_parallel )\n\n # Setup the different layers of the openPMD file\n # (f is None if this processor does not participate is writing data)\n if f is not None:\n\n # Setup the attributes of the top level of the file\n self.setup_openpmd_file( f, iteration, time, dt )\n # Setup the meshes group (contains all the particles)\n f.attrs[\"particlesPath\"] = np.string_(\"particles/\")\n particle_path = \"/data/%d/particles/\" %iteration\n particle_grp = f.require_group(particle_path)\n # Loop through all particle species\n for species_name in sorted(self.species_dict.keys()):\n species = self.species_dict[species_name]\n\n # Check the number of particles to write\n if select_nglobal_dict is not None:\n N = select_nglobal_dict[species_name]\n else:\n N = None\n\n # Create and setup the h5py.Group species_grp\n species_path = particle_path+\"%s/\" %(species_name)\n species_grp = f.require_group( species_path )\n self.setup_openpmd_species_group( species_grp, species, N=N )\n\n # Loop over the different quantities that should be written\n # and setup the corresponding datasets\n for particle_var in self.particle_data:\n\n # Vector quantities\n if particle_var in [\"position\", \"momentum\", \"E\", \"B\"]:\n # Setup the dataset\n quantity_path=species_path+ \"%s/\" %particle_var\n quantity_grp = f.require_group(quantity_path)\n for coord in [\"x\",\"y\",\"z\"]:\n # Create the dataset (fixed size or appendable)\n if N is not None:\n dset = quantity_grp.create_dataset(\n coord, (N,), dtype='f8')\n else:\n dset = quantity_grp.create_dataset(\n coord, (0,), maxshape=(None,), dtype='f8')\n self.setup_openpmd_species_component( dset )\n self.setup_openpmd_species_record( quantity_grp,\n particle_var)\n\n # Scalar quantity\n elif particle_var in [\"weighting\", \"id\", \"t\"]:\n # Choose the type of the output\n if particle_var == \"id\":\n dtype = 'uint64'\n else:\n dtype = 'f8'\n # Create the dataset (fixed size or appendable)\n if N is not None:\n dset = species_grp.create_dataset(\n particle_var, (N,), dtype=dtype )\n else:\n dset = species_grp.create_dataset( particle_var,\n (0,), maxshape=(None,), dtype=dtype)\n self.setup_openpmd_species_component( dset )\n self.setup_openpmd_species_record( dset, particle_var )\n\n # Unknown field\n else:\n raise ValueError(\n \"Invalid string in particletypes: %s\" %particle_var)\n\n # Close the file\n f.close()","def prep_npz(self, input_tsv: Path, output_npz: Path,\n batch_size: int = 512 * 128, minibatch_size: int = 128):\n all_ids, all_sents, all_embs = list(), list(), list()\n\n batch_gen = self.batch_generator(input_tsv, batch_size=batch_size)\n for id_batch, sent_batch in batch_gen:\n embs = self.make_vectors(sent_batch, minibatch_size=minibatch_size)\n\n id_batch = np.array(id_batch, dtype=np.int64)\n emb_batch = np.vstack(embs).astype(np.float32)\n sent_batch = np.array(sent_batch, dtype=np.str)\n\n all_ids.append(id_batch)\n all_embs.append(emb_batch)\n all_sents.append(sent_batch)\n\n all_ids = np.concatenate(all_ids, axis=None)\n all_embs = np.concatenate(all_embs, axis=0)\n all_sents = np.concatenate(all_sents, axis=None)\n\n np.savez(output_npz,\n ids=all_ids, embs=all_embs, sents=all_sents,\n compressed=True)","def write_towhee_restart(self, filename, pbc=(0, 0, 0)):\n seeds = [5054388, 14604618, 6176650, 7526479, 6525, 10097385, 9059353,\n 14349506, 535, 7287374, 12195841, 7272997, 5692437, 11292972,\n 1589479, 16351161, 14342867, 3500530, 14385737, 2924396,\n 11857489, 6765405, 12074244, 5940539, 3050519]\n version = 6\n numcycles = 0\n numboxes = 1\n nummoltypes = 1\n maxtranssingle = 0.5\n maxtranscom = 0.15305334\n maxrot = 0.672082\n maxvoldis = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]\n if type(pbc) is list or type(pbc) is tuple:\n pbc = np.asarray(pbc)\n length = np.asarray(self.length) + pbc\n hmatrix = np.diag(length)\n maxvoldis.extend(list(hmatrix))\n with open(filename, 'w') as f:\n f.write(f\"\\t\\t{version}\\n\")\n f.write(\"\\t\" + \"\\t \".join(map(str, seeds)) + \"\\n\")\n f.write(f\"\\t{numcycles} \\t\\t{numboxes} \\t\\t{nummoltypes}\\n\")\n f.write(f\"\\t{maxtranssingle:20.15f}\\n\")\n f.write(f\"\\t{maxtranscom:20.15f}\\n\")\n f.write(f\"\\t{maxrot:20.15f}\\n\\n\")\n for row in maxvoldis:\n f.write(f\"\\t{row[0]:20.15f} \\t{row[1]:20.15f} \\t{row[2]:20.15f}\\n\")\n f.write(f\"\\t{self.numatom // self.molsize}\\n\")\n f.write(f\"\\t{self.molsize}\\n\")\n f.write(\"\\t \".join(map(str, list(self.contents['Mol_type'].astype(int)))) + \"\\n\")\n f.write(\"\\t \".join(map(str, list(self.contents['Box_ID'].astype(int)))) + \"\\n\")\n df = self.contents[['X', 'Y', 'Z']].copy()\n np.savetxt(f, df.values, fmt=\" %20.15f\"*3)","def write_out(self, niter, locuslen):\n try:\n handle = open(self.output, 'w')\n except OSError:\n print 'Error, you do not have permission to write files here.'\n extit(1)\n # First, write the pop names\n handle.write('#Pop 1: ' + self.popnames[0] + '\\n')\n handle.write('#Pop 2: ' + self.popnames[1] + '\\n')\n # Then write the run parameters\n handle.write('#Model: ' + self.modelname + '\\n')\n handle.write('#Max iterations: ' + str(niter) + '\\n')\n # Then write some model summaries\n handle.write('#Data Likelihoods: ' + ' '.join([str(s) for s in self.mod_like]) + '\\n')\n handle.write('#Optimized Likelihoods: ' + ' '.join([str(s) for s in self.opt_like]) + '\\n')\n handle.write('#AIC: ' + ' '.join([str(s) for s in self.aic]) + '\\n')\n handle.write('#LocusLem: ' + str(locuslen) + '\\n')\n handle.write('#4*Na*u*L: ' + str(self.theta_mean) + '\\n')\n handle.write('#Na: ' + str(self.Na) + '\\n')\n for name, val in zip(self.params['Names'], self.scaled_params):\n towrite = '#' + name + ': ' + str(val) + '\\n'\n handle.write(towrite)\n # Then a table of the parameters that were found\n handle.write('Iteration\\t' + '\\t'.join(self.params['Names']) + '\\n')\n handle.write('Initial\\t' + '\\t'.join([str(s) for s in self.params['Values']]) + '\\n')\n # Write the perturbed parameters\n for index, vals in enumerate(self.p_init):\n name = 'Perturbed_' + str(index) + '\\t'\n handle.write(name + '\\t'.join([str(s) for s in vals]) + '\\n')\n # And the hot annealed values\n for index, vals in enumerate(self.hot_params):\n name = 'Hot_Anneal_' + str(index) + '\\t'\n handle.write(name + '\\t'.join([str(s) for s in vals]) + '\\n')\n # And the cold annealed values\n for index, vals in enumerate(self.cold_params):\n name = 'Cold_Anneal_' + str(index) + '\\t'\n handle.write(name + '\\t'.join([str(s) for s in vals]) + '\\n')\n # And the BFGS parameters\n for index, vals in enumerate(self.opt_params):\n name = 'BFGS_' + str(index) + '\\t'\n handle.write(name + '\\t'.join([str(s) for s in vals]) + '\\n')\n # And the final params\n handle.write('Hot_Mean\\t' + '\\t'.join([str(s) for s in self.hot_mean]) + '\\n')\n handle.write('Cold_Mean\\t' + '\\t'.join([str(s) for s in self.cold_mean]) + '\\n')\n handle.write('BFGS_Mean\\t' + '\\t'.join([str(s) for s in self.bfgs_mean]) + '\\n')\n handle.flush()\n handle.close()\n return","def create_normalization_file(use_controls, use_nofcd, mods):\n\n for j in range(1, mods + 1):\n fcd_paths = sorted(glob.glob(FCD_FOLDER + f'fcd_*.{j}.nii.gz'))\n nofcd_paths = sorted(glob.glob(FCD_FOLDER + f'nofcd_*.{j}.nii.gz'))\n control_paths = sorted(glob.glob(CONTROL_FOLDER + f'control_*.{j}.nii.gz'))\n\n mri_paths = fcd_paths\n if use_nofcd:\n mri_paths += nofcd_paths\n if use_controls:\n mri_paths += control_paths\n\n t1_landmarks_path = Path(f'./data/t1_landmarks_{j}.npy')\n\n if t1_landmarks_path.is_file():\n continue\n # os.remove(f'./data/t1_landmarks_{j}.npy')\n\n t1_landmarks = (\n t1_landmarks_path\n if t1_landmarks_path.is_file()\n else HistogramStandardization.train(mri_paths)\n )\n\n np.save(t1_landmarks_path, t1_landmarks, allow_pickle=True)","def convert_data(self):\n print('Saving data for post-processing in: {}'.format(self.RunDir))\n\n for ig in self.Set:\n print(ig.Name)\n # Phase Array - Convert to CM\n phs = roipy.tools.load_half(ig)\n data = phs * ig.Phs2cm\n outname = os.path.join(self.RunDir, 'd_' + ig.Name.replace('unw','npy')) #d for displacement\n ig.ProcName = outname\n np.save(outname,data)\n\n # Copy rsc\n shutil.copyfile(ig.Path + '.rsc', outname + '.rsc')\n\n # Nan Values as mask array\n maskname = outname.replace('d_', 'nans_')\n nans = np.isnan(data) # NOTE: ROI_PAC saves nans as exact 0.0, risks scraping some true data\n np.save(maskname, nans)","def save_prime_array(number_of_primes) -> None:\n p = prime_array(number_of_primes)\n with open(f'prime{number_of_primes}.bin', 'wb') as prime_file:\n p.tofile(prime_file)\n \n return None","def write_file_simple(self,filename):\n\n output = open(filename,\"w\")\n # write header\n output.write(\"# %1s %3s %22s %6s %22s\\n\"%(\"l\",\"n\",\"nu_theo (muHz)\",\"unused\",\"Inertia\"))\n for i in range(self.modes.shape[0]):\n output.write(\" %1d %3d %22.15e 0.0 %22.15e\\n\"%( \\\n self.modes[\"l\"][i], \\\n self.modes[\"n\"][i], \\\n self.modes[\"freq\"][i]*self.glb[ifreq_ref], \\\n self.modes[\"inertia\"][i]))\n output.close()","def write_xyz_file(allxyz):\n if SAVEXYZ:\n print('+> Saving riverbed topography file...', end='')\n if MODE == 1:\n np.savetxt('kinoshita_topo.xyz', allxyz, fmt='%.6e')\n elif MODE == 2:\n np.savetxt(FNAME.rsplit('.', 1)[0] + '_topo.xyz', allxyz, fmt='%.6e')\n print(' [done]')","def write_input_file(self, outfile='input.dat', **options):\n out = ''\n if 'header' in options:\n out += options['header']\n\n if options['program'] == 'orca':\n ecp = f' NewECP \"{options[\"ecp\"]}\" end'\n\n form = ' {:<4}' + ' '*8 + ' {:> 13.8f}' * 3 + '\\n'\n for atom, xyz, charge in self.qc_mol:\n out += form.format(atom, *xyz)\n\n form = ' {:<4}' + ' {:>7.4f}' + ' {:> 13.8f}' * 3\n for atom, xyz, charge in self.br_mol:\n out += form.format(atom + '>', charge, *xyz) + ecp + '\\n'\n\n if 'separate_pc' in options:\n form = '{:>7.4f}' + ' {:> 13.8f}' * 3\n pc_out = f'{len(self.pc_mol)}\\n'\n for atom, xyz, charge in self.pc_mol:\n pc_out += form.format(charge, *xyz) + '\\n'\n with open(options['separate_pc'], 'w') as f:\n f.write(pc_out)\n else:\n for atom, xyz, charge in self.pc_mol:\n out += form.format('Q', charge, *xyz) + '\\n'\n\n out += '*'\n\n else:\n raise Exception(f'{options[\"program\"]} is not yet supported')\n\n if 'footer' in options:\n out += options['footer']\n\n with open(outfile, 'w') as f:\n f.write(out)","def save(self, ofilename, oname, noisy_only = True):\n ofile = ROOT.TFile(ofilename, 'recreate')\n\n outhists = [h.Clone(oname % (i + 1)) for i, h in enumerate(self.modules)]\n for h, cells in zip(outhists, self.cells):\n if noisy_only: h.Reset()\n for cell in cells: h.SetBinContent(cell[0], cell[1], noisy_only * 1.)\n # h.Write()\n\n ofile.Write()\n ofile.Close()","def _create_pbs_file(self, jobname, num_jobs, pf_pbs, pf_input_package_template, pf_output_package_template):\n\n # create unique compute directory\n pd_compute = None # run_shell_cmd(\"mktemp --tmpdir={}\".format(self._prl_options[\"pbs-pd-root-compute\"]))\n\n pbs_text = PBS._generate_pbs_header_array(num_jobs, jobname, self._prl_options, pd_compute=pd_compute)\n\n pbs_text += \"\\n{}\\n\".format(\n PBS._generate_call_command(self._env,\n pf_input_package_template,\n pf_output_package_template,\n self._prl_options,\n pd_compute=pd_compute\n )\n )\n\n # write to file\n write_to_file(pbs_text, pf_pbs)","def WriteFile( self ):\n with open( \"BasisVector.in\" , \"w\" ) as outfile:\n firstLine = \" \" + str( self.NQ ) + \\\n \" \" + str( self.Nbranches ) + \\\n \" \" + str( self.NatomsUC ) + \\\n \" \" + str( self.dim ) + \"\\n\"\n outfile.write( firstLine )\n for qq in range( self.NQ ): ## loop over Q vectors\n lineQ = [ \"{:15.8f}\".format( x ) for x in \n self.QVectors[ qq , : ] ]\n lineQ = \"\".join( lineQ )\n outfile.write( lineQ + \"\\n\" )\n for branch in range( self.Nbranches ): ## loop over branches\n for atom in range( self.NatomsUC ): ## loop over atoms in unit cell\n line = [ \"{:15.8f}\".format( x ) for x in \n self.EigenVectors[ qq , branch , atom , : ] ]\n line = \"\".join( line )\n outfile.write( line + \"\\n\" )\n outfile.write( \"\\n\" )\n outfile.write( \"\\n\" )","def to_npz(self, file, compressed=False, compute=True):\n labels, nev, masks = self.result()\n\n if compressed:\n saver = numpy.savez_compressed\n else:\n saver = numpy.savez\n\n out = NminusOneToNpz(file, labels, nev, masks, saver)\n if compute:\n out.compute()\n return None\n else:\n return out","def create_and_write_output(predictions_path,output_path,inpDir):\n \n filenames= sorted(os.listdir(predictions_path)) \n for filename in filenames:\n \n # read the 3 channel output image from the neural network\n image=cv2.imread(os.path.join(predictions_path,filename))\n \n # create binary image output using the create_binary function\n out_image=create_binary(image) \n \n # read and store the metadata from the input image\n with BioReader(os.path.join(inpDir,filename)) as br:\n metadata = br.metadata\n\n # Write the binary output consisting of the metadata using bfio.\n output_image_5channel=np.zeros((out_image.shape[0],out_image.shape[1],1,1,1),dtype=np.uint8)\n output_image_5channel[:,:,0,0,0]=out_image \n\n with BioWriter(os.path.join(output_path,filename), metadata=metadata) as bw:\n bw.dtype = output_image_5channel.dtype\n bw.write(output_image_5channel)","def write(self):\n #\n if self.what == 'ecutwfc':\n for i in range(self.Ndata):\n self.pwinput.filename = self.inpFiles[i]\n self.pwinput.SYSTEM.set_ecutwfc(self.values[i])\n self.pwinput.write()\n #\n elif self.what == 'ecutrho':\n for i in range(self.Ndata):\n self.pwinput.filename = self.inpFiles[i]\n self.pwinput.SYSTEM.ecutrho = self.values[i]\n self.pwinput.write()\n elif self.what == 'kpoints':\n for i in range(self.Ndata):\n self.pwinput.filename = self.inpFiles[i]\n self.pwinput.Nk = self.values[i]\n self.pwinput.write()\n #\n else:\n raise RuntimeError('what = %s is not implemented yet' % (self.what))\n #\n self.inputs_have_been_written = True","def write_file(self):\r\n # -open file for writing\r\n f_fbob = open(self.fn_path, 'w')\r\n\r\n # -write header\r\n f_fbob.write('%s\\n' % (self.heading))\r\n\r\n # -write sections 1 & 2 : NOTE- what about NOPRINT?\r\n f_fbob.write('%10i%10i%10i%10i\\n' % (self.nqfb, self.nqcfb,\r\n self.nqtfb, self.iufbobsv))\r\n f_fbob.write('%10e\\n' % (self.tomultfb)) # check format\r\n\r\n # -write sections 3-5 looping through observations groups\r\n c = 0\r\n for i in range(self.nqfb):\r\n # while (i < self.nqfb):\r\n # write section 3\r\n f_fbob.write('{:10d}{:10d}\\n'.format(self.nqobfb[i],\r\n self.nqclfb[i]))\r\n\r\n # Loop through observation times for the groups\r\n for j in range(self.nqobfb[i]):\r\n # -write section 4\r\n f_fbob.write(\r\n '{}{:10d}{:10.4g}{}{:10.4g}\\n'.format(self.obsnam[c],\r\n self.irefsp[c],\r\n self.toffset[c], ' ',\r\n self.flwobs[c]))\r\n c += 1 # index variable\r\n\r\n # -write section 5 - NOTE- need to adjust factor for muliple obs same cell\r\n for j in range(abs(self.nqclfb[i])):\r\n if self.nqclfb[\r\n i] < 0: # set factor to 1.0 for all cells in group\r\n self.factor[i, :] = 1.0\r\n f_fbob.write('{:10d}{:10d}{:10d}{}{:10f}\\n'\r\n .format(self.layer[i, j], (self.row[i, j]),\r\n self.column[i, j],\r\n ' ', self.factor[\r\n i, j])) # note- is 10f good enough here?\r\n\r\n f_fbob.close()\r\n #\r\n # swm: BEGIN hack for writing standard file\r\n sfname = self.fn_path # swm:hack\r\n sfname += '_ins' # swm: hack\r\n # write header\r\n f_ins = open(sfname, 'w') # swm: hack for standard file\r\n f_ins.write('jif @\\n') # swm: hack for standard file\r\n f_ins.write('StandardFile 0 1 %s\\n' % (\r\n self.nqtfb)) # swm: hack for standard file\r\n for i in range(0, self.nqtfb):\r\n f_ins.write(\r\n '{}\\n'.format(self.obsnam[i])) # swm: hack for standard file\r\n\r\n f_ins.close()\r\n # swm: END hack for writing standard file\r\n\r\n return","def analyze_pressure_dump(filename, Lx=200., Ly=200, Lz=900., N=10, bin_divide_flag=False, Natoms=113579):\n myfile = open(filename+'.txt')\n trajectory = []\n traj_pd = []\n frames = []\n\n for _ in range(3):\n next(myfile)\n count = 0\n while EOF(myfile):\n count += 1\n s = next(myfile) # info with the time step\n\n x = np.zeros(N, dtype=[('Chunk',np.float32), ('Coord1',np.float32), ('Ncount',np.float32), ('density',np.float32), ('temp',np.float32), ('vx',np.float32), ('fx',np.float32),('c_pciKE[1]',np.float32), ('c_pciKE[2]',np.float32), ('c_pciKE[3]',np.float32), ('c_pciVIR[1]',np.float32), ('c_pciVIR[2]',np.float32), ('c_pciVIR[3]',np.float32), ('c_pgelELAS[1]',np.float32), ('c_pgelELAS[2]',np.float32), ('c_pgelELAS[3]',np.float32), ('c_pgelVIR[1]', np.float32), ('c_pgelVIR[2]', np.float32), ('c_pgelVIR[3]', np.float32), ('c_pgelPAIR[1]', np.float32), ('c_pgelPAIR[2]', np.float32), ('c_pgelPAIR[3]', np.float32)])\n\n# Chunk Coord1 Ncount density/number temp vx fx c_pciKE[1] c_pciKE[2] c_pciKE[3] c_pciVIR[1] c_pciVIR[2] c_pciVIR[3] c_pgelELAS[1] c_pgelELAS[2] c_pgelELAS[3] c_pgelVIR[1] c_pgelVIR[2] c_pgelVIR[3] c_pgelPAIR[1] c_pgelPAIR[2] c_pgelPAIR[3]\n\n list_line = re.findall(\"[-+]?\\d+[\\.]?\\d*[eE]?[-+]?\\d*\", s)\n frame, _, _ = list_line\n frames.append(int(frame))\n # print( \"reading lines\")\n\n for i in xrange(N):\n count += 1\n s = next(myfile)\n list_line = re.findall(\"[-+]?\\d+[\\.]?\\d*[eE]?[-+]?\\d*\", s)\n # print( \"reading line\", i, list_line)\n for il, l in enumerate(list_line):\n x[i][il] = float(l)\n\n trajectory.append(x)\n\n # names = x.dtype.fields.keys()\n # data = x.dtype.fields.values()\n\n df = pd.DataFrame.from_records(x)\n traj_pd.append(df)\n\n myfile.close()\n\n\n\n # # volume = 218.*44.*44.\n volume = Lx*Ly*Lz\n # N_atoms = 113579\n # if bin_divide_flag:\n # bin_volume = volume / float(N)\n # else:\n # bin_volume = 1.\n\n bin_volume = volume / float(N)\n # bin_volume = volume\n # bin_volume /= float(Natoms)\n\n Combine_PD = pd.concat(traj_pd)\n FINAL_PD = pd.DataFrame()\n\n FINAL_PD['Coord1'] = Combine_PD['Coord1']\n FINAL_PD['p_ciKE'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pciKE[1]'] + Combine_PD['c_pciKE[2]'] + Combine_PD['c_pciKE[3]'])/(3.*bin_volume)\n FINAL_PD['p_ciVIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pciVIR[1]'] + Combine_PD['c_pciVIR[2]'] + Combine_PD['c_pciVIR[3]'])/(3.*bin_volume)\n FINAL_PD['p_gelELAS'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelELAS[1]'] + Combine_PD['c_pgelELAS[2]'] + Combine_PD['c_pgelELAS[3]'])/(3.*bin_volume)\n\n FINAL_PD['p_gelVIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelVIR[1]'] + Combine_PD['c_pgelVIR[2]'] + Combine_PD['c_pgelVIR[3]'])/(3.*bin_volume)\n FINAL_PD['p_gelPAIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelPAIR[1]'] + Combine_PD['c_pgelPAIR[2]'] + Combine_PD['c_pgelPAIR[3]'])/(3.*bin_volume)\n\n # So now I have to\n # P_bin = (sigma_per_atom_xx + ... + sigma_per_atom_zz)/(bin_volume*3)\n # *N_atoms_per_bin\n # N_atoms_per_bin = number_density*N_atoms\n\n\n df_concat = FINAL_PD\n\n by_row_index = df_concat.groupby(df_concat.index)\n df_means = by_row_index.mean()\n by_row_index_2 = df_concat.groupby(df_concat.index)\n df_stds = by_row_index_2.std()\n\n # print( df_means.head())\n # print( df_stds.head())\n return df_means, df_stds","def export_to_phy(waveform_extractor, output_folder, compute_pc_features=True,\n compute_amplitudes=True, sparsity_dict=None, copy_binary=True,\n max_channels_per_template=16, remove_if_exists=False,\n peak_sign='neg', template_mode='median',\n dtype=None, verbose=True, **job_kwargs):\n assert isinstance(waveform_extractor, spikeinterface.core.waveform_extractor.WaveformExtractor), \\\n 'waveform_extractor must be a WaveformExtractor object'\n recording = waveform_extractor.recording\n sorting = waveform_extractor.sorting\n\n assert recording.get_num_segments() == sorting.get_num_segments(), \\\n \"The recording and sorting objects must have the same number of segments!\"\n\n assert recording.get_num_segments() == 1, \"Export to phy work only with one segment\"\n\n if sparsity_dict is None:\n sparsity_dict = dict(method=\"best_channels\", num_channels=16)\n\n channel_ids = recording.channel_ids\n num_chans = recording.get_num_channels()\n fs = recording.get_sampling_frequency()\n\n if max_channels_per_template is None:\n max_channels_per_template = num_chans\n\n empty_flag = False\n non_empty_units = []\n for unit in sorting.get_unit_ids():\n if len(sorting.get_unit_spike_train(unit)) > 0:\n non_empty_units.append(unit)\n else:\n empty_flag = True\n unit_ids = non_empty_units\n if empty_flag:\n print('Warning: empty units have been removed when being exported to Phy')\n\n if not recording.is_filtered():\n print(\"Warning: recording is not filtered! It's recommended to filter the recording before exporting to phy.\\n\"\n \"You can run spikeinterface.toolkit.preprocessing.bandpass_filter(recording)\")\n\n if len(unit_ids) == 0:\n raise Exception(\"No non-empty units in the sorting result, can't save to Phy.\")\n\n output_folder = Path(output_folder).absolute()\n if output_folder.is_dir():\n if remove_if_exists:\n shutil.rmtree(output_folder)\n else:\n raise FileExistsError(f'{output_folder} already exists')\n\n output_folder.mkdir()\n\n # save dat file\n if dtype is None:\n dtype = recording.get_dtype()\n\n if copy_binary:\n rec_path = output_folder / 'recording.dat'\n write_binary_recording(recording, file_paths=rec_path, verbose=verbose, dtype=dtype, **job_kwargs)\n elif isinstance(recording, BinaryRecordingExtractor):\n rec_path = recording._kwargs['file_paths'][0]\n dtype = recording.get_dtype()\n else: # don't save recording.dat\n rec_path = 'None'\n\n dtype_str = np.dtype(dtype).name\n\n # write params.py\n with (output_folder / 'params.py').open('w') as f:\n f.write(f\"dat_path = r'{str(rec_path)}'\\n\")\n f.write(f\"n_channels_dat = {num_chans}\\n\")\n f.write(f\"dtype = '{dtype_str}'\\n\")\n f.write(f\"offset = 0\\n\")\n f.write(f\"sample_rate = {fs}\\n\")\n f.write(f\"hp_filtered = {recording.is_filtered()}\")\n\n # export spike_times/spike_templates/spike_clusters\n # here spike_labels is a remapping to unit_index\n all_spikes = sorting.get_all_spike_trains(outputs='unit_index')\n spike_times, spike_labels = all_spikes[0]\n np.save(str(output_folder / 'spike_times.npy'), spike_times[:, np.newaxis])\n np.save(str(output_folder / 'spike_templates.npy'), spike_labels[:, np.newaxis])\n np.save(str(output_folder / 'spike_clusters.npy'), spike_labels[:, np.newaxis])\n\n # export templates/templates_ind/similar_templates\n # shape (num_units, num_samples, num_channels)\n templates = []\n templates_ind = []\n\n template_sparsity = get_template_channel_sparsity(waveform_extractor,\n outputs=\"id\", **sparsity_dict)\n num_sparse_chans = np.max([len(channels) for channels in template_sparsity.values()])\n num_channels = np.min([max_channels_per_template, num_sparse_chans])\n for unit_id in unit_ids:\n template = waveform_extractor.get_template(unit_id, mode=template_mode, sparsity=template_sparsity)\n inds = waveform_extractor.recording.ids_to_indices(template_sparsity[unit_id])\n if template.shape[-1] < num_channels:\n # fill missing channels\n template_full = np.zeros((template.shape[0], num_channels))\n template_full[:, :template.shape[-1]] = template\n inds_full = np.concatenate((inds, np.array([-1] * (num_channels - template.shape[-1]))))\n else:\n template_full = template\n inds_full = inds\n templates.append(template_full)\n templates_ind.append(inds_full)\n\n template_similarity = compute_template_similarity(waveform_extractor, method='cosine_similarity')\n\n np.save(str(output_folder / 'templates.npy'), templates)\n np.save(str(output_folder / 'template_ind.npy'), templates_ind)\n np.save(str(output_folder / 'similar_templates.npy'), template_similarity)\n\n channel_maps = np.arange(num_chans, dtype='int32')\n channel_map_si = waveform_extractor.recording.get_channel_ids()\n channel_positions = recording.get_channel_locations().astype('float32')\n channel_groups = recording.get_channel_groups()\n if channel_groups is None:\n channel_groups = np.zeros(num_chans, dtype='int32')\n np.save(str(output_folder / 'channel_map.npy'), channel_maps)\n np.save(str(output_folder / 'channel_map_si.npy'), channel_map_si)\n np.save(str(output_folder / 'channel_positions.npy'), channel_positions)\n np.save(str(output_folder / 'channel_groups.npy'), channel_groups)\n\n if compute_amplitudes:\n amplitudes = get_spike_amplitudes(waveform_extractor, peak_sign=peak_sign, outputs='concatenated', **job_kwargs)\n # one segment only\n amplitudes = amplitudes[0][:, np.newaxis]\n np.save(str(output_folder / 'amplitudes.npy'), amplitudes)\n\n if compute_pc_features:\n pc = WaveformPrincipalComponent(waveform_extractor)\n pc.set_params(n_components=5, mode='by_channel_local')\n pc.run_for_all_spikes(output_folder / 'pc_features.npy',\n max_channels_per_template=max_channels_per_template, peak_sign=peak_sign,\n **job_kwargs)\n\n max_channels_per_template = min(max_channels_per_template, len(channel_ids))\n pc_feature_ind = np.zeros((len(unit_ids), max_channels_per_template), dtype='int64')\n best_channels_index = get_template_channel_sparsity(waveform_extractor, method='best_channels',\n peak_sign=peak_sign, num_channels=max_channels_per_template,\n outputs='index')\n\n for u, unit_id in enumerate(sorting.unit_ids):\n pc_feature_ind[u, :] = best_channels_index[unit_id]\n np.save(str(output_folder / 'pc_feature_ind.npy'), pc_feature_ind)\n\n # Save .tsv metadata\n with (output_folder / 'cluster_group.tsv').open('w') as tsvfile:\n writer = csv.writer(tsvfile, delimiter='\\t', lineterminator='\\n')\n writer.writerow(['cluster_id', 'group'])\n for i, u in enumerate(sorting.get_unit_ids()):\n writer.writerow([i, 'unsorted'])\n\n unit_groups = sorting.get_property('group')\n if unit_groups is None:\n unit_groups = np.zeros(len(unit_ids), dtype='int32')\n\n with (output_folder / 'cluster_channel_group.tsv').open('w') as tsvfile:\n writer = csv.writer(tsvfile, delimiter='\\t', lineterminator='\\n')\n writer.writerow(['cluster_id', 'channel_group'])\n for i, unit_id in enumerate(unit_ids):\n writer.writerow([i, unit_groups[i]])\n\n if verbose:\n print('Run:\\nphy template-gui ', str(output_folder / 'params.py'))","def create_dnz_file(args):\n\n file = open(args.o, 'w')\n\n file.write(\"% ----DATA VARIABLES----\\n\\n\")\n file.write(\"t=\" + str(args.t) + \";\" + \"%number of attributes\\n\")\n file.write(\"k=\" + str(args.k) + \";\" + \"%max length of the support set\\n\")\n file.write(\"n=\" + str(args.n) + \";\" + \"%number of positive instances\\n\")\n file.write(\"m=\" + str(args.m) + \";\" + \"%number of negative instances\\n\")\n file.write(\"c=\" + str(args.c) + \";\" + \"%number of atMostOne Constraints\\n\\n\")\n\n file.write(\"% ----OMEGAS----\\n\\n\")\n\n omega_p = generate_omega_data(args.t, args.n, args.b)\n file.write(\"omegap= \" + omega_to_mz(omega_p) + \"\\n\\n\")\n\n omega_n = generate_disjoint_omega_data(omega_p, args.m, args.b)\n file.write(\"omegan= \" + omega_to_mz(omega_n) + \"\\n\\n\")\n\n file.write(\"% ----CONSTRAINS----\\n\\n\")\n at_most_one = generate_at_most_one(int(args.t/2), args.c, 1, args.t)\n file.write(\"atMostOne=\" + at_most_one_to_mz(at_most_one))","def export_prop(self, d, title, t):\r\n self._check_out(title)\r\n np.savez_compressed(os.path.join(self.out_dir, title, title + '_' + str(t)), d)","def dict2file(dict, filename, foldername):\n if foldername:\n if not os.path.exists(\"../Created_QD/\" + foldername):\n os.makedirs(\"../Created_QD/\" + foldername)\n file = open(\"../Created_QD/\" + foldername + \"/\" + filename + \".xyz\", \"w\")\n else:\n file = open(\"../Created_QD/\" + filename + \".xyz\", \"w\")\n file.write(\" \\n\\n\")\n for atom, values in dict.items():\n file.write(values['element'] + \"\\t\" + str(values['coor'][0]) + \"\\t\\t\" +\n str(values['coor'][1]) + \"\\t\\t\" + str(values['coor'][2]) + \"\\n\")\n file.seek(0)\n file.write(str(len(dict)))\n file.close()\n print(\"\\nQuantum Dot created :)\")","def save_results(self, name):\n # metrics in npz format\n # filename = join(self.out_dir, '%s_metrics.npz' % name)\n # np.savez(filename, tel_r=self.tel_r, cable_length=self.cable_length,\n # cable_length_2=self.cable_length_2, uv_hist=self.uv_hist)\n\n if self.cable_length: # Empty dict() evaluate to False\n # ASCII CSV table of radius vs cable length\n filename = join(self.out_dir, '%s_cables.txt' % name)\n data = np.array([[k, v] for k, v in\n self.cable_length.iteritems()])\n data = np.sort(data, axis=0)\n np.savetxt(filename, data, fmt=b'%.10f %.10f')\n\n if self.cable_length_2: # Empty dict() evaluate to False\n # ASCII CSV table of radius vs cable length\n filename = join(self.out_dir, '%s_cables_2.txt' % name)\n data = np.array([[k, v] for k, v in\n self.cable_length_2.iteritems()])\n data = np.sort(data, axis=0)\n np.savetxt(filename, data, fmt=b'%.10f %.10f')\n\n if self.cable_length_3: # Empty dict() evaluates to False\n filename = join(self.out_dir, '%s_cables.txt' % name)\n data = np.array([[k, v] for k, v in\n self.cable_length_3.iteritems()])\n data = np.sort(data, axis=0)\n np.savetxt(filename, data, fmt=b'%.10f %.10f')\n\n # Save a pickle with the PSF comparison info.\n if self.psf:\n filename = join(self.out_dir, '%s_psf.p' % name)\n pickle.dump(self.psf, open(filename, 'wb'))\n\n # Save a pickle of uv hist data.\n if self.uv_hist:\n filename = join(self.out_dir, '%s_uv_hist.p' % name)\n pickle.dump(self.uv_hist, open(filename, 'wb'))","def generate(self):\n self._open_file()\n # copied from GenerateCSPEC.py\n self._write_header_and_defaults()\n self._write_source()\n self._write_sample()\n\n self._write_all_components()\n self._write_mantle_module()\n self._write_segment()\n self._write_all_ids()\n self._write_footer()\n self._close_file()","def PrintCommPkgGZ(self, file, precision=16):\n return _hypre.HypreParMatrix_PrintCommPkgGZ(self, file, precision)","def write_input(self, suffix=''):\n \n out_fname = \"input.plasma_1d\"+suffix\n with open(out_fname, 'w+') as outfile:\n outfile.write('# Input file for ASCOT containing radial 1D information of plasma temperature,density and toroidal rotation \\n')\n outfile.write('# range must cover [0,1] of normalised poloidal rho. It can exceed 1. \\n')\n outfile.write('# {:s} (first 3 lines are comment lines) \\n'.format(time.strftime('%d%b%y')))\n outfile.write('{:d}\\t{:1d}\\t# Nrad,Nion \\n'.format(self.nrho,self.nion))\n strcoll = str(1)+' ' # for electrons\n strZ=''\n strA=''\n for i in range(self.nion):\n strZ += str(self.Z[i]) + ' '\n strA += str(self.A[i]) + ' '\n strcoll += str(int(self.coll_mode[i])) + ' '\n strZ +='\\t\\t# ion Znum \\n'\n strA +='\\t\\t# ion Amass \\n'\n strcoll += '# collision mode (0= no colls, 1=Maxw colls, 2=binary colls, 3=both colls) 1st number is for electrons \\n'\n outfile.write(strZ)\t\t\t\t\n outfile.write(strA)\n outfile.write(strcoll)\n \n lab_len=15\n strlabel='RHO (pol)'.ljust(lab_len)+'Te (eV)'.ljust(lab_len)+'Ne (1/m3)'.ljust(lab_len)+'Vtor_I (rad/s)'.ljust(lab_len)+\\\n 'Ti1 (eV)'.ljust(lab_len)\n for i in range(self.nion):\n tmpstr ='Ni{:d} (1/m3)'.format(i+1)\n strlabel+=tmpstr.ljust(lab_len)\n strlabel+='\\n'\n outfile.write(strlabel)\n data=np.array((self.rho, self.te, self.ne, self.vt, self.ti), dtype=float)\n data = np.concatenate([data, [self.ni[i,:] for i in range(self.nion)]])\n\n data=np.transpose(data)\n #print(data)\n #print(\"if i don't print, it won't work\")\n np.savetxt(outfile, data, fmt='%.5e')","def write_data():\n\n data_location = os.path.realpath(os.path.join(os.path.dirname(__file__), \"..\", DATA_DIR))\n\n sbi_file_name = os.path.join(data_location, SBI_FILE)\n\n sbi = SbiInfo(sbi_file_name)\n\n # the test file is stored in the same directory as the script\n test_file = os.path.splitext(os.path.join(os.path.dirname(__file__), SBI_FILE))[0] + \".pkl\"\n _logger.info(\"Writing header object to {}\".format(os.path.join(os.path.dirname(__file__),\n test_file)))\n sbi.data.to_pickle(test_file)","def write_output(filename, output_data):\n\n folder = os.path.dirname(filename)\n if not os.path.exists(folder):\n os.mkdir(folder)\n\n pickle.dump(output_data, open('dump/' + filename + '.pickle', 'wb'))\n\n with open(filename, 'w') as f:\n f.write(str(len(output_data)) + '\\n')\n for slide in output_data:\n f.write(' '.join(map(str, slide)) + '\\n')","def write(self, stable_poses, min_prob=0):\n R_list = []\n for pose in stable_poses:\n if pose.p >= min_prob:\n R_list.append([pose.p, pose.r, pose.x0])\n\n f = open(self.filepath_[:-4] + \".stp\", \"w\")\n f.write(\"#############################################################\\n\")\n f.write(\"# STP file generated by UC Berkeley Automation Sciences Lab #\\n\")\n f.write(\"# #\\n\")\n f.write(\"# Num Poses: %d\" %len(R_list))\n for _ in range(46 - len(str(len(R_list)))):\n f.write(\" \")\n f.write(\" #\\n\")\n f.write(\"# Min Probability: %s\" %str(min_prob))\n for _ in range(40 - len(str(min_prob))):\n f.write(\" \")\n f.write(\" #\\n\")\n f.write(\"# #\\n\")\n f.write(\"#############################################################\\n\")\n f.write(\"\\n\")\n\n # adding R matrices to .stp file\n pose_index = 1\n for i in range(len(R_list)):\n f.write(\"p %f\\n\" %R_list[i][0])\n f.write(\"r %f %f %f\\n\" %(R_list[i][1][0][0], R_list[i][1][0][1], R_list[i][1][0][2]))\n f.write(\" %f %f %f\\n\" %(R_list[i][1][1][0], R_list[i][1][1][1], R_list[i][1][1][2]))\n f.write(\" %f %f %f\\n\" %(R_list[i][1][2][0], R_list[i][1][2][1], R_list[i][1][2][2]))\n f.write(\"x0 %f %f %f\\n\" %(R_list[i][2][0], R_list[i][2][1], R_list[i][2][2]))\n f.write(\"\\n\\n\")\n f.close()","def write_data_card(spec, data_card, channels, path):\n with open(path, \"w\") as f:\n f.write(f\"imax {str(size(data_card.bins))}\" + \"\\n\")\n f.write(\n \"jmax \"\n + str(size(data_card.processes) - size(data_card.isSignal.keys()))\n + \"\\n\"\n )\n f.write(f\"kmax {str(size(data_card.systs, 0))}\" + \"\\n\")\n\n if data_card.hasShapes:\n for channel in data_card.shapeMap.keys():\n for sample in data_card.shapeMap[channel].keys():\n f.write(\n f\"shapes {sample} {channel} {data_card.shapeMap[channel][sample][0]} {data_card.shapeMap[channel][sample][1]}\"\n )\n if size(data_card.shapeMap[channel][sample]) > 2:\n f.write(f\" {data_card.shapeMap[channel][sample][2]}\" + \"\\n\")\n else:\n f.write(\"\\n\")\n\n f.write(\"\\n---------------------------------\\n\")\n f.write(\"bin \")\n for bin in data_card.obs.keys():\n f.write(f\"{bin} \")\n f.write(\"\\n\")\n f.write(\"observation \")\n for channel in data_card.obs.keys():\n f.write(f\"{str(data_card.obs[channel])} \")\n f.write(\"\\n---------------------------------\\n\")\n f.write(\"bin \")\n for channel in data_card.obs.keys():\n for sample in data_card.exp[channel].keys():\n f.write(f\"{channel} \")\n f.write(\"\\n\")\n f.write(\"process \")\n for channel in data_card.bins:\n for sample in data_card.exp[channel].keys():\n f.write(f\"{sample} \")\n f.write(\"\\n\")\n f.write(\"process \")\n for channel in data_card.bins:\n for sample in data_card.exp[channel].keys():\n if sample in data_card.signals:\n f.write(f\"{str(-1 * data_card.processes.index(sample))} \")\n else:\n f.write(f\"{str(data_card.processes.index(sample) + 1)} \")\n f.write(\"\\n\")\n f.write(\"rate \")\n for channel in data_card.bins:\n for sample in data_card.exp[channel].keys():\n\n f.write(f\"{str(data_card.exp[channel][sample])} \")\n f.write(\"\\n---------------------------------\\n\")\n for syst in data_card.systs:\n f.write(f\"{syst[0]} {syst[2]} \")\n for bin in syst[4].keys():\n for sample in data_card.exp[bin].keys():\n if syst[4][bin][sample] != 0:\n f.write(f\"{str(syst[4][bin][sample])} \")\n else:\n f.write(\"- \")\n\n f.write(\"\\n\")\n f.write(\"\\n---------------------------------\\n\")\n for cAp in data_card.rateParams.keys():\n _dir = cAp.split(\"AND\")\n for i in range(size(data_card.rateParams[cAp], 0)):\n if size(data_card.rateParams[cAp][i][0]) > 3:\n f.write(\n f\"{str(data_card.rateParams[cAp][i][0][0])} rateParam {_dir[0]} {_dir[1]} {str(data_card.rateParams[cAp][i][0][1])} {data_card.rateParams[cAp][i][0][3]}\"\n )\n else:\n f.write(\n f\"{str(data_card.rateParams[cAp][i][0][0])} rateParam {_dir[0]} {_dir[1]} {str(data_card.rateParams[cAp][i][0][1])}\"\n )\n f.write(\"\\n\")\n f.write(\"\\n---------------------------------\\n\")\n for idxc, channel in enumerate(channels):\n if (\n channel in data_card.binParFlags.keys()\n and data_card.binParFlags[channel] == True\n ):\n # double check to be safe\n shapesys = False\n staterror = False\n for sample in spec[\"channels\"][idxc][\"samples\"]:\n mod_types = [mod[\"type\"] for mod in sample[\"modifiers\"]]\n if \"shapesys\" in mod_types:\n shapesys = True\n elif \"staterror\" in mod_types:\n staterror = True\n\n if shapesys:\n f.write(f\"{channel} autoMCStats 100000 0 2\" + \"\\n\")\n if staterror:\n f.write(f\"{channel} autoMCStats 0 0 2\" + \"\\n\")","def save_data_to_disk(self):\n Omega_M = self.theta_fid[0]\n for key in self.data.keys():\n np.save(f'./preloaded_data/{Omega_M}_{self.delta_theta[0]}_{key}.npy', self.data[key])","def write_data():","def write_files(neither, both, c_only, f_only, fh_c, data_c_start):\n\n # output arrays for all pixels in input datasets\n write_to_file(neither.astype(np.int16), 'neither_cube.fits')\n write_to_file(both.astype(np.int16), 'both_cube.fits')\n write_to_file(c_only.astype(np.int16), 'c_only_cube.fits')\n write_to_file(f_only.astype(np.int16), 'f_only_cube.fits')\n\n # output arrays for pixels in 2d array\n print(' Within the 2d arrays:')\n if (fh_c[0].header['NAXIS'] == 3): # for nirspec with 1 integration\n write_to_file(neither.sum(axis=0), 'neither_2d.fits')\n write_to_file(both.sum(axis=0), 'both_2d.fits')\n write_to_file(c_only.sum(axis=0), 'c_only_2d.fits')\n write_to_file(f_only.sum(axis=0), 'f_only_2d.fits')\n print(' The fraction of pixels in the 2d array having true CRs:',\\\n float(len(np.where(both.sum(axis=0) != 0.)[0])) / data_c_start.size)\n elif (fh_c[1].header['NAXIS'] == 4): # for miri or nircam cases\n write_to_file(neither.sum(axis=1).sum(axis=0), 'neither_2d.fits')\n write_to_file(both.sum(axis=1).sum(axis=0), 'both_2d.fits')\n write_to_file(c_only.sum(axis=1).sum(axis=0), 'c_only_2d.fits')\n write_to_file(f_only.sum(axis=1).sum(axis=0), 'f_only_2d.fits')\n print(' The fraction of pixels in the 2d array having true CRs:',\\\n float(len(np.where(both.sum(axis=1).sum(axis=0) != 0.)[0])) / \\\n data_c_start.size)\n else:\n print('FATAL ERROR - unexpected case in write_file()')","def write_features(self):\r\n def pack_keypoint(keypoints, descriptors):\r\n kpts = np.array([[kp.pt[0], kp.pt[1], kp.size,\r\n kp.angle, kp.response, kp.octave,\r\n kp.class_id]\r\n for kp in keypoints])\r\n desc = np.array(descriptors)\r\n return kpts, desc\r\n\r\n filename = self.features_path + self.id\r\n kpts, desc = pack_keypoint(self.keypoints, self.descriptors)\r\n logging.info(f'Writing features of image {self.name} to file...')\r\n np.savez(filename, keypoints=kpts, descriptors=desc)\r\n logging.info('Features saved.')","def save_data_01(outputfilepath,summary):\n ext=os.path.splitext(outputfilepath)[-1].lower()\n if ext=='.mat':\n savemat(outputfilepath,summary,long_field_names=True)\n return outputfilepath\n elif ext=='.npz':\n np.savez(outputfilepath,summary=summary)\n elif ext=='.pdb':\n make_pdb_file_01(outputfilepath,np.array(summary['X_evol'][-1]))\n else:\n if ext!='.json':\n outputfilepath+='.json'\n with open(outputfilepath, 'w') as result:\n # serialize\n summary['json_task']='not stored'\n serialized_summary=jsonify_01(summary)\n json.dump(serialized_summary, result,indent=4,sort_keys=True)","def _multiprocessing_save_sp(in_out_path):\n vp, np = in_out_path\n if not os.path.exists(np + '.npz'):\n save_compact_fft(\n np,\n embedding_fft(load_one_sp_embedding(vp))\n )\n return np","def generate_output_file(data, extension, headers):\n output_data = _replace_boolean(data)\n output_name = _generate_output_name(extension)\n with open(output_name, 'a', newline='') as file:\n _file_writer(file, extension, output_data, headers)","def exportData(self):\n self.fileName = QtGui.QFileDialog.getSaveFileName(self, self.tr(\"Save Data\"), \"\", \n self.tr('Atom Positions (*.pdb)'))\n if not self.fileName.isEmpty():\n self.setCursor(QtCore.Qt.WaitCursor)\n selectedChain = self.main_chain\n PDBstring = selectedChain.toPDB( CAlphaPlaceholders=False)\n F = open(self.fileName, 'w')\n F.write(PDBstring)\n F.close()\n self.dirty = False\n self.setCursor(QtCore.Qt.ArrowCursor)","def npz_to_csv(datadir):\n \n files = glob.glob(datadir + '/conmat*.npz')\n for fname in files:\n print fname\n conmat = np.load(fname)['conmat']\n fH = open (fname[:-3] + \"csv\", 'w')\n csw = csv.writer(fH)\n for row in conmat:\n csw.writerow(row)\n fH.close()","def binary_out(array, fnam, dt=np.dtype(np.float64), endianness='big', appendDim=False):\r\n if appendDim == True :\r\n fnam_out = fnam + '_'\r\n for i in array.shape[:-1] :\r\n fnam_out += str(i) + 'x' \r\n fnam_out += str(array.shape[-1]) + '.raw'\r\n else :\r\n fnam_out = fnam\r\n arrayout = np.array(array, dtype=dt)\r\n if sys.byteorder != endianness:\r\n arrayout.byteswap(True)\r\n arrayout.tofile(os.path.abspath(fnam_out))","def save_pca(self, filepath):\n mean_beam, principal_components, variances = self.pca()\n image_shape = np.array(self.image_shape)\n with open(filepath, 'wb') as f:\n np.save(f, image_shape)\n np.save(f, mean_beam)\n np.save(f, principal_components)\n np.save(f, variances)\n np.save(f, self.mask)","def save_to_poscar(self, filename,direct=False,species_line=False): \n with open( filename, 'w' ) as F:\n F.write( self.name )\n F.write( \" 1.0\\n\" )\n F.write( mat2str( self.unit_cell, \"%16.10f\" ) )\n if species_line:\n pos = 0\n for n in self.num_per_type: \n F.write('%s '%self.species[pos])\n pos += n\n F.write('\\n')\n F.write(' '.join([str(n) for n in self.num_per_type]) )\n F.write('\\n')\n if not direct:\n F.write(\"Cart\\n\")\n F.write( mat2str( self.atoms, \"%16.10f\" ) )\n else:\n F.write(\"Direct\\n\")\n F.write( mat2str( dot(self.atoms,self.recip_cell), \"%16.10f\" ) )","def write_ptm_gridfile(self,fn):\n vertex_hdr = \" Vertex Data: vertex_number, x, y\"\n poly_hdr = \" Polygon Data: polygon_number, number_of_sides,center_x, center_y, center_depth, side_indices(number_of_sides), marker(0=internal,1=open boundary)\"\n side_hdr = \" Side Data: side_number, side_depth, node_indices(2), cell_indices(2), marker(0=internal,1=external,2=flow boundary,3=open boundary)\"\n\n with open(fn,'wt') as fp:\n # write header counts\n fp.write(\" Number of Vertices\\n\")\n fp.write(\" %20d\\n\"%self.Nnodes())\n fp.write(\" Number of Polygons\\n\")\n fp.write(\" %20d\\n\"%self.Ncells())\n fp.write(\" Number of Sides\\n\")\n fp.write(\" %20d\\n\"%self.Nedges())\n fp.write(\" NODATA (land) value\\n\")\n fp.write(\" -9999.000000000\\n\")\n\n # write vertex info\n fp.write(vertex_hdr+\"\\n\")\n for v in range(self.Nnodes()):\n fp.write(\" %10d %16.7f %16.7f\\n\"%(v+1,\n self.nodes['x'][v,0],\n self.nodes['x'][v,1]))\n\n # write polygon info\n fp.write(poly_hdr+\"\\n\")\n cell_write_str1 = \" %10d %10d %16.7f %16.7f %16.7f \"\n cell_depths = self.cell_depths()\n for e in range(self.Ncells()):\n edges = self.cells['edges'][e,:]\n edges[edges<0] = -1\n edge_str = \" \".join( [\"%10d\"%(s+1) for s in edges] )\n edge_str = edge_str+\" %10d\\n\"%(self.cells['mark'][e])\n nsides = sum(edges>=0)\n fp.write(cell_write_str1%(e+1,\n nsides,\n self.cells['_center'][e,0],\n self.cells['_center'][e,1],\n cell_depths[e]))\n fp.write(edge_str)\n \n # write side info\n fp.write(side_hdr+\"\\n\")\n edge_depths = self.edge_depths()\n edge_write_str = \" %10d %16.7f %10d %10d %10d %10d %10d\\n\"\n for s in range(self.Nedges()):\n edges = self.edges['cells'][s,:]\n edges[edges<0] = -1 \n nodes = self.edges['nodes'][s,:]\n nodes[nodes<0] = -1\n fp.write(edge_write_str%(s+1,\n edge_depths[s],\n nodes[0]+1,\n nodes[1]+1,\n edges[0]+1,\n edges[1]+1,\n self.edges['mark'][s]))","def to_poly_file(self, filename):\n\n def getinsidepoint(pts):\n direct = (pts[0] + pts[1] + pts[2]) / 3 - pts[0]\n return pts[0] + 0.001 * direct\n\n if self.dim == 2:\n self.leaveonlyphysicalsurfaces()\n if self.dim == 3:\n self.leaveonlyphysicalvolumes()\n\n # write nodes\n nodes = []\n map = {}\n for x in self.d0.values():\n assert isinstance(x, point)\n nodes.append(x.getxyz())\n map[x.getn()] = len(nodes)\n\n\n s = \"# nodes\\n%d %d 0 0\\n\" % (len(nodes), self.dim)\n if self.dim == 2:\n ptstr = \" %d %f %f\\n\"\n ptstr2 = \" %d %f %f %d\\n\"\n else:\n ptstr = \" %d %f %f %f\\n\"\n ptstr2 = \" %d %f %f %f %d\\n\"\n\n for n, x in enumerate(nodes):\n s += ptstr % tuple([n + 1] + list(x[:self.dim]))\n\n # facets\n # first write external polygon, then hole polygons and then point in each\n # hole polygon\n facets = []\n if self.dim == 2:\n\n hole_pts = []\n regions=[]\n for x2 in self.d2.values():\n assert isinstance(x2, surface)\n for x1 in x2.getlines():\n assert isinstance(x1, line)\n p = [map[y.getn()] for y in x1.getpoints()]\n bc = self.getBCnum(x1.getn())\n facets.append((p, bc))\n\n for hole in x2.getholepoints():\n hole_pts.append(hole.getxyz())\n\n # regions\n for x in self.phys2.values():\n assert isinstance(x, physicalsurface)\n for x2 in x.getsurfaces():\n if not x2.is_hole:\n regions.append(x2.getinsidepoint().getxyz() + [x.getn()])\n\n # number of facets, boundary markers=yes\n s += \"# segments\\n%d 1\\n\" % len(facets)\n for ii, (p, bc) in enumerate(facets):\n # number of corners, corner 1, corner 2, ...\n s += \" %d %s %d\\n\" % (ii + 1, ' '.join([str(ii) for ii in p]), bc)\n # holes\n s += \"# holes\\n%d\\n\" % len(hole_pts)\n for ii, x0 in enumerate(hole_pts):\n # number of corners, corner 1, corner 2, ...\n s += \" %d %s\\n\" % (ii + 1, ' '.join([str(ii) for ii in x0]))\n # regions\n s += \"# regions\\n%d\\n\" % len(regions)\n for ii, x0 in enumerate(regions):\n s += \" %d %f %f %d\\n\" % tuple([ii + 1] + x0)\n\n if self.dim == 3:\n\n for x in self.d2.values():\n assert isinstance(x, surface)\n p = [map[y.getn()] for y in x.getpoints()]\n h = []\n pts = []\n for hole in x.getholepoints():\n h.append([map[y.getn()] for y in hole])\n pts.append(getinsidepoint(hole).getxyz())\n bc = self.getBCnum(x.getn())\n facets.append((p, bc, h, pts))\n # number of facets, boundary markers=yes\n s += \"# segments\\n%d 1\\n\" % len(facets)\n for p, bc, h, holes in facets:\n # number of polygons, # of holes, boundary marker\n s += \" %d %d %d\\n\" % (1 + len(h), len(h), bc)\n # number of corners, corner 1, corner 2, ...\n s += \" %d %s\\n\" % (len(p), ' '.join([str(ii) for ii in p]))\n for x in h:\n # number of corners, corner 1, corner 2, ...\n s += \" %d %s\\n\" % (len(x), ' '.join([str(ii) for ii in p]))\n for i, pt in enumerate(holes):\n # hole #, x, y, z\n s += ptstr % tuple([i + 1] + list(pt))\n\n # volume holes\n s += \"# holes\\n0\\n\"\n # regions\n regions=[]\n for x in self.phys3.values():\n assert isinstance(x, physicalvolume)\n for v in x.getvolumes():\n regions.append(v.getinsidepoint().getxyz()+[x.getn()])\n s += \"# regions\\n%d\\n\" % len(regions)\n for i, x in enumerate(regions):\n s += ptstr2 % tuple([i + 1] + list(x))\n\n open(filename, \"w\").write(s)","def write_quanty_opps(x, nf, n=1, op_name='Opp', ishift=0, jshift=0,\n filename='tmp.lua', mode='a'):\n\n if not (mode == 'a' or mode == 'w'):\n print('Warning: writing mode not supported.')\n return\n\n # create the three lists needed in Quanty: i,j,e\n i, j, e = scipy.sparse.find(x)\n\n # take care that indices may be shifted\n i += ishift\n j += jshift\n\n # divide up the elements in bunches of\n # max n in each bunch\n k = 0\n d = []\n while k + n < len(i):\n d.append([i[k:k + n], j[k:k + n], e[k:k + n]])\n k += n\n d.append([i[k:], j[k:], e[k:]])\n\n # write operators to file\n f = open(filename, mode)\n # loop over the operators\n for k, (ii, jj, ee) in enumerate(d):\n s = \"\\n\"\n s += op_name + '_' + str(k)\n s += \" = NewOperator(\\\"Number\\\",\"\n s += str(nf)\n s += \",\\n {\"\n s += ', '.join([str(el) for el in ii])\n s += '},\\n {'\n s += ', '.join([str(el) for el in jj])\n s += '},\\n'\n s_onerow = '{'\n s_onerow += ', '.join([(str(el.real) + '+I*'\n + str(el.imag)) for el in ee])\n s_onerow += '})'\n s += s_onerow\n s += '\\n'\n if len(s_onerow) > 3000:\n print('Warning: Lua does not support '\n 'lines longer than 3000')\n f.write(s)\n f.close()","def create_grp_file(data, model_name, gp_var, outputModelFilesDirectory):\n\n dimx = None\n dimy = None\n if len(data.shape) == 1:\n dimy = 1\n dimx = data.shape[0]\n else:\n dimx, dimy = data.shape\n data = np.ones(dimx)\n\n if not (gp_var == None):\n i = 1\n for key in sorted(gp_var.keys()):\n\n for index in gp_var[key]:\n data[index] = i\n\n i += 1\n\n\n f = open(os.path.join(outputModelFilesDirectory, model_name + '.grp'), 'w')\n\n print >>f, '/NumWaves\\t1'\n print >>f, '/NumPoints\\t%d\\n' %dimx\n print >>f, '/Matrix'\n np.savetxt(f, data, fmt='%d', delimiter='\\t')\n\n f.close()","def generate_structure(flag):\r\n if os.path.isfile(\"packing.nfo\"):\r\n os.remove(os.path.abspath(\"packing.nfo\"))\r\n proc = subprocess.Popen(['PackingGeneration.exe', flag])\r\n proc.wait()\r\n if not os.path.isfile(\"packing.nfo\"):\r\n print('Try to change number of particles or size distribution.')\r\n raise Exception('Packing algorithm failed.')","def polyencode(ctx, inputfiles, output, poly_order, reflect):\n with rio.open(inputfiles[0]) as src:\n metaprof = src.profile.copy()\n\n metaprof.update(dtype=np.float32, count=(poly_order + 1))\n\n data = read_all(inputfiles, reflect=reflect)\n\n out = poly_multid(data, poly_order).astype(np.float32)\n\n with rio.open(output, \"w\", **metaprof) as dst:\n for i in range(poly_order + 1):\n dst.write(out[:, :, i], i + 1)","def write_pos(sposcar,ngrid,nspecies,filename):\n pos=np.dot(sposcar[\"lattvec\"],sposcar[\"positions\"])\n ntot=ngrid[0]*ngrid[1]*ngrid[2]*nspecies\n np_icell=np.empty((3,ntot),dtype=np.intc)\n car=pos\n np_ispecies=np.empty(ntot,dtype=np.intc)\n icell=np_icell\n ispecies=np_ispecies\n\n f=StringIO.StringIO()\n\n for ii in xrange(ntot):\n tmp,ispecies[ii]=divmod(ii,nspecies)\n tmp,icell[0,ii]=divmod(tmp,ngrid[0])\n icell[2,ii],icell[1,ii]=divmod(tmp,ngrid[1])\n car[0,ii],car[1,ii],car[2,ii]=np.dot(sposcar[\"lattvec\"],sposcar[\"positions\"][:,ii])*10\n f.write(\"{:>6d} {:>6d} {:>15.10f} {:>15.10f} {:>15.10f}\\n\".\n format(ii+1,ispecies[ii]+1, car[0,ii],car[1,ii],car[2,ii]))\n ffinal=open(filename,\"w\")\n ffinal.write(f.getvalue())\n f.close()\n ffinal.close()","def _write_dx(self, FN, data):\n n_points = data['counts'][0] * data['counts'][1] * data['counts'][2]\n if FN.endswith('.dx'):\n F = open(FN, 'w')\n else:\n import gzip\n F = gzip.open(FN, 'w')\n\n F.write(\"\"\"object 1 class gridpositions counts {0[0]} {0[1]} {0[2]}\norigin {1[0]} {1[1]} {1[2]}\ndelta {2[0]} 0.0 0.0\ndelta 0.0 {2[1]} 0.0\ndelta 0.0 0.0 {2[2]}\nobject 2 class gridconnections counts {0[0]} {0[1]} {0[2]}\nobject 3 class array type double rank 0 items {3} data follows\n\"\"\".format(data['counts'], data['origin'], data['spacing'], n_points))\n\n for start_n in range(0, len(data['vals']), 3):\n F.write(' '.join(['%6e' % c\n for c in data['vals'][start_n:start_n + 3]]) + '\\n')\n\n F.write('object 4 class field\\n')\n F.write('component \"positions\" value 1\\n')\n F.write('component \"connections\" value 2\\n')\n F.write('component \"data\" value 3\\n')\n F.close()"],"string":"[\n \"def generate(self):\\n super().generate()\\n records = random.random((self._dimension, self._dimension, self.num_samples))\\n record_labels = [0] * self.num_samples\\n prev_out_spec =\\\"\\\"\\n count = 0\\n for i in range(0, int(self.num_files)):\\n if i % self.comm_size == self.my_rank:\\n progress(i+1, self.num_files, \\\"Generating NPZ Data\\\")\\n out_path_spec = \\\"{}_{}_of_{}.npz\\\".format(self._file_prefix, i, self.num_files)\\n if count == 0:\\n prev_out_spec = out_path_spec\\n if self.compression != Compression.ZIP:\\n np.savez(out_path_spec, x=records, y=record_labels)\\n else:\\n np.savez_compressed(out_path_spec, x=records, y=record_labels)\\n count += 1\\n else:\\n copyfile(prev_out_spec, out_path_spec)\",\n \"def make_npz_file(data_type):\\n\\n\\tdata_folder = data_type + \\\"_images\\\"\\n\\tlabel_file = os.path.join(dataset_params.data_path, data_type + \\\"_lables.csv\\\")\\n\\toutput_file = os.path.join(dataset_params.data_path, \\\"synthetic_\\\" + data_type + \\\"_data\\\")\\n\\tline_reader = csv.DictReader(open(label_file,\\\"r\\\"))\\n\\n\\tdata = []\\n\\tlabels = []\\n\\tdata_points = 0\\n\\tfor row in line_reader:\\n\\t\\timage_name = os.path.join(dataset_params.data_path,data_folder,row[\\\"figNum\\\"] + \\\".png\\\")\\n\\t\\timage_data = cv2.imread(image_name, cv2.IMREAD_COLOR)\\n\\t\\timage_data = cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB)\\n\\t\\timage_label = [int(dataset_params.shapes[row[\\\"shape\\\"]]), int(dataset_params.colors[row[\\\"color\\\"]]), int(dataset_params.sizes[row[\\\"size\\\"]]), int(row[\\\"quadrant\\\"]), int(dataset_params.backgrounds[row[\\\"background\\\"]]) ]\\n\\t\\tdata.append(image_data)\\n\\t\\tlabels.append(image_label)\\n\\t\\tdata_points += 1\\n\\n\\t# Converting list to data to np array\\n\\tdata = np.asarray(data)\\n\\tlabels = np.asarray(labels)\\n\\n\\t# Printing log information\\n\\tprint(data_type, \\\"statistics being saved: \\\")\\n\\tprint(data_type, \\\"data shape\\\", data.shape)\\n\\tprint(data_type, \\\"label shape\\\", labels.shape)\\n\\n\\t# saveing the file as npz file\\n\\tnp.savez_compressed(output_file, data=data, lables=labels)\",\n \"def test_save_npz_file(save_npz) -> None:\\n save_npz\",\n \"def outputPulses(self,filename):\\n np.save(filename,self.getData())\\n return\",\n \"def write_file(_data, _label, _clinical, _contour, _type):\\n pickle.dump(np.array(_data), open(_type + '_data.pxl', 'wb'))\\n pickle.dump(np.array(_label), open(_type + '_label.pxl', 'wb'))\\n pickle.dump(np.array(_clinical), open(_type + '_clinical.pxl', 'wb'))\\n pickle.dump(np.array(_contour), open(_type + '_contour.pxl', 'wb'))\",\n \"def export_npz(self, filename, dtype=np.float32):\\n np.savez(\\n filename, impulse_response=self.in_time.astype(dtype), samplerate=self.fs\\n )\",\n \"def save(self, filename):\\n np.savez(temp_dir + '/' + filename + '.npz', chip_ids=self.chip_ids, core_ids=self.core_ids, cx_ids=self.cx_ids)\",\n \"def main():\\n train_src = read_file(SRC_TRAIN)\\n train_tgt = read_file(TRGT_TRAIN)\\n val_src = read_file(SRC_VAL)\\n val_tgt = read_file(TRGT_VAL)\\n # val = read_files(VAL_FILES)\\n np.savez(\\n DATA_NPZ_NAME, train_src=train_src, train_tgt=train_tgt, val_src=val_src, val_tgt=val_tgt)\",\n \"def write_to_file_z(path):\\n path1 = path + \\\"/z_Macros\\\"\\n if not os.path.exists(path1):\\n os.mkdir(path1)\\n for e in range(int(e_steps)+1):\\n filename = \\\"x0y0z%ske%s.mac\\\" %(dz*z + z_min, e*de + e_min)\\n path = path1\\n fullpath = os.path.join(path, filename)\\n f = open(fullpath, \\\"w\\\")\\n f.write('/rat/physics_list/OmitMuonicProcesses true\\\\n')\\n f.write(\\\"/rat/physics_list/OmitHadronicProcesses true \\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n f.write('/rat/db/set DETECTOR geo_file \\\"geo/snoplus.geo\\\"\\\\n')\\n f.write('/rat/db/set GEO[scint] material \\\"labppo_scintillator\\\"\\\\n')\\n f.write('/rat/db/set DAQ dqxx_info 0 \\\\n')\\n f.write(\\\"/run/initialize \\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"/rat/proc frontend\\\\n\\\")\\n f.write(\\\"/rat/proc trigger\\\\n\\\")\\n f.write(\\\"/rat/proc eventbuilder\\\\n\\\")\\n f.write(\\\"/rat/proc count\\\\n\\\")\\n f.write(\\\"/rat/procset update 100\\\\n\\\")\\n f.write(\\\"/rat/proc calibratePMT\\\\n\\\")\\n f.write(\\\"/rat/proc scintFitter\\\\n\\\")\\n f.write(\\\"/rat/proclast outroot\\\\n\\\")\\n f.write('/rat/procset file \\\"x0y0z%ske%s.root\\\"\\\\n' %(dz*z + z_min, e*de + e_min))\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"/generator/add combo gun:point:poisson\\\\n\\\")\\n f.write(\\\"# want random, isotropic momentum distribution; energy given in MeV\\\\n\\\")\\n f.write(\\\"/generator/vtx/set e- 0 0 0 %s\\\\n\\\" %(e*de + e_min))\\n f.write(\\\"# position given in Cartesians, relative to detector center, in mm\\\\n\\\")\\n f.write(\\\"/generator/pos/set 0 0 %s\\\\n\\\" % (dz*z + z_min))\\n f.write(\\\"/generator/rate/set 1\\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"/rat/run/start %s\\\\n\\\" %(n))\\n f.write(\\\"exit\\\")\",\n \"def dump_npz(filename: str, obj, **kwargs):\\n return np.savez(filename, obj)\",\n \"def save(self, filename):\\n np.savez(temp_dir + '/' + filename + '.npz', core_ids=self.core_ids, cx_ids=self.cx_ids)\",\n \"def write_psts(self,prefix,existing_jco=None,noptmax=None):\\n self.log(\\\"writing realized pest control files\\\")\\n # get a copy of the pest control file\\n pst = self.pst.get(par_names=self.pst.par_names,obs_names=self.pst.obs_names)\\n\\n if noptmax is not None:\\n pst.control_data.noptmax = noptmax\\n pst.control_data.noptmax = noptmax\\n\\n if existing_jco is not None:\\n pst.pestpp_options[\\\"BASE_JACOBIAN\\\"] = existing_jco\\n\\n # set the indices\\n pst.parameter_data.index = pst.parameter_data.parnme\\n pst.observation_data.index = pst.observation_data.obsnme\\n\\n if self.parensemble.istransformed:\\n par_en = self.parensemble._back_transform(inplace=False)\\n else:\\n par_en = self.parensemble\\n\\n for i in range(self.num_reals):\\n pst_name = prefix + \\\"{0:d}.pst\\\".format(i)\\n self.log(\\\"writing realized pest control file \\\" + pst_name)\\n pst.parameter_data.loc[par_en.columns,\\\"parval1\\\"] = par_en.iloc[i, :].T\\n\\n # reset the regularization\\n #if pst.control_data.pestmode == \\\"regularization\\\":\\n #pst.zero_order_tikhonov(parbounds=True)\\n #zero_order_tikhonov(pst,parbounds=True)\\n # add the obs noise realization if needed\\n if self.obsensemble.shape[0] == self.num_reals:\\n pst.observation_data.loc[self.obsensemble.columns,\\\"obsval\\\"] = \\\\\\n self.obsensemble.iloc[i, :].T\\n\\n # write\\n pst.write(pst_name)\\n self.log(\\\"writing realized pest control file \\\" + pst_name)\\n self.log(\\\"writing realized pest control files\\\")\",\n \"def _cmd_export_nbo(args):\\n cnarr = read_cna(args.filename)\\n varr = load_het_snps(\\n args.vcf,\\n args.sample_id,\\n args.normal_id,\\n args.min_variant_depth,\\n args.zygosity_freq,\\n )\\n table = export.export_nexus_ogt(cnarr, varr, args.min_weight)\\n write_dataframe(args.output, table)\",\n \"def write_to_file(output, data, datafields):\\n if (len(data) != len(datafields)):\\n print \\\"Error! number of data fields != number of headers!\\\"\\n print 'len: ', len(data), len(datafields)\\n print 'shape: ', np.shape(data), np.shape(datafields)\\n\\n ofile = open(output,'w')\\n ofile.write(\\\"# g(r) in the xy-plane from 2Drdf.py\\\\n\\\")\\n header = \\\"# chunk \\\"\\n for element in datafields:\\n header += element + \\\" \\\"\\n\\n header = header + '\\\\n'\\n ofile.write(header)\\n \\n it = 0\\n for i in xrange(len(data[0])):\\n line = str(it) + \\\" \\\"\\n it += 1\\n for j in xrange(len(data)):\\n line += str(float(data[j][i])) + \\\" \\\"\\n line += \\\"\\\\n\\\"\\n ofile.write(line)\\n \\n ofile.close()\\n print \\\"Finished writing file: \\\", output\",\n \"def generate_POSCAR(formu,mat_list,my_ordered_elements,my_ordered_numbers,revise_dos):\\n out_name='POSCAR.'+formu\\n out_name='POSCAR_files/'+out_name.replace(' ','')\\n openfile = open(out_name,'wt')\\n openfile.write(formu+'\\\\n')\\n openfile.write(str(1.0)+'\\\\n')\\n for str_lines in mat_list:\\n openfile.write(str_lines+'\\\\n')\\n openfile.write(my_ordered_elements+'\\\\n')\\n openfile.write(my_ordered_numbers+'\\\\n')\\n openfile.write('direct'+'\\\\n')\\n for string_lines in revise_dos:\\n openfile.write(string_lines+'\\\\n')\\n openfile.close()\",\n \"def save(uNetwork, uDir):\\n ## first of all, save the network spec\\n out = open(uDir + \\\"net_spec.py\\\", \\\"w\\\")\\n out.write(\\\"spec = \\\" + str(uNetwork.spec))\\n out.close()\\n \\n ## then, save the state of each layer\\n layers = uNetwork.layers\\n\\n for l in range(len(layers) - 1):\\n if layers[l].node_sharing:\\n ## save just one node\\n layers[l].pipes[0][0].send(\\\"clone_state\\\")\\n state = layers[l].pipes[0][0].recv()\\n \\n ## save coincidences and PCG\\n np.save(uDir + str(l) + \\\".0.0.coincidences\\\", state['coincidences'])\\n np.save(uDir + str(l) + \\\".0.0.PCG\\\", state['PCG'])\\n\\n else:\\n for i in range(len(layers[l].nodes)): \\n for j in range(len(layers[l].nodes[i])):\\n layers[l].pipes[i][j].send(\\\"clone_state\\\")\\n state = layers[l].pipes[i][j].recv()\\n\\n np.save(uDir + str(l) + \\\".\\\" + str(i) + \\\".\\\" + str(j) + \\\".coincidences\\\", state['coincidences'])\\n np.save(uDir + str(l) + \\\".\\\" + str(i) + \\\".\\\" + str(j) + \\\".PCG\\\", state['PCG'])\\n\\n ## then, save also the output layer\\n layers[-1].pipes[0][0].send(\\\"clone_state\\\")\\n state = layers[-1].pipes[0][0].recv()\\n np.save(uDir + str(len(layers) - 1) + \\\".0.0.coincidences\\\", state['coincidences'])\\n np.save(uDir + str(len(layers) - 1) + \\\".0.0.cls_prior_prob\\\", state['cls_prior_prob'])\\n np.save(uDir + str(len(layers) - 1) + \\\".0.0.PCW\\\", state['PCW'])\",\n \"def generate(inputFilename, outputFilename = defaultFileName, \\n sizeOfReducedSample = DEFSIZEOFREDUCEDSAMPLE, \\n centerEta = DEFCENTERETA, centerPhi = DEFCENTERPHI): \\n listOfSignals = convert(inputFilename)\\n arrayOfSignals = np.array(listOfSignals)\\n arrayOfSignals.shape\\n np.save(outputFilename, arrayOfSignals, allow_pickle=False)\\n print(\\\"npy array name: \\\",outputFilename)\",\n \"def preprocess_data(data_dir, export_dir, npz_name=\\\"smp_all.npz\\\", overwrite=False,**params):\\n print(\\\"Starting to export and/or convert data\\\")\\n # export, unite and label smp data\\n start = time.time()\\n # export data from pnt to csv or npz\\n export_pnt(pnt_dir=data_dir, target_dir=export_dir, export_as=\\\"npz\\\", overwrite=overwrite, **params)\\n # load pd.DataFrame from all npz files and save this pd as united DataFrame in npz\\n smp_first = npz_to_pd(export_dir, is_dir=True)\\n dict = smp_first.to_dict(orient=\\\"list\\\")\\n np.savez_compressed(npz_name, **dict)\\n\\n end = time.time()\\n print(\\\"Elapsed time for export and dataframe creation: \\\", end-start)\\n print(\\\"\\\\nUnited smp data was stored in {}.\\\".format(npz_name))\",\n \"def write_compressed(self, filename):\\n\\n # Define which molecules to use \\n # (counting indices of processed data set)\\n indices = np.arange(len(self))\\n # All charges and position arrays have the same size\\n # (the one of the biggest molecule)\\n size = np.max( self.num_atoms )\\n # Initialize arrays\\n num_atoms = np.zeros(len(indices))\\n labels = np.zeros(len(indices))\\n charges = np.zeros([len(indices),size])\\n positions = np.zeros([len(indices),size,3])\\n # For each molecule ...\\n for j,idx in enumerate(indices):\\n # load the data\\n sample = self[idx]\\n # assign per-molecule data\\n labels[j] = sample['data']\\n num_atoms[j] = sample['num_atoms']\\n # ... and for each atom:\\n for ia in range(sample['num_atoms']):\\n charges[j,ia] = sample['charges'][ia]\\n positions[j,ia,0] = sample['positions'][ia][0] \\n positions[j,ia,1] = sample['positions'][ia][1] \\n positions[j,ia,2] = sample['positions'][ia][2]\\n\\n # Merge pairs\\n print(labels.shape,charges.shape,positions.shape)\\n labels = labels[0::2]\\n charges = np.array([np.concatenate((charges[i],charges[i+1])) for i in indices[0::2]])\\n positions = np.array([np.concatenate((positions[i],positions[i+1])) for i in indices[0::2]])\\n print(labels.shape,charges.shape,positions.shape)\\n \\n # Create a dictionary with all the values to save\\n save_dict = {}\\n save_dict['label'] = labels\\n save_dict['charges'] = charges\\n save_dict['positions'] = positions\\n\\n # Save as a compressed array \\n np.savez_compressed(filename,**save_dict)\\n \\n return\",\n \"def serialize(self, data):\\n buffer = io.BytesIO()\\n scipy.sparse.save_npz(buffer, data)\\n return buffer.getvalue()\",\n \"def _write(self):\\n f = FortranFile(self.filename,mode='w')\\n # Default omnivor binary header\\n f.writeInts ( self.data['MK'] , 'i' ) \\n f.writeInts ( self.data['itime'] , 'i' ) \\n f.writeString ( self.data['version'] ) \\n f.writeInts ( self.data['file_id'] , 'i' ) \\n f.writeString ( self.data['sversion'] ) \\n # Velocity field\\n f.writeString ( self.data['stype'] ) \\n f.writeInts ( self.data['is_grid'] , 'i' ) \\n f.writeInts ( self.data['nCPs'] , 'i' ) \\n if self.data['MK'] == 8:\\n real_char='d'\\n else:\\n real_char='f'\\n if self.data['is_grid']:\\n f.writeInts ( self.data['n1'] , 'i' ) \\n f.writeInts ( self.data['n2'] , 'i' ) \\n f.writeInts ( self.data['n3'] , 'i' ) \\n f.writeInts ( self.data['is_straight'] , 'i' ) \\n f.writeReals ( self.data['v1'] , real_char ) \\n f.writeReals ( self.data['v2'] , real_char ) \\n f.writeReals ( self.data['v3'] , real_char ) \\n\\n CPs = self.data['CPs'].flatten(order = 'F')\\n Utot = self.data['Utot'].flatten(order = 'F')\\n f.writeReals(CPs,real_char)\\n f.writeReals(Utot,real_char)\",\n \"def to_file(self, filename):\\n self.header['n'] = self.n\\n save_gyre(filename, self.header, self.data)\",\n \"def write_prb_file(\\n nchans=64, radius=1, y=1, num_tetrodes=16, \\n tetrodes_to_use=[], out_loc=\\\"channel_map.prb\\\",\\n num_chans_for_clust=4):\\n if tetrodes_to_use == []:\\n tetrodes_to_use = [j+1 for j in range(num_tetrodes+1)]\\n\\n # Actual code here\\n channel_groups = {}\\n for i in range(num_tetrodes):\\n # Skip tetrodes which should not be clustered\\n if not i + 1 in tetrodes_to_use:\\n continue\\n start = i*4\\n chans = [j for j in range(start, start + num_chans_for_clust)]\\n geometry = {}\\n label_letters = [\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\"]\\n label = [str(i+1) + label_letters[k] for k in range(num_chans_for_clust)]\\n graph = []\\n for j, c in enumerate(chans):\\n geometry[c] = [i * 2 * radius, j*y]\\n for j in range(len(chans)):\\n for k in range(j+1, len(chans)):\\n graph.append((chans[j], chans[k]))\\n channel_groups[i] = {\\n 'channels': chans,\\n 'geometry': geometry,\\n 'label': label,\\n 'graph': graph}\\n\\n with open(out_loc, \\\"w\\\") as f:\\n f.write(\\\"channel_groups = {\\\\n\\\")\\n for k, v in channel_groups.items():\\n f.write(\\\"\\\\t{}:\\\\n\\\".format(k))\\n f.write(\\\"\\\\t\\\\t{\\\\n\\\")\\n for k2, v2 in v.items():\\n f.write(\\\"\\\\t\\\\t \\\\'{}\\\\': {},\\\\n\\\".format(k2, v2))\\n f.write(\\\"\\\\t\\\\t},\\\\n\\\")\\n f.write(\\\"\\\\n\\\\t}\\\")\",\n \"def _cmd_export_nb(args):\\n cnarr = read_cna(args.filename)\\n table = export.export_nexus_basic(cnarr)\\n write_dataframe(args.output, table)\",\n \"def save_npz(tmpdir_factory: _pytest.tmpdir.TempdirFactory):\\n filename = \\\"test.npz\\\"\\n fn1 = tmpdir_factory.mktemp(\\\"data\\\").join(filename)\\n data = {\\\"test1\\\": np.arange(0, 50), \\\"test2\\\": np.arange(50, 100)}\\n\\n loader.save_npz_file(str(fn1), data)\\n return str(fn1), data\",\n \"def export_wells(self, w, title):\\r\\n self._check_out(title)\\r\\n np.savez_compressed(os.path.join(self.out_dir, title, title), w)\",\n \"def write_input_file(y,z,fname):\\n file = open('c:/4nec2/out/' + fname + '.nec', 'w')\\n file.write('CM Seeddesign \\\\n')\\n file.write('CM Zigzag Antenna \\\\n')\\n file.write('CE File generated by python \\\\n')\\n seg = 1\\n\\n #write the antenna\\n for i in range(0,len(y)-1):\\n file.write('GW %3i %3i %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\\\\n' % (1,seg,0,y[i],z[i],0,y[i+1],z[i+1],1))\\n\\n file.write('GE 0 \\\\n')\\n file.write('EK \\\\n')\\n file.write('EX %3i %3i %3i %3i %3i %3i %3i\\\\n' % (0,1,1,1,1,0,0))\\n file.write('GN -1 \\\\n')\\n \\n file.write('FR %3i %3i %3i %3i %8.4f %8.4f\\\\n' % (0,1,0,0,900,0))\\n file.write('FR %3i %3i %3i %3i %8.4f %8.4f\\\\n' % (0,11,0,0,850,10))\\n\\n file.write('LD %3i %3i %3i %3i %8.4f %8.4f\\\\n' % (5,1,0,0,58000000,2))\\n file.write('RP %3i %3i %3i %3i %8.4f %8.4f %8.4f %8.4f\\\\n' % (0,1,1,1000,90,0,0,0))\\n\\n file.write('EN \\\\n')\\n file.close()\",\n \"def processfile(file, out, me):\\n if out.endswith('.npz'):\\n out = out[:-4]\\n out = out + ('-%03d' % me) + '.npz'\\n if os.path.exists(out):\\n return\\n f = QPM.QPMSubsampleFile(file)\\n xis = []\\n mybf = QPM.LogWindowBiasFunction(edges[me], edges[me + 1])\\n mymock = QPM.halomock(f, 1e6, mybf, continuous=False)\\n for i in range(me + 1):\\n bf = QPM.LogWindowBiasFunction(edges[i], edges[i + 1])\\n mock = QPM.halomock(f, 1e6, bf, continuous=False)\\n # the error is nonsense since we have just one sample\\n # deal with it in combine\\n xi = QPM.xi([mock], mocks2=[mymock])\\n r = xi[0]\\n xis.append(xi[1])\\n print edges[i], edges[i + 1], len(mock[0]), mock[1].sum()\\n numpy.savez(out, r=r, xi=xis, edges=edges, N=1e6, me=me)\",\n \"def write_program(file_prefix=None, point_file=None, dir=None, inc=None, N=None, num_points=None, labels=None, tick_locations=None, tick_values=None, date=None, chi_surface=[7.0, 20.0, 100.0, 500.0]):\\n\\n # Print out.\\n print(\\\"\\\\nCreating the OpenDX .net program file.\\\")\\n\\n # Open the file.\\n file = open_write_file(file_name=file_prefix+\\\".net\\\", dir=dir, force=True)\\n\\n # Corners.\\n corners = \\\"{[0\\\"\\n for i in range(N - 1):\\n corners += \\\" 0\\\"\\n corners += \\\"] [%s\\\" % inc\\n for i in range(N - 1):\\n corners += \\\" %s\\\" % inc\\n corners += \\\"]}\\\"\\n\\n # Sphere size.\\n sphere_size = repr(0.025 * (inc + 1.0))\\n\\n # Image setup.\\n image_array1 = \\\"[%s %s %s]\\\" % (0.6 * (inc + 1.0), 0.3 * (inc + 1.0), 0.6 * (inc + 1.0))\\n image_array2 = \\\"[%s %s %s]\\\" % (0.6 * (inc + 1.0), 0.3 * (inc + 1.0), 6.0 * (inc + 1.0))\\n image_val = repr(3.0 * (inc + 1.0))\\n\\n # Unpacking the values for the 4 isosurfaces.\\n innermost_isosurface_value, inner_isosurface_value, middle_isosurface_value, outer_isosurface_value = chi_surface\\n\\n # Generate the text of the program.\\n ###################################\\n\\n file.write(\\\"//\\\\n\\\")\\n file.write(\\\"// time: %s\\\\n\\\" % date)\\n file.write(\\\"//\\\\n\\\")\\n file.write(\\\"// version: 3.2.0 (format), 4.3.2 (DX)\\\\n\\\")\\n file.write(\\\"//\\\\n\\\")\\n file.write(\\\"//\\\\n\\\")\\n file.write(\\\"// MODULE main\\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"// page assignment: Colour Space\\\\torder=3, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: ColourScene\\\\t\\\\torder=5, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: Glyph\\\\t\\\\torder=2, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: Grey Space\\\\t\\\\torder=4, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: GreyScene\\\\t\\\\torder=6, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: Image\\\\t\\\\t\\\\torder=7, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: Isosurfaces\\\\t\\\\torder=1, windowed=0, showing=1\\\\n\\\")\\n\\n # No sphere.\\n else:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"// page assignment: Colour Space\\\\torder=4, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: ColourScene\\\\t\\\\torder=6, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: Grey Space\\\\t\\\\torder=5, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: GreyScene\\\\t\\\\torder=7, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: Image\\\\t\\\\t\\\\torder=8, windowed=0, showing=0\\\\n\\\")\\n file.write(\\\"// page assignment: Isosurfaces\\\\t\\\\torder=2, windowed=0, showing=1\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"// workspace: width = 474, height = 354\\\\n\\\")\\n file.write(\\\"// layout: snap = 0, width = 50, height = 50, align = NN\\\\n\\\")\\n file.write(\\\"//\\\\n\\\")\\n file.write(\\\"macro main(\\\\n\\\")\\n file.write(\\\") -> (\\\\n\\\")\\n file.write(\\\") {\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // node Import[4]: x = 177, y = 62, inputs = 6, label = %s\\\\n\\\" % point_file)\\n file.write(\\\" // input[1]: defaulting = 0, visible = 1, type = 32, value = \\\\\\\"%s.general\\\\\\\"\\\\n\\\" % point_file)\\n file.write(\\\" // input[3]: defaulting = 1, visible = 1, type = 32, value = \\\\\\\"general\\\\\\\"\\\\n\\\")\\n file.write(\\\" // page group: Glyph\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Import_4_out_1 = \\\\n\\\")\\n file.write(\\\" Import(\\\\n\\\")\\n file.write(\\\" main_Import_4_in_1,\\\\n\\\")\\n file.write(\\\" main_Import_4_in_2,\\\\n\\\")\\n file.write(\\\" main_Import_4_in_3,\\\\n\\\")\\n file.write(\\\" main_Import_4_in_4,\\\\n\\\")\\n file.write(\\\" main_Import_4_in_5,\\\\n\\\")\\n file.write(\\\" main_Import_4_in_6\\\\n\\\")\\n file.write(\\\" ) [instance: 4, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Glyph[2]: x = 201, y = 182, inputs = 7, label = Glyph\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 32, value = \\\\\\\"sphere\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 1, visible = 1, type = 5, value = 10.0\\\\n\\\")\\n file.write(\\\" // input[4]: defaulting = 0, visible = 1, type = 5, value = %s\\\\n\\\" % sphere_size)\\n file.write(\\\" // input[5]: defaulting = 0, visible = 1, type = 5, value = 0.0\\\\n\\\")\\n file.write(\\\" // page group: Glyph\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Glyph_2_out_1 = \\\\n\\\")\\n file.write(\\\" Glyph(\\\\n\\\")\\n file.write(\\\" main_Import_4_out_1,\\\\n\\\")\\n file.write(\\\" main_Glyph_2_in_2,\\\\n\\\")\\n file.write(\\\" main_Glyph_2_in_3,\\\\n\\\")\\n file.write(\\\" main_Glyph_2_in_4,\\\\n\\\")\\n file.write(\\\" main_Glyph_2_in_5,\\\\n\\\")\\n file.write(\\\" main_Glyph_2_in_6,\\\\n\\\")\\n file.write(\\\" main_Glyph_2_in_7\\\\n\\\")\\n file.write(\\\" ) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[10]: x = 357, y = 278, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0 0 0]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 1.0\\\\n\\\")\\n file.write(\\\" // page group: Glyph\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_10_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Glyph_2_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_10_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_10_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_10_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_10_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 10, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[1]: x = 352, y = 386, inputs = 1, label = GreySphere\\\\n\\\")\\n file.write(\\\" // page group: Glyph\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"GreySphere = main_Color_10_out_1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[2]: x = 190, y = 350, inputs = 1, label = GreySphere\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_2_out_1[cache: 0] = GreySphere;\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Import[3]: x = 225, y = 84, inputs = 6, label = %s\\\\n\\\" % file_prefix)\\n file.write(\\\" // input[1]: defaulting = 0, visible = 1, type = 32, value = \\\\\\\"%s.general\\\\\\\"\\\\n\\\" % file_prefix)\\n file.write(\\\" // input[3]: defaulting = 1, visible = 1, type = 32, value = \\\\\\\"general\\\\\\\"\\\\n\\\")\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Import_3_out_1 = \\\\n\\\")\\n file.write(\\\" Import(\\\\n\\\")\\n file.write(\\\" main_Import_3_in_1,\\\\n\\\")\\n file.write(\\\" main_Import_3_in_2,\\\\n\\\")\\n file.write(\\\" main_Import_3_in_3,\\\\n\\\")\\n file.write(\\\" main_Import_3_in_4,\\\\n\\\")\\n file.write(\\\" main_Import_3_in_5,\\\\n\\\")\\n file.write(\\\" main_Import_3_in_6\\\\n\\\")\\n file.write(\\\" ) [instance: 3, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Isosurface[5]: x = 102, y = 191, inputs = 6, label = Outer Isosurface\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 5, value = %.1f\\\\n\\\"%(outer_isosurface_value))\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Isosurface_5_out_1 = \\\\n\\\")\\n file.write(\\\" Isosurface(\\\\n\\\")\\n file.write(\\\" main_Import_3_out_1,\\\\n\\\")\\n file.write(\\\" main_Isosurface_5_in_2,\\\\n\\\")\\n file.write(\\\" main_Isosurface_5_in_3,\\\\n\\\")\\n file.write(\\\" main_Isosurface_5_in_4,\\\\n\\\")\\n file.write(\\\" main_Isosurface_5_in_5,\\\\n\\\")\\n file.write(\\\" main_Isosurface_5_in_6\\\\n\\\")\\n file.write(\\\" ) [instance: 5, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[7]: x = 110, y = 292, inputs = 1, label = Surface4\\\\n\\\")\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"Surface4 = main_Isosurface_5_out_1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[14]: x = 123, y = 51, inputs = 1, label = Surface4\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_14_out_1[cache: 0] = Surface4;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[6]: x = 142, y = 145, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0 0 0]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.2\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_6_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Receiver_14_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_6_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_6_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_6_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_6_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 6, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Isosurface[6]: x = 200, y = 191, inputs = 6, label = Middle Isosurface\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 5, value = %.1f\\\\n\\\"%(middle_isosurface_value))\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Isosurface_6_out_1 = \\\\n\\\")\\n file.write(\\\" Isosurface(\\\\n\\\")\\n file.write(\\\" main_Import_3_out_1,\\\\n\\\")\\n file.write(\\\" main_Isosurface_6_in_2,\\\\n\\\")\\n file.write(\\\" main_Isosurface_6_in_3,\\\\n\\\")\\n file.write(\\\" main_Isosurface_6_in_4,\\\\n\\\")\\n file.write(\\\" main_Isosurface_6_in_5,\\\\n\\\")\\n file.write(\\\" main_Isosurface_6_in_6\\\\n\\\")\\n file.write(\\\" ) [instance: 6, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[8]: x = 208, y = 292, inputs = 1, label = Surface3\\\\n\\\")\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"Surface3 = main_Isosurface_6_out_1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[13]: x = 227, y = 51, inputs = 1, label = Surface3\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_13_out_1[cache: 0] = Surface3;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[7]: x = 246, y = 145, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0.2 0.2 0.2]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.4\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_7_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Receiver_13_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_7_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_7_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_7_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_7_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 7, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Collect[5]: x = 203, y = 236, inputs = 2, label = Collect\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Collect_5_out_1 = \\\\n\\\")\\n file.write(\\\" Collect(\\\\n\\\")\\n file.write(\\\" main_Color_6_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_7_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 5, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Isosurface[7]: x = 298, y = 191, inputs = 6, label = Inner Isosurface\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 5, value = %.1f\\\\n\\\"%(inner_isosurface_value))\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Isosurface_7_out_1 = \\\\n\\\")\\n file.write(\\\" Isosurface(\\\\n\\\")\\n file.write(\\\" main_Import_3_out_1,\\\\n\\\")\\n file.write(\\\" main_Isosurface_7_in_2,\\\\n\\\")\\n file.write(\\\" main_Isosurface_7_in_3,\\\\n\\\")\\n file.write(\\\" main_Isosurface_7_in_4,\\\\n\\\")\\n file.write(\\\" main_Isosurface_7_in_5,\\\\n\\\")\\n file.write(\\\" main_Isosurface_7_in_6\\\\n\\\")\\n file.write(\\\" ) [instance: 7, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[9]: x = 306, y = 292, inputs = 1, label = Surface2\\\\n\\\")\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"Surface2 = main_Isosurface_7_out_1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[12]: x = 331, y = 51, inputs = 1, label = Surface2\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_12_out_1[cache: 0] = Surface2;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[8]: x = 350, y = 145, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0.5 0.5 0.5]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.5\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_8_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Receiver_12_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_8_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_8_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_8_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_8_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 8, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Isosurface[8]: x = 396, y = 191, inputs = 6, label = Innermost Isosurface\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 5, value = %.1f\\\\n\\\"%(innermost_isosurface_value))\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Isosurface_8_out_1 = \\\\n\\\")\\n file.write(\\\" Isosurface(\\\\n\\\")\\n file.write(\\\" main_Import_3_out_1,\\\\n\\\")\\n file.write(\\\" main_Isosurface_8_in_2,\\\\n\\\")\\n file.write(\\\" main_Isosurface_8_in_3,\\\\n\\\")\\n file.write(\\\" main_Isosurface_8_in_4,\\\\n\\\")\\n file.write(\\\" main_Isosurface_8_in_5,\\\\n\\\")\\n file.write(\\\" main_Isosurface_8_in_6\\\\n\\\")\\n file.write(\\\" ) [instance: 8, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[10]: x = 404, y = 292, inputs = 1, label = Surface1\\\\n\\\")\\n file.write(\\\" // page group: Isosurfaces\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"Surface1 = main_Isosurface_8_out_1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[11]: x = 434, y = 51, inputs = 1, label = Surface1\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_11_out_1[cache: 0] = Surface1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[9]: x = 453, y = 145, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 32, value = \\\\\\\"white\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.7\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_9_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Receiver_11_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_9_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_9_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_9_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_9_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 9, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Collect[6]: x = 409, y = 236, inputs = 2, label = Collect\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Collect_6_out_1 = \\\\n\\\")\\n file.write(\\\" Collect(\\\\n\\\")\\n file.write(\\\" main_Color_8_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_9_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 6, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Collect[7]: x = 307, y = 327, inputs = 2, label = Collect\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Collect_7_out_1 = \\\\n\\\")\\n file.write(\\\" Collect(\\\\n\\\")\\n file.write(\\\" main_Collect_5_out_1,\\\\n\\\")\\n file.write(\\\" main_Collect_6_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 7, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // node Collect[8]: x = 293, y = 431, inputs = 2, label = Collect\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Collect_8_out_1 = \\\\n\\\")\\n file.write(\\\" Collect(\\\\n\\\")\\n file.write(\\\" main_Receiver_2_out_1,\\\\n\\\")\\n file.write(\\\" main_Collect_7_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 8, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[4]: x = 282, y = 517, inputs = 1, label = GreySpace\\\\n\\\")\\n\\n # No sphere.\\n else:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // node Transmitter[4]: x = 296, y = 439, inputs = 1, label = GreySpace\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // page group: Grey Space\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"GreySpace = main_Collect_8_out_1;\\\\n\\\")\\n\\n # No sphere.\\n else:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"GreySpace = main_Collect_7_out_1;\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[3]: x = 137, y = 57, inputs = 1, label = GreySpace\\\\n\\\")\\n file.write(\\\" // page group: GreyScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_3_out_1[cache: 0] = GreySpace;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Scale[3]: x = 163, y = 159, inputs = 2, label = Scale\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 1 1]\\\\n\\\")\\n file.write(\\\" // page group: GreyScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Scale_3_out_1 = \\\\n\\\")\\n file.write(\\\" Scale(\\\\n\\\")\\n file.write(\\\" main_Receiver_3_out_1,\\\\n\\\")\\n file.write(\\\" main_Scale_3_in_2\\\\n\\\")\\n file.write(\\\" ) [instance: 3, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node AutoCamera[2]: x = 273, y = 264, inputs = 9, label = AutoCamera\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 -1 1]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 1, visible = 1, type = 5, value = 500.0\\\\n\\\")\\n file.write(\\\" // input[4]: defaulting = 0, visible = 0, type = 1, value = 640\\\\n\\\")\\n file.write(\\\" // input[5]: defaulting = 0, visible = 0, type = 5, value = .75\\\\n\\\")\\n file.write(\\\" // input[6]: defaulting = 0, visible = 0, type = 8, value = [-1 1 0 ]\\\\n\\\")\\n file.write(\\\" // input[7]: defaulting = 0, visible = 0, type = 3, value = 0\\\\n\\\")\\n file.write(\\\" // input[8]: defaulting = 0, visible = 0, type = 5, value = 30.0\\\\n\\\")\\n file.write(\\\" // input[9]: defaulting = 0, visible = 1, type = 32, value = \\\\\\\"white\\\\\\\"\\\\n\\\")\\n file.write(\\\" // page group: GreyScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_out_1 = \\\\n\\\")\\n file.write(\\\" AutoCamera(\\\\n\\\")\\n file.write(\\\" main_Scale_3_out_1,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_in_2,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_in_3,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_in_4,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_in_5,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_in_6,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_in_7,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_in_8,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_in_9\\\\n\\\")\\n file.write(\\\" ) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node AutoAxes[2]: x = 175, y = 379, inputs = 19, label = AutoAxes\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 16777248, value = %s\\\\n\\\" % labels)\\n file.write(\\\" // input[4]: defaulting = 0, visible = 0, type = 1, value = 30\\\\n\\\")\\n file.write(\\\" // input[5]: defaulting = 0, visible = 1, type = 16777224, value = %s\\\\n\\\" % corners)\\n file.write(\\\" // input[6]: defaulting = 0, visible = 1, type = 3, value = 1\\\\n\\\")\\n file.write(\\\" // input[7]: defaulting = 1, visible = 0, type = 3, value = 1\\\\n\\\")\\n file.write(\\\" // input[9]: defaulting = 0, visible = 1, type = 3, value = 1\\\\n\\\")\\n file.write(\\\" // input[10]: defaulting = 0, visible = 1, type = 16777224, value = {[1 1 1] [0.1 0.1 0.1] [0 0 0] [0 0 0]}\\\\n\\\")\\n file.write(\\\" // input[11]: defaulting = 0, visible = 1, type = 16777248, value = {\\\\\\\"background\\\\\\\" \\\\\\\"grid\\\\\\\" \\\\\\\"labels\\\\\\\" \\\\\\\"ticks\\\\\\\"}\\\\n\\\")\\n file.write(\\\" // input[12]: defaulting = 1, visible = 0, type = 5, value = 0.4\\\\n\\\")\\n file.write(\\\" // input[13]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"area\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[14]: defaulting = 0, visible = 1, type = 16777221, value = %s\\\\n\\\" % tick_locations[0])\\n file.write(\\\" // input[15]: defaulting = 0, visible = 1, type = 16777221, value = %s\\\\n\\\" % tick_locations[1])\\n file.write(\\\" // input[16]: defaulting = 0, visible = 1, type = 16777221, value = %s\\\\n\\\" % tick_locations[2])\\n file.write(\\\" // input[17]: defaulting = 0, visible = 1, type = 16777248, value = %s\\\\n\\\" % tick_values[0])\\n file.write(\\\" // input[18]: defaulting = 0, visible = 1, type = 16777248, value = %s\\\\n\\\" % tick_values[1])\\n file.write(\\\" // input[19]: defaulting = 0, visible = 1, type = 16777248, value = %s\\\\n\\\" % tick_values[2])\\n file.write(\\\" // page group: GreyScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_out_1 = \\\\n\\\")\\n file.write(\\\" AutoAxes(\\\\n\\\")\\n file.write(\\\" main_Scale_3_out_1,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_2_out_1,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_3,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_4,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_5,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_6,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_7,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_8,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_9,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_10,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_11,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_12,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_13,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_14,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_15,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_16,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_17,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_18,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_2_in_19\\\\n\\\")\\n file.write(\\\" ) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // node Color[11]: x = 133, y = 278, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 0 0]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 1.0\\\\n\\\")\\n file.write(\\\" // page group: Glyph\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_11_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Glyph_2_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_11_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_11_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_11_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_11_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 11, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[2]: x = 122, y = 386, inputs = 1, label = ColourSphere\\\\n\\\")\\n file.write(\\\" // page group: Glyph\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"ColourSphere = main_Color_11_out_1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[1]: x = 179, y = 349, inputs = 1, label = ColourSphere\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_1_out_1[cache: 0] = ColourSphere;\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[10]: x = 123, y = 51, inputs = 1, label = Surface4\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_10_out_1[cache: 0] = Surface4;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[12]: x = 142, y = 145, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0 0 0.2]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.3\\\\n\\\")\\n file.write(\\\" // input[4]: defaulting = 1, visible = 0, type = 32, value = NULL\\\\n\\\")\\n file.write(\\\" // input[5]: defaulting = 1, visible = 0, type = 3, value = NULL\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_12_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Receiver_10_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_12_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_12_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_12_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_12_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 12, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[9]: x = 227, y = 51, inputs = 1, label = Surface3\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_9_out_1[cache: 0] = Surface3;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[13]: x = 246, y = 145, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 32, value = \\\\\\\"blue\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.3\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_13_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Receiver_9_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_13_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_13_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_13_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_13_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 13, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Collect[9]: x = 203, y = 236, inputs = 2, label = Collect\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Collect_9_out_1 = \\\\n\\\")\\n file.write(\\\" Collect(\\\\n\\\")\\n file.write(\\\" main_Color_12_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_13_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 9, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[8]: x = 331, y = 51, inputs = 1, label = Surface2\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_8_out_1[cache: 0] = Surface2;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[14]: x = 350, y = 145, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [0.5 0.5 1]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.2\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_14_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Receiver_8_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_14_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_14_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_14_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_14_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 14, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[7]: x = 434, y = 51, inputs = 1, label = Surface1\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_7_out_1[cache: 0] = Surface1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Color[15]: x = 453, y = 145, inputs = 5, label = Color\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 32, value = \\\\\\\"white\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 5, value = 0.55\\\\n\\\")\\n file.write(\\\" // input[4]: defaulting = 1, visible = 0, type = 32, value = \\\\\\\"positions\\\\\\\"\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Color_15_out_1 = \\\\n\\\")\\n file.write(\\\" Color(\\\\n\\\")\\n file.write(\\\" main_Receiver_7_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_15_in_2,\\\\n\\\")\\n file.write(\\\" main_Color_15_in_3,\\\\n\\\")\\n file.write(\\\" main_Color_15_in_4,\\\\n\\\")\\n file.write(\\\" main_Color_15_in_5\\\\n\\\")\\n file.write(\\\" ) [instance: 15, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Collect[10]: x = 409, y = 236, inputs = 2, label = Collect\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Collect_10_out_1 = \\\\n\\\")\\n file.write(\\\" Collect(\\\\n\\\")\\n file.write(\\\" main_Color_14_out_1,\\\\n\\\")\\n file.write(\\\" main_Color_15_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 10, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Collect[11]: x = 307, y = 327, inputs = 2, label = Collect\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Collect_11_out_1 = \\\\n\\\")\\n file.write(\\\" Collect(\\\\n\\\")\\n file.write(\\\" main_Collect_9_out_1,\\\\n\\\")\\n file.write(\\\" main_Collect_10_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 11, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // node Collect[12]: x = 293, y = 431, inputs = 2, label = Collect\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Collect_12_out_1 = \\\\n\\\")\\n file.write(\\\" Collect(\\\\n\\\")\\n file.write(\\\" main_Receiver_1_out_1,\\\\n\\\")\\n file.write(\\\" main_Collect_11_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 12, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[3]: x = 276, y = 517, inputs = 1, label = ColourSpace\\\\n\\\")\\n\\n # No sphere.\\n else:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // node Transmitter[3]: x = 290, y = 440, inputs = 1, label = ColourSpace\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // page group: Colour Space\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"ColourSpace = main_Collect_12_out_1;\\\\n\\\")\\n\\n # No sphere.\\n else:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"ColourSpace = main_Collect_11_out_1;\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[4]: x = 131, y = 58, inputs = 1, label = ColourSpace\\\\n\\\")\\n file.write(\\\" // page group: ColourScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_4_out_1[cache: 0] = ColourSpace;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Scale[5]: x = 163, y = 159, inputs = 2, label = Scale\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 1 1]\\\\n\\\")\\n file.write(\\\" // page group: ColourScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Scale_5_out_1 = \\\\n\\\")\\n file.write(\\\" Scale(\\\\n\\\")\\n file.write(\\\" main_Receiver_4_out_1,\\\\n\\\")\\n file.write(\\\" main_Scale_5_in_2\\\\n\\\")\\n file.write(\\\" ) [instance: 5, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node AutoCamera[4]: x = 273, y = 264, inputs = 9, label = AutoCamera\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 8, value = [1 -1 1]\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 1, visible = 1, type = 5, value = 500.0\\\\n\\\")\\n file.write(\\\" // input[5]: defaulting = 0, visible = 0, type = 5, value = .75\\\\n\\\")\\n file.write(\\\" // input[6]: defaulting = 0, visible = 0, type = 8, value = [-1 1 0 ]\\\\n\\\")\\n file.write(\\\" // input[7]: defaulting = 0, visible = 0, type = 3, value = 0\\\\n\\\")\\n file.write(\\\" // input[8]: defaulting = 0, visible = 0, type = 5, value = 30.0\\\\n\\\")\\n file.write(\\\" // input[9]: defaulting = 0, visible = 1, type = 32, value = \\\\\\\"black\\\\\\\"\\\\n\\\")\\n file.write(\\\" // page group: ColourScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_out_1 = \\\\n\\\")\\n file.write(\\\" AutoCamera(\\\\n\\\")\\n file.write(\\\" main_Scale_5_out_1,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_in_2,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_in_3,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_in_4,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_in_5,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_in_6,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_in_7,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_in_8,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_in_9\\\\n\\\")\\n file.write(\\\" ) [instance: 4, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node AutoAxes[4]: x = 175, y = 379, inputs = 19, label = AutoAxes\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 16777248, value = %s\\\\n\\\" % labels)\\n file.write(\\\" // input[4]: defaulting = 0, visible = 0, type = 1, value = 30\\\\n\\\")\\n file.write(\\\" // input[5]: defaulting = 0, visible = 1, type = 16777224, value = %s\\\\n\\\" % corners)\\n file.write(\\\" // input[6]: defaulting = 0, visible = 1, type = 3, value = 1\\\\n\\\")\\n file.write(\\\" // input[7]: defaulting = 1, visible = 0, type = 3, value = 1\\\\n\\\")\\n file.write(\\\" // input[9]: defaulting = 0, visible = 1, type = 3, value = 1\\\\n\\\")\\n file.write(\\\" // input[10]: defaulting = 0, visible = 1, type = 16777224, value = {[0.05 0.05 0.05] [0.3 0.3 0.3] [1 1 1] [1 1 0]}\\\\n\\\")\\n file.write(\\\" // input[11]: defaulting = 0, visible = 1, type = 16777248, value = {\\\\\\\"background\\\\\\\" \\\\\\\"grid\\\\\\\" \\\\\\\"labels\\\\\\\" \\\\\\\"ticks\\\\\\\"}\\\\n\\\")\\n file.write(\\\" // input[12]: defaulting = 1, visible = 0, type = 5, value = 0.4\\\\n\\\")\\n file.write(\\\" // input[13]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"area\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[14]: defaulting = 0, visible = 1, type = 16777221, value = %s\\\\n\\\" % tick_locations[0])\\n file.write(\\\" // input[15]: defaulting = 0, visible = 1, type = 16777221, value = %s\\\\n\\\" % tick_locations[1])\\n file.write(\\\" // input[16]: defaulting = 0, visible = 1, type = 16777221, value = %s\\\\n\\\" % tick_locations[2])\\n file.write(\\\" // input[17]: defaulting = 0, visible = 1, type = 16777248, value = %s\\\\n\\\" % tick_values[0])\\n file.write(\\\" // input[18]: defaulting = 0, visible = 1, type = 16777248, value = %s\\\\n\\\" % tick_values[1])\\n file.write(\\\" // input[19]: defaulting = 0, visible = 1, type = 16777248, value = %s\\\\n\\\" % tick_values[2])\\n file.write(\\\" // page group: ColourScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_out_1 = \\\\n\\\")\\n file.write(\\\" AutoAxes(\\\\n\\\")\\n file.write(\\\" main_Scale_5_out_1,\\\\n\\\")\\n file.write(\\\" main_AutoCamera_4_out_1,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_3,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_4,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_5,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_6,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_7,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_8,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_9,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_10,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_11,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_12,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_13,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_14,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_15,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_16,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_17,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_18,\\\\n\\\")\\n file.write(\\\" main_AutoAxes_4_in_19\\\\n\\\")\\n file.write(\\\" ) [instance: 4, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Selector[1]: x = 245, y = 66, inputs = 7, label = Selector\\\\n\\\")\\n file.write(\\\" // input[1]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"Selector_1\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"Colour\\\\\\\" \\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 0, type = 29, value = 1 \\\\n\\\")\\n file.write(\\\" // input[4]: defaulting = 1, visible = 1, type = 16777248, value = { \\\\\\\"Colour\\\\\\\" \\\\\\\"Grey\\\\\\\" }\\\\n\\\")\\n file.write(\\\" // input[5]: defaulting = 1, visible = 0, type = 16777245, value = { 1 2 }\\\\n\\\")\\n file.write(\\\" // output[1]: visible = 1, type = 29, value = 1 \\\\n\\\")\\n file.write(\\\" // output[2]: visible = 1, type = 32, value = \\\\\\\"Colour\\\\\\\" \\\\n\\\")\\n file.write(\\\" // page group: Image\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[6]: x = 299, y = 487, inputs = 1, label = ColourImage\\\\n\\\")\\n file.write(\\\" // page group: ColourScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"ColourImage = main_AutoAxes_4_out_1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[5]: x = 76, y = 190, inputs = 1, label = ColourImage\\\\n\\\")\\n file.write(\\\" // page group: Image\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_5_out_1[cache: 0] = ColourImage;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Transmitter[5]: x = 305, y = 489, inputs = 1, label = GreyImage\\\\n\\\")\\n file.write(\\\" // page group: GreyScene\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"GreyImage = main_AutoAxes_2_out_1;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Receiver[6]: x = 199, y = 190, inputs = 1, label = GreyImage\\\\n\\\")\\n file.write(\\\" // page group: Image\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Receiver_6_out_1[cache: 0] = GreyImage;\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Switch[1]: x = 177, y = 293, inputs = 3, label = Switch\\\\n\\\")\\n file.write(\\\" // page group: Image\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Switch_1_out_1 = \\\\n\\\")\\n file.write(\\\" Switch(\\\\n\\\")\\n file.write(\\\" main_Selector_1_out_1,\\\\n\\\")\\n file.write(\\\" main_Receiver_5_out_1,\\\\n\\\")\\n file.write(\\\" main_Receiver_6_out_1\\\\n\\\")\\n file.write(\\\" ) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Switch[14]: x = 325, y = 293, inputs = 3, label = Switch\\\\n\\\")\\n file.write(\\\" // input[2]: defaulting = 0, visible = 1, type = 67108863, value = \\\\\\\"black\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[3]: defaulting = 0, visible = 1, type = 67108863, value = \\\\\\\"white\\\\\\\"\\\\n\\\")\\n file.write(\\\" // page group: Image\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Switch_14_out_1 = \\\\n\\\")\\n file.write(\\\" Switch(\\\\n\\\")\\n file.write(\\\" main_Selector_1_out_1,\\\\n\\\")\\n file.write(\\\" main_Switch_14_in_2,\\\\n\\\")\\n file.write(\\\" main_Switch_14_in_3\\\\n\\\")\\n file.write(\\\" ) [instance: 14, cache: 1];\\\\n\\\")\\n file.write(\\\" // \\\\n\\\")\\n file.write(\\\" // node Image[3]: x = 252, y = 424, inputs = 49, label = Image\\\\n\\\")\\n file.write(\\\" // input[1]: defaulting = 0, visible = 0, type = 67108863, value = \\\\\\\"Image_3\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[4]: defaulting = 0, visible = 0, type = 1, value = 1\\\\n\\\")\\n file.write(\\\" // input[5]: defaulting = 0, visible = 0, type = 8, value = %s\\\\n\\\" % image_array1)\\n file.write(\\\" // input[6]: defaulting = 0, visible = 0, type = 8, value = %s\\\\n\\\" % image_array2)\\n file.write(\\\" // input[7]: defaulting = 0, visible = 0, type = 5, value = %s\\\\n\\\" % image_val)\\n file.write(\\\" // input[8]: defaulting = 0, visible = 0, type = 1, value = 1376\\\\n\\\")\\n file.write(\\\" // input[9]: defaulting = 0, visible = 0, type = 5, value = 0.678\\\\n\\\")\\n file.write(\\\" // input[10]: defaulting = 0, visible = 0, type = 8, value = [-0.109685 0.243133 0.963772]\\\\n\\\")\\n file.write(\\\" // input[11]: defaulting = 1, visible = 0, type = 5, value = 30.9877\\\\n\\\")\\n file.write(\\\" // input[12]: defaulting = 0, visible = 0, type = 1, value = 0\\\\n\\\")\\n file.write(\\\" // input[14]: defaulting = 0, visible = 0, type = 1, value = 1\\\\n\\\")\\n file.write(\\\" // input[15]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"none\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[16]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"none\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[17]: defaulting = 1, visible = 0, type = 1, value = 1\\\\n\\\")\\n file.write(\\\" // input[18]: defaulting = 1, visible = 0, type = 1, value = 1\\\\n\\\")\\n file.write(\\\" // input[19]: defaulting = 0, visible = 0, type = 1, value = 0\\\\n\\\")\\n file.write(\\\" // input[22]: defaulting = 1, visible = 1, type = 32, value = \\\\\\\"black\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[25]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"iso\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[26]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"tiff\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[29]: defaulting = 0, visible = 0, type = 3, value = 0\\\\n\\\")\\n file.write(\\\" // input[30]: defaulting = 1, visible = 0, type = 16777248, value = %s\\\\n\\\" % labels)\\n file.write(\\\" // input[32]: defaulting = 1, visible = 0, type = 16777224, value = %s\\\\n\\\" % corners)\\n file.write(\\\" // input[33]: defaulting = 0, visible = 0, type = 3, value = 1\\\\n\\\")\\n file.write(\\\" // input[34]: defaulting = 0, visible = 0, type = 3, value = 0\\\\n\\\")\\n file.write(\\\" // input[36]: defaulting = 0, visible = 0, type = 3, value = 1\\\\n\\\")\\n file.write(\\\" // input[41]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"rotate\\\\\\\"\\\\n\\\")\\n file.write(\\\" // input[42]: defaulting = 0, visible = 0, type = 32, value = \\\\\\\"Surface\\\\\\\"\\\\n\\\")\\n file.write(\\\" // page group: Image\\\\n\\\")\\n file.write(\\\" // title: value = Surface\\\\n\\\")\\n file.write(\\\" // depth: value = 24\\\\n\\\")\\n file.write(\\\" // window: position = (0.0000,0.0400), size = 0.9929x0.9276\\\\n\\\")\\n file.write(\\\" // internal caching: 1\\\\n\\\")\\n file.write(\\\" //\\\\n\\\")\\n file.write(\\\"main_Image_3_out_1,\\\\n\\\")\\n file.write(\\\"main_Image_3_out_2,\\\\n\\\")\\n file.write(\\\"main_Image_3_out_3 = \\\\n\\\")\\n file.write(\\\" Image(\\\\n\\\")\\n file.write(\\\" main_Image_3_in_1,\\\\n\\\")\\n file.write(\\\" main_Switch_1_out_1,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_3,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_4,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_5,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_6,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_7,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_8,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_9,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_10,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_11,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_12,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_13,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_14,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_15,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_16,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_17,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_18,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_19,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_20,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_21,\\\\n\\\")\\n file.write(\\\" main_Switch_14_out_1,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_23,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_24,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_25,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_26,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_27,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_28,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_29,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_30,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_31,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_32,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_33,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_34,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_35,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_36,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_37,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_38,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_39,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_40,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_41,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_42,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_43,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_44,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_45,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_46,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_47,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_48,\\\\n\\\")\\n file.write(\\\" main_Image_3_in_49\\\\n\\\")\\n file.write(\\\" ) [instance: 3, cache: 1];\\\\n\\\")\\n file.write(\\\"// network: end of macro body\\\\n\\\")\\n file.write(\\\"CacheScene(main_Image_3_in_1, main_Image_3_out_1, main_Image_3_out_2);\\\\n\\\")\\n file.write(\\\"}\\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"main_Import_4_in_1 = \\\\\\\"%s.general\\\\\\\";\\\\n\\\" % point_file)\\n file.write(\\\"main_Import_4_in_2 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_4_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_4_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_4_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_4_in_6 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_4_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Glyph_2_in_2 = \\\\\\\"sphere\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Glyph_2_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_Glyph_2_in_4 = %s;\\\\n\\\" % sphere_size)\\n file.write(\\\"main_Glyph_2_in_5 = 0.0;\\\\n\\\")\\n file.write(\\\"main_Glyph_2_in_6 = NULL;\\\\n\\\")\\n file.write(\\\"main_Glyph_2_in_7 = NULL;\\\\n\\\")\\n file.write(\\\"main_Glyph_2_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_10_in_2 = [0 0 0];\\\\n\\\")\\n file.write(\\\"main_Color_10_in_3 = 1.0;\\\\n\\\")\\n file.write(\\\"main_Color_10_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_10_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_10_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Transmitter_1_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_2_out_1 = NULL;\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"main_Import_3_in_1 = \\\\\\\"%s.general\\\\\\\";\\\\n\\\" % file_prefix)\\n file.write(\\\"main_Import_3_in_2 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_3_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_3_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_3_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_3_in_6 = NULL;\\\\n\\\")\\n file.write(\\\"main_Import_3_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_5_in_2 = 500.0;\\\\n\\\")\\n file.write(\\\"main_Isosurface_5_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_5_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_5_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_5_in_6 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_5_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Transmitter_7_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_14_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_6_in_2 = [0 0 0];\\\\n\\\")\\n file.write(\\\"main_Color_6_in_3 = 0.2;\\\\n\\\")\\n file.write(\\\"main_Color_6_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_6_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_6_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_6_in_2 = 100.0;\\\\n\\\")\\n file.write(\\\"main_Isosurface_6_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_6_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_6_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_6_in_6 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_6_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Transmitter_8_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_13_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_7_in_2 = [0.2 0.2 0.2];\\\\n\\\")\\n file.write(\\\"main_Color_7_in_3 = 0.4;\\\\n\\\")\\n file.write(\\\"main_Color_7_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_7_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_7_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Collect_5_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_7_in_2 = 20.0;\\\\n\\\")\\n file.write(\\\"main_Isosurface_7_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_7_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_7_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_7_in_6 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_7_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Transmitter_9_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_12_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_8_in_2 = [0.5 0.5 0.5];\\\\n\\\")\\n file.write(\\\"main_Color_8_in_3 = 0.5;\\\\n\\\")\\n file.write(\\\"main_Color_8_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_8_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_8_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_8_in_2 = 7.0;\\\\n\\\")\\n file.write(\\\"main_Isosurface_8_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_8_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_8_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_8_in_6 = NULL;\\\\n\\\")\\n file.write(\\\"main_Isosurface_8_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Transmitter_10_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_11_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_9_in_2 = \\\\\\\"white\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Color_9_in_3 = 0.7;\\\\n\\\")\\n file.write(\\\"main_Color_9_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_9_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_9_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Collect_6_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Collect_7_out_1 = NULL;\\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"main_Collect_8_out_1 = NULL;\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"main_Transmitter_4_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_3_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Scale_3_in_2 = [1 1 1];\\\\n\\\")\\n file.write(\\\"main_Scale_3_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_in_2 = [1 -1 1];\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_in_4 = 640;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_in_5 = .75;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_in_6 = [-1 1 0 ];\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_in_7 = 0;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_in_8 = 30.0;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_in_9 = \\\\\\\"white\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_AutoCamera_2_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_3 = %s;\\\\n\\\" % labels)\\n file.write(\\\"main_AutoAxes_2_in_4 = 30;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_5 = %s;\\\\n\\\" % corners)\\n file.write(\\\"main_AutoAxes_2_in_6 = 1;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_7 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_8 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_9 = 1;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_10 = {[1 1 1] [0.1 0.1 0.1] [0 0 0] [0 0 0]};\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_11 = {\\\\\\\"background\\\\\\\" \\\\\\\"grid\\\\\\\" \\\\\\\"labels\\\\\\\" \\\\\\\"ticks\\\\\\\"};\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_12 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_13 = \\\\\\\"area\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_AutoAxes_2_in_14 = %s;\\\\n\\\" % tick_locations[0])\\n file.write(\\\"main_AutoAxes_2_in_15 = %s;\\\\n\\\" % tick_locations[1])\\n file.write(\\\"main_AutoAxes_2_in_16 = %s;\\\\n\\\" % tick_locations[2])\\n file.write(\\\"main_AutoAxes_2_in_17 = %s;\\\\n\\\" % tick_values[0])\\n file.write(\\\"main_AutoAxes_2_in_18 = %s;\\\\n\\\" % tick_values[1])\\n file.write(\\\"main_AutoAxes_2_in_19 = %s;\\\\n\\\" % tick_values[2])\\n file.write(\\\"main_AutoAxes_2_out_1 = NULL;\\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"main_Color_11_in_2 = [1 0 0];\\\\n\\\")\\n file.write(\\\"main_Color_11_in_3 = 1.0;\\\\n\\\")\\n file.write(\\\"main_Color_11_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_11_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_11_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Transmitter_2_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_1_out_1 = NULL;\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"main_Receiver_10_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_12_in_2 = [0 0 0.2];\\\\n\\\")\\n file.write(\\\"main_Color_12_in_3 = 0.3;\\\\n\\\")\\n file.write(\\\"main_Color_12_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_12_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_12_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_9_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_13_in_2 = \\\\\\\"blue\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Color_13_in_3 = 0.3;\\\\n\\\")\\n file.write(\\\"main_Color_13_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_13_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_13_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Collect_9_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_8_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_14_in_2 = [0.5 0.5 1];\\\\n\\\")\\n file.write(\\\"main_Color_14_in_3 = 0.2;\\\\n\\\")\\n file.write(\\\"main_Color_14_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_14_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_14_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_7_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_15_in_2 = \\\\\\\"white\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Color_15_in_3 = 0.55;\\\\n\\\")\\n file.write(\\\"main_Color_15_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_15_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Color_15_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Collect_10_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Collect_11_out_1 = NULL;\\\\n\\\")\\n\\n # Include the sphere.\\n if num_points > 0:\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"main_Collect_12_out_1 = NULL;\\\\n\\\")\\n\\n # Common code.\\n file.write(\\\"\\\\n\\\")\\n file.write(\\\"main_Transmitter_3_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_4_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Scale_5_in_2 = [1 1 1];\\\\n\\\")\\n file.write(\\\"main_Scale_5_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_in_2 = [1 -1 1];\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_in_3 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_in_5 = .75;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_in_6 = [-1 1 0 ];\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_in_7 = 0;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_in_8 = 30.0;\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_in_9 = \\\\\\\"black\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_AutoCamera_4_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_3 = %s;\\\\n\\\" % labels)\\n file.write(\\\"main_AutoAxes_4_in_4 = 30;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_5 = %s;\\\\n\\\" % corners)\\n file.write(\\\"main_AutoAxes_4_in_6 = 1;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_7 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_8 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_9 = 1;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_10 = {[0.05 0.05 0.05] [0.3 0.3 0.3] [1 1 1] [1 1 0]};\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_11 = {\\\\\\\"background\\\\\\\" \\\\\\\"grid\\\\\\\" \\\\\\\"labels\\\\\\\" \\\\\\\"ticks\\\\\\\"};\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_12 = NULL;\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_13 = \\\\\\\"area\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_AutoAxes_4_in_14 = %s;\\\\n\\\" % tick_locations[0])\\n file.write(\\\"main_AutoAxes_4_in_15 = %s;\\\\n\\\" % tick_locations[1])\\n file.write(\\\"main_AutoAxes_4_in_16 = %s;\\\\n\\\" % tick_locations[2])\\n file.write(\\\"main_AutoAxes_4_in_17 = %s;\\\\n\\\" % tick_values[0])\\n file.write(\\\"main_AutoAxes_4_in_18 = %s;\\\\n\\\" % tick_values[1])\\n file.write(\\\"main_AutoAxes_4_in_19 = %s;\\\\n\\\" % tick_values[2])\\n file.write(\\\"main_AutoAxes_4_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Selector_1_in_1 = \\\\\\\"Selector_1\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Selector_1_in_2 = \\\\\\\"Colour\\\\\\\" ;\\\\n\\\")\\n file.write(\\\"main_Selector_1_in_3 = 1 ;\\\\n\\\")\\n file.write(\\\"main_Selector_1_in_4 = NULL;\\\\n\\\")\\n file.write(\\\"main_Selector_1_in_5 = NULL;\\\\n\\\")\\n file.write(\\\"main_Selector_1_in_6 = NULL;\\\\n\\\")\\n file.write(\\\"main_Selector_1_in_7 = NULL;\\\\n\\\")\\n file.write(\\\"main_Selector_1_out_1 = 1 ;\\\\n\\\")\\n file.write(\\\"main_Transmitter_6_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_5_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Transmitter_5_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Receiver_6_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Switch_1_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"main_Switch_14_in_2 = \\\\\\\"black\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Switch_14_in_3 = \\\\\\\"white\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Switch_14_out_1 = NULL;\\\\n\\\")\\n file.write(\\\"macro Image(\\\\n\\\")\\n file.write(\\\" id,\\\\n\\\")\\n file.write(\\\" object,\\\\n\\\")\\n file.write(\\\" where,\\\\n\\\")\\n file.write(\\\" useVector,\\\\n\\\")\\n file.write(\\\" to,\\\\n\\\")\\n file.write(\\\" from,\\\\n\\\")\\n file.write(\\\" width,\\\\n\\\")\\n file.write(\\\" resolution,\\\\n\\\")\\n file.write(\\\" aspect,\\\\n\\\")\\n file.write(\\\" up,\\\\n\\\")\\n file.write(\\\" viewAngle,\\\\n\\\")\\n file.write(\\\" perspective,\\\\n\\\")\\n file.write(\\\" options,\\\\n\\\")\\n file.write(\\\" buttonState = 1,\\\\n\\\")\\n file.write(\\\" buttonUpApprox = \\\\\\\"none\\\\\\\",\\\\n\\\")\\n file.write(\\\" buttonDownApprox = \\\\\\\"none\\\\\\\",\\\\n\\\")\\n file.write(\\\" buttonUpDensity = 1,\\\\n\\\")\\n file.write(\\\" buttonDownDensity = 1,\\\\n\\\")\\n file.write(\\\" renderMode = 0,\\\\n\\\")\\n file.write(\\\" defaultCamera,\\\\n\\\")\\n file.write(\\\" reset,\\\\n\\\")\\n file.write(\\\" backgroundColor,\\\\n\\\")\\n file.write(\\\" throttle,\\\\n\\\")\\n file.write(\\\" RECenable = 0,\\\\n\\\")\\n file.write(\\\" RECfile,\\\\n\\\")\\n file.write(\\\" RECformat,\\\\n\\\")\\n file.write(\\\" RECresolution,\\\\n\\\")\\n file.write(\\\" RECaspect,\\\\n\\\")\\n file.write(\\\" AAenable = 0,\\\\n\\\")\\n file.write(\\\" AAlabels,\\\\n\\\")\\n file.write(\\\" AAticks,\\\\n\\\")\\n file.write(\\\" AAcorners,\\\\n\\\")\\n file.write(\\\" AAframe,\\\\n\\\")\\n file.write(\\\" AAadjust,\\\\n\\\")\\n file.write(\\\" AAcursor,\\\\n\\\")\\n file.write(\\\" AAgrid,\\\\n\\\")\\n file.write(\\\" AAcolors,\\\\n\\\")\\n file.write(\\\" AAannotation,\\\\n\\\")\\n file.write(\\\" AAlabelscale,\\\\n\\\")\\n file.write(\\\" AAfont,\\\\n\\\")\\n file.write(\\\" interactionMode,\\\\n\\\")\\n file.write(\\\" title,\\\\n\\\")\\n file.write(\\\" AAxTickLocs,\\\\n\\\")\\n file.write(\\\" AAyTickLocs,\\\\n\\\")\\n file.write(\\\" AAzTickLocs,\\\\n\\\")\\n file.write(\\\" AAxTickLabels,\\\\n\\\")\\n file.write(\\\" AAyTickLabels,\\\\n\\\")\\n file.write(\\\" AAzTickLabels,\\\\n\\\")\\n file.write(\\\" webOptions) -> (\\\\n\\\")\\n file.write(\\\" object,\\\\n\\\")\\n file.write(\\\" camera,\\\\n\\\")\\n file.write(\\\" where)\\\\n\\\")\\n file.write(\\\"{\\\\n\\\")\\n file.write(\\\" ImageMessage(\\\\n\\\")\\n file.write(\\\" id,\\\\n\\\")\\n file.write(\\\" backgroundColor,\\\\n\\\")\\n file.write(\\\" throttle,\\\\n\\\")\\n file.write(\\\" RECenable,\\\\n\\\")\\n file.write(\\\" RECfile,\\\\n\\\")\\n file.write(\\\" RECformat,\\\\n\\\")\\n file.write(\\\" RECresolution,\\\\n\\\")\\n file.write(\\\" RECaspect,\\\\n\\\")\\n file.write(\\\" AAenable,\\\\n\\\")\\n file.write(\\\" AAlabels,\\\\n\\\")\\n file.write(\\\" AAticks,\\\\n\\\")\\n file.write(\\\" AAcorners,\\\\n\\\")\\n file.write(\\\" AAframe,\\\\n\\\")\\n file.write(\\\" AAadjust,\\\\n\\\")\\n file.write(\\\" AAcursor,\\\\n\\\")\\n file.write(\\\" AAgrid,\\\\n\\\")\\n file.write(\\\" AAcolors,\\\\n\\\")\\n file.write(\\\" AAannotation,\\\\n\\\")\\n file.write(\\\" AAlabelscale,\\\\n\\\")\\n file.write(\\\" AAfont,\\\\n\\\")\\n file.write(\\\" AAxTickLocs,\\\\n\\\")\\n file.write(\\\" AAyTickLocs,\\\\n\\\")\\n file.write(\\\" AAzTickLocs,\\\\n\\\")\\n file.write(\\\" AAxTickLabels,\\\\n\\\")\\n file.write(\\\" AAyTickLabels,\\\\n\\\")\\n file.write(\\\" AAzTickLabels,\\\\n\\\")\\n file.write(\\\" interactionMode,\\\\n\\\")\\n file.write(\\\" title,\\\\n\\\")\\n file.write(\\\" renderMode,\\\\n\\\")\\n file.write(\\\" buttonUpApprox,\\\\n\\\")\\n file.write(\\\" buttonDownApprox,\\\\n\\\")\\n file.write(\\\" buttonUpDensity,\\\\n\\\")\\n file.write(\\\" buttonDownDensity) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" autoCamera =\\\\n\\\")\\n file.write(\\\" AutoCamera(\\\\n\\\")\\n file.write(\\\" object,\\\\n\\\")\\n file.write(\\\" \\\\\\\"front\\\\\\\",\\\\n\\\")\\n file.write(\\\" object,\\\\n\\\")\\n file.write(\\\" resolution,\\\\n\\\")\\n file.write(\\\" aspect,\\\\n\\\")\\n file.write(\\\" [0,1,0],\\\\n\\\")\\n file.write(\\\" perspective,\\\\n\\\")\\n file.write(\\\" viewAngle,\\\\n\\\")\\n file.write(\\\" backgroundColor) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" realCamera =\\\\n\\\")\\n file.write(\\\" Camera(\\\\n\\\")\\n file.write(\\\" to,\\\\n\\\")\\n file.write(\\\" from,\\\\n\\\")\\n file.write(\\\" width,\\\\n\\\")\\n file.write(\\\" resolution,\\\\n\\\")\\n file.write(\\\" aspect,\\\\n\\\")\\n file.write(\\\" up,\\\\n\\\")\\n file.write(\\\" perspective,\\\\n\\\")\\n file.write(\\\" viewAngle,\\\\n\\\")\\n file.write(\\\" backgroundColor) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" coloredDefaultCamera = \\\\n\\\")\\n file.write(\\\" UpdateCamera(defaultCamera,\\\\n\\\")\\n file.write(\\\" background=backgroundColor) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" nullDefaultCamera =\\\\n\\\")\\n file.write(\\\" Inquire(defaultCamera,\\\\n\\\")\\n file.write(\\\" \\\\\\\"is null + 1\\\\\\\") [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" resetCamera =\\\\n\\\")\\n file.write(\\\" Switch(\\\\n\\\")\\n file.write(\\\" nullDefaultCamera,\\\\n\\\")\\n file.write(\\\" coloredDefaultCamera,\\\\n\\\")\\n file.write(\\\" autoCamera) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" resetNull = \\\\n\\\")\\n file.write(\\\" Inquire(\\\\n\\\")\\n file.write(\\\" reset,\\\\n\\\")\\n file.write(\\\" \\\\\\\"is null + 1\\\\\\\") [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" reset =\\\\n\\\")\\n file.write(\\\" Switch(\\\\n\\\")\\n file.write(\\\" resetNull,\\\\n\\\")\\n file.write(\\\" reset,\\\\n\\\")\\n file.write(\\\" 0) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" whichCamera =\\\\n\\\")\\n file.write(\\\" Compute(\\\\n\\\")\\n file.write(\\\" \\\\\\\"($0 != 0 || $1 == 0) ? 1 : 2\\\\\\\",\\\\n\\\")\\n file.write(\\\" reset,\\\\n\\\")\\n file.write(\\\" useVector) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" camera = Switch(\\\\n\\\")\\n file.write(\\\" whichCamera,\\\\n\\\")\\n file.write(\\\" resetCamera,\\\\n\\\")\\n file.write(\\\" realCamera) [instance: 3, cache: 1];\\\\n\\\")\\n file.write(\\\" AAobject =\\\\n\\\")\\n file.write(\\\" AutoAxes(\\\\n\\\")\\n file.write(\\\" object,\\\\n\\\")\\n file.write(\\\" camera,\\\\n\\\")\\n file.write(\\\" AAlabels,\\\\n\\\")\\n file.write(\\\" AAticks,\\\\n\\\")\\n file.write(\\\" AAcorners,\\\\n\\\")\\n file.write(\\\" AAframe,\\\\n\\\")\\n file.write(\\\" AAadjust,\\\\n\\\")\\n file.write(\\\" AAcursor,\\\\n\\\")\\n file.write(\\\" AAgrid,\\\\n\\\")\\n file.write(\\\" AAcolors,\\\\n\\\")\\n file.write(\\\" AAannotation,\\\\n\\\")\\n file.write(\\\" AAlabelscale,\\\\n\\\")\\n file.write(\\\" AAfont,\\\\n\\\")\\n file.write(\\\" AAxTickLocs,\\\\n\\\")\\n file.write(\\\" AAyTickLocs,\\\\n\\\")\\n file.write(\\\" AAzTickLocs,\\\\n\\\")\\n file.write(\\\" AAxTickLabels,\\\\n\\\")\\n file.write(\\\" AAyTickLabels,\\\\n\\\")\\n file.write(\\\" AAzTickLabels) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" switchAAenable = Compute(\\\\\\\"$0+1\\\\\\\",\\\\n\\\")\\n file.write(\\\" AAenable) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" object = Switch(\\\\n\\\")\\n file.write(\\\" switchAAenable,\\\\n\\\")\\n file.write(\\\" object,\\\\n\\\")\\n file.write(\\\" AAobject) [instance:4, cache: 1];\\\\n\\\")\\n file.write(\\\" SWapproximation_options =\\\\n\\\")\\n file.write(\\\" Switch(\\\\n\\\")\\n file.write(\\\" buttonState,\\\\n\\\")\\n file.write(\\\" buttonUpApprox,\\\\n\\\")\\n file.write(\\\" buttonDownApprox) [instance: 5, cache: 1];\\\\n\\\")\\n file.write(\\\" SWdensity_options =\\\\n\\\")\\n file.write(\\\" Switch(\\\\n\\\")\\n file.write(\\\" buttonState,\\\\n\\\")\\n file.write(\\\" buttonUpDensity,\\\\n\\\")\\n file.write(\\\" buttonDownDensity) [instance: 6, cache: 1];\\\\n\\\")\\n file.write(\\\" HWapproximation_options =\\\\n\\\")\\n file.write(\\\" Format(\\\\n\\\")\\n file.write(\\\" \\\\\\\"%s,%s\\\\\\\",\\\\n\\\")\\n file.write(\\\" buttonDownApprox,\\\\n\\\")\\n file.write(\\\" buttonUpApprox) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" HWdensity_options =\\\\n\\\")\\n file.write(\\\" Format(\\\\n\\\")\\n file.write(\\\" \\\\\\\"%d,%d\\\\\\\",\\\\n\\\")\\n file.write(\\\" buttonDownDensity,\\\\n\\\")\\n file.write(\\\" buttonUpDensity) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" switchRenderMode = Compute(\\\\n\\\")\\n file.write(\\\" \\\\\\\"$0+1\\\\\\\",\\\\n\\\")\\n file.write(\\\" renderMode) [instance: 3, cache: 1];\\\\n\\\")\\n file.write(\\\" approximation_options = Switch(\\\\n\\\")\\n file.write(\\\" switchRenderMode,\\\\n\\\")\\n file.write(\\\" SWapproximation_options,\\\\n\\\")\\n file.write(\\\" HWapproximation_options) [instance: 7, cache: 1];\\\\n\\\")\\n file.write(\\\" density_options = Switch(\\\\n\\\")\\n file.write(\\\" switchRenderMode,\\\\n\\\")\\n file.write(\\\" SWdensity_options,\\\\n\\\")\\n file.write(\\\" HWdensity_options) [instance: 8, cache: 1];\\\\n\\\")\\n file.write(\\\" renderModeString = Switch(\\\\n\\\")\\n file.write(\\\" switchRenderMode,\\\\n\\\")\\n file.write(\\\" \\\\\\\"software\\\\\\\",\\\\n\\\")\\n file.write(\\\" \\\\\\\"hardware\\\\\\\")[instance: 9, cache: 1];\\\\n\\\")\\n file.write(\\\" object_tag = Inquire(\\\\n\\\")\\n file.write(\\\" object,\\\\n\\\")\\n file.write(\\\" \\\\\\\"object tag\\\\\\\")[instance: 3, cache: 1];\\\\n\\\")\\n file.write(\\\" annoted_object =\\\\n\\\")\\n file.write(\\\" Options(\\\\n\\\")\\n file.write(\\\" object,\\\\n\\\")\\n file.write(\\\" \\\\\\\"send boxes\\\\\\\",\\\\n\\\")\\n file.write(\\\" 0,\\\\n\\\")\\n file.write(\\\" \\\\\\\"cache\\\\\\\",\\\\n\\\")\\n file.write(\\\" 1,\\\\n\\\")\\n file.write(\\\" \\\\\\\"object tag\\\\\\\",\\\\n\\\")\\n file.write(\\\" object_tag,\\\\n\\\")\\n file.write(\\\" \\\\\\\"ddcamera\\\\\\\",\\\\n\\\")\\n file.write(\\\" whichCamera,\\\\n\\\")\\n file.write(\\\" \\\\\\\"rendering approximation\\\\\\\",\\\\n\\\")\\n file.write(\\\" approximation_options,\\\\n\\\")\\n file.write(\\\" \\\\\\\"render every\\\\\\\",\\\\n\\\")\\n file.write(\\\" density_options,\\\\n\\\")\\n file.write(\\\" \\\\\\\"button state\\\\\\\",\\\\n\\\")\\n file.write(\\\" buttonState,\\\\n\\\")\\n file.write(\\\" \\\\\\\"rendering mode\\\\\\\",\\\\n\\\")\\n file.write(\\\" renderModeString) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" RECresNull =\\\\n\\\")\\n file.write(\\\" Inquire(\\\\n\\\")\\n file.write(\\\" RECresolution,\\\\n\\\")\\n file.write(\\\" \\\\\\\"is null + 1\\\\\\\") [instance: 4, cache: 1];\\\\n\\\")\\n file.write(\\\" ImageResolution =\\\\n\\\")\\n file.write(\\\" Inquire(\\\\n\\\")\\n file.write(\\\" camera,\\\\n\\\")\\n file.write(\\\" \\\\\\\"camera resolution\\\\\\\") [instance: 5, cache: 1];\\\\n\\\")\\n file.write(\\\" RECresolution =\\\\n\\\")\\n file.write(\\\" Switch(\\\\n\\\")\\n file.write(\\\" RECresNull,\\\\n\\\")\\n file.write(\\\" RECresolution,\\\\n\\\")\\n file.write(\\\" ImageResolution) [instance: 10, cache: 1];\\\\n\\\")\\n file.write(\\\" RECaspectNull =\\\\n\\\")\\n file.write(\\\" Inquire(\\\\n\\\")\\n file.write(\\\" RECaspect,\\\\n\\\")\\n file.write(\\\" \\\\\\\"is null + 1\\\\\\\") [instance: 6, cache: 1];\\\\n\\\")\\n file.write(\\\" ImageAspect =\\\\n\\\")\\n file.write(\\\" Inquire(\\\\n\\\")\\n file.write(\\\" camera,\\\\n\\\")\\n file.write(\\\" \\\\\\\"camera aspect\\\\\\\") [instance: 7, cache: 1];\\\\n\\\")\\n file.write(\\\" RECaspect =\\\\n\\\")\\n file.write(\\\" Switch(\\\\n\\\")\\n file.write(\\\" RECaspectNull,\\\\n\\\")\\n file.write(\\\" RECaspect,\\\\n\\\")\\n file.write(\\\" ImageAspect) [instance: 11, cache: 1];\\\\n\\\")\\n file.write(\\\" switchRECenable = Compute(\\\\n\\\")\\n file.write(\\\" \\\\\\\"$0 == 0 ? 1 : (($2 == $3) && ($4 == $5)) ? ($1 == 1 ? 2 : 3) : 4\\\\\\\",\\\\n\\\")\\n file.write(\\\" RECenable,\\\\n\\\")\\n file.write(\\\" switchRenderMode,\\\\n\\\")\\n file.write(\\\" RECresolution,\\\\n\\\")\\n file.write(\\\" ImageResolution,\\\\n\\\")\\n file.write(\\\" RECaspect,\\\\n\\\")\\n file.write(\\\" ImageAspect) [instance: 4, cache: 1];\\\\n\\\")\\n file.write(\\\" NoRECobject, RECNoRerenderObject, RECNoRerHW, RECRerenderObject = Route(switchRECenable, annoted_object);\\\\n\\\")\\n file.write(\\\" Display(\\\\n\\\")\\n file.write(\\\" NoRECobject,\\\\n\\\")\\n file.write(\\\" camera,\\\\n\\\")\\n file.write(\\\" where,\\\\n\\\")\\n file.write(\\\" throttle) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" image =\\\\n\\\")\\n file.write(\\\" Render(\\\\n\\\")\\n file.write(\\\" RECNoRerenderObject,\\\\n\\\")\\n file.write(\\\" camera) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" Display(\\\\n\\\")\\n file.write(\\\" image,\\\\n\\\")\\n file.write(\\\" NULL,\\\\n\\\")\\n file.write(\\\" where,\\\\n\\\")\\n file.write(\\\" throttle) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" WriteImage(\\\\n\\\")\\n file.write(\\\" image,\\\\n\\\")\\n file.write(\\\" RECfile,\\\\n\\\")\\n file.write(\\\" RECformat) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" rec_where = Display(\\\\n\\\")\\n file.write(\\\" RECNoRerHW,\\\\n\\\")\\n file.write(\\\" camera,\\\\n\\\")\\n file.write(\\\" where,\\\\n\\\")\\n file.write(\\\" throttle) [instance: 1, cache: 0];\\\\n\\\")\\n file.write(\\\" rec_image = ReadImageWindow(\\\\n\\\")\\n file.write(\\\" rec_where) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" WriteImage(\\\\n\\\")\\n file.write(\\\" rec_image,\\\\n\\\")\\n file.write(\\\" RECfile,\\\\n\\\")\\n file.write(\\\" RECformat) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" RECupdateCamera =\\\\n\\\")\\n file.write(\\\" UpdateCamera(\\\\n\\\")\\n file.write(\\\" camera,\\\\n\\\")\\n file.write(\\\" resolution=RECresolution,\\\\n\\\")\\n file.write(\\\" aspect=RECaspect) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" Display(\\\\n\\\")\\n file.write(\\\" RECRerenderObject,\\\\n\\\")\\n file.write(\\\" camera,\\\\n\\\")\\n file.write(\\\" where,\\\\n\\\")\\n file.write(\\\" throttle) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" RECRerenderObject =\\\\n\\\")\\n file.write(\\\" ScaleScreen(\\\\n\\\")\\n file.write(\\\" RECRerenderObject,\\\\n\\\")\\n file.write(\\\" NULL,\\\\n\\\")\\n file.write(\\\" RECresolution,\\\\n\\\")\\n file.write(\\\" camera) [instance: 1, cache: 1];\\\\n\\\")\\n file.write(\\\" image =\\\\n\\\")\\n file.write(\\\" Render(\\\\n\\\")\\n file.write(\\\" RECRerenderObject,\\\\n\\\")\\n file.write(\\\" RECupdateCamera) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\" WriteImage(\\\\n\\\")\\n file.write(\\\" image,\\\\n\\\")\\n file.write(\\\" RECfile,\\\\n\\\")\\n file.write(\\\" RECformat) [instance: 2, cache: 1];\\\\n\\\")\\n file.write(\\\"}\\\\n\\\")\\n file.write(\\\"main_Image_3_in_1 = \\\\\\\"Image_3\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Image_3_in_3 = \\\\\\\"X24,,Surface\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Image_3_in_4 = 1;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_5 = %s;\\\\n\\\" % image_array1)\\n file.write(\\\"main_Image_3_in_6 = %s;\\\\n\\\" % image_array2)\\n file.write(\\\"main_Image_3_in_7 = %s;\\\\n\\\" % image_val)\\n file.write(\\\"main_Image_3_in_8 = 1376;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_9 = 0.678;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_10 = [-0.109685 0.243133 0.963772];\\\\n\\\")\\n file.write(\\\"main_Image_3_in_11 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_12 = 0;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_13 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_14 = 1;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_15 = \\\\\\\"none\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Image_3_in_16 = \\\\\\\"none\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Image_3_in_17 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_18 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_19 = 0;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_20 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_21 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_23 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_25 = \\\\\\\"iso\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Image_3_in_26 = \\\\\\\"tiff\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Image_3_in_27 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_28 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_29 = 0;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_30 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_31 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_32 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_33 = 1;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_34 = 0;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_35 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_36 = 1;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_37 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_38 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_39 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_40 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_41 = \\\\\\\"rotate\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Image_3_in_42 = \\\\\\\"Surface\\\\\\\";\\\\n\\\")\\n file.write(\\\"main_Image_3_in_43 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_44 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_45 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_46 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_47 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_48 = NULL;\\\\n\\\")\\n file.write(\\\"main_Image_3_in_49 = NULL;\\\\n\\\")\\n file.write(\\\"Executive(\\\\\\\"product version 4 3 2\\\\\\\");\\\\n\\\")\\n file.write(\\\"$sync\\\\n\\\")\\n file.write(\\\"main();\\\\n\\\")\\n\\n # Close the file.\\n file.close()\",\n \"def write_probin(prob_param_files, cxx_prefix):\\n\\n params = []\\n\\n print(\\\" \\\")\\n print(f\\\"write_probdata.py: creating prob_param C++ files\\\")\\n\\n # read the parameters defined in the parameter files\\n\\n for f in prob_param_files:\\n err = parse_param_file(params, f)\\n if err:\\n abort(f\\\"Error parsing {f}\\\")\\n\\n\\n # now handle the C++ -- we need to write a header and a .cpp file\\n # for the parameters\\n\\n cxx_base = os.path.basename(cxx_prefix)\\n\\n ofile = f\\\"{cxx_prefix}_parameters.H\\\"\\n with open(ofile, \\\"w\\\") as fout:\\n fout.write(CXX_HEADER)\\n\\n fout.write(f\\\" void init_{cxx_base}_parameters();\\\\n\\\\n\\\")\\n\\n fout.write(\\\" namespace problem {\\\\n\\\\n\\\")\\n\\n for p in params:\\n if p.dtype == \\\"string\\\":\\n fout.write(f\\\" extern std::string {p.name};\\\\n\\\\n\\\")\\n else:\\n if p.is_array():\\n if p.size == \\\"nspec\\\":\\n fout.write(f\\\" extern AMREX_GPU_MANAGED {p.get_cxx_decl()} {p.name}[NumSpec];\\\\n\\\\n\\\")\\n else:\\n fout.write(f\\\" extern AMREX_GPU_MANAGED {p.get_cxx_decl()} {p.name}[{p.size}];\\\\n\\\\n\\\")\\n else:\\n fout.write(f\\\" extern AMREX_GPU_MANAGED {p.get_cxx_decl()} {p.name};\\\\n\\\\n\\\")\\n\\n fout.write(\\\" }\\\\n\\\\n\\\")\\n\\n fout.write(CXX_FOOTER)\\n\\n # now the C++ job_info tests\\n ofile = f\\\"{cxx_prefix}_job_info_tests.H\\\"\\n with open(ofile, \\\"w\\\") as fout:\\n for p in params:\\n if not p.is_array():\\n if p.in_namelist:\\n fout.write(p.get_job_info_test())\\n\\n # now the C++ initialization routines\\n ofile = f\\\"{cxx_prefix}_parameters.cpp\\\"\\n with open(ofile, \\\"w\\\") as fout:\\n fout.write(f\\\"#include <{cxx_base}_parameters.H>\\\\n\\\")\\n fout.write(\\\"#include <AMReX_ParmParse.H>\\\\n\\\")\\n fout.write(\\\"#include <AMReX_REAL.H>\\\\n\\\\n\\\")\\n for p in params:\\n if p.dtype == \\\"string\\\":\\n fout.write(f\\\" std::string problem::{p.name};\\\\n\\\\n\\\")\\n else:\\n if p.is_array():\\n if p.size == \\\"nspec\\\":\\n fout.write(f\\\" AMREX_GPU_MANAGED {p.get_cxx_decl()} problem::{p.name}[NumSpec];\\\\n\\\\n\\\")\\n else:\\n fout.write(f\\\" AMREX_GPU_MANAGED {p.get_cxx_decl()} problem::{p.name}[{p.size}];\\\\n\\\\n\\\")\\n else:\\n fout.write(f\\\" AMREX_GPU_MANAGED {p.get_cxx_decl()} problem::{p.name};\\\\n\\\\n\\\")\\n\\n fout.write(\\\"\\\\n\\\")\\n fout.write(f\\\" void init_{cxx_base}_parameters() {{\\\\n\\\")\\n\\n\\n # now write the parmparse code to get the value from the C++\\n # inputs.\\n\\n # open namespace\\n fout.write(\\\" {\\\\n\\\")\\n\\n # we need access to _rt\\n fout.write(\\\" using namespace amrex;\\\\n\\\")\\n\\n fout.write(f\\\" amrex::ParmParse pp(\\\\\\\"problem\\\\\\\");\\\\n\\\\n\\\")\\n for p in params:\\n if p.is_array():\\n size = p.size\\n if (size == \\\"nspec\\\"):\\n size = \\\"NumSpec\\\"\\n fout.write(f\\\" for (int n = 0; n < {size}; n++) {{\\\\n\\\")\\n fout.write(f\\\" problem::{p.name}[n] = {p.default_format(lang='C++')};\\\\n\\\")\\n fout.write(f\\\" }}\\\\n\\\")\\n else:\\n fout.write(f\\\" {p.get_default_string()}\\\")\\n\\n if p.in_namelist:\\n fout.write(f\\\" {p.get_query_string('C++')}\\\")\\n fout.write(\\\"\\\\n\\\")\\n fout.write(\\\" }\\\\n\\\")\\n\\n fout.write(\\\" }\\\\n\\\")\",\n \"def compress_nparr(nparr):\\n bytestream = io.BytesIO()\\n np.save(bytestream, nparr)\\n uncompressed = bytestream.getvalue()\\n compressed = zlib.compress(uncompressed)\\n return compressed, len(uncompressed), len(compressed)\",\n \"def save_to_ascii(self, filename, print_progress=True):\\n out=StringIO.StringIO()\\n # --------------------------------\\n # create string with data\\n # --------------------------------\\n # params_data:\\n out.write('#>>params_data:\\\\n')\\n out.write('nx=%d\\\\n' % self.x.size) \\n out.write('np=%d\\\\n' % self.p.size)\\n # x:\\n out.write('\\\\n#>>x:\\\\n')\\n np.savetxt(out, self.x, fmt='%1.8E')\\n # p:\\n out.write('\\\\n#>>p:\\\\n')\\n np.savetxt(out, self.p, fmt='%1.8E')\\n # fmci_XP:\\n out.write('\\\\n#>>XP: (rows:%4d, columns:%4d)\\\\n' % (self.x.size,self.p.size))\\n np.savetxt(out, self.fmci_XP, fmt='%1.8E')\\n # params_physics:\\n out.write('\\\\n#>>params_physics:\\\\n')\\n out.write('particle=%s\\\\n' % self.__particle_short_names[self.name]) \\n out.write('time=%1.8E\\\\n' % self.time)\\n #\\n out.write('P=%1.8E\\\\n' % self.PSR_P)\\n out.write('B12=%1.8E\\\\n' % self.PSR_B12)\\n out.write('Lcm=%1.8E\\\\n' % self.PSR_Lcm)\\n #\\n out.write('Theta=%1.8E\\\\n' % self.PSR_Theta)\\n out.write('Chi=%1.8E\\\\n' % self.PSR_Chi)\\n # params_TDC:\\n out.write('\\\\n#>>params_TDC:\\\\n')\\n out.write('calc_id=%s\\\\n' % self.calc_id) \\n out.write('i_ts=%d\\\\n' % self.i_ts)\\n # --------------------------------\\n # save to file \\n # --------------------------------\\n f=open(filename,'w')\\n f.write(out.getvalue())\\n f.close()\\n out.close()\\n if print_progress:\\n print '\\\\nContent saved in \\\"%s\\\" \\\\n' % filename\",\n \"def write(self, cull=False):\\n if cull:\\n cull_prefixes(self).write()\\n else:\\n ser = self.g.serialize(format='nifttl', encoding='utf-8')\\n with open(self.filename, 'wb') as f:\\n f.write(ser)\\n #print('yes we wrote the first version...', self.name)\",\n \"def dump(self, step=None):\\n if self._compressed:\\n np.savez_compressed(self._file_path, **self._data)\\n else:\\n np.savez(self._file_path, **self._data)\",\n \"def write_data(self, file_prefix, **kwargs):\\n # Add a dot to separate the prefix from the population label if it\\n # doesn't already have one and isn't a directory\\n if (not os.path.isdir(file_prefix) and\\n not file_prefix.endswith('.') and\\n not file_prefix.endswith(os.path.sep)):\\n file_prefix += '.'\\n for comp_array in self.component_arrays.values():\\n # @UndefinedVariable\\n comp_array.write_data(file_prefix + comp_array.name + '.pkl',\\n **kwargs)\",\n \"def _multiprocessing_save_srp(in_out_path):\\n vp, np = in_out_path\\n if not os.path.exists(np + '.npz'):\\n save_compact_fft(\\n np,\\n embedding_fft(load_one_srp_embedding(vp))\\n )\\n return np\",\n \"def savez(d,file):\\n np.savez(file,row=d.row,col=d.col,data=d.data,shape=d.shape)\",\n \"def write_file(self):\\n print 'Writing '+self.name+' binary...'\\n if self.vals is not None:\\n if len(self.vals) == self.size:\\n stream = self.pack_mem()\\n with open(self.name+'.bin','wb') as f:\\n f.write(stream)\\n print 'File written: '+self.name+'.bin'\\n else:\\n print 'Error: input array for '+self.name+'is not the right '+\\\\\\n 'size (should be '+str(self.size)+'). Skipping.'\\n else:\\n print 'No array provided, skipping.'\",\n \"def writexyz(self,fname):\\n xyzfile = open(fname + \\\".xyz\\\",\\\"a+\\\")\\n xyzfile.write(str(self.natoms) + \\\"\\\\n\\\\n\\\")\\n for a in self.atoms:\\n \\tcxyz = a.xyz - np.array(self.pbc_correction(a.xyz))\\n\\t\\t\\txyzfile.write(str(a.type) + \\\"\\\\t\\\" + str(cxyz[0]) + \\\"\\\\t\\\" + str(cxyz[1]) + \\\"\\\\t\\\" + str(cxyz[2]) + \\\"\\\\n\\\")\\n xyzfile.close()\",\n \"def writeNonZeroEntry(prep_fn, tensor_folder, tensor_output_path, slab_inds, vocab_size):\\n \\\"\\\"\\\"\\n f = open(prep_fn, \\\"r\\\")\\n lines = f.readlines()\\n f.close()\\n slab_num = len(lines)\\n \\\"\\\"\\\"\\n g = open(tensor_output_path, \\\"a+\\\")\\n for slab_ind in slab_inds:\\n with open(tensor_folder+str(slab_ind)+\\\".sav\\\", \\\"rb\\\") as handle:\\n count_mat = joblib.load(handle)\\n # only get most frequent vocab\\n print \\\"count_mat original shape:\\\", count_mat.shape,\\n count_mat = count_mat[:vocab_size, :vocab_size]\\n print \\\"count_mat is reduced to:\\\", count_mat.shape\\n\\n nonzero_inds = np.nonzero(count_mat)\\n nonzero_values = count_mat[nonzero_inds]\\n nonzero_coords = np.transpose(nonzero_inds) + 1\\n for ind in range(len(nonzero_values)):\\n\\t val = nonzero_values[ind]\\n #log_value = np.log(nonzero_values[ind]+1)\\n seq = [str(v) for v in nonzero_coords[ind]] + [str(slab_ind+1)] + [str(val)]\\n print >> g, \\\" \\\".join(seq)\\n g.close()\\n print \\\"done formatting\\\", slab_inds\",\n \"def write_po(self, outputfile):\\n raise NotImplementedError(\\n \\\"Writing to this file format is not yet implemented\\\")\",\n \"def pcwrite(filename, xyzrgb):\\n xyz = xyzrgb[:, :3]\\n rgb = xyzrgb[:, 3:].astype(np.uint8)\\n\\n # Write header\\n ply_file = open(filename, 'w')\\n ply_file.write(\\\"ply\\\\n\\\")\\n ply_file.write(\\\"format ascii 1.0\\\\n\\\")\\n ply_file.write(\\\"element vertex %d\\\\n\\\" % (xyz.shape[0]))\\n ply_file.write(\\\"property float x\\\\n\\\")\\n ply_file.write(\\\"property float y\\\\n\\\")\\n ply_file.write(\\\"property float z\\\\n\\\")\\n ply_file.write(\\\"property uchar red\\\\n\\\")\\n ply_file.write(\\\"property uchar green\\\\n\\\")\\n ply_file.write(\\\"property uchar blue\\\\n\\\")\\n ply_file.write(\\\"end_header\\\\n\\\")\\n\\n # Write vertex list\\n for i in range(xyz.shape[0]):\\n ply_file.write(\\\"%f %f %f %d %d %d\\\\n\\\" % (\\n xyz[i, 0], xyz[i, 1], xyz[i, 2],\\n rgb[i, 0], rgb[i, 1], rgb[i, 2],\\n ))\",\n \"def writeHoc(self):\\n print('Writing output file %s ...' % self.outFile)\\n with open(self.outFile, 'w') as fOut:\\n \\n def createSection(secNum):\\n fOut.write('create section_%i\\\\n' %secNum)\\n fOut.write('section_%i {\\\\n' %secNum)\\n fOut.write('pt3dclear()\\\\n')\\n for node in xrange(len(self.sections[secNum])):\\n fOut.write('pt3dadd(%.6f, %.6f, %.6f, %.6f)\\\\n' \\\\\\n % (self.sections[secNum][node][0],\\n self.sections[secNum][node][1],\\n self.sections[secNum][node][2],\\n self.secRads[secNum][node]))\\n fOut.write('}\\\\n')\\n \\n def createConnection():\\n for c in xrange(len(self.connections)):\\n fOut.write('connect section_%i(1), section_%i(0)\\\\n' \\\\\\n % (self.connections[c][0],self.connections[c][1]))\\n \\n \\n for sec in self.sections.keys():\\n createSection(sec)\\n createConnection()\\n \\n \\n return\",\n \"def save_numpy_array(self):\\n np.save(\\\"smallest_particles.npy\\\", self.smallest_particles)\\n np.save(\\\"number_counts.npy\\\", self.number_counts)\\n np.save(\\\"number_counts_2.npy\\\", self.number_counts_2)\",\n \"def to_netcdf(self, outfile):\",\n \"def save_predictions_npz(fn, predictions):\\n np.savez_compressed(fn, predictions=predictions)\\n print(\\\"[save_predictions_npz] Wrote to\\\", fn)\",\n \"def writedata(filename, inputs, outputs):#=None, noutput=1):\\n\\t#if outputs == None:\\n\\t#\\toutputs = np.zeros(shape=(np.shape(inputs)[0], noutput))\\n\\tassert np.shape(inputs)[0] == np.shape(outputs)[0]\\n\\tndp = np.shape(inputs)[0]\\n\\tnin = np.shape(inputs)[1]\\n\\tnout = np.shape(outputs)[1]\\n\\tf = open(filename, \\\"w\\\")\\n\\tf.write(\\\"%i %i %i\\\\n\\\" % (ndp, nin, nout))\\n\\tfor inputline, outputline in zip(inputs, outputs):\\n\\t\\tf.write(\\\" \\\".join([\\\"%.6f\\\" % item for item in inputline]) + \\\"\\\\n\\\")\\n\\t\\tf.write(\\\" \\\".join([\\\"%.6f\\\" % item for item in outputline]) + \\\"\\\\n\\\")\\n\\tf.close()\\n\\tlogger.debug(\\\"Wrote %s\\\\n(datapoints : %i, inputs : %i, outputs : %i)\\\" % (filename, ndp,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t nin, nout))\",\n \"def to_npz(self, file, compressed=False, compute=True):\\n labels, nevonecut, nevcutflow, masksonecut, maskscutflow = self.result()\\n\\n if compressed:\\n saver = numpy.savez_compressed\\n else:\\n saver = numpy.savez\\n\\n out = CutflowToNpz(\\n file, labels, nevonecut, nevcutflow, masksonecut, maskscutflow, saver\\n )\\n if compute:\\n out.compute()\\n return None\\n else:\\n return out\",\n \"def genPSFimage(filename=None):\\n hdu=pf.open(filename)\\n nn = len(hdu)\\n for i in range(1,nn):\\n img = hdu[i].data[0][4:].reshape(npix,npix)\\n img = img/img.sum()\\n hdu[i].data = img\\n #hdu.scale('int16', '', bzero=32768)\\n newfilename = filename[:-7]+'_stamp.fits'\\n hdu.writeto(newfilename)\\n os.system('gzip '+newfilename)\",\n \"def _save_pca_matrix(self, filename):\\n\\n np.savetxt(filename, self.pca_matrix, delimiter=\\\",\\\")\",\n \"def create_file_empty_particles( self, fullpath, iteration,\\n time, dt, select_nglobal_dict=None ):\\n # Create the file (can be done by one proc or in parallel)\\n f = self.open_file( fullpath,\\n parallel_open=self.write_metadata_parallel )\\n\\n # Setup the different layers of the openPMD file\\n # (f is None if this processor does not participate is writing data)\\n if f is not None:\\n\\n # Setup the attributes of the top level of the file\\n self.setup_openpmd_file( f, iteration, time, dt )\\n # Setup the meshes group (contains all the particles)\\n f.attrs[\\\"particlesPath\\\"] = np.string_(\\\"particles/\\\")\\n particle_path = \\\"/data/%d/particles/\\\" %iteration\\n particle_grp = f.require_group(particle_path)\\n # Loop through all particle species\\n for species_name in sorted(self.species_dict.keys()):\\n species = self.species_dict[species_name]\\n\\n # Check the number of particles to write\\n if select_nglobal_dict is not None:\\n N = select_nglobal_dict[species_name]\\n else:\\n N = None\\n\\n # Create and setup the h5py.Group species_grp\\n species_path = particle_path+\\\"%s/\\\" %(species_name)\\n species_grp = f.require_group( species_path )\\n self.setup_openpmd_species_group( species_grp, species, N=N )\\n\\n # Loop over the different quantities that should be written\\n # and setup the corresponding datasets\\n for particle_var in self.particle_data:\\n\\n # Vector quantities\\n if particle_var in [\\\"position\\\", \\\"momentum\\\", \\\"E\\\", \\\"B\\\"]:\\n # Setup the dataset\\n quantity_path=species_path+ \\\"%s/\\\" %particle_var\\n quantity_grp = f.require_group(quantity_path)\\n for coord in [\\\"x\\\",\\\"y\\\",\\\"z\\\"]:\\n # Create the dataset (fixed size or appendable)\\n if N is not None:\\n dset = quantity_grp.create_dataset(\\n coord, (N,), dtype='f8')\\n else:\\n dset = quantity_grp.create_dataset(\\n coord, (0,), maxshape=(None,), dtype='f8')\\n self.setup_openpmd_species_component( dset )\\n self.setup_openpmd_species_record( quantity_grp,\\n particle_var)\\n\\n # Scalar quantity\\n elif particle_var in [\\\"weighting\\\", \\\"id\\\", \\\"t\\\"]:\\n # Choose the type of the output\\n if particle_var == \\\"id\\\":\\n dtype = 'uint64'\\n else:\\n dtype = 'f8'\\n # Create the dataset (fixed size or appendable)\\n if N is not None:\\n dset = species_grp.create_dataset(\\n particle_var, (N,), dtype=dtype )\\n else:\\n dset = species_grp.create_dataset( particle_var,\\n (0,), maxshape=(None,), dtype=dtype)\\n self.setup_openpmd_species_component( dset )\\n self.setup_openpmd_species_record( dset, particle_var )\\n\\n # Unknown field\\n else:\\n raise ValueError(\\n \\\"Invalid string in particletypes: %s\\\" %particle_var)\\n\\n # Close the file\\n f.close()\",\n \"def prep_npz(self, input_tsv: Path, output_npz: Path,\\n batch_size: int = 512 * 128, minibatch_size: int = 128):\\n all_ids, all_sents, all_embs = list(), list(), list()\\n\\n batch_gen = self.batch_generator(input_tsv, batch_size=batch_size)\\n for id_batch, sent_batch in batch_gen:\\n embs = self.make_vectors(sent_batch, minibatch_size=minibatch_size)\\n\\n id_batch = np.array(id_batch, dtype=np.int64)\\n emb_batch = np.vstack(embs).astype(np.float32)\\n sent_batch = np.array(sent_batch, dtype=np.str)\\n\\n all_ids.append(id_batch)\\n all_embs.append(emb_batch)\\n all_sents.append(sent_batch)\\n\\n all_ids = np.concatenate(all_ids, axis=None)\\n all_embs = np.concatenate(all_embs, axis=0)\\n all_sents = np.concatenate(all_sents, axis=None)\\n\\n np.savez(output_npz,\\n ids=all_ids, embs=all_embs, sents=all_sents,\\n compressed=True)\",\n \"def write_towhee_restart(self, filename, pbc=(0, 0, 0)):\\n seeds = [5054388, 14604618, 6176650, 7526479, 6525, 10097385, 9059353,\\n 14349506, 535, 7287374, 12195841, 7272997, 5692437, 11292972,\\n 1589479, 16351161, 14342867, 3500530, 14385737, 2924396,\\n 11857489, 6765405, 12074244, 5940539, 3050519]\\n version = 6\\n numcycles = 0\\n numboxes = 1\\n nummoltypes = 1\\n maxtranssingle = 0.5\\n maxtranscom = 0.15305334\\n maxrot = 0.672082\\n maxvoldis = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]\\n if type(pbc) is list or type(pbc) is tuple:\\n pbc = np.asarray(pbc)\\n length = np.asarray(self.length) + pbc\\n hmatrix = np.diag(length)\\n maxvoldis.extend(list(hmatrix))\\n with open(filename, 'w') as f:\\n f.write(f\\\"\\\\t\\\\t{version}\\\\n\\\")\\n f.write(\\\"\\\\t\\\" + \\\"\\\\t \\\".join(map(str, seeds)) + \\\"\\\\n\\\")\\n f.write(f\\\"\\\\t{numcycles} \\\\t\\\\t{numboxes} \\\\t\\\\t{nummoltypes}\\\\n\\\")\\n f.write(f\\\"\\\\t{maxtranssingle:20.15f}\\\\n\\\")\\n f.write(f\\\"\\\\t{maxtranscom:20.15f}\\\\n\\\")\\n f.write(f\\\"\\\\t{maxrot:20.15f}\\\\n\\\\n\\\")\\n for row in maxvoldis:\\n f.write(f\\\"\\\\t{row[0]:20.15f} \\\\t{row[1]:20.15f} \\\\t{row[2]:20.15f}\\\\n\\\")\\n f.write(f\\\"\\\\t{self.numatom // self.molsize}\\\\n\\\")\\n f.write(f\\\"\\\\t{self.molsize}\\\\n\\\")\\n f.write(\\\"\\\\t \\\".join(map(str, list(self.contents['Mol_type'].astype(int)))) + \\\"\\\\n\\\")\\n f.write(\\\"\\\\t \\\".join(map(str, list(self.contents['Box_ID'].astype(int)))) + \\\"\\\\n\\\")\\n df = self.contents[['X', 'Y', 'Z']].copy()\\n np.savetxt(f, df.values, fmt=\\\" %20.15f\\\"*3)\",\n \"def write_out(self, niter, locuslen):\\n try:\\n handle = open(self.output, 'w')\\n except OSError:\\n print 'Error, you do not have permission to write files here.'\\n extit(1)\\n # First, write the pop names\\n handle.write('#Pop 1: ' + self.popnames[0] + '\\\\n')\\n handle.write('#Pop 2: ' + self.popnames[1] + '\\\\n')\\n # Then write the run parameters\\n handle.write('#Model: ' + self.modelname + '\\\\n')\\n handle.write('#Max iterations: ' + str(niter) + '\\\\n')\\n # Then write some model summaries\\n handle.write('#Data Likelihoods: ' + ' '.join([str(s) for s in self.mod_like]) + '\\\\n')\\n handle.write('#Optimized Likelihoods: ' + ' '.join([str(s) for s in self.opt_like]) + '\\\\n')\\n handle.write('#AIC: ' + ' '.join([str(s) for s in self.aic]) + '\\\\n')\\n handle.write('#LocusLem: ' + str(locuslen) + '\\\\n')\\n handle.write('#4*Na*u*L: ' + str(self.theta_mean) + '\\\\n')\\n handle.write('#Na: ' + str(self.Na) + '\\\\n')\\n for name, val in zip(self.params['Names'], self.scaled_params):\\n towrite = '#' + name + ': ' + str(val) + '\\\\n'\\n handle.write(towrite)\\n # Then a table of the parameters that were found\\n handle.write('Iteration\\\\t' + '\\\\t'.join(self.params['Names']) + '\\\\n')\\n handle.write('Initial\\\\t' + '\\\\t'.join([str(s) for s in self.params['Values']]) + '\\\\n')\\n # Write the perturbed parameters\\n for index, vals in enumerate(self.p_init):\\n name = 'Perturbed_' + str(index) + '\\\\t'\\n handle.write(name + '\\\\t'.join([str(s) for s in vals]) + '\\\\n')\\n # And the hot annealed values\\n for index, vals in enumerate(self.hot_params):\\n name = 'Hot_Anneal_' + str(index) + '\\\\t'\\n handle.write(name + '\\\\t'.join([str(s) for s in vals]) + '\\\\n')\\n # And the cold annealed values\\n for index, vals in enumerate(self.cold_params):\\n name = 'Cold_Anneal_' + str(index) + '\\\\t'\\n handle.write(name + '\\\\t'.join([str(s) for s in vals]) + '\\\\n')\\n # And the BFGS parameters\\n for index, vals in enumerate(self.opt_params):\\n name = 'BFGS_' + str(index) + '\\\\t'\\n handle.write(name + '\\\\t'.join([str(s) for s in vals]) + '\\\\n')\\n # And the final params\\n handle.write('Hot_Mean\\\\t' + '\\\\t'.join([str(s) for s in self.hot_mean]) + '\\\\n')\\n handle.write('Cold_Mean\\\\t' + '\\\\t'.join([str(s) for s in self.cold_mean]) + '\\\\n')\\n handle.write('BFGS_Mean\\\\t' + '\\\\t'.join([str(s) for s in self.bfgs_mean]) + '\\\\n')\\n handle.flush()\\n handle.close()\\n return\",\n \"def create_normalization_file(use_controls, use_nofcd, mods):\\n\\n for j in range(1, mods + 1):\\n fcd_paths = sorted(glob.glob(FCD_FOLDER + f'fcd_*.{j}.nii.gz'))\\n nofcd_paths = sorted(glob.glob(FCD_FOLDER + f'nofcd_*.{j}.nii.gz'))\\n control_paths = sorted(glob.glob(CONTROL_FOLDER + f'control_*.{j}.nii.gz'))\\n\\n mri_paths = fcd_paths\\n if use_nofcd:\\n mri_paths += nofcd_paths\\n if use_controls:\\n mri_paths += control_paths\\n\\n t1_landmarks_path = Path(f'./data/t1_landmarks_{j}.npy')\\n\\n if t1_landmarks_path.is_file():\\n continue\\n # os.remove(f'./data/t1_landmarks_{j}.npy')\\n\\n t1_landmarks = (\\n t1_landmarks_path\\n if t1_landmarks_path.is_file()\\n else HistogramStandardization.train(mri_paths)\\n )\\n\\n np.save(t1_landmarks_path, t1_landmarks, allow_pickle=True)\",\n \"def convert_data(self):\\n print('Saving data for post-processing in: {}'.format(self.RunDir))\\n\\n for ig in self.Set:\\n print(ig.Name)\\n # Phase Array - Convert to CM\\n phs = roipy.tools.load_half(ig)\\n data = phs * ig.Phs2cm\\n outname = os.path.join(self.RunDir, 'd_' + ig.Name.replace('unw','npy')) #d for displacement\\n ig.ProcName = outname\\n np.save(outname,data)\\n\\n # Copy rsc\\n shutil.copyfile(ig.Path + '.rsc', outname + '.rsc')\\n\\n # Nan Values as mask array\\n maskname = outname.replace('d_', 'nans_')\\n nans = np.isnan(data) # NOTE: ROI_PAC saves nans as exact 0.0, risks scraping some true data\\n np.save(maskname, nans)\",\n \"def save_prime_array(number_of_primes) -> None:\\n p = prime_array(number_of_primes)\\n with open(f'prime{number_of_primes}.bin', 'wb') as prime_file:\\n p.tofile(prime_file)\\n \\n return None\",\n \"def write_file_simple(self,filename):\\n\\n output = open(filename,\\\"w\\\")\\n # write header\\n output.write(\\\"# %1s %3s %22s %6s %22s\\\\n\\\"%(\\\"l\\\",\\\"n\\\",\\\"nu_theo (muHz)\\\",\\\"unused\\\",\\\"Inertia\\\"))\\n for i in range(self.modes.shape[0]):\\n output.write(\\\" %1d %3d %22.15e 0.0 %22.15e\\\\n\\\"%( \\\\\\n self.modes[\\\"l\\\"][i], \\\\\\n self.modes[\\\"n\\\"][i], \\\\\\n self.modes[\\\"freq\\\"][i]*self.glb[ifreq_ref], \\\\\\n self.modes[\\\"inertia\\\"][i]))\\n output.close()\",\n \"def write_xyz_file(allxyz):\\n if SAVEXYZ:\\n print('+> Saving riverbed topography file...', end='')\\n if MODE == 1:\\n np.savetxt('kinoshita_topo.xyz', allxyz, fmt='%.6e')\\n elif MODE == 2:\\n np.savetxt(FNAME.rsplit('.', 1)[0] + '_topo.xyz', allxyz, fmt='%.6e')\\n print(' [done]')\",\n \"def write_input_file(self, outfile='input.dat', **options):\\n out = ''\\n if 'header' in options:\\n out += options['header']\\n\\n if options['program'] == 'orca':\\n ecp = f' NewECP \\\"{options[\\\"ecp\\\"]}\\\" end'\\n\\n form = ' {:<4}' + ' '*8 + ' {:> 13.8f}' * 3 + '\\\\n'\\n for atom, xyz, charge in self.qc_mol:\\n out += form.format(atom, *xyz)\\n\\n form = ' {:<4}' + ' {:>7.4f}' + ' {:> 13.8f}' * 3\\n for atom, xyz, charge in self.br_mol:\\n out += form.format(atom + '>', charge, *xyz) + ecp + '\\\\n'\\n\\n if 'separate_pc' in options:\\n form = '{:>7.4f}' + ' {:> 13.8f}' * 3\\n pc_out = f'{len(self.pc_mol)}\\\\n'\\n for atom, xyz, charge in self.pc_mol:\\n pc_out += form.format(charge, *xyz) + '\\\\n'\\n with open(options['separate_pc'], 'w') as f:\\n f.write(pc_out)\\n else:\\n for atom, xyz, charge in self.pc_mol:\\n out += form.format('Q', charge, *xyz) + '\\\\n'\\n\\n out += '*'\\n\\n else:\\n raise Exception(f'{options[\\\"program\\\"]} is not yet supported')\\n\\n if 'footer' in options:\\n out += options['footer']\\n\\n with open(outfile, 'w') as f:\\n f.write(out)\",\n \"def save(self, ofilename, oname, noisy_only = True):\\n ofile = ROOT.TFile(ofilename, 'recreate')\\n\\n outhists = [h.Clone(oname % (i + 1)) for i, h in enumerate(self.modules)]\\n for h, cells in zip(outhists, self.cells):\\n if noisy_only: h.Reset()\\n for cell in cells: h.SetBinContent(cell[0], cell[1], noisy_only * 1.)\\n # h.Write()\\n\\n ofile.Write()\\n ofile.Close()\",\n \"def _create_pbs_file(self, jobname, num_jobs, pf_pbs, pf_input_package_template, pf_output_package_template):\\n\\n # create unique compute directory\\n pd_compute = None # run_shell_cmd(\\\"mktemp --tmpdir={}\\\".format(self._prl_options[\\\"pbs-pd-root-compute\\\"]))\\n\\n pbs_text = PBS._generate_pbs_header_array(num_jobs, jobname, self._prl_options, pd_compute=pd_compute)\\n\\n pbs_text += \\\"\\\\n{}\\\\n\\\".format(\\n PBS._generate_call_command(self._env,\\n pf_input_package_template,\\n pf_output_package_template,\\n self._prl_options,\\n pd_compute=pd_compute\\n )\\n )\\n\\n # write to file\\n write_to_file(pbs_text, pf_pbs)\",\n \"def WriteFile( self ):\\n with open( \\\"BasisVector.in\\\" , \\\"w\\\" ) as outfile:\\n firstLine = \\\" \\\" + str( self.NQ ) + \\\\\\n \\\" \\\" + str( self.Nbranches ) + \\\\\\n \\\" \\\" + str( self.NatomsUC ) + \\\\\\n \\\" \\\" + str( self.dim ) + \\\"\\\\n\\\"\\n outfile.write( firstLine )\\n for qq in range( self.NQ ): ## loop over Q vectors\\n lineQ = [ \\\"{:15.8f}\\\".format( x ) for x in \\n self.QVectors[ qq , : ] ]\\n lineQ = \\\"\\\".join( lineQ )\\n outfile.write( lineQ + \\\"\\\\n\\\" )\\n for branch in range( self.Nbranches ): ## loop over branches\\n for atom in range( self.NatomsUC ): ## loop over atoms in unit cell\\n line = [ \\\"{:15.8f}\\\".format( x ) for x in \\n self.EigenVectors[ qq , branch , atom , : ] ]\\n line = \\\"\\\".join( line )\\n outfile.write( line + \\\"\\\\n\\\" )\\n outfile.write( \\\"\\\\n\\\" )\\n outfile.write( \\\"\\\\n\\\" )\",\n \"def to_npz(self, file, compressed=False, compute=True):\\n labels, nev, masks = self.result()\\n\\n if compressed:\\n saver = numpy.savez_compressed\\n else:\\n saver = numpy.savez\\n\\n out = NminusOneToNpz(file, labels, nev, masks, saver)\\n if compute:\\n out.compute()\\n return None\\n else:\\n return out\",\n \"def create_and_write_output(predictions_path,output_path,inpDir):\\n \\n filenames= sorted(os.listdir(predictions_path)) \\n for filename in filenames:\\n \\n # read the 3 channel output image from the neural network\\n image=cv2.imread(os.path.join(predictions_path,filename))\\n \\n # create binary image output using the create_binary function\\n out_image=create_binary(image) \\n \\n # read and store the metadata from the input image\\n with BioReader(os.path.join(inpDir,filename)) as br:\\n metadata = br.metadata\\n\\n # Write the binary output consisting of the metadata using bfio.\\n output_image_5channel=np.zeros((out_image.shape[0],out_image.shape[1],1,1,1),dtype=np.uint8)\\n output_image_5channel[:,:,0,0,0]=out_image \\n\\n with BioWriter(os.path.join(output_path,filename), metadata=metadata) as bw:\\n bw.dtype = output_image_5channel.dtype\\n bw.write(output_image_5channel)\",\n \"def write(self):\\n #\\n if self.what == 'ecutwfc':\\n for i in range(self.Ndata):\\n self.pwinput.filename = self.inpFiles[i]\\n self.pwinput.SYSTEM.set_ecutwfc(self.values[i])\\n self.pwinput.write()\\n #\\n elif self.what == 'ecutrho':\\n for i in range(self.Ndata):\\n self.pwinput.filename = self.inpFiles[i]\\n self.pwinput.SYSTEM.ecutrho = self.values[i]\\n self.pwinput.write()\\n elif self.what == 'kpoints':\\n for i in range(self.Ndata):\\n self.pwinput.filename = self.inpFiles[i]\\n self.pwinput.Nk = self.values[i]\\n self.pwinput.write()\\n #\\n else:\\n raise RuntimeError('what = %s is not implemented yet' % (self.what))\\n #\\n self.inputs_have_been_written = True\",\n \"def write_file(self):\\r\\n # -open file for writing\\r\\n f_fbob = open(self.fn_path, 'w')\\r\\n\\r\\n # -write header\\r\\n f_fbob.write('%s\\\\n' % (self.heading))\\r\\n\\r\\n # -write sections 1 & 2 : NOTE- what about NOPRINT?\\r\\n f_fbob.write('%10i%10i%10i%10i\\\\n' % (self.nqfb, self.nqcfb,\\r\\n self.nqtfb, self.iufbobsv))\\r\\n f_fbob.write('%10e\\\\n' % (self.tomultfb)) # check format\\r\\n\\r\\n # -write sections 3-5 looping through observations groups\\r\\n c = 0\\r\\n for i in range(self.nqfb):\\r\\n # while (i < self.nqfb):\\r\\n # write section 3\\r\\n f_fbob.write('{:10d}{:10d}\\\\n'.format(self.nqobfb[i],\\r\\n self.nqclfb[i]))\\r\\n\\r\\n # Loop through observation times for the groups\\r\\n for j in range(self.nqobfb[i]):\\r\\n # -write section 4\\r\\n f_fbob.write(\\r\\n '{}{:10d}{:10.4g}{}{:10.4g}\\\\n'.format(self.obsnam[c],\\r\\n self.irefsp[c],\\r\\n self.toffset[c], ' ',\\r\\n self.flwobs[c]))\\r\\n c += 1 # index variable\\r\\n\\r\\n # -write section 5 - NOTE- need to adjust factor for muliple obs same cell\\r\\n for j in range(abs(self.nqclfb[i])):\\r\\n if self.nqclfb[\\r\\n i] < 0: # set factor to 1.0 for all cells in group\\r\\n self.factor[i, :] = 1.0\\r\\n f_fbob.write('{:10d}{:10d}{:10d}{}{:10f}\\\\n'\\r\\n .format(self.layer[i, j], (self.row[i, j]),\\r\\n self.column[i, j],\\r\\n ' ', self.factor[\\r\\n i, j])) # note- is 10f good enough here?\\r\\n\\r\\n f_fbob.close()\\r\\n #\\r\\n # swm: BEGIN hack for writing standard file\\r\\n sfname = self.fn_path # swm:hack\\r\\n sfname += '_ins' # swm: hack\\r\\n # write header\\r\\n f_ins = open(sfname, 'w') # swm: hack for standard file\\r\\n f_ins.write('jif @\\\\n') # swm: hack for standard file\\r\\n f_ins.write('StandardFile 0 1 %s\\\\n' % (\\r\\n self.nqtfb)) # swm: hack for standard file\\r\\n for i in range(0, self.nqtfb):\\r\\n f_ins.write(\\r\\n '{}\\\\n'.format(self.obsnam[i])) # swm: hack for standard file\\r\\n\\r\\n f_ins.close()\\r\\n # swm: END hack for writing standard file\\r\\n\\r\\n return\",\n \"def analyze_pressure_dump(filename, Lx=200., Ly=200, Lz=900., N=10, bin_divide_flag=False, Natoms=113579):\\n myfile = open(filename+'.txt')\\n trajectory = []\\n traj_pd = []\\n frames = []\\n\\n for _ in range(3):\\n next(myfile)\\n count = 0\\n while EOF(myfile):\\n count += 1\\n s = next(myfile) # info with the time step\\n\\n x = np.zeros(N, dtype=[('Chunk',np.float32), ('Coord1',np.float32), ('Ncount',np.float32), ('density',np.float32), ('temp',np.float32), ('vx',np.float32), ('fx',np.float32),('c_pciKE[1]',np.float32), ('c_pciKE[2]',np.float32), ('c_pciKE[3]',np.float32), ('c_pciVIR[1]',np.float32), ('c_pciVIR[2]',np.float32), ('c_pciVIR[3]',np.float32), ('c_pgelELAS[1]',np.float32), ('c_pgelELAS[2]',np.float32), ('c_pgelELAS[3]',np.float32), ('c_pgelVIR[1]', np.float32), ('c_pgelVIR[2]', np.float32), ('c_pgelVIR[3]', np.float32), ('c_pgelPAIR[1]', np.float32), ('c_pgelPAIR[2]', np.float32), ('c_pgelPAIR[3]', np.float32)])\\n\\n# Chunk Coord1 Ncount density/number temp vx fx c_pciKE[1] c_pciKE[2] c_pciKE[3] c_pciVIR[1] c_pciVIR[2] c_pciVIR[3] c_pgelELAS[1] c_pgelELAS[2] c_pgelELAS[3] c_pgelVIR[1] c_pgelVIR[2] c_pgelVIR[3] c_pgelPAIR[1] c_pgelPAIR[2] c_pgelPAIR[3]\\n\\n list_line = re.findall(\\\"[-+]?\\\\d+[\\\\.]?\\\\d*[eE]?[-+]?\\\\d*\\\", s)\\n frame, _, _ = list_line\\n frames.append(int(frame))\\n # print( \\\"reading lines\\\")\\n\\n for i in xrange(N):\\n count += 1\\n s = next(myfile)\\n list_line = re.findall(\\\"[-+]?\\\\d+[\\\\.]?\\\\d*[eE]?[-+]?\\\\d*\\\", s)\\n # print( \\\"reading line\\\", i, list_line)\\n for il, l in enumerate(list_line):\\n x[i][il] = float(l)\\n\\n trajectory.append(x)\\n\\n # names = x.dtype.fields.keys()\\n # data = x.dtype.fields.values()\\n\\n df = pd.DataFrame.from_records(x)\\n traj_pd.append(df)\\n\\n myfile.close()\\n\\n\\n\\n # # volume = 218.*44.*44.\\n volume = Lx*Ly*Lz\\n # N_atoms = 113579\\n # if bin_divide_flag:\\n # bin_volume = volume / float(N)\\n # else:\\n # bin_volume = 1.\\n\\n bin_volume = volume / float(N)\\n # bin_volume = volume\\n # bin_volume /= float(Natoms)\\n\\n Combine_PD = pd.concat(traj_pd)\\n FINAL_PD = pd.DataFrame()\\n\\n FINAL_PD['Coord1'] = Combine_PD['Coord1']\\n FINAL_PD['p_ciKE'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pciKE[1]'] + Combine_PD['c_pciKE[2]'] + Combine_PD['c_pciKE[3]'])/(3.*bin_volume)\\n FINAL_PD['p_ciVIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pciVIR[1]'] + Combine_PD['c_pciVIR[2]'] + Combine_PD['c_pciVIR[3]'])/(3.*bin_volume)\\n FINAL_PD['p_gelELAS'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelELAS[1]'] + Combine_PD['c_pgelELAS[2]'] + Combine_PD['c_pgelELAS[3]'])/(3.*bin_volume)\\n\\n FINAL_PD['p_gelVIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelVIR[1]'] + Combine_PD['c_pgelVIR[2]'] + Combine_PD['c_pgelVIR[3]'])/(3.*bin_volume)\\n FINAL_PD['p_gelPAIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelPAIR[1]'] + Combine_PD['c_pgelPAIR[2]'] + Combine_PD['c_pgelPAIR[3]'])/(3.*bin_volume)\\n\\n # So now I have to\\n # P_bin = (sigma_per_atom_xx + ... + sigma_per_atom_zz)/(bin_volume*3)\\n # *N_atoms_per_bin\\n # N_atoms_per_bin = number_density*N_atoms\\n\\n\\n df_concat = FINAL_PD\\n\\n by_row_index = df_concat.groupby(df_concat.index)\\n df_means = by_row_index.mean()\\n by_row_index_2 = df_concat.groupby(df_concat.index)\\n df_stds = by_row_index_2.std()\\n\\n # print( df_means.head())\\n # print( df_stds.head())\\n return df_means, df_stds\",\n \"def export_to_phy(waveform_extractor, output_folder, compute_pc_features=True,\\n compute_amplitudes=True, sparsity_dict=None, copy_binary=True,\\n max_channels_per_template=16, remove_if_exists=False,\\n peak_sign='neg', template_mode='median',\\n dtype=None, verbose=True, **job_kwargs):\\n assert isinstance(waveform_extractor, spikeinterface.core.waveform_extractor.WaveformExtractor), \\\\\\n 'waveform_extractor must be a WaveformExtractor object'\\n recording = waveform_extractor.recording\\n sorting = waveform_extractor.sorting\\n\\n assert recording.get_num_segments() == sorting.get_num_segments(), \\\\\\n \\\"The recording and sorting objects must have the same number of segments!\\\"\\n\\n assert recording.get_num_segments() == 1, \\\"Export to phy work only with one segment\\\"\\n\\n if sparsity_dict is None:\\n sparsity_dict = dict(method=\\\"best_channels\\\", num_channels=16)\\n\\n channel_ids = recording.channel_ids\\n num_chans = recording.get_num_channels()\\n fs = recording.get_sampling_frequency()\\n\\n if max_channels_per_template is None:\\n max_channels_per_template = num_chans\\n\\n empty_flag = False\\n non_empty_units = []\\n for unit in sorting.get_unit_ids():\\n if len(sorting.get_unit_spike_train(unit)) > 0:\\n non_empty_units.append(unit)\\n else:\\n empty_flag = True\\n unit_ids = non_empty_units\\n if empty_flag:\\n print('Warning: empty units have been removed when being exported to Phy')\\n\\n if not recording.is_filtered():\\n print(\\\"Warning: recording is not filtered! It's recommended to filter the recording before exporting to phy.\\\\n\\\"\\n \\\"You can run spikeinterface.toolkit.preprocessing.bandpass_filter(recording)\\\")\\n\\n if len(unit_ids) == 0:\\n raise Exception(\\\"No non-empty units in the sorting result, can't save to Phy.\\\")\\n\\n output_folder = Path(output_folder).absolute()\\n if output_folder.is_dir():\\n if remove_if_exists:\\n shutil.rmtree(output_folder)\\n else:\\n raise FileExistsError(f'{output_folder} already exists')\\n\\n output_folder.mkdir()\\n\\n # save dat file\\n if dtype is None:\\n dtype = recording.get_dtype()\\n\\n if copy_binary:\\n rec_path = output_folder / 'recording.dat'\\n write_binary_recording(recording, file_paths=rec_path, verbose=verbose, dtype=dtype, **job_kwargs)\\n elif isinstance(recording, BinaryRecordingExtractor):\\n rec_path = recording._kwargs['file_paths'][0]\\n dtype = recording.get_dtype()\\n else: # don't save recording.dat\\n rec_path = 'None'\\n\\n dtype_str = np.dtype(dtype).name\\n\\n # write params.py\\n with (output_folder / 'params.py').open('w') as f:\\n f.write(f\\\"dat_path = r'{str(rec_path)}'\\\\n\\\")\\n f.write(f\\\"n_channels_dat = {num_chans}\\\\n\\\")\\n f.write(f\\\"dtype = '{dtype_str}'\\\\n\\\")\\n f.write(f\\\"offset = 0\\\\n\\\")\\n f.write(f\\\"sample_rate = {fs}\\\\n\\\")\\n f.write(f\\\"hp_filtered = {recording.is_filtered()}\\\")\\n\\n # export spike_times/spike_templates/spike_clusters\\n # here spike_labels is a remapping to unit_index\\n all_spikes = sorting.get_all_spike_trains(outputs='unit_index')\\n spike_times, spike_labels = all_spikes[0]\\n np.save(str(output_folder / 'spike_times.npy'), spike_times[:, np.newaxis])\\n np.save(str(output_folder / 'spike_templates.npy'), spike_labels[:, np.newaxis])\\n np.save(str(output_folder / 'spike_clusters.npy'), spike_labels[:, np.newaxis])\\n\\n # export templates/templates_ind/similar_templates\\n # shape (num_units, num_samples, num_channels)\\n templates = []\\n templates_ind = []\\n\\n template_sparsity = get_template_channel_sparsity(waveform_extractor,\\n outputs=\\\"id\\\", **sparsity_dict)\\n num_sparse_chans = np.max([len(channels) for channels in template_sparsity.values()])\\n num_channels = np.min([max_channels_per_template, num_sparse_chans])\\n for unit_id in unit_ids:\\n template = waveform_extractor.get_template(unit_id, mode=template_mode, sparsity=template_sparsity)\\n inds = waveform_extractor.recording.ids_to_indices(template_sparsity[unit_id])\\n if template.shape[-1] < num_channels:\\n # fill missing channels\\n template_full = np.zeros((template.shape[0], num_channels))\\n template_full[:, :template.shape[-1]] = template\\n inds_full = np.concatenate((inds, np.array([-1] * (num_channels - template.shape[-1]))))\\n else:\\n template_full = template\\n inds_full = inds\\n templates.append(template_full)\\n templates_ind.append(inds_full)\\n\\n template_similarity = compute_template_similarity(waveform_extractor, method='cosine_similarity')\\n\\n np.save(str(output_folder / 'templates.npy'), templates)\\n np.save(str(output_folder / 'template_ind.npy'), templates_ind)\\n np.save(str(output_folder / 'similar_templates.npy'), template_similarity)\\n\\n channel_maps = np.arange(num_chans, dtype='int32')\\n channel_map_si = waveform_extractor.recording.get_channel_ids()\\n channel_positions = recording.get_channel_locations().astype('float32')\\n channel_groups = recording.get_channel_groups()\\n if channel_groups is None:\\n channel_groups = np.zeros(num_chans, dtype='int32')\\n np.save(str(output_folder / 'channel_map.npy'), channel_maps)\\n np.save(str(output_folder / 'channel_map_si.npy'), channel_map_si)\\n np.save(str(output_folder / 'channel_positions.npy'), channel_positions)\\n np.save(str(output_folder / 'channel_groups.npy'), channel_groups)\\n\\n if compute_amplitudes:\\n amplitudes = get_spike_amplitudes(waveform_extractor, peak_sign=peak_sign, outputs='concatenated', **job_kwargs)\\n # one segment only\\n amplitudes = amplitudes[0][:, np.newaxis]\\n np.save(str(output_folder / 'amplitudes.npy'), amplitudes)\\n\\n if compute_pc_features:\\n pc = WaveformPrincipalComponent(waveform_extractor)\\n pc.set_params(n_components=5, mode='by_channel_local')\\n pc.run_for_all_spikes(output_folder / 'pc_features.npy',\\n max_channels_per_template=max_channels_per_template, peak_sign=peak_sign,\\n **job_kwargs)\\n\\n max_channels_per_template = min(max_channels_per_template, len(channel_ids))\\n pc_feature_ind = np.zeros((len(unit_ids), max_channels_per_template), dtype='int64')\\n best_channels_index = get_template_channel_sparsity(waveform_extractor, method='best_channels',\\n peak_sign=peak_sign, num_channels=max_channels_per_template,\\n outputs='index')\\n\\n for u, unit_id in enumerate(sorting.unit_ids):\\n pc_feature_ind[u, :] = best_channels_index[unit_id]\\n np.save(str(output_folder / 'pc_feature_ind.npy'), pc_feature_ind)\\n\\n # Save .tsv metadata\\n with (output_folder / 'cluster_group.tsv').open('w') as tsvfile:\\n writer = csv.writer(tsvfile, delimiter='\\\\t', lineterminator='\\\\n')\\n writer.writerow(['cluster_id', 'group'])\\n for i, u in enumerate(sorting.get_unit_ids()):\\n writer.writerow([i, 'unsorted'])\\n\\n unit_groups = sorting.get_property('group')\\n if unit_groups is None:\\n unit_groups = np.zeros(len(unit_ids), dtype='int32')\\n\\n with (output_folder / 'cluster_channel_group.tsv').open('w') as tsvfile:\\n writer = csv.writer(tsvfile, delimiter='\\\\t', lineterminator='\\\\n')\\n writer.writerow(['cluster_id', 'channel_group'])\\n for i, unit_id in enumerate(unit_ids):\\n writer.writerow([i, unit_groups[i]])\\n\\n if verbose:\\n print('Run:\\\\nphy template-gui ', str(output_folder / 'params.py'))\",\n \"def create_dnz_file(args):\\n\\n file = open(args.o, 'w')\\n\\n file.write(\\\"% ----DATA VARIABLES----\\\\n\\\\n\\\")\\n file.write(\\\"t=\\\" + str(args.t) + \\\";\\\" + \\\"%number of attributes\\\\n\\\")\\n file.write(\\\"k=\\\" + str(args.k) + \\\";\\\" + \\\"%max length of the support set\\\\n\\\")\\n file.write(\\\"n=\\\" + str(args.n) + \\\";\\\" + \\\"%number of positive instances\\\\n\\\")\\n file.write(\\\"m=\\\" + str(args.m) + \\\";\\\" + \\\"%number of negative instances\\\\n\\\")\\n file.write(\\\"c=\\\" + str(args.c) + \\\";\\\" + \\\"%number of atMostOne Constraints\\\\n\\\\n\\\")\\n\\n file.write(\\\"% ----OMEGAS----\\\\n\\\\n\\\")\\n\\n omega_p = generate_omega_data(args.t, args.n, args.b)\\n file.write(\\\"omegap= \\\" + omega_to_mz(omega_p) + \\\"\\\\n\\\\n\\\")\\n\\n omega_n = generate_disjoint_omega_data(omega_p, args.m, args.b)\\n file.write(\\\"omegan= \\\" + omega_to_mz(omega_n) + \\\"\\\\n\\\\n\\\")\\n\\n file.write(\\\"% ----CONSTRAINS----\\\\n\\\\n\\\")\\n at_most_one = generate_at_most_one(int(args.t/2), args.c, 1, args.t)\\n file.write(\\\"atMostOne=\\\" + at_most_one_to_mz(at_most_one))\",\n \"def export_prop(self, d, title, t):\\r\\n self._check_out(title)\\r\\n np.savez_compressed(os.path.join(self.out_dir, title, title + '_' + str(t)), d)\",\n \"def dict2file(dict, filename, foldername):\\n if foldername:\\n if not os.path.exists(\\\"../Created_QD/\\\" + foldername):\\n os.makedirs(\\\"../Created_QD/\\\" + foldername)\\n file = open(\\\"../Created_QD/\\\" + foldername + \\\"/\\\" + filename + \\\".xyz\\\", \\\"w\\\")\\n else:\\n file = open(\\\"../Created_QD/\\\" + filename + \\\".xyz\\\", \\\"w\\\")\\n file.write(\\\" \\\\n\\\\n\\\")\\n for atom, values in dict.items():\\n file.write(values['element'] + \\\"\\\\t\\\" + str(values['coor'][0]) + \\\"\\\\t\\\\t\\\" +\\n str(values['coor'][1]) + \\\"\\\\t\\\\t\\\" + str(values['coor'][2]) + \\\"\\\\n\\\")\\n file.seek(0)\\n file.write(str(len(dict)))\\n file.close()\\n print(\\\"\\\\nQuantum Dot created :)\\\")\",\n \"def save_results(self, name):\\n # metrics in npz format\\n # filename = join(self.out_dir, '%s_metrics.npz' % name)\\n # np.savez(filename, tel_r=self.tel_r, cable_length=self.cable_length,\\n # cable_length_2=self.cable_length_2, uv_hist=self.uv_hist)\\n\\n if self.cable_length: # Empty dict() evaluate to False\\n # ASCII CSV table of radius vs cable length\\n filename = join(self.out_dir, '%s_cables.txt' % name)\\n data = np.array([[k, v] for k, v in\\n self.cable_length.iteritems()])\\n data = np.sort(data, axis=0)\\n np.savetxt(filename, data, fmt=b'%.10f %.10f')\\n\\n if self.cable_length_2: # Empty dict() evaluate to False\\n # ASCII CSV table of radius vs cable length\\n filename = join(self.out_dir, '%s_cables_2.txt' % name)\\n data = np.array([[k, v] for k, v in\\n self.cable_length_2.iteritems()])\\n data = np.sort(data, axis=0)\\n np.savetxt(filename, data, fmt=b'%.10f %.10f')\\n\\n if self.cable_length_3: # Empty dict() evaluates to False\\n filename = join(self.out_dir, '%s_cables.txt' % name)\\n data = np.array([[k, v] for k, v in\\n self.cable_length_3.iteritems()])\\n data = np.sort(data, axis=0)\\n np.savetxt(filename, data, fmt=b'%.10f %.10f')\\n\\n # Save a pickle with the PSF comparison info.\\n if self.psf:\\n filename = join(self.out_dir, '%s_psf.p' % name)\\n pickle.dump(self.psf, open(filename, 'wb'))\\n\\n # Save a pickle of uv hist data.\\n if self.uv_hist:\\n filename = join(self.out_dir, '%s_uv_hist.p' % name)\\n pickle.dump(self.uv_hist, open(filename, 'wb'))\",\n \"def generate(self):\\n self._open_file()\\n # copied from GenerateCSPEC.py\\n self._write_header_and_defaults()\\n self._write_source()\\n self._write_sample()\\n\\n self._write_all_components()\\n self._write_mantle_module()\\n self._write_segment()\\n self._write_all_ids()\\n self._write_footer()\\n self._close_file()\",\n \"def PrintCommPkgGZ(self, file, precision=16):\\n return _hypre.HypreParMatrix_PrintCommPkgGZ(self, file, precision)\",\n \"def write_input(self, suffix=''):\\n \\n out_fname = \\\"input.plasma_1d\\\"+suffix\\n with open(out_fname, 'w+') as outfile:\\n outfile.write('# Input file for ASCOT containing radial 1D information of plasma temperature,density and toroidal rotation \\\\n')\\n outfile.write('# range must cover [0,1] of normalised poloidal rho. It can exceed 1. \\\\n')\\n outfile.write('# {:s} (first 3 lines are comment lines) \\\\n'.format(time.strftime('%d%b%y')))\\n outfile.write('{:d}\\\\t{:1d}\\\\t# Nrad,Nion \\\\n'.format(self.nrho,self.nion))\\n strcoll = str(1)+' ' # for electrons\\n strZ=''\\n strA=''\\n for i in range(self.nion):\\n strZ += str(self.Z[i]) + ' '\\n strA += str(self.A[i]) + ' '\\n strcoll += str(int(self.coll_mode[i])) + ' '\\n strZ +='\\\\t\\\\t# ion Znum \\\\n'\\n strA +='\\\\t\\\\t# ion Amass \\\\n'\\n strcoll += '# collision mode (0= no colls, 1=Maxw colls, 2=binary colls, 3=both colls) 1st number is for electrons \\\\n'\\n outfile.write(strZ)\\t\\t\\t\\t\\n outfile.write(strA)\\n outfile.write(strcoll)\\n \\n lab_len=15\\n strlabel='RHO (pol)'.ljust(lab_len)+'Te (eV)'.ljust(lab_len)+'Ne (1/m3)'.ljust(lab_len)+'Vtor_I (rad/s)'.ljust(lab_len)+\\\\\\n 'Ti1 (eV)'.ljust(lab_len)\\n for i in range(self.nion):\\n tmpstr ='Ni{:d} (1/m3)'.format(i+1)\\n strlabel+=tmpstr.ljust(lab_len)\\n strlabel+='\\\\n'\\n outfile.write(strlabel)\\n data=np.array((self.rho, self.te, self.ne, self.vt, self.ti), dtype=float)\\n data = np.concatenate([data, [self.ni[i,:] for i in range(self.nion)]])\\n\\n data=np.transpose(data)\\n #print(data)\\n #print(\\\"if i don't print, it won't work\\\")\\n np.savetxt(outfile, data, fmt='%.5e')\",\n \"def write_data():\\n\\n data_location = os.path.realpath(os.path.join(os.path.dirname(__file__), \\\"..\\\", DATA_DIR))\\n\\n sbi_file_name = os.path.join(data_location, SBI_FILE)\\n\\n sbi = SbiInfo(sbi_file_name)\\n\\n # the test file is stored in the same directory as the script\\n test_file = os.path.splitext(os.path.join(os.path.dirname(__file__), SBI_FILE))[0] + \\\".pkl\\\"\\n _logger.info(\\\"Writing header object to {}\\\".format(os.path.join(os.path.dirname(__file__),\\n test_file)))\\n sbi.data.to_pickle(test_file)\",\n \"def write_output(filename, output_data):\\n\\n folder = os.path.dirname(filename)\\n if not os.path.exists(folder):\\n os.mkdir(folder)\\n\\n pickle.dump(output_data, open('dump/' + filename + '.pickle', 'wb'))\\n\\n with open(filename, 'w') as f:\\n f.write(str(len(output_data)) + '\\\\n')\\n for slide in output_data:\\n f.write(' '.join(map(str, slide)) + '\\\\n')\",\n \"def write(self, stable_poses, min_prob=0):\\n R_list = []\\n for pose in stable_poses:\\n if pose.p >= min_prob:\\n R_list.append([pose.p, pose.r, pose.x0])\\n\\n f = open(self.filepath_[:-4] + \\\".stp\\\", \\\"w\\\")\\n f.write(\\\"#############################################################\\\\n\\\")\\n f.write(\\\"# STP file generated by UC Berkeley Automation Sciences Lab #\\\\n\\\")\\n f.write(\\\"# #\\\\n\\\")\\n f.write(\\\"# Num Poses: %d\\\" %len(R_list))\\n for _ in range(46 - len(str(len(R_list)))):\\n f.write(\\\" \\\")\\n f.write(\\\" #\\\\n\\\")\\n f.write(\\\"# Min Probability: %s\\\" %str(min_prob))\\n for _ in range(40 - len(str(min_prob))):\\n f.write(\\\" \\\")\\n f.write(\\\" #\\\\n\\\")\\n f.write(\\\"# #\\\\n\\\")\\n f.write(\\\"#############################################################\\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n\\n # adding R matrices to .stp file\\n pose_index = 1\\n for i in range(len(R_list)):\\n f.write(\\\"p %f\\\\n\\\" %R_list[i][0])\\n f.write(\\\"r %f %f %f\\\\n\\\" %(R_list[i][1][0][0], R_list[i][1][0][1], R_list[i][1][0][2]))\\n f.write(\\\" %f %f %f\\\\n\\\" %(R_list[i][1][1][0], R_list[i][1][1][1], R_list[i][1][1][2]))\\n f.write(\\\" %f %f %f\\\\n\\\" %(R_list[i][1][2][0], R_list[i][1][2][1], R_list[i][1][2][2]))\\n f.write(\\\"x0 %f %f %f\\\\n\\\" %(R_list[i][2][0], R_list[i][2][1], R_list[i][2][2]))\\n f.write(\\\"\\\\n\\\\n\\\")\\n f.close()\",\n \"def write_data_card(spec, data_card, channels, path):\\n with open(path, \\\"w\\\") as f:\\n f.write(f\\\"imax {str(size(data_card.bins))}\\\" + \\\"\\\\n\\\")\\n f.write(\\n \\\"jmax \\\"\\n + str(size(data_card.processes) - size(data_card.isSignal.keys()))\\n + \\\"\\\\n\\\"\\n )\\n f.write(f\\\"kmax {str(size(data_card.systs, 0))}\\\" + \\\"\\\\n\\\")\\n\\n if data_card.hasShapes:\\n for channel in data_card.shapeMap.keys():\\n for sample in data_card.shapeMap[channel].keys():\\n f.write(\\n f\\\"shapes {sample} {channel} {data_card.shapeMap[channel][sample][0]} {data_card.shapeMap[channel][sample][1]}\\\"\\n )\\n if size(data_card.shapeMap[channel][sample]) > 2:\\n f.write(f\\\" {data_card.shapeMap[channel][sample][2]}\\\" + \\\"\\\\n\\\")\\n else:\\n f.write(\\\"\\\\n\\\")\\n\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n f.write(\\\"bin \\\")\\n for bin in data_card.obs.keys():\\n f.write(f\\\"{bin} \\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"observation \\\")\\n for channel in data_card.obs.keys():\\n f.write(f\\\"{str(data_card.obs[channel])} \\\")\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n f.write(\\\"bin \\\")\\n for channel in data_card.obs.keys():\\n for sample in data_card.exp[channel].keys():\\n f.write(f\\\"{channel} \\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"process \\\")\\n for channel in data_card.bins:\\n for sample in data_card.exp[channel].keys():\\n f.write(f\\\"{sample} \\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"process \\\")\\n for channel in data_card.bins:\\n for sample in data_card.exp[channel].keys():\\n if sample in data_card.signals:\\n f.write(f\\\"{str(-1 * data_card.processes.index(sample))} \\\")\\n else:\\n f.write(f\\\"{str(data_card.processes.index(sample) + 1)} \\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"rate \\\")\\n for channel in data_card.bins:\\n for sample in data_card.exp[channel].keys():\\n\\n f.write(f\\\"{str(data_card.exp[channel][sample])} \\\")\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n for syst in data_card.systs:\\n f.write(f\\\"{syst[0]} {syst[2]} \\\")\\n for bin in syst[4].keys():\\n for sample in data_card.exp[bin].keys():\\n if syst[4][bin][sample] != 0:\\n f.write(f\\\"{str(syst[4][bin][sample])} \\\")\\n else:\\n f.write(\\\"- \\\")\\n\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n for cAp in data_card.rateParams.keys():\\n _dir = cAp.split(\\\"AND\\\")\\n for i in range(size(data_card.rateParams[cAp], 0)):\\n if size(data_card.rateParams[cAp][i][0]) > 3:\\n f.write(\\n f\\\"{str(data_card.rateParams[cAp][i][0][0])} rateParam {_dir[0]} {_dir[1]} {str(data_card.rateParams[cAp][i][0][1])} {data_card.rateParams[cAp][i][0][3]}\\\"\\n )\\n else:\\n f.write(\\n f\\\"{str(data_card.rateParams[cAp][i][0][0])} rateParam {_dir[0]} {_dir[1]} {str(data_card.rateParams[cAp][i][0][1])}\\\"\\n )\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n for idxc, channel in enumerate(channels):\\n if (\\n channel in data_card.binParFlags.keys()\\n and data_card.binParFlags[channel] == True\\n ):\\n # double check to be safe\\n shapesys = False\\n staterror = False\\n for sample in spec[\\\"channels\\\"][idxc][\\\"samples\\\"]:\\n mod_types = [mod[\\\"type\\\"] for mod in sample[\\\"modifiers\\\"]]\\n if \\\"shapesys\\\" in mod_types:\\n shapesys = True\\n elif \\\"staterror\\\" in mod_types:\\n staterror = True\\n\\n if shapesys:\\n f.write(f\\\"{channel} autoMCStats 100000 0 2\\\" + \\\"\\\\n\\\")\\n if staterror:\\n f.write(f\\\"{channel} autoMCStats 0 0 2\\\" + \\\"\\\\n\\\")\",\n \"def save_data_to_disk(self):\\n Omega_M = self.theta_fid[0]\\n for key in self.data.keys():\\n np.save(f'./preloaded_data/{Omega_M}_{self.delta_theta[0]}_{key}.npy', self.data[key])\",\n \"def write_data():\",\n \"def write_files(neither, both, c_only, f_only, fh_c, data_c_start):\\n\\n # output arrays for all pixels in input datasets\\n write_to_file(neither.astype(np.int16), 'neither_cube.fits')\\n write_to_file(both.astype(np.int16), 'both_cube.fits')\\n write_to_file(c_only.astype(np.int16), 'c_only_cube.fits')\\n write_to_file(f_only.astype(np.int16), 'f_only_cube.fits')\\n\\n # output arrays for pixels in 2d array\\n print(' Within the 2d arrays:')\\n if (fh_c[0].header['NAXIS'] == 3): # for nirspec with 1 integration\\n write_to_file(neither.sum(axis=0), 'neither_2d.fits')\\n write_to_file(both.sum(axis=0), 'both_2d.fits')\\n write_to_file(c_only.sum(axis=0), 'c_only_2d.fits')\\n write_to_file(f_only.sum(axis=0), 'f_only_2d.fits')\\n print(' The fraction of pixels in the 2d array having true CRs:',\\\\\\n float(len(np.where(both.sum(axis=0) != 0.)[0])) / data_c_start.size)\\n elif (fh_c[1].header['NAXIS'] == 4): # for miri or nircam cases\\n write_to_file(neither.sum(axis=1).sum(axis=0), 'neither_2d.fits')\\n write_to_file(both.sum(axis=1).sum(axis=0), 'both_2d.fits')\\n write_to_file(c_only.sum(axis=1).sum(axis=0), 'c_only_2d.fits')\\n write_to_file(f_only.sum(axis=1).sum(axis=0), 'f_only_2d.fits')\\n print(' The fraction of pixels in the 2d array having true CRs:',\\\\\\n float(len(np.where(both.sum(axis=1).sum(axis=0) != 0.)[0])) / \\\\\\n data_c_start.size)\\n else:\\n print('FATAL ERROR - unexpected case in write_file()')\",\n \"def write_features(self):\\r\\n def pack_keypoint(keypoints, descriptors):\\r\\n kpts = np.array([[kp.pt[0], kp.pt[1], kp.size,\\r\\n kp.angle, kp.response, kp.octave,\\r\\n kp.class_id]\\r\\n for kp in keypoints])\\r\\n desc = np.array(descriptors)\\r\\n return kpts, desc\\r\\n\\r\\n filename = self.features_path + self.id\\r\\n kpts, desc = pack_keypoint(self.keypoints, self.descriptors)\\r\\n logging.info(f'Writing features of image {self.name} to file...')\\r\\n np.savez(filename, keypoints=kpts, descriptors=desc)\\r\\n logging.info('Features saved.')\",\n \"def save_data_01(outputfilepath,summary):\\n ext=os.path.splitext(outputfilepath)[-1].lower()\\n if ext=='.mat':\\n savemat(outputfilepath,summary,long_field_names=True)\\n return outputfilepath\\n elif ext=='.npz':\\n np.savez(outputfilepath,summary=summary)\\n elif ext=='.pdb':\\n make_pdb_file_01(outputfilepath,np.array(summary['X_evol'][-1]))\\n else:\\n if ext!='.json':\\n outputfilepath+='.json'\\n with open(outputfilepath, 'w') as result:\\n # serialize\\n summary['json_task']='not stored'\\n serialized_summary=jsonify_01(summary)\\n json.dump(serialized_summary, result,indent=4,sort_keys=True)\",\n \"def _multiprocessing_save_sp(in_out_path):\\n vp, np = in_out_path\\n if not os.path.exists(np + '.npz'):\\n save_compact_fft(\\n np,\\n embedding_fft(load_one_sp_embedding(vp))\\n )\\n return np\",\n \"def generate_output_file(data, extension, headers):\\n output_data = _replace_boolean(data)\\n output_name = _generate_output_name(extension)\\n with open(output_name, 'a', newline='') as file:\\n _file_writer(file, extension, output_data, headers)\",\n \"def exportData(self):\\n self.fileName = QtGui.QFileDialog.getSaveFileName(self, self.tr(\\\"Save Data\\\"), \\\"\\\", \\n self.tr('Atom Positions (*.pdb)'))\\n if not self.fileName.isEmpty():\\n self.setCursor(QtCore.Qt.WaitCursor)\\n selectedChain = self.main_chain\\n PDBstring = selectedChain.toPDB( CAlphaPlaceholders=False)\\n F = open(self.fileName, 'w')\\n F.write(PDBstring)\\n F.close()\\n self.dirty = False\\n self.setCursor(QtCore.Qt.ArrowCursor)\",\n \"def npz_to_csv(datadir):\\n \\n files = glob.glob(datadir + '/conmat*.npz')\\n for fname in files:\\n print fname\\n conmat = np.load(fname)['conmat']\\n fH = open (fname[:-3] + \\\"csv\\\", 'w')\\n csw = csv.writer(fH)\\n for row in conmat:\\n csw.writerow(row)\\n fH.close()\",\n \"def binary_out(array, fnam, dt=np.dtype(np.float64), endianness='big', appendDim=False):\\r\\n if appendDim == True :\\r\\n fnam_out = fnam + '_'\\r\\n for i in array.shape[:-1] :\\r\\n fnam_out += str(i) + 'x' \\r\\n fnam_out += str(array.shape[-1]) + '.raw'\\r\\n else :\\r\\n fnam_out = fnam\\r\\n arrayout = np.array(array, dtype=dt)\\r\\n if sys.byteorder != endianness:\\r\\n arrayout.byteswap(True)\\r\\n arrayout.tofile(os.path.abspath(fnam_out))\",\n \"def save_pca(self, filepath):\\n mean_beam, principal_components, variances = self.pca()\\n image_shape = np.array(self.image_shape)\\n with open(filepath, 'wb') as f:\\n np.save(f, image_shape)\\n np.save(f, mean_beam)\\n np.save(f, principal_components)\\n np.save(f, variances)\\n np.save(f, self.mask)\",\n \"def save_to_poscar(self, filename,direct=False,species_line=False): \\n with open( filename, 'w' ) as F:\\n F.write( self.name )\\n F.write( \\\" 1.0\\\\n\\\" )\\n F.write( mat2str( self.unit_cell, \\\"%16.10f\\\" ) )\\n if species_line:\\n pos = 0\\n for n in self.num_per_type: \\n F.write('%s '%self.species[pos])\\n pos += n\\n F.write('\\\\n')\\n F.write(' '.join([str(n) for n in self.num_per_type]) )\\n F.write('\\\\n')\\n if not direct:\\n F.write(\\\"Cart\\\\n\\\")\\n F.write( mat2str( self.atoms, \\\"%16.10f\\\" ) )\\n else:\\n F.write(\\\"Direct\\\\n\\\")\\n F.write( mat2str( dot(self.atoms,self.recip_cell), \\\"%16.10f\\\" ) )\",\n \"def write_ptm_gridfile(self,fn):\\n vertex_hdr = \\\" Vertex Data: vertex_number, x, y\\\"\\n poly_hdr = \\\" Polygon Data: polygon_number, number_of_sides,center_x, center_y, center_depth, side_indices(number_of_sides), marker(0=internal,1=open boundary)\\\"\\n side_hdr = \\\" Side Data: side_number, side_depth, node_indices(2), cell_indices(2), marker(0=internal,1=external,2=flow boundary,3=open boundary)\\\"\\n\\n with open(fn,'wt') as fp:\\n # write header counts\\n fp.write(\\\" Number of Vertices\\\\n\\\")\\n fp.write(\\\" %20d\\\\n\\\"%self.Nnodes())\\n fp.write(\\\" Number of Polygons\\\\n\\\")\\n fp.write(\\\" %20d\\\\n\\\"%self.Ncells())\\n fp.write(\\\" Number of Sides\\\\n\\\")\\n fp.write(\\\" %20d\\\\n\\\"%self.Nedges())\\n fp.write(\\\" NODATA (land) value\\\\n\\\")\\n fp.write(\\\" -9999.000000000\\\\n\\\")\\n\\n # write vertex info\\n fp.write(vertex_hdr+\\\"\\\\n\\\")\\n for v in range(self.Nnodes()):\\n fp.write(\\\" %10d %16.7f %16.7f\\\\n\\\"%(v+1,\\n self.nodes['x'][v,0],\\n self.nodes['x'][v,1]))\\n\\n # write polygon info\\n fp.write(poly_hdr+\\\"\\\\n\\\")\\n cell_write_str1 = \\\" %10d %10d %16.7f %16.7f %16.7f \\\"\\n cell_depths = self.cell_depths()\\n for e in range(self.Ncells()):\\n edges = self.cells['edges'][e,:]\\n edges[edges<0] = -1\\n edge_str = \\\" \\\".join( [\\\"%10d\\\"%(s+1) for s in edges] )\\n edge_str = edge_str+\\\" %10d\\\\n\\\"%(self.cells['mark'][e])\\n nsides = sum(edges>=0)\\n fp.write(cell_write_str1%(e+1,\\n nsides,\\n self.cells['_center'][e,0],\\n self.cells['_center'][e,1],\\n cell_depths[e]))\\n fp.write(edge_str)\\n \\n # write side info\\n fp.write(side_hdr+\\\"\\\\n\\\")\\n edge_depths = self.edge_depths()\\n edge_write_str = \\\" %10d %16.7f %10d %10d %10d %10d %10d\\\\n\\\"\\n for s in range(self.Nedges()):\\n edges = self.edges['cells'][s,:]\\n edges[edges<0] = -1 \\n nodes = self.edges['nodes'][s,:]\\n nodes[nodes<0] = -1\\n fp.write(edge_write_str%(s+1,\\n edge_depths[s],\\n nodes[0]+1,\\n nodes[1]+1,\\n edges[0]+1,\\n edges[1]+1,\\n self.edges['mark'][s]))\",\n \"def to_poly_file(self, filename):\\n\\n def getinsidepoint(pts):\\n direct = (pts[0] + pts[1] + pts[2]) / 3 - pts[0]\\n return pts[0] + 0.001 * direct\\n\\n if self.dim == 2:\\n self.leaveonlyphysicalsurfaces()\\n if self.dim == 3:\\n self.leaveonlyphysicalvolumes()\\n\\n # write nodes\\n nodes = []\\n map = {}\\n for x in self.d0.values():\\n assert isinstance(x, point)\\n nodes.append(x.getxyz())\\n map[x.getn()] = len(nodes)\\n\\n\\n s = \\\"# nodes\\\\n%d %d 0 0\\\\n\\\" % (len(nodes), self.dim)\\n if self.dim == 2:\\n ptstr = \\\" %d %f %f\\\\n\\\"\\n ptstr2 = \\\" %d %f %f %d\\\\n\\\"\\n else:\\n ptstr = \\\" %d %f %f %f\\\\n\\\"\\n ptstr2 = \\\" %d %f %f %f %d\\\\n\\\"\\n\\n for n, x in enumerate(nodes):\\n s += ptstr % tuple([n + 1] + list(x[:self.dim]))\\n\\n # facets\\n # first write external polygon, then hole polygons and then point in each\\n # hole polygon\\n facets = []\\n if self.dim == 2:\\n\\n hole_pts = []\\n regions=[]\\n for x2 in self.d2.values():\\n assert isinstance(x2, surface)\\n for x1 in x2.getlines():\\n assert isinstance(x1, line)\\n p = [map[y.getn()] for y in x1.getpoints()]\\n bc = self.getBCnum(x1.getn())\\n facets.append((p, bc))\\n\\n for hole in x2.getholepoints():\\n hole_pts.append(hole.getxyz())\\n\\n # regions\\n for x in self.phys2.values():\\n assert isinstance(x, physicalsurface)\\n for x2 in x.getsurfaces():\\n if not x2.is_hole:\\n regions.append(x2.getinsidepoint().getxyz() + [x.getn()])\\n\\n # number of facets, boundary markers=yes\\n s += \\\"# segments\\\\n%d 1\\\\n\\\" % len(facets)\\n for ii, (p, bc) in enumerate(facets):\\n # number of corners, corner 1, corner 2, ...\\n s += \\\" %d %s %d\\\\n\\\" % (ii + 1, ' '.join([str(ii) for ii in p]), bc)\\n # holes\\n s += \\\"# holes\\\\n%d\\\\n\\\" % len(hole_pts)\\n for ii, x0 in enumerate(hole_pts):\\n # number of corners, corner 1, corner 2, ...\\n s += \\\" %d %s\\\\n\\\" % (ii + 1, ' '.join([str(ii) for ii in x0]))\\n # regions\\n s += \\\"# regions\\\\n%d\\\\n\\\" % len(regions)\\n for ii, x0 in enumerate(regions):\\n s += \\\" %d %f %f %d\\\\n\\\" % tuple([ii + 1] + x0)\\n\\n if self.dim == 3:\\n\\n for x in self.d2.values():\\n assert isinstance(x, surface)\\n p = [map[y.getn()] for y in x.getpoints()]\\n h = []\\n pts = []\\n for hole in x.getholepoints():\\n h.append([map[y.getn()] for y in hole])\\n pts.append(getinsidepoint(hole).getxyz())\\n bc = self.getBCnum(x.getn())\\n facets.append((p, bc, h, pts))\\n # number of facets, boundary markers=yes\\n s += \\\"# segments\\\\n%d 1\\\\n\\\" % len(facets)\\n for p, bc, h, holes in facets:\\n # number of polygons, # of holes, boundary marker\\n s += \\\" %d %d %d\\\\n\\\" % (1 + len(h), len(h), bc)\\n # number of corners, corner 1, corner 2, ...\\n s += \\\" %d %s\\\\n\\\" % (len(p), ' '.join([str(ii) for ii in p]))\\n for x in h:\\n # number of corners, corner 1, corner 2, ...\\n s += \\\" %d %s\\\\n\\\" % (len(x), ' '.join([str(ii) for ii in p]))\\n for i, pt in enumerate(holes):\\n # hole #, x, y, z\\n s += ptstr % tuple([i + 1] + list(pt))\\n\\n # volume holes\\n s += \\\"# holes\\\\n0\\\\n\\\"\\n # regions\\n regions=[]\\n for x in self.phys3.values():\\n assert isinstance(x, physicalvolume)\\n for v in x.getvolumes():\\n regions.append(v.getinsidepoint().getxyz()+[x.getn()])\\n s += \\\"# regions\\\\n%d\\\\n\\\" % len(regions)\\n for i, x in enumerate(regions):\\n s += ptstr2 % tuple([i + 1] + list(x))\\n\\n open(filename, \\\"w\\\").write(s)\",\n \"def write_quanty_opps(x, nf, n=1, op_name='Opp', ishift=0, jshift=0,\\n filename='tmp.lua', mode='a'):\\n\\n if not (mode == 'a' or mode == 'w'):\\n print('Warning: writing mode not supported.')\\n return\\n\\n # create the three lists needed in Quanty: i,j,e\\n i, j, e = scipy.sparse.find(x)\\n\\n # take care that indices may be shifted\\n i += ishift\\n j += jshift\\n\\n # divide up the elements in bunches of\\n # max n in each bunch\\n k = 0\\n d = []\\n while k + n < len(i):\\n d.append([i[k:k + n], j[k:k + n], e[k:k + n]])\\n k += n\\n d.append([i[k:], j[k:], e[k:]])\\n\\n # write operators to file\\n f = open(filename, mode)\\n # loop over the operators\\n for k, (ii, jj, ee) in enumerate(d):\\n s = \\\"\\\\n\\\"\\n s += op_name + '_' + str(k)\\n s += \\\" = NewOperator(\\\\\\\"Number\\\\\\\",\\\"\\n s += str(nf)\\n s += \\\",\\\\n {\\\"\\n s += ', '.join([str(el) for el in ii])\\n s += '},\\\\n {'\\n s += ', '.join([str(el) for el in jj])\\n s += '},\\\\n'\\n s_onerow = '{'\\n s_onerow += ', '.join([(str(el.real) + '+I*'\\n + str(el.imag)) for el in ee])\\n s_onerow += '})'\\n s += s_onerow\\n s += '\\\\n'\\n if len(s_onerow) > 3000:\\n print('Warning: Lua does not support '\\n 'lines longer than 3000')\\n f.write(s)\\n f.close()\",\n \"def create_grp_file(data, model_name, gp_var, outputModelFilesDirectory):\\n\\n dimx = None\\n dimy = None\\n if len(data.shape) == 1:\\n dimy = 1\\n dimx = data.shape[0]\\n else:\\n dimx, dimy = data.shape\\n data = np.ones(dimx)\\n\\n if not (gp_var == None):\\n i = 1\\n for key in sorted(gp_var.keys()):\\n\\n for index in gp_var[key]:\\n data[index] = i\\n\\n i += 1\\n\\n\\n f = open(os.path.join(outputModelFilesDirectory, model_name + '.grp'), 'w')\\n\\n print >>f, '/NumWaves\\\\t1'\\n print >>f, '/NumPoints\\\\t%d\\\\n' %dimx\\n print >>f, '/Matrix'\\n np.savetxt(f, data, fmt='%d', delimiter='\\\\t')\\n\\n f.close()\",\n \"def generate_structure(flag):\\r\\n if os.path.isfile(\\\"packing.nfo\\\"):\\r\\n os.remove(os.path.abspath(\\\"packing.nfo\\\"))\\r\\n proc = subprocess.Popen(['PackingGeneration.exe', flag])\\r\\n proc.wait()\\r\\n if not os.path.isfile(\\\"packing.nfo\\\"):\\r\\n print('Try to change number of particles or size distribution.')\\r\\n raise Exception('Packing algorithm failed.')\",\n \"def polyencode(ctx, inputfiles, output, poly_order, reflect):\\n with rio.open(inputfiles[0]) as src:\\n metaprof = src.profile.copy()\\n\\n metaprof.update(dtype=np.float32, count=(poly_order + 1))\\n\\n data = read_all(inputfiles, reflect=reflect)\\n\\n out = poly_multid(data, poly_order).astype(np.float32)\\n\\n with rio.open(output, \\\"w\\\", **metaprof) as dst:\\n for i in range(poly_order + 1):\\n dst.write(out[:, :, i], i + 1)\",\n \"def write_pos(sposcar,ngrid,nspecies,filename):\\n pos=np.dot(sposcar[\\\"lattvec\\\"],sposcar[\\\"positions\\\"])\\n ntot=ngrid[0]*ngrid[1]*ngrid[2]*nspecies\\n np_icell=np.empty((3,ntot),dtype=np.intc)\\n car=pos\\n np_ispecies=np.empty(ntot,dtype=np.intc)\\n icell=np_icell\\n ispecies=np_ispecies\\n\\n f=StringIO.StringIO()\\n\\n for ii in xrange(ntot):\\n tmp,ispecies[ii]=divmod(ii,nspecies)\\n tmp,icell[0,ii]=divmod(tmp,ngrid[0])\\n icell[2,ii],icell[1,ii]=divmod(tmp,ngrid[1])\\n car[0,ii],car[1,ii],car[2,ii]=np.dot(sposcar[\\\"lattvec\\\"],sposcar[\\\"positions\\\"][:,ii])*10\\n f.write(\\\"{:>6d} {:>6d} {:>15.10f} {:>15.10f} {:>15.10f}\\\\n\\\".\\n format(ii+1,ispecies[ii]+1, car[0,ii],car[1,ii],car[2,ii]))\\n ffinal=open(filename,\\\"w\\\")\\n ffinal.write(f.getvalue())\\n f.close()\\n ffinal.close()\",\n \"def _write_dx(self, FN, data):\\n n_points = data['counts'][0] * data['counts'][1] * data['counts'][2]\\n if FN.endswith('.dx'):\\n F = open(FN, 'w')\\n else:\\n import gzip\\n F = gzip.open(FN, 'w')\\n\\n F.write(\\\"\\\"\\\"object 1 class gridpositions counts {0[0]} {0[1]} {0[2]}\\norigin {1[0]} {1[1]} {1[2]}\\ndelta {2[0]} 0.0 0.0\\ndelta 0.0 {2[1]} 0.0\\ndelta 0.0 0.0 {2[2]}\\nobject 2 class gridconnections counts {0[0]} {0[1]} {0[2]}\\nobject 3 class array type double rank 0 items {3} data follows\\n\\\"\\\"\\\".format(data['counts'], data['origin'], data['spacing'], n_points))\\n\\n for start_n in range(0, len(data['vals']), 3):\\n F.write(' '.join(['%6e' % c\\n for c in data['vals'][start_n:start_n + 3]]) + '\\\\n')\\n\\n F.write('object 4 class field\\\\n')\\n F.write('component \\\"positions\\\" value 1\\\\n')\\n F.write('component \\\"connections\\\" value 2\\\\n')\\n F.write('component \\\"data\\\" value 3\\\\n')\\n F.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66370714","0.65234995","0.63456404","0.6172922","0.6125077","0.60102993","0.59520173","0.5935673","0.59053946","0.58978695","0.58922994","0.5873275","0.58675355","0.58388406","0.58206254","0.5806083","0.57888585","0.57863545","0.57822645","0.5781454","0.5780015","0.5778276","0.57391256","0.5734234","0.57236046","0.5707929","0.5706203","0.56791115","0.56710863","0.5670155","0.5658984","0.56392473","0.563228","0.5624349","0.5605117","0.5577179","0.55769384","0.55645686","0.55481505","0.5545723","0.55453825","0.5535557","0.55257833","0.5519188","0.55162555","0.5513216","0.5510281","0.5506505","0.5497271","0.54869545","0.548379","0.5483576","0.5470726","0.54679626","0.5461137","0.5459585","0.545696","0.54472226","0.54401726","0.5419815","0.54167914","0.5410688","0.54089785","0.5408472","0.5407124","0.5405003","0.5402687","0.5400294","0.5396695","0.53943026","0.5389252","0.5380503","0.537666","0.53735524","0.53690946","0.53652954","0.5361811","0.5361733","0.53578705","0.5356821","0.5354099","0.53533393","0.53518873","0.53495026","0.53387785","0.53382313","0.5329129","0.5322876","0.53223276","0.53215873","0.53183174","0.5316758","0.5315156","0.5310216","0.5308329","0.5308086","0.530265","0.5297513","0.52974117","0.52831435"],"string":"[\n \"0.66370714\",\n \"0.65234995\",\n \"0.63456404\",\n \"0.6172922\",\n \"0.6125077\",\n \"0.60102993\",\n \"0.59520173\",\n \"0.5935673\",\n \"0.59053946\",\n \"0.58978695\",\n \"0.58922994\",\n \"0.5873275\",\n \"0.58675355\",\n \"0.58388406\",\n \"0.58206254\",\n \"0.5806083\",\n \"0.57888585\",\n \"0.57863545\",\n \"0.57822645\",\n \"0.5781454\",\n \"0.5780015\",\n \"0.5778276\",\n \"0.57391256\",\n \"0.5734234\",\n \"0.57236046\",\n \"0.5707929\",\n \"0.5706203\",\n \"0.56791115\",\n \"0.56710863\",\n \"0.5670155\",\n \"0.5658984\",\n \"0.56392473\",\n \"0.563228\",\n \"0.5624349\",\n \"0.5605117\",\n \"0.5577179\",\n \"0.55769384\",\n \"0.55645686\",\n \"0.55481505\",\n \"0.5545723\",\n \"0.55453825\",\n \"0.5535557\",\n \"0.55257833\",\n \"0.5519188\",\n \"0.55162555\",\n \"0.5513216\",\n \"0.5510281\",\n \"0.5506505\",\n \"0.5497271\",\n \"0.54869545\",\n \"0.548379\",\n \"0.5483576\",\n \"0.5470726\",\n \"0.54679626\",\n \"0.5461137\",\n \"0.5459585\",\n \"0.545696\",\n \"0.54472226\",\n \"0.54401726\",\n \"0.5419815\",\n \"0.54167914\",\n \"0.5410688\",\n \"0.54089785\",\n \"0.5408472\",\n \"0.5407124\",\n \"0.5405003\",\n \"0.5402687\",\n \"0.5400294\",\n \"0.5396695\",\n \"0.53943026\",\n \"0.5389252\",\n \"0.5380503\",\n \"0.537666\",\n \"0.53735524\",\n \"0.53690946\",\n \"0.53652954\",\n \"0.5361811\",\n \"0.5361733\",\n \"0.53578705\",\n \"0.5356821\",\n \"0.5354099\",\n \"0.53533393\",\n \"0.53518873\",\n \"0.53495026\",\n \"0.53387785\",\n \"0.53382313\",\n \"0.5329129\",\n \"0.5322876\",\n \"0.53223276\",\n \"0.53215873\",\n \"0.53183174\",\n \"0.5316758\",\n \"0.5315156\",\n \"0.5310216\",\n \"0.5308329\",\n \"0.5308086\",\n \"0.530265\",\n \"0.5297513\",\n \"0.52974117\",\n \"0.52831435\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70493346"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93781,"cells":{"query":{"kind":"string","value":"Returns the chemical formation energy of the major MS at pH 7. If the compound is part of the training set, returns the value that was calculated during training. Otherwise, we use pure group contribution (if possible) on the groups of the major MS."},"document":{"kind":"string","value":"def get_major_ms_dG0_f(self, compound_id):\n if compound_id is None:\n raise ValueError('given compound ID is None')\n if self.params is None:\n self.train()\n\n if compound_id in self.cids:\n i = self.cids.index(compound_id)\n return self.params['dG0_cc'][i, 0]\n else:\n # Decompose the compound and calculate the 'formation energy'\n # using the group contributions.\n # Note that the length of the group contribution vector we get\n # from CC is longer than the number of groups in \"groups_data\"\n # since we artifically added fictive groups to represent all the\n # non-decomposable compounds. Therefore, we truncate the\n # dG0_gc vector since here we only use GC for compounds which\n # are not in cids anyway.\n comp = ccache.get_compound(compound_id)\n try:\n group_vec = self.decomposer.smiles_to_groupvec(comp.smiles)\n g = group_vec.as_array()\n dG0_gc = self.params['dG0_gc'][0:self.Ng]\n return g @ dG0_gc\n except inchi2gv.GroupDecompositionError:\n return np.nan"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_energy(self):\n return self.momentum*self.momentum/(2*self.mass)","def get_hoomd_energy(key, hoomd_force_groups, calc_group):\n\n total_energy = 0\n total_energy = sum([a.get_energy(calc_group) \n for a in hoomd_force_groups[key]])\n return total_energy","def computeEnergy(self):\n\t\tGmo = self.Gmo\n\t\te = self.e\n\t\tself.Ec = 0.0\n\n\t\tfor i in range( self.nocc ):\n\t\t\tfor j in range( self.nocc ):\n\t\t\t\tfor a in range( self.nocc,self.norb ):\n\t\t\t\t\tfor b in range( self.nocc,self.norb ):\n\t\t\t\t\t\tself.Ec += 0.25*(Gmo[i,j,a,b]*Gmo[a,b,i,j])/(e[i]+e[j]-e[a]-e[b])\n\n\t\treturn self.E0 + self.Ec","def computeEnergy(self):\n _cgco.gcoComputeEnergy(self.handle, self.energyTempArray)\n return self._convertEnergyBack(self.energyTempArray[0])","def Find_Lowest_Energy_Structure_Electrostatics(self):\n n_Na = self.structure.composition['Na']\n n_S = self.structure.composition['S']\n n_O = self.structure.composition['O']\n n_N = self.structure.composition['N']\n n_Fe = self.structure.composition['Fe']\n\n n_Fe_reduced = self.variable_magnetization_dict['Fe']['n_reduced']\n n_Fe_oxidized = n_Fe-n_Fe_reduced \n\n N_charge = ( 2.*n_O-6.*n_S-n_Na-3.*n_Fe_oxidized-2.*n_Fe_reduced )/n_N\n\n oxidation_states = {'Na':+1, 'Fe':+3, 'O':-2,'S':+6,'N':N_charge}\n Fe_2plus = pymatgen.Specie('Fe',oxidation_state=+2)\n\n structure_with_charges = self.structure.copy()\n structure_with_charges.add_oxidation_state_by_element(oxidation_states) \n\n # identify Fe sites\n list_Fe_indices = []\n for i,site in enumerate(structure_with_charges):\n if site.specie.symbol == 'Fe':\n list_Fe_indices.append(i)\n\n # Generate all possible permutation of sites and compute \n # Ewald energy\n ewald_model = EwaldElectrostaticModel(acc_factor=6)\n list_reduced_sets = []\n list_ewald_energy = []\n for reduced_set in itertools.combinations(list_Fe_indices,n_Fe_reduced):\n list_reduced_sets.append(reduced_set) \n\n struct = structure_with_charges.copy()\n for i in reduced_set:\n struct.replace(i, Fe_2plus)\n\n list_ewald_energy.append(ewald_model.get_energy(struct))\n\n if len(list_ewald_energy) == 0:\n # all sites are oxidized. No sorting involved \n list_reduced_site_indices = []\n list_oxidized_site_indices = list_Fe_indices\n else:\n # some reduction takes place. Identify best electrostatic choice\n\n imin = np.argmin(list_ewald_energy)\n\n list_reduced_site_indices = list_reduced_sets[imin] \n list_oxidized_site_indices = []\n for i in list_Fe_indices:\n if i not in list_reduced_site_indices: \n list_oxidized_site_indices.append(i) \n\n\n return list_reduced_site_indices, list_oxidized_site_indices","def intEnergy_mass(self):\n return _cantera.reactor_intEnergy_mass(self.__reactor_id)","def cf_mean(self):\n net_power = self['annual_gross_energy'] \\\n - self['annual_thermal_consumption'] # kW-hr\n # q_pb_des is in MW, convert to kW-hr\n name_plate = self['q_pb_design'] * 8760 * 1000\n\n return net_power / name_plate","def energy(self):\n nocc, gmo, e = self.nocc, self.gmo, self.e\n\n Ec = 0.0\n for i in range(nocc):\n for j in range(nocc):\n for a in range(nocc, len(e)):\n for b in range(nocc, len(e)):\n Ec += (1/4.0) * gmo[i, j, a, b]**2 / (e[i]+e[j]-e[a]-e[b])\n\n self.Ec = Ec\n self.E_mp2 = Ec + self.E_scf\n\n print('@MP2 correlation energy: {:15.10f}\\n'.format(self.Ec))\n print('@Total MP2 energy: {:15.10f}\\n'.format(self.E_mp2))\n\n return self.E_mp2","def cf_mean(self):\n net_power = self['annual_field_energy'] \\\n - self['annual_thermal_consumption'] # kW-hr\n # q_pb_des is in MW, convert to kW-hr\n name_plate = self['q_pb_des'] * 8760 * 1000\n\n return net_power / name_plate","def energy(self):\n nocc, ntot, gmo, e = self.nocc, self.ntot, self.gmo, self.e\n\n Ec = 0.0\n for i in range(nocc):\n for j in range(nocc):\n for a in range(nocc, ntot):\n for b in range(nocc, ntot):\n Ec += gmo[i, a, j, b]*(2*gmo[i, a, j, b] - gmo[i, b, j, a])/\\\n (e[i] + e[j] - e[a] - e[b])\n\n self.Ec = Ec\n self.E_mp2 = Ec + self.E_scf\n\n print('@MP2 correlation energy: {:15.10f}\\n'.format(self.Ec))\n print('@Total MP2 energy: {:15.10f}\\n'.format(self.E_mp2))\n\n return self.E_mp2","def Energy(molecules, experiment = None, which = \"all\"):\n # Validate the molecules\n molecules, gn = validate_molecules(molecules)\n # Create the procedure\n procedure = validate_procedure(\"energy\")\n # Determine who is doing the calculation\n try:\n user = experiment[\"User\"]\n except (KeyError, TypeError, AttributeError, IPRO_Error):\n user = defaultUser\n # Determine if solvation is or should be used\n solvation = SOLVATION.get_string(experiment, procedure)\n # Generate the CHARMM script to do the energy calculation\n if isinstance(gn, int):\n script = introduction(\"Energy Calculation for Design Group \" + str(gn))\n else:\n script = introduction(\"A CHARMM energy calculation\")\n script += load_input_files(experiment)\n script += load_molecules(molecules, procedure, user, which)\n script += ic_fill() + solvation\n script += energy_line(experiment, procedure, '', '', '', solvation)\n # Declare the name of the output file\n energyFile = \"energy_values.txt\"\n script += \"\\nener\\n\\nset tot ?ener\\n\\nopen write card unit 10 name \"\n script += energyFile + \"\\n\\nwrite title unit 10\\n* @tot\\n*\\nclose unit 10\\n\"\n script += \"stop\\n\"\n # Run CHARMM\n input, output = execute_CHARMM_script(script, procedure, gn)\n # Read in the calculated energy\n try:\n file = open(energyFile, \"r\")\n energy = float(file.readline())\n file.close()\n except IOError:\n text = \"A CHARMM energy calculation failed to calculate an energy. \"\n text += \"Please review \" + output + \" to determine why.\"\n raise CHARMM_Error(text)\n # Clean up the folder\n #clean_up(molecules, input, output, procedure, energyFile)\n # Return the calculated energy\n return energy","def E(self):\n return self.generic_getter(get_energy, \"E\", \"convert_energy\")","def read_energy(self, fname):\n outfile = open(fname)\n lines = outfile.readlines()\n outfile.close()\n\n energy = None\n for line in lines:\n if line.find('HEAT OF FORMATION') != -1:\n words = line.split()\n energy = float(words[5])\n if line.find('H.o.F. per unit cell') != -1:\n words = line.split()\n energy = float(words[5])\n if line.find('UNABLE TO ACHIEVE SELF-CONSISTENCE') != -1:\n energy = None\n if energy is None:\n raise RuntimeError('MOPAC: could not find total energy')\n### do not change unit for mopac\n energy *= (kcal / mol)\n return energy","def energyK(k):\r\n C1 = 9.7846113e-07\r\n C2 = 12.263868e0 \r\n E = (-1.0 + np.sqrt(1.0 + 4.0 * C1 * C2**2 * k**2))/(2.0 * C1)\r\n return E","def mass_energy():\n c2 = _si.c.value**2\n return Equivalency(\n [\n (si.kg, si.J, lambda x: x * c2, lambda x: x / c2),\n (si.kg / si.m**2, si.J / si.m**2, lambda x: x * c2, lambda x: x / c2),\n (si.kg / si.m**3, si.J / si.m**3, lambda x: x * c2, lambda x: x / c2),\n (si.kg / si.s, si.J / si.s, lambda x: x * c2, lambda x: x / c2),\n ],\n \"mass_energy\",\n )","def get_Ec_min(self):\n return self.Ec_min","def estimate_emissions(excess_CH4):\n import math\n #a = 0.4630664\n #b = 0.7443749\n a1 = 1.2889\n b1 = 0.35232\n a2 = 1.755891\n b2 = 0.4438203\n\n a= -1.370\n b= 0.823\n\n m = math.exp((excess_CH4 - a) / b)\n # if m < math.exp(3.157):\n # if m < math.exp(2):\n # m = math.exp((np.log(m) - a1)/b1)\n # if m > math.exp(2):\n # m = math.exp((np.log(m) - a2)/b2)\n return (m)","def get_energy():\n\n # open the psi4 log file\n with open('output.dat', 'r') as log:\n lines = log.readlines()\n\n # find the total converged energy\n for line in lines:\n if 'Total Energy =' in line:\n energy = float(line.split()[3])\n break\n else:\n raise EOFError('Cannot find energy in output.dat file.')\n\n return energy","def energy(self):\n return self.mc.energy(self.chain)","def getEnergy(self):\n if not hasattr(self,\"energy\"):\n self.energy = self.calcEnergy()\n return self.energy","def _calc_Em(self):\n return (self.parameters.E0 +\n self.x * sqrt2 * self.parameters.sigma * self.mt)","def _get_mean(self):\n return self._get_conditional_negative_energy()","def get_omm_energy(key, omm_force_groups, omm_context):\n if key == 'all':\n return (omm_context.getState(getEnergy=True).getPotentialEnergy())\n if len(omm_force_groups[key]) == 0:\n return 0 * unit.kilojoule_per_mole\n return (omm_context.getState(getEnergy=True, groups=omm_force_groups[key])\n .getPotentialEnergy())","def computeDataEnergy(self):\n _cgco.gcoComputeDataEnergy(self.handle, self.energyTempArray)\n return self._convertEnergyBack(self.energyTempArray[0])","def get_energy():\n\n # open the psi4 log file\n with open(\"output.dat\", \"r\") as log:\n for line in log:\n if \"Total Energy =\" in line:\n return float(line.split()[3])\n\n raise EOFError(\"Cannot find energy in output.dat file.\")","def get_energy(self):\r\n return self._energy","def energy(self):\n return self._energy","def energy_func(self):\n i = self.inl[0].to_flow()\n o = self.outl[0].to_flow()\n\n T_m = (T_mix_ph(i, T0=self.inl[0].T.val_SI) +\n T_mix_ph(o, T0=self.outl[0].T.val_SI)) / 2\n\n iam = (\n 1 - self.iam_1.val * abs(self.aoi.val) -\n self.iam_2.val * self.aoi.val ** 2)\n\n return (i[0] * (o[2] - i[2]) -\n self.A.val * (\n self.E.val * self.eta_opt.val * self.doc.val ** 1.5 * iam -\n (T_m - self.Tamb.val_SI) * self.c_1.val -\n self.c_2.val * (T_m - self.Tamb.val_SI) ** 2))","def get_molpro_energy(species,wellorts):\n fname = str(species.chemid)\n if wellorts: fname = species.name\n \n status = os.path.exists('molpro/' + fname + '.out')\n \n if status:\n with open('molpro/' + fname + '.out') as f:\n lines = f.readlines()\n \n for index, line in enumerate(reversed(lines)):\n if 'SETTING MYENA' in line:\n return 1, float(line.split()[2])\n else:\n return 0, -1","def get_o_energies(mol):\n try:\n ev_to_hartree = 1./convertor(1,'hartree','eV')\n g=hack_parser.Gaussian(mol.calc.log, loglevel=50)\n d=g.parse()\n #lm, hm, lr\n o_component_es = np.array(d.oniomenergies)\n except AttributeError:\n return 0\n\n return (ev_to_hartree * o_component_es * [-1,1,1]).sum(axis=1)","def mhu(self, sign, spin):\n index = self.Frontier_MO_index(sign=sign, spin=spin)\n if spin == \"alpha\":\n return self._molecule.mo.energies[index[0]]\n else:\n return self._molecule.mo.energies[index[1]]","def energy(self):\n energy = -0.5*np.sum(self.phi)+0.5*np.sum(self.mass*np.sqrt(self.particles.momentum[:,0]**2+self.particles.momentum[:,1]**2)**2)\n return energy","def compute_energy(x, input_HP_sequence):\n # add code here, feel free to change the argument list\n # Given a input HP sequence, we already which points are H's.\n return U","def sound_horizon_EH(self):\n om_m = self.omega_cb\n om_b = self.omega_b\n om_n = np.sum(self.omega_nu)\n h = self.h \n if self.M_nu_tot == 0.: rs = 44.5*np.log(9.83/om_m)/np.sqrt(1+10*om_b**0.75)*h\n else: rs = 55.154*np.exp(-72.3*(om_n+0.0006)**2.)/(om_m**0.25351*om_b**0.12807)*h\n return rs","def getEnergy(self, normalized=True, mask=None):\n psi = self.psi.get()\n n = self.n.get()\n density = np.absolute(psi) ** 2\n gradx = np.gradient(psi)[0]\n normFactor = density.sum() if normalized else 1.0\n return np.ma.array(-(0.25 * np.gradient(\n np.gradient(density)[0])[0]\n - 0.5 * np.absolute(gradx) ** 2\n - (self.g_C * density + self.g_R * n)\n * density), mask=mask).sum() / normFactor","def _emiss_ep(self,Eph):\n if self.weight_ep == 0.0:\n return np.zeros_like(Eph)\n\n gam = np.vstack(self._gam)\n eps = (Eph / mec2).decompose().value\n # compute integral with electron distribution\n emiss = c.cgs * trapz_loglog(np.vstack(self._nelec) * self._sigma_1(gam,eps),\n self._gam, axis=0).to(u.cm**2 / Eph.unit)\n return emiss","def energy_func(self):\n i = self.inl[0].to_flow()\n o = self.outl[0].to_flow()\n\n T_m = (T_mix_ph(i, T0=self.inl[0].T.val_SI) +\n T_mix_ph(o, T0=self.outl[0].T.val_SI)) / 2\n\n return (i[0] * (o[2] - i[2]) -\n self.A.val * (\n self.E.val * self.eta_opt.val -\n (T_m - self.Tamb.val_SI) * self.lkf_lin.val -\n self.lkf_quad.val * (T_m - self.Tamb.val_SI) ** 2))","def buildEint(self, version = \"classical\", verbose = 1):\n keys_c = [\"sigma_c_12\", \"sigma_c_21\"]\n keys_c = [\"sigma_d_12\", \"sigma_d_21\"]\n\n if \"classical\".startswith(version.lower()):\n if np.sum([self.parameters[k] == 0 for k in keys_c]) > 0:\n string = \"Surface energies not set\"\n ut.infoPrint(string)\n return\n else:\n \"\"\"Calculate the interfacial energies\"\"\"\n sigma = np.sum([self.parameters[k] for k in keys_c])\n self.e_int_c = sigma - self.w_seps_c\n \"\"\"Set to zero in all places where w_seps is not set\"\"\"\n self.e_int_c[self.w_seps_c == 0] = 0\n \n if verbose > 0:\n string = \"e_int_c set from w_seps_c data of shape (%i,%i)\" % (self.w_seps_c.shape)\n ut.infoPrint(string)\n\n if \"dft\".startswith(version.lower()):\n if np.sum([self.parameters[k] == 0 for k in keys_d]) > 0:\n string = \"Surface energies not set\"\n ut.infoPrint(string)\n return\n else:\n \"\"\"Calculate the interfacial energies\"\"\"\n sigma = np.sum([self.parameters[k] for k in keys_c])\n self.e_int_d = sigma - self.w_seps_d\n \"\"\"Set to zero in all places where w_seps is not set\"\"\"\n self.e_int_d[self.w_seps_d == 0] = 0\n\n if verbose > 0:\n string = \"e_int_d set from w_seps_d data of shape (%i,%i)\" % (self.w_seps_d.shape)\n ut.infoPrint(string)","def enthalpy_mass(self):\n return _cantera.reactor_enthalpy_mass(self.__reactor_id)","def comptcptotalquantum(self) :\n\t\ttry :\n\t\t\treturn self._comptcptotalquantum\n\t\texcept Exception as e:\n\t\t\traise e","def calc_excess_energy (self):\n #~ print sorted(self.cd.keys())\n self.excess_energy = \\\n (self.generation_wind_proposed - self.transmission_losses) * \\\n (self.cd['percent excess energy'] / 100.0)\n #~ print 'self.excess_energy',self.excess_energy","def get_effective_mass():\n\n H_BAR = 6.582119514e-16 # eV*s\n M_0 = 9.10938356e-31 # kg\n N_KPTS = 6 # Number of k-points included in the parabola.\n\n spin_up = Spin(1)\n\n band_structure = Vasprun('vasprun.xml').get_band_structure()\n\n # Locations of CBM and VBM in band_structure.bands\n cbm_band_index = band_structure.get_cbm()['band_index'][spin_up][0]\n cbm_kpoint_index = band_structure.get_cbm()['kpoint_index'][0]\n\n vbm_band_index = band_structure.get_vbm()['band_index'][spin_up][0]\n vbm_kpoint_index = band_structure.get_vbm()['kpoint_index'][0]\n\n k = {'electron': {'left': [], 'right': []},\n 'hole': {'left': [], 'right': []}}\n E = {'electron': {'left': [], 'right': []},\n 'hole': {'left': [], 'right': []}}\n\n e_ref_coords = band_structure.kpoints[cbm_kpoint_index]._ccoords\n h_ref_coords = band_structure.kpoints[vbm_kpoint_index]._ccoords\n\n for n in range(-N_KPTS, 1):\n e_coords = band_structure.kpoints[cbm_kpoint_index + n]._ccoords\n h_coords = band_structure.kpoints[vbm_kpoint_index + n]._ccoords\n\n k['electron']['left'].append(\n ((e_coords[0] - e_ref_coords[0])**2 +\n (e_coords[1] - e_ref_coords[1])**2 +\n (e_coords[2] - e_ref_coords[2])**2)**0.5\n )\n k['hole']['left'].append(\n ((h_coords[0] - h_ref_coords[0])**2 +\n (h_coords[1] - h_ref_coords[1])**2 +\n (h_coords[2] - h_ref_coords[2])**2)**0.5\n )\n\n e_energy = band_structure.bands[\n spin_up][cbm_band_index][cbm_kpoint_index + n]\n h_energy = band_structure.bands[\n spin_up][vbm_band_index][vbm_kpoint_index + n]\n\n E['electron']['left'].append(e_energy)\n E['hole']['left'].append(h_energy)\n\n for n in range(1, 1 + N_KPTS):\n e_coords = band_structure.kpoints[cbm_kpoint_index + n]._ccoords\n h_coords = band_structure.kpoints[vbm_kpoint_index + n]._ccoords\n\n k['electron']['right'].append(\n ((e_coords[0] - e_ref_coords[0])**2 +\n (e_coords[1] - e_ref_coords[1])**2 +\n (e_coords[2] - e_ref_coords[2])**2)**0.5\n )\n k['hole']['right'].append(\n ((h_coords[0] - h_ref_coords[0])**2 +\n (h_coords[1] - h_ref_coords[1])**2 +\n (h_coords[2] - h_ref_coords[2])**2)**0.5\n )\n\n e_energy = band_structure.bands[\n spin_up][cbm_band_index][cbm_kpoint_index + n]\n h_energy = band_structure.bands[\n spin_up][vbm_band_index][vbm_kpoint_index + n]\n\n E['electron']['right'].append(e_energy)\n E['hole']['right'].append(h_energy)\n\n # 2nd order fits\n e_l_fit = np.poly1d(\n np.polyfit(k['electron']['left'], E['electron']['left'], 2))\n e_r_fit = np.poly1d(\n np.polyfit(k['electron']['right'], E['electron']['right'], 2))\n h_l_fit = np.poly1d(\n np.polyfit(k['hole']['left'], E['hole']['left'], 2))\n h_r_fit = np.poly1d(\n np.polyfit(k['hole']['right'], E['hole']['right'], 2))\n\n # Curvatures\n e_l_curvature = e_l_fit.deriv().deriv()[0]\n e_r_curvature = e_r_fit.deriv().deriv()[0]\n h_l_curvature = h_l_fit.deriv().deriv()[0]\n h_r_curvature = h_r_fit.deriv().deriv()[0]\n\n # Unit conversion\n e_m_eff_l = 10 * ((H_BAR ** 2) / e_l_curvature) / M_0\n e_m_eff_r = 10 * ((H_BAR ** 2) / e_r_curvature) / M_0\n h_m_eff_l = -10 * ((H_BAR ** 2) / h_l_curvature) / M_0\n h_m_eff_r = -10 * ((H_BAR ** 2) / h_r_curvature) / M_0\n\n return {'electron': {'left': e_m_eff_l, 'right': e_m_eff_r},\n 'hole': {'left': h_m_eff_l, 'right': h_m_eff_r}}","def estimated_energy(self):\n energy = 0j\n for pauli_string, coef in self._pauli_coef_terms:\n a = self._zeros[pauli_string]\n b = self._ones[pauli_string]\n if a + b:\n energy += coef * (a - b) / (a + b)\n energy = complex(energy)\n if energy.imag == 0:\n energy = energy.real\n energy += self._identity_offset\n return energy","def get_kwh(self):\n\n svc = \"urn:micasaverde-com:serviceId:EnergyMetering1\"\n if not svc in self.services:\n raise RuntimeError, \"Device doesn't support the service\"\n\n return self.get_variable(svc, \"KWH\")","def getMeanE(self):\n\n\n\t\tEBefore, EAfter = self.getEnergyEvolution()\n\n\t\tmeanBefore = np.mean(EBefore[-self.__Nkicks//5:])\n\t\tmeanAfter = np.mean(EAfter[-self.__Nkicks//5:])\n\t\tmeanTot = (meanBefore+meanAfter)/2\n\n\t\treturn meanBefore, meanAfter, meanTot","def get_ENMIN(potcar):\n with open(potcar) as f:\n for line in f:\n if 'ENMIN' in line:\n regex = 'ENMIN\\s*=\\s*(?P<ENMIN>[0-9]+.[0-9]+)\\s+eV'\n m = re.search(regex, line)\n return float(m.groupdict()['ENMIN'])","def energy_pfu(self):\n return self._energy_pfu","def helmholtzenergy(temp,pres):\n g = liq_g(0,0,temp,pres)\n g_p = liq_g(0,1,temp,pres)\n f = g - pres*g_p\n return f","def energy_yield(self):\n return self['kwh_per_kw']","def getEnergyEvolution(self):\n\n\t\tEBefore = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleBefore]\n\t\tEAfter = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleAfter]\n\n\t\treturn EBefore, EAfter","def get_total_energy_produced (self):\n return self.pre_intertie_generation[:self.actual_project_life]","def kinetic_energy(self):\r\n position, velocity, escaped_particles,impact, wall_collision,mom = self.box_collision_info()\r\n for j in xrange(1,self.n):\r\n abs_velocity = np.sqrt(velocity[:,0]**2+velocity[:,1]**2\r\n + velocity[:,2]**2)\r\n KE = 0.5*self.m*abs_velocity**2\r\n total_KE = np.sum(KE)\r\n invid_KE = total_KE/self.Npart\r\n\r\n return total_KE, invid_KE","def get_energy(self, circuit, sampler):\n val = 0.0\n for meas in self.hamiltonian:\n c = circuit.copy()\n for op in meas.ops:\n if op.op == \"X\":\n c.h[op.n]\n elif op.op == \"Y\":\n c.rx(-np.pi / 2)[op.n]\n measured = sampler(c, meas.n_iter())\n for bits, prob in measured.items():\n if sum(bits) % 2:\n val -= prob * meas.coeff\n else:\n val += prob * meas.coeff\n return val.real","def send_energy(self) -> float:\n # Ensure that the molecule currently passes validation\n if not self.molecule_validated:\n raise Exception(\"MDI attempting to compute energy on an unvalidated molecule\")\n self.run_energy()\n properties = self.compute_return.properties.dict()\n energy = properties[\"return_energy\"]\n MDI_Send(energy, 1, MDI_DOUBLE, self.comm)\n return energy","def calcEnergy(self):\n speed_light = constants.physical_constants[\"speed of light in vacuum\"][0]#m/sec by default\n if self.mass is None:\n raise CoordinateVector(\"The particle mass needs to be specified to calculate the energy.\")\n return speed_light*math.sqrt(self.p*self.p + (self.mass*speed_light)**2)","def energy(e: float) -> float:\n\n return (1/np.sqrt(2))*(gamma(-e/2+1/2)/(gamma(-e/2+3/4)))","def getEnergy(self, recalculate):\n\n if recalculate:\n try:\n calc = (GULP(keywords=self.params[\"gulp_keywords_calculate_energy\"][\"value\"],\n library=self.params[\"gulp_library\"][\"value\"]))\n self.structure.set_calculator(calc)\n self.energy = self.structure.get_potential_energy()\n self.numberOfSinglePointCalculations += 1\n return True, self.energy / self.numberOfAtoms\n except:\n return False, 0\n\n else:\n return True, self.energy / self.numberOfAtoms","def compute_energy(self):\n energy = 0.5 * self.masses * np.sum(self.velocities * self.velocities, axis=1)\n avg_energy = np.mean(energy) # average kinetic energy of all particles\n return avg_energy","def min_e_score(self, entity):\n return float(entity['es_bb'][0])","def cse_elastic(energy, elements, stoic):\n i = 0\n cs_el = 0\n gamma = float(1.+energy/ElectronMass)\n beta = sqrt(1.-1./(gamma*gamma))\n for Z in elements:\n Z = float(Z)\n cs_el += 1.0e-14*1.4e-6*(Z**1.5)*(1.-(0.26*Z/(137.*beta)))/(beta*beta)*stoic[i]\n i += 1\n\n return cs_el","def getEnergyAdded(self):\n return self.json_state.get(\"charging\").get(\"wh_energy\")","def mean_error(self) -> float:\n self.ME = sum(np.array(self.sim_data[\"Human (mean)\"]) - np.array(self.sim_data[\"assigned_sim\"])) / len(self.sim_data)\n return self.ME","def ReadGaussian():\n # LICHEM calculates this as the optimization energy - self energy.\n # Self energy of the charges = {f} a.u.\n self_line = \" Self energy of the charges\"\n # SCF Done: E({s}) = {f} A.U. after {d} cycles\n SCF_line = \" SCF Done:\"\n with open('LICHM_GaussEnergy_0.log') as f:\n for line in f:\n if line.startswith(self_line):\n # print(line)\n ## Parses unmarked positive and - int and float values\n selfE = re.findall(r'\\-\\d+\\.*\\d*', line)\n selfE = float(selfE[0])\n elif line.startswith(SCF_line):\n # print(line)\n SCFE = re.findall(r'\\-\\d+\\.*\\d*', line)\n SCFE = float(SCFE[0])\n f.close()\n GaussE = SCFE - selfE\n GaussE *= har2ev\n return GaussE","def get_e(self):\n return self.e_min + self.e_ * self.e_range","def compute_energy(alat, nk, ecut, atom, clat):\r\n\r\n potpath = os.path.join(os.environ['ESPRESSO_PSEUDO'], 'Cu.pz-d-rrkjus.UPF')\r\n pseudopots = {'Cu': PseudoPotential(path=potpath, ptype='uspp', element='Cu',\r\n functional='LDA', name='Cu.pz-d-rrkjus.UPF'),\r\n 'Au': PseudoPotential(path=potpath, ptype='uspp', element='Au',\r\n functional='LDA', name='Au.pz-d-rrkjus.UPF')\r\n }\r\n struc = make_struc(alat=alat, atom=atom, clat=clat)\r\n if clat != alat:\r\n kpts = Kpoints(gridsize=[nk, nk, int(numpy.floor(nk/2))], option='automatic', offset=False)\r\n else:\r\n kpts = Kpoints(gridsize=[nk, nk, nk], option='automatic', offset=False)\r\n dirname = '{}_a_{}_ecut_{}_nk_{}_'.format(atom, alat, ecut, nk)\r\n runpath = Dir(path=os.path.join(os.environ['WORKDIR'], \"Lab3/Problem3/Ediff\", dirname))\r\n input_params = PWscf_inparam({\r\n 'CONTROL': {\r\n 'calculation': 'scf',\r\n 'pseudo_dir': os.environ['ESPRESSO_PSEUDO'],\r\n 'outdir': runpath.path,\r\n 'tstress': True,\r\n 'tprnfor': True,\r\n 'disk_io': 'none',\r\n },\r\n 'SYSTEM': {\r\n 'ecutwfc': ecut,\r\n 'ecutrho': ecut * 8,\r\n 'occupations': 'smearing',\r\n 'smearing': 'mp',\r\n 'degauss': 0.02\r\n },\r\n 'ELECTRONS': {\r\n 'diagonalization': 'david',\r\n 'mixing_beta': 0.2,\r\n 'conv_thr': 1e-7,\r\n },\r\n 'IONS': {\r\n 'ion_dynamics': 'bfgs',\r\n },\r\n 'CELL': {\r\n 'cell_dynamics': 'bfgs',\r\n 'cell_factor': 4.0,\r\n },\r\n })\r\n\r\n output_file = run_qe_pwscf(runpath=runpath, struc=struc, pseudopots=pseudopots,\r\n params=input_params, kpoints=kpts, ncpu=1, nkpool=1)\r\n output = parse_qe_pwscf_output(outfile=output_file)\r\n return output['energy']","def mu(self):\n return self.generic_getter(get_chemical_potential, \"mu\", \"convert_energy\")","def energy(data):\n\n return np.real(np.mean(np.abs(data)**2, axis=1))","def _cal_energies(self):\n max_i, max_j, max_k = self._max_grid_indices\n\n corr_func = self._cal_corr_func(\"occupancy\")\n self._free_of_clash = (corr_func < 0.001)\n self._free_of_clash = self._free_of_clash[0:max_i, 0:max_j, 0:max_k] # exclude positions where ligand crosses border\n \n self._meaningful_energies = np.zeros(self._grid[\"counts\"], dtype=float)\n if np.any(self._free_of_clash):\n grid_names = [name for name in self._grid_func_names if name != \"occupancy\"]\n for name in grid_names:\n self._meaningful_energies += self._cal_corr_func(name) \n \n self._meaningful_energies = self._meaningful_energies[0:max_i, 0:max_j, 0:max_k] # exclude positions where ligand crosses border\n \n self._meaningful_energies = self._meaningful_energies[self._free_of_clash] # exclude positions where ligand is in clash with receptor, become 1D array\n self._number_of_meaningful_energies = self._meaningful_energies.shape[0]\n \n return None","def energy(energy_name: str) -> float:\n pass","def getEnergy(self, normalized=True, mask=None):\n if self.gpu:\n psi = self.psi.get()\n V = self.Vdt.get() / self.dt\n else:\n psi = self.psi\n V = self.Vdt.get() / self.dt\n density = np.absolute(psi) ** 2\n gradx = np.gradient(psi)[0]\n normFactor = density.sum() if normalized else 1.0\n return np.ma.array(-(0.25 * np.gradient(\n np.gradient(density)[0])[0]\n - 0.5 * np.absolute(gradx) ** 2\n - (self.g_C * density + V)\n * density), mask=mask).sum() / normFactor","def energy(self) -> Union[int, float]:\n return self.proto.energy","def energy(self) -> Union[int, float]:\n return self.proto.energy","def _calc_energy( self, V_a, eos_d ):\n pass","def compute_energy(self, protein): \n return utils.score_pose(protein.pose, self.scorefxn)","def get_quantum_harmonic_free_energy(self, temperatures=None):\n if temperatures is None:\n temperatures = self.input.temperature\n temperatures = np.clip(temperatures, 1e-6, np.inf)\n beta = 1.0 / (KB * temperatures)\n f = 0\n for m in self.input.structure.get_masses():\n hbar_omega = (\n HBAR\n * np.sqrt(self.input.spring_constant / m)\n * ROOT_EV_PER_ANGSTROM_SQUARE_PER_AMU_IN_S\n )\n f += (3.0 / 2) * hbar_omega + (\n (3.0 / beta) * np.log(1 - np.exp(-beta * hbar_omega))\n )\n return f","def _get_gs_energies(self):\n energy = []\n for ground_state in self._ground_states:\n gs_energy = 0.0\n for key in ground_state[\"eci\"].keys():\n gs_energy += ground_state[\"eci\"][key] * ground_state[\"cf\"][key]\n energy.append(len(ground_state[\"atoms\"]) * gs_energy)\n return energy","def ComputeEnergyConsumption(self):\r\n pass","def _calculate_fuel_simple(self):\n self._fuel_simple = (self.mass // 3) - 2","def hf_energy(hf_state, hamiltonian_sp):\n qpu = get_default_qpu()\n res = qpu.submit(hf_state.to_job(job_type=\"OBS\", observable=hamiltonian_sp))\n return res.value","def E_Dynamic_MavkoEtAl2009(rhob,DTS,PR):\n E = (2*(rhob*1000)*((304800/DTS)**2)*(1+PR))/1000000\n return E","def price_heston_mc(kappa_,theta_,sigma_,rho_,r_,T_,L_,V0_,S0_,K0_,N_):\r\n esp_ = monte_carlo(kappa_,theta_,sigma_,rho_,r_,T_,L_,V0_,S0_,K0_,N_)\r\n return exp(-r_*T_)*esp_","def energies(self, num_levels=-1):\n if not self.solved: self.solve()\n return self.en[:num_levels]","def eci(self):\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\n eci = coefs.copy()\n eci = eci / self._subspace.function_total_multiplicities\n return eci","def compute_energy(self):\n\n # radiation energy\n Qsqrd = self.omega_coords[:,:,1]*self.omega_coords[:,:,1]\n Psqrd = self.omega_coords[:,:,0]*self.omega_coords[:,:,0]\n\n e_rad = (Psqrd/self.mode_mass + (self.mode_mass*self.omega**2)*Qsqrd)*.5\n\n # space charge energy\n Dsqrd = self.dc_coords[:,:,0]*self.dc_coords[:,:,0]\n\n e_drft = Dsqrd/(2.*self.mode_mass)\n\n energy = e_rad+e_drft\n\n return energy","def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\n graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)\n return graph","def get_E(J,k):\n E = -2 * J * np.cos(k) # energyeigenvalue \n return E","def test_energy_increment(self):\n sqw_ws = MuscatSofQW(SampleWorkspace=self._sample_ws,\n ResolutionWorkspace=self._resolution_ws,\n ParameterWorkspace=self._param_ws,\n OutputWorkspace='__MuscatSofQWTest_result',\n EnergyInc=0.1)\n\n self.assertEqual(sqw_ws.getNumberHistograms(), self._sample_ws.getNumberHistograms())\n self.assertEqual(sqw_ws.getAxis(0).getUnit().unitID(), 'Energy')\n self.assertEqual(sqw_ws.getAxis(1).getUnit().unitID(), 'MomentumTransfer')\n\n x_data = sqw_ws.dataX(0)\n self.assertAlmostEqual(x_data[0], -0.5)\n self.assertAlmostEqual(x_data[-1], 0.5)\n self.assertAlmostEqual(x_data[len(x_data)/2], 0.0)\n\n self.assertEquals(sqw_ws.blocksize(), 10)","def temperature_energy():\n e = _si.e.value\n k_B = _si.k_B.value\n return Equivalency(\n [(si.K, si.eV, lambda x: x / (e / k_B), lambda x: x * (e / k_B))],\n \"temperature_energy\",\n )","def load_energy(self, zpe_scale_factor=1.):\n e_elect = None\n with open(self.path, 'r') as f:\n for line in f:\n if 'FINAL SINGLE POINT ENERGY' in line: # for all methods in Orca\n e_elect = float(line.split()[-1])\n if e_elect is None:\n raise LogError('Unable to find energy in Orca output file.')\n return e_elect * constants.E_h * constants.Na","def self_energy(gf_imp0, gf_imp):\n return 1/gf_imp0 - 1/gf_imp","def energy(sequences, structure, ordering = None, material = 'rna',\n dangles = 'some', T = 37, multi = True, pseudo = False,\n sodium = 1.0, magnesium = 0.0):\n \n ## Set up command-line arguments and input\n args, cmd_input = \\\n setup_nupack_input(exec_name = 'energy', sequences = sequences, ordering = ordering,\n structure = structure, material = material,\n sodium = sodium, magnesium = magnesium,\n dangles = dangles, T = T, multi = multi, pseudo = pseudo)\n \n ## Perform call\n output, error = call_with_pipe(args, cmd_input)\n\n ## Parse and return output\n if output[-3] != \"% Energy (kcal/mol):\" :\n raise ValueError('NUPACK output parsing problem')\n\n return float(output[-2])","def read_electrons(self):\n for iline, line in enumerate(self.lines):\n fstring = 'number_of_electrons'\n if line.find(fstring) >= 0:\n index_ele = iline + 1\n break\n return float(self.lines[index_ele].split('\\n')[0])","def keV(E):\n if np.min(E) >= 100:\n return E / 1000\n else:\n return E","def homog_core(self, enrich=0.9, r_cool=0.5, \n PD=1.48, rho_cool=0.087, c=0.031):\n # get volumes, volume fractions\n self.calc_vol_vfrac(r_cool, PD, c)\n \n # volume-weighted densities/masses\n fracs = {'fuel' : {\n 'volfrac' : self.vfrac_cermet*md.vfrac_UN,\n 'rho' : md.rho_UN},\n 'matr' : {\n 'volfrac' : self.vfrac_cermet*(1 - md.vfrac_UN),\n 'rho' : md.rho_W},\n 'cool' : {\n 'volfrac' : self.vfrac_cool,\n 'rho' : rho_cool}, \n 'clad' : {\n 'volfrac' : self.vfrac_clad,\n 'rho' : md.rho_In}\n }\n\n core_mass = 0\n \n self.homog_mat = Material()\n # mix normalized mass fractions\n for mat in fracs:\n # get UN material from custom composition\n if mat == 'fuel':\n pyne_mat = Material(md.enrich_fuel(enrich))\n else:\n pyne_mat = self.matlib[md.mats[mat]]\n mass = fracs[mat]['volfrac'] * fracs[mat]['rho']\n self.homog_mat += pyne_mat * mass\n core_mass += mass\n \n self.homog_mat.normalize()\n # total density [g/cc]\n self.rho = core_mass / self.core_vol","def energy_erg_yr_supernovae(self, time, timestep, metallicity):\n # get the mass limits of the timesteps the user passed in. The\n # lower time corresponds to the higher stellar mass\n m_low = self.lifetimes.turnoff_mass(time + timestep, metallicity)\n m_high = self.lifetimes.turnoff_mass(time, metallicity)\n\n model = self.sn_ii_model\n\n # we want to integrate the instantaneous energy to get the\n # total energy, so we define the IMF-weighted energy\n def instantaneous_energy(mass):\n e_per_star = model.energy_released_erg(mass, metallicity)\n imf_weight = self.imf.normalized_dn_dm(mass)\n\n return e_per_star * imf_weight\n\n # integrate this between our mass limits\n energy = self._integrate_mass_smart(instantaneous_energy,\n m_low, m_high, source=\"massive\")\n return energy / timestep","def amount_4_conto_energia(self, production, verbose=False):\n en_autocons = round( production * self.perc_autocons) \n energia_immessa_in_rete = production - en_autocons\n tot_incentivo_all_prod = production * self.incentivo_all_prod\n if en_autocons < self.used_external_en:\n tot_incentivo_ssp = self.incentivo_ssp * energia_immessa_in_rete\n else:\n tot_incentivo_ssp = self.incentivo_ssp * energia_immessa_in_rete + \\\n self.eccedenze * (en_autocons - self.used_external_en )\n if verbose:\n print(\"\\nincentivo_ssp: \",self.incentivo_ssp)\n print(\"incentivo_all_prod: \",self.incentivo_all_prod)\n print(\"en_autocons \", en_autocons) \n print(\"energia_immessa_in_rete \", energia_immessa_in_rete)\n print(\"tot_incentivo_all_prod \", tot_incentivo_all_prod) \n print(\"tot_incentivo_ssp \", tot_incentivo_ssp)\n \n \n return tot_incentivo_all_prod + tot_incentivo_ssp - self.spese_4","def _emiss_ee(self,Eph):\n if self.weight_ee == 0.0:\n return np.zeros_like(Eph)\n\n gam = np.vstack(self._gam)\n # compute integral with electron distribution\n emiss = c.cgs * trapz_loglog(np.vstack(self._nelec) * self._sigma_ee(gam,Eph),\n self._gam, axis=0)\n return emiss","def getEnergyExpended(self, sample):\r\n if sample is not None:\r\n if len(sample._data) > self.ENERGY_EXPENDED_INDEX:\r\n ee = sample._data[self.ENERGY_EXPENDED_INDEX]\r\n if ee is not None:\r\n return int(ee)\r\n return -1","def mass_energy(particle: Particle, mass_numb: Optional[Integral] = None) -> u.J:\n return particle.mass_energy","def meanEnergy(self, count=False):\n \n _E = (self.E).groupby(pd.cut(self.E, self.binEdges))\n try:\n def wm(x):\n try:\n return np.average(x, weights=self.Weight.loc[x.index])\n except ZeroDivisionError:\n return np.nan\n wc = lambda x: np.sum(self.Weight.loc[x.index])\n if count:\n E_count = _E.apply(wc)\n E_mean = _E.apply(wm)\n except AttributeError:\n if count:\n E_count = _E.count()\n E_mean = _E.mean()\n \n E_masked = np.ma.masked_invalid(E_mean)\n \n if count:\n C_masked = np.ma.masked_array(E_count, mask=E_masked.mask)\n return C_masked, E_masked\n return E_masked"],"string":"[\n \"def get_energy(self):\\n return self.momentum*self.momentum/(2*self.mass)\",\n \"def get_hoomd_energy(key, hoomd_force_groups, calc_group):\\n\\n total_energy = 0\\n total_energy = sum([a.get_energy(calc_group) \\n for a in hoomd_force_groups[key]])\\n return total_energy\",\n \"def computeEnergy(self):\\n\\t\\tGmo = self.Gmo\\n\\t\\te = self.e\\n\\t\\tself.Ec = 0.0\\n\\n\\t\\tfor i in range( self.nocc ):\\n\\t\\t\\tfor j in range( self.nocc ):\\n\\t\\t\\t\\tfor a in range( self.nocc,self.norb ):\\n\\t\\t\\t\\t\\tfor b in range( self.nocc,self.norb ):\\n\\t\\t\\t\\t\\t\\tself.Ec += 0.25*(Gmo[i,j,a,b]*Gmo[a,b,i,j])/(e[i]+e[j]-e[a]-e[b])\\n\\n\\t\\treturn self.E0 + self.Ec\",\n \"def computeEnergy(self):\\n _cgco.gcoComputeEnergy(self.handle, self.energyTempArray)\\n return self._convertEnergyBack(self.energyTempArray[0])\",\n \"def Find_Lowest_Energy_Structure_Electrostatics(self):\\n n_Na = self.structure.composition['Na']\\n n_S = self.structure.composition['S']\\n n_O = self.structure.composition['O']\\n n_N = self.structure.composition['N']\\n n_Fe = self.structure.composition['Fe']\\n\\n n_Fe_reduced = self.variable_magnetization_dict['Fe']['n_reduced']\\n n_Fe_oxidized = n_Fe-n_Fe_reduced \\n\\n N_charge = ( 2.*n_O-6.*n_S-n_Na-3.*n_Fe_oxidized-2.*n_Fe_reduced )/n_N\\n\\n oxidation_states = {'Na':+1, 'Fe':+3, 'O':-2,'S':+6,'N':N_charge}\\n Fe_2plus = pymatgen.Specie('Fe',oxidation_state=+2)\\n\\n structure_with_charges = self.structure.copy()\\n structure_with_charges.add_oxidation_state_by_element(oxidation_states) \\n\\n # identify Fe sites\\n list_Fe_indices = []\\n for i,site in enumerate(structure_with_charges):\\n if site.specie.symbol == 'Fe':\\n list_Fe_indices.append(i)\\n\\n # Generate all possible permutation of sites and compute \\n # Ewald energy\\n ewald_model = EwaldElectrostaticModel(acc_factor=6)\\n list_reduced_sets = []\\n list_ewald_energy = []\\n for reduced_set in itertools.combinations(list_Fe_indices,n_Fe_reduced):\\n list_reduced_sets.append(reduced_set) \\n\\n struct = structure_with_charges.copy()\\n for i in reduced_set:\\n struct.replace(i, Fe_2plus)\\n\\n list_ewald_energy.append(ewald_model.get_energy(struct))\\n\\n if len(list_ewald_energy) == 0:\\n # all sites are oxidized. No sorting involved \\n list_reduced_site_indices = []\\n list_oxidized_site_indices = list_Fe_indices\\n else:\\n # some reduction takes place. Identify best electrostatic choice\\n\\n imin = np.argmin(list_ewald_energy)\\n\\n list_reduced_site_indices = list_reduced_sets[imin] \\n list_oxidized_site_indices = []\\n for i in list_Fe_indices:\\n if i not in list_reduced_site_indices: \\n list_oxidized_site_indices.append(i) \\n\\n\\n return list_reduced_site_indices, list_oxidized_site_indices\",\n \"def intEnergy_mass(self):\\n return _cantera.reactor_intEnergy_mass(self.__reactor_id)\",\n \"def cf_mean(self):\\n net_power = self['annual_gross_energy'] \\\\\\n - self['annual_thermal_consumption'] # kW-hr\\n # q_pb_des is in MW, convert to kW-hr\\n name_plate = self['q_pb_design'] * 8760 * 1000\\n\\n return net_power / name_plate\",\n \"def energy(self):\\n nocc, gmo, e = self.nocc, self.gmo, self.e\\n\\n Ec = 0.0\\n for i in range(nocc):\\n for j in range(nocc):\\n for a in range(nocc, len(e)):\\n for b in range(nocc, len(e)):\\n Ec += (1/4.0) * gmo[i, j, a, b]**2 / (e[i]+e[j]-e[a]-e[b])\\n\\n self.Ec = Ec\\n self.E_mp2 = Ec + self.E_scf\\n\\n print('@MP2 correlation energy: {:15.10f}\\\\n'.format(self.Ec))\\n print('@Total MP2 energy: {:15.10f}\\\\n'.format(self.E_mp2))\\n\\n return self.E_mp2\",\n \"def cf_mean(self):\\n net_power = self['annual_field_energy'] \\\\\\n - self['annual_thermal_consumption'] # kW-hr\\n # q_pb_des is in MW, convert to kW-hr\\n name_plate = self['q_pb_des'] * 8760 * 1000\\n\\n return net_power / name_plate\",\n \"def energy(self):\\n nocc, ntot, gmo, e = self.nocc, self.ntot, self.gmo, self.e\\n\\n Ec = 0.0\\n for i in range(nocc):\\n for j in range(nocc):\\n for a in range(nocc, ntot):\\n for b in range(nocc, ntot):\\n Ec += gmo[i, a, j, b]*(2*gmo[i, a, j, b] - gmo[i, b, j, a])/\\\\\\n (e[i] + e[j] - e[a] - e[b])\\n\\n self.Ec = Ec\\n self.E_mp2 = Ec + self.E_scf\\n\\n print('@MP2 correlation energy: {:15.10f}\\\\n'.format(self.Ec))\\n print('@Total MP2 energy: {:15.10f}\\\\n'.format(self.E_mp2))\\n\\n return self.E_mp2\",\n \"def Energy(molecules, experiment = None, which = \\\"all\\\"):\\n # Validate the molecules\\n molecules, gn = validate_molecules(molecules)\\n # Create the procedure\\n procedure = validate_procedure(\\\"energy\\\")\\n # Determine who is doing the calculation\\n try:\\n user = experiment[\\\"User\\\"]\\n except (KeyError, TypeError, AttributeError, IPRO_Error):\\n user = defaultUser\\n # Determine if solvation is or should be used\\n solvation = SOLVATION.get_string(experiment, procedure)\\n # Generate the CHARMM script to do the energy calculation\\n if isinstance(gn, int):\\n script = introduction(\\\"Energy Calculation for Design Group \\\" + str(gn))\\n else:\\n script = introduction(\\\"A CHARMM energy calculation\\\")\\n script += load_input_files(experiment)\\n script += load_molecules(molecules, procedure, user, which)\\n script += ic_fill() + solvation\\n script += energy_line(experiment, procedure, '', '', '', solvation)\\n # Declare the name of the output file\\n energyFile = \\\"energy_values.txt\\\"\\n script += \\\"\\\\nener\\\\n\\\\nset tot ?ener\\\\n\\\\nopen write card unit 10 name \\\"\\n script += energyFile + \\\"\\\\n\\\\nwrite title unit 10\\\\n* @tot\\\\n*\\\\nclose unit 10\\\\n\\\"\\n script += \\\"stop\\\\n\\\"\\n # Run CHARMM\\n input, output = execute_CHARMM_script(script, procedure, gn)\\n # Read in the calculated energy\\n try:\\n file = open(energyFile, \\\"r\\\")\\n energy = float(file.readline())\\n file.close()\\n except IOError:\\n text = \\\"A CHARMM energy calculation failed to calculate an energy. \\\"\\n text += \\\"Please review \\\" + output + \\\" to determine why.\\\"\\n raise CHARMM_Error(text)\\n # Clean up the folder\\n #clean_up(molecules, input, output, procedure, energyFile)\\n # Return the calculated energy\\n return energy\",\n \"def E(self):\\n return self.generic_getter(get_energy, \\\"E\\\", \\\"convert_energy\\\")\",\n \"def read_energy(self, fname):\\n outfile = open(fname)\\n lines = outfile.readlines()\\n outfile.close()\\n\\n energy = None\\n for line in lines:\\n if line.find('HEAT OF FORMATION') != -1:\\n words = line.split()\\n energy = float(words[5])\\n if line.find('H.o.F. per unit cell') != -1:\\n words = line.split()\\n energy = float(words[5])\\n if line.find('UNABLE TO ACHIEVE SELF-CONSISTENCE') != -1:\\n energy = None\\n if energy is None:\\n raise RuntimeError('MOPAC: could not find total energy')\\n### do not change unit for mopac\\n energy *= (kcal / mol)\\n return energy\",\n \"def energyK(k):\\r\\n C1 = 9.7846113e-07\\r\\n C2 = 12.263868e0 \\r\\n E = (-1.0 + np.sqrt(1.0 + 4.0 * C1 * C2**2 * k**2))/(2.0 * C1)\\r\\n return E\",\n \"def mass_energy():\\n c2 = _si.c.value**2\\n return Equivalency(\\n [\\n (si.kg, si.J, lambda x: x * c2, lambda x: x / c2),\\n (si.kg / si.m**2, si.J / si.m**2, lambda x: x * c2, lambda x: x / c2),\\n (si.kg / si.m**3, si.J / si.m**3, lambda x: x * c2, lambda x: x / c2),\\n (si.kg / si.s, si.J / si.s, lambda x: x * c2, lambda x: x / c2),\\n ],\\n \\\"mass_energy\\\",\\n )\",\n \"def get_Ec_min(self):\\n return self.Ec_min\",\n \"def estimate_emissions(excess_CH4):\\n import math\\n #a = 0.4630664\\n #b = 0.7443749\\n a1 = 1.2889\\n b1 = 0.35232\\n a2 = 1.755891\\n b2 = 0.4438203\\n\\n a= -1.370\\n b= 0.823\\n\\n m = math.exp((excess_CH4 - a) / b)\\n # if m < math.exp(3.157):\\n # if m < math.exp(2):\\n # m = math.exp((np.log(m) - a1)/b1)\\n # if m > math.exp(2):\\n # m = math.exp((np.log(m) - a2)/b2)\\n return (m)\",\n \"def get_energy():\\n\\n # open the psi4 log file\\n with open('output.dat', 'r') as log:\\n lines = log.readlines()\\n\\n # find the total converged energy\\n for line in lines:\\n if 'Total Energy =' in line:\\n energy = float(line.split()[3])\\n break\\n else:\\n raise EOFError('Cannot find energy in output.dat file.')\\n\\n return energy\",\n \"def energy(self):\\n return self.mc.energy(self.chain)\",\n \"def getEnergy(self):\\n if not hasattr(self,\\\"energy\\\"):\\n self.energy = self.calcEnergy()\\n return self.energy\",\n \"def _calc_Em(self):\\n return (self.parameters.E0 +\\n self.x * sqrt2 * self.parameters.sigma * self.mt)\",\n \"def _get_mean(self):\\n return self._get_conditional_negative_energy()\",\n \"def get_omm_energy(key, omm_force_groups, omm_context):\\n if key == 'all':\\n return (omm_context.getState(getEnergy=True).getPotentialEnergy())\\n if len(omm_force_groups[key]) == 0:\\n return 0 * unit.kilojoule_per_mole\\n return (omm_context.getState(getEnergy=True, groups=omm_force_groups[key])\\n .getPotentialEnergy())\",\n \"def computeDataEnergy(self):\\n _cgco.gcoComputeDataEnergy(self.handle, self.energyTempArray)\\n return self._convertEnergyBack(self.energyTempArray[0])\",\n \"def get_energy():\\n\\n # open the psi4 log file\\n with open(\\\"output.dat\\\", \\\"r\\\") as log:\\n for line in log:\\n if \\\"Total Energy =\\\" in line:\\n return float(line.split()[3])\\n\\n raise EOFError(\\\"Cannot find energy in output.dat file.\\\")\",\n \"def get_energy(self):\\r\\n return self._energy\",\n \"def energy(self):\\n return self._energy\",\n \"def energy_func(self):\\n i = self.inl[0].to_flow()\\n o = self.outl[0].to_flow()\\n\\n T_m = (T_mix_ph(i, T0=self.inl[0].T.val_SI) +\\n T_mix_ph(o, T0=self.outl[0].T.val_SI)) / 2\\n\\n iam = (\\n 1 - self.iam_1.val * abs(self.aoi.val) -\\n self.iam_2.val * self.aoi.val ** 2)\\n\\n return (i[0] * (o[2] - i[2]) -\\n self.A.val * (\\n self.E.val * self.eta_opt.val * self.doc.val ** 1.5 * iam -\\n (T_m - self.Tamb.val_SI) * self.c_1.val -\\n self.c_2.val * (T_m - self.Tamb.val_SI) ** 2))\",\n \"def get_molpro_energy(species,wellorts):\\n fname = str(species.chemid)\\n if wellorts: fname = species.name\\n \\n status = os.path.exists('molpro/' + fname + '.out')\\n \\n if status:\\n with open('molpro/' + fname + '.out') as f:\\n lines = f.readlines()\\n \\n for index, line in enumerate(reversed(lines)):\\n if 'SETTING MYENA' in line:\\n return 1, float(line.split()[2])\\n else:\\n return 0, -1\",\n \"def get_o_energies(mol):\\n try:\\n ev_to_hartree = 1./convertor(1,'hartree','eV')\\n g=hack_parser.Gaussian(mol.calc.log, loglevel=50)\\n d=g.parse()\\n #lm, hm, lr\\n o_component_es = np.array(d.oniomenergies)\\n except AttributeError:\\n return 0\\n\\n return (ev_to_hartree * o_component_es * [-1,1,1]).sum(axis=1)\",\n \"def mhu(self, sign, spin):\\n index = self.Frontier_MO_index(sign=sign, spin=spin)\\n if spin == \\\"alpha\\\":\\n return self._molecule.mo.energies[index[0]]\\n else:\\n return self._molecule.mo.energies[index[1]]\",\n \"def energy(self):\\n energy = -0.5*np.sum(self.phi)+0.5*np.sum(self.mass*np.sqrt(self.particles.momentum[:,0]**2+self.particles.momentum[:,1]**2)**2)\\n return energy\",\n \"def compute_energy(x, input_HP_sequence):\\n # add code here, feel free to change the argument list\\n # Given a input HP sequence, we already which points are H's.\\n return U\",\n \"def sound_horizon_EH(self):\\n om_m = self.omega_cb\\n om_b = self.omega_b\\n om_n = np.sum(self.omega_nu)\\n h = self.h \\n if self.M_nu_tot == 0.: rs = 44.5*np.log(9.83/om_m)/np.sqrt(1+10*om_b**0.75)*h\\n else: rs = 55.154*np.exp(-72.3*(om_n+0.0006)**2.)/(om_m**0.25351*om_b**0.12807)*h\\n return rs\",\n \"def getEnergy(self, normalized=True, mask=None):\\n psi = self.psi.get()\\n n = self.n.get()\\n density = np.absolute(psi) ** 2\\n gradx = np.gradient(psi)[0]\\n normFactor = density.sum() if normalized else 1.0\\n return np.ma.array(-(0.25 * np.gradient(\\n np.gradient(density)[0])[0]\\n - 0.5 * np.absolute(gradx) ** 2\\n - (self.g_C * density + self.g_R * n)\\n * density), mask=mask).sum() / normFactor\",\n \"def _emiss_ep(self,Eph):\\n if self.weight_ep == 0.0:\\n return np.zeros_like(Eph)\\n\\n gam = np.vstack(self._gam)\\n eps = (Eph / mec2).decompose().value\\n # compute integral with electron distribution\\n emiss = c.cgs * trapz_loglog(np.vstack(self._nelec) * self._sigma_1(gam,eps),\\n self._gam, axis=0).to(u.cm**2 / Eph.unit)\\n return emiss\",\n \"def energy_func(self):\\n i = self.inl[0].to_flow()\\n o = self.outl[0].to_flow()\\n\\n T_m = (T_mix_ph(i, T0=self.inl[0].T.val_SI) +\\n T_mix_ph(o, T0=self.outl[0].T.val_SI)) / 2\\n\\n return (i[0] * (o[2] - i[2]) -\\n self.A.val * (\\n self.E.val * self.eta_opt.val -\\n (T_m - self.Tamb.val_SI) * self.lkf_lin.val -\\n self.lkf_quad.val * (T_m - self.Tamb.val_SI) ** 2))\",\n \"def buildEint(self, version = \\\"classical\\\", verbose = 1):\\n keys_c = [\\\"sigma_c_12\\\", \\\"sigma_c_21\\\"]\\n keys_c = [\\\"sigma_d_12\\\", \\\"sigma_d_21\\\"]\\n\\n if \\\"classical\\\".startswith(version.lower()):\\n if np.sum([self.parameters[k] == 0 for k in keys_c]) > 0:\\n string = \\\"Surface energies not set\\\"\\n ut.infoPrint(string)\\n return\\n else:\\n \\\"\\\"\\\"Calculate the interfacial energies\\\"\\\"\\\"\\n sigma = np.sum([self.parameters[k] for k in keys_c])\\n self.e_int_c = sigma - self.w_seps_c\\n \\\"\\\"\\\"Set to zero in all places where w_seps is not set\\\"\\\"\\\"\\n self.e_int_c[self.w_seps_c == 0] = 0\\n \\n if verbose > 0:\\n string = \\\"e_int_c set from w_seps_c data of shape (%i,%i)\\\" % (self.w_seps_c.shape)\\n ut.infoPrint(string)\\n\\n if \\\"dft\\\".startswith(version.lower()):\\n if np.sum([self.parameters[k] == 0 for k in keys_d]) > 0:\\n string = \\\"Surface energies not set\\\"\\n ut.infoPrint(string)\\n return\\n else:\\n \\\"\\\"\\\"Calculate the interfacial energies\\\"\\\"\\\"\\n sigma = np.sum([self.parameters[k] for k in keys_c])\\n self.e_int_d = sigma - self.w_seps_d\\n \\\"\\\"\\\"Set to zero in all places where w_seps is not set\\\"\\\"\\\"\\n self.e_int_d[self.w_seps_d == 0] = 0\\n\\n if verbose > 0:\\n string = \\\"e_int_d set from w_seps_d data of shape (%i,%i)\\\" % (self.w_seps_d.shape)\\n ut.infoPrint(string)\",\n \"def enthalpy_mass(self):\\n return _cantera.reactor_enthalpy_mass(self.__reactor_id)\",\n \"def comptcptotalquantum(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._comptcptotalquantum\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def calc_excess_energy (self):\\n #~ print sorted(self.cd.keys())\\n self.excess_energy = \\\\\\n (self.generation_wind_proposed - self.transmission_losses) * \\\\\\n (self.cd['percent excess energy'] / 100.0)\\n #~ print 'self.excess_energy',self.excess_energy\",\n \"def get_effective_mass():\\n\\n H_BAR = 6.582119514e-16 # eV*s\\n M_0 = 9.10938356e-31 # kg\\n N_KPTS = 6 # Number of k-points included in the parabola.\\n\\n spin_up = Spin(1)\\n\\n band_structure = Vasprun('vasprun.xml').get_band_structure()\\n\\n # Locations of CBM and VBM in band_structure.bands\\n cbm_band_index = band_structure.get_cbm()['band_index'][spin_up][0]\\n cbm_kpoint_index = band_structure.get_cbm()['kpoint_index'][0]\\n\\n vbm_band_index = band_structure.get_vbm()['band_index'][spin_up][0]\\n vbm_kpoint_index = band_structure.get_vbm()['kpoint_index'][0]\\n\\n k = {'electron': {'left': [], 'right': []},\\n 'hole': {'left': [], 'right': []}}\\n E = {'electron': {'left': [], 'right': []},\\n 'hole': {'left': [], 'right': []}}\\n\\n e_ref_coords = band_structure.kpoints[cbm_kpoint_index]._ccoords\\n h_ref_coords = band_structure.kpoints[vbm_kpoint_index]._ccoords\\n\\n for n in range(-N_KPTS, 1):\\n e_coords = band_structure.kpoints[cbm_kpoint_index + n]._ccoords\\n h_coords = band_structure.kpoints[vbm_kpoint_index + n]._ccoords\\n\\n k['electron']['left'].append(\\n ((e_coords[0] - e_ref_coords[0])**2 +\\n (e_coords[1] - e_ref_coords[1])**2 +\\n (e_coords[2] - e_ref_coords[2])**2)**0.5\\n )\\n k['hole']['left'].append(\\n ((h_coords[0] - h_ref_coords[0])**2 +\\n (h_coords[1] - h_ref_coords[1])**2 +\\n (h_coords[2] - h_ref_coords[2])**2)**0.5\\n )\\n\\n e_energy = band_structure.bands[\\n spin_up][cbm_band_index][cbm_kpoint_index + n]\\n h_energy = band_structure.bands[\\n spin_up][vbm_band_index][vbm_kpoint_index + n]\\n\\n E['electron']['left'].append(e_energy)\\n E['hole']['left'].append(h_energy)\\n\\n for n in range(1, 1 + N_KPTS):\\n e_coords = band_structure.kpoints[cbm_kpoint_index + n]._ccoords\\n h_coords = band_structure.kpoints[vbm_kpoint_index + n]._ccoords\\n\\n k['electron']['right'].append(\\n ((e_coords[0] - e_ref_coords[0])**2 +\\n (e_coords[1] - e_ref_coords[1])**2 +\\n (e_coords[2] - e_ref_coords[2])**2)**0.5\\n )\\n k['hole']['right'].append(\\n ((h_coords[0] - h_ref_coords[0])**2 +\\n (h_coords[1] - h_ref_coords[1])**2 +\\n (h_coords[2] - h_ref_coords[2])**2)**0.5\\n )\\n\\n e_energy = band_structure.bands[\\n spin_up][cbm_band_index][cbm_kpoint_index + n]\\n h_energy = band_structure.bands[\\n spin_up][vbm_band_index][vbm_kpoint_index + n]\\n\\n E['electron']['right'].append(e_energy)\\n E['hole']['right'].append(h_energy)\\n\\n # 2nd order fits\\n e_l_fit = np.poly1d(\\n np.polyfit(k['electron']['left'], E['electron']['left'], 2))\\n e_r_fit = np.poly1d(\\n np.polyfit(k['electron']['right'], E['electron']['right'], 2))\\n h_l_fit = np.poly1d(\\n np.polyfit(k['hole']['left'], E['hole']['left'], 2))\\n h_r_fit = np.poly1d(\\n np.polyfit(k['hole']['right'], E['hole']['right'], 2))\\n\\n # Curvatures\\n e_l_curvature = e_l_fit.deriv().deriv()[0]\\n e_r_curvature = e_r_fit.deriv().deriv()[0]\\n h_l_curvature = h_l_fit.deriv().deriv()[0]\\n h_r_curvature = h_r_fit.deriv().deriv()[0]\\n\\n # Unit conversion\\n e_m_eff_l = 10 * ((H_BAR ** 2) / e_l_curvature) / M_0\\n e_m_eff_r = 10 * ((H_BAR ** 2) / e_r_curvature) / M_0\\n h_m_eff_l = -10 * ((H_BAR ** 2) / h_l_curvature) / M_0\\n h_m_eff_r = -10 * ((H_BAR ** 2) / h_r_curvature) / M_0\\n\\n return {'electron': {'left': e_m_eff_l, 'right': e_m_eff_r},\\n 'hole': {'left': h_m_eff_l, 'right': h_m_eff_r}}\",\n \"def estimated_energy(self):\\n energy = 0j\\n for pauli_string, coef in self._pauli_coef_terms:\\n a = self._zeros[pauli_string]\\n b = self._ones[pauli_string]\\n if a + b:\\n energy += coef * (a - b) / (a + b)\\n energy = complex(energy)\\n if energy.imag == 0:\\n energy = energy.real\\n energy += self._identity_offset\\n return energy\",\n \"def get_kwh(self):\\n\\n svc = \\\"urn:micasaverde-com:serviceId:EnergyMetering1\\\"\\n if not svc in self.services:\\n raise RuntimeError, \\\"Device doesn't support the service\\\"\\n\\n return self.get_variable(svc, \\\"KWH\\\")\",\n \"def getMeanE(self):\\n\\n\\n\\t\\tEBefore, EAfter = self.getEnergyEvolution()\\n\\n\\t\\tmeanBefore = np.mean(EBefore[-self.__Nkicks//5:])\\n\\t\\tmeanAfter = np.mean(EAfter[-self.__Nkicks//5:])\\n\\t\\tmeanTot = (meanBefore+meanAfter)/2\\n\\n\\t\\treturn meanBefore, meanAfter, meanTot\",\n \"def get_ENMIN(potcar):\\n with open(potcar) as f:\\n for line in f:\\n if 'ENMIN' in line:\\n regex = 'ENMIN\\\\s*=\\\\s*(?P<ENMIN>[0-9]+.[0-9]+)\\\\s+eV'\\n m = re.search(regex, line)\\n return float(m.groupdict()['ENMIN'])\",\n \"def energy_pfu(self):\\n return self._energy_pfu\",\n \"def helmholtzenergy(temp,pres):\\n g = liq_g(0,0,temp,pres)\\n g_p = liq_g(0,1,temp,pres)\\n f = g - pres*g_p\\n return f\",\n \"def energy_yield(self):\\n return self['kwh_per_kw']\",\n \"def getEnergyEvolution(self):\\n\\n\\t\\tEBefore = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleBefore]\\n\\t\\tEAfter = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleAfter]\\n\\n\\t\\treturn EBefore, EAfter\",\n \"def get_total_energy_produced (self):\\n return self.pre_intertie_generation[:self.actual_project_life]\",\n \"def kinetic_energy(self):\\r\\n position, velocity, escaped_particles,impact, wall_collision,mom = self.box_collision_info()\\r\\n for j in xrange(1,self.n):\\r\\n abs_velocity = np.sqrt(velocity[:,0]**2+velocity[:,1]**2\\r\\n + velocity[:,2]**2)\\r\\n KE = 0.5*self.m*abs_velocity**2\\r\\n total_KE = np.sum(KE)\\r\\n invid_KE = total_KE/self.Npart\\r\\n\\r\\n return total_KE, invid_KE\",\n \"def get_energy(self, circuit, sampler):\\n val = 0.0\\n for meas in self.hamiltonian:\\n c = circuit.copy()\\n for op in meas.ops:\\n if op.op == \\\"X\\\":\\n c.h[op.n]\\n elif op.op == \\\"Y\\\":\\n c.rx(-np.pi / 2)[op.n]\\n measured = sampler(c, meas.n_iter())\\n for bits, prob in measured.items():\\n if sum(bits) % 2:\\n val -= prob * meas.coeff\\n else:\\n val += prob * meas.coeff\\n return val.real\",\n \"def send_energy(self) -> float:\\n # Ensure that the molecule currently passes validation\\n if not self.molecule_validated:\\n raise Exception(\\\"MDI attempting to compute energy on an unvalidated molecule\\\")\\n self.run_energy()\\n properties = self.compute_return.properties.dict()\\n energy = properties[\\\"return_energy\\\"]\\n MDI_Send(energy, 1, MDI_DOUBLE, self.comm)\\n return energy\",\n \"def calcEnergy(self):\\n speed_light = constants.physical_constants[\\\"speed of light in vacuum\\\"][0]#m/sec by default\\n if self.mass is None:\\n raise CoordinateVector(\\\"The particle mass needs to be specified to calculate the energy.\\\")\\n return speed_light*math.sqrt(self.p*self.p + (self.mass*speed_light)**2)\",\n \"def energy(e: float) -> float:\\n\\n return (1/np.sqrt(2))*(gamma(-e/2+1/2)/(gamma(-e/2+3/4)))\",\n \"def getEnergy(self, recalculate):\\n\\n if recalculate:\\n try:\\n calc = (GULP(keywords=self.params[\\\"gulp_keywords_calculate_energy\\\"][\\\"value\\\"],\\n library=self.params[\\\"gulp_library\\\"][\\\"value\\\"]))\\n self.structure.set_calculator(calc)\\n self.energy = self.structure.get_potential_energy()\\n self.numberOfSinglePointCalculations += 1\\n return True, self.energy / self.numberOfAtoms\\n except:\\n return False, 0\\n\\n else:\\n return True, self.energy / self.numberOfAtoms\",\n \"def compute_energy(self):\\n energy = 0.5 * self.masses * np.sum(self.velocities * self.velocities, axis=1)\\n avg_energy = np.mean(energy) # average kinetic energy of all particles\\n return avg_energy\",\n \"def min_e_score(self, entity):\\n return float(entity['es_bb'][0])\",\n \"def cse_elastic(energy, elements, stoic):\\n i = 0\\n cs_el = 0\\n gamma = float(1.+energy/ElectronMass)\\n beta = sqrt(1.-1./(gamma*gamma))\\n for Z in elements:\\n Z = float(Z)\\n cs_el += 1.0e-14*1.4e-6*(Z**1.5)*(1.-(0.26*Z/(137.*beta)))/(beta*beta)*stoic[i]\\n i += 1\\n\\n return cs_el\",\n \"def getEnergyAdded(self):\\n return self.json_state.get(\\\"charging\\\").get(\\\"wh_energy\\\")\",\n \"def mean_error(self) -> float:\\n self.ME = sum(np.array(self.sim_data[\\\"Human (mean)\\\"]) - np.array(self.sim_data[\\\"assigned_sim\\\"])) / len(self.sim_data)\\n return self.ME\",\n \"def ReadGaussian():\\n # LICHEM calculates this as the optimization energy - self energy.\\n # Self energy of the charges = {f} a.u.\\n self_line = \\\" Self energy of the charges\\\"\\n # SCF Done: E({s}) = {f} A.U. after {d} cycles\\n SCF_line = \\\" SCF Done:\\\"\\n with open('LICHM_GaussEnergy_0.log') as f:\\n for line in f:\\n if line.startswith(self_line):\\n # print(line)\\n ## Parses unmarked positive and - int and float values\\n selfE = re.findall(r'\\\\-\\\\d+\\\\.*\\\\d*', line)\\n selfE = float(selfE[0])\\n elif line.startswith(SCF_line):\\n # print(line)\\n SCFE = re.findall(r'\\\\-\\\\d+\\\\.*\\\\d*', line)\\n SCFE = float(SCFE[0])\\n f.close()\\n GaussE = SCFE - selfE\\n GaussE *= har2ev\\n return GaussE\",\n \"def get_e(self):\\n return self.e_min + self.e_ * self.e_range\",\n \"def compute_energy(alat, nk, ecut, atom, clat):\\r\\n\\r\\n potpath = os.path.join(os.environ['ESPRESSO_PSEUDO'], 'Cu.pz-d-rrkjus.UPF')\\r\\n pseudopots = {'Cu': PseudoPotential(path=potpath, ptype='uspp', element='Cu',\\r\\n functional='LDA', name='Cu.pz-d-rrkjus.UPF'),\\r\\n 'Au': PseudoPotential(path=potpath, ptype='uspp', element='Au',\\r\\n functional='LDA', name='Au.pz-d-rrkjus.UPF')\\r\\n }\\r\\n struc = make_struc(alat=alat, atom=atom, clat=clat)\\r\\n if clat != alat:\\r\\n kpts = Kpoints(gridsize=[nk, nk, int(numpy.floor(nk/2))], option='automatic', offset=False)\\r\\n else:\\r\\n kpts = Kpoints(gridsize=[nk, nk, nk], option='automatic', offset=False)\\r\\n dirname = '{}_a_{}_ecut_{}_nk_{}_'.format(atom, alat, ecut, nk)\\r\\n runpath = Dir(path=os.path.join(os.environ['WORKDIR'], \\\"Lab3/Problem3/Ediff\\\", dirname))\\r\\n input_params = PWscf_inparam({\\r\\n 'CONTROL': {\\r\\n 'calculation': 'scf',\\r\\n 'pseudo_dir': os.environ['ESPRESSO_PSEUDO'],\\r\\n 'outdir': runpath.path,\\r\\n 'tstress': True,\\r\\n 'tprnfor': True,\\r\\n 'disk_io': 'none',\\r\\n },\\r\\n 'SYSTEM': {\\r\\n 'ecutwfc': ecut,\\r\\n 'ecutrho': ecut * 8,\\r\\n 'occupations': 'smearing',\\r\\n 'smearing': 'mp',\\r\\n 'degauss': 0.02\\r\\n },\\r\\n 'ELECTRONS': {\\r\\n 'diagonalization': 'david',\\r\\n 'mixing_beta': 0.2,\\r\\n 'conv_thr': 1e-7,\\r\\n },\\r\\n 'IONS': {\\r\\n 'ion_dynamics': 'bfgs',\\r\\n },\\r\\n 'CELL': {\\r\\n 'cell_dynamics': 'bfgs',\\r\\n 'cell_factor': 4.0,\\r\\n },\\r\\n })\\r\\n\\r\\n output_file = run_qe_pwscf(runpath=runpath, struc=struc, pseudopots=pseudopots,\\r\\n params=input_params, kpoints=kpts, ncpu=1, nkpool=1)\\r\\n output = parse_qe_pwscf_output(outfile=output_file)\\r\\n return output['energy']\",\n \"def mu(self):\\n return self.generic_getter(get_chemical_potential, \\\"mu\\\", \\\"convert_energy\\\")\",\n \"def energy(data):\\n\\n return np.real(np.mean(np.abs(data)**2, axis=1))\",\n \"def _cal_energies(self):\\n max_i, max_j, max_k = self._max_grid_indices\\n\\n corr_func = self._cal_corr_func(\\\"occupancy\\\")\\n self._free_of_clash = (corr_func < 0.001)\\n self._free_of_clash = self._free_of_clash[0:max_i, 0:max_j, 0:max_k] # exclude positions where ligand crosses border\\n \\n self._meaningful_energies = np.zeros(self._grid[\\\"counts\\\"], dtype=float)\\n if np.any(self._free_of_clash):\\n grid_names = [name for name in self._grid_func_names if name != \\\"occupancy\\\"]\\n for name in grid_names:\\n self._meaningful_energies += self._cal_corr_func(name) \\n \\n self._meaningful_energies = self._meaningful_energies[0:max_i, 0:max_j, 0:max_k] # exclude positions where ligand crosses border\\n \\n self._meaningful_energies = self._meaningful_energies[self._free_of_clash] # exclude positions where ligand is in clash with receptor, become 1D array\\n self._number_of_meaningful_energies = self._meaningful_energies.shape[0]\\n \\n return None\",\n \"def energy(energy_name: str) -> float:\\n pass\",\n \"def getEnergy(self, normalized=True, mask=None):\\n if self.gpu:\\n psi = self.psi.get()\\n V = self.Vdt.get() / self.dt\\n else:\\n psi = self.psi\\n V = self.Vdt.get() / self.dt\\n density = np.absolute(psi) ** 2\\n gradx = np.gradient(psi)[0]\\n normFactor = density.sum() if normalized else 1.0\\n return np.ma.array(-(0.25 * np.gradient(\\n np.gradient(density)[0])[0]\\n - 0.5 * np.absolute(gradx) ** 2\\n - (self.g_C * density + V)\\n * density), mask=mask).sum() / normFactor\",\n \"def energy(self) -> Union[int, float]:\\n return self.proto.energy\",\n \"def energy(self) -> Union[int, float]:\\n return self.proto.energy\",\n \"def _calc_energy( self, V_a, eos_d ):\\n pass\",\n \"def compute_energy(self, protein): \\n return utils.score_pose(protein.pose, self.scorefxn)\",\n \"def get_quantum_harmonic_free_energy(self, temperatures=None):\\n if temperatures is None:\\n temperatures = self.input.temperature\\n temperatures = np.clip(temperatures, 1e-6, np.inf)\\n beta = 1.0 / (KB * temperatures)\\n f = 0\\n for m in self.input.structure.get_masses():\\n hbar_omega = (\\n HBAR\\n * np.sqrt(self.input.spring_constant / m)\\n * ROOT_EV_PER_ANGSTROM_SQUARE_PER_AMU_IN_S\\n )\\n f += (3.0 / 2) * hbar_omega + (\\n (3.0 / beta) * np.log(1 - np.exp(-beta * hbar_omega))\\n )\\n return f\",\n \"def _get_gs_energies(self):\\n energy = []\\n for ground_state in self._ground_states:\\n gs_energy = 0.0\\n for key in ground_state[\\\"eci\\\"].keys():\\n gs_energy += ground_state[\\\"eci\\\"][key] * ground_state[\\\"cf\\\"][key]\\n energy.append(len(ground_state[\\\"atoms\\\"]) * gs_energy)\\n return energy\",\n \"def ComputeEnergyConsumption(self):\\r\\n pass\",\n \"def _calculate_fuel_simple(self):\\n self._fuel_simple = (self.mass // 3) - 2\",\n \"def hf_energy(hf_state, hamiltonian_sp):\\n qpu = get_default_qpu()\\n res = qpu.submit(hf_state.to_job(job_type=\\\"OBS\\\", observable=hamiltonian_sp))\\n return res.value\",\n \"def E_Dynamic_MavkoEtAl2009(rhob,DTS,PR):\\n E = (2*(rhob*1000)*((304800/DTS)**2)*(1+PR))/1000000\\n return E\",\n \"def price_heston_mc(kappa_,theta_,sigma_,rho_,r_,T_,L_,V0_,S0_,K0_,N_):\\r\\n esp_ = monte_carlo(kappa_,theta_,sigma_,rho_,r_,T_,L_,V0_,S0_,K0_,N_)\\r\\n return exp(-r_*T_)*esp_\",\n \"def energies(self, num_levels=-1):\\n if not self.solved: self.solve()\\n return self.en[:num_levels]\",\n \"def eci(self):\\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\\n eci = coefs.copy()\\n eci = eci / self._subspace.function_total_multiplicities\\n return eci\",\n \"def compute_energy(self):\\n\\n # radiation energy\\n Qsqrd = self.omega_coords[:,:,1]*self.omega_coords[:,:,1]\\n Psqrd = self.omega_coords[:,:,0]*self.omega_coords[:,:,0]\\n\\n e_rad = (Psqrd/self.mode_mass + (self.mode_mass*self.omega**2)*Qsqrd)*.5\\n\\n # space charge energy\\n Dsqrd = self.dc_coords[:,:,0]*self.dc_coords[:,:,0]\\n\\n e_drft = Dsqrd/(2.*self.mode_mass)\\n\\n energy = e_rad+e_drft\\n\\n return energy\",\n \"def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\\n graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)\\n return graph\",\n \"def get_E(J,k):\\n E = -2 * J * np.cos(k) # energyeigenvalue \\n return E\",\n \"def test_energy_increment(self):\\n sqw_ws = MuscatSofQW(SampleWorkspace=self._sample_ws,\\n ResolutionWorkspace=self._resolution_ws,\\n ParameterWorkspace=self._param_ws,\\n OutputWorkspace='__MuscatSofQWTest_result',\\n EnergyInc=0.1)\\n\\n self.assertEqual(sqw_ws.getNumberHistograms(), self._sample_ws.getNumberHistograms())\\n self.assertEqual(sqw_ws.getAxis(0).getUnit().unitID(), 'Energy')\\n self.assertEqual(sqw_ws.getAxis(1).getUnit().unitID(), 'MomentumTransfer')\\n\\n x_data = sqw_ws.dataX(0)\\n self.assertAlmostEqual(x_data[0], -0.5)\\n self.assertAlmostEqual(x_data[-1], 0.5)\\n self.assertAlmostEqual(x_data[len(x_data)/2], 0.0)\\n\\n self.assertEquals(sqw_ws.blocksize(), 10)\",\n \"def temperature_energy():\\n e = _si.e.value\\n k_B = _si.k_B.value\\n return Equivalency(\\n [(si.K, si.eV, lambda x: x / (e / k_B), lambda x: x * (e / k_B))],\\n \\\"temperature_energy\\\",\\n )\",\n \"def load_energy(self, zpe_scale_factor=1.):\\n e_elect = None\\n with open(self.path, 'r') as f:\\n for line in f:\\n if 'FINAL SINGLE POINT ENERGY' in line: # for all methods in Orca\\n e_elect = float(line.split()[-1])\\n if e_elect is None:\\n raise LogError('Unable to find energy in Orca output file.')\\n return e_elect * constants.E_h * constants.Na\",\n \"def self_energy(gf_imp0, gf_imp):\\n return 1/gf_imp0 - 1/gf_imp\",\n \"def energy(sequences, structure, ordering = None, material = 'rna',\\n dangles = 'some', T = 37, multi = True, pseudo = False,\\n sodium = 1.0, magnesium = 0.0):\\n \\n ## Set up command-line arguments and input\\n args, cmd_input = \\\\\\n setup_nupack_input(exec_name = 'energy', sequences = sequences, ordering = ordering,\\n structure = structure, material = material,\\n sodium = sodium, magnesium = magnesium,\\n dangles = dangles, T = T, multi = multi, pseudo = pseudo)\\n \\n ## Perform call\\n output, error = call_with_pipe(args, cmd_input)\\n\\n ## Parse and return output\\n if output[-3] != \\\"% Energy (kcal/mol):\\\" :\\n raise ValueError('NUPACK output parsing problem')\\n\\n return float(output[-2])\",\n \"def read_electrons(self):\\n for iline, line in enumerate(self.lines):\\n fstring = 'number_of_electrons'\\n if line.find(fstring) >= 0:\\n index_ele = iline + 1\\n break\\n return float(self.lines[index_ele].split('\\\\n')[0])\",\n \"def keV(E):\\n if np.min(E) >= 100:\\n return E / 1000\\n else:\\n return E\",\n \"def homog_core(self, enrich=0.9, r_cool=0.5, \\n PD=1.48, rho_cool=0.087, c=0.031):\\n # get volumes, volume fractions\\n self.calc_vol_vfrac(r_cool, PD, c)\\n \\n # volume-weighted densities/masses\\n fracs = {'fuel' : {\\n 'volfrac' : self.vfrac_cermet*md.vfrac_UN,\\n 'rho' : md.rho_UN},\\n 'matr' : {\\n 'volfrac' : self.vfrac_cermet*(1 - md.vfrac_UN),\\n 'rho' : md.rho_W},\\n 'cool' : {\\n 'volfrac' : self.vfrac_cool,\\n 'rho' : rho_cool}, \\n 'clad' : {\\n 'volfrac' : self.vfrac_clad,\\n 'rho' : md.rho_In}\\n }\\n\\n core_mass = 0\\n \\n self.homog_mat = Material()\\n # mix normalized mass fractions\\n for mat in fracs:\\n # get UN material from custom composition\\n if mat == 'fuel':\\n pyne_mat = Material(md.enrich_fuel(enrich))\\n else:\\n pyne_mat = self.matlib[md.mats[mat]]\\n mass = fracs[mat]['volfrac'] * fracs[mat]['rho']\\n self.homog_mat += pyne_mat * mass\\n core_mass += mass\\n \\n self.homog_mat.normalize()\\n # total density [g/cc]\\n self.rho = core_mass / self.core_vol\",\n \"def energy_erg_yr_supernovae(self, time, timestep, metallicity):\\n # get the mass limits of the timesteps the user passed in. The\\n # lower time corresponds to the higher stellar mass\\n m_low = self.lifetimes.turnoff_mass(time + timestep, metallicity)\\n m_high = self.lifetimes.turnoff_mass(time, metallicity)\\n\\n model = self.sn_ii_model\\n\\n # we want to integrate the instantaneous energy to get the\\n # total energy, so we define the IMF-weighted energy\\n def instantaneous_energy(mass):\\n e_per_star = model.energy_released_erg(mass, metallicity)\\n imf_weight = self.imf.normalized_dn_dm(mass)\\n\\n return e_per_star * imf_weight\\n\\n # integrate this between our mass limits\\n energy = self._integrate_mass_smart(instantaneous_energy,\\n m_low, m_high, source=\\\"massive\\\")\\n return energy / timestep\",\n \"def amount_4_conto_energia(self, production, verbose=False):\\n en_autocons = round( production * self.perc_autocons) \\n energia_immessa_in_rete = production - en_autocons\\n tot_incentivo_all_prod = production * self.incentivo_all_prod\\n if en_autocons < self.used_external_en:\\n tot_incentivo_ssp = self.incentivo_ssp * energia_immessa_in_rete\\n else:\\n tot_incentivo_ssp = self.incentivo_ssp * energia_immessa_in_rete + \\\\\\n self.eccedenze * (en_autocons - self.used_external_en )\\n if verbose:\\n print(\\\"\\\\nincentivo_ssp: \\\",self.incentivo_ssp)\\n print(\\\"incentivo_all_prod: \\\",self.incentivo_all_prod)\\n print(\\\"en_autocons \\\", en_autocons) \\n print(\\\"energia_immessa_in_rete \\\", energia_immessa_in_rete)\\n print(\\\"tot_incentivo_all_prod \\\", tot_incentivo_all_prod) \\n print(\\\"tot_incentivo_ssp \\\", tot_incentivo_ssp)\\n \\n \\n return tot_incentivo_all_prod + tot_incentivo_ssp - self.spese_4\",\n \"def _emiss_ee(self,Eph):\\n if self.weight_ee == 0.0:\\n return np.zeros_like(Eph)\\n\\n gam = np.vstack(self._gam)\\n # compute integral with electron distribution\\n emiss = c.cgs * trapz_loglog(np.vstack(self._nelec) * self._sigma_ee(gam,Eph),\\n self._gam, axis=0)\\n return emiss\",\n \"def getEnergyExpended(self, sample):\\r\\n if sample is not None:\\r\\n if len(sample._data) > self.ENERGY_EXPENDED_INDEX:\\r\\n ee = sample._data[self.ENERGY_EXPENDED_INDEX]\\r\\n if ee is not None:\\r\\n return int(ee)\\r\\n return -1\",\n \"def mass_energy(particle: Particle, mass_numb: Optional[Integral] = None) -> u.J:\\n return particle.mass_energy\",\n \"def meanEnergy(self, count=False):\\n \\n _E = (self.E).groupby(pd.cut(self.E, self.binEdges))\\n try:\\n def wm(x):\\n try:\\n return np.average(x, weights=self.Weight.loc[x.index])\\n except ZeroDivisionError:\\n return np.nan\\n wc = lambda x: np.sum(self.Weight.loc[x.index])\\n if count:\\n E_count = _E.apply(wc)\\n E_mean = _E.apply(wm)\\n except AttributeError:\\n if count:\\n E_count = _E.count()\\n E_mean = _E.mean()\\n \\n E_masked = np.ma.masked_invalid(E_mean)\\n \\n if count:\\n C_masked = np.ma.masked_array(E_count, mask=E_masked.mask)\\n return C_masked, E_masked\\n return E_masked\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.620082","0.5894757","0.58618146","0.5841101","0.5817328","0.578734","0.5772486","0.57676405","0.5644884","0.5638012","0.56097746","0.5535166","0.5525179","0.5514855","0.5491314","0.54726696","0.5452643","0.5449478","0.54388803","0.54244787","0.54218894","0.54208773","0.5359031","0.53490794","0.5303772","0.5281641","0.5258539","0.52530444","0.5248017","0.5241477","0.5237614","0.523448","0.52278656","0.52233225","0.52148396","0.5212178","0.52032936","0.5184004","0.51792026","0.5150901","0.51357204","0.5126996","0.5126715","0.5123597","0.51233417","0.5123196","0.5119402","0.5118984","0.51134574","0.5105686","0.51010674","0.5084992","0.5082482","0.50756615","0.50516635","0.5049907","0.5047004","0.5043688","0.5038799","0.50378853","0.5033408","0.50311714","0.50295657","0.50270224","0.5019778","0.5018208","0.50176054","0.50110215","0.5010843","0.5009805","0.50069743","0.50069743","0.50055534","0.50011873","0.49912348","0.49860382","0.49722946","0.49721697","0.49703655","0.49502763","0.4947895","0.49467093","0.49461535","0.4938968","0.49372968","0.49345836","0.49341992","0.49296975","0.49253005","0.49210775","0.49129614","0.49074778","0.49073002","0.49065137","0.49031326","0.48965514","0.4894323","0.48937243","0.48930538","0.48892415"],"string":"[\n \"0.620082\",\n \"0.5894757\",\n \"0.58618146\",\n \"0.5841101\",\n \"0.5817328\",\n \"0.578734\",\n \"0.5772486\",\n \"0.57676405\",\n \"0.5644884\",\n \"0.5638012\",\n \"0.56097746\",\n \"0.5535166\",\n \"0.5525179\",\n \"0.5514855\",\n \"0.5491314\",\n \"0.54726696\",\n \"0.5452643\",\n \"0.5449478\",\n \"0.54388803\",\n \"0.54244787\",\n \"0.54218894\",\n \"0.54208773\",\n \"0.5359031\",\n \"0.53490794\",\n \"0.5303772\",\n \"0.5281641\",\n \"0.5258539\",\n \"0.52530444\",\n \"0.5248017\",\n \"0.5241477\",\n \"0.5237614\",\n \"0.523448\",\n \"0.52278656\",\n \"0.52233225\",\n \"0.52148396\",\n \"0.5212178\",\n \"0.52032936\",\n \"0.5184004\",\n \"0.51792026\",\n \"0.5150901\",\n \"0.51357204\",\n \"0.5126996\",\n \"0.5126715\",\n \"0.5123597\",\n \"0.51233417\",\n \"0.5123196\",\n \"0.5119402\",\n \"0.5118984\",\n \"0.51134574\",\n \"0.5105686\",\n \"0.51010674\",\n \"0.5084992\",\n \"0.5082482\",\n \"0.50756615\",\n \"0.50516635\",\n \"0.5049907\",\n \"0.5047004\",\n \"0.5043688\",\n \"0.5038799\",\n \"0.50378853\",\n \"0.5033408\",\n \"0.50311714\",\n \"0.50295657\",\n \"0.50270224\",\n \"0.5019778\",\n \"0.5018208\",\n \"0.50176054\",\n \"0.50110215\",\n \"0.5010843\",\n \"0.5009805\",\n \"0.50069743\",\n \"0.50069743\",\n \"0.50055534\",\n \"0.50011873\",\n \"0.49912348\",\n \"0.49860382\",\n \"0.49722946\",\n \"0.49721697\",\n \"0.49703655\",\n \"0.49502763\",\n \"0.4947895\",\n \"0.49467093\",\n \"0.49461535\",\n \"0.4938968\",\n \"0.49372968\",\n \"0.49345836\",\n \"0.49341992\",\n \"0.49296975\",\n \"0.49253005\",\n \"0.49210775\",\n \"0.49129614\",\n \"0.49074778\",\n \"0.49073002\",\n \"0.49065137\",\n \"0.49031326\",\n \"0.48965514\",\n \"0.4894323\",\n \"0.48937243\",\n \"0.48930538\",\n \"0.48892415\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.591733"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":93782,"cells":{"query":{"kind":"string","value":"adds the componentcontribution estimation to the JSON"},"document":{"kind":"string","value":"def to_dict(self, compound_id):\n if compound_id is None:\n raise ValueError('given compound ID is None')\n if self.params is None:\n self.train()\n\n comp = ccache.get_compound(compound_id)\n d = {'compound_id': compound_id, 'inchi_key': comp.inchi_key}\n gv = None\n\n if compound_id in self.cids:\n i = self.cids.index(compound_id)\n gv = self.params['G'][i, :]\n major_ms_dG0_f = self.params['dG0_cc'][i]\n d['compound_index'] = i\n elif comp.smiles is not None:\n # decompose the compounds in the training_data and add to G\n try:\n group_def = self.decomposer.smiles_to_groupvec(comp.smiles)\n gv = group_def.as_array()\n # we need to truncate the dG0_gc matrix from all the group\n # dimensions that correspond to non-decomposable compounds\n # from the training set\n dG0_gc = self.params['dG0_gc'][0:self.Ng]\n major_ms_dG0_f = float(gv @ dG0_gc)\n except inchi2gv.GroupDecompositionError:\n d['error'] = ('We cannot estimate the formation energy of '\n 'this compound because its structure is too '\n 'small or too complex to decompose to groups')\n major_ms_dG0_f = np.nan\n else:\n d['error'] = ('We cannot estimate the formation energy of this '\n 'compound because it has no defined structure')\n major_ms_dG0_f = np.nan\n\n if gv is not None:\n sparse_gv = [(i, int(g)) for (i, g) in enumerate(gv.flat)\n if g != 0]\n d['group_vector'] = sparse_gv\n\n if not np.isnan(major_ms_dG0_f):\n _species = list(comp.get_species(major_ms_dG0_f, default_T))\n d['pmap'] = {'source': 'Component Contribution (2013)',\n 'species': _species}\n\n d['num_electrons'] = comp.atom_bag.get('e-', 0)\n\n if comp.inchi is not None:\n d['InChI'] = comp.inchi\n try:\n mol = Molecule.FromInChI(str(comp.inchi))\n d['mass'] = mol.GetExactMass()\n d['formula'] = mol.GetFormula()\n except OpenBabelError:\n if compound_id == 'C00282': # an exception for hydrogen\n d['mass'] = 2.0157\n d['formula'] = 'H2'\n else:\n d['mass'] = 0\n d['formula'] = ''\n\n return d"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, contribution):\n self._contribution = contribution","def add_comp(self, component, mole_fraction=np.nan):\n self['pore.mole_fraction.' + component.name] = mole_fraction","def execute_contribution(self):\n contributions = [v for v in self.action_register if v['action'] == 'contribution']\n for contribution in contributions:\n self.model.schedule.agents_by_type['Customer'][contribution['unique_id']].hour_wallet -= contribution['value']\n self.model.schedule.agents_by_type['Customer'][contribution['unique_id']].contributed_hours += contribution['value']","def add_contribution(contribution_info):\n NotificationsHandler.handle_contribution(\n contribution_info[\"USER_ID\"], contribution_info[\"PROJECT_ID\"], status=True\n )","def add_component(self, component, targets):\n data = [{\n 'targets': targets,\n 'labels': {'service': self.name, 'component': component},\n }]\n with open(self.filename) as f:\n old_data = json.load(f)\n for s in old_data:\n if s['labels']['service'] == self.name and s['labels'].get('component','') == component:\n data[0]['targets'].extend(s['targets'])\n else:\n data.append(s)\n logging.debug('set(%s) %r', self.filename, data)\n with open(self.filename, 'w') as f:\n json.dump(data, f)","def calculate_contributions(self, request, parent_lookup_client, pk, format=None):\n # TODO: Make this work\n return Response({'btc_amount': 1111, 'atc_amount': 0})","def update_compartments(self, food_glucose):\n self.g_t, self.m_t = self.new_values(food_glucose, self.get_variables())","def addProvisionToCommission(commission):\n provision=Decimal('0.0025')\n if(commission.source_wallet.cryptocurrency.name=='BTC'):\n prov = commission.source_amount * Decimal(provision) \n commission.source_amount += prov \n elif (commission.destination_wallet.cryptocurrency.name=='BTC'):\n prov = commission.destination_amount * Decimal(provision)\n commission.destination_amount += prov\n commission.save()\n provision = Provisions(commission=commission, provision=prov)\n provision.save()\n return 0","def _add_compartments(self, comps):\n return self._cm.add_compartments(comps)","def get_contribution(self, cid):\n fields = list(\n self.swagger_spec.definitions.get(\"ContributionsSchema\")._properties.keys()\n ) # don't return dynamic fields (card_*)\n fields.remove(\"notebook\")\n return Dict(self.contributions.get_entry(pk=cid, _fields=fields).result())","def createDict( self ):\n self.d = {}\n self.d['comp1'] = compensation_channel('comp1', 0, (-479.0, -10.0))\n self.d['comp2'] = compensation_channel('comp2', 1, (-479.0, -10.0))\n self.addCalibration()","def _WriteContribution(self, project_name, contributions_json, output_writer):\n # https://developer.github.com/v3/repos/statistics/\n # [{\n # \"author\": {\n # \"login\": string containing the login name,\n # ...\n # }\n # \"weeks\": [{\n # \"a\": integer containing the number of lines added,\n # \"c\": integer containing the number of contributions,\n # \"d\": integer containing the number of lines deleted,\n # \"w\": integer containing a POSIX timestamp of the start of the week,\n # }, ...],\n # }, ...]\n\n contributions_per_week = {}\n for contributions_per_author_json in contributions_json:\n author_json = contributions_per_author_json.get(\"author\", None)\n if not author_json:\n logging.error(u'Missing author JSON dictionary.')\n continue\n\n weeks_json = contributions_per_author_json.get(\"weeks\", None)\n if not weeks_json:\n logging.error(u'Missing weeks JSON list.')\n continue\n\n login_name = author_json.get(\"login\", None)\n if not login_name:\n logging.error(u'Missing login name JSON value.')\n continue\n\n # TODO: map login name to username\n\n for week_json in weeks_json:\n number_of_lines_added = week_json.get(u'a', 0)\n number_of_contributions = week_json.get(u'c', 0)\n number_of_lines_deleted = week_json.get(u'd', 0)\n\n if (not number_of_lines_added and not number_of_contributions and\n not number_of_lines_deleted):\n continue\n\n week_timestamp = week_json.get(u'w', None)\n if not week_timestamp:\n logging.error(u'Missing week timestamp JSON value.')\n continue\n\n time_elements = time.gmtime(week_timestamp)\n year = time.strftime(u'%Y', time_elements)\n week_number = time.strftime(u'%U', time_elements)\n\n if week_number not in contributions_per_week:\n contributions_per_week[week_number] = {}\n\n contributions_per_week[week_number][login_name] = (\n number_of_contributions, number_of_lines_added,\n number_of_lines_deleted)\n\n if not self._alternate_output_format:\n output_line = (\n u'{0:s}\\t{1:s}\\t{2:s}\\t{3:s}\\t{4:d}\\t{5:d}\\t{6:d}\\n').format(\n year, week_number, login_name, project_name,\n number_of_contributions, number_of_lines_added,\n number_of_lines_deleted)\n\n else:\n username = login_name.lower()\n username = self._user_mappings.get(username, None)\n if not username:\n # Skip login names without a username mapping.\n continue\n\n date_time_string = u'{0:s}-W{1:s}-0'.format(year, week_number)\n date_time = datetime.datetime.strptime(date_time_string, u'%Y-W%W-%w')\n date_time_string = date_time.isoformat()\n\n output_line = (\n u'{0:s} [github] ~ author:{1:s} ~ project:{2:s} ~ '\n u'number_of_cls:{3:d} ~ delta_added:{4:d} ~ delta_deleted:{5:d} '\n u'~ py:{4:d} ~ file_type:py ~ op_type:ADD ~\\n').format(\n date_time_string, username, project_name,\n number_of_contributions, number_of_lines_added,\n number_of_lines_deleted)\n\n output_writer.Write(output_line.encode(u'utf-8'))","def save_component_csv (self, directory):\n #~ return\n if not self.was_run:\n #~ fname = os.path.join(directory,\n #~ self.component_name + \"_output.csv\")\n #~ fname = fname.replace(\" \",\"_\")\n\n #~ fd = open(fname, 'w')\n #~ fd.write(\"Wind Power minimum requirements not met\\n\")\n #~ fd.close()\n return\n\n\n years = np.array(range(self.project_life)) + self.start_year\n\n # ??? +/-\n # ???\n df = DataFrame({\n 'Wind: Capacity (kW)':self.load_offset_proposed,\n \"Wind: Generation (kWh/year)\": self.net_generation_wind,\n 'Wind: Energy Captured by Secondary Load'\n ' (gallons of heating oil equivalent)':\n self.diesel_equiv_captured,\n 'Wind: Assumed capacity factor':\n float(self.comp_specs['capacity factor']),\n 'Wind: Utility Diesel Displaced (gallons/year)':\n self.electric_diesel_reduction,\n 'Wind: Heat Recovery Lost (gallons/year)':\n self.loss_heat_recovery,\n \"Wind: Heat Recovery Cost Savings ($/year)\":\n self.get_heating_savings_costs(),\n \"Wind: Electricity Cost Savings ($/year)\":\n self.get_electric_savings_costs(),\n \"Wind: Project Capital Cost ($/year)\":\n self.get_capital_costs(),\n \"Wind: Total Cost Savings ($/year)\":\n self.get_total_savings_costs(),\n \"Wind: Net Benefit ($/year)\": self.get_net_benefit(),\n }, years)\n\n df[\"Community\"] = self.cd['name']\n\n ol = [\"Community\",\n 'Wind: Capacity (kW)',\n 'Wind: Energy Captured by Secondary Load'\n ' (gallons of heating oil equivalent)',\n 'Wind: Assumed capacity factor',\n 'Wind: Utility Diesel Displaced (gallons/year)',\n 'Wind: Heat Recovery Lost (gallons/year)',\n \"Wind: Generation (kWh/year)\",\n \"Wind: Heat Recovery Cost Savings ($/year)\",\n \"Wind: Electricity Cost Savings ($/year)\",\n \"Wind: Project Capital Cost ($/year)\",\n \"Wind: Total Cost Savings ($/year)\",\n \"Wind: Net Benefit ($/year)\"]\n fname = os.path.join(directory,\n self.cd['name'] + '_' + \\\n self.component_name.lower() + \"_output.csv\")\n fname = fname.replace(\" \",\"_\")\n\n df[ol].ix[:self.actual_end_year].to_csv(fname, index_label=\"Year\")","def refresh_counterfactual_json(request):\t\n\n\n \"\"\"\n the following json are for compound exposures\n \"\"\"\n #json_file = '/home/zhou/Downloads/jsons/compound/json_9.json'\n\tjson_file = '/Users/jiaozi/Downloads/jsons/compound/json_9.json'\n\n exposure_sequence = read_json(json_file)#list of exposures{mean,sd,non_rate}\n\n\tprimeCoordinator = PrimeCoordinator()\n\tprimeCoordinator.get_counterfactual_compound_exposures(exposure_sequence)\n\t\n\t# get the data in the interface\n\tb_output_mortality \t= primeCoordinator.output_baseline_mortality # baseline mortality list for all outcomes\n\tb_output_mortality_num \t= primeCoordinator.output_baseline_mortality_num # baseline mortality sum up for each outcome\n\tb_total_mortality \t= primeCoordinator.output_baseline_mortality_total# baseline mortality sum up for all outcomes\n\tc_output_mortality \t= primeCoordinator.output_counterfactual_mortality# counterfactual mortality for all outcomes\n\tc_output_mortality_num \t= primeCoordinator.output_counterfactual_mortality_num# counterfactual mortality for each outcome\n\tc_total_mortality \t= primeCoordinator.output_counterfactual_mortality_total# counterfactual mortality sum up for all outcomes\n\ttotal_population\t= primeCoordinator.output_total_population\n\tall_mortality_exposure\t= primeCoordinator.output_all_mortality_exposure\n\ttotal_death_averted\t= str(round(primeCoordinator.output_total_death_averted,0))\n\ttotal_death_baseline\t= str(primeCoordinator.output_total_death_baseline)\n\n\t#transmit the parameters\n\ttemplate = loader.get_template('primemodel/index.html')\n\tpara_view = {\n\t\t\t'b_output_mortality_num':\tb_output_mortality_num,\n\t\t\t'b_total_mortality':\t\tb_total_mortality,\n\t\t\t'c_output_mortality_num':\tc_output_mortality_num,\n\t\t\t'c_total_mortality':\t\tc_total_mortality,\n\t\t\t'total_population':\t\ttotal_population,\n\t\t\t'total_death_averted':\t\ttotal_death_averted,\n\t\t\t'total_death_baseline':\t\ttotal_death_baseline,\n\t\t\t'all_mortality_exposure':\tall_mortality_exposure\n\t\t\t}\n\n\t#context to transmit the parameters to show\n\tcontext = Context(para_view)\n\tresponse = template.render(context)\n\treturn HttpResponse(response)","def get_contribution_for_year(self, year: int):","def contribution_to_dict(contribution):\n user = contribution.user\n stats = contribution.get_statistics()\n # show only normalized values for the reputation fields\n stats['engaged_reputation'] = stats['engaged_reputation_normal']\n del stats['engaged_reputation_normal']\n for val in stats['evaluations']:\n stats['evaluations'][val]['reputation'] = stats['evaluations'][val]['reputation_normal']\n del stats['evaluations'][val]['reputation_normal']\n\n return {\n 'id': contribution.id,\n 'contributor': user_to_dict(user),\n 'type': contribution.contribution_type,\n 'stats': stats,\n }","def add_comp(mol, projects):\n\n # Now attribute all this meta-deta to the compound object\n new_comp = Compound()\n comp = calc_cpd(new_comp, mol, projects)\n return comp","def gpa_calculator():\n gpa = 0.0\n grade_array = []\n credit_array = []\n grade_converter = {\"A\": 4.00, \"A-\":3.67, \"B+\": 3.33, \"B\": 3.00, \"B-\": 2.67, \"C+\": 2.33, \"C\": 2.00, \"C-\": 1.67, \"D\": 1.00, \"F\": 0.0}\n with open('full_courses.json', 'r') as fp:\n full_courses = json.load(fp)\n for val in full_courses.values():\n if val[2] == 'C':\n credit_array.append(val[0])\n for i, val2 in grade_converter.items():\n if val[1] == i:\n grade_array.append(val2)\n final_array = [val*val1 for val,val1 in zip(grade_array, credit_array)]\n gpa = round(sum(final_array)/sum(credit_array),2)\n print(\"GPA CALCULATED AS: \"+str(gpa))\n return gpa","def calc_contributions(self):\n\n if self.iterations >= self.statistics_batch_num:\n return\n for wrapper in self.get_modules_wrapper():\n filters = wrapper.module.weight.size(0)\n contribution = (wrapper.module.weight*wrapper.module.weight.grad).data.pow(2).view(filters, -1).sum(dim=1)\n if wrapper.contribution is None:\n wrapper.contribution = contribution\n else:\n wrapper.contribution += contribution\n\n self.iterations += 1","def _updateFinalJsonString(self, statistic, data, completed):\n\n if statistic.final_json:\n items = simplejson.loads(statistic.final_json)\n else:\n items = {}\n\n items.update(data)\n\n properties = {\n 'choices_json': None,\n 'working_json': None,\n 'next_entity': None,\n 'final_json': simplejson.dumps(items),\n 'calculated_on': datetime.utcnow(),\n }\n\n return self.updateEntityProperties(statistic, properties)","def reward_contributions(self): \n contribution_reward_per_hour = get_contribution_reward_per_hour(self.model)\n #payout to agents according to the number of hours they contributed\n for agent_id, agent in self.model.schedule.agents_by_type['Customer'].items():\n agent.teo_wallet += agent.contributed_hours * contribution_reward_per_hour\n agent.contribution_surplus += agent.contributed_hours * contribution_reward_per_hour","def insert_contribution(request):\n try:\n is_loggedin, username = get_session_variables(request)\n # User is not logged in\n if not logged_in(request):\n return HttpResponseRedirect('/register/login')\n\n # User is logged in\n else:\n if request.method == 'POST':\n form = AddContributionForm(request.POST)\n\n # Invalid form imput\n if not form.is_valid():\n error = \"Invalid inputs\"\n return render_to_response('achievement/new_contribution.html', \\\n {'form': form, \\\n 'error':error, \\\n 'is_loggedin':is_loggedin, \\\n 'username':username}, \\\n RequestContext(request))\n\n # Form is valid\n else:\n # Get the new achievement_id\n achievement_id = get_achievement_id(request)\t\n achievement_type = \"contribution\"\n\n # Saving inputs\n achievement_obj = Achievement(achievement_id, \\\n achievement_type, \\\n username)\n achievement_obj.save()\n contribution_obj = form.save(commit = False)\n contribution_obj.achievement_id = achievement_obj\n contribution_obj.achieve_typ = achievement_type\n user_obj = get_object_or_404(User_info, username = username)\n contribution_obj.username = user_obj\n contribution_obj.save()\n return render_to_response('achievement/success.html', \\\n {'achievement_type':achievement_type, \\\n 'is_loggedin':is_loggedin, \\\n 'username':username}, \\\n RequestContext(request))\n # Method is not POST\n else:\n return render_to_response('achievement/new_contribution.html', \\\n {'form': AddContributionForm, \\\n 'is_loggedin':is_loggedin, \\\n 'username':username}, \\\n RequestContext(request))\n except KeyError:\n return error_key(request)","def calc_comp(self, selected_ions=None):\n pass","def add_modifications(self, dset):\n dset_keys = [\"entity_ids\", \"comp_ids\"]\n for key in dset_keys:\n self.model_dict[key] = dset[key]\n self.model_dict[\"code_version\"] = np.array(mbgdml_version)\n self.model_dict[\"md5\"] = np.array(self.md5)","def add_computed_gas_concentrations(self):\n # Extract the z-coordinate and T, S, P profile\n zs = self.interp_ds.coords[self.ztsp[0]].values\n Ts = self.interp_ds[self.ztsp[1]].values\n Ss = self.interp_ds[self.ztsp[2]].values\n Ps = self.interp_ds[self.ztsp[3]].values\n \n # Create an air object\n air_names = ['nitrogen', 'oxygen', 'argon', 'carbon_dioxide']\n yk = np.array([0.78084, 0.20946, 0.009340, 0.00036])\n from tamoc import dbm\n air = dbm.FluidMixture(air_names)\n m = air.masses(yk)\n \n # Compute the concentrations adjusted for depth\n Cs = np.zeros((len(zs), len(air_names)))\n for i in range(len(zs)):\n Cs[i,:] = air.solubility(m, Ts[i], 101325., Ss[i])[0,:] * \\\n seawater.density(Ts[i], Ss[i], Ps[i]) / \\\n seawater.density(Ts[i], Ss[i], 101325.)\n \n # Make sure none of these gases are already in the measured profile\n for name in air_names:\n if name in self.interp_ds:\n air_names[air_names.index(name)] = 'computed_' + name\n \n # Add these data to the Profile object\n data = np.hstack((np.atleast_2d(zs).transpose(), Cs))\n names = [self.ztsp[0]] + air_names \n units = [self.ztsp_units[0]] + 4*['kg/m^3']\n self.append(data, names, units)\n \n # Rebuild the interpolator\n self._build_interpolator()","def set_contribution(self):\r\n \r\n # WX = X*Q\r\n # WY = Y*Q\r\n # see how these are assembled in the Farm class.\r\n \r\n # x-row of big X:\r\n self.x = np.hstack((np.hstack((\\\r\n [xi + ni for xi, ni in zip(self.cx, self.bn)]\\\r\n )), np.hstack((\\\r\n [yi + ti for yi, ti in zip(self.cy, self.bt)]\\\r\n ))))\r\n \r\n # y-row of big Y:\r\n self.y = np.hstack((np.hstack((self.cy)), np.hstack((\\\r\n [-xi for xi in self.cx]))))","def do_TRANSFER_COMPONENTS(self):\n self.logger = SimpleLogger(conf=None).get_logger_object()\n self.conf = SimpleLogger(conf=None).get_conf()\n self.msg = GlobalVariables(self.logger)\n self._request_handler = Req()\n transfer_component_timeout = int(self.conf.get('\\\n transfer_component_timeout', 600))\n self.ll_port = int(self.conf.get('llport', 61014))\n self.service_id = self.msg.get_service_id()\n self.deleted_comp_list = [] \n transfer_component_map = {} #dictionary containing{'(dest_node_obj)':'[comp_list]'}\n #eg: {('169.254.1.12', '61009', 'HN0101_61014_account-updater'):'['1', '2']'}\n self.final_transfer_status_list = [] #final component status list which will be send to GL\n #[(1, True),(2, False),(3, True)] \n self.final_status = False #final response to GL\n self.check_transfer_component_map = {} # to check if component transfer completed or failed: \n #{dest_node_obj1:True, dest_node_obj2:\"Failed\", dest_node_obj3:False}\n all_transfer_event_received = []\n self.protocol_version = \"HTTP/1.1\"\n self.logger.info(\"Account-updater received Transfer component request\")\n try:\n content_length = int(self.headers['Content-Length'])\n #sending acknowledgement of TRANSFER_COMPONENT to GL \n self.send_response(HTTP_CONTINUE, \"Continue\\r\\n\\r\\n\")\n\n data = self.rfile.read(content_length)\n message = TransferCompMessage()\n message.de_serialize(data)\n transfer_component_map = message.service_comp_map()\n self.logger.info(\"Deserialized transfer component map:%s\" \\\n %transfer_component_map)\n except Exception, ex:\n self.logger.error(\"Exception: %s\" % ex)\n self.send_header('Message-Type', \\\n typeEnums.TRANSFER_COMPONENT_RESPONSE)\n self.send_response(HTTP_INTERNAL_SERVER_ERROR)\n self.end_headers()\n return\n\n #if transfer_component_map is empty then send HTTP_OK to GL\n if not transfer_component_map:\n self.logger.info(\"Deserialized transfer component map is empty, return HTTP OK!\")\n comp_transfer_response = TransferCompResponseMessage(\\\n self.final_transfer_status_list, True)\n serialized_body = comp_transfer_response.serialize()\n\n self.send_header('Message-Type', \\\n typeEnums.TRANSFER_COMPONENT_RESPONSE)\n self.send_header('Content-Length', len(serialized_body))\n self.send_response(HTTP_OK)\n self.end_headers()\n self.wfile.write(serialized_body)\n return\n \n for dest_node_obj, comp_list in transfer_component_map.items():\n for comp in comp_list:\n self.deleted_comp_list.append(comp)\n self.final_transfer_status_list.append((comp, False))\n self.check_transfer_component_map[dest_node_obj] = False\n self.logger.debug(\"Ownership for components:%s will be removed\" %self.deleted_comp_list)\n self.delete_self_ownership()\n transfer_comp_event = self.msg.get_transfer_cmp_event()\n transfer_comp_event.set()\n\n try:\n #sending accept component request to other nodes\n self.logger.info(\"Sending accept component request to \" \\\n \"destination nodes\")\n for target_service_obj, comp_list in transfer_component_map.items():\n thread_connecting_node = threading.Thread(target = \\\n self.send_accept_component_request, args = \\\n (\"ACCEPT_COMPONENT_TRANSFER\", target_service_obj, \\\n comp_list, ))\n thread_connecting_node.setDaemon(True)\n thread_connecting_node.start()\n\n #Checking if transfer component completed and intermediate \n #response sent or not\n self.logger.info(\"Checking if transfer component completed\")\n thread_check_transfer_status = threading.Thread(target = \\\n self.check_component_transfer_completion)\n thread_check_transfer_status.setDaemon(True)\n thread_check_transfer_status.start()\n thread_check_transfer_status.join(transfer_component_timeout)\n\n # sending final response to GL\n self.logger.info(\"Sending final response to GL :%s\" \\\n % self.final_transfer_status_list)\n if self.final_transfer_status_list:\n for comp_status_tuple in self.final_transfer_status_list:\n if not comp_status_tuple[1]:\n self.logger.warning(\"Final transfer component list having failed\" \\\n \"component:%s, %s\" %(comp_status_tuple[0], comp_status_tuple[1]))\n self.msg.set_ownershipList(self.old_ownership_list)\n break\n else:\n self.final_status = True\n comp_transfer_response = TransferCompResponseMessage(\\\n self.final_transfer_status_list, self.final_status)\n serialized_body = comp_transfer_response.serialize()\n \n while len(all_transfer_event_received) != 4:\n all_transfer_event_received.append(self.msg.get_from_Queue())\n \n self.logger.info(\"Completed TRANSFER_COMPONENTS request\")\n self.send_header('Message-Type', \\\n typeEnums.TRANSFER_COMPONENT_RESPONSE)\n self.send_header('Content-Length', len(serialized_body))\n self.send_response(HTTP_OK)\n self.end_headers()\n self.wfile.write(serialized_body)\n transfer_comp_event.clear()\n return\n except Exception as ex:\n self.logger.error(\"Exception raised: %s\" % ex)\n transfer_comp_event.clear()\n self.msg.set_ownershipList(self.old_ownership_list)\n self.logger.error(\"old_ownership_list:%s\" %self.old_ownership_list)\n self.send_header('Message-Type', \\\n typeEnums.TRANSFER_COMPONENT_RESPONSE)\n self.send_response(HTTP_INTERNAL_SERVER_ERROR)\n self.end_headers()","def add_com_jac(ui):\n global com_jac_list\n\n content = content_fk_jac_loops(ui, \"com_jac\")\n if content in com_jac_list:\n return\n com_jac_list.append(content)\n ui.listWidget_com_jac.addItem(f\"Center of Mass Jacobian \"\n f\"{parse_content(content)}\")","def register_contribution(self, agent, value):\n if value <= agent.hour_wallet:\n action = {\n 'unique_id': agent.unique_id,\n 'action': 'contribution',\n 'value': value\n }\n self.action_register.append(action)","def json_formatter(components):\n columns = cfg['columns']\n\n newList = [] # New list of only dictionaries with column attributes to marshall\n\n for component in components:\n newComp = {}\n\n for column in columns:\n try:\n newComp[column] = component[column]\n except:\n newComp[column] = cfg['emptyValue']\n\n newList.append(newComp)\n\n result = json.dumps(newList)\n\n # Save the json file\n save_path = args.output_file\n try:\n with open(save_path, \"w\") as file:\n file.write(result)\n\n Logger.Debug(\"Output saved to\", save_path)\n\n return save_path\n\n except:\n Logger.Error(\"Could not save output to\", save_path)","def __init__(self, capitalCommitment, contributionRates, bow, growthRate, fundYield, lastInvestmentYear, lifeOfFund, segments, startDate):\n self.segments = int(segments)\n self.calculate = ModelCalculations(segments)\n if isinstance(startDate, datetime.date):\n self.startDate = startDate\n else:\n dateConverter = ConvertDate.ConvertDate()\n self.startDate = dateConverter(startDate)\n self.endDate = self.calculate.endDate(int(lifeOfFund), self.startDate)\n self.lastInvestmentYear = int(lastInvestmentYear) * self.segments\n self.lifeOfFund = int(lifeOfFund) * self.segments\n self.capitalCommitment = int(capitalCommitment)\n # old code below (before abstraction of rate expansion)\n #self.contributionRates = self._expandContributionRates(self.segments, contributionRates)\n contributionRates = self._appendExtraRates(contributionRates, self.lastInvestmentYear / self.segments)\n self.contributionRates = self.calculate.expandRates(contributionRates, self.segments, False)\n #self._validateContributionRates(self.contributionRates)\n self.bow = float(bow)\n self.growthRate = self.calculate.segmentInterest(self.segments, float(growthRate))#growthRate / self.segments\n self.fundYield = float(fundYield) / self.segments\n\n\n self._contributionList = []\n self._distributionList = []\n self._navList = []\n self._commitmentRemainingList = []\n self._netCashFlowList = []\n self._cummulativeCashFlowList = []\n self._dateList = []\n self._typeList = []\n self._distributionRates = []\n\n self._distributionRates = self.calculate.expandRates(self._getBaseDistributionRates(), self.segments, True)","def add(self, component):\n\n self.append(component)\n self.sum.Add(component.th1f)","def calculate_balance_contributions(self, request, parent_lookup_client, pk, format=None):\n # TODO: Make this work\n return Response(6666666)","def calculate_contributions_balance(self, request, parent_lookup_client, pk, format=None):\n # TODO: Make this work\n return Response({'btc_amount': 2222, 'atc_amount': 88})","def __contribution_from_parameters(self, parameter_names):\n if not isinstance(parameter_names, list):\n parameter_names = [parameter_names]\n\n for iname, name in enumerate(parameter_names):\n parameter_names[iname] = name.lower()\n assert name.lower() in self.jco.col_names,\\\n \"contribution parameter \" + name + \" not found jco\"\n keep_names = []\n for name in self.jco.col_names:\n if name not in keep_names:\n keep_names.append(name)\n if len(keep_names) == 0:\n raise Exception(\"Schur.contribution_from_parameters: \" +\n \"atleast one parameter must remain uncertain\")\n #get the reduced predictions\n if self.predictions is None:\n raise Exception(\"Schur.contribution_from_parameters: \" +\n \"no predictions have been set\")\n cond_preds = []\n for pred in self.predictions:\n cond_preds.append(pred.get(keep_names, pred.col_names))\n la_cond = Schur(jco=self.jco.get(self.jco.row_names, keep_names),\n parcov=self.parcov.condition_on(parameter_names),\n obscov=self.obscov, predictions=cond_preds,verbose=False)\n\n #get the prior and posterior for the base case\n bprior,bpost = self.prior_prediction, self.posterior_prediction\n #get the prior and posterior for the conditioned case\n cprior,cpost = la_cond.prior_prediction, la_cond.posterior_prediction\n return cprior,cpost","def to_json(self) -> collections.OrderedDict:\n self['hparams_json_file'] = os.path.join(self.get_workspace_dir(), 'hparams.json')\n hparam_dict = self.to_dict()\n save_json(self.hparams_json_file, hparam_dict)","def modifications(self, components=['model', 'sheets', 'projections']):\n mapping = {'model': [self],\n 'sheets':self.sheets,\n 'projections':self.projections}\n\n lines = []\n for component in components:\n heading = \"=\" * len(component)\n lines.extend([heading, component.capitalize(), heading, ''])\n specs = mapping[component]\n padding = max(len(str(spec)) for spec in specs)\n for spec in sorted(specs):\n modified = [str(el) for el in sorted(spec.modified_parameters)]\n lines.append(\"%s : [%s]\" % (str(spec).ljust(padding), \", \".join(modified)))\n lines.append('')\n print \"\\n\".join(lines)","def get_contribution_dataframe(self,parlist_dict=None):\n self.log(\"calculating contribution from parameters\")\n if parlist_dict is None:\n parlist_dict = dict(zip(self.pst.parameter_data.parnme,self.pst.parameter_data.parnme))\n\n results = {}\n names = [\"base\"]\n for forecast in self.prior_forecast.keys():\n pr = self.prior_forecast[forecast]\n pt = self.posterior_forecast[forecast]\n reduce = 100.0 * ((pr - pt) / pr)\n results[(forecast,\"prior\")] = [pr]\n results[(forecast,\"post\")] = [pt]\n results[(forecast,\"percent_reduce\")] = [reduce]\n for case_name,par_list in parlist_dict.items():\n names.append(case_name)\n self.log(\"calculating contribution from: \" + str(par_list) + '\\n')\n case_prior,case_post = self.__contribution_from_parameters(par_list)\n self.log(\"calculating contribution from: \" + str(par_list) + '\\n')\n for forecast in case_prior.keys():\n pr = case_prior[forecast]\n pt = case_post[forecast]\n reduce = 100.0 * ((pr - pt) / pr)\n results[(forecast, \"prior\")].append(pr)\n results[(forecast, \"post\")].append(pt)\n results[(forecast, \"percent_reduce\")].append(reduce)\n\n df = pd.DataFrame(results,index=names)\n self.log(\"calculating contribution from parameters\")\n return df","def data_from_json(self, json_file):\n\n param_list = ['name', 'version', 'dir', 'description']\n push_param = list()\n dep_str = ''\n dep_node_str = ''\n with open(json_file) as f:\n param_dict = json.load(f)\n for i in range(len(param_list)):\n push_param.append(param_dict[param_list[i]])\n push_param.append(param_dict['maintainer']['name'])\n push_param.append(param_dict['maintainer']['email'])\n for dep in param_dict['depend']:\n dep_str = dep_str + dep + ', '\n push_param.append(dep_str)\n push_param.append(param_dict['node']['name'])\n for line, val in zip(self.full_ed_lines, push_param):\n line.setText(val)\n self.manager.wid.pub_list = param_dict['node']['publishers']\n self.manager.wid.sub_list = param_dict['node']['subscribers']\n self.manager.wid.reload_table()\n self.changed = False\n self.msg2Statusbar.emit('Выполнена выгрузка данных из JSON')","def add_observations(self, json_data):\n record = self.record\n update_id = uuid.uuid4().hex\n self.add_pending_update(update_id)\n\n new_data = json.loads(json_data)\n calculator = Calculator(self)\n\n new_dframe_raw = calculator.dframe_from_update(\n new_data, self.schema.labels_to_slugs)\n calculator._check_update_is_valid(new_dframe_raw)\n\n call_async(calculator.calculate_updates, calculator, new_data,\n new_dframe_raw=new_dframe_raw, update_id=update_id)","def define_components(mod):\n\n mod.PROJ_DISPATCH_POINTS_DISCRETE = Set(\n initialize=mod.PROJ_DISPATCH_POINTS,\n filter=lambda m, pr, t: (\n m.proj_gen_tech[pr] in m.GEN_TECH_WITH_UNIT_SIZES))\n mod.CommitUnits = Var(\n mod.PROJ_DISPATCH_POINTS_DISCRETE,\n within=NonNegativeIntegers)\n mod.Commit_Units_Consistency = Constraint(\n mod.PROJ_DISPATCH_POINTS_DISCRETE,\n rule=lambda m, pr, t: (\n m.CommitProject[pr, t] ==\n m.CommitUnits[pr, t] * m.g_unit_size[m.proj_gen_tech[pr]] *\n m.proj_availability[pr]))","def sr_add_c():\n req_data = request.get_json()\n logging.debug(\"req_data = \" + str(req_data))\n\n product_name = req_data['product_name']\n version_number = req_data['version_number']\n name = req_data['name']\n version = req_data['version']\n destination = req_data['destination']\n\n if destination == \"\":\n destination = \".\"\n\n outcome = {\"name\": \"Fail\"}\n\n try:\n # create new association\n c = Component.query.filter_by(name=name, version=version).first()\n sr = SoftwareRelease.query.filter_by(product_name=product_name, version_number=version_number).first()\n\n a = Association(destination=destination)\n a.component = c\n sr.components.append(a)\n\n db.session.commit()\n outcome['name'] = \"Success\"\n except:\n db.session.rollback()\n raise\n finally:\n db.session.close()\n return jsonify(outcome)","def dump_analysis(self, ecosystem, component, version, json_response):\n timestamp_str = datetime.datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%S.%f\")\n filename = API_RESULTS_DIRECTORY + \"/\"\n filename += \"component_analysis_{e}_{c}_{v}_{t}.json\".format(e=ecosystem,\n c=component,\n v=version,\n t=timestamp_str)\n with open(filename, 'w') as fout:\n json.dump(json_response, fout)","def get_computes(self):\n _url = f\"{self.base_url}/computes\"\n return self.http_call(\"get\", _url).json()","def minority_aggregation_as_json(request):\n msa_target_lma_sum = 0\n msa_target_mma_sum = 0\n msa_target_hma_sum = 0\n\n msa_peer_lma_sum = 0\n msa_peer_mma_sum = 0\n msa_peer_hma_sum = 0\n\n\n msa_stats = {}\n\n lar_data = loan_originations_as_json(request)\n lender = get_object_or_404(Institution, pk=request.GET.get('lender'))\n metro = get_object_or_404(Geo, geo_type=Geo.METRO_TYPE, geoid=request.GET.get('metro'))\n peer_request = HttpRequest()\n peer_request.GET['lender'] = lender.institution_id\n peer_request.GET['metro']= metro.geoid\n peer_request.GET['peers'] = 'true'\n peer_lar_data = loan_originations_as_json(peer_request)\n\n msa_counties = Geo.objects.filter(geo_type=Geo.COUNTY_TYPE, cbsa=metro.cbsa, year=metro.year)\n county_stats = {}\n for county in msa_counties:\n county_tracts = Geo.objects.filter(geo_type=Geo.TRACT_TYPE, state=county.state, county=county.county, year=metro.year)\n minority_area_stats = get_minority_area_stats(lar_data, peer_lar_data, county_tracts)\n county_stats[county.geoid] = assemble_stats(*minority_area_stats)\n county_stats[county.geoid]['name'] = county.name\n #tally target msa counts\n msa_target_lma_sum += county_stats[county.geoid]['lma']\n msa_target_mma_sum += county_stats[county.geoid]['mma']\n msa_target_hma_sum += county_stats[county.geoid]['hma']\n #tally peer msa counts\n msa_peer_lma_sum += county_stats[county.geoid]['peer_lma']\n msa_peer_mma_sum += county_stats[county.geoid]['peer_mma']\n msa_peer_hma_sum += county_stats[county.geoid]['peer_hma']\n #msa\n msa_minority_area_stats = (msa_target_lma_sum, msa_target_mma_sum, msa_target_hma_sum, msa_peer_lma_sum, msa_peer_mma_sum, msa_peer_hma_sum)\n msa_stats = assemble_stats(*msa_minority_area_stats)\n \n return {\n 'msa': msa_stats,\n 'counties': county_stats,\n }","def set_component(self, component, targets):\n data = []\n if targets:\n data.append({\n 'targets': targets,\n 'labels': {'service': self.name, 'component': component},\n })\n with open(self.filename) as f:\n old_data = json.load(f)\n for s in old_data:\n if s['labels']['service'] != self.name or s['labels'].get('component','') != component:\n data.append(s)\n logging.debug('set(%s) %r', self.filename, data)\n with open(self.filename, 'w') as f:\n json.dump(data, f)","def update_generator_parameters(self):\n gens = self.get_attribute('generator_parameters')\n if not len (gens):\n genInfo = generator_parameters()\n else:\n genInfo = generator_parameters(gens[-1])\n genInfo.set_attribute('submission_details', self._json_base__get_submission_details())\n genInfo.set_attribute('version', genInfo.get_attribute('version')+1)\n\n gens.append(genInfo.json())\n self.set_attribute('generator_parameters', gens)","def componentResult(self, *xpars):\n if len(xpars) == 0:\n xpars = self.__x\n else:\n assert len(xpars) == self.__nx # The input parameter number should be consistent.\n #-> Calculate the add model components\n result = OrderedDict()\n for modelName in self._addList:\n mf = self.__modelDict[modelName]\n result[modelName] = mf(*xpars)\n #-> Manipulate the model components\n for modelName in self._mltList:\n mf = self.__modelDict[modelName]\n my = mf(*xpars) # multiplied y component\n #--> Multiply the current component to the target models\n for tmn in mf.multiList:\n result[tmn] *= my\n return result","def save_output_json(ycomp, tmesh, ystar=None, fstring=None):\n if fstring is None:\n fstring = 'nonspecified_output'\n\n jsfile = open(fstring, mode='w')\n jsfile.write(json.dumps(dict(ycomp=ycomp,\n tmesh=tmesh,\n ystar=ystar)))\n\n print 'output saved to ' + fstring\n print '\\n to plot run the commands \\n'\n print 'import plot_output as plo'\n print 'import optcont_main as ocm'\n print 'jsf = ocm.load_json_dicts(\"' + fstring + '\")'\n print 'plo.plot_optcont_json(jsf, fname=\"' + fstring + '\")\\n'","def request_contribution(project_info):\n owner = user_collection.find_one({\"_id\": project_info[\"OWNER_ID\"]})\n incoming_list = owner[\"incoming\"]\n incoming_list.append(\n {\n \"user_id\": project_info[\"USER_ID\"],\n \"project_id\": project_info[\"PROJECT_ID\"],\n }\n )\n user_collection.find_one_and_update(\n {\"_id\": project_info[\"OWNER_ID\"]},\n {\n \"$set\": {\n \"incoming\": incoming_list,\n }\n },\n upsert=False,\n )\n user = user_collection.find_one({\"_id\": project_info[\"USER_ID\"]})\n user_outgoing = user[\"outgoing\"]\n user_outgoing.append(project_info[\"PROJECT_ID\"])\n user_collection.find_one_and_update(\n {\"_id\": project_info[\"USER_ID\"]},\n {\n \"$set\": {\n \"outgoing\": user_outgoing,\n }\n },\n upsert=False,\n )","def json_out(self):\n temp_json = json.dumps(self.ecat_info, indent=4)\n print(temp_json)","def collection_post(request):\n user_id = request.validated['contributor_id']\n user = request.contract.get_user(user_id)\n contribution_type = request.validated['type']\n try:\n contribution = request.contract.create_contribution(user=user, contribution_type=contribution_type)\n except InvalidContributionTypeError as error:\n raise exc.HTTPBadRequest(error)\n return contribution_to_dict(contribution)","def add_to_pr_export(self, exp_template):","def add(cm_response, **data):\n return cm_response","def componentAddResult(self, *xpars):\n if len(xpars) == 0:\n xpars = self.__x\n else:\n assert len(xpars) == self.__nx # The input parameter number should be consistent.\n result = OrderedDict()\n for modelName in self._addList:\n result[modelName] = self.__modelDict[modelName](*xpars)\n return result","def to_json(self):\n\t\t\n\t\treturn { \"total_sentences\": self.total_sentences }","def cc_json():\n return sh(\"intercept-build ./build.py compile:\\\\* -R; ./build.py -c compile:\\\\*\")","def get_contribution_info(self, project):\n drafts = 0\n pending_personal = 0\n personal = 0\n pending_all = None\n\n user = self.context.get('user')\n if not user.is_anonymous():\n personal = project.observations.filter(creator=user).count()\n pending_personal = project.observations.filter(\n creator=user, status='pending').count()\n drafts = project.observations.filter(\n creator=user, status='draft').count()\n\n if project.can_moderate(user):\n pending_all = project.observations.filter(\n status='pending').count()\n\n return {\n 'total': self.get_num_contributions(project),\n 'personal': personal,\n 'pending_all': pending_all,\n 'pending_personal': pending_personal,\n 'drafts': drafts\n }","def get_contribution(self):\n salary = self._get_salary()\n if not salary:\n return 0\n # Class 1 NIC.\n contribution = 0\n st = 702\n if salary > st:\n contribution = (salary - st) * 0.138\n return contribution","def compService():\n return jsonify(compileService.getItems())","def handle(component_id, cognigy_json_response, response_time_seconds=0.0):\n coco_standard_response = copy.deepcopy(COCO_STANDARD_RESPONSE)\n\n response = cognigy_json_response.get(\"text\", \"\")\n\n coco_standard_response[\"action_name\"] = user_input_handler.fetch_action_name(\n response)\n\n coco_standard_response.update(user_input_handler.calculate_status_flags(\n response))\n\n coco_standard_response[\"response\"] = \\\n user_input_handler.clean_placeholders_from_text(response)\n\n coco_standard_response[\"response_time\"] = response_time_seconds\n\n return coco_standard_response","def _writeInputJSON(self, tstep):\n\n # update json based on the Properties:\n # print(\"Property keys:\")\n for key in self.properties.index:\n # print(key)\n prop = self.properties[key]\n\n if(key[0] == PropertyID.PID_RefractiveIndex and\n key[2] == tstep and key[1] == objID.OBJ_CONE):\n print(\"PID_RefractiveIndex, \", prop.getValue())\n parent = self._jsondata['materials']\n parent[3][\"refractiveIndex\"] = prop.getValue()\n\n elif(key[0] == PropertyID.PID_NumberOfRays and\n key[2] == tstep):\n print(\"PID_NumberOfRays, \", prop.getValue())\n parent = self._jsondata['sources']\n for item in parent:\n item[\"rays\"] = prop.getValue()\n\n elif(key[0] == PropertyID.PID_ChipSpectrum and\n key[2] == tstep):\n print(\"PID_ChipSpectrum, \", prop.getValue())\n parent = self._jsondata['sources']\n for item in parent:\n item[\"wavelengths\"] = prop.getValue()[\n \"wavelengths\"].tolist()\n item[\"intensities\"] = prop.getValue()[\n \"intensities\"].tolist()\n\n elif(key[0] == PropertyID.PID_ParticleNumberDensity and\n key[2] == tstep):\n print(\"PID_ParticleNumberDensity, \", prop.getValue())\n parent = self._jsondata['materials']\n if prop.getValueType() is ValueType.Scalar:\n parent[3][\"particleDensities\"] = [prop.getValue()]\n else:\n parent[3][\"particleDensities\"] = prop.getValue()\n\n elif(key[0] == PropertyID.PID_ScatteringCrossSections and\n key[2] == tstep):\n pass\n elif(key[0] == PropertyID.PID_InverseCumulativeDist and\n key[2] == tstep):\n pass\n # Datafiles:\n parent = self._jsondata['materials']\n\n n_particle_key = (\n PropertyID.PID_NumberOfFluorescentParticles, objID.OBJ_CONE, tstep)\n n_particles = self.properties[n_particle_key]\n parent[3]['numberOfFluorescentParticles'] = n_particles.getValue()\n print(\"n_particles=\", n_particles.getValue(),\n parent[3]['numberOfFluorescentParticles'])\n\n # Phosphor efficiences:\n if PropertyID.PID_PhosphorEfficiency in\\\n self.properties.index.get_level_values('propertyID'):\n effs = self.properties.xs(\n (PropertyID.PID_PhosphorEfficiency, tstep), level=('propertyID', 'tstep'))\n\n efflist = []\n for i, row in effs.iteritems():\n p = row.getValue()\n efflist.extend([p])\n\n parent[3][\"phosphorEfficiencies\"] = efflist\n if len(efflist) != n_particles.getValue():\n raise APIError.APIError(\"Number of phosphor efficiencies \\\n (%d) does not match with number of \\\n fluorescent particles (%d)\" %\n (len(efflist),\n n_particles.getValue())\n )\n else:\n raise APIError.APIError(\n \"Did not find phosphor efficiencies in Properties!\")\n\n # Properties containing emission spectrums\n if PropertyID.PID_EmissionSpectrum in\\\n self.properties.index.get_level_values('propertyID'):\n em = self.properties.xs((PropertyID.PID_EmissionSpectrum, tstep),\n level=('propertyID', 'tstep'))\n\n em_fname = []\n for i, row in em.iteritems():\n p = row.getValue()\n fname = 'InvCumul_EM_%d.dat' % i\n gS.writeEmissionSpectrumFile(p[\"wavelengths\"],\n p[\"intensities\"],\n fname)\n em_fname.extend([fname])\n\n parent[3][\"cumulativeEmissionSpectrumFilenames\"] = em_fname\n if len(em_fname) != n_particles.getValue():\n raise APIError.APIError(\"Number of em spectrum file names \\\n (%d) does not match with number of \\\n fluorescent particles (%d)\" %\n (len(em_fname),\n n_particles.getValue())\n )\n else:\n raise APIError.APIError(\n \"Did not find em spectrum file names in Properties!\")\n\n # Excitation spectrum property\n if PropertyID.PID_ExcitationSpectrum in\\\n self.properties.index.get_level_values('propertyID'):\n ex = self.properties.xs((PropertyID.PID_ExcitationSpectrum, tstep),\n level=('propertyID', 'tstep'))\n\n ex_fname = []\n for i, row in ex.iteritems():\n p = row.getValue()\n fname = 'InvCumul_EX_%d.dat' % i\n gS.writeExAbsSpectrumFile(p[\"wavelengths\"],\n p[\"intensities\"],\n fname)\n ex_fname.extend([fname])\n parent[3][\"excitationSpectrumFilenames\"] = ex_fname\n\n if len(ex_fname) != n_particles.getValue():\n raise APIError.APIError(\"Number of ex spectrum file names \\\n (%d) does not match with number of \\\n fluorescent particles (%d)\" %\n (len(ex_fname),\n n_particles.getValue())\n )\n else:\n raise APIError.APIError(\n \"Did not find ex spectrum file names in Properties!\")\n\n # Absorption spectrum property\n if PropertyID.PID_AsorptionSpectrum in\\\n self.properties.index.get_level_values('propertyID'):\n aabs = self.properties.xs((PropertyID.PID_AsorptionSpectrum,\n tstep),\n level=('propertyID', 'tstep'))\n\n aabs_fname = []\n for i, row in aabs.iteritems():\n p = row.getValue()\n fname = 'InvCumul_ABS_%d.dat' % i\n gS.writeExAbsSpectrumFile(p[\"wavelengths\"],\n p[\"intensities\"],\n fname)\n aabs_fname.extend([fname])\n parent[3][\"absorptionSpectrumFilenames\"] = aabs_fname\n if len(aabs_fname) != n_particles.getValue():\n raise APIError.APIError(\"Number of abs spectrum \\\n file names (%d) does not match \\\n with number of fluorescent \\\n particles (%d)\" %\n (len(aabs_fname),\n n_particles.getValue()))\n else:\n raise APIError.APIError(\n \"Did not find abs spectrum file names in Properties!\")\n\n # write the json file:\n f = open('input.json', 'w')\n f.write(json.dumps(self._jsondata))\n f.close()","def handle_contribution(user_id, project_id, status):\n user = user_collection.find_one({\"_id\": user_id})\n contribution_list = user[\"contributions\"]\n if status:\n contribution_list.append(project_id)\n else:\n contribution_list.remove(project_id)\n user_collection.find_one_and_update(\n {\"_id\": user_id},\n {\n \"$set\": {\n \"contributions\": contribution_list,\n }\n },\n upsert=False,\n )","def unit_contribution(self):\n\n _unit_contribution = np.array([self.base_margin * (1 - self.annual_margin_decr) ** n\n for n in range(self.num_years)])\n\n return _unit_contribution","def manufacturing_manifest_json(self):\n\n file_name = os.path.join(self.cur_8digit_dir, \"manufacturing.manifest.json\")\n\n with open(file_name) as f_in:\n man_manifest = json.load(f_in)\n\n for component in man_manifest[\"ComponentManufactureList\"]:\n # Get ManufacturingModel (if exists)\n if \"ManufacturingModel\" in component:\n rel_path_from_results = os.path.join(self.cur_8digit_dir, component[\"ManufacturingModel\"])\n if os.path.exists(rel_path_from_results):\n self.files_for_export.append(os.path.join('results', rel_path_from_results))\n\n # Get STEPModel (if exists)\n if \"STEPModel\" in component:\n rel_path_from_results = os.path.join(self.cur_8digit_dir, component[\"STEPModel\"])\n if os.path.exists(rel_path_from_results):\n self.files_for_export.append(os.path.join('results', rel_path_from_results))\n\n # Since this is a PrepareForIFAB folder, gather all *.stp files in ./AP203_E2_SEPERATE_PART_FILES/\n path_to_stp_files = os.path.join(self.cur_8digit_dir,'AP203_E2_SEPERATE_PART_FILES')\n filter_for_stp_files = os.path.join(path_to_stp_files,'*.stp')\n stp_files = glob(filter_for_stp_files)\n for stp_file in stp_files:\n self.files_for_export.append(os.path.join('results',stp_file))\n\n # If there's a TDP tag, \n # add the referred file,\n\t# then crawl that file and gather its file references\n if \"TDP\" in man_manifest:\n tdp_json_path = os.path.join(self.cur_8digit_dir,man_manifest[\"TDP\"])\n self.tdp_json(tdp_json_path)\n self.files_for_export.append(os.path.join('results', tdp_json_path))","def register_contribution(self, contribution_intent):\n if contribution_intent > 0:\n self.teo.register_contribution(self, contribution_intent)","def to_json(self):\n formatted_date = self.created_at.strftime(\"%m/%d/%Y\")\n json_gain = {\n 'id': self.id,\n 'description':self.description,\n 'amount':self.amount,\n 'created_at': formatted_date\n }\n return json_gain","def update_electricity_exchanges(self, d_act):\n d_act = self.remove_exchanges(d_act, ['electricity'])\n\n for act in d_act:\n\n new_exchanges = []\n electricity_needed = self.iam_data.gnr_data.loc[dict(\n variables='Power consumption',\n region=self.geo.iam_to_iam_region(act) if self.model == \"image\" else act\n )].values / 1000\n electricity_recovered = self.iam_data.gnr_data.loc[dict(\n variables='Power generation',\n region=self.geo.iam_to_iam_region(act) if self.model == \"image\" else act\n )].values / 1000\n\n\n electricity_suppliers = self.get_shares_from_production_volume(\n self.get_suppliers_of_a_region(\n iam_region=act,\n ecoinvent_technologies=[\"electricity, medium voltage\"],\n reference_product=\"electricity, medium voltage\",\n unit=\"kilowatt hour\",\n look_for_locations_in=act\n )\n )\n\n if len(electricity_suppliers) == 0:\n electricity_suppliers = self.get_shares_from_production_volume(\n self.get_suppliers_of_a_region(\n iam_region=act,\n ecoinvent_technologies=[\"electricity, medium voltage\"],\n reference_product=\"electricity, medium voltage\",\n unit=\"kilowatt hour\",\n look_for_locations_in=\"ecoinvent\"\n )\n )\n\n for s, supplier in enumerate(electricity_suppliers):\n share = electricity_suppliers[supplier]\n new_exchanges.append(\n {\n \"uncertainty type\": 0,\n \"loc\": 1,\n \"amount\": (electricity_needed - electricity_recovered) * share,\n \"type\": \"technosphere\",\n \"production volume\": 0,\n \"product\": supplier[2],\n \"name\": supplier[0],\n \"unit\": supplier[3],\n \"location\": supplier[1],\n }\n )\n\n d_act[act][\"exchanges\"].extend(new_exchanges)\n d_act[act]['exchanges'] = [v for v in d_act[act][\"exchanges\"] if v]\n\n d_act[act][\"comment\"] = (\"WARNING: Dataset modified by `premise` based on WBCSD's GNR data and 2018 IEA roadmap for the cement industry.\\n \" +\n \"Electricity consumption per kg cement: {} kWh.\\n\".format(electricity_needed) +\n \"Of which {} kWh were generated from on-site waste heat recovery.\\n\".format(electricity_recovered)\n ) + d_act[act][\"comment\"]\n\n return d_act","def _update_ghg_coefs(self) -> None:\n ghg_data, bird_data = self._datasets\n model = self._selection.get_model(ghg_data, bird_data)\n page = self.pages[self.current_page]\n for button in page.buttons:\n if button.tag == 'display':\n gas = button.name[0:3]\n weight = '{:.2e}'.format(model.coef[gas])\n button.update_name(f'{gas} Weight: {weight}')","def append_estimates(self, node: Node) -> None:\n self.estimate.update(\n {node.left_child.key: node.left_child.noncoverage.estimate})\n self.estimate.update(\n {node.right_child.key: node.right_child.noncoverage.estimate})","def _add_component(self, __assistant):\r\n\r\n # Retrieve the hardware ID.\r\n _model = self.cmbHardware.get_model()\r\n _row = self.cmbHardware.get_active_iter()\r\n _hardware_id = int(_model.get_value(_row, 1))\r\n\r\n self._controller.add_component(self._incident_id, _hardware_id)\r\n self._workbook.load_component_list()\r\n\r\n return False","def read(self):\n import cclib\n from pybel import readfile\n\n molecule = next(readfile(\"orca\", self._file))\n\n atom_numbers = []\n coordinates = []\n\n for atom in molecule:\n atom_number = atom.atomicnum\n atom_numbers.append(atom_number)\n for coord in atom.coords:\n coordinates.append(coord)\n\n molecular_mass = molecule.exactmass\n\n cjson = {\n \"chemical json\": 0,\n \"atoms\": {\n \"elements\": {\"number\": atom_numbers},\n \"coords\": {\"3d\": coordinates},\n },\n \"properties\": {\"molecular mass\": molecular_mass},\n }\n\n data = cclib.io.ccread(self._file)\n\n # Add calculated properties\n if hasattr(data, \"scfenergies\"):\n if len(data.scfenergies) > 0:\n energy = data.scfenergies[-1] * EV_TO_J_MOL\n cjson.setdefault(\"properties\", {})[\"totalEnergy\"] = energy\n\n if hasattr(data, \"gbasis\"):\n basis = _cclib_to_cjson_basis(data.gbasis)\n cjson[\"basisSet\"] = basis\n\n if hasattr(data, \"vibfreqs\"):\n vibfreqs = list(data.vibfreqs)\n cjson.setdefault(\"vibrations\", {})[\"frequencies\"] = vibfreqs\n\n if hasattr(data, \"vibdisps\"):\n vibdisps = _cclib_to_cjson_vibdisps(data.vibdisps)\n cjson.setdefault(\"vibrations\", {})[\"eigenVectors\"] = vibdisps\n\n # Add a placeholder intensities array\n if \"vibrations\" in cjson and \"frequencies\" in cjson[\"vibrations\"]:\n if \"intensities\" not in cjson[\"vibrations\"]:\n cjson[\"vibrations\"][\"intensities\"] = [\n 1 for i in range(len(cjson[\"vibrations\"][\"frequencies\"]))\n ]\n if \"modes\" not in cjson[\"vibrations\"]:\n cjson[\"vibrations\"][\"modes\"] = [\n i + 1 for i in range(len(cjson[\"vibrations\"][\"frequencies\"]))\n ]\n\n return cjson","def add_conversions_for_component(self, comp):\n model = self.model\n if self.special_conversions:\n self.model._cml_special_units_converter = self._apply_special_conversion_for_nested_expr\n assignments = model.search_for_assignments(comp)\n self.convert_assignments(assignments)\n if self.special_conversions:\n del self.model._cml_special_units_converter\n for conn in getattr(model, u'connection', []):\n cname1 = conn.map_components.component_1\n cname2 = conn.map_components.component_2\n if comp.name in [cname1, cname2]:\n comp1 = model.get_component_by_name(cname1)\n comp2 = model.get_component_by_name(cname2)\n for mapping in conn.map_variables:\n var1 = model.get_variable_by_name(cname1, mapping.variable_1)\n var2 = model.get_variable_by_name(cname2, mapping.variable_2)\n self.convert_mapping(mapping, comp1, comp2, var1, var2)","def toJSON(self):\r\n\r\n jsonToRet = []\r\n rowJson = []\r\n matrixJson = []\r\n\r\n if len(self.slctData) > 100:\r\n self.getSimMatSummary(100)\r\n jsonToRet.append(self.summaryOrdering)\r\n for i in range(0,len(self.simMatSmm)):\r\n for n in self.simMatSmm[i]:\r\n rowJson.append(n)\r\n matrixJson.append(rowJson)\r\n rowJson = []\r\n jsonToRet.append(matrixJson)\r\n\r\n jsonToRet.append(self.patchOrdering)\r\n # jsonToRet = []\r\n rowJson = []\r\n matrixJson = []\r\n\r\n for i in range(0,len(self.simMat)):\r\n for n in self.simMat[i]:\r\n rowJson.append(n)\r\n matrixJson.append(rowJson)\r\n rowJson = []\r\n jsonToRet.append(matrixJson)\r\n return jsonToRet","def test_composition_adds_to_100_percent(self):","def as_json(self):\n result = super().as_json()\n result[\"generator\"].update({\n \"block\": self.vein.with_purity(100).as_json(),\n \"cluster-size\": self.cluster_size,\n \"type\": \"cluster\",\n })\n return result","def makecalc():\n modelfile = 'winequality-red.pickleRF.dat'\n model = pickle.load(open(os.path.join(APP_STATIC, modelfile), 'rb'))\n\n jsonfile = request.get_json()\n data = pd.read_json(json.dumps(jsonfile),orient='index')\n print(data)\n \n res = dict()\n\n # Headers of Data\n # \"fixed acidity\",\"volatile acidity\",\"citric acid\",\"residual sugar\",\"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\"pH\",\"sulphates\",\"alcohol\"\n\n # create array from JSON data from service\n X = np.array(data[['fixed acidity','volatile acidity','citric acid','residual sugar','chlorides','free sulfur dioxide','total sulfur dioxide','density','pH','sulphates','alcohol']])\n\n print(X)\n\n ypred = model.predict(X)\n \n for i in range(len(ypred)):\n res[i] = ypred[i]\n \n return jsonify(res)","def getJSON(self):\n text = super().getJSON() + f', \"exchange\": \"{self.__exchange}\"'\n text += f', \"market pair\": \"{self.__market_pairs}\"'\n text += f', \"interval\": \"{self.__interval}\"}}'\n return text","def jec_combination(args=None, additional_dictionary=None, algo = 'CHS'):\n\tmpl_plots = []\n\troot_plots = []\n\tlabel_dict = {\n\t\t'ptbalance': 'PtBal',\n\t\t'mpf': 'MPF',\n\t\t'rawmpf': 'MPF-notypeI',\n\t\t'zmass': 'ZMass',\n\t\t'npumean': 'Mu',\n\t\t'rho': 'Rho',\n\t\t'npv': 'NPV',\n\t}\n#\talpha_limits = [0.4]\n\talpha_limits = [0.1, 0.15, 0.2, 0.3, 0.4]\n\talpha_cuts = ['(alpha<{})'.format(limit) for limit in alpha_limits]\n\talpha_strings = ['a'+str(int(100*limit)) for limit in alpha_limits]\n\n\teta_borders = [0, 0.783, 1.305, 1.93, 2.5, 2.964, 3.2, 5.191]\n\teta_cuts = [\"({0}<=abs(jet1eta)&&abs(jet1eta)<{1})\".format(*b) for b in zip(eta_borders[:-1], eta_borders[1:])]\n\teta_cuts = [\"(0<=abs(jet1eta)&&abs(jet1eta)<1.3)\"] + eta_cuts # also include standard barrel jet selection\n\teta_strings = [\"eta_{0:0>2d}_{1:0>2d}\".format(int(round(10*up)), int(round(10*low))) for up, low in zip(eta_borders[:-1], eta_borders[1:])]\n\teta_strings = [\"eta_00_13\"] + eta_strings\n\ttry:\n\t\tnpv_weights = additional_dictionary.pop(\"_npv_weights\")\n\t\twarnings.warn(\"Usage of '_npv_weights' is deprecated. Use PUWeights in Excalibur instead.\")\n\texcept (AttributeError, KeyError):\n\t\tnpv_weights = [\"1\"]\n\ttry:\n\t\tfile_label = additional_dictionary.pop(\"file_label\")\n\texcept (AttributeError, KeyError):\n\t\tfile_label = \"\"\n\n\tnow = time.localtime()\n\tdef mpl_to_root(mpl_plot_dict):\n\t\t\"\"\"Create root plot dict from mpl plot dict\"\"\"\n\t\troot_plot_dict = mpl_plot_dict.copy()\n\t\troot_plot_dict.update({\n\t\t\t'plot_modules': ['ExportRoot'],\n\t\t\t'filename': 'combination_ZJet_' + file_label + time.strftime(\"%Y-%m-%d\", now),\n\t\t\t'file_mode': ('RECREATE' if mpl_to_root.first else 'UPDATE'),\n\t\t})\n\t\tmpl_to_root.first = False\n\t\treturn root_plot_dict\n\tmpl_to_root.first = False\n\n\tfor alphacut, alphastring in zip(alpha_cuts, alpha_strings):\n\t\tfor etacut, etastring in zip(eta_cuts, eta_strings):\n\t\t\tfor correction in ['L1L2L3']: # no L1L2L3Res available atm\n\t\t\t\teta_alpha_cut = '&&'.join((alphacut, etacut))\n\t\t\t\tbase_plot = {\n\t\t\t\t\t'nicks': ['data', 'mc'],\n\t\t\t\t\t'corrections': [correction],\n\t\t\t\t\t'zjetfolders': ['noalphanoetacuts'],\n\t\t\t\t\t'weights': [\"(%s)*(%s)\" % (eta_alpha_cut, npv_weight) for npv_weight in npv_weights],\n\t\t\t\t\t'tree_draw_options' : ['prof'],\n\t\t\t\t\t# ratio\n\t\t\t\t\t'analysis_modules': ['Ratio'],\n\t\t\t\t\t'ratio_numerator_nicks':['data'],\n\t\t\t\t\t'ratio_denominator_nicks':['mc'],\n\t\t\t\t\t'ratio_denominator_no_errors': False,\n\t\t\t\t}\n\t\t\t\t# histograms - raw event counts\n\t\t\t\tlabelsuffix = '_'.join(['RawNEvents', algo, alphastring, etastring, correction])\n\t\t\t\td_mpl = {\n\t\t\t\t\t'x_expressions': ['zpt'],\n\t\t\t\t\t'x_bins': 'zpt',\n\t\t\t\t\t'labels': ['_'.join([item, labelsuffix]) for item in ['Data', 'MC', 'Ratio']],\n\t\t\t\t\t'filename': labelsuffix + file_label,\n\t\t\t\t\t'no_weight': True, #Remove reweights in MC\n\t\t\t\t}\n\t\t\t\td_mpl.update(base_plot)\n\t\t\t\tif additional_dictionary is not None:\n\t\t\t\t\td_mpl.update(additional_dictionary)\n\t\t\t\tdel d_mpl['tree_draw_options']\n\t\t\t\td_root = mpl_to_root(d_mpl)\n\t\t\t\t# make plots comparable to jec_comparison\n\t\t\t\td_mpl['x_log'] = True\n\t\t\t\td_mpl['x_ticks'] = [30, 50, 70, 100, 200, 400, 1000]\n\t\t\t\tmpl_plots.append(d_mpl)\n\t\t\t\troot_plots.append(d_root)\n\t\t\t\t# profiles - responses\n\t\t\t\tfor method in ['mpf', 'ptbalance', 'rawmpf', 'zmass']:\n\t\t\t\t\tlabelsuffix = '_'.join([label_dict[method], algo, alphastring, etastring, correction])\n\t\t\t\t\td_mpl = {\n\t\t\t\t\t\t'x_expressions': ['zpt'],\n\t\t\t\t\t\t'y_expressions': [method],\n\t\t\t\t\t\t'x_bins': 'zpt',\n\t\t\t\t\t\t'y_label': method,\n\t\t\t\t\t\t'labels': ['_'.join([item, labelsuffix]) for item in ['Data', 'MC', 'Ratio']],\n\t\t\t\t\t\t'filename': labelsuffix + file_label,\n\t\t\t\t\t}\n\t\t\t\t\td_mpl.update(base_plot)\n\t\t\t\t\tif additional_dictionary is not None:\n\t\t\t\t\t\td_mpl.update(additional_dictionary)\n\t\t\t\t\tapply_double_profile(d_mpl, args)\n\t\t\t\t\td_root = mpl_to_root(d_mpl)\n\t\t\t\t\t# make plots comparable to jec_comparison\n\t\t\t\t\td_mpl['x_log'] = True\n\t\t\t\t\td_mpl['x_ticks'] = [30, 50, 70, 100, 200, 400, 1000]\n\t\t\t\t\tmpl_plots.append(d_mpl)\n\t\t\t\t\troot_plots.append(d_root)\n\t\t\t\t# pileup info\n\t\t\t\tfor x_expression, y_expression in [(\"npumean\", \"rho\"), (\"npumean\", \"npv\")]:\n\t\t\t\t\tlabelsuffix = \"_\".join((y_expression, \"vs\", x_expression, algo, alphastring, etastring, correction))\n\t\t\t\t\td_mpl = {\n\t\t\t\t\t\t'x_expressions': [x_expression],\n\t\t\t\t\t\t'y_expressions': [y_expression],\n\t\t\t\t\t\t'y_label': y_expression,\n\t\t\t\t\t\t'cutlabel': True,\n\t\t\t\t\t\t'x_bins': \"25,0.5,25.5\",\n\t\t\t\t\t\t'legend': 'upper left',\n\t\t\t\t\t\t'labels': ['_'.join([item, labelsuffix]) for item in ['Data', 'MC', 'Ratio']],\n\t\t\t\t\t\t'filename': labelsuffix + file_label,\n\t\t\t\t\t}\n\t\t\t\t\td_mpl.update(base_plot)\n\t\t\t\t\tif additional_dictionary is not None:\n\t\t\t\t\t\td_mpl.update(additional_dictionary)\n\t\t\t\t\tapply_double_profile(d_mpl, args)\n\t\t\t\t\td_root = mpl_to_root(d_mpl)\n\t\t\t\t\tmpl_plots.append(d_mpl)\n\t\t\t\t\troot_plots.append(d_root)\n\tharryinterface.harry_interface(mpl_plots, args)\n\tharryinterface.harry_interface(root_plots, args + ['--max-processes', '1'])","def json(self, update=False):\n return json.dumps(self.export(update=update), indent=4)","def update_from_json(course_key, jsondict, user):\r\n descriptor = modulestore('direct').get_course(course_key)\r\n\r\n graders_parsed = [CourseGradingModel.parse_grader(jsonele) for jsonele in jsondict['graders']]\r\n\r\n descriptor.raw_grader = graders_parsed\r\n descriptor.grade_cutoffs = jsondict['grade_cutoffs']\r\n\r\n modulestore('direct').update_item(descriptor, user.id)\r\n\r\n CourseGradingModel.update_grace_period_from_json(course_key, jsondict['grace_period'], user)\r\n\r\n return CourseGradingModel.fetch(course_key)","def get_cloned_plugin_data(self, update={}):\n form = self.get_form()\n\n cloned_data = copy.copy(self.data)\n data = {}\n\n for field, default_value in form.plugin_data_fields:\n data.update({field: getattr(cloned_data, field, '')})\n\n for prop, value in update.items():\n data.update({prop: value})\n\n return json.dumps(data)","def add_file_contribution(self, doc, value):\n if self.has_package(doc) and self.has_file(doc):\n self.file(doc).add_contrib(value)\n else:\n raise OrderError('File::Contributor')","def _add_component_to_software_profile(self, kit, comp_name, comp_version,\n software_profile):\n best_match_component = \\\n self._component_db_api.getBestMatchComponent(\n comp_name, comp_version, software_profile.getOsInfo(),\n kit.getId())\n self._component_db_api.addComponentToSoftwareProfile(\n best_match_component.getId(), software_profile.getId())\n\n return best_match_component","def toJSON(self):\n return {'actLib' : self.nomAct,'comLib' : self.comAct,'id' : self.Id,'equID' : self.EquipementId}","def update_csevo(z, j, e_kin, dr_fwhm, tmax, cni):\n tmax /= 1000\n cni = True if \"Active\" in cni else False\n\n try:\n res = cached_basic_sim(int(z), j, e_kin, tmax, dr_fwhm=dr_fwhm, CNI=bool(cni))\n except:\n res = None\n\n data = []\n if res:\n for cs in range(res.N.shape[0]):\n data.append(\n {\"x\": res.t, \"y\":res.N[cs, :]/res.N.sum(axis=0), \"name\":str(cs)+\"+\", \"type\":\"line\"}\n )\n\n highlim = math.log10(tmax)\n lowlim = math.floor(math.log10(.01/j))\n lowlim = lowlim if (lowlim < highlim) else highlim-1\n layout = {\n \"title\":'Charge state evolution',\n \"template\":\"plotly_dark\",\n \"xaxis\":{\"title\":\"Time (s)\", \"type\":\"log\", \"range\":[lowlim, highlim]},\n \"yaxis\":{\"title\":\"Relative abundance\"}\n }\n\n return {\"data\":data, \"layout\":layout}","def __init__(self, jsondict=None, strict=True):\n \n self.amount = None\n \"\"\" Financial total for the category.\n Type `Money` (represented as `dict` in JSON). \"\"\"\n \n self.category = None\n \"\"\" Type of adjudication information.\n Type `CodeableConcept` (represented as `dict` in JSON). \"\"\"\n \n super(ClaimResponseTotal, self).__init__(jsondict=jsondict, strict=strict)","def __call__( self, json_blob, regression_var ):\n return super()._process( self.__load( json_blob ), regression_var )","def new_labor_subcontract(self) -> float:\n driver_labor_cost = self.driver_labor_cost(self.inputs.trucks_total)\n depot_labor_cost = self.depot_labor_cost(self.inputs.trucks_total)\n landfill_labor_cost = self.landfill_labor_cost()\n return driver_labor_cost + depot_labor_cost + landfill_labor_cost","def getJSONfromUI(self):\n newParams = JSONObject()\n try:\n if not self.name.getText().isEmpty():\n newParams.put(\"name\", self.name.getText())\n newParams.put(\"strategy\", self.strategy.getSelectedItem().__str__())\n newParams.put(\"metagameStrategy\", self.metagameStrategy.getSelectedItem().__str__())\n newParams.put(\"stateMachine\", self.stateMachine.getSelectedItem().__str__())\n newParams.put(\"cacheStateMachine\", self.cacheStateMachine.isSelected())\n newParams.put(\"maxPlys\", self.maxPlys.getModel().getValue())\n newParams.put(\"heuristicFocus\", self.heuristicFocus.getModel().getValue())\n newParams.put(\"heuristicMobility\", self.heuristicMobility.getModel().getValue())\n newParams.put(\"heuristicOpponentFocus\", self.heuristicOpponentFocus.getModel().getValue())\n newParams.put(\"heuristicOpponentMobility\", self.heuristicOpponentMobility.getModel().getValue())\n newParams.put(\"mcDecayRate\", self.mcDecayRate.getModel().getValue())\n except JSONException as je:\n je.printStackTrace()\n return newParams","def amt(self, comp):\n if isinstance(comp, Phase):\n comp = comp.comp\n elif isinstance(comp, str):\n comp = parse_comp(comp)\n residual = defaultdict(float, self.comp)\n tot = sum(residual.values())\n for c, amt in list(dict(comp).items()):\n pres = residual[c] / amt\n for c2, amt2 in list(comp.items()):\n residual[c2] -= pres * amt2\n residual[\"var\"] = tot - sum(residual.values())\n residual[\"var\"] /= float(sum(comp.values()))\n return residual","def magento_modify_composer(composer):\n\n composer['name']= \"platformsh/{0}\".format(projectName)\n composer['description']= \"Magento 2 CE(Community Edition) for Platform.sh\"\n\n composer['repositories'] = {\n \"Magento Repo Auth Required\": {\n \"type\": \"composer\",\n \"url\": \"https://repo.magento.com\"\n },\n \"ECE-Tools\": {\n \"type\": \"git\",\n \"url\": \"https://github.com/magento/ece-tools.git\"\n },\n \"Magento Cloud Components\": {\n \"type\": \"git\",\n \"url\": \"https://github.com/magento/magento-cloud-components.git\"\n },\n \"Magento Cloud Patches\": {\n \"type\": \"git\",\n \"url\": \"https://github.com/magento/magento-cloud-patches.git\"\n },\n \"Magento Quality Patches\": {\n \"type\": \"git\",\n \"url\": \"https://github.com/magento/quality-patches.git\"\n }\n }\n\n return composer","def __init__(self):\n price1 = weight1 = 0\n with open(\"Json_Files/DataMngRPW.json\") as json_file:\n data = json.load(json_file)\n for i in data:\n print(\"***********\", i, \"*************\")\n for j in data[i]:\n price1 += int(j[\"price\"])\n weight1 += int(j[\"weight\"])\n print(\"Price : \", price1)\n print(\"Weight : \", weight1)","def update_json(self):\n self.set_version_to_default()\n self.remove_null_fields()\n self.remove_unnecessary_keys()\n self.set_fromVersion(from_version=self.from_version)","def coefficient(self) -> float:\n ...","def _course_json(request, course_key):\r\n course_module = _get_course_module(course_key, request.user, depth=None)\r\n return _xmodule_json(course_module, course_module.id)","def calculate_output(self):","def transit_compare(coeffs, t, y, y_err, name, mega_dict):\n mega_dict[name] = {'coeffs': coeffs, 't': list(t), 'y': list(y), 'y_err':list(y_err)}\n with open('results.json', 'w') as out:\n json.dump(mega_dict, out)\n print('Writing out results for {} to results.json'.format(name))\n return","def added(self, comp):\n\t\tpass","def to_json(self):\n return {'vendor': self.vendor, 'product': self.product}"],"string":"[\n \"def __init__(self, contribution):\\n self._contribution = contribution\",\n \"def add_comp(self, component, mole_fraction=np.nan):\\n self['pore.mole_fraction.' + component.name] = mole_fraction\",\n \"def execute_contribution(self):\\n contributions = [v for v in self.action_register if v['action'] == 'contribution']\\n for contribution in contributions:\\n self.model.schedule.agents_by_type['Customer'][contribution['unique_id']].hour_wallet -= contribution['value']\\n self.model.schedule.agents_by_type['Customer'][contribution['unique_id']].contributed_hours += contribution['value']\",\n \"def add_contribution(contribution_info):\\n NotificationsHandler.handle_contribution(\\n contribution_info[\\\"USER_ID\\\"], contribution_info[\\\"PROJECT_ID\\\"], status=True\\n )\",\n \"def add_component(self, component, targets):\\n data = [{\\n 'targets': targets,\\n 'labels': {'service': self.name, 'component': component},\\n }]\\n with open(self.filename) as f:\\n old_data = json.load(f)\\n for s in old_data:\\n if s['labels']['service'] == self.name and s['labels'].get('component','') == component:\\n data[0]['targets'].extend(s['targets'])\\n else:\\n data.append(s)\\n logging.debug('set(%s) %r', self.filename, data)\\n with open(self.filename, 'w') as f:\\n json.dump(data, f)\",\n \"def calculate_contributions(self, request, parent_lookup_client, pk, format=None):\\n # TODO: Make this work\\n return Response({'btc_amount': 1111, 'atc_amount': 0})\",\n \"def update_compartments(self, food_glucose):\\n self.g_t, self.m_t = self.new_values(food_glucose, self.get_variables())\",\n \"def addProvisionToCommission(commission):\\n provision=Decimal('0.0025')\\n if(commission.source_wallet.cryptocurrency.name=='BTC'):\\n prov = commission.source_amount * Decimal(provision) \\n commission.source_amount += prov \\n elif (commission.destination_wallet.cryptocurrency.name=='BTC'):\\n prov = commission.destination_amount * Decimal(provision)\\n commission.destination_amount += prov\\n commission.save()\\n provision = Provisions(commission=commission, provision=prov)\\n provision.save()\\n return 0\",\n \"def _add_compartments(self, comps):\\n return self._cm.add_compartments(comps)\",\n \"def get_contribution(self, cid):\\n fields = list(\\n self.swagger_spec.definitions.get(\\\"ContributionsSchema\\\")._properties.keys()\\n ) # don't return dynamic fields (card_*)\\n fields.remove(\\\"notebook\\\")\\n return Dict(self.contributions.get_entry(pk=cid, _fields=fields).result())\",\n \"def createDict( self ):\\n self.d = {}\\n self.d['comp1'] = compensation_channel('comp1', 0, (-479.0, -10.0))\\n self.d['comp2'] = compensation_channel('comp2', 1, (-479.0, -10.0))\\n self.addCalibration()\",\n \"def _WriteContribution(self, project_name, contributions_json, output_writer):\\n # https://developer.github.com/v3/repos/statistics/\\n # [{\\n # \\\"author\\\": {\\n # \\\"login\\\": string containing the login name,\\n # ...\\n # }\\n # \\\"weeks\\\": [{\\n # \\\"a\\\": integer containing the number of lines added,\\n # \\\"c\\\": integer containing the number of contributions,\\n # \\\"d\\\": integer containing the number of lines deleted,\\n # \\\"w\\\": integer containing a POSIX timestamp of the start of the week,\\n # }, ...],\\n # }, ...]\\n\\n contributions_per_week = {}\\n for contributions_per_author_json in contributions_json:\\n author_json = contributions_per_author_json.get(\\\"author\\\", None)\\n if not author_json:\\n logging.error(u'Missing author JSON dictionary.')\\n continue\\n\\n weeks_json = contributions_per_author_json.get(\\\"weeks\\\", None)\\n if not weeks_json:\\n logging.error(u'Missing weeks JSON list.')\\n continue\\n\\n login_name = author_json.get(\\\"login\\\", None)\\n if not login_name:\\n logging.error(u'Missing login name JSON value.')\\n continue\\n\\n # TODO: map login name to username\\n\\n for week_json in weeks_json:\\n number_of_lines_added = week_json.get(u'a', 0)\\n number_of_contributions = week_json.get(u'c', 0)\\n number_of_lines_deleted = week_json.get(u'd', 0)\\n\\n if (not number_of_lines_added and not number_of_contributions and\\n not number_of_lines_deleted):\\n continue\\n\\n week_timestamp = week_json.get(u'w', None)\\n if not week_timestamp:\\n logging.error(u'Missing week timestamp JSON value.')\\n continue\\n\\n time_elements = time.gmtime(week_timestamp)\\n year = time.strftime(u'%Y', time_elements)\\n week_number = time.strftime(u'%U', time_elements)\\n\\n if week_number not in contributions_per_week:\\n contributions_per_week[week_number] = {}\\n\\n contributions_per_week[week_number][login_name] = (\\n number_of_contributions, number_of_lines_added,\\n number_of_lines_deleted)\\n\\n if not self._alternate_output_format:\\n output_line = (\\n u'{0:s}\\\\t{1:s}\\\\t{2:s}\\\\t{3:s}\\\\t{4:d}\\\\t{5:d}\\\\t{6:d}\\\\n').format(\\n year, week_number, login_name, project_name,\\n number_of_contributions, number_of_lines_added,\\n number_of_lines_deleted)\\n\\n else:\\n username = login_name.lower()\\n username = self._user_mappings.get(username, None)\\n if not username:\\n # Skip login names without a username mapping.\\n continue\\n\\n date_time_string = u'{0:s}-W{1:s}-0'.format(year, week_number)\\n date_time = datetime.datetime.strptime(date_time_string, u'%Y-W%W-%w')\\n date_time_string = date_time.isoformat()\\n\\n output_line = (\\n u'{0:s} [github] ~ author:{1:s} ~ project:{2:s} ~ '\\n u'number_of_cls:{3:d} ~ delta_added:{4:d} ~ delta_deleted:{5:d} '\\n u'~ py:{4:d} ~ file_type:py ~ op_type:ADD ~\\\\n').format(\\n date_time_string, username, project_name,\\n number_of_contributions, number_of_lines_added,\\n number_of_lines_deleted)\\n\\n output_writer.Write(output_line.encode(u'utf-8'))\",\n \"def save_component_csv (self, directory):\\n #~ return\\n if not self.was_run:\\n #~ fname = os.path.join(directory,\\n #~ self.component_name + \\\"_output.csv\\\")\\n #~ fname = fname.replace(\\\" \\\",\\\"_\\\")\\n\\n #~ fd = open(fname, 'w')\\n #~ fd.write(\\\"Wind Power minimum requirements not met\\\\n\\\")\\n #~ fd.close()\\n return\\n\\n\\n years = np.array(range(self.project_life)) + self.start_year\\n\\n # ??? +/-\\n # ???\\n df = DataFrame({\\n 'Wind: Capacity (kW)':self.load_offset_proposed,\\n \\\"Wind: Generation (kWh/year)\\\": self.net_generation_wind,\\n 'Wind: Energy Captured by Secondary Load'\\n ' (gallons of heating oil equivalent)':\\n self.diesel_equiv_captured,\\n 'Wind: Assumed capacity factor':\\n float(self.comp_specs['capacity factor']),\\n 'Wind: Utility Diesel Displaced (gallons/year)':\\n self.electric_diesel_reduction,\\n 'Wind: Heat Recovery Lost (gallons/year)':\\n self.loss_heat_recovery,\\n \\\"Wind: Heat Recovery Cost Savings ($/year)\\\":\\n self.get_heating_savings_costs(),\\n \\\"Wind: Electricity Cost Savings ($/year)\\\":\\n self.get_electric_savings_costs(),\\n \\\"Wind: Project Capital Cost ($/year)\\\":\\n self.get_capital_costs(),\\n \\\"Wind: Total Cost Savings ($/year)\\\":\\n self.get_total_savings_costs(),\\n \\\"Wind: Net Benefit ($/year)\\\": self.get_net_benefit(),\\n }, years)\\n\\n df[\\\"Community\\\"] = self.cd['name']\\n\\n ol = [\\\"Community\\\",\\n 'Wind: Capacity (kW)',\\n 'Wind: Energy Captured by Secondary Load'\\n ' (gallons of heating oil equivalent)',\\n 'Wind: Assumed capacity factor',\\n 'Wind: Utility Diesel Displaced (gallons/year)',\\n 'Wind: Heat Recovery Lost (gallons/year)',\\n \\\"Wind: Generation (kWh/year)\\\",\\n \\\"Wind: Heat Recovery Cost Savings ($/year)\\\",\\n \\\"Wind: Electricity Cost Savings ($/year)\\\",\\n \\\"Wind: Project Capital Cost ($/year)\\\",\\n \\\"Wind: Total Cost Savings ($/year)\\\",\\n \\\"Wind: Net Benefit ($/year)\\\"]\\n fname = os.path.join(directory,\\n self.cd['name'] + '_' + \\\\\\n self.component_name.lower() + \\\"_output.csv\\\")\\n fname = fname.replace(\\\" \\\",\\\"_\\\")\\n\\n df[ol].ix[:self.actual_end_year].to_csv(fname, index_label=\\\"Year\\\")\",\n \"def refresh_counterfactual_json(request):\\t\\n\\n\\n \\\"\\\"\\\"\\n the following json are for compound exposures\\n \\\"\\\"\\\"\\n #json_file = '/home/zhou/Downloads/jsons/compound/json_9.json'\\n\\tjson_file = '/Users/jiaozi/Downloads/jsons/compound/json_9.json'\\n\\n exposure_sequence = read_json(json_file)#list of exposures{mean,sd,non_rate}\\n\\n\\tprimeCoordinator = PrimeCoordinator()\\n\\tprimeCoordinator.get_counterfactual_compound_exposures(exposure_sequence)\\n\\t\\n\\t# get the data in the interface\\n\\tb_output_mortality \\t= primeCoordinator.output_baseline_mortality # baseline mortality list for all outcomes\\n\\tb_output_mortality_num \\t= primeCoordinator.output_baseline_mortality_num # baseline mortality sum up for each outcome\\n\\tb_total_mortality \\t= primeCoordinator.output_baseline_mortality_total# baseline mortality sum up for all outcomes\\n\\tc_output_mortality \\t= primeCoordinator.output_counterfactual_mortality# counterfactual mortality for all outcomes\\n\\tc_output_mortality_num \\t= primeCoordinator.output_counterfactual_mortality_num# counterfactual mortality for each outcome\\n\\tc_total_mortality \\t= primeCoordinator.output_counterfactual_mortality_total# counterfactual mortality sum up for all outcomes\\n\\ttotal_population\\t= primeCoordinator.output_total_population\\n\\tall_mortality_exposure\\t= primeCoordinator.output_all_mortality_exposure\\n\\ttotal_death_averted\\t= str(round(primeCoordinator.output_total_death_averted,0))\\n\\ttotal_death_baseline\\t= str(primeCoordinator.output_total_death_baseline)\\n\\n\\t#transmit the parameters\\n\\ttemplate = loader.get_template('primemodel/index.html')\\n\\tpara_view = {\\n\\t\\t\\t'b_output_mortality_num':\\tb_output_mortality_num,\\n\\t\\t\\t'b_total_mortality':\\t\\tb_total_mortality,\\n\\t\\t\\t'c_output_mortality_num':\\tc_output_mortality_num,\\n\\t\\t\\t'c_total_mortality':\\t\\tc_total_mortality,\\n\\t\\t\\t'total_population':\\t\\ttotal_population,\\n\\t\\t\\t'total_death_averted':\\t\\ttotal_death_averted,\\n\\t\\t\\t'total_death_baseline':\\t\\ttotal_death_baseline,\\n\\t\\t\\t'all_mortality_exposure':\\tall_mortality_exposure\\n\\t\\t\\t}\\n\\n\\t#context to transmit the parameters to show\\n\\tcontext = Context(para_view)\\n\\tresponse = template.render(context)\\n\\treturn HttpResponse(response)\",\n \"def get_contribution_for_year(self, year: int):\",\n \"def contribution_to_dict(contribution):\\n user = contribution.user\\n stats = contribution.get_statistics()\\n # show only normalized values for the reputation fields\\n stats['engaged_reputation'] = stats['engaged_reputation_normal']\\n del stats['engaged_reputation_normal']\\n for val in stats['evaluations']:\\n stats['evaluations'][val]['reputation'] = stats['evaluations'][val]['reputation_normal']\\n del stats['evaluations'][val]['reputation_normal']\\n\\n return {\\n 'id': contribution.id,\\n 'contributor': user_to_dict(user),\\n 'type': contribution.contribution_type,\\n 'stats': stats,\\n }\",\n \"def add_comp(mol, projects):\\n\\n # Now attribute all this meta-deta to the compound object\\n new_comp = Compound()\\n comp = calc_cpd(new_comp, mol, projects)\\n return comp\",\n \"def gpa_calculator():\\n gpa = 0.0\\n grade_array = []\\n credit_array = []\\n grade_converter = {\\\"A\\\": 4.00, \\\"A-\\\":3.67, \\\"B+\\\": 3.33, \\\"B\\\": 3.00, \\\"B-\\\": 2.67, \\\"C+\\\": 2.33, \\\"C\\\": 2.00, \\\"C-\\\": 1.67, \\\"D\\\": 1.00, \\\"F\\\": 0.0}\\n with open('full_courses.json', 'r') as fp:\\n full_courses = json.load(fp)\\n for val in full_courses.values():\\n if val[2] == 'C':\\n credit_array.append(val[0])\\n for i, val2 in grade_converter.items():\\n if val[1] == i:\\n grade_array.append(val2)\\n final_array = [val*val1 for val,val1 in zip(grade_array, credit_array)]\\n gpa = round(sum(final_array)/sum(credit_array),2)\\n print(\\\"GPA CALCULATED AS: \\\"+str(gpa))\\n return gpa\",\n \"def calc_contributions(self):\\n\\n if self.iterations >= self.statistics_batch_num:\\n return\\n for wrapper in self.get_modules_wrapper():\\n filters = wrapper.module.weight.size(0)\\n contribution = (wrapper.module.weight*wrapper.module.weight.grad).data.pow(2).view(filters, -1).sum(dim=1)\\n if wrapper.contribution is None:\\n wrapper.contribution = contribution\\n else:\\n wrapper.contribution += contribution\\n\\n self.iterations += 1\",\n \"def _updateFinalJsonString(self, statistic, data, completed):\\n\\n if statistic.final_json:\\n items = simplejson.loads(statistic.final_json)\\n else:\\n items = {}\\n\\n items.update(data)\\n\\n properties = {\\n 'choices_json': None,\\n 'working_json': None,\\n 'next_entity': None,\\n 'final_json': simplejson.dumps(items),\\n 'calculated_on': datetime.utcnow(),\\n }\\n\\n return self.updateEntityProperties(statistic, properties)\",\n \"def reward_contributions(self): \\n contribution_reward_per_hour = get_contribution_reward_per_hour(self.model)\\n #payout to agents according to the number of hours they contributed\\n for agent_id, agent in self.model.schedule.agents_by_type['Customer'].items():\\n agent.teo_wallet += agent.contributed_hours * contribution_reward_per_hour\\n agent.contribution_surplus += agent.contributed_hours * contribution_reward_per_hour\",\n \"def insert_contribution(request):\\n try:\\n is_loggedin, username = get_session_variables(request)\\n # User is not logged in\\n if not logged_in(request):\\n return HttpResponseRedirect('/register/login')\\n\\n # User is logged in\\n else:\\n if request.method == 'POST':\\n form = AddContributionForm(request.POST)\\n\\n # Invalid form imput\\n if not form.is_valid():\\n error = \\\"Invalid inputs\\\"\\n return render_to_response('achievement/new_contribution.html', \\\\\\n {'form': form, \\\\\\n 'error':error, \\\\\\n 'is_loggedin':is_loggedin, \\\\\\n 'username':username}, \\\\\\n RequestContext(request))\\n\\n # Form is valid\\n else:\\n # Get the new achievement_id\\n achievement_id = get_achievement_id(request)\\t\\n achievement_type = \\\"contribution\\\"\\n\\n # Saving inputs\\n achievement_obj = Achievement(achievement_id, \\\\\\n achievement_type, \\\\\\n username)\\n achievement_obj.save()\\n contribution_obj = form.save(commit = False)\\n contribution_obj.achievement_id = achievement_obj\\n contribution_obj.achieve_typ = achievement_type\\n user_obj = get_object_or_404(User_info, username = username)\\n contribution_obj.username = user_obj\\n contribution_obj.save()\\n return render_to_response('achievement/success.html', \\\\\\n {'achievement_type':achievement_type, \\\\\\n 'is_loggedin':is_loggedin, \\\\\\n 'username':username}, \\\\\\n RequestContext(request))\\n # Method is not POST\\n else:\\n return render_to_response('achievement/new_contribution.html', \\\\\\n {'form': AddContributionForm, \\\\\\n 'is_loggedin':is_loggedin, \\\\\\n 'username':username}, \\\\\\n RequestContext(request))\\n except KeyError:\\n return error_key(request)\",\n \"def calc_comp(self, selected_ions=None):\\n pass\",\n \"def add_modifications(self, dset):\\n dset_keys = [\\\"entity_ids\\\", \\\"comp_ids\\\"]\\n for key in dset_keys:\\n self.model_dict[key] = dset[key]\\n self.model_dict[\\\"code_version\\\"] = np.array(mbgdml_version)\\n self.model_dict[\\\"md5\\\"] = np.array(self.md5)\",\n \"def add_computed_gas_concentrations(self):\\n # Extract the z-coordinate and T, S, P profile\\n zs = self.interp_ds.coords[self.ztsp[0]].values\\n Ts = self.interp_ds[self.ztsp[1]].values\\n Ss = self.interp_ds[self.ztsp[2]].values\\n Ps = self.interp_ds[self.ztsp[3]].values\\n \\n # Create an air object\\n air_names = ['nitrogen', 'oxygen', 'argon', 'carbon_dioxide']\\n yk = np.array([0.78084, 0.20946, 0.009340, 0.00036])\\n from tamoc import dbm\\n air = dbm.FluidMixture(air_names)\\n m = air.masses(yk)\\n \\n # Compute the concentrations adjusted for depth\\n Cs = np.zeros((len(zs), len(air_names)))\\n for i in range(len(zs)):\\n Cs[i,:] = air.solubility(m, Ts[i], 101325., Ss[i])[0,:] * \\\\\\n seawater.density(Ts[i], Ss[i], Ps[i]) / \\\\\\n seawater.density(Ts[i], Ss[i], 101325.)\\n \\n # Make sure none of these gases are already in the measured profile\\n for name in air_names:\\n if name in self.interp_ds:\\n air_names[air_names.index(name)] = 'computed_' + name\\n \\n # Add these data to the Profile object\\n data = np.hstack((np.atleast_2d(zs).transpose(), Cs))\\n names = [self.ztsp[0]] + air_names \\n units = [self.ztsp_units[0]] + 4*['kg/m^3']\\n self.append(data, names, units)\\n \\n # Rebuild the interpolator\\n self._build_interpolator()\",\n \"def set_contribution(self):\\r\\n \\r\\n # WX = X*Q\\r\\n # WY = Y*Q\\r\\n # see how these are assembled in the Farm class.\\r\\n \\r\\n # x-row of big X:\\r\\n self.x = np.hstack((np.hstack((\\\\\\r\\n [xi + ni for xi, ni in zip(self.cx, self.bn)]\\\\\\r\\n )), np.hstack((\\\\\\r\\n [yi + ti for yi, ti in zip(self.cy, self.bt)]\\\\\\r\\n ))))\\r\\n \\r\\n # y-row of big Y:\\r\\n self.y = np.hstack((np.hstack((self.cy)), np.hstack((\\\\\\r\\n [-xi for xi in self.cx]))))\",\n \"def do_TRANSFER_COMPONENTS(self):\\n self.logger = SimpleLogger(conf=None).get_logger_object()\\n self.conf = SimpleLogger(conf=None).get_conf()\\n self.msg = GlobalVariables(self.logger)\\n self._request_handler = Req()\\n transfer_component_timeout = int(self.conf.get('\\\\\\n transfer_component_timeout', 600))\\n self.ll_port = int(self.conf.get('llport', 61014))\\n self.service_id = self.msg.get_service_id()\\n self.deleted_comp_list = [] \\n transfer_component_map = {} #dictionary containing{'(dest_node_obj)':'[comp_list]'}\\n #eg: {('169.254.1.12', '61009', 'HN0101_61014_account-updater'):'['1', '2']'}\\n self.final_transfer_status_list = [] #final component status list which will be send to GL\\n #[(1, True),(2, False),(3, True)] \\n self.final_status = False #final response to GL\\n self.check_transfer_component_map = {} # to check if component transfer completed or failed: \\n #{dest_node_obj1:True, dest_node_obj2:\\\"Failed\\\", dest_node_obj3:False}\\n all_transfer_event_received = []\\n self.protocol_version = \\\"HTTP/1.1\\\"\\n self.logger.info(\\\"Account-updater received Transfer component request\\\")\\n try:\\n content_length = int(self.headers['Content-Length'])\\n #sending acknowledgement of TRANSFER_COMPONENT to GL \\n self.send_response(HTTP_CONTINUE, \\\"Continue\\\\r\\\\n\\\\r\\\\n\\\")\\n\\n data = self.rfile.read(content_length)\\n message = TransferCompMessage()\\n message.de_serialize(data)\\n transfer_component_map = message.service_comp_map()\\n self.logger.info(\\\"Deserialized transfer component map:%s\\\" \\\\\\n %transfer_component_map)\\n except Exception, ex:\\n self.logger.error(\\\"Exception: %s\\\" % ex)\\n self.send_header('Message-Type', \\\\\\n typeEnums.TRANSFER_COMPONENT_RESPONSE)\\n self.send_response(HTTP_INTERNAL_SERVER_ERROR)\\n self.end_headers()\\n return\\n\\n #if transfer_component_map is empty then send HTTP_OK to GL\\n if not transfer_component_map:\\n self.logger.info(\\\"Deserialized transfer component map is empty, return HTTP OK!\\\")\\n comp_transfer_response = TransferCompResponseMessage(\\\\\\n self.final_transfer_status_list, True)\\n serialized_body = comp_transfer_response.serialize()\\n\\n self.send_header('Message-Type', \\\\\\n typeEnums.TRANSFER_COMPONENT_RESPONSE)\\n self.send_header('Content-Length', len(serialized_body))\\n self.send_response(HTTP_OK)\\n self.end_headers()\\n self.wfile.write(serialized_body)\\n return\\n \\n for dest_node_obj, comp_list in transfer_component_map.items():\\n for comp in comp_list:\\n self.deleted_comp_list.append(comp)\\n self.final_transfer_status_list.append((comp, False))\\n self.check_transfer_component_map[dest_node_obj] = False\\n self.logger.debug(\\\"Ownership for components:%s will be removed\\\" %self.deleted_comp_list)\\n self.delete_self_ownership()\\n transfer_comp_event = self.msg.get_transfer_cmp_event()\\n transfer_comp_event.set()\\n\\n try:\\n #sending accept component request to other nodes\\n self.logger.info(\\\"Sending accept component request to \\\" \\\\\\n \\\"destination nodes\\\")\\n for target_service_obj, comp_list in transfer_component_map.items():\\n thread_connecting_node = threading.Thread(target = \\\\\\n self.send_accept_component_request, args = \\\\\\n (\\\"ACCEPT_COMPONENT_TRANSFER\\\", target_service_obj, \\\\\\n comp_list, ))\\n thread_connecting_node.setDaemon(True)\\n thread_connecting_node.start()\\n\\n #Checking if transfer component completed and intermediate \\n #response sent or not\\n self.logger.info(\\\"Checking if transfer component completed\\\")\\n thread_check_transfer_status = threading.Thread(target = \\\\\\n self.check_component_transfer_completion)\\n thread_check_transfer_status.setDaemon(True)\\n thread_check_transfer_status.start()\\n thread_check_transfer_status.join(transfer_component_timeout)\\n\\n # sending final response to GL\\n self.logger.info(\\\"Sending final response to GL :%s\\\" \\\\\\n % self.final_transfer_status_list)\\n if self.final_transfer_status_list:\\n for comp_status_tuple in self.final_transfer_status_list:\\n if not comp_status_tuple[1]:\\n self.logger.warning(\\\"Final transfer component list having failed\\\" \\\\\\n \\\"component:%s, %s\\\" %(comp_status_tuple[0], comp_status_tuple[1]))\\n self.msg.set_ownershipList(self.old_ownership_list)\\n break\\n else:\\n self.final_status = True\\n comp_transfer_response = TransferCompResponseMessage(\\\\\\n self.final_transfer_status_list, self.final_status)\\n serialized_body = comp_transfer_response.serialize()\\n \\n while len(all_transfer_event_received) != 4:\\n all_transfer_event_received.append(self.msg.get_from_Queue())\\n \\n self.logger.info(\\\"Completed TRANSFER_COMPONENTS request\\\")\\n self.send_header('Message-Type', \\\\\\n typeEnums.TRANSFER_COMPONENT_RESPONSE)\\n self.send_header('Content-Length', len(serialized_body))\\n self.send_response(HTTP_OK)\\n self.end_headers()\\n self.wfile.write(serialized_body)\\n transfer_comp_event.clear()\\n return\\n except Exception as ex:\\n self.logger.error(\\\"Exception raised: %s\\\" % ex)\\n transfer_comp_event.clear()\\n self.msg.set_ownershipList(self.old_ownership_list)\\n self.logger.error(\\\"old_ownership_list:%s\\\" %self.old_ownership_list)\\n self.send_header('Message-Type', \\\\\\n typeEnums.TRANSFER_COMPONENT_RESPONSE)\\n self.send_response(HTTP_INTERNAL_SERVER_ERROR)\\n self.end_headers()\",\n \"def add_com_jac(ui):\\n global com_jac_list\\n\\n content = content_fk_jac_loops(ui, \\\"com_jac\\\")\\n if content in com_jac_list:\\n return\\n com_jac_list.append(content)\\n ui.listWidget_com_jac.addItem(f\\\"Center of Mass Jacobian \\\"\\n f\\\"{parse_content(content)}\\\")\",\n \"def register_contribution(self, agent, value):\\n if value <= agent.hour_wallet:\\n action = {\\n 'unique_id': agent.unique_id,\\n 'action': 'contribution',\\n 'value': value\\n }\\n self.action_register.append(action)\",\n \"def json_formatter(components):\\n columns = cfg['columns']\\n\\n newList = [] # New list of only dictionaries with column attributes to marshall\\n\\n for component in components:\\n newComp = {}\\n\\n for column in columns:\\n try:\\n newComp[column] = component[column]\\n except:\\n newComp[column] = cfg['emptyValue']\\n\\n newList.append(newComp)\\n\\n result = json.dumps(newList)\\n\\n # Save the json file\\n save_path = args.output_file\\n try:\\n with open(save_path, \\\"w\\\") as file:\\n file.write(result)\\n\\n Logger.Debug(\\\"Output saved to\\\", save_path)\\n\\n return save_path\\n\\n except:\\n Logger.Error(\\\"Could not save output to\\\", save_path)\",\n \"def __init__(self, capitalCommitment, contributionRates, bow, growthRate, fundYield, lastInvestmentYear, lifeOfFund, segments, startDate):\\n self.segments = int(segments)\\n self.calculate = ModelCalculations(segments)\\n if isinstance(startDate, datetime.date):\\n self.startDate = startDate\\n else:\\n dateConverter = ConvertDate.ConvertDate()\\n self.startDate = dateConverter(startDate)\\n self.endDate = self.calculate.endDate(int(lifeOfFund), self.startDate)\\n self.lastInvestmentYear = int(lastInvestmentYear) * self.segments\\n self.lifeOfFund = int(lifeOfFund) * self.segments\\n self.capitalCommitment = int(capitalCommitment)\\n # old code below (before abstraction of rate expansion)\\n #self.contributionRates = self._expandContributionRates(self.segments, contributionRates)\\n contributionRates = self._appendExtraRates(contributionRates, self.lastInvestmentYear / self.segments)\\n self.contributionRates = self.calculate.expandRates(contributionRates, self.segments, False)\\n #self._validateContributionRates(self.contributionRates)\\n self.bow = float(bow)\\n self.growthRate = self.calculate.segmentInterest(self.segments, float(growthRate))#growthRate / self.segments\\n self.fundYield = float(fundYield) / self.segments\\n\\n\\n self._contributionList = []\\n self._distributionList = []\\n self._navList = []\\n self._commitmentRemainingList = []\\n self._netCashFlowList = []\\n self._cummulativeCashFlowList = []\\n self._dateList = []\\n self._typeList = []\\n self._distributionRates = []\\n\\n self._distributionRates = self.calculate.expandRates(self._getBaseDistributionRates(), self.segments, True)\",\n \"def add(self, component):\\n\\n self.append(component)\\n self.sum.Add(component.th1f)\",\n \"def calculate_balance_contributions(self, request, parent_lookup_client, pk, format=None):\\n # TODO: Make this work\\n return Response(6666666)\",\n \"def calculate_contributions_balance(self, request, parent_lookup_client, pk, format=None):\\n # TODO: Make this work\\n return Response({'btc_amount': 2222, 'atc_amount': 88})\",\n \"def __contribution_from_parameters(self, parameter_names):\\n if not isinstance(parameter_names, list):\\n parameter_names = [parameter_names]\\n\\n for iname, name in enumerate(parameter_names):\\n parameter_names[iname] = name.lower()\\n assert name.lower() in self.jco.col_names,\\\\\\n \\\"contribution parameter \\\" + name + \\\" not found jco\\\"\\n keep_names = []\\n for name in self.jco.col_names:\\n if name not in keep_names:\\n keep_names.append(name)\\n if len(keep_names) == 0:\\n raise Exception(\\\"Schur.contribution_from_parameters: \\\" +\\n \\\"atleast one parameter must remain uncertain\\\")\\n #get the reduced predictions\\n if self.predictions is None:\\n raise Exception(\\\"Schur.contribution_from_parameters: \\\" +\\n \\\"no predictions have been set\\\")\\n cond_preds = []\\n for pred in self.predictions:\\n cond_preds.append(pred.get(keep_names, pred.col_names))\\n la_cond = Schur(jco=self.jco.get(self.jco.row_names, keep_names),\\n parcov=self.parcov.condition_on(parameter_names),\\n obscov=self.obscov, predictions=cond_preds,verbose=False)\\n\\n #get the prior and posterior for the base case\\n bprior,bpost = self.prior_prediction, self.posterior_prediction\\n #get the prior and posterior for the conditioned case\\n cprior,cpost = la_cond.prior_prediction, la_cond.posterior_prediction\\n return cprior,cpost\",\n \"def to_json(self) -> collections.OrderedDict:\\n self['hparams_json_file'] = os.path.join(self.get_workspace_dir(), 'hparams.json')\\n hparam_dict = self.to_dict()\\n save_json(self.hparams_json_file, hparam_dict)\",\n \"def modifications(self, components=['model', 'sheets', 'projections']):\\n mapping = {'model': [self],\\n 'sheets':self.sheets,\\n 'projections':self.projections}\\n\\n lines = []\\n for component in components:\\n heading = \\\"=\\\" * len(component)\\n lines.extend([heading, component.capitalize(), heading, ''])\\n specs = mapping[component]\\n padding = max(len(str(spec)) for spec in specs)\\n for spec in sorted(specs):\\n modified = [str(el) for el in sorted(spec.modified_parameters)]\\n lines.append(\\\"%s : [%s]\\\" % (str(spec).ljust(padding), \\\", \\\".join(modified)))\\n lines.append('')\\n print \\\"\\\\n\\\".join(lines)\",\n \"def get_contribution_dataframe(self,parlist_dict=None):\\n self.log(\\\"calculating contribution from parameters\\\")\\n if parlist_dict is None:\\n parlist_dict = dict(zip(self.pst.parameter_data.parnme,self.pst.parameter_data.parnme))\\n\\n results = {}\\n names = [\\\"base\\\"]\\n for forecast in self.prior_forecast.keys():\\n pr = self.prior_forecast[forecast]\\n pt = self.posterior_forecast[forecast]\\n reduce = 100.0 * ((pr - pt) / pr)\\n results[(forecast,\\\"prior\\\")] = [pr]\\n results[(forecast,\\\"post\\\")] = [pt]\\n results[(forecast,\\\"percent_reduce\\\")] = [reduce]\\n for case_name,par_list in parlist_dict.items():\\n names.append(case_name)\\n self.log(\\\"calculating contribution from: \\\" + str(par_list) + '\\\\n')\\n case_prior,case_post = self.__contribution_from_parameters(par_list)\\n self.log(\\\"calculating contribution from: \\\" + str(par_list) + '\\\\n')\\n for forecast in case_prior.keys():\\n pr = case_prior[forecast]\\n pt = case_post[forecast]\\n reduce = 100.0 * ((pr - pt) / pr)\\n results[(forecast, \\\"prior\\\")].append(pr)\\n results[(forecast, \\\"post\\\")].append(pt)\\n results[(forecast, \\\"percent_reduce\\\")].append(reduce)\\n\\n df = pd.DataFrame(results,index=names)\\n self.log(\\\"calculating contribution from parameters\\\")\\n return df\",\n \"def data_from_json(self, json_file):\\n\\n param_list = ['name', 'version', 'dir', 'description']\\n push_param = list()\\n dep_str = ''\\n dep_node_str = ''\\n with open(json_file) as f:\\n param_dict = json.load(f)\\n for i in range(len(param_list)):\\n push_param.append(param_dict[param_list[i]])\\n push_param.append(param_dict['maintainer']['name'])\\n push_param.append(param_dict['maintainer']['email'])\\n for dep in param_dict['depend']:\\n dep_str = dep_str + dep + ', '\\n push_param.append(dep_str)\\n push_param.append(param_dict['node']['name'])\\n for line, val in zip(self.full_ed_lines, push_param):\\n line.setText(val)\\n self.manager.wid.pub_list = param_dict['node']['publishers']\\n self.manager.wid.sub_list = param_dict['node']['subscribers']\\n self.manager.wid.reload_table()\\n self.changed = False\\n self.msg2Statusbar.emit('Выполнена выгрузка данных из JSON')\",\n \"def add_observations(self, json_data):\\n record = self.record\\n update_id = uuid.uuid4().hex\\n self.add_pending_update(update_id)\\n\\n new_data = json.loads(json_data)\\n calculator = Calculator(self)\\n\\n new_dframe_raw = calculator.dframe_from_update(\\n new_data, self.schema.labels_to_slugs)\\n calculator._check_update_is_valid(new_dframe_raw)\\n\\n call_async(calculator.calculate_updates, calculator, new_data,\\n new_dframe_raw=new_dframe_raw, update_id=update_id)\",\n \"def define_components(mod):\\n\\n mod.PROJ_DISPATCH_POINTS_DISCRETE = Set(\\n initialize=mod.PROJ_DISPATCH_POINTS,\\n filter=lambda m, pr, t: (\\n m.proj_gen_tech[pr] in m.GEN_TECH_WITH_UNIT_SIZES))\\n mod.CommitUnits = Var(\\n mod.PROJ_DISPATCH_POINTS_DISCRETE,\\n within=NonNegativeIntegers)\\n mod.Commit_Units_Consistency = Constraint(\\n mod.PROJ_DISPATCH_POINTS_DISCRETE,\\n rule=lambda m, pr, t: (\\n m.CommitProject[pr, t] ==\\n m.CommitUnits[pr, t] * m.g_unit_size[m.proj_gen_tech[pr]] *\\n m.proj_availability[pr]))\",\n \"def sr_add_c():\\n req_data = request.get_json()\\n logging.debug(\\\"req_data = \\\" + str(req_data))\\n\\n product_name = req_data['product_name']\\n version_number = req_data['version_number']\\n name = req_data['name']\\n version = req_data['version']\\n destination = req_data['destination']\\n\\n if destination == \\\"\\\":\\n destination = \\\".\\\"\\n\\n outcome = {\\\"name\\\": \\\"Fail\\\"}\\n\\n try:\\n # create new association\\n c = Component.query.filter_by(name=name, version=version).first()\\n sr = SoftwareRelease.query.filter_by(product_name=product_name, version_number=version_number).first()\\n\\n a = Association(destination=destination)\\n a.component = c\\n sr.components.append(a)\\n\\n db.session.commit()\\n outcome['name'] = \\\"Success\\\"\\n except:\\n db.session.rollback()\\n raise\\n finally:\\n db.session.close()\\n return jsonify(outcome)\",\n \"def dump_analysis(self, ecosystem, component, version, json_response):\\n timestamp_str = datetime.datetime.utcnow().strftime(\\\"%Y-%m-%dT%H:%M:%S.%f\\\")\\n filename = API_RESULTS_DIRECTORY + \\\"/\\\"\\n filename += \\\"component_analysis_{e}_{c}_{v}_{t}.json\\\".format(e=ecosystem,\\n c=component,\\n v=version,\\n t=timestamp_str)\\n with open(filename, 'w') as fout:\\n json.dump(json_response, fout)\",\n \"def get_computes(self):\\n _url = f\\\"{self.base_url}/computes\\\"\\n return self.http_call(\\\"get\\\", _url).json()\",\n \"def minority_aggregation_as_json(request):\\n msa_target_lma_sum = 0\\n msa_target_mma_sum = 0\\n msa_target_hma_sum = 0\\n\\n msa_peer_lma_sum = 0\\n msa_peer_mma_sum = 0\\n msa_peer_hma_sum = 0\\n\\n\\n msa_stats = {}\\n\\n lar_data = loan_originations_as_json(request)\\n lender = get_object_or_404(Institution, pk=request.GET.get('lender'))\\n metro = get_object_or_404(Geo, geo_type=Geo.METRO_TYPE, geoid=request.GET.get('metro'))\\n peer_request = HttpRequest()\\n peer_request.GET['lender'] = lender.institution_id\\n peer_request.GET['metro']= metro.geoid\\n peer_request.GET['peers'] = 'true'\\n peer_lar_data = loan_originations_as_json(peer_request)\\n\\n msa_counties = Geo.objects.filter(geo_type=Geo.COUNTY_TYPE, cbsa=metro.cbsa, year=metro.year)\\n county_stats = {}\\n for county in msa_counties:\\n county_tracts = Geo.objects.filter(geo_type=Geo.TRACT_TYPE, state=county.state, county=county.county, year=metro.year)\\n minority_area_stats = get_minority_area_stats(lar_data, peer_lar_data, county_tracts)\\n county_stats[county.geoid] = assemble_stats(*minority_area_stats)\\n county_stats[county.geoid]['name'] = county.name\\n #tally target msa counts\\n msa_target_lma_sum += county_stats[county.geoid]['lma']\\n msa_target_mma_sum += county_stats[county.geoid]['mma']\\n msa_target_hma_sum += county_stats[county.geoid]['hma']\\n #tally peer msa counts\\n msa_peer_lma_sum += county_stats[county.geoid]['peer_lma']\\n msa_peer_mma_sum += county_stats[county.geoid]['peer_mma']\\n msa_peer_hma_sum += county_stats[county.geoid]['peer_hma']\\n #msa\\n msa_minority_area_stats = (msa_target_lma_sum, msa_target_mma_sum, msa_target_hma_sum, msa_peer_lma_sum, msa_peer_mma_sum, msa_peer_hma_sum)\\n msa_stats = assemble_stats(*msa_minority_area_stats)\\n \\n return {\\n 'msa': msa_stats,\\n 'counties': county_stats,\\n }\",\n \"def set_component(self, component, targets):\\n data = []\\n if targets:\\n data.append({\\n 'targets': targets,\\n 'labels': {'service': self.name, 'component': component},\\n })\\n with open(self.filename) as f:\\n old_data = json.load(f)\\n for s in old_data:\\n if s['labels']['service'] != self.name or s['labels'].get('component','') != component:\\n data.append(s)\\n logging.debug('set(%s) %r', self.filename, data)\\n with open(self.filename, 'w') as f:\\n json.dump(data, f)\",\n \"def update_generator_parameters(self):\\n gens = self.get_attribute('generator_parameters')\\n if not len (gens):\\n genInfo = generator_parameters()\\n else:\\n genInfo = generator_parameters(gens[-1])\\n genInfo.set_attribute('submission_details', self._json_base__get_submission_details())\\n genInfo.set_attribute('version', genInfo.get_attribute('version')+1)\\n\\n gens.append(genInfo.json())\\n self.set_attribute('generator_parameters', gens)\",\n \"def componentResult(self, *xpars):\\n if len(xpars) == 0:\\n xpars = self.__x\\n else:\\n assert len(xpars) == self.__nx # The input parameter number should be consistent.\\n #-> Calculate the add model components\\n result = OrderedDict()\\n for modelName in self._addList:\\n mf = self.__modelDict[modelName]\\n result[modelName] = mf(*xpars)\\n #-> Manipulate the model components\\n for modelName in self._mltList:\\n mf = self.__modelDict[modelName]\\n my = mf(*xpars) # multiplied y component\\n #--> Multiply the current component to the target models\\n for tmn in mf.multiList:\\n result[tmn] *= my\\n return result\",\n \"def save_output_json(ycomp, tmesh, ystar=None, fstring=None):\\n if fstring is None:\\n fstring = 'nonspecified_output'\\n\\n jsfile = open(fstring, mode='w')\\n jsfile.write(json.dumps(dict(ycomp=ycomp,\\n tmesh=tmesh,\\n ystar=ystar)))\\n\\n print 'output saved to ' + fstring\\n print '\\\\n to plot run the commands \\\\n'\\n print 'import plot_output as plo'\\n print 'import optcont_main as ocm'\\n print 'jsf = ocm.load_json_dicts(\\\"' + fstring + '\\\")'\\n print 'plo.plot_optcont_json(jsf, fname=\\\"' + fstring + '\\\")\\\\n'\",\n \"def request_contribution(project_info):\\n owner = user_collection.find_one({\\\"_id\\\": project_info[\\\"OWNER_ID\\\"]})\\n incoming_list = owner[\\\"incoming\\\"]\\n incoming_list.append(\\n {\\n \\\"user_id\\\": project_info[\\\"USER_ID\\\"],\\n \\\"project_id\\\": project_info[\\\"PROJECT_ID\\\"],\\n }\\n )\\n user_collection.find_one_and_update(\\n {\\\"_id\\\": project_info[\\\"OWNER_ID\\\"]},\\n {\\n \\\"$set\\\": {\\n \\\"incoming\\\": incoming_list,\\n }\\n },\\n upsert=False,\\n )\\n user = user_collection.find_one({\\\"_id\\\": project_info[\\\"USER_ID\\\"]})\\n user_outgoing = user[\\\"outgoing\\\"]\\n user_outgoing.append(project_info[\\\"PROJECT_ID\\\"])\\n user_collection.find_one_and_update(\\n {\\\"_id\\\": project_info[\\\"USER_ID\\\"]},\\n {\\n \\\"$set\\\": {\\n \\\"outgoing\\\": user_outgoing,\\n }\\n },\\n upsert=False,\\n )\",\n \"def json_out(self):\\n temp_json = json.dumps(self.ecat_info, indent=4)\\n print(temp_json)\",\n \"def collection_post(request):\\n user_id = request.validated['contributor_id']\\n user = request.contract.get_user(user_id)\\n contribution_type = request.validated['type']\\n try:\\n contribution = request.contract.create_contribution(user=user, contribution_type=contribution_type)\\n except InvalidContributionTypeError as error:\\n raise exc.HTTPBadRequest(error)\\n return contribution_to_dict(contribution)\",\n \"def add_to_pr_export(self, exp_template):\",\n \"def add(cm_response, **data):\\n return cm_response\",\n \"def componentAddResult(self, *xpars):\\n if len(xpars) == 0:\\n xpars = self.__x\\n else:\\n assert len(xpars) == self.__nx # The input parameter number should be consistent.\\n result = OrderedDict()\\n for modelName in self._addList:\\n result[modelName] = self.__modelDict[modelName](*xpars)\\n return result\",\n \"def to_json(self):\\n\\t\\t\\n\\t\\treturn { \\\"total_sentences\\\": self.total_sentences }\",\n \"def cc_json():\\n return sh(\\\"intercept-build ./build.py compile:\\\\\\\\* -R; ./build.py -c compile:\\\\\\\\*\\\")\",\n \"def get_contribution_info(self, project):\\n drafts = 0\\n pending_personal = 0\\n personal = 0\\n pending_all = None\\n\\n user = self.context.get('user')\\n if not user.is_anonymous():\\n personal = project.observations.filter(creator=user).count()\\n pending_personal = project.observations.filter(\\n creator=user, status='pending').count()\\n drafts = project.observations.filter(\\n creator=user, status='draft').count()\\n\\n if project.can_moderate(user):\\n pending_all = project.observations.filter(\\n status='pending').count()\\n\\n return {\\n 'total': self.get_num_contributions(project),\\n 'personal': personal,\\n 'pending_all': pending_all,\\n 'pending_personal': pending_personal,\\n 'drafts': drafts\\n }\",\n \"def get_contribution(self):\\n salary = self._get_salary()\\n if not salary:\\n return 0\\n # Class 1 NIC.\\n contribution = 0\\n st = 702\\n if salary > st:\\n contribution = (salary - st) * 0.138\\n return contribution\",\n \"def compService():\\n return jsonify(compileService.getItems())\",\n \"def handle(component_id, cognigy_json_response, response_time_seconds=0.0):\\n coco_standard_response = copy.deepcopy(COCO_STANDARD_RESPONSE)\\n\\n response = cognigy_json_response.get(\\\"text\\\", \\\"\\\")\\n\\n coco_standard_response[\\\"action_name\\\"] = user_input_handler.fetch_action_name(\\n response)\\n\\n coco_standard_response.update(user_input_handler.calculate_status_flags(\\n response))\\n\\n coco_standard_response[\\\"response\\\"] = \\\\\\n user_input_handler.clean_placeholders_from_text(response)\\n\\n coco_standard_response[\\\"response_time\\\"] = response_time_seconds\\n\\n return coco_standard_response\",\n \"def _writeInputJSON(self, tstep):\\n\\n # update json based on the Properties:\\n # print(\\\"Property keys:\\\")\\n for key in self.properties.index:\\n # print(key)\\n prop = self.properties[key]\\n\\n if(key[0] == PropertyID.PID_RefractiveIndex and\\n key[2] == tstep and key[1] == objID.OBJ_CONE):\\n print(\\\"PID_RefractiveIndex, \\\", prop.getValue())\\n parent = self._jsondata['materials']\\n parent[3][\\\"refractiveIndex\\\"] = prop.getValue()\\n\\n elif(key[0] == PropertyID.PID_NumberOfRays and\\n key[2] == tstep):\\n print(\\\"PID_NumberOfRays, \\\", prop.getValue())\\n parent = self._jsondata['sources']\\n for item in parent:\\n item[\\\"rays\\\"] = prop.getValue()\\n\\n elif(key[0] == PropertyID.PID_ChipSpectrum and\\n key[2] == tstep):\\n print(\\\"PID_ChipSpectrum, \\\", prop.getValue())\\n parent = self._jsondata['sources']\\n for item in parent:\\n item[\\\"wavelengths\\\"] = prop.getValue()[\\n \\\"wavelengths\\\"].tolist()\\n item[\\\"intensities\\\"] = prop.getValue()[\\n \\\"intensities\\\"].tolist()\\n\\n elif(key[0] == PropertyID.PID_ParticleNumberDensity and\\n key[2] == tstep):\\n print(\\\"PID_ParticleNumberDensity, \\\", prop.getValue())\\n parent = self._jsondata['materials']\\n if prop.getValueType() is ValueType.Scalar:\\n parent[3][\\\"particleDensities\\\"] = [prop.getValue()]\\n else:\\n parent[3][\\\"particleDensities\\\"] = prop.getValue()\\n\\n elif(key[0] == PropertyID.PID_ScatteringCrossSections and\\n key[2] == tstep):\\n pass\\n elif(key[0] == PropertyID.PID_InverseCumulativeDist and\\n key[2] == tstep):\\n pass\\n # Datafiles:\\n parent = self._jsondata['materials']\\n\\n n_particle_key = (\\n PropertyID.PID_NumberOfFluorescentParticles, objID.OBJ_CONE, tstep)\\n n_particles = self.properties[n_particle_key]\\n parent[3]['numberOfFluorescentParticles'] = n_particles.getValue()\\n print(\\\"n_particles=\\\", n_particles.getValue(),\\n parent[3]['numberOfFluorescentParticles'])\\n\\n # Phosphor efficiences:\\n if PropertyID.PID_PhosphorEfficiency in\\\\\\n self.properties.index.get_level_values('propertyID'):\\n effs = self.properties.xs(\\n (PropertyID.PID_PhosphorEfficiency, tstep), level=('propertyID', 'tstep'))\\n\\n efflist = []\\n for i, row in effs.iteritems():\\n p = row.getValue()\\n efflist.extend([p])\\n\\n parent[3][\\\"phosphorEfficiencies\\\"] = efflist\\n if len(efflist) != n_particles.getValue():\\n raise APIError.APIError(\\\"Number of phosphor efficiencies \\\\\\n (%d) does not match with number of \\\\\\n fluorescent particles (%d)\\\" %\\n (len(efflist),\\n n_particles.getValue())\\n )\\n else:\\n raise APIError.APIError(\\n \\\"Did not find phosphor efficiencies in Properties!\\\")\\n\\n # Properties containing emission spectrums\\n if PropertyID.PID_EmissionSpectrum in\\\\\\n self.properties.index.get_level_values('propertyID'):\\n em = self.properties.xs((PropertyID.PID_EmissionSpectrum, tstep),\\n level=('propertyID', 'tstep'))\\n\\n em_fname = []\\n for i, row in em.iteritems():\\n p = row.getValue()\\n fname = 'InvCumul_EM_%d.dat' % i\\n gS.writeEmissionSpectrumFile(p[\\\"wavelengths\\\"],\\n p[\\\"intensities\\\"],\\n fname)\\n em_fname.extend([fname])\\n\\n parent[3][\\\"cumulativeEmissionSpectrumFilenames\\\"] = em_fname\\n if len(em_fname) != n_particles.getValue():\\n raise APIError.APIError(\\\"Number of em spectrum file names \\\\\\n (%d) does not match with number of \\\\\\n fluorescent particles (%d)\\\" %\\n (len(em_fname),\\n n_particles.getValue())\\n )\\n else:\\n raise APIError.APIError(\\n \\\"Did not find em spectrum file names in Properties!\\\")\\n\\n # Excitation spectrum property\\n if PropertyID.PID_ExcitationSpectrum in\\\\\\n self.properties.index.get_level_values('propertyID'):\\n ex = self.properties.xs((PropertyID.PID_ExcitationSpectrum, tstep),\\n level=('propertyID', 'tstep'))\\n\\n ex_fname = []\\n for i, row in ex.iteritems():\\n p = row.getValue()\\n fname = 'InvCumul_EX_%d.dat' % i\\n gS.writeExAbsSpectrumFile(p[\\\"wavelengths\\\"],\\n p[\\\"intensities\\\"],\\n fname)\\n ex_fname.extend([fname])\\n parent[3][\\\"excitationSpectrumFilenames\\\"] = ex_fname\\n\\n if len(ex_fname) != n_particles.getValue():\\n raise APIError.APIError(\\\"Number of ex spectrum file names \\\\\\n (%d) does not match with number of \\\\\\n fluorescent particles (%d)\\\" %\\n (len(ex_fname),\\n n_particles.getValue())\\n )\\n else:\\n raise APIError.APIError(\\n \\\"Did not find ex spectrum file names in Properties!\\\")\\n\\n # Absorption spectrum property\\n if PropertyID.PID_AsorptionSpectrum in\\\\\\n self.properties.index.get_level_values('propertyID'):\\n aabs = self.properties.xs((PropertyID.PID_AsorptionSpectrum,\\n tstep),\\n level=('propertyID', 'tstep'))\\n\\n aabs_fname = []\\n for i, row in aabs.iteritems():\\n p = row.getValue()\\n fname = 'InvCumul_ABS_%d.dat' % i\\n gS.writeExAbsSpectrumFile(p[\\\"wavelengths\\\"],\\n p[\\\"intensities\\\"],\\n fname)\\n aabs_fname.extend([fname])\\n parent[3][\\\"absorptionSpectrumFilenames\\\"] = aabs_fname\\n if len(aabs_fname) != n_particles.getValue():\\n raise APIError.APIError(\\\"Number of abs spectrum \\\\\\n file names (%d) does not match \\\\\\n with number of fluorescent \\\\\\n particles (%d)\\\" %\\n (len(aabs_fname),\\n n_particles.getValue()))\\n else:\\n raise APIError.APIError(\\n \\\"Did not find abs spectrum file names in Properties!\\\")\\n\\n # write the json file:\\n f = open('input.json', 'w')\\n f.write(json.dumps(self._jsondata))\\n f.close()\",\n \"def handle_contribution(user_id, project_id, status):\\n user = user_collection.find_one({\\\"_id\\\": user_id})\\n contribution_list = user[\\\"contributions\\\"]\\n if status:\\n contribution_list.append(project_id)\\n else:\\n contribution_list.remove(project_id)\\n user_collection.find_one_and_update(\\n {\\\"_id\\\": user_id},\\n {\\n \\\"$set\\\": {\\n \\\"contributions\\\": contribution_list,\\n }\\n },\\n upsert=False,\\n )\",\n \"def unit_contribution(self):\\n\\n _unit_contribution = np.array([self.base_margin * (1 - self.annual_margin_decr) ** n\\n for n in range(self.num_years)])\\n\\n return _unit_contribution\",\n \"def manufacturing_manifest_json(self):\\n\\n file_name = os.path.join(self.cur_8digit_dir, \\\"manufacturing.manifest.json\\\")\\n\\n with open(file_name) as f_in:\\n man_manifest = json.load(f_in)\\n\\n for component in man_manifest[\\\"ComponentManufactureList\\\"]:\\n # Get ManufacturingModel (if exists)\\n if \\\"ManufacturingModel\\\" in component:\\n rel_path_from_results = os.path.join(self.cur_8digit_dir, component[\\\"ManufacturingModel\\\"])\\n if os.path.exists(rel_path_from_results):\\n self.files_for_export.append(os.path.join('results', rel_path_from_results))\\n\\n # Get STEPModel (if exists)\\n if \\\"STEPModel\\\" in component:\\n rel_path_from_results = os.path.join(self.cur_8digit_dir, component[\\\"STEPModel\\\"])\\n if os.path.exists(rel_path_from_results):\\n self.files_for_export.append(os.path.join('results', rel_path_from_results))\\n\\n # Since this is a PrepareForIFAB folder, gather all *.stp files in ./AP203_E2_SEPERATE_PART_FILES/\\n path_to_stp_files = os.path.join(self.cur_8digit_dir,'AP203_E2_SEPERATE_PART_FILES')\\n filter_for_stp_files = os.path.join(path_to_stp_files,'*.stp')\\n stp_files = glob(filter_for_stp_files)\\n for stp_file in stp_files:\\n self.files_for_export.append(os.path.join('results',stp_file))\\n\\n # If there's a TDP tag, \\n # add the referred file,\\n\\t# then crawl that file and gather its file references\\n if \\\"TDP\\\" in man_manifest:\\n tdp_json_path = os.path.join(self.cur_8digit_dir,man_manifest[\\\"TDP\\\"])\\n self.tdp_json(tdp_json_path)\\n self.files_for_export.append(os.path.join('results', tdp_json_path))\",\n \"def register_contribution(self, contribution_intent):\\n if contribution_intent > 0:\\n self.teo.register_contribution(self, contribution_intent)\",\n \"def to_json(self):\\n formatted_date = self.created_at.strftime(\\\"%m/%d/%Y\\\")\\n json_gain = {\\n 'id': self.id,\\n 'description':self.description,\\n 'amount':self.amount,\\n 'created_at': formatted_date\\n }\\n return json_gain\",\n \"def update_electricity_exchanges(self, d_act):\\n d_act = self.remove_exchanges(d_act, ['electricity'])\\n\\n for act in d_act:\\n\\n new_exchanges = []\\n electricity_needed = self.iam_data.gnr_data.loc[dict(\\n variables='Power consumption',\\n region=self.geo.iam_to_iam_region(act) if self.model == \\\"image\\\" else act\\n )].values / 1000\\n electricity_recovered = self.iam_data.gnr_data.loc[dict(\\n variables='Power generation',\\n region=self.geo.iam_to_iam_region(act) if self.model == \\\"image\\\" else act\\n )].values / 1000\\n\\n\\n electricity_suppliers = self.get_shares_from_production_volume(\\n self.get_suppliers_of_a_region(\\n iam_region=act,\\n ecoinvent_technologies=[\\\"electricity, medium voltage\\\"],\\n reference_product=\\\"electricity, medium voltage\\\",\\n unit=\\\"kilowatt hour\\\",\\n look_for_locations_in=act\\n )\\n )\\n\\n if len(electricity_suppliers) == 0:\\n electricity_suppliers = self.get_shares_from_production_volume(\\n self.get_suppliers_of_a_region(\\n iam_region=act,\\n ecoinvent_technologies=[\\\"electricity, medium voltage\\\"],\\n reference_product=\\\"electricity, medium voltage\\\",\\n unit=\\\"kilowatt hour\\\",\\n look_for_locations_in=\\\"ecoinvent\\\"\\n )\\n )\\n\\n for s, supplier in enumerate(electricity_suppliers):\\n share = electricity_suppliers[supplier]\\n new_exchanges.append(\\n {\\n \\\"uncertainty type\\\": 0,\\n \\\"loc\\\": 1,\\n \\\"amount\\\": (electricity_needed - electricity_recovered) * share,\\n \\\"type\\\": \\\"technosphere\\\",\\n \\\"production volume\\\": 0,\\n \\\"product\\\": supplier[2],\\n \\\"name\\\": supplier[0],\\n \\\"unit\\\": supplier[3],\\n \\\"location\\\": supplier[1],\\n }\\n )\\n\\n d_act[act][\\\"exchanges\\\"].extend(new_exchanges)\\n d_act[act]['exchanges'] = [v for v in d_act[act][\\\"exchanges\\\"] if v]\\n\\n d_act[act][\\\"comment\\\"] = (\\\"WARNING: Dataset modified by `premise` based on WBCSD's GNR data and 2018 IEA roadmap for the cement industry.\\\\n \\\" +\\n \\\"Electricity consumption per kg cement: {} kWh.\\\\n\\\".format(electricity_needed) +\\n \\\"Of which {} kWh were generated from on-site waste heat recovery.\\\\n\\\".format(electricity_recovered)\\n ) + d_act[act][\\\"comment\\\"]\\n\\n return d_act\",\n \"def _update_ghg_coefs(self) -> None:\\n ghg_data, bird_data = self._datasets\\n model = self._selection.get_model(ghg_data, bird_data)\\n page = self.pages[self.current_page]\\n for button in page.buttons:\\n if button.tag == 'display':\\n gas = button.name[0:3]\\n weight = '{:.2e}'.format(model.coef[gas])\\n button.update_name(f'{gas} Weight: {weight}')\",\n \"def append_estimates(self, node: Node) -> None:\\n self.estimate.update(\\n {node.left_child.key: node.left_child.noncoverage.estimate})\\n self.estimate.update(\\n {node.right_child.key: node.right_child.noncoverage.estimate})\",\n \"def _add_component(self, __assistant):\\r\\n\\r\\n # Retrieve the hardware ID.\\r\\n _model = self.cmbHardware.get_model()\\r\\n _row = self.cmbHardware.get_active_iter()\\r\\n _hardware_id = int(_model.get_value(_row, 1))\\r\\n\\r\\n self._controller.add_component(self._incident_id, _hardware_id)\\r\\n self._workbook.load_component_list()\\r\\n\\r\\n return False\",\n \"def read(self):\\n import cclib\\n from pybel import readfile\\n\\n molecule = next(readfile(\\\"orca\\\", self._file))\\n\\n atom_numbers = []\\n coordinates = []\\n\\n for atom in molecule:\\n atom_number = atom.atomicnum\\n atom_numbers.append(atom_number)\\n for coord in atom.coords:\\n coordinates.append(coord)\\n\\n molecular_mass = molecule.exactmass\\n\\n cjson = {\\n \\\"chemical json\\\": 0,\\n \\\"atoms\\\": {\\n \\\"elements\\\": {\\\"number\\\": atom_numbers},\\n \\\"coords\\\": {\\\"3d\\\": coordinates},\\n },\\n \\\"properties\\\": {\\\"molecular mass\\\": molecular_mass},\\n }\\n\\n data = cclib.io.ccread(self._file)\\n\\n # Add calculated properties\\n if hasattr(data, \\\"scfenergies\\\"):\\n if len(data.scfenergies) > 0:\\n energy = data.scfenergies[-1] * EV_TO_J_MOL\\n cjson.setdefault(\\\"properties\\\", {})[\\\"totalEnergy\\\"] = energy\\n\\n if hasattr(data, \\\"gbasis\\\"):\\n basis = _cclib_to_cjson_basis(data.gbasis)\\n cjson[\\\"basisSet\\\"] = basis\\n\\n if hasattr(data, \\\"vibfreqs\\\"):\\n vibfreqs = list(data.vibfreqs)\\n cjson.setdefault(\\\"vibrations\\\", {})[\\\"frequencies\\\"] = vibfreqs\\n\\n if hasattr(data, \\\"vibdisps\\\"):\\n vibdisps = _cclib_to_cjson_vibdisps(data.vibdisps)\\n cjson.setdefault(\\\"vibrations\\\", {})[\\\"eigenVectors\\\"] = vibdisps\\n\\n # Add a placeholder intensities array\\n if \\\"vibrations\\\" in cjson and \\\"frequencies\\\" in cjson[\\\"vibrations\\\"]:\\n if \\\"intensities\\\" not in cjson[\\\"vibrations\\\"]:\\n cjson[\\\"vibrations\\\"][\\\"intensities\\\"] = [\\n 1 for i in range(len(cjson[\\\"vibrations\\\"][\\\"frequencies\\\"]))\\n ]\\n if \\\"modes\\\" not in cjson[\\\"vibrations\\\"]:\\n cjson[\\\"vibrations\\\"][\\\"modes\\\"] = [\\n i + 1 for i in range(len(cjson[\\\"vibrations\\\"][\\\"frequencies\\\"]))\\n ]\\n\\n return cjson\",\n \"def add_conversions_for_component(self, comp):\\n model = self.model\\n if self.special_conversions:\\n self.model._cml_special_units_converter = self._apply_special_conversion_for_nested_expr\\n assignments = model.search_for_assignments(comp)\\n self.convert_assignments(assignments)\\n if self.special_conversions:\\n del self.model._cml_special_units_converter\\n for conn in getattr(model, u'connection', []):\\n cname1 = conn.map_components.component_1\\n cname2 = conn.map_components.component_2\\n if comp.name in [cname1, cname2]:\\n comp1 = model.get_component_by_name(cname1)\\n comp2 = model.get_component_by_name(cname2)\\n for mapping in conn.map_variables:\\n var1 = model.get_variable_by_name(cname1, mapping.variable_1)\\n var2 = model.get_variable_by_name(cname2, mapping.variable_2)\\n self.convert_mapping(mapping, comp1, comp2, var1, var2)\",\n \"def toJSON(self):\\r\\n\\r\\n jsonToRet = []\\r\\n rowJson = []\\r\\n matrixJson = []\\r\\n\\r\\n if len(self.slctData) > 100:\\r\\n self.getSimMatSummary(100)\\r\\n jsonToRet.append(self.summaryOrdering)\\r\\n for i in range(0,len(self.simMatSmm)):\\r\\n for n in self.simMatSmm[i]:\\r\\n rowJson.append(n)\\r\\n matrixJson.append(rowJson)\\r\\n rowJson = []\\r\\n jsonToRet.append(matrixJson)\\r\\n\\r\\n jsonToRet.append(self.patchOrdering)\\r\\n # jsonToRet = []\\r\\n rowJson = []\\r\\n matrixJson = []\\r\\n\\r\\n for i in range(0,len(self.simMat)):\\r\\n for n in self.simMat[i]:\\r\\n rowJson.append(n)\\r\\n matrixJson.append(rowJson)\\r\\n rowJson = []\\r\\n jsonToRet.append(matrixJson)\\r\\n return jsonToRet\",\n \"def test_composition_adds_to_100_percent(self):\",\n \"def as_json(self):\\n result = super().as_json()\\n result[\\\"generator\\\"].update({\\n \\\"block\\\": self.vein.with_purity(100).as_json(),\\n \\\"cluster-size\\\": self.cluster_size,\\n \\\"type\\\": \\\"cluster\\\",\\n })\\n return result\",\n \"def makecalc():\\n modelfile = 'winequality-red.pickleRF.dat'\\n model = pickle.load(open(os.path.join(APP_STATIC, modelfile), 'rb'))\\n\\n jsonfile = request.get_json()\\n data = pd.read_json(json.dumps(jsonfile),orient='index')\\n print(data)\\n \\n res = dict()\\n\\n # Headers of Data\\n # \\\"fixed acidity\\\",\\\"volatile acidity\\\",\\\"citric acid\\\",\\\"residual sugar\\\",\\\"chlorides\\\",\\\"free sulfur dioxide\\\",\\\"total sulfur dioxide\\\",\\\"density\\\",\\\"pH\\\",\\\"sulphates\\\",\\\"alcohol\\\"\\n\\n # create array from JSON data from service\\n X = np.array(data[['fixed acidity','volatile acidity','citric acid','residual sugar','chlorides','free sulfur dioxide','total sulfur dioxide','density','pH','sulphates','alcohol']])\\n\\n print(X)\\n\\n ypred = model.predict(X)\\n \\n for i in range(len(ypred)):\\n res[i] = ypred[i]\\n \\n return jsonify(res)\",\n \"def getJSON(self):\\n text = super().getJSON() + f', \\\"exchange\\\": \\\"{self.__exchange}\\\"'\\n text += f', \\\"market pair\\\": \\\"{self.__market_pairs}\\\"'\\n text += f', \\\"interval\\\": \\\"{self.__interval}\\\"}}'\\n return text\",\n \"def jec_combination(args=None, additional_dictionary=None, algo = 'CHS'):\\n\\tmpl_plots = []\\n\\troot_plots = []\\n\\tlabel_dict = {\\n\\t\\t'ptbalance': 'PtBal',\\n\\t\\t'mpf': 'MPF',\\n\\t\\t'rawmpf': 'MPF-notypeI',\\n\\t\\t'zmass': 'ZMass',\\n\\t\\t'npumean': 'Mu',\\n\\t\\t'rho': 'Rho',\\n\\t\\t'npv': 'NPV',\\n\\t}\\n#\\talpha_limits = [0.4]\\n\\talpha_limits = [0.1, 0.15, 0.2, 0.3, 0.4]\\n\\talpha_cuts = ['(alpha<{})'.format(limit) for limit in alpha_limits]\\n\\talpha_strings = ['a'+str(int(100*limit)) for limit in alpha_limits]\\n\\n\\teta_borders = [0, 0.783, 1.305, 1.93, 2.5, 2.964, 3.2, 5.191]\\n\\teta_cuts = [\\\"({0}<=abs(jet1eta)&&abs(jet1eta)<{1})\\\".format(*b) for b in zip(eta_borders[:-1], eta_borders[1:])]\\n\\teta_cuts = [\\\"(0<=abs(jet1eta)&&abs(jet1eta)<1.3)\\\"] + eta_cuts # also include standard barrel jet selection\\n\\teta_strings = [\\\"eta_{0:0>2d}_{1:0>2d}\\\".format(int(round(10*up)), int(round(10*low))) for up, low in zip(eta_borders[:-1], eta_borders[1:])]\\n\\teta_strings = [\\\"eta_00_13\\\"] + eta_strings\\n\\ttry:\\n\\t\\tnpv_weights = additional_dictionary.pop(\\\"_npv_weights\\\")\\n\\t\\twarnings.warn(\\\"Usage of '_npv_weights' is deprecated. Use PUWeights in Excalibur instead.\\\")\\n\\texcept (AttributeError, KeyError):\\n\\t\\tnpv_weights = [\\\"1\\\"]\\n\\ttry:\\n\\t\\tfile_label = additional_dictionary.pop(\\\"file_label\\\")\\n\\texcept (AttributeError, KeyError):\\n\\t\\tfile_label = \\\"\\\"\\n\\n\\tnow = time.localtime()\\n\\tdef mpl_to_root(mpl_plot_dict):\\n\\t\\t\\\"\\\"\\\"Create root plot dict from mpl plot dict\\\"\\\"\\\"\\n\\t\\troot_plot_dict = mpl_plot_dict.copy()\\n\\t\\troot_plot_dict.update({\\n\\t\\t\\t'plot_modules': ['ExportRoot'],\\n\\t\\t\\t'filename': 'combination_ZJet_' + file_label + time.strftime(\\\"%Y-%m-%d\\\", now),\\n\\t\\t\\t'file_mode': ('RECREATE' if mpl_to_root.first else 'UPDATE'),\\n\\t\\t})\\n\\t\\tmpl_to_root.first = False\\n\\t\\treturn root_plot_dict\\n\\tmpl_to_root.first = False\\n\\n\\tfor alphacut, alphastring in zip(alpha_cuts, alpha_strings):\\n\\t\\tfor etacut, etastring in zip(eta_cuts, eta_strings):\\n\\t\\t\\tfor correction in ['L1L2L3']: # no L1L2L3Res available atm\\n\\t\\t\\t\\teta_alpha_cut = '&&'.join((alphacut, etacut))\\n\\t\\t\\t\\tbase_plot = {\\n\\t\\t\\t\\t\\t'nicks': ['data', 'mc'],\\n\\t\\t\\t\\t\\t'corrections': [correction],\\n\\t\\t\\t\\t\\t'zjetfolders': ['noalphanoetacuts'],\\n\\t\\t\\t\\t\\t'weights': [\\\"(%s)*(%s)\\\" % (eta_alpha_cut, npv_weight) for npv_weight in npv_weights],\\n\\t\\t\\t\\t\\t'tree_draw_options' : ['prof'],\\n\\t\\t\\t\\t\\t# ratio\\n\\t\\t\\t\\t\\t'analysis_modules': ['Ratio'],\\n\\t\\t\\t\\t\\t'ratio_numerator_nicks':['data'],\\n\\t\\t\\t\\t\\t'ratio_denominator_nicks':['mc'],\\n\\t\\t\\t\\t\\t'ratio_denominator_no_errors': False,\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t# histograms - raw event counts\\n\\t\\t\\t\\tlabelsuffix = '_'.join(['RawNEvents', algo, alphastring, etastring, correction])\\n\\t\\t\\t\\td_mpl = {\\n\\t\\t\\t\\t\\t'x_expressions': ['zpt'],\\n\\t\\t\\t\\t\\t'x_bins': 'zpt',\\n\\t\\t\\t\\t\\t'labels': ['_'.join([item, labelsuffix]) for item in ['Data', 'MC', 'Ratio']],\\n\\t\\t\\t\\t\\t'filename': labelsuffix + file_label,\\n\\t\\t\\t\\t\\t'no_weight': True, #Remove reweights in MC\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\td_mpl.update(base_plot)\\n\\t\\t\\t\\tif additional_dictionary is not None:\\n\\t\\t\\t\\t\\td_mpl.update(additional_dictionary)\\n\\t\\t\\t\\tdel d_mpl['tree_draw_options']\\n\\t\\t\\t\\td_root = mpl_to_root(d_mpl)\\n\\t\\t\\t\\t# make plots comparable to jec_comparison\\n\\t\\t\\t\\td_mpl['x_log'] = True\\n\\t\\t\\t\\td_mpl['x_ticks'] = [30, 50, 70, 100, 200, 400, 1000]\\n\\t\\t\\t\\tmpl_plots.append(d_mpl)\\n\\t\\t\\t\\troot_plots.append(d_root)\\n\\t\\t\\t\\t# profiles - responses\\n\\t\\t\\t\\tfor method in ['mpf', 'ptbalance', 'rawmpf', 'zmass']:\\n\\t\\t\\t\\t\\tlabelsuffix = '_'.join([label_dict[method], algo, alphastring, etastring, correction])\\n\\t\\t\\t\\t\\td_mpl = {\\n\\t\\t\\t\\t\\t\\t'x_expressions': ['zpt'],\\n\\t\\t\\t\\t\\t\\t'y_expressions': [method],\\n\\t\\t\\t\\t\\t\\t'x_bins': 'zpt',\\n\\t\\t\\t\\t\\t\\t'y_label': method,\\n\\t\\t\\t\\t\\t\\t'labels': ['_'.join([item, labelsuffix]) for item in ['Data', 'MC', 'Ratio']],\\n\\t\\t\\t\\t\\t\\t'filename': labelsuffix + file_label,\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\td_mpl.update(base_plot)\\n\\t\\t\\t\\t\\tif additional_dictionary is not None:\\n\\t\\t\\t\\t\\t\\td_mpl.update(additional_dictionary)\\n\\t\\t\\t\\t\\tapply_double_profile(d_mpl, args)\\n\\t\\t\\t\\t\\td_root = mpl_to_root(d_mpl)\\n\\t\\t\\t\\t\\t# make plots comparable to jec_comparison\\n\\t\\t\\t\\t\\td_mpl['x_log'] = True\\n\\t\\t\\t\\t\\td_mpl['x_ticks'] = [30, 50, 70, 100, 200, 400, 1000]\\n\\t\\t\\t\\t\\tmpl_plots.append(d_mpl)\\n\\t\\t\\t\\t\\troot_plots.append(d_root)\\n\\t\\t\\t\\t# pileup info\\n\\t\\t\\t\\tfor x_expression, y_expression in [(\\\"npumean\\\", \\\"rho\\\"), (\\\"npumean\\\", \\\"npv\\\")]:\\n\\t\\t\\t\\t\\tlabelsuffix = \\\"_\\\".join((y_expression, \\\"vs\\\", x_expression, algo, alphastring, etastring, correction))\\n\\t\\t\\t\\t\\td_mpl = {\\n\\t\\t\\t\\t\\t\\t'x_expressions': [x_expression],\\n\\t\\t\\t\\t\\t\\t'y_expressions': [y_expression],\\n\\t\\t\\t\\t\\t\\t'y_label': y_expression,\\n\\t\\t\\t\\t\\t\\t'cutlabel': True,\\n\\t\\t\\t\\t\\t\\t'x_bins': \\\"25,0.5,25.5\\\",\\n\\t\\t\\t\\t\\t\\t'legend': 'upper left',\\n\\t\\t\\t\\t\\t\\t'labels': ['_'.join([item, labelsuffix]) for item in ['Data', 'MC', 'Ratio']],\\n\\t\\t\\t\\t\\t\\t'filename': labelsuffix + file_label,\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\td_mpl.update(base_plot)\\n\\t\\t\\t\\t\\tif additional_dictionary is not None:\\n\\t\\t\\t\\t\\t\\td_mpl.update(additional_dictionary)\\n\\t\\t\\t\\t\\tapply_double_profile(d_mpl, args)\\n\\t\\t\\t\\t\\td_root = mpl_to_root(d_mpl)\\n\\t\\t\\t\\t\\tmpl_plots.append(d_mpl)\\n\\t\\t\\t\\t\\troot_plots.append(d_root)\\n\\tharryinterface.harry_interface(mpl_plots, args)\\n\\tharryinterface.harry_interface(root_plots, args + ['--max-processes', '1'])\",\n \"def json(self, update=False):\\n return json.dumps(self.export(update=update), indent=4)\",\n \"def update_from_json(course_key, jsondict, user):\\r\\n descriptor = modulestore('direct').get_course(course_key)\\r\\n\\r\\n graders_parsed = [CourseGradingModel.parse_grader(jsonele) for jsonele in jsondict['graders']]\\r\\n\\r\\n descriptor.raw_grader = graders_parsed\\r\\n descriptor.grade_cutoffs = jsondict['grade_cutoffs']\\r\\n\\r\\n modulestore('direct').update_item(descriptor, user.id)\\r\\n\\r\\n CourseGradingModel.update_grace_period_from_json(course_key, jsondict['grace_period'], user)\\r\\n\\r\\n return CourseGradingModel.fetch(course_key)\",\n \"def get_cloned_plugin_data(self, update={}):\\n form = self.get_form()\\n\\n cloned_data = copy.copy(self.data)\\n data = {}\\n\\n for field, default_value in form.plugin_data_fields:\\n data.update({field: getattr(cloned_data, field, '')})\\n\\n for prop, value in update.items():\\n data.update({prop: value})\\n\\n return json.dumps(data)\",\n \"def add_file_contribution(self, doc, value):\\n if self.has_package(doc) and self.has_file(doc):\\n self.file(doc).add_contrib(value)\\n else:\\n raise OrderError('File::Contributor')\",\n \"def _add_component_to_software_profile(self, kit, comp_name, comp_version,\\n software_profile):\\n best_match_component = \\\\\\n self._component_db_api.getBestMatchComponent(\\n comp_name, comp_version, software_profile.getOsInfo(),\\n kit.getId())\\n self._component_db_api.addComponentToSoftwareProfile(\\n best_match_component.getId(), software_profile.getId())\\n\\n return best_match_component\",\n \"def toJSON(self):\\n return {'actLib' : self.nomAct,'comLib' : self.comAct,'id' : self.Id,'equID' : self.EquipementId}\",\n \"def update_csevo(z, j, e_kin, dr_fwhm, tmax, cni):\\n tmax /= 1000\\n cni = True if \\\"Active\\\" in cni else False\\n\\n try:\\n res = cached_basic_sim(int(z), j, e_kin, tmax, dr_fwhm=dr_fwhm, CNI=bool(cni))\\n except:\\n res = None\\n\\n data = []\\n if res:\\n for cs in range(res.N.shape[0]):\\n data.append(\\n {\\\"x\\\": res.t, \\\"y\\\":res.N[cs, :]/res.N.sum(axis=0), \\\"name\\\":str(cs)+\\\"+\\\", \\\"type\\\":\\\"line\\\"}\\n )\\n\\n highlim = math.log10(tmax)\\n lowlim = math.floor(math.log10(.01/j))\\n lowlim = lowlim if (lowlim < highlim) else highlim-1\\n layout = {\\n \\\"title\\\":'Charge state evolution',\\n \\\"template\\\":\\\"plotly_dark\\\",\\n \\\"xaxis\\\":{\\\"title\\\":\\\"Time (s)\\\", \\\"type\\\":\\\"log\\\", \\\"range\\\":[lowlim, highlim]},\\n \\\"yaxis\\\":{\\\"title\\\":\\\"Relative abundance\\\"}\\n }\\n\\n return {\\\"data\\\":data, \\\"layout\\\":layout}\",\n \"def __init__(self, jsondict=None, strict=True):\\n \\n self.amount = None\\n \\\"\\\"\\\" Financial total for the category.\\n Type `Money` (represented as `dict` in JSON). \\\"\\\"\\\"\\n \\n self.category = None\\n \\\"\\\"\\\" Type of adjudication information.\\n Type `CodeableConcept` (represented as `dict` in JSON). \\\"\\\"\\\"\\n \\n super(ClaimResponseTotal, self).__init__(jsondict=jsondict, strict=strict)\",\n \"def __call__( self, json_blob, regression_var ):\\n return super()._process( self.__load( json_blob ), regression_var )\",\n \"def new_labor_subcontract(self) -> float:\\n driver_labor_cost = self.driver_labor_cost(self.inputs.trucks_total)\\n depot_labor_cost = self.depot_labor_cost(self.inputs.trucks_total)\\n landfill_labor_cost = self.landfill_labor_cost()\\n return driver_labor_cost + depot_labor_cost + landfill_labor_cost\",\n \"def getJSONfromUI(self):\\n newParams = JSONObject()\\n try:\\n if not self.name.getText().isEmpty():\\n newParams.put(\\\"name\\\", self.name.getText())\\n newParams.put(\\\"strategy\\\", self.strategy.getSelectedItem().__str__())\\n newParams.put(\\\"metagameStrategy\\\", self.metagameStrategy.getSelectedItem().__str__())\\n newParams.put(\\\"stateMachine\\\", self.stateMachine.getSelectedItem().__str__())\\n newParams.put(\\\"cacheStateMachine\\\", self.cacheStateMachine.isSelected())\\n newParams.put(\\\"maxPlys\\\", self.maxPlys.getModel().getValue())\\n newParams.put(\\\"heuristicFocus\\\", self.heuristicFocus.getModel().getValue())\\n newParams.put(\\\"heuristicMobility\\\", self.heuristicMobility.getModel().getValue())\\n newParams.put(\\\"heuristicOpponentFocus\\\", self.heuristicOpponentFocus.getModel().getValue())\\n newParams.put(\\\"heuristicOpponentMobility\\\", self.heuristicOpponentMobility.getModel().getValue())\\n newParams.put(\\\"mcDecayRate\\\", self.mcDecayRate.getModel().getValue())\\n except JSONException as je:\\n je.printStackTrace()\\n return newParams\",\n \"def amt(self, comp):\\n if isinstance(comp, Phase):\\n comp = comp.comp\\n elif isinstance(comp, str):\\n comp = parse_comp(comp)\\n residual = defaultdict(float, self.comp)\\n tot = sum(residual.values())\\n for c, amt in list(dict(comp).items()):\\n pres = residual[c] / amt\\n for c2, amt2 in list(comp.items()):\\n residual[c2] -= pres * amt2\\n residual[\\\"var\\\"] = tot - sum(residual.values())\\n residual[\\\"var\\\"] /= float(sum(comp.values()))\\n return residual\",\n \"def magento_modify_composer(composer):\\n\\n composer['name']= \\\"platformsh/{0}\\\".format(projectName)\\n composer['description']= \\\"Magento 2 CE(Community Edition) for Platform.sh\\\"\\n\\n composer['repositories'] = {\\n \\\"Magento Repo Auth Required\\\": {\\n \\\"type\\\": \\\"composer\\\",\\n \\\"url\\\": \\\"https://repo.magento.com\\\"\\n },\\n \\\"ECE-Tools\\\": {\\n \\\"type\\\": \\\"git\\\",\\n \\\"url\\\": \\\"https://github.com/magento/ece-tools.git\\\"\\n },\\n \\\"Magento Cloud Components\\\": {\\n \\\"type\\\": \\\"git\\\",\\n \\\"url\\\": \\\"https://github.com/magento/magento-cloud-components.git\\\"\\n },\\n \\\"Magento Cloud Patches\\\": {\\n \\\"type\\\": \\\"git\\\",\\n \\\"url\\\": \\\"https://github.com/magento/magento-cloud-patches.git\\\"\\n },\\n \\\"Magento Quality Patches\\\": {\\n \\\"type\\\": \\\"git\\\",\\n \\\"url\\\": \\\"https://github.com/magento/quality-patches.git\\\"\\n }\\n }\\n\\n return composer\",\n \"def __init__(self):\\n price1 = weight1 = 0\\n with open(\\\"Json_Files/DataMngRPW.json\\\") as json_file:\\n data = json.load(json_file)\\n for i in data:\\n print(\\\"***********\\\", i, \\\"*************\\\")\\n for j in data[i]:\\n price1 += int(j[\\\"price\\\"])\\n weight1 += int(j[\\\"weight\\\"])\\n print(\\\"Price : \\\", price1)\\n print(\\\"Weight : \\\", weight1)\",\n \"def update_json(self):\\n self.set_version_to_default()\\n self.remove_null_fields()\\n self.remove_unnecessary_keys()\\n self.set_fromVersion(from_version=self.from_version)\",\n \"def coefficient(self) -> float:\\n ...\",\n \"def _course_json(request, course_key):\\r\\n course_module = _get_course_module(course_key, request.user, depth=None)\\r\\n return _xmodule_json(course_module, course_module.id)\",\n \"def calculate_output(self):\",\n \"def transit_compare(coeffs, t, y, y_err, name, mega_dict):\\n mega_dict[name] = {'coeffs': coeffs, 't': list(t), 'y': list(y), 'y_err':list(y_err)}\\n with open('results.json', 'w') as out:\\n json.dump(mega_dict, out)\\n print('Writing out results for {} to results.json'.format(name))\\n return\",\n \"def added(self, comp):\\n\\t\\tpass\",\n \"def to_json(self):\\n return {'vendor': self.vendor, 'product': self.product}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5488856","0.546339","0.5305696","0.524301","0.5230317","0.5165457","0.51322114","0.5111415","0.50932914","0.5080458","0.50602216","0.503721","0.50131637","0.4996776","0.499471","0.497496","0.49216732","0.4919991","0.48574156","0.48565018","0.4780565","0.4724434","0.47011533","0.46937972","0.4682328","0.4658481","0.46424368","0.46410102","0.4640655","0.46395513","0.46282464","0.46253073","0.4620594","0.45878512","0.45872223","0.45569202","0.45438346","0.45359108","0.4527366","0.45265782","0.45225063","0.45201632","0.45172003","0.4516277","0.45109272","0.4508673","0.45043555","0.44977507","0.44954973","0.44930956","0.4486362","0.44806838","0.4477541","0.44770223","0.44701353","0.44672355","0.44654706","0.44634706","0.4451289","0.44494176","0.4447454","0.44472012","0.44442207","0.44411796","0.44371274","0.44363707","0.44212887","0.44186562","0.44174594","0.44163173","0.44119698","0.44104347","0.44049007","0.4404543","0.44017997","0.4400224","0.43992248","0.439866","0.4397518","0.4396184","0.43923607","0.439229","0.43846935","0.4381726","0.4379099","0.4378236","0.43604326","0.43535116","0.43483362","0.4339508","0.43342635","0.43291333","0.43253782","0.4322041","0.43209532","0.43086556","0.4303547","0.42997316","0.42907602","0.42858285"],"string":"[\n \"0.5488856\",\n \"0.546339\",\n \"0.5305696\",\n \"0.524301\",\n \"0.5230317\",\n \"0.5165457\",\n \"0.51322114\",\n \"0.5111415\",\n \"0.50932914\",\n \"0.5080458\",\n \"0.50602216\",\n \"0.503721\",\n \"0.50131637\",\n \"0.4996776\",\n \"0.499471\",\n \"0.497496\",\n \"0.49216732\",\n \"0.4919991\",\n \"0.48574156\",\n \"0.48565018\",\n \"0.4780565\",\n \"0.4724434\",\n \"0.47011533\",\n \"0.46937972\",\n \"0.4682328\",\n \"0.4658481\",\n \"0.46424368\",\n \"0.46410102\",\n \"0.4640655\",\n \"0.46395513\",\n \"0.46282464\",\n \"0.46253073\",\n \"0.4620594\",\n \"0.45878512\",\n \"0.45872223\",\n \"0.45569202\",\n \"0.45438346\",\n \"0.45359108\",\n \"0.4527366\",\n \"0.45265782\",\n \"0.45225063\",\n \"0.45201632\",\n \"0.45172003\",\n \"0.4516277\",\n \"0.45109272\",\n \"0.4508673\",\n \"0.45043555\",\n \"0.44977507\",\n \"0.44954973\",\n \"0.44930956\",\n \"0.4486362\",\n \"0.44806838\",\n \"0.4477541\",\n \"0.44770223\",\n \"0.44701353\",\n \"0.44672355\",\n \"0.44654706\",\n \"0.44634706\",\n \"0.4451289\",\n \"0.44494176\",\n \"0.4447454\",\n \"0.44472012\",\n \"0.44442207\",\n \"0.44411796\",\n \"0.44371274\",\n \"0.44363707\",\n \"0.44212887\",\n \"0.44186562\",\n \"0.44174594\",\n \"0.44163173\",\n \"0.44119698\",\n \"0.44104347\",\n \"0.44049007\",\n \"0.4404543\",\n \"0.44017997\",\n \"0.4400224\",\n \"0.43992248\",\n \"0.439866\",\n \"0.4397518\",\n \"0.4396184\",\n \"0.43923607\",\n \"0.439229\",\n \"0.43846935\",\n \"0.4381726\",\n \"0.4379099\",\n \"0.4378236\",\n \"0.43604326\",\n \"0.43535116\",\n \"0.43483362\",\n \"0.4339508\",\n \"0.43342635\",\n \"0.43291333\",\n \"0.43253782\",\n \"0.4322041\",\n \"0.43209532\",\n \"0.43086556\",\n \"0.4303547\",\n \"0.42997316\",\n \"0.42907602\",\n \"0.42858285\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.46383762"},"document_rank":{"kind":"string","value":"30"}}},{"rowIdx":93783,"cells":{"query":{"kind":"string","value":"Initialize G matrix, and then use the python script \"inchi2gv.py\" to decompose each of the compounds that has an InChI and save the decomposition as a row in the G matrix."},"document":{"kind":"string","value":"def create_group_incidence_matrix(self):\n\n gv_data = []\n # decompose the compounds in the training_data and add to G\n for compound_id in self.cids:\n smiles = ccache.get_compound(compound_id).smiles\n try:\n gv_data.append(\n list(self.decomposer.smiles_to_groupvec(smiles).flat))\n except inchi2gv.GroupDecompositionError:\n gv_data.append([0] * len(self.group_names))\n\n G = pd.DataFrame(index=self.cids,\n columns=self.group_names,\n dtype=float,\n data=gv_data)\n\n for compound_id in G.index[(G == 0).all(1)]:\n # add a column for this compound, representing itself\n # as a new group\n G[compound_id] = 0.0\n\n # place a single '1' for this compound group decomposition\n G.at[compound_id, compound_id] = 1.0\n\n return G.values"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def invcell2Gmat(invcell):\n G = fillgmat(invcell)\n g = nl.inv(G)\n return G, g","def complete_disp(IBC, nodes, UG):\r\n nnodes = nodes.shape[0]\r\n UC = np.zeros([nnodes, 3], dtype=np.float)\r\n for row in range(nnodes):\r\n for col in range(3):\r\n cons = IBC[row, col]\r\n if cons == -1:\r\n UC[row, col] = 0.0\r\n else:\r\n UC[row, col] = UG[cons]\r\n\r\n return UC","def biotite():\n\n rho = 2800.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 186.; C[0,1] = 32.4; C[0,2] = 11.6; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 186.; C[1,2] = 11.6; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 54.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 5.8; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 5.8; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 76.8\n\n return C, rho","def get_UBmat(self, i, j, hi, hj):\n h1c = (self.Bmat * vec(*hi)).T\n h2c = (self.Bmat * vec(*hj)).T\n\n t1c = norm_vec(h1c)\n t3c = norm_vec(np.cross(h1c, h2c))\n t2c = norm_vec(np.cross(h1c, t3c))\n Tc = np.concatenate((t1c, t2c, t3c)).T\n\n g1 = self.Gvec(self.xp[i], self.yp[i], self.zp[i]).T\n g2 = self.Gvec(self.xp[j], self.yp[j], self.zp[j]).T\n\n t1g = norm_vec(g1)\n t3g = norm_vec(np.cross(g1, g2))\n t2g = norm_vec(np.cross(g1, t3g))\n Tg = np.concatenate((t1g, t2g, t3g)).T\n\n return Tg * np.linalg.inv(Tc)","def cell2Gmat(cell):\n g = fillgmat(cell)\n G = nl.inv(g)\n return G, g","def convImg2gMatrix(self):\n self.file = self.file.convert(\"L\")\n matrix = numpy.asarray(self.file)\n # convert image to gray scale image\n # in the array, 0 represents black\n # 255 represents white\n # array[a,b] => a represents the line, b represents the columns\n # array[0,0] is the pixel in the top-left hand corner\n return matrix","def main():\r\n # set print options to use lower precision\r\n printopt = np.get_printoptions()\r\n np.set_printoptions(formatter={'float': '{:8.2g}'.format}, linewidth=200)\r\n\r\n # create special matrix, the so-called Hilbert-matrix Aij = 1 / (i + j + 1)\r\n A = hilbert(10)\r\n Q = gramschmidt(A)\r\n\r\n # matrix according to theory should be unit matrix:\r\n I = np.dot(Q.T, Q)\r\n print('I = \\n{}'.format(I))\r\n\r\n # numpy's internal orthogonaliztation by QR-decomposition\r\n Q1, R1 = np.linalg.qr(A)\r\n D = A - np.dot(Q1, R1)\r\n print('D = \\n{}'.format(D))\r\n I1 = np.dot(Q1.T, Q1)\r\n print('I1 = \\n{}'.format(I1))\r\n\r\n np.set_printoptions(**printopt)","def setup(self):\n igd = self.options['input_grid_data']\n ogd = self.options['output_grid_data']\n output_subset = self.options['output_subset']\n\n if ogd is None:\n ogd = igd\n\n # Build the interpolation matrix which maps from the input grid to the output grid.\n # Rather than a single phase-wide interpolating polynomial, map each segment.\n # To do this, find the nodes in the output grid which fall in each segment of the input\n # grid. Then build a Lagrange interpolating polynomial for that segment\n L_blocks = []\n output_nodes_ptau = list(ogd.node_ptau[ogd.subset_node_indices[output_subset]])\n\n for iseg in range(igd.num_segments):\n i1, i2 = igd.segment_indices[iseg]\n iptau_segi = np.take(igd.node_ptau, (i1, i2-1))\n istau_segi = np.take(igd.node_stau, (i1, i2-1))\n\n # The indices of the output grid that fall within this segment of the input grid\n if ogd is igd:\n optau_segi = iptau_segi\n else:\n ptau_hi = igd.segment_ends[iseg+1]\n if iseg < igd.num_segments - 1:\n idxs_in_iseg = np.where(output_nodes_ptau <= ptau_hi)[0]\n else:\n idxs_in_iseg = np.arange(len(output_nodes_ptau))\n optau_segi = np.asarray(output_nodes_ptau)[idxs_in_iseg]\n # Remove the captured nodes so we don't accidentally include them again\n output_nodes_ptau = output_nodes_ptau[len(idxs_in_iseg):]\n\n # Now get the output nodes which fall in iseg in iseg's segment tau space.\n ostau_segi = 2.0 * (optau_segi - iptau_segi[0]) / (iptau_segi[-1] - iptau_segi[0]) - 1\n\n # Create the interpolation matrix and add it to the blocks\n L, _ = lagrange_matrices(istau_segi, ostau_segi)\n L_blocks.append(L)\n\n self.interpolation_matrix = block_diag(*L_blocks)\n r, c = np.nonzero(self.interpolation_matrix)\n\n output_num_nodes, input_num_nodes = self.interpolation_matrix.shape\n\n for (name, kwargs) in self._timeseries_outputs:\n\n input_kwargs = {k: kwargs[k] for k in ('units', 'desc')}\n input_name = 'input_values:{0}'.format(name)\n self.add_input(input_name,\n shape=(input_num_nodes,) + kwargs['shape'],\n **input_kwargs)\n\n output_name = name\n output_kwargs = {k: kwargs[k] for k in ('units', 'desc')}\n output_kwargs['shape'] = (output_num_nodes,) + kwargs['shape']\n self.add_output(output_name, **output_kwargs)\n\n self._vars.append((input_name, output_name, kwargs['shape']))\n\n size = np.prod(kwargs['shape'])\n val_jac = np.zeros((output_num_nodes, size, input_num_nodes, size))\n\n for i in range(size):\n val_jac[:, i, :, i] = self.interpolation_matrix\n\n val_jac = val_jac.reshape((output_num_nodes * size, input_num_nodes * size),\n order='C')\n\n val_jac_rows, val_jac_cols = np.where(val_jac != 0)\n\n rs, cs = val_jac_rows, val_jac_cols\n self.declare_partials(of=output_name,\n wrt=input_name,\n rows=rs, cols=cs, val=val_jac[rs, cs])","def importIGG():\n df = load_file(\"data-luminex-igg\")\n df = pd.melt(df, id_vars=[\"subject\"])\n\n df[\"variable\"] = df[\"variable\"].str.replace(\"IgG.\", \"\", regex=False)\n\n return df","def _gto_from_ccdata(self):\n\n gbasis = self.ccdata.gbasis\n lines = []\n\n for no, basis in enumerate(gbasis):\n lines.append(f\"{no + 1:3d} 0\")\n for prims in basis:\n lines.append(f\"{prims[0].lower():s} {len(prims[1]):5d} 1.00\")\n for prim in prims[1]:\n lines.append(f\"{prim[0]:15.9e} {prim[1]:15.9e}\")\n lines.append('')\n lines.append('')\n return lines","def matrix_readin(suffix='full'):\n\n ## Define arrays for covariance matrices\n c2s = np.zeros([2, 2, n_bins, n_bins])\n c3s = np.zeros([2, 2, 2, n_bins, n_bins])\n c4s = np.zeros([2, 2, 2, 2, n_bins, n_bins])\n ## Normalization arrays for covariance matrices\n n2s = np.zeros([2, 2])\n n3s = np.zeros([2, 2, 2])\n n4s = np.zeros([2, 2, 2, 2])\n\n for ii in range(len(I1)):\n index4 = \"%d%d,%d%d\" % (I1[ii], I2[ii], I3[ii], I4[ii])\n index3 = \"%d,%d%d\" % (I2[ii], I1[ii], I3[ii])\n index2 = \"%d%d\" % (I1[ii], I2[ii])\n\n j1, j2, j3,j4=I1[ii]-1, I2[ii]-1, I3[ii]-1, I4[ii]-1 # internal indexing\n\n # Define input files\n file_root_all = os.path.join(file_root, 'CovMatricesAll/')\n\n if suffix=='full':\n counts_file = file_root_all + 'total_counts_n%d_m%d_%s.txt' % (n, m, index4)\n # Load total number of counts\n try:\n total_counts=np.loadtxt(counts_file)\n print(\"Reading in integral components for C_{%s}, which used %.2e pairs, %.2e triples and %.2e quads of particles\" % (index4,total_counts[0], total_counts[1], total_counts[2]))\n except (FileNotFoundError, IOError): pass\n else:\n pass\n #print(\"Reading in integral components for C_{%s}, iteration %s\"%(index4,suffix))\n\n # Load full integrals\n c2 = np.diag(np.loadtxt(file_root_all + 'c2_n%d_m%d_%s_%s.txt' % (n, m, index2, suffix))[skip_bins:])\n c3 = np.loadtxt(file_root_all + 'c3_n%d_m%d_%s_%s.txt' % (n, m, index3, suffix))[skip_bins:, skip_bins:]\n c4 = np.loadtxt(file_root_all + 'c4_n%d_m%d_%s_%s.txt' % (n, m, index4, suffix))[skip_bins:, skip_bins:]\n\n # Now save components\n c2s[j1, j2] += c2\n n2s[j1, j2] += 1\n c3s[j2, j1, j3] += c3\n n3s[j2, j1, j3] += 1\n # will deal with c4s/n4s later\n\n # c2 symmetry - indices interchanged, ensures matrix symmetry if they are equal\n c2s[j2, j1] += c2\n n2s[j2, j1] += 1\n\n # c3 symmetry - last two indices interchanged, ensures matrix symmetry if they are equal\n c3s[j2, j3, j1] += c3.T\n n3s[j2, j3, j1] += 1\n \n # All symmetries possible for c4 without transpositions\n permutations4 = ((j1, j2, j3, j4), # original\n (j2, j1, j3, j4), # first two indices interchanged\n (j1, j2, j4, j3), # last two indices interchanged\n (j2, j1, j4, j3), # first and last two indices interchanged at the same time\n )\n \n for (i1, i2, i3, i4) in permutations4:\n c4s[i1, i2, i3, i4] += c4\n n4s[i1, i2, i3, i4] += 1\n # now swap indices and transpose\n c4s[i3, i4, i1, i2] += c4.T\n n4s[i3, i4, i1, i2] += 1\n \n # normalize the covariances\n c2s /= n2s[:, :, None, None]\n c3s /= n3s[:, :, :, None, None]\n c4s /= n4s[:, :, :, :, None, None]\n\n def construct_fields(j1, j2, j3, j4, alpha1, alpha2):\n # Reconstruct the full field for given input fields and rescaling parameters\n\n # Create kronecker deltas\n d_xw = (j1 == j4)\n d_xz = (j1 == j3)\n d_yw = (j2 == j4)\n d_yz = (j2 == j3)\n\n full = c4s[j1, j2, j3, j4] + 0.25 * alpha1 * (d_xw * c3s[j1, j2, j3] + d_xz * c3s[j1, j2, j4]) + 0.25 * alpha2 * (d_yw * c3s[j2, j1, j3] + d_yz * c3s[j2, j1, j4]) + 0.5 * alpha1 * alpha2 * (d_xw * d_yz + d_xz * d_yw) * c2s[j1, j2]\n return full\n\n # Index in ordering (P_11,P_12,P_22)\n cov_indices = [[0, 0], [0, 1], [1, 1]]\n\n c_tot = np.zeros([3, 3, n_bins, n_bins]) # array with each individual covariance accessible\n c_comb = np.zeros([3*n_bins, 3*n_bins]) # full array suitable for inversion\n\n for j1 in range(3):\n ind1, ind2 = cov_indices[j1]\n alpha1, alpha2 = alpha_best[[ind1, ind2]]\n for j2 in range(3):\n ind3,ind4 = cov_indices[j2]\n tmp = construct_fields(ind1, ind2, ind3, ind4, alpha1, alpha2)\n c_tot[j1, j2] = tmp\n c_comb[j1*n_bins:(j1+1)*n_bins, j2*n_bins:(j2+1)*n_bins] = tmp\n\n return c_tot, 0.5*(c_comb+c_comb.T) # add all remaining symmetries","def harzburgite():\n\n rho = 3200.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 226.5; C[0,1] = 75.34; C[0,2] = 74.73; C[0,3] = -0.27; C[0,4] = -2.00; C[0,5] = 1.85\n C[1,0] = C[0,1]; C[1,1] = 242.8; C[1,2] = 73.68; C[1,3] = -3.6; C[1,4] = -1.91; C[1,5] = 4.14\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 230.; C[2,3] = -4.36; C[2,4] = -4.27; C[2,5] = -0.27\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 80.75; C[3,4] = 1.81; C[3,5] = -2.19\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 76.94; C[4,5] = -1.88\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.15\n\n return C, rho","def make_Gr(mlat, *J):\n\n if (len(J)!=3):\n print(\"Number of paramaters are not right, must be 5!\")\n \n NN = 2*mlat # # of sites in one super unitcell\n tau = -np.zeros((NN, NN),dtype=complex)\n h = np.zeros((NN,NN), dtype=complex)\n\n # translational cell's Hamiltonian\n for i in range(mlat-1):\n if (i%2==0):\n h[i,i+1] = J[0]\n h[mlat+i,mlat+i+1] = J[1]\n h[i,mlat+i] = J[2] # horizoltal connection\n elif (i%2==1):\n h[i,i+1] = J[1]\n h[mlat+i,mlat+i+1] = J[0]\n # longitudinal connection of the last sites\n if (mlat-1)%2 == 0:\n h[mlat-1,2*mlat-1] = J[2]\n \n h = h + h.conj().T # make it hermitian\n\n # Hopping matrix\n for i in range(1,mlat,2):\n tau[i+mlat,i] = J[2]\n\n return h, tau","def systematize_algorithm(H: np.array) -> Tuple[np.array, np.array, np.array]:\n n, c = H.shape\n m = np.abs(n-c)\n\n G_s = np.zeros((m, c), dtype=int)\n G_s[:, :m] = np.identity(m)\n\n H_s, permutation = systematize_matrix(H, post_system=True)\n\n rev_permutation = reverse_permutation(permutation)\n\n P = H_s[:, :m]\n\n G_s[:, m:] = P.T\n\n G = G_s[:, rev_permutation]\n\n return G, G_s, H_s","def test_inverse_non_interacting(self, size):\n t_nn = 1.2\n idx = np.arange(size)\n g0_inv_full = np.zeros((size, size), dtype=complex)\n g0_inv_full[idx[:-1], idx[1:]] = g0_inv_full[idx[1:], idx[:-1]] = t_nn\n for g0 in self.g0_loc_inv:\n g0_inv_full[idx, idx] = g0\n rv, h, rv_inv = gt.matrix.decompose_gf(g0_inv_full)\n g0 = gt.matrix.construct_gf(rv_inv=rv_inv, diag_inv=h**-1, rv=rv)\n assert_allclose(g0.dot(g0_inv_full), np.identity(size), atol=1e-14)\n assert_allclose(g0, la.inv(g0_inv_full))\n g0_alt = gt.matrix.Decomposition(rv, h**-1, rv_inv).reconstruct(kind='full')\n assert_allclose(g0, g0_alt)","def Matrix_G(r,xi,E,ops,j):\r\n #Array of multipliers for operators in V14\r\n #----------------------------------------\r\n raw_pot = av14.V14(r)\r\n #----------------------------------------\r\n \r\n #Operator Values \r\n #---------------------------------------- \r\n op00,op01,op10,op11 = ops\r\n \r\n #Matrix Values\r\n #----------------------------------------\r\n G00 = (j-1)*j/r**2 + xi*(np.sum(op00*raw_pot) - E)\r\n G01 = xi*(np.sum(op01*raw_pot))\r\n G10 = G01#xi*(np.sum(operators10*raw_pot))\r\n G11 = (j+1)*(j+2)/r**2 + xi*(np.sum(op11*raw_pot) - E)\r\n #Generate and return (2x2)\r\n #----------------------------------------\r\n return np.array([[G00,G01],[G10,G11]])","def zoisite():\n\n rho = 3343.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 279.8; C[0,1] = 94.7; C[0,2] = 88.7; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 249.2; C[1,2] = 27.5; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 209.4; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 51.8; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 81.4; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 66.3\n\n return C, rho","def init_ising_H_mpo(g,J,L):\n s0 = np.eye(2)\n sx = np.array([[0.,1.],[1.,0.]])\n sy = np.array([[0.,-1j],[1j,0.]])\n sz = np.array([[1.,0.],[0.,-1.]])\n d = 2\n\n w = np.zeros((3,3,d,d),dtype=np.float)\n w[0,:2] = [s0,sz]\n w[0:,2] = [g*sx, -J*sz, s0]\n return w","def get_stain_matrix(I):","def Gmat2AB(G):\n # cellstar = Gmat2cell(G)\n g = nl.inv(G)\n cell = Gmat2cell(g)\n # A = np.zeros(shape=(3,3))\n return cell2AB(cell)","def T(self):\n # TODO - your code here\n transpose = []\n for col in range(self.w):\n new_row = []\n for row in range(self.h):\n new_row.append(self.g[row][col])\n transpose.append(new_row)\n return Matrix(transpose)\n # TODO - your code here","def integration_matrix(grid):\n I_blocks = []\n\n for iseg in range(grid.num_segments):\n i1, i2 = grid.subset_segment_indices['all'][iseg, :]\n indices = grid.subset_node_indices['all'][i1:i2]\n nodes_given = grid.node_stau[indices]\n\n i1, i2 = grid.subset_segment_indices['all'][iseg, :]\n indices = grid.subset_node_indices['all'][i1:i2]\n nodes_eval = grid.node_stau[indices][1:]\n\n _, D_block = lagrange_matrices(nodes_given, nodes_eval)\n I_block = np.linalg.inv(D_block[:, 1:])\n I_blocks.append(I_block)\n\n I = block_diag(*I_blocks)\n\n return I","def to_dense_grid( fname_in, fname_out = None, dim=2 ):\n import numpy as np\n import insect_tools\n import matplotlib.pyplot as plt\n\n # read data\n time, x0, dx, box, data, treecode = read_wabbit_hdf5( fname_in )\n\n # convert blocks to complete matrix\n field, box = dense_matrix( x0, dx, data, treecode, dim=dim )\n\n # write data to FLUSI-type hdf file\n if fname_out:\n insect_tools.write_flusi_HDF5( fname_out, time, box, field)\n else: \n dx = [b/(np.size(field,k)) for k,b in enumerate(box)]\n X = [np.arange(0,np.size(field,k))*dx[k] for k,b in enumerate(box)]\n return field, box, dx, X","def casdetude_genetics():\n file_path = PROJECT_PATH + \"/geographycal_data/Monterusciello/MontEdo_buildings\"\n router = Router(building_file=file_path)\n\n router.design_aqueduct(0)\n\n router.write2epanet(router.acqueduct, PROJECT_PATH + \"/Monterusciello_solution/Monterusciello_acqueduct\",\n diam=False)\n\n read_epanet = graphIO.graph_reader(router.acqueduct)\n read_epanet.read_epanet(PROJECT_PATH + \"/geographycal_data/SolvedNet/MonteSolution\")\n kpi_calculator(router.acqueduct)\n\n minimal = router.design_minimal_aqueduct(router.acqueduct, \"Q*H\")\n router.write2epanet(minimal, PROJECT_PATH + \"/Monterusciello_solution/Monterusciello_acqueduct\", diam=False)","def build_matrix(G):\n # Create an n by n matrix of all zeros\n matrix = np.zeros(shape=(len(G), len(G)))\n for v in G:\n for u in G:\n if v == u:\n matrix[v][u] = 0\n elif u in G[v]:\n matrix[v][u] = G[v][u][\"weight\"]\n else:\n matrix[v][u] = np.inf\n\n return matrix","def mtf2ss(G):\n # Description to help the user\n\n # calculating the number of outputs\n p = len(G)\n # calculating the number of inputs\n m = len(G[0])\n\n # creating a list for each matrix\n A = []\n B = []\n C = []\n D = []\n\n nss = 0\n # loop that get the SISO state-space transformations\n for i in range(0, p):\n # outputs - first index of the MIMO process list\n A.append([])\n B.append([])\n C.append([])\n D.append([])\n for j in range(0, m):\n # inputs - second index of the MIMO process list\n if G[i][j] != 0:\n # transform the individual SISO systems to a state-space model\n Aij, Bij, Cij, Dij = signal.tf2ss(G[i][j].num, G[i][j].den)\n # calculate the size of the A matrix\n nss = nss + Aij.shape[0]\n # organizing the matrices on a list\n A[i].append(Aij)\n B[i].append(Bij)\n C[i].append(Cij)\n D[i].append(Dij)\n else:\n A[i].append([])\n B[i].append([])\n C[i].append([])\n D[i].append([])\n\n # preallocation of the system's matrices\n Ass = np.zeros((nss, nss))\n Bss = np.zeros((nss, m))\n Css = np.zeros((p, nss))\n Dss = np.zeros((p, m))\n # counters\n ct = 0\n\n # loop that organize the MIMO list obtained above on the state-space model\n for i in range(0, p):\n # loop on the outputs\n for j in range(0, m):\n # loop on the inputs\n # test if the matrix isn't zero\n if len(A[i][j]) > 0:\n # calculate the size of the dynamic matrix\n nij = A[i][j].shape[0]\n # organizing the dynamic matrix\n Ass[ct : ct + nij, ct : ct + nij] = A[i][j]\n # organizing the input matrix\n Bss[ct : ct + nij, j] = B[i][j][:, 0]\n # organizing the output matrix\n Css[i, ct : ct + nij] = C[i][j][0, :]\n # organizing the feedforward matrix\n Dss[i, j] = D[i][j]\n # increase the counter\n ct = ct + nij\n\n return Ass, Bss, Css, Dss","def blueschist_mafic():\n\n rho = 3190.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 190.79; C[0,1] = 62.28; C[0,2] = 52.94; C[0,3] = -0.44; C[0,4] = 4.68; C[0,5] = 0.6\n C[1,0] = C[0,1]; C[1,1] = 218.38; C[1,2] = 53.1; C[1,3] = -0.87; C[1,4] = 1.57; C[1,5] = 0.28\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 158.04; C[2,3] = -0.44; C[2,4] = 2.66; C[2,5] = -0.35\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 60.86; C[3,4] = -0.29; C[3,5] = 1.86\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 58.94; C[4,5] = -0.2\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 69.63\n\n return C, rho","def test_inverse_eigenvectors_non_interacting(self, size):\n t_nn = 1.2\n idx = np.arange(size)\n g0_inv_full = np.zeros((size, size), dtype=complex)\n g0_inv_full[idx[:-1], idx[1:]] = g0_inv_full[idx[1:], idx[:-1]] = t_nn\n for g0 in self.g0_loc_inv:\n g0_inv_full[idx, idx] = g0\n rv, h, rv_inv = gt.matrix.decompose_gf(g0_inv_full)\n assert_allclose(rv.dot(rv_inv), np.identity(*h.shape), atol=1e-14)","def build_integer_basis_of_orthogonal_complement_of_binomial_matrix(messi_network):\n binomial_matrix = build_binomial_matrix(messi_network)\n\n\n #print(\"G1\", messi_network.G1.edges())\n #print(\"G2\", messi_network.G2.edges())\n #print(\"G2 circle\", messi_network.G2_circle.edges())\n #print(\"B\", binomial_matrix)\n\n\n #print(\"binomial matrix\")\n #print(binomial_matrix)\n #print(\"build_integer_basis_of_orthogonal_complement_of_binomial_matrix\")\n ret = build_integer_basis_matrix_of_orthogonal_complement_of_matrix(binomial_matrix.transpose(), positive=False).transpose()\n #M = binomial_matrix.transpose()\n #M_ort = ret.transpose()\n #print(M @ M_ort)\n return ret","def Cijkl(C):\n c = np.zeros(shape=(3, 3, 3, 3))\n CC = np.zeros(shape=(9, 9))\n CC[0:6, 0:6] = C[0:6, 0:6]\n CC[6:9, 6:9] = C[3:6, 3:6]\n CC[0:6, 6:9] = C[0:6, 3:6]\n CC[6:9, 0:6] = C[3:6, 0:6]\n\n c[0, 0, 0, 0] = CC[0, 0]\n c[0, 0, 1, 1] = CC[0, 1]\n c[0, 0, 2, 2] = CC[0, 2]\n c[0, 0, 1, 2] = CC[0, 3]\n c[0, 0, 2, 0] = CC[0, 4]\n c[0, 0, 0, 1] = CC[0, 5]\n c[0, 0, 2, 1] = CC[0, 6]\n c[0, 0, 0, 2] = CC[0, 7]\n c[0, 0, 1, 0] = CC[0, 8]\n\n c[1, 1, 0, 0] = CC[1, 0]\n c[1, 1, 1, 1] = CC[1, 1]\n c[1, 1, 2, 2] = CC[1, 2]\n c[1, 1, 1, 2] = CC[1, 3]\n c[1, 1, 2, 0] = CC[1, 4]\n c[1, 1, 0, 1] = CC[1, 5]\n c[1, 1, 2, 1] = CC[1, 6]\n c[1, 1, 0, 2] = CC[1, 7]\n c[1, 1, 1, 0] = CC[1, 8]\n\n c[2, 2, 0, 0] = CC[2, 0]\n c[2, 2, 1, 1] = CC[2, 1]\n c[2, 2, 2, 2] = CC[2, 2]\n c[2, 2, 1, 2] = CC[2, 3]\n c[2, 2, 2, 0] = CC[2, 4]\n c[2, 2, 0, 1] = CC[2, 5]\n c[2, 2, 2, 1] = CC[2, 6]\n c[2, 2, 0, 2] = CC[2, 7]\n c[2, 2, 1, 0] = CC[2, 8]\n\n c[1, 2, 0, 0] = CC[3, 0]\n c[1, 2, 1, 1] = CC[3, 1]\n c[1, 2, 2, 2] = CC[3, 2]\n c[1, 2, 1, 2] = CC[3, 3]\n c[1, 2, 2, 0] = CC[3, 4]\n c[1, 2, 0, 1] = CC[3, 5]\n c[1, 2, 2, 1] = CC[3, 6]\n c[1, 2, 0, 2] = CC[3, 7]\n c[1, 2, 1, 0] = CC[3, 8]\n\n c[2, 0, 0, 0] = CC[4, 0]\n c[2, 0, 1, 1] = CC[4, 1]\n c[2, 0, 2, 2] = CC[4, 2]\n c[2, 0, 1, 2] = CC[4, 3]\n c[2, 0, 2, 0] = CC[4, 4]\n c[2, 0, 0, 1] = CC[4, 5]\n c[2, 0, 2, 1] = CC[4, 6]\n c[2, 0, 0, 2] = CC[4, 7]\n c[2, 0, 1, 0] = CC[4, 8]\n\n c[0, 1, 0, 0] = CC[5, 0]\n c[0, 1, 1, 1] = CC[5, 1]\n c[0, 1, 2, 2] = CC[5, 2]\n c[0, 1, 1, 2] = CC[5, 3]\n c[0, 1, 2, 0] = CC[5, 4]\n c[0, 1, 0, 1] = CC[5, 5]\n c[0, 1, 2, 1] = CC[5, 6]\n c[0, 1, 0, 2] = CC[5, 7]\n c[0, 1, 1, 0] = CC[5, 8]\n\n c[2, 1, 0, 0] = CC[6, 0]\n c[2, 1, 1, 1] = CC[6, 1]\n c[2, 1, 2, 2] = CC[6, 2]\n c[2, 1, 1, 2] = CC[6, 3]\n c[2, 1, 2, 0] = CC[6, 4]\n c[2, 1, 0, 1] = CC[6, 5]\n c[2, 1, 2, 1] = CC[6, 6]\n c[2, 1, 0, 2] = CC[6, 7]\n c[2, 1, 1, 0] = CC[6, 8]\n\n c[0, 2, 0, 0] = CC[7, 0]\n c[0, 2, 1, 1] = CC[7, 1]\n c[0, 2, 2, 2] = CC[7, 2]\n c[0, 2, 1, 2] = CC[7, 3]\n c[0, 2, 2, 0] = CC[7, 4]\n c[0, 2, 0, 1] = CC[7, 5]\n c[0, 2, 2, 1] = CC[7, 6]\n c[0, 2, 0, 2] = CC[7, 7]\n c[0, 2, 1, 0] = CC[7, 8]\n\n c[1, 0, 0, 0] = CC[8, 0]\n c[1, 0, 1, 1] = CC[8, 1]\n c[1, 0, 2, 2] = CC[8, 2]\n c[1, 0, 1, 2] = CC[8, 3]\n c[1, 0, 2, 0] = CC[8, 4]\n c[1, 0, 0, 1] = CC[8, 5]\n c[1, 0, 2, 1] = CC[8, 6]\n c[1, 0, 0, 2] = CC[8, 7]\n c[1, 0, 1, 0] = CC[8, 8]\n return c","def inverse(self):\n if not self.is_square():\n raise(ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n # TODO - your code here\n inverse = []\n if self.h == 1:\n temp = []\n temp.append(1/self.g[0][0])\n inverse.append(temp)\n else:\n identity_matrix = identity(self.h)\n det_term = 1/self.determinant()\n trace_term = self.trace()\n # implement intermediate scaling step locally\n # trace_x_I = trace_term * identity_matrix\n trace_x_I = []\n for i in range(len(self.g)):\n temp_row = []\n for j in range(len(self.g[i])):\n temp_row.append(trace_term * identity_matrix[i][j])\n trace_x_I.append(temp_row)\n # implement sub-traction locally\n # sub_term = trace_x_I - self.g\n sub_term = []\n for i in range(len(trace_x_I)):\n temp_row = []\n for j in range(len(trace_x_I[i])):\n temp_row.append(trace_x_I[i][j] - self.g[i][j])\n sub_term.append(temp_row)\n # implement final scaling step locally\n # inverse = det_term * sub_term\n inverse = []\n for i in range(len(sub_term)):\n temp_row = []\n for j in range(len(sub_term[i])):\n temp_row.append(det_term * sub_term[i][j])\n inverse.append(temp_row)\n return Matrix(inverse)\n # TODO - your code here","def multi_grid_iou(orig_bed_pcd, cfgs, voxel_size=0.05):\n ious = np.zeros(len(cfgs))\n for i in range(cfgs.shape[0]):\n cfg = cfgs[i]\n bed_pcd = orig_bed_pcd - cfg[:2]\n theta = np.radians(cfg[2])\n rotmat = np.array([\n [np.cos(theta), np.sin(theta)],\n [-np.sin(theta), np.cos(theta)],\n ])\n bed_pcd = bed_pcd@rotmat\n\n bed_pcd = np.round_(bed_pcd/voxel_size, 0, np.zeros_like(bed_pcd))\n bed_pcd_summed = np.unique(1024*bed_pcd[:,0]+bed_pcd[:,1])\n bed_pcd_0 = np.round_(bed_pcd_summed/1024, 0, np.zeros_like(bed_pcd_summed))\n bed_pcd_1 = bed_pcd_summed-bed_pcd_0*1024\n x_ok = (bed_pcd_1*2 <= int(cfg[4]/voxel_size)) & (bed_pcd_1*2 >= int(-cfg[4]/voxel_size))\n y_ok = (bed_pcd_0*2 <= int(cfg[3]/voxel_size)) & (bed_pcd_0*2 >= int(-cfg[3]/voxel_size))\n n_ok = np.sum(x_ok & y_ok)\n n_elem = len(bed_pcd_summed)\n n_grid = (cfg[3]+voxel_size)*(cfg[4]+voxel_size)/(voxel_size*voxel_size)\n ious[i] = n_ok/(n_grid+n_elem-n_ok)\n return ious","def Intgrl(N, R, Zeta1, Zeta2, Za, Zb):\n\n global S12, T11, T12, T22, V11A, V12A, V22A, V11B, V12B, V22B, V1111, V2111, V2121, V2211, V2221, V2222\n\n S12 = 0.0\n T11 = 0.0\n T12 = 0.0\n T22 = 0.0\n V11A = 0.0\n V12A = 0.0\n V22A = 0.0\n V11B = 0.0\n V12B = 0.0\n V22B = 0.0\n V1111 = 0.0\n V2111 = 0.0\n V2121 = 0.0\n V2211 = 0.0\n V2221 = 0.0\n V2222 = 0.0\n\n R2 = R * R\n\n # The coefficients for the contracted Gaussian functions are below\n Coeff = np.array([[1.00000, 0.0000000, 0.000000],\n [0.678914, 0.430129, 0.000000],\n [0.444635, 0.535328, 0.154329]])\n\n Expon = np.array([[0.270950, 0.000000, 0.000000],\n [0.151623, 0.851819, 0.000000],\n [0.109818, 0.405771, 2.227660]])\n D1 = np.zeros([3])\n A1 = np.zeros([3])\n D2 = np.zeros([3])\n A2 = np.zeros([3])\n\n # This loop constructs the contracted Gaussian functions\n for i in range(N):\n A1[i] = Expon[N - 1, i] * (Zeta1 ** 2)\n D1[i] = Coeff[N - 1, i] * ((2.0 * A1[i] / np.pi) ** 0.75)\n A2[i] = Expon[N - 1, i] * (Zeta2 ** 2)\n D2[i] = Coeff[N - 1, i] * ((2.0 * A2[i] / np.pi) ** 0.75)\n\n # Calculate one electron integrals\n # Centre A is first atom centre B is second atom\n # Origin is on second atom\n # V12A - off diagonal nuclear attraction to centre A etc.\n for i in range(N):\n for j in range(N):\n # Rap2 - squared distance between centre A and centre P\n Rap = A2[j] * R / (A1[i] + A2[j])\n Rap2 = Rap ** 2\n Rbp2 = (R - Rap) ** 2\n S12 = S12 + S_int(A1[i], A2[j], R2) * D1[i] * D2[j]\n T11 = T11 + T_int(A1[i], A1[j], 0.0) * D1[i] * D1[j]\n T12 = T12 + T_int(A1[i], A2[j], R2) * D1[i] * D2[j]\n T22 = T22 + T_int(A2[i], A2[j], 0.0) * D2[i] * D2[j]\n V11A = V11A + V_int(A1[i], A1[j], 0.0, 0.0, Za) * D1[i] * D1[j]\n V12A = V12A + V_int(A1[i], A2[j], R2, Rap2, Za) * D1[i] * D2[j]\n V22A = V22A + V_int(A2[i], A2[j], 0.0, R2, Za) * D2[i] * D2[j]\n V11B = V11B + V_int(A1[i], A1[j], 0.0, R2, Zb) * D1[i] * D1[j]\n V12B = V12B + V_int(A1[i], A2[j], R2, Rbp2, Zb) * D1[i] * D2[j]\n V22B = V22B + V_int(A2[i], A2[j], 0.0, 0.0, Zb) * D2[i] * D2[j]\n\n # Calculate two electron integrals\n\n for i in range(N):\n for j in range(N):\n for k in range(N):\n for l in range(N):\n Rap = A2[i] * R / (A2[i] + A1[j])\n Rbp = R - Rap\n Raq = A2[k] * R / (A2[k] + A1[l])\n Rbq = R - Raq\n Rpq = Rap - Raq\n Rap2 = Rap * Rap\n Rbp2 = Rbp * Rbp\n Raq2 = Raq * Raq\n Rbq2 = Rbq * Rbq\n Rpq2 = Rpq * Rpq\n V1111 = V1111 + TwoE(A1[i], A1[j], A1[k], A1[l], 0.0, 0.0, 0.0) * D1[i] * D1[j] * D1[k] * D1[l]\n V2111 = V2111 + TwoE(A2[i], A1[j], A1[k], A1[l], R2, 0.0, Rap2) * D2[i] * D1[j] * D1[k] * D1[l]\n V2121 = V2121 + TwoE(A2[i], A1[j], A2[k], A1[l], R2, R2, Rpq2) * D2[i] * D1[j] * D2[k] * D1[l]\n V2211 = V2211 + TwoE(A2[i], A2[j], A1[k], A1[l], 0.0, 0.0, R2) * D2[i] * D2[j] * D1[k] * D1[l]\n V2221 = V2221 + TwoE(A2[i], A2[j], A2[k], A1[l], 0.0, R2, Rbq2) * D2[i] * D2[j] * D2[k] * D1[l]\n V2222 = V2222 + TwoE(A2[i], A2[j], A2[k], A2[l], 0.0, 0.0, 0.0) * D2[i] * D2[j] * D2[k] * D2[l]\n return","def solve_prep(self):\n\n par = self.par\n sol = self.sol\n\n # a. retirement\n sol.m_ret = np.zeros((par.T,par.Nm_ret))\n sol.c_ret = np.zeros((par.T,par.Nm_ret))\n sol.a_ret = np.zeros((par.T,par.Nm_ret))\n sol.inv_v_ret = np.zeros((par.T,par.Nm_ret))\n sol.inv_vm_ret = np.zeros((par.T,par.Nm_ret))\n sol.inv_vn_ret = np.zeros((par.T,par.Nm_ret))\n\n # b. working\n if par.solmethod == 'G2EGM':\n\n sol.c = np.zeros((par.T,par.Nn,par.Nm))\n sol.d = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_v = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_vm = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_vn = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.ucon_c = np.zeros((par.T,par.Nn,par.Nm))\n sol.ucon_d = np.zeros((par.T,par.Nn,par.Nm))\n sol.ucon_v = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.dcon_c = np.zeros((par.T,par.Nn,par.Nm))\n sol.dcon_d = np.zeros((par.T,par.Nn,par.Nm))\n sol.dcon_v = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.acon_c = np.zeros((par.T,par.Nn,par.Nm))\n sol.acon_d = np.zeros((par.T,par.Nn,par.Nm))\n sol.acon_v = np.zeros((par.T,par.Nn,par.Nm))\n sol.con_c = np.zeros((par.T,par.Nn,par.Nm))\n sol.con_d = np.zeros((par.T,par.Nn,par.Nm))\n sol.con_v = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.z = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.w = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n sol.wa = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n sol.wb = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n \n elif par.solmethod == 'NEGM':\n\n sol.c = np.zeros((par.T,par.Nn,par.Nm))\n sol.d = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_v = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_vn = np.zeros((0,0,0))\n sol.inv_vm = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.w = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n sol.wa = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n sol.wb = np.zeros((0,0,0))\n \n sol.c_pure_c = np.zeros((par.T,par.Nb_pd,par.Nm))\n sol.inv_v_pure_c = np.zeros((par.T,par.Nb_pd,par.Nm))","def computeInverse2DDCT(self, imge):\n \n # Assuming a square image\n N = imge.shape[0]\n finalInverse2DDCT = np.zeros([N, N], dtype=float)\n for x in xrange(N):\n for y in xrange(N):\n #Compute the DCT value for each cells/points in the resulting transformed image.\n finalInverse2DDCT[x, y] = DCT.__computeSinglePointInverse2DCT(imge, x, y, N)\n return finalInverse2DDCT","def G(U):\n n = U.shape[0]\n G_U = np.zeros([n,1])\n DELTA_x = float(2*L)/(n-1)\n for i in range(n):\n G_U[i][0] = U[(i+1)%n][0]\n G_U[i][0] -= U[(i-1)%n][0]\n G_U[i][0] /= (2* DELTA_x)\n G_U[i][0] += (float(epsilon) * (U[(i+1)%n][0]- U[(i-1)%n][0]) * (U[(i-1)%n][0]+U[(i+1)%n][0]+ U[i][0])/ (4* DELTA_x))\n G_U[i][0] += (float(epsilon) * (U[(i+2)%n][0]- 2*U[(i+1)%n][0]+ 2*U[(i-1)%n][0]- U[(i-2)%n][0]) / (12*( DELTA_x**3)))\n return G_U","def build_binomial_matrix(messi_network):\n binomial_basis = get_binomial_basis(messi_network)\n\n #print(\"Binomial basis: \")\n #print(binomial_basis)\n\n #Las filas de B tienen los binomios\n column_basis = extract_column_basis(np.array(binomial_basis).transpose())\n\n #print(\"Column basis: \")\n #print(column_basis)\n\n return column_basis.transpose()\n #return get_positive_matrix(column_basis.transpose())","def two_qubit_local_invariants(U: np.ndarray) -> np.ndarray:\n U = np.asarray(U)\n if U.shape != (4, 4):\n raise ValueError(\"Unitary must correspond to a two-qubit gate.\")\n\n # Transform to bell basis\n Um = MAGIC.conj().T.dot(U.dot(MAGIC))\n # Get determinate since +- one is allowed.\n det_um = np.linalg.det(Um)\n M = Um.T.dot(Um)\n # trace(M)**2\n m_tr2 = M.trace()\n m_tr2 *= m_tr2\n\n # Table II of Ref. 1 or Eq. 28 of Ref. 2.\n G1 = m_tr2 / (16 * det_um)\n G2 = (m_tr2 - np.trace(M.dot(M))) / (4 * det_um)\n\n # Here we split the real and imag pieces of G1 into two so as\n # to better equate to the Weyl chamber coordinates (c0,c1,c2)\n # and explore the parameter space.\n # Also do a FP trick -0.0 + 0.0 = 0.0\n return np.round([G1.real, G1.imag, G2.real], 12) + 0.0","def inout_creator(df = pd.DataFrame(), features='datosrahm.csv'):\r\n df = df\r\n \r\n start=time.time()\r\n \r\n datos=pd.read_csv(features)\r\n datos=datos.fillna(-1)\r\n\r\n dicc=dict(datos[['Symbol','Z']].values)\r\n\r\n dicc['D']=1\r\n dicc['Bk']=97\r\n dicc['Cf']=98\r\n dicc['Es']=99\r\n dicc['Fm']=100\r\n dicc['Md']=101\r\n dicc['No']=102\r\n dicc['Lr']=103\r\n \r\n max_sitios = max(df['sitios'].values)\r\n \r\n X=np.zeros((len(df),max_sitios,104))\r\n\r\n mult=np.zeros((len(df),max_sitios))\r\n wyckmul=np.load('support/WyckoffSG_dict.npy').item()['wyckmul']\r\n \r\n todelete = list()\r\n \r\n for row in range(len(df)):\r\n item=df['WyckOcc'][row]\r\n sitios=list(item.values()) \r\n sitocc=np.zeros((len(sitios),104)) \r\n spacegroup = str(df['sgnum'][row]).zfill(3)\r\n \r\n try:\r\n \r\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\r\n sitios] for i in j]\r\n \r\n except:\r\n print(row)\r\n print('There exists an error concerning with the space group of CIF ', df['cif'][row],'\\n')\r\n print('Please check in www.crystallography.net to provide the correct space group number of that CIF',\r\n '\\n','\\n')\r\n spacegroup=input('Give me the correct spacegroup:'+'\\n'+'\\n')\r\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\r\n list(df['WyckOcc'][row].values())] for i in j]\r\n \r\n occs=[]\r\n for i in range(len(sitios)):\r\n\r\n for j in list(sitios[i].values()):\r\n \r\n ocupacion=np.array(list(j.values()))\r\n llaves=[llave.replace('+','').replace('-','').replace('1',\r\n '').replace('2','').replace('3','').replace('4',\r\n '') for llave in np.array(list(j.keys()))]\r\n llaves=[llave.replace('.','') for llave in llaves]\r\n llaves=[llave.replace('5','').replace('6','').replace('7',\r\n '').replace('8','').replace('9','').replace('0',\r\n '') for llave in llaves]\r\n vector=np.zeros((1,104))\r\n occs=[sum(ocupacion)]+occs\r\n \r\n try:\r\n \r\n idx=[dicc[k] for k in llaves]\r\n \r\n except:\r\n print('The compound with the cif ', df['cif'][row], ' will be deleted')\r\n print('The database will be updated')\r\n todelete += [row]\r\n \r\n for k in idx:\r\n vector[0][k-1] = ocupacion[idx.index(k)]\r\n \r\n sitocc[i]=vector\r\n \r\n while sitocc.shape[0] != max_sitios:\r\n sitocc=np.concatenate((np.zeros((1,104)),sitocc))\r\n s=[0]+s\r\n \r\n X[row,:,:]=sitocc\r\n mult[row]=s\r\n \r\n features=datos.iloc[:,2:].values\r\n x=X[:,:,:96]\r\n \r\n fracsum = np.expand_dims(np.sum(x,axis=2), axis=2)\r\n \r\n x=np.dot(x,features) \r\n \r\n x = np.delete(x, todelete,axis=0)\r\n df = df.drop(df.index[todelete]).reset_index(drop=True)\r\n \r\n print('inout_creator lasted ',round(time.time()-start,2),' s') \r\n return x, fracsum, df","def chi(nuc, temp=300.0, group_struct=None, phi_g=None, xs_cache=None, eres=101):\n xs_cache = cache.xs_cache if xs_cache is None else xs_cache\n _prep_cache(xs_cache, group_struct, phi_g)\n if isinstance(nuc, collectionsAbc.Iterable) and not isinstance(nuc, basestring):\n return _atom_mass_channel(chi, nuc, temp=temp, xs_cache=xs_cache, eres=eres)\n nuc = nucname.id(nuc)\n key = (nuc, \"chi\", temp)\n\n # Don't recalculate anything if you don't have to\n if key in xs_cache:\n return xs_cache[key]\n\n # Get the the set of nuclides we know we need chi for.\n if \"fissionable_nucs\" not in xs_cache:\n with tb.open_file(nuc_data, \"r\") as f:\n if \"/neutron/cinder_xs/fission\" in f:\n fn = set(f.root.neutron.cinder_xs.fission.cols.nuc)\n else:\n fn = set()\n xs_cache[\"fissionable_nucs\"] = fn\n fissionable_nucs = xs_cache[\"fissionable_nucs\"]\n if (nuc not in fissionable_nucs) and (86 <= nucname.znum(nuc)):\n fissionable_nucs.add(nuc)\n\n # Perform the group collapse on a continuous chi\n E_g = xs_cache[\"E_g\"]\n G = len(E_g) - 1\n chi_g = np.zeros(G, dtype=\"f8\")\n if nuc in fissionable_nucs:\n for g in range(G):\n E_space = np.logspace(np.log10(E_g[g]), np.log10(E_g[g + 1]), eres)\n dnumer = models.chi(E_space)\n numer = scipy.integrate.trapz(dnumer, E_space)\n denom = E_g[g + 1] - E_g[g]\n chi_g[g] = numer / denom\n # renormalize chi\n chi_g = chi_g / chi_g.sum()\n # Put this value back into the cache, with the appropriate label\n xs_cache[key] = chi_g\n return chi_g","def garnet():\n\n rho = 3660.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 306.2; C[0,1] = 112.5; C[0,2] = 112.5; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 306.2; C[1,2] = 112.5; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 306.2; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 92.7; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 92.7; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 92.7\n\n return C, rho","def generate_GS_matrix(self, matrix):\n GS_matrix = Matrix(QQ, matrix.transpose().gram_schmidt()[0]).transpose()\n return GS_matrix","def inverse_gc(g):\n i = g\n j = 1\n while j<N:\n i = i ^ (g >> j)\n j = j + 1\n return i","def blueschist_felsic():\n\n rho = 2970.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 149.85; C[0,1] = 38.7; C[0,2] = 32.59; C[0,3] = -0.15; C[0,4] = -1.; C[0,5] = -0.19\n C[1,0] = C[0,1]; C[1,1] = 163.55; C[1,2] = 30.03; C[1,3] = 1.05; C[1,4] = -1.81; C[1,5] = -1.78\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 121.62; C[2,3] = 0.22; C[2,4] = -0.95; C[2,5] = -0.13\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 48.03; C[3,4] = -0.63; C[3,5] = -1.14\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 48.62; C[4,5] = -0.01\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 58.42\n\n return C, rho","def __init__(self, cnf_grammar):\n self.grammar = cnf_grammar\n self.matrix = []\n self.i = self.k = self.j = 0","def getInverseMatrix(self) -> CMatrix4:\n ...","def initialize_grid(self):\r\n for i in range(self.height):\r\n for j in range(self.width):\r\n self.grid[i][j] = 0\r\n \r\n # fill up unvisited cells\r\n for r in range(self.height):\r\n for c in range(self.width):\r\n if r % 2 == 0 and c % 2 == 0:\r\n self.unvisited.append((r,c))\r\n\r\n self.visited = []\r\n self.path = dict()\r\n self.generated = False","def test_pybel_to_inchi(self):\n pbmol1 = converter.xyz_to_pybel_mol(self.xyz1['dict'])\n pbmol2 = converter.xyz_to_pybel_mol(self.xyz5['dict'])\n pbmol3 = converter.xyz_to_pybel_mol(self.xyz2['dict'])\n pbmol4 = converter.xyz_to_pybel_mol(self.xyz6['dict'])\n\n inchi1 = converter.pybel_to_inchi(pbmol1)\n inchi2 = converter.pybel_to_inchi(pbmol2)\n inchi3 = converter.pybel_to_inchi(pbmol3)\n inchi4 = converter.pybel_to_inchi(pbmol4)\n\n self.assertEqual(inchi1, 'InChI=1/CH4/h1H4')\n self.assertEqual(inchi2, 'InChI=1/CH2NO/c1-2-3/h1H2')\n self.assertEqual(inchi3, 'InChI=1/CH3NOS/c1-2-3-4-1/h2H,1H2')\n self.assertEqual(inchi4, 'InChI=1/CH5NO2S/c1-5(2,3)4/h2-3H,1H3')","def interaction_matrix(self):\n\n self.int_M_called = True\n int_M = np.zeros((self.n, self.n))\n for k in range(self.n):\n for j in range(k+1):\n o = self.attribute_interactions(k, j)\n int_M[k, j] = o.rel_total_ig_ab # Store total information gain\n int_M[j, k] = o.rel_total_ig_ab # TODO: Maybe storing interactions too is not a bad idea\n # TODO: We can than easily sort either by total gain or by positive interaction\n for k in range(self.n):\n int_M[k, k] = self.info_gains[self.data.domain.attributes[k].name]\n self.int_matrix = Orange.misc.distmatrix.DistMatrix(int_M)","def set_interaction(self):\n\n dirInd = \"/Users/asedeki/Drive/environement/Quasi1D/quasi1d/data/inddata\" # My laptop\n N = self.N\n array = np.load(f\"{dirInd}/array_index_n{N}.npy\")\n\n Temps = np.loadtxt(self.g_file, usecols=[\n 1], unpack=True, dtype=np.double)\n if self._temperatures.size == 0:\n self.temperatures = np.unique(Temps)\n\n self.g = {}\n for i in [1, 2, 3]:\n self.g[i] = {}\n G_i = np.loadtxt(self.g_file, usecols=[\n 4+i], unpack=True, dtype=np.double)\n for Ti in self.temperatures:\n GT = G_i[np.where(Temps == Ti)]\n self.g[i][Ti] = np.zeros([N, N, N], dtype=np.double)\n self.set_g(self.g[i][Ti], array, GT)","def homothin():\n\n Iab = se2hmt(binary([[0,0,0],\n [0,1,0],\n [1,1,1]]),\n binary([[1,1,1],\n [0,0,0],\n [0,0,0]]))\n return Iab","def loadasem(loads, IBC, neq):\r\n RHSG = np.zeros([neq])\r\n nl = loads.shape[0]\r\n for i in range(nl):\r\n il = int(loads[i, 0])\r\n ilx = IBC[il, 0]\r\n ily = IBC[il, 1]\r\n ilT = IBC[il, 2]\r\n if ilx != -1:\r\n RHSG[ilx] = loads[i, 1]\r\n if ily != -1:\r\n RHSG[ily] = loads[i, 2]\r\n if ilT != -1:\r\n RHSG[ilT] = loads[i, 3]\r\n return RHSG","def EmptyStateMat(nX,nU,nY):\n Xx = np.zeros((nX,nX)) # Ac \n Yx = np.zeros((nY,nX)) # Gc \n Xu = np.zeros((nX,nU)) # Xu \n Yu = np.zeros((nY,nU)) # Jc \n return Xx,Xu,Yx,Yu","def test_rmg_mol_from_inchi(self):\n mol1 = converter.rmg_mol_from_inchi('InChI=1S/NO2/c2-1-3')\n mol2 = converter.rmg_mol_from_inchi('InChI=1/CH3NOS/c1-2-3-4-1/h2H,1H2')\n\n self.assertTrue(isinstance(mol1, Molecule))\n self.assertTrue(isinstance(mol2, Molecule))\n\n self.assertEqual(mol1.to_smiles(), '[O]N=O')\n self.assertEqual(mol2.to_smiles(), 'C1NOS1')","def build_Xij_inv_matrix(self,Nmesh=64):\n H0, F = self.cosmo.H0, self.cosmo.F\n Lbox = self.attrs['Lbox']\n kgrid = initialize_kgrid(Nmesh,Lbox)\n kmag_grid = np.linalg.norm(kgrid,axis=3)\n w_grid = self.cosmo.Pk_lin(kmag_grid)*(1/Lbox**3)*np.exp(-kmag_grid*kmag_grid*self.RG*self.RG)\n k2 = kmag_grid**2\n k2[0,0,0] = 1 \n #----------------------------------------------------\n cspace = np.arange(0,18)\n \n xij_tensor = [[np.sum(np.conj(Hhats[i](kgrid,k2,H0,F))*Hhats[j](kgrid,k2,H0,F)*w_grid)\n for j in cspace[self.cmask]] for i in cspace[self.cmask]]\n \n xij_tensor = np.array(xij_tensor)\n self.xij_tensor_inv = np.linalg.inv(xij_tensor.real)","def inchi(smi):\n\n ich = automol.inchi.base.hardcoded_object_to_inchi_by_key(\n 'smiles', smi, comp=_compare)\n\n if ich is None:\n rdm = rdkit_.from_smiles(smi)\n ich = rdkit_.to_inchi(rdm)\n return ich","def calc_Hij(self):\n\t\n\tself.Hij = np.zeros((3,3, 3,3, self.N),dtype=np.complex_)\n\t\n\tself.Hij[0,0,:,:,:] = self.Hpij*self.ep[0,0] + self.Hcij*self.ec[0,0]\n\tself.Hij[0,1,:,:,:] = self.Hpij*self.ep[0,1] + self.Hcij*self.ec[0,1]\n\tself.Hij[0,2,:,:,:] = self.Hpij*self.ep[0,2] + self.Hcij*self.ec[0,2]\n\t\n\tself.Hij[1,0,:,:,:] = self.Hpij*self.ep[1,0] + self.Hcij*self.ec[1,0] \n\tself.Hij[1,1,:,:,:] = self.Hpij*self.ep[1,1] + self.Hcij*self.ec[1,1]\n\tself.Hij[1,2,:,:,:] = self.Hpij*self.ep[1,2] + self.Hcij*self.ec[1,2]\n\t\n\tself.Hij[2,0,:,:,:] = self.Hpij*self.ep[2,0] + self.Hcij*self.ec[2,0] \n\tself.Hij[2,1,:,:,:] = self.Hpij*self.ep[2,1] + self.Hcij*self.ec[2,1] \n\tself.Hij[2,2,:,:,:] = self.Hpij*self.ep[2,2] + self.Hcij*self.ec[2,2]\n\t\n\treturn","def setUp(self):\n self.grid = SudukuGrid(BaseCase)\n for i in range(81):\n self.grid[i] = SudukuAlphabet.VALUES[(i+(i//9)*3+i//27)%9]","def get_V(self):\n if not self.gpu:\n self.rho[...] = conj_square(self.psi)\n self.fourier_grid[...] = fft.rfftn(self.rho)\n ft_inv_laplace(self.fourier_grid)\n self.fourier_grid *= 4*np.pi*G\n self.V[...] = fft.irfftn(self.fourier_grid)\n self.V[...] += self.lam*self.rho**2\n else:\n self.g_conj_square(self.g_psi, self.g_rho)\n cufft.cufftExecD2Z(self.rho_plan, self.g_rho.ptr, self.g_fourier.ptr)\n self.g_fourier /= self.psi.shape[0]**3\n self.g_pot_func(self.g_fourier, np.float64(4*np.pi*G/self.N), np.int64(self.fourier_grid.shape[0]), np.int64(self.fourier_grid.shape[1]), np.int64(self.fourier_grid.shape[2]), block=(8,8,8), grid=tuple([(i+7)/8 for i in self.psi_hat.shape]))\n cufft.cufftExecZ2D(self.inv_plan, self.g_fourier.ptr, self.g_V.ptr)\n self.g_V += self.lam*self.g_rho**2","def dolomite():\n\n rho = 2840.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 205.; C[0,1] = 71.; C[0,2] = 57.4; C[0,3] = -19.5; C[0,4] = 13.7; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 205.; C[1,2] = 57.4; C[1,3] = 19.5; C[1,4] = -13.7; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 113.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.8; C[3,4] = 0.; C[3,5] = -13.7\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 39.8; C[4,5] = -19.5\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 67.\n\n return C, rho","def muscovite():\n\n rho = 2834.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 181.; C[0,1] = 48.8; C[0,2] = 25.6; C[0,3] = 0.; C[0,4] = -14.2; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 178.4; C[1,2] = 21.2; C[1,3] = 0.; C[1,4] = 1.1; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 58.6; C[2,3] = 0.; C[2,4] = 1.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 16.5; C[3,4] = 0.; C[3,5] = -5.2\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 19.5; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 72.\n\n return C, rho","def A2Gmat(A, inverse=True):\n G = np.array([\n [A[0], A[3] / 2., A[4] / 2.],\n [A[3] / 2., A[1], A[5] / 2.],\n [A[4] / 2., A[5] / 2., A[2]]])\n if inverse:\n g = nl.inv(G)\n return G, g\n else:\n return G","def fock_ueg(sys, G):\n nbsf = sys.nbasis\n nq = len(sys.qvecs)\n assert nq == len(sys.vqvec)\n\n Fock = numpy.zeros((2, nbsf, nbsf), dtype=numpy.complex128)\n Gkpq = numpy.zeros((2, nq), dtype=numpy.complex128)\n Gpmq = numpy.zeros((2, nq), dtype=numpy.complex128)\n\n for s in [0, 1]:\n coulomb_greens_function(nq, sys.ikpq_i, sys.ikpq_kpq,\n sys.ipmq_i, sys.ipmq_pmq,\n Gkpq[s], Gpmq[s], G[s])\n\n J = build_J_opt(nq, sys.vqvec, sys.vol, sys.nbasis,\n sys.ikpq_i, sys.ikpq_kpq, sys.ipmq_i, sys.ipmq_pmq,\n Gkpq, Gpmq)\n\n K = build_K_opt(nq, sys.vqvec, sys.vol, sys.nbasis,\n sys.ikpq_i, sys.ikpq_kpq, sys.ipmq_i, sys.ipmq_pmq,\n G)\n\n for s in [0, 1]:\n Fock[s] = sys.H1[s] + J[s] + K[s]\n\n return Fock","def Generate(self,the_scf):\n self.dip_ints = the_scf.mol.intor('cint1e_r_sph', comp=3) # component,ao,ao.\n charges = the_scf.mol.atom_charges()\n coords = the_scf.mol.atom_coords()\n self.nuc_dip = np.einsum('i,ix->x', charges, coords)\n return","def FormG():\n for i in range(2):\n for j in range(2):\n G[i, j] = 0.0\n for k in range(2):\n for l in range(2):\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])","def unstructured_grid(self, X, Y, Z):\r\n uX = []\r\n uY = []\r\n uZ = []\r\n l = self.size[0] * self.size[1]\r\n self.Grid = vtk.vtkUnstructuredGrid()\r\n for k in range(self.size[2]):\r\n n = 0\r\n for j in range(self.size[1]):\r\n for i in range(self.size[0]):\r\n # SW-B\r\n swb = 8 * k * l + 4 * l + 2 * self.size[0] * (j + 1) + 2 * n\r\n uX.append(X[swb])\r\n uY.append(Y[swb])\r\n uZ.append(Z[swb])\r\n # SE-B\r\n seb = 8 * k * l + 4 * l + 2 * self.size[0] * (j + 1) + 2 * n + 1\r\n uX.append(X[seb])\r\n uY.append(Y[seb])\r\n uZ.append(Z[seb])\r\n # NE-B\r\n neb = 8 * k * l + 4 * l + 2 * self.size[0] * j + 2 * n + 1\r\n uX.append(X[neb])\r\n uY.append(Y[neb])\r\n uZ.append(Z[neb])\r\n # NW-B\r\n nwb = 8 * k * l + 4 * l + 2 * self.size[0] * j + 2 * n\r\n uX.append(X[nwb])\r\n uY.append(Y[nwb])\r\n uZ.append(Z[nwb])\r\n # SW-T\r\n swt = 8 * k * l + 2 * self.size[0] * (j + 1) + 2 * n\r\n uX.append(X[swt])\r\n uY.append(Y[swt])\r\n uZ.append(Z[swt])\r\n # SE-T\r\n sett = 8 * k * l + 2 * self.size[0] * (j + 1) + 2 * n + 1\r\n uX.append(X[sett])\r\n uY.append(Y[sett])\r\n uZ.append(Z[sett])\r\n # NE-T\r\n net = 8 * k * l + 2 * self.size[0] * j + 2 * n + 1\r\n uX.append(X[net])\r\n uY.append(Y[net])\r\n uZ.append(Z[net])\r\n # NW-T\r\n nwt = 8 * k * l + 2 * self.size[0] * j + 2 * n\r\n uX.append(X[nwt])\r\n uY.append(Y[nwt])\r\n uZ.append(Z[nwt])\r\n\r\n n += 1\r\n\r\n # Set points\r\n points = vtk.vtkPoints()\r\n points.SetNumberOfPoints(2 * self.size[0] * 2 * self.size[1] * 2 * self.size[2])\r\n for i in range(len(uX)):\r\n points.SetPoint(i, [uX[i], uY[i], uZ[i]])\r\n self.Grid.SetPoints(points)\r\n\r\n # Set cells\r\n cells = vtk.vtkCellArray()\r\n cell = vtk.vtkHexahedron()\r\n for i in range(len(uX)):\r\n if i > 0 and i % 8 == 0:\r\n cells.InsertNextCell(cell)\r\n cell.GetPointIds().SetId(i % 8, i)\r\n cells.InsertNextCell(cell)\r\n\r\n self.Grid.SetCells(cell.GetCellType(), cells)","def MS_to_galactic():\n return matrix_transpose(MS_MATRIX)","def diamond(T, Vg_start, Vg_end, Ng, Vd_start, Vd_end, Nd, Cs, Cd, Cg, Gs, Gd, num_e, mode='difcon', dVg=False, filename='simData.dat'):\n Vg = scipy.linspace(Vg_start, Vg_end, Ng)\n Vd = scipy.linspace(Vd_start, Vd_end, Nd)\n data_matrix = []\n for (i_vg, vg) in enumerate(Vg):\n myset=system(vg, Cs, Cd, Cg, Gs, Gd, num_e)\n myset.set_temperature(T)\n myset.pre_processing()\n I = []\n P = []\n V_dot = []\n print \"Vg = \", vg\n for vd in Vd:\n myset.tunnel_rate([0, vd, vg]) \n myset.solver() \n I.append(myset.current('drain','dot'))\n P.append(myset.proba('dot'))\n V_dot.append(myset.voltage('dot'))\n # convert lists to scipy arrays\n I = scipy.array(I)\n P = scipy.array(P)\n V_dot = scipy.array(V_dot)\n # compute the diffential conductance\n if mode == 'current':\n Y = Vd\n F = I\n elif mode == 'difcon':\n F, Y = derive(I, Vd)\n F *= 1e3\n elif mode == 'voltage':\n Y = Vd\n F = V_dot\n elif mode == 'francis':\n F_1, Y = derive(I, Vd)\n F_2, Y = derive(Vd-V_dot, Vd)\n F = F_1/F_2\n F *= 1e3\n elif mode == 'sourcis':\n F_1, Y = derive(I, Vd)\n F_2, Y = derive(V_dot, Vd)\n F = F_1/F_2\n F *= 1e3\n data_matrix.append(F)\n data_matrix = array(data_matrix)\n data_matrix = transpose(data_matrix)\n X = Vg\n \n # Derivate with Vg\n if dVg:\n data_dVg = []\n for vd_i in arange(len(Y)):\n F_dVg, X_dVg = derive(data_matrix[vd_i,:], X)\n F_dVg *= 1e3\n data_dVg.append(F_dVg)\n data_matrix = array(data_dVg)\n X = X_dVg\n \n if filename != 0: \n write_file(data_matrix, filename)\n return data_matrix, X, Y","def local_energy_ueg(system, G, Ghalf=None, two_rdm=None):\n if (system.diagH1):\n ke = numpy.einsum('sii,sii->',system.H1,G)\n else:\n ke = numpy.einsum('sij,sij->',system.H1,G)\n\n Gkpq = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\n Gpmq = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\n Gprod = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\n\n ne = [system.nup, system.ndown]\n nq = numpy.shape(system.qvecs)[0]\n\n for s in [0, 1]:\n # exchange_greens_function(nq, system.ikpq_i, system.ikpq_kpq, system.ipmq_i,system.ipmq_pmq, Gprod[s],G[s])\n # coulomb_greens_function(nq, system.ikpq_i, system.ikpq_kpq, system.ipmq_i, system.ipmq_pmq,Gkpq[s], Gpmq[s],G[s])\n for iq in range(nq):\n Gkpq[s,iq], Gpmq[s,iq] = coulomb_greens_function_per_qvec(system.ikpq_i[iq],\n system.ikpq_kpq[iq],\n system.ipmq_i[iq],\n system.ipmq_pmq[iq],\n G[s])\n Gprod[s,iq] = exchange_greens_function_per_qvec(system.ikpq_i[iq],\n system.ikpq_kpq[iq],\n system.ipmq_i[iq],\n system.ipmq_pmq[iq],\n G[s])\n\n if two_rdm is None:\n two_rdm = numpy.zeros((2,2,len(system.qvecs)), dtype=numpy.complex128)\n two_rdm[0,0] = numpy.multiply(Gkpq[0],Gpmq[0]) - Gprod[0]\n essa = (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[0,0])\n\n two_rdm[1,1] = numpy.multiply(Gkpq[1],Gpmq[1]) - Gprod[1]\n essb = (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[1,1])\n\n two_rdm[0,1] = numpy.multiply(Gkpq[0],Gpmq[1])\n two_rdm[1,0] = numpy.multiply(Gkpq[1],Gpmq[0])\n eos = (\n (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[0,1])\n + (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[1,0])\n )\n\n pe = essa + essb + eos\n\n return (ke+pe, ke, pe)","def HaarGOB(treeG):\n # number of level of the chain (or tree)\n Ntr = len(treeG)\n # reorder chain (optional) \n # reordering each level so that in each level the nodes are in the\n # descent order of degrees\n # compute u_l^c for level J_0 (top level)\n clusterJ0 = treeG[Ntr-1]['clusters']\n N0 = len(clusterJ0)\n # generate indicator function on G^c\n chic = np.identity(N0)\n uc = [None]*N0\n uc[0] = 1/np.sqrt(N0,dtype=np.float64)*np.ones(N0)\n for l in np.arange(1, N0):\n uc[l] = np.sqrt((N0-l)/(N0-l+1))*(chic[l-1,:]-1/(N0-l)*np.sum(chic[l:,:], axis=0))\n# u = copy.deepcopy(uc)\n treeG[Ntr-1]['u'] = uc\n # compute the next level orthonormal basis ulk and stored into u\n for j_tr in np.arange(0, Ntr-1)[::-1]:\n N1 = len(treeG[j_tr]['clusters'])\n u = [None]*N1\n i = N0\n for l in range(N0):\n clusterl = treeG[j_tr+1]['clusters'][l]\n kl = len(clusterl)\n #for k==1\n ucl = uc[l]\n ul1 = np.zeros(N1)\n for j in range(N0):\n idxj = treeG[j_tr+1]['clusters'][j]\n ul1[idxj] = ucl[j]/np.sqrt(len(idxj))\n u[l] = ul1\n if kl>1:\n chil = np.zeros((kl,N1))\n for k in range(kl):\n idxl = treeG[j_tr+1]['clusters'][l]\n chil[k,idxl[k]] = 1;\n \n for k in np.arange(1,kl):\n i = i+1\n ulk = np.sqrt((kl-k)/(kl-k+1))*(chil[k-1,:]-1/(kl-k)*np.sum(chil[k:,:],axis=0))\n u[i-1] = ulk\n treeG[j_tr]['u'] = u\n # update uc and N0\n# uc = copy.deepcopy(u)\n uc = u\n N0 = N1\n return treeG","def to_cdo_grid(self, outfile):","def read_isbi2013_2shell():\n dipy_home = pjoin(os.path.expanduser('~'), '.dipy')\n folder = pjoin(dipy_home, 'isbi2013')\n fraw = pjoin(folder, 'phantom64.nii.gz')\n fbval = pjoin(folder, 'phantom64.bval')\n fbvec = pjoin(folder, 'phantom64.bvec')\n\n md5_dict = {'data': '42911a70f232321cf246315192d69c42',\n 'bval': '90e8cf66e0f4d9737a3b3c0da24df5ea',\n 'bvec': '4b7aa2757a1ccab140667b76e8075cb1'}\n\n check_md5(fraw, md5_dict['data'])\n check_md5(fbval, md5_dict['bval'])\n check_md5(fbvec, md5_dict['bvec'])\n\n bvals, bvecs = read_bvals_bvecs(fbval, fbvec)\n\n gtab = gradient_table(bvals, bvecs)\n img = nib.load(fraw)\n return img, gtab","def incidence_matrix(self):\n try: \n return self._incidence_matrix\n except AttributeError:\n self._incidence_matrix = matrix(ZZ, len(self.Vrepresentation()), \n len(self.Hrepresentation()), 0)\n for V in self.Vrep_generator():\n for H in self.Hrep_generator():\n if self._is_zero(H*V):\n self._incidence_matrix[V.index(),H.index()] = 1\n\n return self._incidence_matrix","def form_population_matrix(N, hb, Nb, ht, Nt, pac, age_and_gender):\n \n # 1 allocated to iso-boxes and tents\n household_column = create_household_column(hb, Nb, ht, Nt)\n # 2 allocate an infector\n disease_column = create_diseasestate_column(N)\n # 3 weibull distribution\n dsymptom_column = create_daystosymptoms_column(N)\n # 4 np.zeros(num_ppl)\n daycount_column = create_daycount_column(N)\n # 5 hard coded number of chronics\n asymp_column = create_asymp_column(N, pac)\n # 6 age\n age_column = create_age_column(age_and_gender[:,0])\n # 7 gender\n gender_column = create_gender_column(age_and_gender[:,1])\n # 8 formula based on age\n chronic_column = create_chronic_column(N, age_column)\n # 5 new_asymp_column[chronic_column==1]=0\n new_asymp_column = adjust_asymp_with_chronic(asymp_column, chronic_column)\n # 9\n wanderer_column = create_wanderer_column(gender_column, age_column)\n\n pop_matrix = np.column_stack((household_column, disease_column, dsymptom_column,\n daycount_column, new_asymp_column, age_column,\n gender_column, chronic_column, wanderer_column))\n \n assert pop_matrix.shape == (N, 9)\n return pop_matrix","def eclogite_massive():\n\n rho = 3490.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 238.85; C[0,1] = 82.01; C[0,2] = 81.44; C[0,3] = 0.3; C[0,4] = -0.02; C[0,5] = 0.5\n C[1,0] = C[0,1]; C[1,1] = 242.12; C[1,2] = 81.11; C[1,3] = -0.66; C[1,4] = 0.33; C[1,5] = 0.12\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 235.57; C[2,3] = -0.28; C[2,4] = 0.22; C[2,5] = 0.31\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 78.72; C[3,4] = 0.27; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 78.37; C[4,5] = 0.25\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 77.91\n\n return C, rho","def run_cgne(data):\r\n\r\n try:\r\n name = data['name']\r\n print(f\"Iniciando algoritmo CGNE\")\r\n\r\n img_path = '/images/' + name\r\n start_time = datetime.now()\r\n\r\n # Carrega a matriz de modelo\r\n matrix_path = \"./files/H-1.csv\"\r\n matrix = pd.read_csv(matrix_path, header=None)\r\n\r\n # Transforma os valores em valores numéricos e caso um deles\r\n # tenha um tipo diferente é registrado como NaN\r\n matrix = matrix.apply(pd.to_numeric, errors='coerce')\r\n matrix = matrix.to_numpy()\r\n\r\n # Inicializa as variáveis descritas no algoritmo\r\n r = np.array(data['signal'])\r\n p = np.dot(np.transpose(matrix), r)\r\n image = np.zeros(len(p))\r\n\r\n for i in range(30):\r\n print(f\"[ITERAÇÃO {i + 1}]\")\r\n # Linha 1 do algoritmo\r\n alpha = np.dot(np.transpose(r), r) / np.dot(np.transpose(p), p)\r\n\r\n # Linha 2 do algoritmo\r\n image = np.add(image, alpha * np.transpose(p))\r\n\r\n # Linha 3 do algoritmo\r\n r_aux = np.copy(r)\r\n r = r - alpha * np.dot(matrix, p)\r\n\r\n # Cálculo de erro. Quando o valor é atingido o algoritmo para.\r\n erro = la.norm(r, 2) - la.norm(r_aux, 2)\r\n if erro < 1e-4:\r\n break\r\n\r\n # Linha 4 do algoritmo\r\n beta = np.dot(np.transpose(r), r) / np.dot(np.transpose(r_aux), r_aux)\r\n\r\n #Linha 5 do algoritmo\r\n p = np.add(np.matmul(np.transpose(matrix), r), beta * p)\r\n\r\n # Encontra o valor absoluto do menor valor no vetor da imagem\r\n min_value = abs(image.min())\r\n\r\n # Encontra o maior valor no vetor da imagem e soma com o menor\r\n max_value = image.max() + min_value\r\n\r\n # Percorre o vetor da imagem alterando-o para que os valores finais\r\n # fiquem entre 0 e 255, assim podendo salvar a imagem em preto e branco\r\n for i in range(3600):\r\n image[0][i] = (image[0][i] + min_value) / max_value * 255\r\n #image[0][i] *= 255\r\n\r\n # Transforma o vetor em uma matriz de 60x60\r\n image = image.reshape((60, 60), order='F')\r\n\r\n # Amplia a imagem em 10 vezes\r\n res = cv.resize(image, (600, 600), interpolation = cv.INTER_NEAREST)\r\n\r\n # Salva a imagem\r\n cv.imwrite('.' + img_path, res)\r\n\r\n # Calcula a duração total do algoritmo\r\n finish_time = datetime.now()\r\n duration = (finish_time - start_time).total_seconds()\r\n duration = str(duration) + 's'\r\n\r\n # Cria um objeto contendo as informações da imagem\r\n image = Images(username=data['user'], \r\n name=data['name'], \r\n algorithm=data['algorithm'],\r\n path=img_path,\r\n size='600x600',\r\n n_iterations=2,\r\n status='Concluído',\r\n start_time=start_time,\r\n finish_time=finish_time,\r\n duration=duration)\r\n\r\n # Salva as informações no banco de dados\r\n db.session.add(image)\r\n db.session.commit()\r\n\r\n print(\"Imagem salva\")\r\n \r\n except Exception as e:\r\n print(\"Não foi possível gerar a imagem\")\r\n print(e)","def olivine():\n\n rho = 3355.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 320.5; C[0,1] = 68.15; C[0,2] = 71.6; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 196.5; C[1,2] = 76.8; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 233.5; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 64.; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 77.; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 78.7\n\n return C, rho","def get_chisq(g, nu, g_h, bins, bin_num2, ig_num): # checked 2017-7-9!!!\n G_h = g - nu * g_h\n num = numpy.histogram(G_h, bins)[0]\n n1 = numpy.flip(num[0:bin_num2],axis=0)\n n2 = num[bin_num2:]\n xi = (n1 - n2) ** 2 / (n1 + n2)\n return numpy.sum(xi[:len(xi)-ig_num]) * 0.5,n1,n2","def casdetude_genetics_sk():\n file_path = PROJECT_PATH + \"/geographycal_data/Monterusciello/MontEdo_buildings\"\n router = Router(building_file=file_path)\n\n router.design_aqueduct(0)\n\n router.write2epanet(router.acqueduct, PROJECT_PATH + \"/Monterusciello_solution/Monterusciello_acqueduct\",\n diam=False)\n\n read_epanet = graphIO.graph_reader(router.acqueduct)\n read_epanet.read_epanet(PROJECT_PATH + \"/geographycal_data/SolvedNet/MonteSolution\")\n\n minimal = router.design_minimal_aqueduct(router.acqueduct, \"Q*H\")\n router.write2epanet(minimal, PROJECT_PATH + \"/Monterusciello_solution/Monterusciello_acqueduct\", diam=False)\n\n read_epanet = graphIO.graph_reader(router.acqueduct)\n read_epanet.read_epanet(PROJECT_PATH + \"/geographycal_data/SolvedNet/prova\")\n kpi_calculator(router.acqueduct)","def cell_cnc_tracker(Out, U, V, W, t, cell0, cellG, cellD, SHP, cryoconite_locations):\n\n Cells = np.random.rand(len(t),SHP[0],SHP[1],SHP[2]) * cell0\n CellD = np.zeros(shape=(SHP[0],SHP[1],SHP[2])) + cellD\n CellG = np.zeros(shape=(SHP[0],SHP[1],SHP[2])) + cellG\n \n \n for i in np.arange(0,SHP[0],1):\n CellG[i,:,:] = np.where(cryoconite_locations==True, CellG[i,:,:]*1000, CellG[i,:,:])\n \n for t in np.arange(0,len(Out.Qz[:,0,0,0]),1):\n \n for layer in np.arange(0,len(Out.Qz[0,:,0,0]),1):\n\n # normalise lateral flow so that -ve= flow out of cells towards edges\n # and positive flow is towards centre line. In Qx -ve = leftwards flow\n # and +ve = rightwards flow. This leads to a rightwards drift in cell\n # fluxes if not normalised in this way.\n U[t,layer,:,0:int(U.shape[2]/2)] = 0-U[t,layer,:,0:int(U.shape[2]/2)]\n\n # nabla is the inverted delta operator used to denote the divergence \n # of a vector field, here applied to hydrological flow in m3/d \n # calculated as dx/dt + dy/dt + dz/dt\n\n nabla = (U[t,layer,:,:] + V[t,layer,:,:] + W[t,layer,:,:])\n \n # divergence gives net in/outflow in m3/t\n # cells/m3 = cells/mL *1000\n\n delC = Out.Q[t,layer,:,:] * (1+CellG[layer,:,:] - CellD[layer,:,:])\n\n Cells[t,layer,:,:] = Cells[t,layer,:,:] + (delC * 1000) \n\n Cells[Cells<0] = 0\n \n CellColumnTot = Cells.sum(axis=1)\n \n\n return Cells, CellColumnTot","def createGloveDic():\n saveGloveDicIntoFile(_extractGloveVects())","def qchannel_to_qiskit(representation):\n\n rep = representation.representation\n # Find what representation it is.\n # Then create the corresponding matrix and shape it like qiskit is expecting it.\n # Finally, create the qiskit representation from that matrix.\n if rep in (RepresentationType.PTM, RepresentationType.CHOI):\n matri = representation.matrix\n data_re = []\n data_im = []\n for i in range(matri.nRows):\n for j in range(matri.nCols):\n data_re.append(matri.data[i * matri.nRows + j].re + 0.j)\n data_im.append(matri.data[i * matri.nRows + j].im)\n data = np.array(data_re)\n data.imag = np.array(data_im)\n data = data.reshape((matri.nRows, matri.nCols))\n return PTM(data) if (rep == RepresentationType.PTM) else Choi(data)\n if rep in (RepresentationType.CHI, RepresentationType.SUPEROP):\n final_data = []\n for matri in representation.basis:\n data_re = []\n data_im = []\n for i in range(matri.nRows):\n for j in range(matri.nCols):\n data_re.append(matri.data[i * matri.nRows + j].re + 0.j)\n data_im.append(matri.data[i * matri.nRows + j].im)\n data = np.array(data_re)\n data.imag = np.array(data_im)\n data = data.reshape((matri.nRows, matri.nCols))\n final_data.append(data)\n if rep == RepresentationType.CHI:\n return Chi(final_data) if len(final_data) > 1 else Chi(final_data[0])\n return SuperOp(final_data) if len(final_data) > 1 else SuperOp(final_data[0])\n if rep == RepresentationType.KRAUS:\n final_data = []\n for matri in representation.kraus_ops:\n data_re = []\n data_im = []\n for i in range(matri.nRows):\n for j in range(matri.nCols):\n data_re.append(matri.data[i * matri.nRows + j].re + 0.j)\n data_im.append(matri.data[i * matri.nRows + j].im)\n data = np.array(data_re)\n data.imag = np.array(data_im)\n data = data.reshape((matri.nRows, matri.nCols))\n final_data.append(data)\n return Kraus(final_data)\n return None","def g(self, t, s, u):\n P, g = s\n return np.matrix([self.Toc * P])","def convert(self):\n with open(self.path) as csvfile:\n reader = csv.reader(csvfile)\n d = []\n for row in reader:\n d+=[row]\n if self.n_class==2:\n dd = []\n for i in range(len(d)):\n if d[i][0] in ['EI','IE']:\n dd+=[d[i]]\n d = dd\n\n random.seed(0)\n random.shuffle(d)\n\n self.x = np.zeros((len(d),len(d[0][2].strip()),4))\n self.y = np.zeros((len(d),self.n_class))\n self.count = Counter([x[0] for x in d])\n for i in range(len(d)):\n self.x_raw += [d[i][2].strip()]\n tmp = [self.base[x] for x in d[i][2].strip()]\n for j in range(len(tmp)):\n if tmp[j]==4:\n # N: A or G or C or T\n self.x[i][j][0] = .25\n self.x[i][j][1] = .25\n self.x[i][j][2] = .25\n self.x[i][j][3] = .25\n elif tmp[j]==5:\n # D: A or G or T\n self.x[i][j][0] = .33\n self.x[i][j][1] = .33\n self.x[i][j][2] = .33\n elif tmp[j]==6:\n # R: A or G\n self.x[i][j][0] = .50\n self.x[i][j][2] = .50\n elif tmp[j]==7:\n # S: C or G\n self.x[i][j][2] = .50\n self.x[i][j][3] = .50\n else:\n self.x[i][j][tmp[j]] = 1\n\n #self.x[i][range(len(tmp)),tmp] = 1\n self.y[i][self.result[d[i][0]]] = 1","def _get_grid_representations(self):\n\n representation = '-----------Loading Sequence----------------------------------------------------------------\\n'\n for row in self.grid:\n for col in row:\n if col == -1:\n representation += 'X\\t'\n elif col == 0:\n representation += '-\\t'\n else:\n representation += str(int(col)) + '\\t'\n representation += '\\n\\n'\n\n representation += '-----------VehicleType--------------------------------------------------------------------\\n'\n for row in self.grid_vehicle_type:\n for col in row:\n if col == -2:\n representation += 'X\\t'\n elif col == -1:\n representation += '-\\t'\n else:\n representation += str(int(col)) + '\\t'\n representation += '\\n\\n'\n\n representation += '-----------Destination--------------------------------------------------------------------\\n'\n for row in self.grid_destination:\n for col in row:\n if col == -1:\n representation += 'X\\t'\n elif col == 0:\n representation += '-\\t'\n else:\n representation += str(int(col)) + '\\t'\n representation += '\\n\\n'\n\n return representation","def _init_numeric(self):\n from scipy.special import erf\n covbasis = np.zeros((self.nmax, self.nmax))\n overbasis = np.zeros((self.nmax, self.nmax))\n #Get local references to these variables so that we don't need `self`\n #all over in the overbasis calculation below.\n alpha = self.alpha\n rb = self.rb\n \n for i in range(self.nmax):\n for j in range(self.nmax):\n covbasis[j,i] = np.exp(-alpha * (rb[i] - rb[j])**2)\n overbasis[j,i] = (np.exp(-alpha*(rb[i]**2+rb[j]**2))*np.sqrt(2.)* \n alpha**1.5*(rb[i] + rb[j]) + \n alpha*np.exp(-0.5*alpha*(rb[i] - rb[j])**2)*\n np.sqrt(np.pi)*\n (1. + alpha*(rb[i] + rb[j])**2)*\n (1.0 + erf(np.sqrt(alpha/2.0)*(rb[i]+rb[j]))))\n \n overbasis /= np.sqrt(128. * alpha**5)\n\n from numpy.linalg import cholesky\n choloverlap = cholesky(overbasis)\n\n for i in range(self.nmax):\n for j in range(i):\n choloverlap[j,i] = 0.0\n\n from numpy.linalg import solve\n self.transformbasis = solve(covbasis, choloverlap)","def reformat_gittins(g, v=None):\n n = g.shape[0]\n g_reformat = np.zeros(g.shape)\n\n for row in range(0, n):\n g_reformat[row, :n - row] = g[row, row:]\n return g_reformat","def pv2ssh(lon, lat, q, hg, c, nitr=1, name_grd=''):\n def compute_avec(vec,aaa,bbb,grd):\n\n avec=np.empty(grd.np0,)\n avec[grd.vp2] = aaa[grd.vp2]*((vec[grd.vp2e]+vec[grd.vp2w]-2*vec[grd.vp2])/(grd.dx1d[grd.vp2]**2)+(vec[grd.vp2n]+vec[grd.vp2s]-2*vec[grd.vp2])/(grd.dy1d[grd.vp2]**2)) + bbb[grd.vp2]*vec[grd.vp2]\n avec[grd.vp1] = vec[grd.vp1]\n\n return avec,\n if name_grd is not None:\n if os.path.isfile(name_grd):\n with open(name_grd, 'rb') as f:\n grd = pickle.load(f)\n else:\n grd = Grid(lon,lat)\n with open(name_grd, 'wb') as f:\n pickle.dump(grd, f)\n f.close()\n else:\n grd = Grid(lon,lat)\n\n ny,nx,=np.shape(hg)\n g=grd.g\n\n\n x=hg[grd.indi,grd.indj]\n q1d=q[grd.indi,grd.indj]\n\n aaa=g/grd.f01d\n bbb=-g*grd.f01d/c**2\n ccc=+q1d\n\n aaa[grd.vp1]=0\n bbb[grd.vp1]=1\n ccc[grd.vp1]=x[grd.vp1] ##boundary condition\n\n vec=+x\n\n avec,=compute_avec(vec,aaa,bbb,grd)\n gg=avec-ccc\n p=-gg\n\n for itr in range(nitr-1):\n vec=+p\n avec,=compute_avec(vec,aaa,bbb,grd)\n tmp=np.dot(p,avec)\n\n if tmp!=0. : s=-np.dot(p,gg)/tmp\n else: s=1.\n\n a1=np.dot(gg,gg)\n x=x+s*p\n vec=+x\n avec,=compute_avec(vec,aaa,bbb,grd)\n gg=avec-ccc\n a2=np.dot(gg,gg)\n\n if a1!=0: beta=a2/a1\n else: beta=1.\n\n p=-gg+beta*p\n\n vec=+p\n avec,=compute_avec(vec,aaa,bbb,grd)\n val1=-np.dot(p,gg)\n val2=np.dot(p,avec)\n if (val2==0.):\n s=1.\n else:\n s=val1/val2\n\n a1=np.dot(gg,gg)\n x=x+s*p\n\n # back to 2D\n h=np.empty((ny,nx))\n h[:,:]=np.NAN\n h[grd.indi,grd.indj]=x[:]\n\n\n return h","def lizardite():\n\n rho = 2610.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 245.; C[0,1] = 50.; C[0,2] = 31.; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 245.; C[1,2] = 31.; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 23.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 11.6; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 11.6; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 97.5\n\n return C, rho","def set_global_problem_vf_3_gr1_bif(self):\n #0\n std_map = Epetra.Map(len(self.all_fine_vols_ic),0,self.comm)\n self.trans_fine = Epetra.CrsMatrix(Epetra.Copy, std_map, 7)\n self.b = Epetra.Vector(std_map)\n for volume in self.all_fine_vols_ic - set(self.neigh_wells_d):\n #1\n soma = 0.0\n soma2 = 0.0\n soma3 = 0.0\n volume_centroid = self.mesh_topo_util.get_average_position([volume])\n adj_volumes = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n global_volume = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\n lamb_w_vol = self.mb.tag_get_data(self.lamb_w_tag, volume)[0][0]\n lamb_o_vol = self.mb.tag_get_data(self.lamb_o_tag, volume)[0][0]\n lbt_vol = lamb_w_vol + lamb_o_vol\n z_vol = self.tz - volume_centroid[2]\n soma = 0.0\n temp_glob_adj = []\n temp_k = []\n flux_gr = []\n for adj in adj_volumes:\n #2\n global_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\n adj_centroid = self.mesh_topo_util.get_average_position([adj])\n z_adj = self.tz - adj_centroid[2]\n altura = adj_centroid[2]\n direction = adj_centroid - volume_centroid\n uni = self.unitary(direction)\n kvol = np.dot(np.dot(kvol,uni),uni)\n #kvol = kvol*(lamb_w_vol + lamb_o_vol)\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\n kadj = np.dot(np.dot(kadj,uni),uni)\n lamb_w_adj = self.mb.tag_get_data(self.lamb_w_tag, adj)[0][0]\n lamb_o_adj = self.mb.tag_get_data(self.lamb_o_tag, adj)[0][0]\n lbt_adj = lamb_w_adj + lamb_o_adj\n\n #kadj = kadj*(lamb_w_adj + lamb_o_adj)\n keq = self.kequiv(kvol, kadj)*((lbt_adj + lbt_vol)/2.0)\n keq = keq*(np.dot(self.A, uni)/float(abs(np.dot(direction, uni))))\n grad_z = (z_adj - z_vol)\n q_grad_z = grad_z*self.gama*keq\n flux_gr.append(q_grad_z)\n\n temp_glob_adj.append(self.map_vols_ic[adj])\n temp_k.append(-keq)\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n #1\n soma2 = -sum(flux_gr)\n temp_k.append(-sum(temp_k))\n temp_glob_adj.append(self.map_vols_ic[volume])\n self.trans_fine.InsertGlobalValues(self.map_vols_ic[volume], temp_k, temp_glob_adj)\n if volume in self.wells_n:\n #2\n index = self.wells_n.index(volume)\n # tipo_de_poco = self.mb.tag_get_data(self.tipo_de_poco_tag, volume)\n if volume in self.wells_inj:\n #3\n self.b[self.map_vols_ic[volume]] += self.set_q[index] + soma2\n #2\n else:\n #3\n self.b[self.map_vols_ic[volume]] += -self.set_q[index] + soma2\n #1\n else:\n #2\n self.b[self.map_vols_ic[volume]] += soma2\n #0\n for volume in self.neigh_wells_d:\n #1\n soma2 = 0.0\n soma3 = 0.0\n volume_centroid = self.mesh_topo_util.get_average_position([volume])\n z_vol = self.tz - volume_centroid[2]\n adj_volumes = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n global_volume = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\n lamb_w_vol = self.mb.tag_get_data(self.lamb_w_tag, volume)[0][0]\n lamb_o_vol = self.mb.tag_get_data(self.lamb_o_tag, volume)[0][0]\n lbt_vol = lamb_w_vol + lamb_o_vol\n soma = 0.0\n temp_glob_adj = []\n temp_k = []\n flux_gr = []\n for adj in adj_volumes:\n #2\n global_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\n adj_centroid = self.mesh_topo_util.get_average_position([adj])\n z_adj = self.tz - adj_centroid[2]\n altura = adj_centroid[2]\n direction = adj_centroid - volume_centroid\n uni = self.unitary(direction)\n z = uni[2]\n kvol = np.dot(np.dot(kvol,uni),uni)\n #kvol = kvol*(lamb_w_vol + lamb_o_vol)\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\n kadj = np.dot(np.dot(kadj,uni),uni)\n lamb_w_adj = self.mb.tag_get_data(self.lamb_w_tag, adj)[0][0]\n lamb_o_adj = self.mb.tag_get_data(self.lamb_o_tag, adj)[0][0]\n lbt_adj = lamb_o_adj + lamb_o_adj\n #kadj = kadj*(lamb_w_adj + lamb_o_adj)\n keq = self.kequiv(kvol, kadj)*((lbt_adj + lbt_vol)/2.0)\n keq = keq*(np.dot(self.A, uni)/(abs(np.dot(direction, uni))))\n grad_z = (z_adj - z_vol)\n q_grad_z = grad_z*self.gama*keq\n flux_gr.append(q_grad_z)\n #2\n if adj in self.wells_d:\n #3\n soma = soma + keq\n index = self.wells_d.index(adj)\n self.b[self.map_vols_ic[volume]] += self.set_p[index]*(keq)\n #2\n else:\n #3\n temp_glob_adj.append(self.map_vols_ic[adj])\n temp_k.append(-keq)\n soma = soma + keq\n #2\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n #1\n soma2 = -sum(flux_gr)\n temp_k.append(soma)\n temp_glob_adj.append(self.map_vols_ic[volume])\n self.trans_fine.InsertGlobalValues(self.map_vols_ic[volume], temp_k, temp_glob_adj)\n if volume in self.wells_n:\n #2\n index = self.wells_n.index(volume)\n # tipo_de_poco = self.mb.tag_get_data(self.tipo_de_poco_tag, volume)\n if volume in self.wells_inj:\n #3\n self.b[self.map_vols_ic[volume]] += self.set_q[index] + soma2\n #2\n else:\n #3\n self.b[self.map_vols_ic[volume]] += -self.set_q[index] + soma2\n #1\n else:\n #2\n self.b[self.map_vols_ic[volume]] += soma2\n #0\n self.trans_fine.FillComplete()","def connect_gfluct(self, E_e=0, E_i=-65):\n if self.do_run:\n \n for m in self.flucts:\n del m \n del self.flucts\n \n for m in self.noises:\n del m \n del self.noises\n \n self.flucts = []\n self.noises = []\n \n for n in range(self.n_celltypes):\n \n fluct_g_i0_n = self.fluct_g_i0[n]\n \n if type(fluct_g_i0_n) is not ndarray: fluct_g_i0_n = np.array([fluct_g_i0_n])\n \n if len(fluct_g_i0_n) == len(self.global_gidlist[n]):\n pass\n else:\n fluct_g_i0_n = np.ones(int(len(self.global_gidlist[n])))*fluct_g_i0_n[0]\n if self.id == 0: print \"- single value in fluct_g_i0_n\"\n \n #print fluct_g_i0_n\n \n for i, gid in enumerate(self.gidlist[n]): # for every cell in the gidlist \n \n #h.mcell_ran4_init(gid)\n \n noiseRandObj = h.Random() # provides NOISE with random stream\n self.noises.append(noiseRandObj) # has to be set here not inside the nmodl function!! \n \n # print str(gid) + \": \" + str(noiseRandObj.normal(0,1))\n \n fluct = h.Gfluct3(self.cells[n][i].soma(0.5))\n fluct.E_e = E_e/mV # [mV]\n fluct.E_i = E_i/mV # [mV]\n fluct.g_e0 = self.fluct_g_e0[n]/uS # [uS]\n fluct.g_i0 = fluct_g_i0_n[i]/uS # [uS]\n fluct.std_e = self.fluct_std_e[n]/uS # [uS] \n fluct.std_i = self.fluct_std_i[n]/uS # [uS] \n fluct.tau_e = self.fluct_tau_e/ms #tau_e/ms # [ms] \n fluct.tau_i = self.fluct_tau_i/ms #tau_i/ms # [ms]\n \n self.flucts.append(fluct) # add to list \n self.flucts[-1].noiseFromRandom(self.noises[-1]) # connect random generator!\n \n self.noises[-1].MCellRan4(1, gid+1) # set lowindex to gid+1, set highindex to > 0 \n self.noises[-1].normal(0,1)","def to_ijv(self):\n row_indices = []\n column_indices = []\n nonzero_elements = []\n k = 0\n for key, value in self.data.items():\n if value == 0:\n continue\n row, col = key[:2]\n row_indices.append(row)\n column_indices.append(col)\n nonzero_elements.append(value)\n k += 1\n return row_indices, column_indices, nonzero_elements","def transform(self,G):\n\n n = len(self.G_train_)\n nt = len(G)\n #Ks = sp.zeros((n,1))\n kernel_matrix = sp.zeros((nt,n))\n \n# for j in range(n):\n# Ks[j] = sp.sqrt(aGMKernel(self.G_train_[j],self.G_train_[j],self.alpha,self.gamma))\n# \n# for i in range(nt):\n# Kts = sp.sqrt(aGMKernel(G[i],G[i],self.alpha,self.gamma))\n# for j in range(n):\n# kernel_matrix[i,j] = aGMKernel(G[i],self.G_train_[j],self.alpha,self.gamma)/Kts/Ks[j]\n \n for i in range (nt):\n for j in range(n):\n kernel_matrix[i,j] = aGMKernel(G[i],self.G_train_[j],self.alpha, self.gamma)\n \n \n return kernel_matrix","def read_input():\n \n argv = sys.argv\n\n # Read file names from sd input\n f_dy = argv[1] # matdyn.modes\n f_pat = argv[2] # path.out (should be in crystal coords)\n f_ph = argv[3] # ph.x output (Gamma point)\n\n # Read input card\n f_inp = open(\"input.dat\",'r')\n l1 = f_inp.readline()\n l2 = f_inp.readline()\n l3 = f_inp.readline().split()\n f_inp.close()\n\n # Open files\n\n f = open(f_dy,'r') # matdyn.modes \n f_dyn = f.readlines()\n f.close()\n\n f = open(f_pat,'r') # path.out\n f_path = f.readlines()\n f.close()\n\n f = open(f_ph,'r') # ph.x output\n f_zs = f.readlines()\n f.close()\n\n # Assign values to a0, nat, M, nqp\n a0, vol = float(l1.split()[0]), float(l1.split()[1])\n nat = int(l2) \n mass = np.zeros(nat)\n for iat in range(nat):\n mass[iat] = float(l3[iat])\n\n # Assign values to G (reciprocal lattice vec)\n ig = 0 ; i = 0\n for line in f_zs:\n if \"reciprocal axes:\" in line:\n ig = i + 1 \n break\n i += 1 \n\n rG = np.zeros((3,3))\n for ic in range(3):\n rGtext = f_zs[ig+ic][23:48].split()\n rG[ic,:] = np.array([float(rGtext[0]), float(rGtext[1]), float(rGtext[2])])\n\n # Read Z* tensor from f_zs\n i = 0\n iz = 0\n zstart = []\n for line in f_zs:\n if \"(d P / du)\" in line:\n iz = i + 3\n if \"Px\" in line:\n zstart.append(i)\n\n i += 1\n\n # Read the dielectric tensor from f_zs\n i = 0\n ie = 0\n for line in f_zs:\n if \"Dielectric constant in cartesian axis\" in line:\n ie = i + 2\n break\n\n i += 1\n\n # Assign Z* values\n zs = np.zeros((nat,3,3)) # initialize Z*\n\n for iat in range(nat):\n for ic in range(3):\n ztext = f_zs[zstart[iat]+ic][19:56].split()\n for jc in range(3):\n zs[iat][ic][jc] = float(ztext[jc])\n\n # Assing the dielectric tensor\n eps = np.zeros((3,3))\n\n for ic in range(3):\n epstext = f_zs[ie+ic][16:66].split()\n for jc in range(3):\n eps[ic][jc] = float(epstext[jc])\n\n # Number of modes and q-points\n nmodes = 3 * nat\n nqpt = int(f_path[0].split()[0])\n\n # Read the q-points\n q = np.zeros((nqpt,4)) # 4th dimension is lenght for q-points on a line, weights for q-points on a grid \n for iq in range(1,nqpt+1):\n q[iq-1,] = np.array([float(f_path[iq].split()[0]),float(f_path[iq].split()[1]), \\\n float(f_path[iq].split()[2]),float(f_path[iq].split()[3])])\n\n # Read the eigenvalues(om) and eigenvectors(eig) \n # Initiate first\n om = np.zeros((nmodes,nqpt))\n eig = np.zeros((nmodes,nqpt,nat,3), dtype=complex) \n\n # Get the starting lines for each q-pt\n i = 0\n i_q = []\n for line in f_dyn:\n if \"q =\" in line:\n i_q.append(i+2)\n i += 1\n\n #Assign values to om and eig\n for iq in range(nqpt):\n for imod in range(nmodes):\n omtext = f_dyn[i_q[iq]+imod*(nat+1)][43:55]\n om[imod][iq] = float(omtext)\n for iat in range(nat):\n etext = f_dyn[i_q[iq]+imod*(nat+1)+iat+1][2:72].split()\n for ic in range(3):\n eig.real[imod][iq][iat][ic]=float(etext[2*ic])*np.sqrt(mass[iat])\n eig.imag[imod][iq][iat][ic]=float(etext[2*ic+1])*np.sqrt(mass[iat])\n\n #Normalize the eigenvectors\n t1 = eig[imod,iq,:,:]\n t_nu = np.sum(np.sum(np.conjugate(t1)*t1,axis=0))\n eig[imod,iq,:,:] = eig[imod,iq,:,:]/np.sqrt(np.abs(t_nu))\n\n # Check normalization\n delta = np.zeros((nmodes,nmodes), dtype=complex)\n for iat in range(nat):\n for ic in range(3):\n t2 = eig[:,iq,iat,ic]\n delta += np.outer(np.conjugate(t2),t2)\n\n unit = np.diag(np.diag(np.ones((nmodes,nmodes)))) # Unit vector\n test = np.abs( (delta-unit) )\n if ( np.max(test) > 1e-3):\n print \"Non-orthonormal eigenvector at iq=\", q[iq,:]\n\n return om, eig, q, zs, eps, mass, a0, vol, rG, nmodes, nqpt, nat","def local_composition(self, outfile):\n # TODO Rewrite if I ever need this again\n radius = 3.6 * 2\n npix = 64\n #mat = np.zeros((npix,npix,npix),dtype=np.float)\n #mat = np.zeros((npix,npix,npix),dtype={'names':['col1', 'col2', 'col3'], 'formats':['f4','f4','f4']})\n #mat = np.zeros((npix,npix,npix),dtype={'names':['40', '13', '29'], 'formats':['f4','f4','f4']})\n #mat = np.zeros((npix,npix,npix),dtype={'names':['id','data'], 'formats':['f4','f4']})\n #names = ['id','data']\n #formats = ['i4',('f4','f4','f4')]\n #mat = np.zeros((npix,npix,npix),dtype=dict(names = names, formats=formats))\n #mat = np.zeros((npix,npix,npix),dtype={'40':('i4',0), '29':('f4',0), '13':('f4',0)})\n print(\"Creating matrix...\")\n mat = [[[{} for i in range(npix)] for j in range(npix)] for k in range(npix)]\n print(\"Finished creating matrix.\")\n #print(repr(mat))\n dx = self.xsize/npix\n dy = self.ysize/npix\n dz = self.zsize/npix\n for ii,i in enumerate(drange(-npix/2*dx,npix/2*dx-dx,dx)):\n print(\"On ii = {0}\".format(ii))\n for jj,j in enumerate(drange(-npix/2*dy,npix/2*dy-dy,dy)):\n for kk,k in enumerate(drange(-npix/2*dz,npix/2*dz-dz,dz)):\n atoms = self.get_atoms_in_cutoff( (i,j,k), radius )\n comp = {}\n for atom in atoms:\n comp[str(atom.z)] = comp.get(str(atom.z),0) + 1.0\n for key in comp:\n comp[key] /= len(atoms)\n #print(comp)\n #mat[ii][jj][kk] = copy.copy(comp)\n mat[ii][jj][kk] = comp\n of = open(outfile,'w')\n of.write('IGOR\\n')\n for atomtype in self.atomtypes:\n of.write('\\nWAVES/N=({0},{1},{2})\\t {3}\\nBEGIN\\n'.format(npix,npix,npix,'partial_comp_'+znum2sym.z2sym(atomtype)))\n for layer in mat:\n for column in layer:\n for value in column:\n try:\n of.write(\"{0} \".format(value[str(atomtype)]))\n except KeyError:\n of.write(\"{0} \".format(0.0))\n of.write(\"\\n\")\n of.write('END\\n')\n of.write('X SetScale/P x 0,1,\"\", {0}; SetScale/P y 0,1,\"\", {0}; SetScale/P z 0,1,\"\", {0}; SetScale d 0,0,\"\", {0}\\n'.format('partial_comp_'+znum2sym.z2sym(atomtype)))\n of.close()\n return mat","def __init__(self):\n #self.solitaire = np.zeros((7, 13), dtype=object)\n #self.leaf_cards = np.zeros(7, dtype=object)\n # # Keep track of the facedown cards in each of the 7 piles\n #self.col_facedown: list = [0, 1, 2, 3, 4, 5, 6]\n pass","def assign_vertices(self):\n CV_matrix = np.zeros((self.n_c, self.n_v, 3))\n for i in range(3):\n CV_matrix[self.tris[:, i], np.arange(self.n_v), i] = 1\n self.CV_matrix = CV_matrix\n return self.CV_matrix","def assemble_matrices(self):\n \n #Pointer reassignment for convenience\n N = self.ngrids\n\n #Begin with a linked-list data structure for the transmissibilities,\n #and one-dimenstional arrays for the diagonal of B and the flux vector\n T = lil_matrix((N, N), dtype=np.double)\n B = np.zeros(N, dtype=np.double)\n Q = np.zeros(N, dtype=np.double)\n\n #Read in boundary condition types and values\n bcs = self.input_data['boundary conditions']\n bc_type_1 = bcs['left']['type'].lower()\n bc_type_2 = bcs['right']['type'].lower()\n bc_value_1 = bcs['left']['value']\n bc_value_2 = bcs['right']['value']\n \n #Loop over all grid cells\n for i in range(N):\n\n #Apply left BC\n if i == 0:\n T[i, i+1] = -self.compute_transmissibility(i, i + 1)\n\n if bc_type_1 == 'neumann':\n T[i, i] = T[i,i] - T[i, i+1]\n elif bc_type_1 == 'dirichlet':\n #Computes the transmissibility of the ith block\n T0 = self.compute_transmissibility(i, i)\n T[i, i] = T[i,i] - T[i, i+1] + 2.0 * T0\n Q[i] = 2.0 * T0 * bc_value_1\n else:\n pass #TODO: Add error checking here if no bc is specified\n\n #Apply right BC\n elif i == (N - 1):\n T[i, i-1] = -self.compute_transmissibility(i, i - 1)\n\n if bc_type_2 == 'neumann':\n T[i, i] = T[i,i] - T[i, i-1]\n elif bc_type_2 == 'dirichlet':\n #Computes the transmissibility of the ith block\n T0 = self.compute_transmissibility(i, i)\n T[i, i] = T[i, i] - T[i, i-1] + 2.0 * T0\n Q[i] = 2.0 * T0 * bc_value_2\n else:\n pass #TODO:Add error checking here if no bc is specified\n\n #If there is no boundary condition compute interblock transmissibilties\n else:\n T[i, i-1] = -self.compute_transmissibility(i, i-1)\n T[i, i+1] = -self.compute_transmissibility(i, i+1)\n T[i, i] = (self.compute_transmissibility(i, i-1) +\n self.compute_transmissibility(i, i+1))\n\n #Compute accumulations\n B[i] = self.compute_accumulation(i)\n\n #If constant-rate wells are present, add them to the flux vector\n if self.rate_well_grids is not None:\n Q[self.rate_well_grids] += self.rate_well_values\n\n \n #Return sparse data-structures\n return (T.tocsr(), \n csr_matrix((B, (np.arange(N), np.arange(N))), shape=(N,N)), \n Q)","def H(self) -> BaseMatrix:","def H(self) -> BaseMatrix:"],"string":"[\n \"def invcell2Gmat(invcell):\\n G = fillgmat(invcell)\\n g = nl.inv(G)\\n return G, g\",\n \"def complete_disp(IBC, nodes, UG):\\r\\n nnodes = nodes.shape[0]\\r\\n UC = np.zeros([nnodes, 3], dtype=np.float)\\r\\n for row in range(nnodes):\\r\\n for col in range(3):\\r\\n cons = IBC[row, col]\\r\\n if cons == -1:\\r\\n UC[row, col] = 0.0\\r\\n else:\\r\\n UC[row, col] = UG[cons]\\r\\n\\r\\n return UC\",\n \"def biotite():\\n\\n rho = 2800.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 186.; C[0,1] = 32.4; C[0,2] = 11.6; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 186.; C[1,2] = 11.6; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 54.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 5.8; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 5.8; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 76.8\\n\\n return C, rho\",\n \"def get_UBmat(self, i, j, hi, hj):\\n h1c = (self.Bmat * vec(*hi)).T\\n h2c = (self.Bmat * vec(*hj)).T\\n\\n t1c = norm_vec(h1c)\\n t3c = norm_vec(np.cross(h1c, h2c))\\n t2c = norm_vec(np.cross(h1c, t3c))\\n Tc = np.concatenate((t1c, t2c, t3c)).T\\n\\n g1 = self.Gvec(self.xp[i], self.yp[i], self.zp[i]).T\\n g2 = self.Gvec(self.xp[j], self.yp[j], self.zp[j]).T\\n\\n t1g = norm_vec(g1)\\n t3g = norm_vec(np.cross(g1, g2))\\n t2g = norm_vec(np.cross(g1, t3g))\\n Tg = np.concatenate((t1g, t2g, t3g)).T\\n\\n return Tg * np.linalg.inv(Tc)\",\n \"def cell2Gmat(cell):\\n g = fillgmat(cell)\\n G = nl.inv(g)\\n return G, g\",\n \"def convImg2gMatrix(self):\\n self.file = self.file.convert(\\\"L\\\")\\n matrix = numpy.asarray(self.file)\\n # convert image to gray scale image\\n # in the array, 0 represents black\\n # 255 represents white\\n # array[a,b] => a represents the line, b represents the columns\\n # array[0,0] is the pixel in the top-left hand corner\\n return matrix\",\n \"def main():\\r\\n # set print options to use lower precision\\r\\n printopt = np.get_printoptions()\\r\\n np.set_printoptions(formatter={'float': '{:8.2g}'.format}, linewidth=200)\\r\\n\\r\\n # create special matrix, the so-called Hilbert-matrix Aij = 1 / (i + j + 1)\\r\\n A = hilbert(10)\\r\\n Q = gramschmidt(A)\\r\\n\\r\\n # matrix according to theory should be unit matrix:\\r\\n I = np.dot(Q.T, Q)\\r\\n print('I = \\\\n{}'.format(I))\\r\\n\\r\\n # numpy's internal orthogonaliztation by QR-decomposition\\r\\n Q1, R1 = np.linalg.qr(A)\\r\\n D = A - np.dot(Q1, R1)\\r\\n print('D = \\\\n{}'.format(D))\\r\\n I1 = np.dot(Q1.T, Q1)\\r\\n print('I1 = \\\\n{}'.format(I1))\\r\\n\\r\\n np.set_printoptions(**printopt)\",\n \"def setup(self):\\n igd = self.options['input_grid_data']\\n ogd = self.options['output_grid_data']\\n output_subset = self.options['output_subset']\\n\\n if ogd is None:\\n ogd = igd\\n\\n # Build the interpolation matrix which maps from the input grid to the output grid.\\n # Rather than a single phase-wide interpolating polynomial, map each segment.\\n # To do this, find the nodes in the output grid which fall in each segment of the input\\n # grid. Then build a Lagrange interpolating polynomial for that segment\\n L_blocks = []\\n output_nodes_ptau = list(ogd.node_ptau[ogd.subset_node_indices[output_subset]])\\n\\n for iseg in range(igd.num_segments):\\n i1, i2 = igd.segment_indices[iseg]\\n iptau_segi = np.take(igd.node_ptau, (i1, i2-1))\\n istau_segi = np.take(igd.node_stau, (i1, i2-1))\\n\\n # The indices of the output grid that fall within this segment of the input grid\\n if ogd is igd:\\n optau_segi = iptau_segi\\n else:\\n ptau_hi = igd.segment_ends[iseg+1]\\n if iseg < igd.num_segments - 1:\\n idxs_in_iseg = np.where(output_nodes_ptau <= ptau_hi)[0]\\n else:\\n idxs_in_iseg = np.arange(len(output_nodes_ptau))\\n optau_segi = np.asarray(output_nodes_ptau)[idxs_in_iseg]\\n # Remove the captured nodes so we don't accidentally include them again\\n output_nodes_ptau = output_nodes_ptau[len(idxs_in_iseg):]\\n\\n # Now get the output nodes which fall in iseg in iseg's segment tau space.\\n ostau_segi = 2.0 * (optau_segi - iptau_segi[0]) / (iptau_segi[-1] - iptau_segi[0]) - 1\\n\\n # Create the interpolation matrix and add it to the blocks\\n L, _ = lagrange_matrices(istau_segi, ostau_segi)\\n L_blocks.append(L)\\n\\n self.interpolation_matrix = block_diag(*L_blocks)\\n r, c = np.nonzero(self.interpolation_matrix)\\n\\n output_num_nodes, input_num_nodes = self.interpolation_matrix.shape\\n\\n for (name, kwargs) in self._timeseries_outputs:\\n\\n input_kwargs = {k: kwargs[k] for k in ('units', 'desc')}\\n input_name = 'input_values:{0}'.format(name)\\n self.add_input(input_name,\\n shape=(input_num_nodes,) + kwargs['shape'],\\n **input_kwargs)\\n\\n output_name = name\\n output_kwargs = {k: kwargs[k] for k in ('units', 'desc')}\\n output_kwargs['shape'] = (output_num_nodes,) + kwargs['shape']\\n self.add_output(output_name, **output_kwargs)\\n\\n self._vars.append((input_name, output_name, kwargs['shape']))\\n\\n size = np.prod(kwargs['shape'])\\n val_jac = np.zeros((output_num_nodes, size, input_num_nodes, size))\\n\\n for i in range(size):\\n val_jac[:, i, :, i] = self.interpolation_matrix\\n\\n val_jac = val_jac.reshape((output_num_nodes * size, input_num_nodes * size),\\n order='C')\\n\\n val_jac_rows, val_jac_cols = np.where(val_jac != 0)\\n\\n rs, cs = val_jac_rows, val_jac_cols\\n self.declare_partials(of=output_name,\\n wrt=input_name,\\n rows=rs, cols=cs, val=val_jac[rs, cs])\",\n \"def importIGG():\\n df = load_file(\\\"data-luminex-igg\\\")\\n df = pd.melt(df, id_vars=[\\\"subject\\\"])\\n\\n df[\\\"variable\\\"] = df[\\\"variable\\\"].str.replace(\\\"IgG.\\\", \\\"\\\", regex=False)\\n\\n return df\",\n \"def _gto_from_ccdata(self):\\n\\n gbasis = self.ccdata.gbasis\\n lines = []\\n\\n for no, basis in enumerate(gbasis):\\n lines.append(f\\\"{no + 1:3d} 0\\\")\\n for prims in basis:\\n lines.append(f\\\"{prims[0].lower():s} {len(prims[1]):5d} 1.00\\\")\\n for prim in prims[1]:\\n lines.append(f\\\"{prim[0]:15.9e} {prim[1]:15.9e}\\\")\\n lines.append('')\\n lines.append('')\\n return lines\",\n \"def matrix_readin(suffix='full'):\\n\\n ## Define arrays for covariance matrices\\n c2s = np.zeros([2, 2, n_bins, n_bins])\\n c3s = np.zeros([2, 2, 2, n_bins, n_bins])\\n c4s = np.zeros([2, 2, 2, 2, n_bins, n_bins])\\n ## Normalization arrays for covariance matrices\\n n2s = np.zeros([2, 2])\\n n3s = np.zeros([2, 2, 2])\\n n4s = np.zeros([2, 2, 2, 2])\\n\\n for ii in range(len(I1)):\\n index4 = \\\"%d%d,%d%d\\\" % (I1[ii], I2[ii], I3[ii], I4[ii])\\n index3 = \\\"%d,%d%d\\\" % (I2[ii], I1[ii], I3[ii])\\n index2 = \\\"%d%d\\\" % (I1[ii], I2[ii])\\n\\n j1, j2, j3,j4=I1[ii]-1, I2[ii]-1, I3[ii]-1, I4[ii]-1 # internal indexing\\n\\n # Define input files\\n file_root_all = os.path.join(file_root, 'CovMatricesAll/')\\n\\n if suffix=='full':\\n counts_file = file_root_all + 'total_counts_n%d_m%d_%s.txt' % (n, m, index4)\\n # Load total number of counts\\n try:\\n total_counts=np.loadtxt(counts_file)\\n print(\\\"Reading in integral components for C_{%s}, which used %.2e pairs, %.2e triples and %.2e quads of particles\\\" % (index4,total_counts[0], total_counts[1], total_counts[2]))\\n except (FileNotFoundError, IOError): pass\\n else:\\n pass\\n #print(\\\"Reading in integral components for C_{%s}, iteration %s\\\"%(index4,suffix))\\n\\n # Load full integrals\\n c2 = np.diag(np.loadtxt(file_root_all + 'c2_n%d_m%d_%s_%s.txt' % (n, m, index2, suffix))[skip_bins:])\\n c3 = np.loadtxt(file_root_all + 'c3_n%d_m%d_%s_%s.txt' % (n, m, index3, suffix))[skip_bins:, skip_bins:]\\n c4 = np.loadtxt(file_root_all + 'c4_n%d_m%d_%s_%s.txt' % (n, m, index4, suffix))[skip_bins:, skip_bins:]\\n\\n # Now save components\\n c2s[j1, j2] += c2\\n n2s[j1, j2] += 1\\n c3s[j2, j1, j3] += c3\\n n3s[j2, j1, j3] += 1\\n # will deal with c4s/n4s later\\n\\n # c2 symmetry - indices interchanged, ensures matrix symmetry if they are equal\\n c2s[j2, j1] += c2\\n n2s[j2, j1] += 1\\n\\n # c3 symmetry - last two indices interchanged, ensures matrix symmetry if they are equal\\n c3s[j2, j3, j1] += c3.T\\n n3s[j2, j3, j1] += 1\\n \\n # All symmetries possible for c4 without transpositions\\n permutations4 = ((j1, j2, j3, j4), # original\\n (j2, j1, j3, j4), # first two indices interchanged\\n (j1, j2, j4, j3), # last two indices interchanged\\n (j2, j1, j4, j3), # first and last two indices interchanged at the same time\\n )\\n \\n for (i1, i2, i3, i4) in permutations4:\\n c4s[i1, i2, i3, i4] += c4\\n n4s[i1, i2, i3, i4] += 1\\n # now swap indices and transpose\\n c4s[i3, i4, i1, i2] += c4.T\\n n4s[i3, i4, i1, i2] += 1\\n \\n # normalize the covariances\\n c2s /= n2s[:, :, None, None]\\n c3s /= n3s[:, :, :, None, None]\\n c4s /= n4s[:, :, :, :, None, None]\\n\\n def construct_fields(j1, j2, j3, j4, alpha1, alpha2):\\n # Reconstruct the full field for given input fields and rescaling parameters\\n\\n # Create kronecker deltas\\n d_xw = (j1 == j4)\\n d_xz = (j1 == j3)\\n d_yw = (j2 == j4)\\n d_yz = (j2 == j3)\\n\\n full = c4s[j1, j2, j3, j4] + 0.25 * alpha1 * (d_xw * c3s[j1, j2, j3] + d_xz * c3s[j1, j2, j4]) + 0.25 * alpha2 * (d_yw * c3s[j2, j1, j3] + d_yz * c3s[j2, j1, j4]) + 0.5 * alpha1 * alpha2 * (d_xw * d_yz + d_xz * d_yw) * c2s[j1, j2]\\n return full\\n\\n # Index in ordering (P_11,P_12,P_22)\\n cov_indices = [[0, 0], [0, 1], [1, 1]]\\n\\n c_tot = np.zeros([3, 3, n_bins, n_bins]) # array with each individual covariance accessible\\n c_comb = np.zeros([3*n_bins, 3*n_bins]) # full array suitable for inversion\\n\\n for j1 in range(3):\\n ind1, ind2 = cov_indices[j1]\\n alpha1, alpha2 = alpha_best[[ind1, ind2]]\\n for j2 in range(3):\\n ind3,ind4 = cov_indices[j2]\\n tmp = construct_fields(ind1, ind2, ind3, ind4, alpha1, alpha2)\\n c_tot[j1, j2] = tmp\\n c_comb[j1*n_bins:(j1+1)*n_bins, j2*n_bins:(j2+1)*n_bins] = tmp\\n\\n return c_tot, 0.5*(c_comb+c_comb.T) # add all remaining symmetries\",\n \"def harzburgite():\\n\\n rho = 3200.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 226.5; C[0,1] = 75.34; C[0,2] = 74.73; C[0,3] = -0.27; C[0,4] = -2.00; C[0,5] = 1.85\\n C[1,0] = C[0,1]; C[1,1] = 242.8; C[1,2] = 73.68; C[1,3] = -3.6; C[1,4] = -1.91; C[1,5] = 4.14\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 230.; C[2,3] = -4.36; C[2,4] = -4.27; C[2,5] = -0.27\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 80.75; C[3,4] = 1.81; C[3,5] = -2.19\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 76.94; C[4,5] = -1.88\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.15\\n\\n return C, rho\",\n \"def make_Gr(mlat, *J):\\n\\n if (len(J)!=3):\\n print(\\\"Number of paramaters are not right, must be 5!\\\")\\n \\n NN = 2*mlat # # of sites in one super unitcell\\n tau = -np.zeros((NN, NN),dtype=complex)\\n h = np.zeros((NN,NN), dtype=complex)\\n\\n # translational cell's Hamiltonian\\n for i in range(mlat-1):\\n if (i%2==0):\\n h[i,i+1] = J[0]\\n h[mlat+i,mlat+i+1] = J[1]\\n h[i,mlat+i] = J[2] # horizoltal connection\\n elif (i%2==1):\\n h[i,i+1] = J[1]\\n h[mlat+i,mlat+i+1] = J[0]\\n # longitudinal connection of the last sites\\n if (mlat-1)%2 == 0:\\n h[mlat-1,2*mlat-1] = J[2]\\n \\n h = h + h.conj().T # make it hermitian\\n\\n # Hopping matrix\\n for i in range(1,mlat,2):\\n tau[i+mlat,i] = J[2]\\n\\n return h, tau\",\n \"def systematize_algorithm(H: np.array) -> Tuple[np.array, np.array, np.array]:\\n n, c = H.shape\\n m = np.abs(n-c)\\n\\n G_s = np.zeros((m, c), dtype=int)\\n G_s[:, :m] = np.identity(m)\\n\\n H_s, permutation = systematize_matrix(H, post_system=True)\\n\\n rev_permutation = reverse_permutation(permutation)\\n\\n P = H_s[:, :m]\\n\\n G_s[:, m:] = P.T\\n\\n G = G_s[:, rev_permutation]\\n\\n return G, G_s, H_s\",\n \"def test_inverse_non_interacting(self, size):\\n t_nn = 1.2\\n idx = np.arange(size)\\n g0_inv_full = np.zeros((size, size), dtype=complex)\\n g0_inv_full[idx[:-1], idx[1:]] = g0_inv_full[idx[1:], idx[:-1]] = t_nn\\n for g0 in self.g0_loc_inv:\\n g0_inv_full[idx, idx] = g0\\n rv, h, rv_inv = gt.matrix.decompose_gf(g0_inv_full)\\n g0 = gt.matrix.construct_gf(rv_inv=rv_inv, diag_inv=h**-1, rv=rv)\\n assert_allclose(g0.dot(g0_inv_full), np.identity(size), atol=1e-14)\\n assert_allclose(g0, la.inv(g0_inv_full))\\n g0_alt = gt.matrix.Decomposition(rv, h**-1, rv_inv).reconstruct(kind='full')\\n assert_allclose(g0, g0_alt)\",\n \"def Matrix_G(r,xi,E,ops,j):\\r\\n #Array of multipliers for operators in V14\\r\\n #----------------------------------------\\r\\n raw_pot = av14.V14(r)\\r\\n #----------------------------------------\\r\\n \\r\\n #Operator Values \\r\\n #---------------------------------------- \\r\\n op00,op01,op10,op11 = ops\\r\\n \\r\\n #Matrix Values\\r\\n #----------------------------------------\\r\\n G00 = (j-1)*j/r**2 + xi*(np.sum(op00*raw_pot) - E)\\r\\n G01 = xi*(np.sum(op01*raw_pot))\\r\\n G10 = G01#xi*(np.sum(operators10*raw_pot))\\r\\n G11 = (j+1)*(j+2)/r**2 + xi*(np.sum(op11*raw_pot) - E)\\r\\n #Generate and return (2x2)\\r\\n #----------------------------------------\\r\\n return np.array([[G00,G01],[G10,G11]])\",\n \"def zoisite():\\n\\n rho = 3343.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 279.8; C[0,1] = 94.7; C[0,2] = 88.7; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 249.2; C[1,2] = 27.5; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 209.4; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 51.8; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 81.4; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 66.3\\n\\n return C, rho\",\n \"def init_ising_H_mpo(g,J,L):\\n s0 = np.eye(2)\\n sx = np.array([[0.,1.],[1.,0.]])\\n sy = np.array([[0.,-1j],[1j,0.]])\\n sz = np.array([[1.,0.],[0.,-1.]])\\n d = 2\\n\\n w = np.zeros((3,3,d,d),dtype=np.float)\\n w[0,:2] = [s0,sz]\\n w[0:,2] = [g*sx, -J*sz, s0]\\n return w\",\n \"def get_stain_matrix(I):\",\n \"def Gmat2AB(G):\\n # cellstar = Gmat2cell(G)\\n g = nl.inv(G)\\n cell = Gmat2cell(g)\\n # A = np.zeros(shape=(3,3))\\n return cell2AB(cell)\",\n \"def T(self):\\n # TODO - your code here\\n transpose = []\\n for col in range(self.w):\\n new_row = []\\n for row in range(self.h):\\n new_row.append(self.g[row][col])\\n transpose.append(new_row)\\n return Matrix(transpose)\\n # TODO - your code here\",\n \"def integration_matrix(grid):\\n I_blocks = []\\n\\n for iseg in range(grid.num_segments):\\n i1, i2 = grid.subset_segment_indices['all'][iseg, :]\\n indices = grid.subset_node_indices['all'][i1:i2]\\n nodes_given = grid.node_stau[indices]\\n\\n i1, i2 = grid.subset_segment_indices['all'][iseg, :]\\n indices = grid.subset_node_indices['all'][i1:i2]\\n nodes_eval = grid.node_stau[indices][1:]\\n\\n _, D_block = lagrange_matrices(nodes_given, nodes_eval)\\n I_block = np.linalg.inv(D_block[:, 1:])\\n I_blocks.append(I_block)\\n\\n I = block_diag(*I_blocks)\\n\\n return I\",\n \"def to_dense_grid( fname_in, fname_out = None, dim=2 ):\\n import numpy as np\\n import insect_tools\\n import matplotlib.pyplot as plt\\n\\n # read data\\n time, x0, dx, box, data, treecode = read_wabbit_hdf5( fname_in )\\n\\n # convert blocks to complete matrix\\n field, box = dense_matrix( x0, dx, data, treecode, dim=dim )\\n\\n # write data to FLUSI-type hdf file\\n if fname_out:\\n insect_tools.write_flusi_HDF5( fname_out, time, box, field)\\n else: \\n dx = [b/(np.size(field,k)) for k,b in enumerate(box)]\\n X = [np.arange(0,np.size(field,k))*dx[k] for k,b in enumerate(box)]\\n return field, box, dx, X\",\n \"def casdetude_genetics():\\n file_path = PROJECT_PATH + \\\"/geographycal_data/Monterusciello/MontEdo_buildings\\\"\\n router = Router(building_file=file_path)\\n\\n router.design_aqueduct(0)\\n\\n router.write2epanet(router.acqueduct, PROJECT_PATH + \\\"/Monterusciello_solution/Monterusciello_acqueduct\\\",\\n diam=False)\\n\\n read_epanet = graphIO.graph_reader(router.acqueduct)\\n read_epanet.read_epanet(PROJECT_PATH + \\\"/geographycal_data/SolvedNet/MonteSolution\\\")\\n kpi_calculator(router.acqueduct)\\n\\n minimal = router.design_minimal_aqueduct(router.acqueduct, \\\"Q*H\\\")\\n router.write2epanet(minimal, PROJECT_PATH + \\\"/Monterusciello_solution/Monterusciello_acqueduct\\\", diam=False)\",\n \"def build_matrix(G):\\n # Create an n by n matrix of all zeros\\n matrix = np.zeros(shape=(len(G), len(G)))\\n for v in G:\\n for u in G:\\n if v == u:\\n matrix[v][u] = 0\\n elif u in G[v]:\\n matrix[v][u] = G[v][u][\\\"weight\\\"]\\n else:\\n matrix[v][u] = np.inf\\n\\n return matrix\",\n \"def mtf2ss(G):\\n # Description to help the user\\n\\n # calculating the number of outputs\\n p = len(G)\\n # calculating the number of inputs\\n m = len(G[0])\\n\\n # creating a list for each matrix\\n A = []\\n B = []\\n C = []\\n D = []\\n\\n nss = 0\\n # loop that get the SISO state-space transformations\\n for i in range(0, p):\\n # outputs - first index of the MIMO process list\\n A.append([])\\n B.append([])\\n C.append([])\\n D.append([])\\n for j in range(0, m):\\n # inputs - second index of the MIMO process list\\n if G[i][j] != 0:\\n # transform the individual SISO systems to a state-space model\\n Aij, Bij, Cij, Dij = signal.tf2ss(G[i][j].num, G[i][j].den)\\n # calculate the size of the A matrix\\n nss = nss + Aij.shape[0]\\n # organizing the matrices on a list\\n A[i].append(Aij)\\n B[i].append(Bij)\\n C[i].append(Cij)\\n D[i].append(Dij)\\n else:\\n A[i].append([])\\n B[i].append([])\\n C[i].append([])\\n D[i].append([])\\n\\n # preallocation of the system's matrices\\n Ass = np.zeros((nss, nss))\\n Bss = np.zeros((nss, m))\\n Css = np.zeros((p, nss))\\n Dss = np.zeros((p, m))\\n # counters\\n ct = 0\\n\\n # loop that organize the MIMO list obtained above on the state-space model\\n for i in range(0, p):\\n # loop on the outputs\\n for j in range(0, m):\\n # loop on the inputs\\n # test if the matrix isn't zero\\n if len(A[i][j]) > 0:\\n # calculate the size of the dynamic matrix\\n nij = A[i][j].shape[0]\\n # organizing the dynamic matrix\\n Ass[ct : ct + nij, ct : ct + nij] = A[i][j]\\n # organizing the input matrix\\n Bss[ct : ct + nij, j] = B[i][j][:, 0]\\n # organizing the output matrix\\n Css[i, ct : ct + nij] = C[i][j][0, :]\\n # organizing the feedforward matrix\\n Dss[i, j] = D[i][j]\\n # increase the counter\\n ct = ct + nij\\n\\n return Ass, Bss, Css, Dss\",\n \"def blueschist_mafic():\\n\\n rho = 3190.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 190.79; C[0,1] = 62.28; C[0,2] = 52.94; C[0,3] = -0.44; C[0,4] = 4.68; C[0,5] = 0.6\\n C[1,0] = C[0,1]; C[1,1] = 218.38; C[1,2] = 53.1; C[1,3] = -0.87; C[1,4] = 1.57; C[1,5] = 0.28\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 158.04; C[2,3] = -0.44; C[2,4] = 2.66; C[2,5] = -0.35\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 60.86; C[3,4] = -0.29; C[3,5] = 1.86\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 58.94; C[4,5] = -0.2\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 69.63\\n\\n return C, rho\",\n \"def test_inverse_eigenvectors_non_interacting(self, size):\\n t_nn = 1.2\\n idx = np.arange(size)\\n g0_inv_full = np.zeros((size, size), dtype=complex)\\n g0_inv_full[idx[:-1], idx[1:]] = g0_inv_full[idx[1:], idx[:-1]] = t_nn\\n for g0 in self.g0_loc_inv:\\n g0_inv_full[idx, idx] = g0\\n rv, h, rv_inv = gt.matrix.decompose_gf(g0_inv_full)\\n assert_allclose(rv.dot(rv_inv), np.identity(*h.shape), atol=1e-14)\",\n \"def build_integer_basis_of_orthogonal_complement_of_binomial_matrix(messi_network):\\n binomial_matrix = build_binomial_matrix(messi_network)\\n\\n\\n #print(\\\"G1\\\", messi_network.G1.edges())\\n #print(\\\"G2\\\", messi_network.G2.edges())\\n #print(\\\"G2 circle\\\", messi_network.G2_circle.edges())\\n #print(\\\"B\\\", binomial_matrix)\\n\\n\\n #print(\\\"binomial matrix\\\")\\n #print(binomial_matrix)\\n #print(\\\"build_integer_basis_of_orthogonal_complement_of_binomial_matrix\\\")\\n ret = build_integer_basis_matrix_of_orthogonal_complement_of_matrix(binomial_matrix.transpose(), positive=False).transpose()\\n #M = binomial_matrix.transpose()\\n #M_ort = ret.transpose()\\n #print(M @ M_ort)\\n return ret\",\n \"def Cijkl(C):\\n c = np.zeros(shape=(3, 3, 3, 3))\\n CC = np.zeros(shape=(9, 9))\\n CC[0:6, 0:6] = C[0:6, 0:6]\\n CC[6:9, 6:9] = C[3:6, 3:6]\\n CC[0:6, 6:9] = C[0:6, 3:6]\\n CC[6:9, 0:6] = C[3:6, 0:6]\\n\\n c[0, 0, 0, 0] = CC[0, 0]\\n c[0, 0, 1, 1] = CC[0, 1]\\n c[0, 0, 2, 2] = CC[0, 2]\\n c[0, 0, 1, 2] = CC[0, 3]\\n c[0, 0, 2, 0] = CC[0, 4]\\n c[0, 0, 0, 1] = CC[0, 5]\\n c[0, 0, 2, 1] = CC[0, 6]\\n c[0, 0, 0, 2] = CC[0, 7]\\n c[0, 0, 1, 0] = CC[0, 8]\\n\\n c[1, 1, 0, 0] = CC[1, 0]\\n c[1, 1, 1, 1] = CC[1, 1]\\n c[1, 1, 2, 2] = CC[1, 2]\\n c[1, 1, 1, 2] = CC[1, 3]\\n c[1, 1, 2, 0] = CC[1, 4]\\n c[1, 1, 0, 1] = CC[1, 5]\\n c[1, 1, 2, 1] = CC[1, 6]\\n c[1, 1, 0, 2] = CC[1, 7]\\n c[1, 1, 1, 0] = CC[1, 8]\\n\\n c[2, 2, 0, 0] = CC[2, 0]\\n c[2, 2, 1, 1] = CC[2, 1]\\n c[2, 2, 2, 2] = CC[2, 2]\\n c[2, 2, 1, 2] = CC[2, 3]\\n c[2, 2, 2, 0] = CC[2, 4]\\n c[2, 2, 0, 1] = CC[2, 5]\\n c[2, 2, 2, 1] = CC[2, 6]\\n c[2, 2, 0, 2] = CC[2, 7]\\n c[2, 2, 1, 0] = CC[2, 8]\\n\\n c[1, 2, 0, 0] = CC[3, 0]\\n c[1, 2, 1, 1] = CC[3, 1]\\n c[1, 2, 2, 2] = CC[3, 2]\\n c[1, 2, 1, 2] = CC[3, 3]\\n c[1, 2, 2, 0] = CC[3, 4]\\n c[1, 2, 0, 1] = CC[3, 5]\\n c[1, 2, 2, 1] = CC[3, 6]\\n c[1, 2, 0, 2] = CC[3, 7]\\n c[1, 2, 1, 0] = CC[3, 8]\\n\\n c[2, 0, 0, 0] = CC[4, 0]\\n c[2, 0, 1, 1] = CC[4, 1]\\n c[2, 0, 2, 2] = CC[4, 2]\\n c[2, 0, 1, 2] = CC[4, 3]\\n c[2, 0, 2, 0] = CC[4, 4]\\n c[2, 0, 0, 1] = CC[4, 5]\\n c[2, 0, 2, 1] = CC[4, 6]\\n c[2, 0, 0, 2] = CC[4, 7]\\n c[2, 0, 1, 0] = CC[4, 8]\\n\\n c[0, 1, 0, 0] = CC[5, 0]\\n c[0, 1, 1, 1] = CC[5, 1]\\n c[0, 1, 2, 2] = CC[5, 2]\\n c[0, 1, 1, 2] = CC[5, 3]\\n c[0, 1, 2, 0] = CC[5, 4]\\n c[0, 1, 0, 1] = CC[5, 5]\\n c[0, 1, 2, 1] = CC[5, 6]\\n c[0, 1, 0, 2] = CC[5, 7]\\n c[0, 1, 1, 0] = CC[5, 8]\\n\\n c[2, 1, 0, 0] = CC[6, 0]\\n c[2, 1, 1, 1] = CC[6, 1]\\n c[2, 1, 2, 2] = CC[6, 2]\\n c[2, 1, 1, 2] = CC[6, 3]\\n c[2, 1, 2, 0] = CC[6, 4]\\n c[2, 1, 0, 1] = CC[6, 5]\\n c[2, 1, 2, 1] = CC[6, 6]\\n c[2, 1, 0, 2] = CC[6, 7]\\n c[2, 1, 1, 0] = CC[6, 8]\\n\\n c[0, 2, 0, 0] = CC[7, 0]\\n c[0, 2, 1, 1] = CC[7, 1]\\n c[0, 2, 2, 2] = CC[7, 2]\\n c[0, 2, 1, 2] = CC[7, 3]\\n c[0, 2, 2, 0] = CC[7, 4]\\n c[0, 2, 0, 1] = CC[7, 5]\\n c[0, 2, 2, 1] = CC[7, 6]\\n c[0, 2, 0, 2] = CC[7, 7]\\n c[0, 2, 1, 0] = CC[7, 8]\\n\\n c[1, 0, 0, 0] = CC[8, 0]\\n c[1, 0, 1, 1] = CC[8, 1]\\n c[1, 0, 2, 2] = CC[8, 2]\\n c[1, 0, 1, 2] = CC[8, 3]\\n c[1, 0, 2, 0] = CC[8, 4]\\n c[1, 0, 0, 1] = CC[8, 5]\\n c[1, 0, 2, 1] = CC[8, 6]\\n c[1, 0, 0, 2] = CC[8, 7]\\n c[1, 0, 1, 0] = CC[8, 8]\\n return c\",\n \"def inverse(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n # TODO - your code here\\n inverse = []\\n if self.h == 1:\\n temp = []\\n temp.append(1/self.g[0][0])\\n inverse.append(temp)\\n else:\\n identity_matrix = identity(self.h)\\n det_term = 1/self.determinant()\\n trace_term = self.trace()\\n # implement intermediate scaling step locally\\n # trace_x_I = trace_term * identity_matrix\\n trace_x_I = []\\n for i in range(len(self.g)):\\n temp_row = []\\n for j in range(len(self.g[i])):\\n temp_row.append(trace_term * identity_matrix[i][j])\\n trace_x_I.append(temp_row)\\n # implement sub-traction locally\\n # sub_term = trace_x_I - self.g\\n sub_term = []\\n for i in range(len(trace_x_I)):\\n temp_row = []\\n for j in range(len(trace_x_I[i])):\\n temp_row.append(trace_x_I[i][j] - self.g[i][j])\\n sub_term.append(temp_row)\\n # implement final scaling step locally\\n # inverse = det_term * sub_term\\n inverse = []\\n for i in range(len(sub_term)):\\n temp_row = []\\n for j in range(len(sub_term[i])):\\n temp_row.append(det_term * sub_term[i][j])\\n inverse.append(temp_row)\\n return Matrix(inverse)\\n # TODO - your code here\",\n \"def multi_grid_iou(orig_bed_pcd, cfgs, voxel_size=0.05):\\n ious = np.zeros(len(cfgs))\\n for i in range(cfgs.shape[0]):\\n cfg = cfgs[i]\\n bed_pcd = orig_bed_pcd - cfg[:2]\\n theta = np.radians(cfg[2])\\n rotmat = np.array([\\n [np.cos(theta), np.sin(theta)],\\n [-np.sin(theta), np.cos(theta)],\\n ])\\n bed_pcd = bed_pcd@rotmat\\n\\n bed_pcd = np.round_(bed_pcd/voxel_size, 0, np.zeros_like(bed_pcd))\\n bed_pcd_summed = np.unique(1024*bed_pcd[:,0]+bed_pcd[:,1])\\n bed_pcd_0 = np.round_(bed_pcd_summed/1024, 0, np.zeros_like(bed_pcd_summed))\\n bed_pcd_1 = bed_pcd_summed-bed_pcd_0*1024\\n x_ok = (bed_pcd_1*2 <= int(cfg[4]/voxel_size)) & (bed_pcd_1*2 >= int(-cfg[4]/voxel_size))\\n y_ok = (bed_pcd_0*2 <= int(cfg[3]/voxel_size)) & (bed_pcd_0*2 >= int(-cfg[3]/voxel_size))\\n n_ok = np.sum(x_ok & y_ok)\\n n_elem = len(bed_pcd_summed)\\n n_grid = (cfg[3]+voxel_size)*(cfg[4]+voxel_size)/(voxel_size*voxel_size)\\n ious[i] = n_ok/(n_grid+n_elem-n_ok)\\n return ious\",\n \"def Intgrl(N, R, Zeta1, Zeta2, Za, Zb):\\n\\n global S12, T11, T12, T22, V11A, V12A, V22A, V11B, V12B, V22B, V1111, V2111, V2121, V2211, V2221, V2222\\n\\n S12 = 0.0\\n T11 = 0.0\\n T12 = 0.0\\n T22 = 0.0\\n V11A = 0.0\\n V12A = 0.0\\n V22A = 0.0\\n V11B = 0.0\\n V12B = 0.0\\n V22B = 0.0\\n V1111 = 0.0\\n V2111 = 0.0\\n V2121 = 0.0\\n V2211 = 0.0\\n V2221 = 0.0\\n V2222 = 0.0\\n\\n R2 = R * R\\n\\n # The coefficients for the contracted Gaussian functions are below\\n Coeff = np.array([[1.00000, 0.0000000, 0.000000],\\n [0.678914, 0.430129, 0.000000],\\n [0.444635, 0.535328, 0.154329]])\\n\\n Expon = np.array([[0.270950, 0.000000, 0.000000],\\n [0.151623, 0.851819, 0.000000],\\n [0.109818, 0.405771, 2.227660]])\\n D1 = np.zeros([3])\\n A1 = np.zeros([3])\\n D2 = np.zeros([3])\\n A2 = np.zeros([3])\\n\\n # This loop constructs the contracted Gaussian functions\\n for i in range(N):\\n A1[i] = Expon[N - 1, i] * (Zeta1 ** 2)\\n D1[i] = Coeff[N - 1, i] * ((2.0 * A1[i] / np.pi) ** 0.75)\\n A2[i] = Expon[N - 1, i] * (Zeta2 ** 2)\\n D2[i] = Coeff[N - 1, i] * ((2.0 * A2[i] / np.pi) ** 0.75)\\n\\n # Calculate one electron integrals\\n # Centre A is first atom centre B is second atom\\n # Origin is on second atom\\n # V12A - off diagonal nuclear attraction to centre A etc.\\n for i in range(N):\\n for j in range(N):\\n # Rap2 - squared distance between centre A and centre P\\n Rap = A2[j] * R / (A1[i] + A2[j])\\n Rap2 = Rap ** 2\\n Rbp2 = (R - Rap) ** 2\\n S12 = S12 + S_int(A1[i], A2[j], R2) * D1[i] * D2[j]\\n T11 = T11 + T_int(A1[i], A1[j], 0.0) * D1[i] * D1[j]\\n T12 = T12 + T_int(A1[i], A2[j], R2) * D1[i] * D2[j]\\n T22 = T22 + T_int(A2[i], A2[j], 0.0) * D2[i] * D2[j]\\n V11A = V11A + V_int(A1[i], A1[j], 0.0, 0.0, Za) * D1[i] * D1[j]\\n V12A = V12A + V_int(A1[i], A2[j], R2, Rap2, Za) * D1[i] * D2[j]\\n V22A = V22A + V_int(A2[i], A2[j], 0.0, R2, Za) * D2[i] * D2[j]\\n V11B = V11B + V_int(A1[i], A1[j], 0.0, R2, Zb) * D1[i] * D1[j]\\n V12B = V12B + V_int(A1[i], A2[j], R2, Rbp2, Zb) * D1[i] * D2[j]\\n V22B = V22B + V_int(A2[i], A2[j], 0.0, 0.0, Zb) * D2[i] * D2[j]\\n\\n # Calculate two electron integrals\\n\\n for i in range(N):\\n for j in range(N):\\n for k in range(N):\\n for l in range(N):\\n Rap = A2[i] * R / (A2[i] + A1[j])\\n Rbp = R - Rap\\n Raq = A2[k] * R / (A2[k] + A1[l])\\n Rbq = R - Raq\\n Rpq = Rap - Raq\\n Rap2 = Rap * Rap\\n Rbp2 = Rbp * Rbp\\n Raq2 = Raq * Raq\\n Rbq2 = Rbq * Rbq\\n Rpq2 = Rpq * Rpq\\n V1111 = V1111 + TwoE(A1[i], A1[j], A1[k], A1[l], 0.0, 0.0, 0.0) * D1[i] * D1[j] * D1[k] * D1[l]\\n V2111 = V2111 + TwoE(A2[i], A1[j], A1[k], A1[l], R2, 0.0, Rap2) * D2[i] * D1[j] * D1[k] * D1[l]\\n V2121 = V2121 + TwoE(A2[i], A1[j], A2[k], A1[l], R2, R2, Rpq2) * D2[i] * D1[j] * D2[k] * D1[l]\\n V2211 = V2211 + TwoE(A2[i], A2[j], A1[k], A1[l], 0.0, 0.0, R2) * D2[i] * D2[j] * D1[k] * D1[l]\\n V2221 = V2221 + TwoE(A2[i], A2[j], A2[k], A1[l], 0.0, R2, Rbq2) * D2[i] * D2[j] * D2[k] * D1[l]\\n V2222 = V2222 + TwoE(A2[i], A2[j], A2[k], A2[l], 0.0, 0.0, 0.0) * D2[i] * D2[j] * D2[k] * D2[l]\\n return\",\n \"def solve_prep(self):\\n\\n par = self.par\\n sol = self.sol\\n\\n # a. retirement\\n sol.m_ret = np.zeros((par.T,par.Nm_ret))\\n sol.c_ret = np.zeros((par.T,par.Nm_ret))\\n sol.a_ret = np.zeros((par.T,par.Nm_ret))\\n sol.inv_v_ret = np.zeros((par.T,par.Nm_ret))\\n sol.inv_vm_ret = np.zeros((par.T,par.Nm_ret))\\n sol.inv_vn_ret = np.zeros((par.T,par.Nm_ret))\\n\\n # b. working\\n if par.solmethod == 'G2EGM':\\n\\n sol.c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_v = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_vm = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_vn = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.ucon_c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.ucon_d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.ucon_v = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.dcon_c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.dcon_d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.dcon_v = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.acon_c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.acon_d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.acon_v = np.zeros((par.T,par.Nn,par.Nm))\\n sol.con_c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.con_d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.con_v = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.z = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.w = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n sol.wa = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n sol.wb = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n \\n elif par.solmethod == 'NEGM':\\n\\n sol.c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_v = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_vn = np.zeros((0,0,0))\\n sol.inv_vm = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.w = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n sol.wa = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n sol.wb = np.zeros((0,0,0))\\n \\n sol.c_pure_c = np.zeros((par.T,par.Nb_pd,par.Nm))\\n sol.inv_v_pure_c = np.zeros((par.T,par.Nb_pd,par.Nm))\",\n \"def computeInverse2DDCT(self, imge):\\n \\n # Assuming a square image\\n N = imge.shape[0]\\n finalInverse2DDCT = np.zeros([N, N], dtype=float)\\n for x in xrange(N):\\n for y in xrange(N):\\n #Compute the DCT value for each cells/points in the resulting transformed image.\\n finalInverse2DDCT[x, y] = DCT.__computeSinglePointInverse2DCT(imge, x, y, N)\\n return finalInverse2DDCT\",\n \"def G(U):\\n n = U.shape[0]\\n G_U = np.zeros([n,1])\\n DELTA_x = float(2*L)/(n-1)\\n for i in range(n):\\n G_U[i][0] = U[(i+1)%n][0]\\n G_U[i][0] -= U[(i-1)%n][0]\\n G_U[i][0] /= (2* DELTA_x)\\n G_U[i][0] += (float(epsilon) * (U[(i+1)%n][0]- U[(i-1)%n][0]) * (U[(i-1)%n][0]+U[(i+1)%n][0]+ U[i][0])/ (4* DELTA_x))\\n G_U[i][0] += (float(epsilon) * (U[(i+2)%n][0]- 2*U[(i+1)%n][0]+ 2*U[(i-1)%n][0]- U[(i-2)%n][0]) / (12*( DELTA_x**3)))\\n return G_U\",\n \"def build_binomial_matrix(messi_network):\\n binomial_basis = get_binomial_basis(messi_network)\\n\\n #print(\\\"Binomial basis: \\\")\\n #print(binomial_basis)\\n\\n #Las filas de B tienen los binomios\\n column_basis = extract_column_basis(np.array(binomial_basis).transpose())\\n\\n #print(\\\"Column basis: \\\")\\n #print(column_basis)\\n\\n return column_basis.transpose()\\n #return get_positive_matrix(column_basis.transpose())\",\n \"def two_qubit_local_invariants(U: np.ndarray) -> np.ndarray:\\n U = np.asarray(U)\\n if U.shape != (4, 4):\\n raise ValueError(\\\"Unitary must correspond to a two-qubit gate.\\\")\\n\\n # Transform to bell basis\\n Um = MAGIC.conj().T.dot(U.dot(MAGIC))\\n # Get determinate since +- one is allowed.\\n det_um = np.linalg.det(Um)\\n M = Um.T.dot(Um)\\n # trace(M)**2\\n m_tr2 = M.trace()\\n m_tr2 *= m_tr2\\n\\n # Table II of Ref. 1 or Eq. 28 of Ref. 2.\\n G1 = m_tr2 / (16 * det_um)\\n G2 = (m_tr2 - np.trace(M.dot(M))) / (4 * det_um)\\n\\n # Here we split the real and imag pieces of G1 into two so as\\n # to better equate to the Weyl chamber coordinates (c0,c1,c2)\\n # and explore the parameter space.\\n # Also do a FP trick -0.0 + 0.0 = 0.0\\n return np.round([G1.real, G1.imag, G2.real], 12) + 0.0\",\n \"def inout_creator(df = pd.DataFrame(), features='datosrahm.csv'):\\r\\n df = df\\r\\n \\r\\n start=time.time()\\r\\n \\r\\n datos=pd.read_csv(features)\\r\\n datos=datos.fillna(-1)\\r\\n\\r\\n dicc=dict(datos[['Symbol','Z']].values)\\r\\n\\r\\n dicc['D']=1\\r\\n dicc['Bk']=97\\r\\n dicc['Cf']=98\\r\\n dicc['Es']=99\\r\\n dicc['Fm']=100\\r\\n dicc['Md']=101\\r\\n dicc['No']=102\\r\\n dicc['Lr']=103\\r\\n \\r\\n max_sitios = max(df['sitios'].values)\\r\\n \\r\\n X=np.zeros((len(df),max_sitios,104))\\r\\n\\r\\n mult=np.zeros((len(df),max_sitios))\\r\\n wyckmul=np.load('support/WyckoffSG_dict.npy').item()['wyckmul']\\r\\n \\r\\n todelete = list()\\r\\n \\r\\n for row in range(len(df)):\\r\\n item=df['WyckOcc'][row]\\r\\n sitios=list(item.values()) \\r\\n sitocc=np.zeros((len(sitios),104)) \\r\\n spacegroup = str(df['sgnum'][row]).zfill(3)\\r\\n \\r\\n try:\\r\\n \\r\\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\\\\r\\n sitios] for i in j]\\r\\n \\r\\n except:\\r\\n print(row)\\r\\n print('There exists an error concerning with the space group of CIF ', df['cif'][row],'\\\\n')\\r\\n print('Please check in www.crystallography.net to provide the correct space group number of that CIF',\\r\\n '\\\\n','\\\\n')\\r\\n spacegroup=input('Give me the correct spacegroup:'+'\\\\n'+'\\\\n')\\r\\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\\\\r\\n list(df['WyckOcc'][row].values())] for i in j]\\r\\n \\r\\n occs=[]\\r\\n for i in range(len(sitios)):\\r\\n\\r\\n for j in list(sitios[i].values()):\\r\\n \\r\\n ocupacion=np.array(list(j.values()))\\r\\n llaves=[llave.replace('+','').replace('-','').replace('1',\\r\\n '').replace('2','').replace('3','').replace('4',\\r\\n '') for llave in np.array(list(j.keys()))]\\r\\n llaves=[llave.replace('.','') for llave in llaves]\\r\\n llaves=[llave.replace('5','').replace('6','').replace('7',\\r\\n '').replace('8','').replace('9','').replace('0',\\r\\n '') for llave in llaves]\\r\\n vector=np.zeros((1,104))\\r\\n occs=[sum(ocupacion)]+occs\\r\\n \\r\\n try:\\r\\n \\r\\n idx=[dicc[k] for k in llaves]\\r\\n \\r\\n except:\\r\\n print('The compound with the cif ', df['cif'][row], ' will be deleted')\\r\\n print('The database will be updated')\\r\\n todelete += [row]\\r\\n \\r\\n for k in idx:\\r\\n vector[0][k-1] = ocupacion[idx.index(k)]\\r\\n \\r\\n sitocc[i]=vector\\r\\n \\r\\n while sitocc.shape[0] != max_sitios:\\r\\n sitocc=np.concatenate((np.zeros((1,104)),sitocc))\\r\\n s=[0]+s\\r\\n \\r\\n X[row,:,:]=sitocc\\r\\n mult[row]=s\\r\\n \\r\\n features=datos.iloc[:,2:].values\\r\\n x=X[:,:,:96]\\r\\n \\r\\n fracsum = np.expand_dims(np.sum(x,axis=2), axis=2)\\r\\n \\r\\n x=np.dot(x,features) \\r\\n \\r\\n x = np.delete(x, todelete,axis=0)\\r\\n df = df.drop(df.index[todelete]).reset_index(drop=True)\\r\\n \\r\\n print('inout_creator lasted ',round(time.time()-start,2),' s') \\r\\n return x, fracsum, df\",\n \"def chi(nuc, temp=300.0, group_struct=None, phi_g=None, xs_cache=None, eres=101):\\n xs_cache = cache.xs_cache if xs_cache is None else xs_cache\\n _prep_cache(xs_cache, group_struct, phi_g)\\n if isinstance(nuc, collectionsAbc.Iterable) and not isinstance(nuc, basestring):\\n return _atom_mass_channel(chi, nuc, temp=temp, xs_cache=xs_cache, eres=eres)\\n nuc = nucname.id(nuc)\\n key = (nuc, \\\"chi\\\", temp)\\n\\n # Don't recalculate anything if you don't have to\\n if key in xs_cache:\\n return xs_cache[key]\\n\\n # Get the the set of nuclides we know we need chi for.\\n if \\\"fissionable_nucs\\\" not in xs_cache:\\n with tb.open_file(nuc_data, \\\"r\\\") as f:\\n if \\\"/neutron/cinder_xs/fission\\\" in f:\\n fn = set(f.root.neutron.cinder_xs.fission.cols.nuc)\\n else:\\n fn = set()\\n xs_cache[\\\"fissionable_nucs\\\"] = fn\\n fissionable_nucs = xs_cache[\\\"fissionable_nucs\\\"]\\n if (nuc not in fissionable_nucs) and (86 <= nucname.znum(nuc)):\\n fissionable_nucs.add(nuc)\\n\\n # Perform the group collapse on a continuous chi\\n E_g = xs_cache[\\\"E_g\\\"]\\n G = len(E_g) - 1\\n chi_g = np.zeros(G, dtype=\\\"f8\\\")\\n if nuc in fissionable_nucs:\\n for g in range(G):\\n E_space = np.logspace(np.log10(E_g[g]), np.log10(E_g[g + 1]), eres)\\n dnumer = models.chi(E_space)\\n numer = scipy.integrate.trapz(dnumer, E_space)\\n denom = E_g[g + 1] - E_g[g]\\n chi_g[g] = numer / denom\\n # renormalize chi\\n chi_g = chi_g / chi_g.sum()\\n # Put this value back into the cache, with the appropriate label\\n xs_cache[key] = chi_g\\n return chi_g\",\n \"def garnet():\\n\\n rho = 3660.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 306.2; C[0,1] = 112.5; C[0,2] = 112.5; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 306.2; C[1,2] = 112.5; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 306.2; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 92.7; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 92.7; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 92.7\\n\\n return C, rho\",\n \"def generate_GS_matrix(self, matrix):\\n GS_matrix = Matrix(QQ, matrix.transpose().gram_schmidt()[0]).transpose()\\n return GS_matrix\",\n \"def inverse_gc(g):\\n i = g\\n j = 1\\n while j<N:\\n i = i ^ (g >> j)\\n j = j + 1\\n return i\",\n \"def blueschist_felsic():\\n\\n rho = 2970.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 149.85; C[0,1] = 38.7; C[0,2] = 32.59; C[0,3] = -0.15; C[0,4] = -1.; C[0,5] = -0.19\\n C[1,0] = C[0,1]; C[1,1] = 163.55; C[1,2] = 30.03; C[1,3] = 1.05; C[1,4] = -1.81; C[1,5] = -1.78\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 121.62; C[2,3] = 0.22; C[2,4] = -0.95; C[2,5] = -0.13\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 48.03; C[3,4] = -0.63; C[3,5] = -1.14\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 48.62; C[4,5] = -0.01\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 58.42\\n\\n return C, rho\",\n \"def __init__(self, cnf_grammar):\\n self.grammar = cnf_grammar\\n self.matrix = []\\n self.i = self.k = self.j = 0\",\n \"def getInverseMatrix(self) -> CMatrix4:\\n ...\",\n \"def initialize_grid(self):\\r\\n for i in range(self.height):\\r\\n for j in range(self.width):\\r\\n self.grid[i][j] = 0\\r\\n \\r\\n # fill up unvisited cells\\r\\n for r in range(self.height):\\r\\n for c in range(self.width):\\r\\n if r % 2 == 0 and c % 2 == 0:\\r\\n self.unvisited.append((r,c))\\r\\n\\r\\n self.visited = []\\r\\n self.path = dict()\\r\\n self.generated = False\",\n \"def test_pybel_to_inchi(self):\\n pbmol1 = converter.xyz_to_pybel_mol(self.xyz1['dict'])\\n pbmol2 = converter.xyz_to_pybel_mol(self.xyz5['dict'])\\n pbmol3 = converter.xyz_to_pybel_mol(self.xyz2['dict'])\\n pbmol4 = converter.xyz_to_pybel_mol(self.xyz6['dict'])\\n\\n inchi1 = converter.pybel_to_inchi(pbmol1)\\n inchi2 = converter.pybel_to_inchi(pbmol2)\\n inchi3 = converter.pybel_to_inchi(pbmol3)\\n inchi4 = converter.pybel_to_inchi(pbmol4)\\n\\n self.assertEqual(inchi1, 'InChI=1/CH4/h1H4')\\n self.assertEqual(inchi2, 'InChI=1/CH2NO/c1-2-3/h1H2')\\n self.assertEqual(inchi3, 'InChI=1/CH3NOS/c1-2-3-4-1/h2H,1H2')\\n self.assertEqual(inchi4, 'InChI=1/CH5NO2S/c1-5(2,3)4/h2-3H,1H3')\",\n \"def interaction_matrix(self):\\n\\n self.int_M_called = True\\n int_M = np.zeros((self.n, self.n))\\n for k in range(self.n):\\n for j in range(k+1):\\n o = self.attribute_interactions(k, j)\\n int_M[k, j] = o.rel_total_ig_ab # Store total information gain\\n int_M[j, k] = o.rel_total_ig_ab # TODO: Maybe storing interactions too is not a bad idea\\n # TODO: We can than easily sort either by total gain or by positive interaction\\n for k in range(self.n):\\n int_M[k, k] = self.info_gains[self.data.domain.attributes[k].name]\\n self.int_matrix = Orange.misc.distmatrix.DistMatrix(int_M)\",\n \"def set_interaction(self):\\n\\n dirInd = \\\"/Users/asedeki/Drive/environement/Quasi1D/quasi1d/data/inddata\\\" # My laptop\\n N = self.N\\n array = np.load(f\\\"{dirInd}/array_index_n{N}.npy\\\")\\n\\n Temps = np.loadtxt(self.g_file, usecols=[\\n 1], unpack=True, dtype=np.double)\\n if self._temperatures.size == 0:\\n self.temperatures = np.unique(Temps)\\n\\n self.g = {}\\n for i in [1, 2, 3]:\\n self.g[i] = {}\\n G_i = np.loadtxt(self.g_file, usecols=[\\n 4+i], unpack=True, dtype=np.double)\\n for Ti in self.temperatures:\\n GT = G_i[np.where(Temps == Ti)]\\n self.g[i][Ti] = np.zeros([N, N, N], dtype=np.double)\\n self.set_g(self.g[i][Ti], array, GT)\",\n \"def homothin():\\n\\n Iab = se2hmt(binary([[0,0,0],\\n [0,1,0],\\n [1,1,1]]),\\n binary([[1,1,1],\\n [0,0,0],\\n [0,0,0]]))\\n return Iab\",\n \"def loadasem(loads, IBC, neq):\\r\\n RHSG = np.zeros([neq])\\r\\n nl = loads.shape[0]\\r\\n for i in range(nl):\\r\\n il = int(loads[i, 0])\\r\\n ilx = IBC[il, 0]\\r\\n ily = IBC[il, 1]\\r\\n ilT = IBC[il, 2]\\r\\n if ilx != -1:\\r\\n RHSG[ilx] = loads[i, 1]\\r\\n if ily != -1:\\r\\n RHSG[ily] = loads[i, 2]\\r\\n if ilT != -1:\\r\\n RHSG[ilT] = loads[i, 3]\\r\\n return RHSG\",\n \"def EmptyStateMat(nX,nU,nY):\\n Xx = np.zeros((nX,nX)) # Ac \\n Yx = np.zeros((nY,nX)) # Gc \\n Xu = np.zeros((nX,nU)) # Xu \\n Yu = np.zeros((nY,nU)) # Jc \\n return Xx,Xu,Yx,Yu\",\n \"def test_rmg_mol_from_inchi(self):\\n mol1 = converter.rmg_mol_from_inchi('InChI=1S/NO2/c2-1-3')\\n mol2 = converter.rmg_mol_from_inchi('InChI=1/CH3NOS/c1-2-3-4-1/h2H,1H2')\\n\\n self.assertTrue(isinstance(mol1, Molecule))\\n self.assertTrue(isinstance(mol2, Molecule))\\n\\n self.assertEqual(mol1.to_smiles(), '[O]N=O')\\n self.assertEqual(mol2.to_smiles(), 'C1NOS1')\",\n \"def build_Xij_inv_matrix(self,Nmesh=64):\\n H0, F = self.cosmo.H0, self.cosmo.F\\n Lbox = self.attrs['Lbox']\\n kgrid = initialize_kgrid(Nmesh,Lbox)\\n kmag_grid = np.linalg.norm(kgrid,axis=3)\\n w_grid = self.cosmo.Pk_lin(kmag_grid)*(1/Lbox**3)*np.exp(-kmag_grid*kmag_grid*self.RG*self.RG)\\n k2 = kmag_grid**2\\n k2[0,0,0] = 1 \\n #----------------------------------------------------\\n cspace = np.arange(0,18)\\n \\n xij_tensor = [[np.sum(np.conj(Hhats[i](kgrid,k2,H0,F))*Hhats[j](kgrid,k2,H0,F)*w_grid)\\n for j in cspace[self.cmask]] for i in cspace[self.cmask]]\\n \\n xij_tensor = np.array(xij_tensor)\\n self.xij_tensor_inv = np.linalg.inv(xij_tensor.real)\",\n \"def inchi(smi):\\n\\n ich = automol.inchi.base.hardcoded_object_to_inchi_by_key(\\n 'smiles', smi, comp=_compare)\\n\\n if ich is None:\\n rdm = rdkit_.from_smiles(smi)\\n ich = rdkit_.to_inchi(rdm)\\n return ich\",\n \"def calc_Hij(self):\\n\\t\\n\\tself.Hij = np.zeros((3,3, 3,3, self.N),dtype=np.complex_)\\n\\t\\n\\tself.Hij[0,0,:,:,:] = self.Hpij*self.ep[0,0] + self.Hcij*self.ec[0,0]\\n\\tself.Hij[0,1,:,:,:] = self.Hpij*self.ep[0,1] + self.Hcij*self.ec[0,1]\\n\\tself.Hij[0,2,:,:,:] = self.Hpij*self.ep[0,2] + self.Hcij*self.ec[0,2]\\n\\t\\n\\tself.Hij[1,0,:,:,:] = self.Hpij*self.ep[1,0] + self.Hcij*self.ec[1,0] \\n\\tself.Hij[1,1,:,:,:] = self.Hpij*self.ep[1,1] + self.Hcij*self.ec[1,1]\\n\\tself.Hij[1,2,:,:,:] = self.Hpij*self.ep[1,2] + self.Hcij*self.ec[1,2]\\n\\t\\n\\tself.Hij[2,0,:,:,:] = self.Hpij*self.ep[2,0] + self.Hcij*self.ec[2,0] \\n\\tself.Hij[2,1,:,:,:] = self.Hpij*self.ep[2,1] + self.Hcij*self.ec[2,1] \\n\\tself.Hij[2,2,:,:,:] = self.Hpij*self.ep[2,2] + self.Hcij*self.ec[2,2]\\n\\t\\n\\treturn\",\n \"def setUp(self):\\n self.grid = SudukuGrid(BaseCase)\\n for i in range(81):\\n self.grid[i] = SudukuAlphabet.VALUES[(i+(i//9)*3+i//27)%9]\",\n \"def get_V(self):\\n if not self.gpu:\\n self.rho[...] = conj_square(self.psi)\\n self.fourier_grid[...] = fft.rfftn(self.rho)\\n ft_inv_laplace(self.fourier_grid)\\n self.fourier_grid *= 4*np.pi*G\\n self.V[...] = fft.irfftn(self.fourier_grid)\\n self.V[...] += self.lam*self.rho**2\\n else:\\n self.g_conj_square(self.g_psi, self.g_rho)\\n cufft.cufftExecD2Z(self.rho_plan, self.g_rho.ptr, self.g_fourier.ptr)\\n self.g_fourier /= self.psi.shape[0]**3\\n self.g_pot_func(self.g_fourier, np.float64(4*np.pi*G/self.N), np.int64(self.fourier_grid.shape[0]), np.int64(self.fourier_grid.shape[1]), np.int64(self.fourier_grid.shape[2]), block=(8,8,8), grid=tuple([(i+7)/8 for i in self.psi_hat.shape]))\\n cufft.cufftExecZ2D(self.inv_plan, self.g_fourier.ptr, self.g_V.ptr)\\n self.g_V += self.lam*self.g_rho**2\",\n \"def dolomite():\\n\\n rho = 2840.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 205.; C[0,1] = 71.; C[0,2] = 57.4; C[0,3] = -19.5; C[0,4] = 13.7; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 205.; C[1,2] = 57.4; C[1,3] = 19.5; C[1,4] = -13.7; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 113.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.8; C[3,4] = 0.; C[3,5] = -13.7\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 39.8; C[4,5] = -19.5\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 67.\\n\\n return C, rho\",\n \"def muscovite():\\n\\n rho = 2834.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 181.; C[0,1] = 48.8; C[0,2] = 25.6; C[0,3] = 0.; C[0,4] = -14.2; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 178.4; C[1,2] = 21.2; C[1,3] = 0.; C[1,4] = 1.1; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 58.6; C[2,3] = 0.; C[2,4] = 1.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 16.5; C[3,4] = 0.; C[3,5] = -5.2\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 19.5; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 72.\\n\\n return C, rho\",\n \"def A2Gmat(A, inverse=True):\\n G = np.array([\\n [A[0], A[3] / 2., A[4] / 2.],\\n [A[3] / 2., A[1], A[5] / 2.],\\n [A[4] / 2., A[5] / 2., A[2]]])\\n if inverse:\\n g = nl.inv(G)\\n return G, g\\n else:\\n return G\",\n \"def fock_ueg(sys, G):\\n nbsf = sys.nbasis\\n nq = len(sys.qvecs)\\n assert nq == len(sys.vqvec)\\n\\n Fock = numpy.zeros((2, nbsf, nbsf), dtype=numpy.complex128)\\n Gkpq = numpy.zeros((2, nq), dtype=numpy.complex128)\\n Gpmq = numpy.zeros((2, nq), dtype=numpy.complex128)\\n\\n for s in [0, 1]:\\n coulomb_greens_function(nq, sys.ikpq_i, sys.ikpq_kpq,\\n sys.ipmq_i, sys.ipmq_pmq,\\n Gkpq[s], Gpmq[s], G[s])\\n\\n J = build_J_opt(nq, sys.vqvec, sys.vol, sys.nbasis,\\n sys.ikpq_i, sys.ikpq_kpq, sys.ipmq_i, sys.ipmq_pmq,\\n Gkpq, Gpmq)\\n\\n K = build_K_opt(nq, sys.vqvec, sys.vol, sys.nbasis,\\n sys.ikpq_i, sys.ikpq_kpq, sys.ipmq_i, sys.ipmq_pmq,\\n G)\\n\\n for s in [0, 1]:\\n Fock[s] = sys.H1[s] + J[s] + K[s]\\n\\n return Fock\",\n \"def Generate(self,the_scf):\\n self.dip_ints = the_scf.mol.intor('cint1e_r_sph', comp=3) # component,ao,ao.\\n charges = the_scf.mol.atom_charges()\\n coords = the_scf.mol.atom_coords()\\n self.nuc_dip = np.einsum('i,ix->x', charges, coords)\\n return\",\n \"def FormG():\\n for i in range(2):\\n for j in range(2):\\n G[i, j] = 0.0\\n for k in range(2):\\n for l in range(2):\\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])\",\n \"def unstructured_grid(self, X, Y, Z):\\r\\n uX = []\\r\\n uY = []\\r\\n uZ = []\\r\\n l = self.size[0] * self.size[1]\\r\\n self.Grid = vtk.vtkUnstructuredGrid()\\r\\n for k in range(self.size[2]):\\r\\n n = 0\\r\\n for j in range(self.size[1]):\\r\\n for i in range(self.size[0]):\\r\\n # SW-B\\r\\n swb = 8 * k * l + 4 * l + 2 * self.size[0] * (j + 1) + 2 * n\\r\\n uX.append(X[swb])\\r\\n uY.append(Y[swb])\\r\\n uZ.append(Z[swb])\\r\\n # SE-B\\r\\n seb = 8 * k * l + 4 * l + 2 * self.size[0] * (j + 1) + 2 * n + 1\\r\\n uX.append(X[seb])\\r\\n uY.append(Y[seb])\\r\\n uZ.append(Z[seb])\\r\\n # NE-B\\r\\n neb = 8 * k * l + 4 * l + 2 * self.size[0] * j + 2 * n + 1\\r\\n uX.append(X[neb])\\r\\n uY.append(Y[neb])\\r\\n uZ.append(Z[neb])\\r\\n # NW-B\\r\\n nwb = 8 * k * l + 4 * l + 2 * self.size[0] * j + 2 * n\\r\\n uX.append(X[nwb])\\r\\n uY.append(Y[nwb])\\r\\n uZ.append(Z[nwb])\\r\\n # SW-T\\r\\n swt = 8 * k * l + 2 * self.size[0] * (j + 1) + 2 * n\\r\\n uX.append(X[swt])\\r\\n uY.append(Y[swt])\\r\\n uZ.append(Z[swt])\\r\\n # SE-T\\r\\n sett = 8 * k * l + 2 * self.size[0] * (j + 1) + 2 * n + 1\\r\\n uX.append(X[sett])\\r\\n uY.append(Y[sett])\\r\\n uZ.append(Z[sett])\\r\\n # NE-T\\r\\n net = 8 * k * l + 2 * self.size[0] * j + 2 * n + 1\\r\\n uX.append(X[net])\\r\\n uY.append(Y[net])\\r\\n uZ.append(Z[net])\\r\\n # NW-T\\r\\n nwt = 8 * k * l + 2 * self.size[0] * j + 2 * n\\r\\n uX.append(X[nwt])\\r\\n uY.append(Y[nwt])\\r\\n uZ.append(Z[nwt])\\r\\n\\r\\n n += 1\\r\\n\\r\\n # Set points\\r\\n points = vtk.vtkPoints()\\r\\n points.SetNumberOfPoints(2 * self.size[0] * 2 * self.size[1] * 2 * self.size[2])\\r\\n for i in range(len(uX)):\\r\\n points.SetPoint(i, [uX[i], uY[i], uZ[i]])\\r\\n self.Grid.SetPoints(points)\\r\\n\\r\\n # Set cells\\r\\n cells = vtk.vtkCellArray()\\r\\n cell = vtk.vtkHexahedron()\\r\\n for i in range(len(uX)):\\r\\n if i > 0 and i % 8 == 0:\\r\\n cells.InsertNextCell(cell)\\r\\n cell.GetPointIds().SetId(i % 8, i)\\r\\n cells.InsertNextCell(cell)\\r\\n\\r\\n self.Grid.SetCells(cell.GetCellType(), cells)\",\n \"def MS_to_galactic():\\n return matrix_transpose(MS_MATRIX)\",\n \"def diamond(T, Vg_start, Vg_end, Ng, Vd_start, Vd_end, Nd, Cs, Cd, Cg, Gs, Gd, num_e, mode='difcon', dVg=False, filename='simData.dat'):\\n Vg = scipy.linspace(Vg_start, Vg_end, Ng)\\n Vd = scipy.linspace(Vd_start, Vd_end, Nd)\\n data_matrix = []\\n for (i_vg, vg) in enumerate(Vg):\\n myset=system(vg, Cs, Cd, Cg, Gs, Gd, num_e)\\n myset.set_temperature(T)\\n myset.pre_processing()\\n I = []\\n P = []\\n V_dot = []\\n print \\\"Vg = \\\", vg\\n for vd in Vd:\\n myset.tunnel_rate([0, vd, vg]) \\n myset.solver() \\n I.append(myset.current('drain','dot'))\\n P.append(myset.proba('dot'))\\n V_dot.append(myset.voltage('dot'))\\n # convert lists to scipy arrays\\n I = scipy.array(I)\\n P = scipy.array(P)\\n V_dot = scipy.array(V_dot)\\n # compute the diffential conductance\\n if mode == 'current':\\n Y = Vd\\n F = I\\n elif mode == 'difcon':\\n F, Y = derive(I, Vd)\\n F *= 1e3\\n elif mode == 'voltage':\\n Y = Vd\\n F = V_dot\\n elif mode == 'francis':\\n F_1, Y = derive(I, Vd)\\n F_2, Y = derive(Vd-V_dot, Vd)\\n F = F_1/F_2\\n F *= 1e3\\n elif mode == 'sourcis':\\n F_1, Y = derive(I, Vd)\\n F_2, Y = derive(V_dot, Vd)\\n F = F_1/F_2\\n F *= 1e3\\n data_matrix.append(F)\\n data_matrix = array(data_matrix)\\n data_matrix = transpose(data_matrix)\\n X = Vg\\n \\n # Derivate with Vg\\n if dVg:\\n data_dVg = []\\n for vd_i in arange(len(Y)):\\n F_dVg, X_dVg = derive(data_matrix[vd_i,:], X)\\n F_dVg *= 1e3\\n data_dVg.append(F_dVg)\\n data_matrix = array(data_dVg)\\n X = X_dVg\\n \\n if filename != 0: \\n write_file(data_matrix, filename)\\n return data_matrix, X, Y\",\n \"def local_energy_ueg(system, G, Ghalf=None, two_rdm=None):\\n if (system.diagH1):\\n ke = numpy.einsum('sii,sii->',system.H1,G)\\n else:\\n ke = numpy.einsum('sij,sij->',system.H1,G)\\n\\n Gkpq = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\\n Gpmq = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\\n Gprod = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\\n\\n ne = [system.nup, system.ndown]\\n nq = numpy.shape(system.qvecs)[0]\\n\\n for s in [0, 1]:\\n # exchange_greens_function(nq, system.ikpq_i, system.ikpq_kpq, system.ipmq_i,system.ipmq_pmq, Gprod[s],G[s])\\n # coulomb_greens_function(nq, system.ikpq_i, system.ikpq_kpq, system.ipmq_i, system.ipmq_pmq,Gkpq[s], Gpmq[s],G[s])\\n for iq in range(nq):\\n Gkpq[s,iq], Gpmq[s,iq] = coulomb_greens_function_per_qvec(system.ikpq_i[iq],\\n system.ikpq_kpq[iq],\\n system.ipmq_i[iq],\\n system.ipmq_pmq[iq],\\n G[s])\\n Gprod[s,iq] = exchange_greens_function_per_qvec(system.ikpq_i[iq],\\n system.ikpq_kpq[iq],\\n system.ipmq_i[iq],\\n system.ipmq_pmq[iq],\\n G[s])\\n\\n if two_rdm is None:\\n two_rdm = numpy.zeros((2,2,len(system.qvecs)), dtype=numpy.complex128)\\n two_rdm[0,0] = numpy.multiply(Gkpq[0],Gpmq[0]) - Gprod[0]\\n essa = (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[0,0])\\n\\n two_rdm[1,1] = numpy.multiply(Gkpq[1],Gpmq[1]) - Gprod[1]\\n essb = (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[1,1])\\n\\n two_rdm[0,1] = numpy.multiply(Gkpq[0],Gpmq[1])\\n two_rdm[1,0] = numpy.multiply(Gkpq[1],Gpmq[0])\\n eos = (\\n (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[0,1])\\n + (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[1,0])\\n )\\n\\n pe = essa + essb + eos\\n\\n return (ke+pe, ke, pe)\",\n \"def HaarGOB(treeG):\\n # number of level of the chain (or tree)\\n Ntr = len(treeG)\\n # reorder chain (optional) \\n # reordering each level so that in each level the nodes are in the\\n # descent order of degrees\\n # compute u_l^c for level J_0 (top level)\\n clusterJ0 = treeG[Ntr-1]['clusters']\\n N0 = len(clusterJ0)\\n # generate indicator function on G^c\\n chic = np.identity(N0)\\n uc = [None]*N0\\n uc[0] = 1/np.sqrt(N0,dtype=np.float64)*np.ones(N0)\\n for l in np.arange(1, N0):\\n uc[l] = np.sqrt((N0-l)/(N0-l+1))*(chic[l-1,:]-1/(N0-l)*np.sum(chic[l:,:], axis=0))\\n# u = copy.deepcopy(uc)\\n treeG[Ntr-1]['u'] = uc\\n # compute the next level orthonormal basis ulk and stored into u\\n for j_tr in np.arange(0, Ntr-1)[::-1]:\\n N1 = len(treeG[j_tr]['clusters'])\\n u = [None]*N1\\n i = N0\\n for l in range(N0):\\n clusterl = treeG[j_tr+1]['clusters'][l]\\n kl = len(clusterl)\\n #for k==1\\n ucl = uc[l]\\n ul1 = np.zeros(N1)\\n for j in range(N0):\\n idxj = treeG[j_tr+1]['clusters'][j]\\n ul1[idxj] = ucl[j]/np.sqrt(len(idxj))\\n u[l] = ul1\\n if kl>1:\\n chil = np.zeros((kl,N1))\\n for k in range(kl):\\n idxl = treeG[j_tr+1]['clusters'][l]\\n chil[k,idxl[k]] = 1;\\n \\n for k in np.arange(1,kl):\\n i = i+1\\n ulk = np.sqrt((kl-k)/(kl-k+1))*(chil[k-1,:]-1/(kl-k)*np.sum(chil[k:,:],axis=0))\\n u[i-1] = ulk\\n treeG[j_tr]['u'] = u\\n # update uc and N0\\n# uc = copy.deepcopy(u)\\n uc = u\\n N0 = N1\\n return treeG\",\n \"def to_cdo_grid(self, outfile):\",\n \"def read_isbi2013_2shell():\\n dipy_home = pjoin(os.path.expanduser('~'), '.dipy')\\n folder = pjoin(dipy_home, 'isbi2013')\\n fraw = pjoin(folder, 'phantom64.nii.gz')\\n fbval = pjoin(folder, 'phantom64.bval')\\n fbvec = pjoin(folder, 'phantom64.bvec')\\n\\n md5_dict = {'data': '42911a70f232321cf246315192d69c42',\\n 'bval': '90e8cf66e0f4d9737a3b3c0da24df5ea',\\n 'bvec': '4b7aa2757a1ccab140667b76e8075cb1'}\\n\\n check_md5(fraw, md5_dict['data'])\\n check_md5(fbval, md5_dict['bval'])\\n check_md5(fbvec, md5_dict['bvec'])\\n\\n bvals, bvecs = read_bvals_bvecs(fbval, fbvec)\\n\\n gtab = gradient_table(bvals, bvecs)\\n img = nib.load(fraw)\\n return img, gtab\",\n \"def incidence_matrix(self):\\n try: \\n return self._incidence_matrix\\n except AttributeError:\\n self._incidence_matrix = matrix(ZZ, len(self.Vrepresentation()), \\n len(self.Hrepresentation()), 0)\\n for V in self.Vrep_generator():\\n for H in self.Hrep_generator():\\n if self._is_zero(H*V):\\n self._incidence_matrix[V.index(),H.index()] = 1\\n\\n return self._incidence_matrix\",\n \"def form_population_matrix(N, hb, Nb, ht, Nt, pac, age_and_gender):\\n \\n # 1 allocated to iso-boxes and tents\\n household_column = create_household_column(hb, Nb, ht, Nt)\\n # 2 allocate an infector\\n disease_column = create_diseasestate_column(N)\\n # 3 weibull distribution\\n dsymptom_column = create_daystosymptoms_column(N)\\n # 4 np.zeros(num_ppl)\\n daycount_column = create_daycount_column(N)\\n # 5 hard coded number of chronics\\n asymp_column = create_asymp_column(N, pac)\\n # 6 age\\n age_column = create_age_column(age_and_gender[:,0])\\n # 7 gender\\n gender_column = create_gender_column(age_and_gender[:,1])\\n # 8 formula based on age\\n chronic_column = create_chronic_column(N, age_column)\\n # 5 new_asymp_column[chronic_column==1]=0\\n new_asymp_column = adjust_asymp_with_chronic(asymp_column, chronic_column)\\n # 9\\n wanderer_column = create_wanderer_column(gender_column, age_column)\\n\\n pop_matrix = np.column_stack((household_column, disease_column, dsymptom_column,\\n daycount_column, new_asymp_column, age_column,\\n gender_column, chronic_column, wanderer_column))\\n \\n assert pop_matrix.shape == (N, 9)\\n return pop_matrix\",\n \"def eclogite_massive():\\n\\n rho = 3490.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 238.85; C[0,1] = 82.01; C[0,2] = 81.44; C[0,3] = 0.3; C[0,4] = -0.02; C[0,5] = 0.5\\n C[1,0] = C[0,1]; C[1,1] = 242.12; C[1,2] = 81.11; C[1,3] = -0.66; C[1,4] = 0.33; C[1,5] = 0.12\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 235.57; C[2,3] = -0.28; C[2,4] = 0.22; C[2,5] = 0.31\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 78.72; C[3,4] = 0.27; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 78.37; C[4,5] = 0.25\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 77.91\\n\\n return C, rho\",\n \"def run_cgne(data):\\r\\n\\r\\n try:\\r\\n name = data['name']\\r\\n print(f\\\"Iniciando algoritmo CGNE\\\")\\r\\n\\r\\n img_path = '/images/' + name\\r\\n start_time = datetime.now()\\r\\n\\r\\n # Carrega a matriz de modelo\\r\\n matrix_path = \\\"./files/H-1.csv\\\"\\r\\n matrix = pd.read_csv(matrix_path, header=None)\\r\\n\\r\\n # Transforma os valores em valores numéricos e caso um deles\\r\\n # tenha um tipo diferente é registrado como NaN\\r\\n matrix = matrix.apply(pd.to_numeric, errors='coerce')\\r\\n matrix = matrix.to_numpy()\\r\\n\\r\\n # Inicializa as variáveis descritas no algoritmo\\r\\n r = np.array(data['signal'])\\r\\n p = np.dot(np.transpose(matrix), r)\\r\\n image = np.zeros(len(p))\\r\\n\\r\\n for i in range(30):\\r\\n print(f\\\"[ITERAÇÃO {i + 1}]\\\")\\r\\n # Linha 1 do algoritmo\\r\\n alpha = np.dot(np.transpose(r), r) / np.dot(np.transpose(p), p)\\r\\n\\r\\n # Linha 2 do algoritmo\\r\\n image = np.add(image, alpha * np.transpose(p))\\r\\n\\r\\n # Linha 3 do algoritmo\\r\\n r_aux = np.copy(r)\\r\\n r = r - alpha * np.dot(matrix, p)\\r\\n\\r\\n # Cálculo de erro. Quando o valor é atingido o algoritmo para.\\r\\n erro = la.norm(r, 2) - la.norm(r_aux, 2)\\r\\n if erro < 1e-4:\\r\\n break\\r\\n\\r\\n # Linha 4 do algoritmo\\r\\n beta = np.dot(np.transpose(r), r) / np.dot(np.transpose(r_aux), r_aux)\\r\\n\\r\\n #Linha 5 do algoritmo\\r\\n p = np.add(np.matmul(np.transpose(matrix), r), beta * p)\\r\\n\\r\\n # Encontra o valor absoluto do menor valor no vetor da imagem\\r\\n min_value = abs(image.min())\\r\\n\\r\\n # Encontra o maior valor no vetor da imagem e soma com o menor\\r\\n max_value = image.max() + min_value\\r\\n\\r\\n # Percorre o vetor da imagem alterando-o para que os valores finais\\r\\n # fiquem entre 0 e 255, assim podendo salvar a imagem em preto e branco\\r\\n for i in range(3600):\\r\\n image[0][i] = (image[0][i] + min_value) / max_value * 255\\r\\n #image[0][i] *= 255\\r\\n\\r\\n # Transforma o vetor em uma matriz de 60x60\\r\\n image = image.reshape((60, 60), order='F')\\r\\n\\r\\n # Amplia a imagem em 10 vezes\\r\\n res = cv.resize(image, (600, 600), interpolation = cv.INTER_NEAREST)\\r\\n\\r\\n # Salva a imagem\\r\\n cv.imwrite('.' + img_path, res)\\r\\n\\r\\n # Calcula a duração total do algoritmo\\r\\n finish_time = datetime.now()\\r\\n duration = (finish_time - start_time).total_seconds()\\r\\n duration = str(duration) + 's'\\r\\n\\r\\n # Cria um objeto contendo as informações da imagem\\r\\n image = Images(username=data['user'], \\r\\n name=data['name'], \\r\\n algorithm=data['algorithm'],\\r\\n path=img_path,\\r\\n size='600x600',\\r\\n n_iterations=2,\\r\\n status='Concluído',\\r\\n start_time=start_time,\\r\\n finish_time=finish_time,\\r\\n duration=duration)\\r\\n\\r\\n # Salva as informações no banco de dados\\r\\n db.session.add(image)\\r\\n db.session.commit()\\r\\n\\r\\n print(\\\"Imagem salva\\\")\\r\\n \\r\\n except Exception as e:\\r\\n print(\\\"Não foi possível gerar a imagem\\\")\\r\\n print(e)\",\n \"def olivine():\\n\\n rho = 3355.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 320.5; C[0,1] = 68.15; C[0,2] = 71.6; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 196.5; C[1,2] = 76.8; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 233.5; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 64.; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 77.; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 78.7\\n\\n return C, rho\",\n \"def get_chisq(g, nu, g_h, bins, bin_num2, ig_num): # checked 2017-7-9!!!\\n G_h = g - nu * g_h\\n num = numpy.histogram(G_h, bins)[0]\\n n1 = numpy.flip(num[0:bin_num2],axis=0)\\n n2 = num[bin_num2:]\\n xi = (n1 - n2) ** 2 / (n1 + n2)\\n return numpy.sum(xi[:len(xi)-ig_num]) * 0.5,n1,n2\",\n \"def casdetude_genetics_sk():\\n file_path = PROJECT_PATH + \\\"/geographycal_data/Monterusciello/MontEdo_buildings\\\"\\n router = Router(building_file=file_path)\\n\\n router.design_aqueduct(0)\\n\\n router.write2epanet(router.acqueduct, PROJECT_PATH + \\\"/Monterusciello_solution/Monterusciello_acqueduct\\\",\\n diam=False)\\n\\n read_epanet = graphIO.graph_reader(router.acqueduct)\\n read_epanet.read_epanet(PROJECT_PATH + \\\"/geographycal_data/SolvedNet/MonteSolution\\\")\\n\\n minimal = router.design_minimal_aqueduct(router.acqueduct, \\\"Q*H\\\")\\n router.write2epanet(minimal, PROJECT_PATH + \\\"/Monterusciello_solution/Monterusciello_acqueduct\\\", diam=False)\\n\\n read_epanet = graphIO.graph_reader(router.acqueduct)\\n read_epanet.read_epanet(PROJECT_PATH + \\\"/geographycal_data/SolvedNet/prova\\\")\\n kpi_calculator(router.acqueduct)\",\n \"def cell_cnc_tracker(Out, U, V, W, t, cell0, cellG, cellD, SHP, cryoconite_locations):\\n\\n Cells = np.random.rand(len(t),SHP[0],SHP[1],SHP[2]) * cell0\\n CellD = np.zeros(shape=(SHP[0],SHP[1],SHP[2])) + cellD\\n CellG = np.zeros(shape=(SHP[0],SHP[1],SHP[2])) + cellG\\n \\n \\n for i in np.arange(0,SHP[0],1):\\n CellG[i,:,:] = np.where(cryoconite_locations==True, CellG[i,:,:]*1000, CellG[i,:,:])\\n \\n for t in np.arange(0,len(Out.Qz[:,0,0,0]),1):\\n \\n for layer in np.arange(0,len(Out.Qz[0,:,0,0]),1):\\n\\n # normalise lateral flow so that -ve= flow out of cells towards edges\\n # and positive flow is towards centre line. In Qx -ve = leftwards flow\\n # and +ve = rightwards flow. This leads to a rightwards drift in cell\\n # fluxes if not normalised in this way.\\n U[t,layer,:,0:int(U.shape[2]/2)] = 0-U[t,layer,:,0:int(U.shape[2]/2)]\\n\\n # nabla is the inverted delta operator used to denote the divergence \\n # of a vector field, here applied to hydrological flow in m3/d \\n # calculated as dx/dt + dy/dt + dz/dt\\n\\n nabla = (U[t,layer,:,:] + V[t,layer,:,:] + W[t,layer,:,:])\\n \\n # divergence gives net in/outflow in m3/t\\n # cells/m3 = cells/mL *1000\\n\\n delC = Out.Q[t,layer,:,:] * (1+CellG[layer,:,:] - CellD[layer,:,:])\\n\\n Cells[t,layer,:,:] = Cells[t,layer,:,:] + (delC * 1000) \\n\\n Cells[Cells<0] = 0\\n \\n CellColumnTot = Cells.sum(axis=1)\\n \\n\\n return Cells, CellColumnTot\",\n \"def createGloveDic():\\n saveGloveDicIntoFile(_extractGloveVects())\",\n \"def qchannel_to_qiskit(representation):\\n\\n rep = representation.representation\\n # Find what representation it is.\\n # Then create the corresponding matrix and shape it like qiskit is expecting it.\\n # Finally, create the qiskit representation from that matrix.\\n if rep in (RepresentationType.PTM, RepresentationType.CHOI):\\n matri = representation.matrix\\n data_re = []\\n data_im = []\\n for i in range(matri.nRows):\\n for j in range(matri.nCols):\\n data_re.append(matri.data[i * matri.nRows + j].re + 0.j)\\n data_im.append(matri.data[i * matri.nRows + j].im)\\n data = np.array(data_re)\\n data.imag = np.array(data_im)\\n data = data.reshape((matri.nRows, matri.nCols))\\n return PTM(data) if (rep == RepresentationType.PTM) else Choi(data)\\n if rep in (RepresentationType.CHI, RepresentationType.SUPEROP):\\n final_data = []\\n for matri in representation.basis:\\n data_re = []\\n data_im = []\\n for i in range(matri.nRows):\\n for j in range(matri.nCols):\\n data_re.append(matri.data[i * matri.nRows + j].re + 0.j)\\n data_im.append(matri.data[i * matri.nRows + j].im)\\n data = np.array(data_re)\\n data.imag = np.array(data_im)\\n data = data.reshape((matri.nRows, matri.nCols))\\n final_data.append(data)\\n if rep == RepresentationType.CHI:\\n return Chi(final_data) if len(final_data) > 1 else Chi(final_data[0])\\n return SuperOp(final_data) if len(final_data) > 1 else SuperOp(final_data[0])\\n if rep == RepresentationType.KRAUS:\\n final_data = []\\n for matri in representation.kraus_ops:\\n data_re = []\\n data_im = []\\n for i in range(matri.nRows):\\n for j in range(matri.nCols):\\n data_re.append(matri.data[i * matri.nRows + j].re + 0.j)\\n data_im.append(matri.data[i * matri.nRows + j].im)\\n data = np.array(data_re)\\n data.imag = np.array(data_im)\\n data = data.reshape((matri.nRows, matri.nCols))\\n final_data.append(data)\\n return Kraus(final_data)\\n return None\",\n \"def g(self, t, s, u):\\n P, g = s\\n return np.matrix([self.Toc * P])\",\n \"def convert(self):\\n with open(self.path) as csvfile:\\n reader = csv.reader(csvfile)\\n d = []\\n for row in reader:\\n d+=[row]\\n if self.n_class==2:\\n dd = []\\n for i in range(len(d)):\\n if d[i][0] in ['EI','IE']:\\n dd+=[d[i]]\\n d = dd\\n\\n random.seed(0)\\n random.shuffle(d)\\n\\n self.x = np.zeros((len(d),len(d[0][2].strip()),4))\\n self.y = np.zeros((len(d),self.n_class))\\n self.count = Counter([x[0] for x in d])\\n for i in range(len(d)):\\n self.x_raw += [d[i][2].strip()]\\n tmp = [self.base[x] for x in d[i][2].strip()]\\n for j in range(len(tmp)):\\n if tmp[j]==4:\\n # N: A or G or C or T\\n self.x[i][j][0] = .25\\n self.x[i][j][1] = .25\\n self.x[i][j][2] = .25\\n self.x[i][j][3] = .25\\n elif tmp[j]==5:\\n # D: A or G or T\\n self.x[i][j][0] = .33\\n self.x[i][j][1] = .33\\n self.x[i][j][2] = .33\\n elif tmp[j]==6:\\n # R: A or G\\n self.x[i][j][0] = .50\\n self.x[i][j][2] = .50\\n elif tmp[j]==7:\\n # S: C or G\\n self.x[i][j][2] = .50\\n self.x[i][j][3] = .50\\n else:\\n self.x[i][j][tmp[j]] = 1\\n\\n #self.x[i][range(len(tmp)),tmp] = 1\\n self.y[i][self.result[d[i][0]]] = 1\",\n \"def _get_grid_representations(self):\\n\\n representation = '-----------Loading Sequence----------------------------------------------------------------\\\\n'\\n for row in self.grid:\\n for col in row:\\n if col == -1:\\n representation += 'X\\\\t'\\n elif col == 0:\\n representation += '-\\\\t'\\n else:\\n representation += str(int(col)) + '\\\\t'\\n representation += '\\\\n\\\\n'\\n\\n representation += '-----------VehicleType--------------------------------------------------------------------\\\\n'\\n for row in self.grid_vehicle_type:\\n for col in row:\\n if col == -2:\\n representation += 'X\\\\t'\\n elif col == -1:\\n representation += '-\\\\t'\\n else:\\n representation += str(int(col)) + '\\\\t'\\n representation += '\\\\n\\\\n'\\n\\n representation += '-----------Destination--------------------------------------------------------------------\\\\n'\\n for row in self.grid_destination:\\n for col in row:\\n if col == -1:\\n representation += 'X\\\\t'\\n elif col == 0:\\n representation += '-\\\\t'\\n else:\\n representation += str(int(col)) + '\\\\t'\\n representation += '\\\\n\\\\n'\\n\\n return representation\",\n \"def _init_numeric(self):\\n from scipy.special import erf\\n covbasis = np.zeros((self.nmax, self.nmax))\\n overbasis = np.zeros((self.nmax, self.nmax))\\n #Get local references to these variables so that we don't need `self`\\n #all over in the overbasis calculation below.\\n alpha = self.alpha\\n rb = self.rb\\n \\n for i in range(self.nmax):\\n for j in range(self.nmax):\\n covbasis[j,i] = np.exp(-alpha * (rb[i] - rb[j])**2)\\n overbasis[j,i] = (np.exp(-alpha*(rb[i]**2+rb[j]**2))*np.sqrt(2.)* \\n alpha**1.5*(rb[i] + rb[j]) + \\n alpha*np.exp(-0.5*alpha*(rb[i] - rb[j])**2)*\\n np.sqrt(np.pi)*\\n (1. + alpha*(rb[i] + rb[j])**2)*\\n (1.0 + erf(np.sqrt(alpha/2.0)*(rb[i]+rb[j]))))\\n \\n overbasis /= np.sqrt(128. * alpha**5)\\n\\n from numpy.linalg import cholesky\\n choloverlap = cholesky(overbasis)\\n\\n for i in range(self.nmax):\\n for j in range(i):\\n choloverlap[j,i] = 0.0\\n\\n from numpy.linalg import solve\\n self.transformbasis = solve(covbasis, choloverlap)\",\n \"def reformat_gittins(g, v=None):\\n n = g.shape[0]\\n g_reformat = np.zeros(g.shape)\\n\\n for row in range(0, n):\\n g_reformat[row, :n - row] = g[row, row:]\\n return g_reformat\",\n \"def pv2ssh(lon, lat, q, hg, c, nitr=1, name_grd=''):\\n def compute_avec(vec,aaa,bbb,grd):\\n\\n avec=np.empty(grd.np0,)\\n avec[grd.vp2] = aaa[grd.vp2]*((vec[grd.vp2e]+vec[grd.vp2w]-2*vec[grd.vp2])/(grd.dx1d[grd.vp2]**2)+(vec[grd.vp2n]+vec[grd.vp2s]-2*vec[grd.vp2])/(grd.dy1d[grd.vp2]**2)) + bbb[grd.vp2]*vec[grd.vp2]\\n avec[grd.vp1] = vec[grd.vp1]\\n\\n return avec,\\n if name_grd is not None:\\n if os.path.isfile(name_grd):\\n with open(name_grd, 'rb') as f:\\n grd = pickle.load(f)\\n else:\\n grd = Grid(lon,lat)\\n with open(name_grd, 'wb') as f:\\n pickle.dump(grd, f)\\n f.close()\\n else:\\n grd = Grid(lon,lat)\\n\\n ny,nx,=np.shape(hg)\\n g=grd.g\\n\\n\\n x=hg[grd.indi,grd.indj]\\n q1d=q[grd.indi,grd.indj]\\n\\n aaa=g/grd.f01d\\n bbb=-g*grd.f01d/c**2\\n ccc=+q1d\\n\\n aaa[grd.vp1]=0\\n bbb[grd.vp1]=1\\n ccc[grd.vp1]=x[grd.vp1] ##boundary condition\\n\\n vec=+x\\n\\n avec,=compute_avec(vec,aaa,bbb,grd)\\n gg=avec-ccc\\n p=-gg\\n\\n for itr in range(nitr-1):\\n vec=+p\\n avec,=compute_avec(vec,aaa,bbb,grd)\\n tmp=np.dot(p,avec)\\n\\n if tmp!=0. : s=-np.dot(p,gg)/tmp\\n else: s=1.\\n\\n a1=np.dot(gg,gg)\\n x=x+s*p\\n vec=+x\\n avec,=compute_avec(vec,aaa,bbb,grd)\\n gg=avec-ccc\\n a2=np.dot(gg,gg)\\n\\n if a1!=0: beta=a2/a1\\n else: beta=1.\\n\\n p=-gg+beta*p\\n\\n vec=+p\\n avec,=compute_avec(vec,aaa,bbb,grd)\\n val1=-np.dot(p,gg)\\n val2=np.dot(p,avec)\\n if (val2==0.):\\n s=1.\\n else:\\n s=val1/val2\\n\\n a1=np.dot(gg,gg)\\n x=x+s*p\\n\\n # back to 2D\\n h=np.empty((ny,nx))\\n h[:,:]=np.NAN\\n h[grd.indi,grd.indj]=x[:]\\n\\n\\n return h\",\n \"def lizardite():\\n\\n rho = 2610.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 245.; C[0,1] = 50.; C[0,2] = 31.; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 245.; C[1,2] = 31.; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 23.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 11.6; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 11.6; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 97.5\\n\\n return C, rho\",\n \"def set_global_problem_vf_3_gr1_bif(self):\\n #0\\n std_map = Epetra.Map(len(self.all_fine_vols_ic),0,self.comm)\\n self.trans_fine = Epetra.CrsMatrix(Epetra.Copy, std_map, 7)\\n self.b = Epetra.Vector(std_map)\\n for volume in self.all_fine_vols_ic - set(self.neigh_wells_d):\\n #1\\n soma = 0.0\\n soma2 = 0.0\\n soma3 = 0.0\\n volume_centroid = self.mesh_topo_util.get_average_position([volume])\\n adj_volumes = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n global_volume = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\\n lamb_w_vol = self.mb.tag_get_data(self.lamb_w_tag, volume)[0][0]\\n lamb_o_vol = self.mb.tag_get_data(self.lamb_o_tag, volume)[0][0]\\n lbt_vol = lamb_w_vol + lamb_o_vol\\n z_vol = self.tz - volume_centroid[2]\\n soma = 0.0\\n temp_glob_adj = []\\n temp_k = []\\n flux_gr = []\\n for adj in adj_volumes:\\n #2\\n global_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\\n adj_centroid = self.mesh_topo_util.get_average_position([adj])\\n z_adj = self.tz - adj_centroid[2]\\n altura = adj_centroid[2]\\n direction = adj_centroid - volume_centroid\\n uni = self.unitary(direction)\\n kvol = np.dot(np.dot(kvol,uni),uni)\\n #kvol = kvol*(lamb_w_vol + lamb_o_vol)\\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\\n kadj = np.dot(np.dot(kadj,uni),uni)\\n lamb_w_adj = self.mb.tag_get_data(self.lamb_w_tag, adj)[0][0]\\n lamb_o_adj = self.mb.tag_get_data(self.lamb_o_tag, adj)[0][0]\\n lbt_adj = lamb_w_adj + lamb_o_adj\\n\\n #kadj = kadj*(lamb_w_adj + lamb_o_adj)\\n keq = self.kequiv(kvol, kadj)*((lbt_adj + lbt_vol)/2.0)\\n keq = keq*(np.dot(self.A, uni)/float(abs(np.dot(direction, uni))))\\n grad_z = (z_adj - z_vol)\\n q_grad_z = grad_z*self.gama*keq\\n flux_gr.append(q_grad_z)\\n\\n temp_glob_adj.append(self.map_vols_ic[adj])\\n temp_k.append(-keq)\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n #1\\n soma2 = -sum(flux_gr)\\n temp_k.append(-sum(temp_k))\\n temp_glob_adj.append(self.map_vols_ic[volume])\\n self.trans_fine.InsertGlobalValues(self.map_vols_ic[volume], temp_k, temp_glob_adj)\\n if volume in self.wells_n:\\n #2\\n index = self.wells_n.index(volume)\\n # tipo_de_poco = self.mb.tag_get_data(self.tipo_de_poco_tag, volume)\\n if volume in self.wells_inj:\\n #3\\n self.b[self.map_vols_ic[volume]] += self.set_q[index] + soma2\\n #2\\n else:\\n #3\\n self.b[self.map_vols_ic[volume]] += -self.set_q[index] + soma2\\n #1\\n else:\\n #2\\n self.b[self.map_vols_ic[volume]] += soma2\\n #0\\n for volume in self.neigh_wells_d:\\n #1\\n soma2 = 0.0\\n soma3 = 0.0\\n volume_centroid = self.mesh_topo_util.get_average_position([volume])\\n z_vol = self.tz - volume_centroid[2]\\n adj_volumes = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n global_volume = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\\n lamb_w_vol = self.mb.tag_get_data(self.lamb_w_tag, volume)[0][0]\\n lamb_o_vol = self.mb.tag_get_data(self.lamb_o_tag, volume)[0][0]\\n lbt_vol = lamb_w_vol + lamb_o_vol\\n soma = 0.0\\n temp_glob_adj = []\\n temp_k = []\\n flux_gr = []\\n for adj in adj_volumes:\\n #2\\n global_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\\n adj_centroid = self.mesh_topo_util.get_average_position([adj])\\n z_adj = self.tz - adj_centroid[2]\\n altura = adj_centroid[2]\\n direction = adj_centroid - volume_centroid\\n uni = self.unitary(direction)\\n z = uni[2]\\n kvol = np.dot(np.dot(kvol,uni),uni)\\n #kvol = kvol*(lamb_w_vol + lamb_o_vol)\\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\\n kadj = np.dot(np.dot(kadj,uni),uni)\\n lamb_w_adj = self.mb.tag_get_data(self.lamb_w_tag, adj)[0][0]\\n lamb_o_adj = self.mb.tag_get_data(self.lamb_o_tag, adj)[0][0]\\n lbt_adj = lamb_o_adj + lamb_o_adj\\n #kadj = kadj*(lamb_w_adj + lamb_o_adj)\\n keq = self.kequiv(kvol, kadj)*((lbt_adj + lbt_vol)/2.0)\\n keq = keq*(np.dot(self.A, uni)/(abs(np.dot(direction, uni))))\\n grad_z = (z_adj - z_vol)\\n q_grad_z = grad_z*self.gama*keq\\n flux_gr.append(q_grad_z)\\n #2\\n if adj in self.wells_d:\\n #3\\n soma = soma + keq\\n index = self.wells_d.index(adj)\\n self.b[self.map_vols_ic[volume]] += self.set_p[index]*(keq)\\n #2\\n else:\\n #3\\n temp_glob_adj.append(self.map_vols_ic[adj])\\n temp_k.append(-keq)\\n soma = soma + keq\\n #2\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n #1\\n soma2 = -sum(flux_gr)\\n temp_k.append(soma)\\n temp_glob_adj.append(self.map_vols_ic[volume])\\n self.trans_fine.InsertGlobalValues(self.map_vols_ic[volume], temp_k, temp_glob_adj)\\n if volume in self.wells_n:\\n #2\\n index = self.wells_n.index(volume)\\n # tipo_de_poco = self.mb.tag_get_data(self.tipo_de_poco_tag, volume)\\n if volume in self.wells_inj:\\n #3\\n self.b[self.map_vols_ic[volume]] += self.set_q[index] + soma2\\n #2\\n else:\\n #3\\n self.b[self.map_vols_ic[volume]] += -self.set_q[index] + soma2\\n #1\\n else:\\n #2\\n self.b[self.map_vols_ic[volume]] += soma2\\n #0\\n self.trans_fine.FillComplete()\",\n \"def connect_gfluct(self, E_e=0, E_i=-65):\\n if self.do_run:\\n \\n for m in self.flucts:\\n del m \\n del self.flucts\\n \\n for m in self.noises:\\n del m \\n del self.noises\\n \\n self.flucts = []\\n self.noises = []\\n \\n for n in range(self.n_celltypes):\\n \\n fluct_g_i0_n = self.fluct_g_i0[n]\\n \\n if type(fluct_g_i0_n) is not ndarray: fluct_g_i0_n = np.array([fluct_g_i0_n])\\n \\n if len(fluct_g_i0_n) == len(self.global_gidlist[n]):\\n pass\\n else:\\n fluct_g_i0_n = np.ones(int(len(self.global_gidlist[n])))*fluct_g_i0_n[0]\\n if self.id == 0: print \\\"- single value in fluct_g_i0_n\\\"\\n \\n #print fluct_g_i0_n\\n \\n for i, gid in enumerate(self.gidlist[n]): # for every cell in the gidlist \\n \\n #h.mcell_ran4_init(gid)\\n \\n noiseRandObj = h.Random() # provides NOISE with random stream\\n self.noises.append(noiseRandObj) # has to be set here not inside the nmodl function!! \\n \\n # print str(gid) + \\\": \\\" + str(noiseRandObj.normal(0,1))\\n \\n fluct = h.Gfluct3(self.cells[n][i].soma(0.5))\\n fluct.E_e = E_e/mV # [mV]\\n fluct.E_i = E_i/mV # [mV]\\n fluct.g_e0 = self.fluct_g_e0[n]/uS # [uS]\\n fluct.g_i0 = fluct_g_i0_n[i]/uS # [uS]\\n fluct.std_e = self.fluct_std_e[n]/uS # [uS] \\n fluct.std_i = self.fluct_std_i[n]/uS # [uS] \\n fluct.tau_e = self.fluct_tau_e/ms #tau_e/ms # [ms] \\n fluct.tau_i = self.fluct_tau_i/ms #tau_i/ms # [ms]\\n \\n self.flucts.append(fluct) # add to list \\n self.flucts[-1].noiseFromRandom(self.noises[-1]) # connect random generator!\\n \\n self.noises[-1].MCellRan4(1, gid+1) # set lowindex to gid+1, set highindex to > 0 \\n self.noises[-1].normal(0,1)\",\n \"def to_ijv(self):\\n row_indices = []\\n column_indices = []\\n nonzero_elements = []\\n k = 0\\n for key, value in self.data.items():\\n if value == 0:\\n continue\\n row, col = key[:2]\\n row_indices.append(row)\\n column_indices.append(col)\\n nonzero_elements.append(value)\\n k += 1\\n return row_indices, column_indices, nonzero_elements\",\n \"def transform(self,G):\\n\\n n = len(self.G_train_)\\n nt = len(G)\\n #Ks = sp.zeros((n,1))\\n kernel_matrix = sp.zeros((nt,n))\\n \\n# for j in range(n):\\n# Ks[j] = sp.sqrt(aGMKernel(self.G_train_[j],self.G_train_[j],self.alpha,self.gamma))\\n# \\n# for i in range(nt):\\n# Kts = sp.sqrt(aGMKernel(G[i],G[i],self.alpha,self.gamma))\\n# for j in range(n):\\n# kernel_matrix[i,j] = aGMKernel(G[i],self.G_train_[j],self.alpha,self.gamma)/Kts/Ks[j]\\n \\n for i in range (nt):\\n for j in range(n):\\n kernel_matrix[i,j] = aGMKernel(G[i],self.G_train_[j],self.alpha, self.gamma)\\n \\n \\n return kernel_matrix\",\n \"def read_input():\\n \\n argv = sys.argv\\n\\n # Read file names from sd input\\n f_dy = argv[1] # matdyn.modes\\n f_pat = argv[2] # path.out (should be in crystal coords)\\n f_ph = argv[3] # ph.x output (Gamma point)\\n\\n # Read input card\\n f_inp = open(\\\"input.dat\\\",'r')\\n l1 = f_inp.readline()\\n l2 = f_inp.readline()\\n l3 = f_inp.readline().split()\\n f_inp.close()\\n\\n # Open files\\n\\n f = open(f_dy,'r') # matdyn.modes \\n f_dyn = f.readlines()\\n f.close()\\n\\n f = open(f_pat,'r') # path.out\\n f_path = f.readlines()\\n f.close()\\n\\n f = open(f_ph,'r') # ph.x output\\n f_zs = f.readlines()\\n f.close()\\n\\n # Assign values to a0, nat, M, nqp\\n a0, vol = float(l1.split()[0]), float(l1.split()[1])\\n nat = int(l2) \\n mass = np.zeros(nat)\\n for iat in range(nat):\\n mass[iat] = float(l3[iat])\\n\\n # Assign values to G (reciprocal lattice vec)\\n ig = 0 ; i = 0\\n for line in f_zs:\\n if \\\"reciprocal axes:\\\" in line:\\n ig = i + 1 \\n break\\n i += 1 \\n\\n rG = np.zeros((3,3))\\n for ic in range(3):\\n rGtext = f_zs[ig+ic][23:48].split()\\n rG[ic,:] = np.array([float(rGtext[0]), float(rGtext[1]), float(rGtext[2])])\\n\\n # Read Z* tensor from f_zs\\n i = 0\\n iz = 0\\n zstart = []\\n for line in f_zs:\\n if \\\"(d P / du)\\\" in line:\\n iz = i + 3\\n if \\\"Px\\\" in line:\\n zstart.append(i)\\n\\n i += 1\\n\\n # Read the dielectric tensor from f_zs\\n i = 0\\n ie = 0\\n for line in f_zs:\\n if \\\"Dielectric constant in cartesian axis\\\" in line:\\n ie = i + 2\\n break\\n\\n i += 1\\n\\n # Assign Z* values\\n zs = np.zeros((nat,3,3)) # initialize Z*\\n\\n for iat in range(nat):\\n for ic in range(3):\\n ztext = f_zs[zstart[iat]+ic][19:56].split()\\n for jc in range(3):\\n zs[iat][ic][jc] = float(ztext[jc])\\n\\n # Assing the dielectric tensor\\n eps = np.zeros((3,3))\\n\\n for ic in range(3):\\n epstext = f_zs[ie+ic][16:66].split()\\n for jc in range(3):\\n eps[ic][jc] = float(epstext[jc])\\n\\n # Number of modes and q-points\\n nmodes = 3 * nat\\n nqpt = int(f_path[0].split()[0])\\n\\n # Read the q-points\\n q = np.zeros((nqpt,4)) # 4th dimension is lenght for q-points on a line, weights for q-points on a grid \\n for iq in range(1,nqpt+1):\\n q[iq-1,] = np.array([float(f_path[iq].split()[0]),float(f_path[iq].split()[1]), \\\\\\n float(f_path[iq].split()[2]),float(f_path[iq].split()[3])])\\n\\n # Read the eigenvalues(om) and eigenvectors(eig) \\n # Initiate first\\n om = np.zeros((nmodes,nqpt))\\n eig = np.zeros((nmodes,nqpt,nat,3), dtype=complex) \\n\\n # Get the starting lines for each q-pt\\n i = 0\\n i_q = []\\n for line in f_dyn:\\n if \\\"q =\\\" in line:\\n i_q.append(i+2)\\n i += 1\\n\\n #Assign values to om and eig\\n for iq in range(nqpt):\\n for imod in range(nmodes):\\n omtext = f_dyn[i_q[iq]+imod*(nat+1)][43:55]\\n om[imod][iq] = float(omtext)\\n for iat in range(nat):\\n etext = f_dyn[i_q[iq]+imod*(nat+1)+iat+1][2:72].split()\\n for ic in range(3):\\n eig.real[imod][iq][iat][ic]=float(etext[2*ic])*np.sqrt(mass[iat])\\n eig.imag[imod][iq][iat][ic]=float(etext[2*ic+1])*np.sqrt(mass[iat])\\n\\n #Normalize the eigenvectors\\n t1 = eig[imod,iq,:,:]\\n t_nu = np.sum(np.sum(np.conjugate(t1)*t1,axis=0))\\n eig[imod,iq,:,:] = eig[imod,iq,:,:]/np.sqrt(np.abs(t_nu))\\n\\n # Check normalization\\n delta = np.zeros((nmodes,nmodes), dtype=complex)\\n for iat in range(nat):\\n for ic in range(3):\\n t2 = eig[:,iq,iat,ic]\\n delta += np.outer(np.conjugate(t2),t2)\\n\\n unit = np.diag(np.diag(np.ones((nmodes,nmodes)))) # Unit vector\\n test = np.abs( (delta-unit) )\\n if ( np.max(test) > 1e-3):\\n print \\\"Non-orthonormal eigenvector at iq=\\\", q[iq,:]\\n\\n return om, eig, q, zs, eps, mass, a0, vol, rG, nmodes, nqpt, nat\",\n \"def local_composition(self, outfile):\\n # TODO Rewrite if I ever need this again\\n radius = 3.6 * 2\\n npix = 64\\n #mat = np.zeros((npix,npix,npix),dtype=np.float)\\n #mat = np.zeros((npix,npix,npix),dtype={'names':['col1', 'col2', 'col3'], 'formats':['f4','f4','f4']})\\n #mat = np.zeros((npix,npix,npix),dtype={'names':['40', '13', '29'], 'formats':['f4','f4','f4']})\\n #mat = np.zeros((npix,npix,npix),dtype={'names':['id','data'], 'formats':['f4','f4']})\\n #names = ['id','data']\\n #formats = ['i4',('f4','f4','f4')]\\n #mat = np.zeros((npix,npix,npix),dtype=dict(names = names, formats=formats))\\n #mat = np.zeros((npix,npix,npix),dtype={'40':('i4',0), '29':('f4',0), '13':('f4',0)})\\n print(\\\"Creating matrix...\\\")\\n mat = [[[{} for i in range(npix)] for j in range(npix)] for k in range(npix)]\\n print(\\\"Finished creating matrix.\\\")\\n #print(repr(mat))\\n dx = self.xsize/npix\\n dy = self.ysize/npix\\n dz = self.zsize/npix\\n for ii,i in enumerate(drange(-npix/2*dx,npix/2*dx-dx,dx)):\\n print(\\\"On ii = {0}\\\".format(ii))\\n for jj,j in enumerate(drange(-npix/2*dy,npix/2*dy-dy,dy)):\\n for kk,k in enumerate(drange(-npix/2*dz,npix/2*dz-dz,dz)):\\n atoms = self.get_atoms_in_cutoff( (i,j,k), radius )\\n comp = {}\\n for atom in atoms:\\n comp[str(atom.z)] = comp.get(str(atom.z),0) + 1.0\\n for key in comp:\\n comp[key] /= len(atoms)\\n #print(comp)\\n #mat[ii][jj][kk] = copy.copy(comp)\\n mat[ii][jj][kk] = comp\\n of = open(outfile,'w')\\n of.write('IGOR\\\\n')\\n for atomtype in self.atomtypes:\\n of.write('\\\\nWAVES/N=({0},{1},{2})\\\\t {3}\\\\nBEGIN\\\\n'.format(npix,npix,npix,'partial_comp_'+znum2sym.z2sym(atomtype)))\\n for layer in mat:\\n for column in layer:\\n for value in column:\\n try:\\n of.write(\\\"{0} \\\".format(value[str(atomtype)]))\\n except KeyError:\\n of.write(\\\"{0} \\\".format(0.0))\\n of.write(\\\"\\\\n\\\")\\n of.write('END\\\\n')\\n of.write('X SetScale/P x 0,1,\\\"\\\", {0}; SetScale/P y 0,1,\\\"\\\", {0}; SetScale/P z 0,1,\\\"\\\", {0}; SetScale d 0,0,\\\"\\\", {0}\\\\n'.format('partial_comp_'+znum2sym.z2sym(atomtype)))\\n of.close()\\n return mat\",\n \"def __init__(self):\\n #self.solitaire = np.zeros((7, 13), dtype=object)\\n #self.leaf_cards = np.zeros(7, dtype=object)\\n # # Keep track of the facedown cards in each of the 7 piles\\n #self.col_facedown: list = [0, 1, 2, 3, 4, 5, 6]\\n pass\",\n \"def assign_vertices(self):\\n CV_matrix = np.zeros((self.n_c, self.n_v, 3))\\n for i in range(3):\\n CV_matrix[self.tris[:, i], np.arange(self.n_v), i] = 1\\n self.CV_matrix = CV_matrix\\n return self.CV_matrix\",\n \"def assemble_matrices(self):\\n \\n #Pointer reassignment for convenience\\n N = self.ngrids\\n\\n #Begin with a linked-list data structure for the transmissibilities,\\n #and one-dimenstional arrays for the diagonal of B and the flux vector\\n T = lil_matrix((N, N), dtype=np.double)\\n B = np.zeros(N, dtype=np.double)\\n Q = np.zeros(N, dtype=np.double)\\n\\n #Read in boundary condition types and values\\n bcs = self.input_data['boundary conditions']\\n bc_type_1 = bcs['left']['type'].lower()\\n bc_type_2 = bcs['right']['type'].lower()\\n bc_value_1 = bcs['left']['value']\\n bc_value_2 = bcs['right']['value']\\n \\n #Loop over all grid cells\\n for i in range(N):\\n\\n #Apply left BC\\n if i == 0:\\n T[i, i+1] = -self.compute_transmissibility(i, i + 1)\\n\\n if bc_type_1 == 'neumann':\\n T[i, i] = T[i,i] - T[i, i+1]\\n elif bc_type_1 == 'dirichlet':\\n #Computes the transmissibility of the ith block\\n T0 = self.compute_transmissibility(i, i)\\n T[i, i] = T[i,i] - T[i, i+1] + 2.0 * T0\\n Q[i] = 2.0 * T0 * bc_value_1\\n else:\\n pass #TODO: Add error checking here if no bc is specified\\n\\n #Apply right BC\\n elif i == (N - 1):\\n T[i, i-1] = -self.compute_transmissibility(i, i - 1)\\n\\n if bc_type_2 == 'neumann':\\n T[i, i] = T[i,i] - T[i, i-1]\\n elif bc_type_2 == 'dirichlet':\\n #Computes the transmissibility of the ith block\\n T0 = self.compute_transmissibility(i, i)\\n T[i, i] = T[i, i] - T[i, i-1] + 2.0 * T0\\n Q[i] = 2.0 * T0 * bc_value_2\\n else:\\n pass #TODO:Add error checking here if no bc is specified\\n\\n #If there is no boundary condition compute interblock transmissibilties\\n else:\\n T[i, i-1] = -self.compute_transmissibility(i, i-1)\\n T[i, i+1] = -self.compute_transmissibility(i, i+1)\\n T[i, i] = (self.compute_transmissibility(i, i-1) +\\n self.compute_transmissibility(i, i+1))\\n\\n #Compute accumulations\\n B[i] = self.compute_accumulation(i)\\n\\n #If constant-rate wells are present, add them to the flux vector\\n if self.rate_well_grids is not None:\\n Q[self.rate_well_grids] += self.rate_well_values\\n\\n \\n #Return sparse data-structures\\n return (T.tocsr(), \\n csr_matrix((B, (np.arange(N), np.arange(N))), shape=(N,N)), \\n Q)\",\n \"def H(self) -> BaseMatrix:\",\n \"def H(self) -> BaseMatrix:\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59091425","0.57651687","0.5696586","0.5588323","0.5519537","0.54216594","0.54123414","0.538351","0.5372897","0.5361422","0.5356955","0.5349356","0.53209347","0.53064805","0.5252595","0.5252","0.5212284","0.52063763","0.51806784","0.51663315","0.5142872","0.5138772","0.51122373","0.510396","0.5097512","0.50875187","0.508736","0.5066477","0.5066057","0.50487655","0.504849","0.5031612","0.5008759","0.5006741","0.49981004","0.49894616","0.49854","0.49831367","0.4972586","0.4959279","0.49528605","0.49510098","0.49460673","0.4937289","0.4933054","0.49304798","0.49094808","0.4905722","0.49017575","0.48997357","0.4893252","0.48907503","0.48905432","0.48881525","0.48839933","0.4881577","0.48747885","0.48739332","0.48541698","0.48488462","0.48456162","0.48437294","0.48412552","0.48357835","0.4827084","0.48234126","0.481894","0.4817497","0.4817378","0.48105782","0.48102045","0.4809813","0.47994518","0.4798995","0.47862753","0.47774914","0.47771752","0.47770956","0.47680345","0.47630635","0.47621292","0.4762073","0.4760376","0.47537696","0.47536445","0.47522822","0.4750145","0.47490245","0.47482976","0.47477973","0.47463214","0.47453153","0.47450316","0.4741647","0.47406816","0.473811","0.47265646","0.4723883","0.4723564","0.4723564"],"string":"[\n \"0.59091425\",\n \"0.57651687\",\n \"0.5696586\",\n \"0.5588323\",\n \"0.5519537\",\n \"0.54216594\",\n \"0.54123414\",\n \"0.538351\",\n \"0.5372897\",\n \"0.5361422\",\n \"0.5356955\",\n \"0.5349356\",\n \"0.53209347\",\n \"0.53064805\",\n \"0.5252595\",\n \"0.5252\",\n \"0.5212284\",\n \"0.52063763\",\n \"0.51806784\",\n \"0.51663315\",\n \"0.5142872\",\n \"0.5138772\",\n \"0.51122373\",\n \"0.510396\",\n \"0.5097512\",\n \"0.50875187\",\n \"0.508736\",\n \"0.5066477\",\n \"0.5066057\",\n \"0.50487655\",\n \"0.504849\",\n \"0.5031612\",\n \"0.5008759\",\n \"0.5006741\",\n \"0.49981004\",\n \"0.49894616\",\n \"0.49854\",\n \"0.49831367\",\n \"0.4972586\",\n \"0.4959279\",\n \"0.49528605\",\n \"0.49510098\",\n \"0.49460673\",\n \"0.4937289\",\n \"0.4933054\",\n \"0.49304798\",\n \"0.49094808\",\n \"0.4905722\",\n \"0.49017575\",\n \"0.48997357\",\n \"0.4893252\",\n \"0.48907503\",\n \"0.48905432\",\n \"0.48881525\",\n \"0.48839933\",\n \"0.4881577\",\n \"0.48747885\",\n \"0.48739332\",\n \"0.48541698\",\n \"0.48488462\",\n \"0.48456162\",\n \"0.48437294\",\n \"0.48412552\",\n \"0.48357835\",\n \"0.4827084\",\n \"0.48234126\",\n \"0.481894\",\n \"0.4817497\",\n \"0.4817378\",\n \"0.48105782\",\n \"0.48102045\",\n \"0.4809813\",\n \"0.47994518\",\n \"0.4798995\",\n \"0.47862753\",\n \"0.47774914\",\n \"0.47771752\",\n \"0.47770956\",\n \"0.47680345\",\n \"0.47630635\",\n \"0.47621292\",\n \"0.4762073\",\n \"0.4760376\",\n \"0.47537696\",\n \"0.47536445\",\n \"0.47522822\",\n \"0.4750145\",\n \"0.47490245\",\n \"0.47482976\",\n \"0.47477973\",\n \"0.47463214\",\n \"0.47453153\",\n \"0.47450316\",\n \"0.4741647\",\n \"0.47406816\",\n \"0.473811\",\n \"0.47265646\",\n \"0.4723883\",\n \"0.4723564\",\n \"0.4723564\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5412369"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":93784,"cells":{"query":{"kind":"string","value":"Estimate standard Gibbs energies of formation"},"document":{"kind":"string","value":"def train(S, G, b, w):\n assert type(S) == np.ndarray\n assert type(G) == np.ndarray\n assert type(b) == np.ndarray\n assert type(w) == np.ndarray\n\n m, n = S.shape\n assert G.shape[0] == m\n assert b.shape == (n,)\n assert w.shape == (n,)\n\n # Apply weighing\n W = np.diag(w.flat)\n GS = G.T @ S\n\n # Linear regression for the reactant layer (aka RC)\n inv_S, r_rc, P_R_rc, P_N_rc = LINALG._invert_project(S @ W)\n\n # Linear regression for the group layer (aka GC)\n inv_GS, r_gc, P_R_gc, P_N_gc = LINALG._invert_project(GS @ W)\n\n # calculate the group contributions\n dG0_gc = np.squeeze(inv_GS.T @ W @ b)\n\n # Calculate the contributions in the stoichiometric space\n dG0_rc = np.squeeze(inv_S.T @ W @ b)\n dG0_cc = np.squeeze(P_R_rc @ dG0_rc + P_N_rc @ G @ dG0_gc)\n\n # Calculate the residual error (unweighted squared error divided\n # by N - rank)\n e_rc = (S.T @ dG0_rc - b)\n MSE_rc = float((e_rc.T @ W @ e_rc) / (n - r_rc))\n # MSE_rc = (e_rc.T @ e_rc) / (n - r_rc)\n\n e_gc = (GS.T @ dG0_gc - b)\n MSE_gc = float((e_gc.T @ W @ e_gc) / (n - r_gc))\n # MSE_gc = (e_gc.T @ e_gc) / (n - r_gc)\n\n # Calculate the MSE of GC residuals for all reactions in ker(G).\n # This will help later to give an estimate of the uncertainty for such\n # reactions, which otherwise would have a 0 uncertainty in the GC\n # method.\n kerG_inds = list(np.where(np.all(GS == 0, 0))[0].flat)\n\n e_kerG = e_gc[kerG_inds]\n MSE_kerG = float((e_kerG.T @ e_kerG) / len(kerG_inds))\n\n MSE_inf = ComponentContribution.MSE_inf\n\n # Calculate the uncertainty covariance matrices\n inv_SWS, _, _, _ = LINALG._invert_project(S @ W @ S.T)\n inv_GSWGS, _, _, _ = LINALG._invert_project(GS @ W @ GS.T)\n\n V_rc = P_R_rc @ inv_SWS @ P_R_rc\n V_gc = P_N_rc @ G @ inv_GSWGS @ G.T @ P_N_rc\n V_inf = P_N_rc @ G @ P_N_gc @ G.T @ P_N_rc\n\n # Calculate the total of the contributions and covariances\n cov_dG0 = MSE_rc * V_rc + MSE_gc * V_gc + MSE_inf * V_inf\n\n # preprocessing matrices (for calculating the contribution of each\n # observation)\n G1 = P_R_rc @ inv_S.T @ W\n G2 = P_N_rc @ G @ inv_GS.T @ W\n G3 = inv_GS.T @ W\n\n S_uniq, P_col = LINALG._col_uniq(S)\n S_counter = np.sum(P_col, 0)\n preprocess_G1 = G1 @ P_col\n preprocess_G2 = G2 @ P_col\n preprocess_G3 = G3 @ P_col\n\n # preprocessing matrices (for quick calculation of uncertainty)\n preprocess_C1 = cov_dG0\n preprocess_C2 = MSE_gc * P_N_rc @ G @ inv_GSWGS + MSE_inf * G @ P_N_gc\n preprocess_C3 = MSE_gc * inv_GSWGS + MSE_inf * P_N_gc\n\n # Put all the calculated data in 'params' for the sake of debugging\n params = {'b': b,\n 'S': S,\n 'w': w,\n 'G': G,\n 'dG0_rc': dG0_rc,\n 'dG0_gc': dG0_gc,\n 'dG0_cc': dG0_cc,\n 'cov_dG0': cov_dG0,\n 'V_rc': V_rc,\n 'V_gc': V_gc,\n 'V_inf': V_inf,\n 'MSE_rc': MSE_rc,\n 'MSE_gc': MSE_gc,\n 'MSE_kerG': MSE_kerG,\n 'MSE_inf': MSE_inf,\n 'P_R_rc': P_R_rc,\n 'P_R_gc': P_R_gc,\n 'P_N_rc': P_N_rc,\n 'P_N_gc': P_N_gc,\n 'inv_S': inv_S,\n 'inv_GS': inv_GS,\n 'inv_SWS': inv_SWS,\n 'inv_GSWGS': inv_GSWGS,\n 'preprocess_v_r': dG0_cc,\n 'preprocess_v_g': dG0_gc,\n 'G1': G1,\n 'G2': G2,\n 'G3': G3,\n 'preprocess_G1': preprocess_G1,\n 'preprocess_G2': preprocess_G2,\n 'preprocess_G3': preprocess_G3,\n 'preprocess_S': S_uniq,\n 'preprocess_S_count': S_counter,\n 'preprocess_C1': preprocess_C1,\n 'preprocess_C2': preprocess_C2,\n 'preprocess_C3': preprocess_C3}\n return params"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def bse_fe(self):\n p = self.model.exog.shape[1]\n return np.sqrt(np.diag(self.cov_params())[0:p])","def gibbs_init(self, sigma2_s_param=None, sigma2_g_param=None):\n #Gibbs : Initialization step\n self.gibbs_init_step(self.nb_days, self.nb_particles, sigma2_s_param, sigma2_g_param)\n\n #Gibbs : step t > 0\n for j in range(1, self.nb_particles):\n if(j%(self.nb_particles/10)==0 or j==1):\n print(\"Gibbs sampling for particle \" + str(j) + \"/\" + str(self.nb_particles))\n\n\n self.s[:,j] = self.s[:,j-1]\n self.g_heat[:,j] = self.g_heat[:,j-1]\n self.sigma_s_star_2[:,j] = self.sigma_s_star_2[:,j-1]\n self.sigma_g_star_2[:,j] = self.sigma_g_star_2[:,j-1]\n\n # Compute s[0] for particle j (j>0)\n self.compute_s_0(j)\n\n # Compute s[n] for particle j (n>0 and j>0)\n for i in range(1, self.nb_days):\n self.compute_s(i,j)\n\n # Compute g_heat[O] for particle j (and j>0)\n self.compute_g_0(j)\n\n # Compute g_heat[n] for particle j (n>0 and j>0)\n for i in range(1, self.nb_days):\n self.compute_g(i,j)\n\n shape = 0.01 + ((self.nb_days - 1)/2)\n # Compute the new sigma_s_star2 for particle j (j>0) (follow Inverse Gamma)\n self.sigma_s_star_2[0, j] = self.compute_sigma_star_2(shape, self.s, j)\n\n # Compute the new sigma_g_star2 for particle j (j>0) (follow Inverse Gamma)\n self.sigma_s_star_2[0, j] = self.compute_sigma_star_2(shape, self.g_heat, j)\n\n #Compute x\n self.compute_x()\n #Compute w\n self.compute_w()","def expected_g(self):\n raise NotImplementedError()\n # \\sum_{b} \\gamma_b\n trm2 = self.mf_gamma.sum(axis=2)\n E_g = self.mf_gamma / trm2[:,:,None]\n return E_g","def gslb(self) :\n try :\n return self._gslb\n except Exception as e:\n raise e","def gibbs(h, s, temp):\n\n return h - s * temp * R","def _reestimate_B(self, obs_seq, gamma):\n B_new = {\n 'numer': [\n np.zeros((self.n_states, self.n_features[i]))\n for i in range(self.n_emissions)\n ],\n 'denom': [\n np.zeros((self.n_states, self.n_features[i]))\n for i in range(self.n_emissions)\n ],\n }\n\n for e in range(self.n_emissions):\n for j in range(self.n_states):\n for k in range(self.n_features[e]):\n numer = 0.0\n denom = 0.0\n for t, obs in enumerate(obs_seq):\n if obs[e] == k:\n numer += gamma[t][j]\n denom += gamma[t][j]\n B_new['numer'][e][j][k] = numer\n B_new['denom'][e][j][k] = denom\n\n return B_new","def bsm_vega(S0, K, T, r, sigma):\n \n from math import log, sqrt\n from scipy import stats\n \n S0 = float(S0)\n d1 = (log(S0 / K) + (r + 0.5 * sigma ** 2) * T / (sigma * sqrt(T))\n vega = S0 * stats.normcdf(d1, 0.0, 1.0) * sqrt(T)\n return vega\n \n# Implied volatility function\n\ndef bsm_call_imp_vol(S0, K, T, r, C0, sigma_est, it = 100):\n \"\"\" \n Implied volatility of European call option in BSM model\n \n Parameters\n ==========\n S0 : Float\n Initial stock/index level\n K : Float\n Strike Price\n T : Float\n Maturity Date (in year fractions)\n r : Float\n Constant risk-free short rate\n sigma_est : Float\n Estimate of impl. volatility\n it : integer\n Number of iterations\n \n Returns\n =======\n sigma_est : Float\n Numerically estimated implied volatility\n \"\"\"\n for i in range(it):\n sigma_est -= ((bsm_call_value(S0, K, T, r, sigma_est) - C0)\n / bsm_vega(S0, K, T, r, sigma_est))\n return sigma_est","def _sigma_ep(self,gam,eps):\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n return self._sigma_1(gam,eps)","def get_total_BMA_effect_size(self):\n \n if self.total_bma_es is None:\n # clean up these long expressions on Isle 2\n log_evidences = [self.results[kernel].summary(b=self.b)['evidence']['md'] \n for kernel in self.kernel_dict.keys()] + \\\n [self.results[kernel].summary(b=self.b)['evidence']['mc'] \n for kernel in self.kernel_dict.keys()]\n \n M = len(log_evidences)\n Z = logSumExp(log_evidences)\n evidences = np.exp(log_evidences - Z)\n disc_stats = [self.results[kernel].summary(b=self.b)['es_disc_stats'] \n for kernel in self.kernel_dict.keys()]\n nsamples = 50000\n samples = list() \n for i in range(int(M/2)):\n samples += list(np.random.normal(loc=disc_stats[i][0], \n scale=disc_stats[i][1], \n size=int(nsamples*evidences[i])))\n samples += list(np.zeros(nsamples - len(samples)))\n \n if np.sum(np.abs(samples))==0:\n xrange = np.linspace(-2, 2, 500)\n ix = np.argmin((xrange-self.b)**2)\n es_bma = np.zeros((500))\n es_bma[ix] = 1.0/ (xrange[1] - xrange[0])\n else: \n kde_fit = stats.gaussian_kde(samples, bw_method='silverman')\n xrange = np.linspace(np.min(samples), np.max(samples), 500)\n es_bma = kde_fit(xrange)\n self.total_bma_es = np.sum(xrange*es_bma) * (xrange[1]-xrange[0])\n self.total_bma_pdf = (xrange, es_bma)\n return self.total_bma_es","def ReadGaussian():\n # LICHEM calculates this as the optimization energy - self energy.\n # Self energy of the charges = {f} a.u.\n self_line = \" Self energy of the charges\"\n # SCF Done: E({s}) = {f} A.U. after {d} cycles\n SCF_line = \" SCF Done:\"\n with open('LICHM_GaussEnergy_0.log') as f:\n for line in f:\n if line.startswith(self_line):\n # print(line)\n ## Parses unmarked positive and - int and float values\n selfE = re.findall(r'\\-\\d+\\.*\\d*', line)\n selfE = float(selfE[0])\n elif line.startswith(SCF_line):\n # print(line)\n SCFE = re.findall(r'\\-\\d+\\.*\\d*', line)\n SCFE = float(SCFE[0])\n f.close()\n GaussE = SCFE - selfE\n GaussE *= har2ev\n return GaussE","def bse_re(self):\n p = self.model.exog.shape[1]\n return np.sqrt(self.scale * np.diag(self.cov_params())[p:])","def get_gibbs_state(self):\n # Set the initial state and propagate\n self.set_wignerfunction(lambda _, x, p: 0*x + 0*p + 1.)\n return self.propagate(self.num_beta_steps)","def expected_log_g(self):\n raise NotImplementedError()\n E_lng = np.zeros_like(self.mf_gamma)\n\n # \\psi(\\sum_{b} \\gamma_b)\n trm2 = psi(self.mf_gamma.sum(axis=2))\n for b in xrange(self.B):\n E_lng[:,:,b] = psi(self.mf_gamma[:,:,b]) - trm2\n\n return E_lng","def gibbs_init_step(self, nb_days, nb_particles, sigma2_s_param=None, sigma2_g_param=None):\n\n #Gibbs step 0\n if sigma2_s_param is None:\n self.sigma_s_star_2[0, 0] = invgamma.rvs(a=10**(-2), scale=10**(2), size = 1)[0]\n else :\n self.sigma_s_star_2[0, 0] = sigma2_s_param\n if sigma2_g_param is None:\n self.sigma_g_star_2[0, 0] = invgamma.rvs(a=10**(-2), scale=10**(2), size = 1)[0]\n else :\n self.sigma_g_star_2[0, 0] = sigma2_g_param\n\n self.s[0,0] = self.truncated_norm(0, inf, 0, 10**8)\n self.g_heat[0,0] = self.truncated_norm(-inf, 0, 0, 10**8)\n for i in range(1, nb_days):\n self.s[i,0] = self.s[i-1,0] + self.truncated_norm(-self.s[i-1,0], inf, 0, self.sigma_s_star_2[0,0])\n self.g_heat[i,0] = self.g_heat[i-1,0] + self.truncated_norm(-inf, - self.g_heat[i-1,0], 0, self.sigma_g_star_2[0,0])","def bn(x, e=1e-7):\n mu = np.mean(x)\n sigma2 = np.var(x)\n x_ = (x - mu) / np.sqrt(sigma2 + e)\n return x_","def _se_beta(self):\n return _handle_ab(self._se_all, self.use_const)[1]","def beta_Sonoi2015(self):\n return 10.0**(-3.86*self.string_to_param(\"log_Teff\") + 0.235*self.string_to_param(\"log_g\")+14.2)","def EGWD_fg(f):\n A = 4.2e-47\n res = np.zeros((len(f)))\n for i,freq in enumerate(f): \n if freq >=3e-3:\n # strain \n res[i] = A * freq**(-7/3) * np.exp(-2*(freq/5e-2)**2) \n else:\n res[i] = np.NaN\n return np.array(res)","def update(self, es, **kwargs):\n es.sigma = self.coefficient * es.sp.weights.mueff * _norm(es.mean) / es.N / es.sp.cmean","def f0(E, fermi, T):\n return 1. / (1. + np.exp((E - fermi) / (k_B * T)))","def energy(e: float) -> float:\n\n return (1/np.sqrt(2))*(gamma(-e/2+1/2)/(gamma(-e/2+3/4)))","def Two_EP_B(params, ns):\r\n nu1,nu2,nu3,T1,T2 = params\r\n\r\n nu1_func = lambda t: nu2 * (nu3/nu2)**(t/T2)\r\n nu_func = lambda t: [nu1_func(t)]\r\n\r\n\r\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0])\r\n fs = moments.Spectrum(sts)\r\n fs.integrate([nu1], T1)\r\n fs.integrate(nu_func, T2)\r\n \r\n return fs","def f4():\n n = 4\n v = np.arange(n)**0.75 * 0.2\n e = (np.arange(n)+1)**0.7 * 1e-1\n\n n = 12\n v = np.arange(n)\n e = np.array([0.1]*n) * 10e-0\n\n print(Sumb(v,e))\n\n f = plt.figure()\n a = f.add_subplot(111)\n\n dx = 0.0001\n x = np.arange(-1,v[-1]+1,dx)\n y = x.copy()\n y[:] = 0.\n for i in range(n):\n yx = lg(x,v[i],e[i])\n a.plot(x,np.exp(yx),label='{:d}'.format(i))\n y += yx\n y = np.exp((y - np.max(y))/n**2)\n y /= np.sum(y) * dx \n a.plot(x,y,label='sum')\n s = np.argsort(y)[::-1]\n ys = np.cumsum(y[s]) * dx\n yi = np.argwhere(ys > 0.682689492137)[0][0]\n print('mean = {:2f}'.format(x[s[0]]))\n print('sigma = {:2f}'.format(yi*dx/2))\n xy = np.ndarray((yi+2,2))\n i0,i1 = min(s[:yi]), max(s[:yi])\n xy[:yi,0] = x[i0:i1+1]\n xy[:yi,1] = y[i0:i1+1]\n xy[yi:,1] = 0\n xy[yi:,0] = x[[i1,i0]]\n a.add_patch(Polygon(xy,fill=True,color='green',ec='none',alpha=0.25))\n \n leg = plt.legend()\n plt.draw()","def sigma2_RG(self):\n sigma = np.sqrt(self.cosmo.gs_spectral_moment(l=2,RG=self.RG))\n return sigma","def verhulst(nb_init, t0, tf, eps, methode, gamma, K) :\n f=lambda y,t : gamma*y*(1-y/K)\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n return Y","def sigma0_RG(self):\n sigma = np.sqrt(self.cosmo.gs_spectral_moment(l=0,RG=self.RG))\n return sigma","def sigma_e(re, n, q, Ftot=1., c0=0.):\n from scipy.special import gamma\n kap = get_kappa(n)\n return Ftot/(2*np.pi*re**2*np.exp(kap)*n*kap**(-2*n)*gamma(2*n)*q/Rc(c0)), kap","def bergman_input(self):\n return self.m_t","def womgau(hop):\n import numpy as np\n import logging\n import matplotlib.pyplot as plt\n from scipy.optimize import curve_fit\n from tmath.wombat.womwaverange import womwaverange\n from tmath.wombat.womget_element import womget_element\n from tmath.wombat.inputter import inputter\n from tmath.wombat.inputter_single import inputter_single\n from tmath.wombat.gauss import gauss\n from tmath.wombat.gauss_cont import gauss_cont\n from tmath.wombat.yesno import yesno\n print(' ')\n logging.info('Object is {}'.format(hop[0].obname))\n print(' ')\n print('Spectrum runs from {} to {}'.format(hop[0].wave[0],hop[0].wave[-1]))\n print(' ')\n print('This routine expects the spectrum to be in flambda units.')\n print('It also expects a linear wavelength scale.')\n print(' ')\n print('Choose general region of spectrum\\n')\n nwave,nflux,mode=womwaverange(hop[0].wave,hop[0].flux,'none')\n print('\\nNow pick the exact range for the fit')\n waveint,fluxint,mode=womwaverange(nwave,nflux,mode)\n indexblue=womget_element(nwave, waveint[0])\n indexred=womget_element(nwave,waveint[-1])\n if (mode == 'w'):\n done = False\n while (not done):\n print(' ')\n wavecenter=inputter('Enter approximate center of Gaussian : ','float',False)\n indexcenter=womget_element(waveint,wavecenter)\n if (indexcenter <= 0) or (wavecenter > waveint[-1]):\n print('Bad central wavelength, try again')\n else:\n done = True\n else:\n done=False\n while (not done):\n print('Mark the approximate center of the Gaussian')\n pickcent=plt.ginput(1,timeout=-1)\n indexcenter=womget_element(waveint,pickcent[0][0])\n print('\\nApproximate center at {}'.format(waveint[indexcenter]))\n print('\\nIs this OK?')\n answer=yesno('y')\n if (answer == 'y'):\n done=True\n weights=np.sqrt(hop[0].var[indexblue:indexred+1])\n print(' ')\n continuum=inputter_single('Do you want to fit gaussian with (c)ontinuum, or (n)o continuum? ','cn')\n if (continuum == 'c'):\n p=[fluxint[indexcenter], waveint[indexcenter],3.0,1.0,waveint[0]]\n result=curve_fit(gauss_cont,waveint,fluxint,sigma=weights,p0=p,absolute_sigma=True,full_output=True)\n else:\n p=[fluxint[indexcenter], waveint[indexcenter],3.0]\n result=curve_fit(gauss,waveint,fluxint,sigma=weights,p0=p,absolute_sigma=True,full_output=True)\n coefferr=np.sqrt(np.diag(result[1]))\n coeff=result[0]\n # make 'finer-grained' version of fit, 0.2A/pix for calculations\n wavecalc=np.arange(2*5*50*abs(coeff[2]))*0.2+coeff[1]-0.2*5*50*abs(coeff[2])\n calccenter=womget_element(wavecalc,coeff[1])\n if (continuum == 'c'):\n fluxcalc=gauss_cont(wavecalc,*coeff)\n fluxcont=wavecalc*coeff[3]+coeff[4]\n fluxgaussian=fluxcalc-fluxcont\n linecont=fluxcont[calccenter]\n else:\n fluxcalc=gauss(wavecalc,*coeff)\n \n \n deltafit=wavecalc[1]-wavecalc[0]\n calcindexblue=womget_element(wavecalc,waveint[0])\n calcindexred=womget_element(wavecalc,waveint[-1])\n sumfluxcalc=np.sum(fluxcalc[calcindexblue:calcindexred+1]*deltafit)\n sumallfluxcalc=np.sum(fluxcalc*deltafit)\n chi=(result[2]['fvec']**2).sum()\n redchi=chi/(len(waveint)-len(coeff))\n if (continuum == 'c'):\n sumfluxgaussian=np.sum(fluxgaussian[calcindexblue:calcindexred+1]*deltafit)\n sumallfluxgaussian=np.sum(fluxgaussian*deltafit)\n sumfluxcont=np.sum(fluxcont[calcindexblue:calcindexred+1]*deltafit)\n sumallfluxcont=np.sum(fluxcont*deltafit)\n sumallfluxcont_test=np.sum(fluxcont)\n # propagate uncertainty (from old version) not sure this is correct\n height_pct=coefferr[0]/coeff[0]\n sigma_pct=coefferr[2]/coeff[2]\n flux_pct=np.sqrt(height_pct**2+sigma_pct**2)\n sumfluxgaussiansig=sumfluxgaussian*flux_pct\n sumallfluxgaussiansig=sumallfluxgaussian*flux_pct\n plt.cla()\n plt.plot(nwave,nflux,drawstyle='steps-mid',color='k')\n plt.ylabel('Flux')\n plt.xlabel('Wavelength')\n xmin,xmax=plt.xlim()\n ymin,ymax=plt.ylim()\n plt.plot(wavecalc,fluxcalc,drawstyle='steps-mid',color='b')\n if (continuum == 'c'):\n plt.plot(wavecalc,fluxgaussian,drawstyle='steps-mid',color='r')\n plt.plot(wavecalc,fluxcont,drawstyle='steps-mid',color='g')\n plt.plot([waveint[0],waveint[0]],[ymin,ymax],color='k',linestyle='--')\n plt.plot([waveint[-1],waveint[-1]],[ymin,ymax],color='k',linestyle='--')\n plt.xlim([xmin,xmax])\n plt.ylim([ymin,ymax])\n logging.info('For object {} Gaussian fit'.format(hop[0].obname))\n if (continuum == 'c'):\n print('\\nData = Black, Fit = Blue, Continuum = Green, Fit-Continuum = Red\\n')\n else:\n print('\\nData = Black, Fit = Blue\\n')\n logging.info('Height {:16.8f}+/-{:16.8f}'.format(coeff[0],coefferr[0]))\n logging.info('Center {:16.8f}+/-{:16.8f}'.format(coeff[1],coefferr[1]))\n logging.info('Sigma {:16.8f}+/-{:16.8f}'.format(coeff[2],coefferr[2]))\n if (continuum == 'c'):\n FWHM = 2.35482*np.abs(coeff[2])\n rest_wave = input('Rest wavelength [N/A]: ') or None\n redshift = input('Redshift [N/A]: ') or None\n if rest_wave:\n rest_wave = float(rest_wave)\n w1 = (rest_wave - FWHM/2.)/(1.+float(redshift)) \n w2 = (rest_wave + FWHM/2.)/(1.+float(redshift)) \n c = 299792.458\n v1 = -1.*c*((rest_wave/w1)**2. - 1)/(1+((rest_wave/w1)**2.))\n v2 = -1.*c*((rest_wave/w2)**2. - 1)/(1+((rest_wave/w2)**2.))\n logging.info('Slope {:16.8f}+/-{:16.8f}'.format(coeff[3],coefferr[3]))\n logging.info('Y-intercept {:16.8f}+/-{:16.8f}'.format(coeff[4],coefferr[4]))\n logging.info('FWHM {:16.8f}+/-{:16.8f}'.format(2.35482*np.abs(coeff[2]),2.35482*coefferr[2]))\n logging.info('FWHM (velocity) {:16.8f} km/s'.format(v2-v1))\n logging.info('Flux between dotted lines (Gaussian): {:16.8f}+/-{:16.8f}'.format(sumfluxgaussian, sumfluxgaussiansig))\n logging.info('EW between dotted lines (Gaussian): {:16.8f}'.format(sumfluxgaussian/linecont))\n logging.info('Flux for full (Gaussian): {:16.8f}+/-{:16.8f}'.format(sumallfluxgaussian, sumallfluxgaussiansig))\n logging.info('EW for full (Gaussian): {:16.8f}'.format(sumallfluxgaussian/linecont))\n logging.info('Continuum flux at line center: {:16.8f}'.format(linecont))\n\n \n logging.info('Chi^2: {}'.format(chi))\n logging.info('Reduced chi^2: {}'.format(redchi))\n logging.info('All fluxes might need to be scaled by 1e-15')\n print(' ')\n return hop","def gibbs_(dict_, T):\n\tdST = dict_['S298'] + CpT(dict_, T)[0]\n\t\t\n\tdHT = dict_['dH298'] + CpT(dict_, T)[1]/1000\n\t\t\n\treturn (dHT - T*dST/1000)","def gmae(self) -> float:\n return _geometric_mean(np.abs(self._error()))","def EVBMF(Y, sigma2=None, H=None): \n L,M = Y.shape #has to be L<=M\n\n if H is None:\n H = L\n\n alpha = L/M\n tauubar = 2.5129*np.sqrt(alpha)\n \n #SVD of the input matrix, max rank of H\n _,s,_ = torch.svd(Y)\n s = s[:H]\n\n #Calculate residual\n residual = 0.\n if H<L:\n # residual = np.sum(np.sum(Y**2)-np.sum(s**2))\n residual = torch.sum(torch.sum(Y**2)-torch.sum(s**2))\n\n #Estimation of the variance when sigma2 is unspecified\n if sigma2 is None: \n xubar = (1+tauubar)*(1+alpha/tauubar)\n eH_ub = int(np.min([np.ceil(L/(1+alpha))-1, H]))-1\n\n upper_bound = (torch.sum(s**2)+residual)/(L*M)\n lower_bound = np.max([s[eH_ub+1]**2/(M*xubar), torch.mean(s[eH_ub+1:]**2)/M])\n\n scale = 1.#/lower_bound\n s = s*np.sqrt(scale)\n residual = residual*scale\n lower_bound = float(lower_bound)*scale\n upper_bound = float(upper_bound)*scale\n\n sigma2_opt = minimize_scalar(EVBsigma2, args=(L,M,s,residual,xubar), bounds=[lower_bound, upper_bound], method='Bounded')\n sigma2 = sigma2_opt.x\n\n #Threshold gamma term\n threshold = np.sqrt(M*sigma2*(1+tauubar)*(1+alpha/tauubar))\n\n pos = torch.sum(s>threshold)\n if pos == 0: return np.array([])\n #Formula (15) from [2]\n d = torch.mul(s[:pos]/2, 1-(L+M)*sigma2/s[:pos]**2 + torch.sqrt( (1-((L+M)*sigma2)/s[:pos]**2)**2 - (4*L*M*sigma2**2)/s[:pos]**4) )\n return torch.diag(d)","def test_Gaussian_NB_estimators():","def _sigma_ee_nonrel(self,gam,eps):\n s0 = 4 * r0**2 * alpha / (15 * eps)\n x = 4 * eps / (gam**2 - 1)\n sigma_nonrel = s0 * self._F(x,gam)\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\n return sigma_nonrel / mec2_unit","def compute_derbeko_2007_gibbs_bound(empirical_gibbs_risk, m, N, KLQP, delta=0.05): \n m_over_N = float(m)/N \n log_complexity_term = 7 * log( m*(N+1) )\n return empirical_gibbs_risk + sqrt( ((1.-m_over_N)/(2*m-2)) * (KLQP + log_complexity_term - log(delta) ) )","def Bates_Granger_5(df_train, df_test, W):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n\n # check whether there is enough observations, so sigma is invertible\n if K > T:\n raise ValueError('No. forecasts must be <= length of training sample')\n\n # forecast errors\n errors = df_train.iloc[:, 1:].subtract(df_train.iloc[:, 0], axis=0)\n\n # initialize the covariance matrix sigma\n sigma = np.full((K, K), fill_value=0, dtype=float)\n\n # exponential error weights\n error_w = np.full(T, fill_value=W, dtype=float)**(np.arange(T)+1)\n error_w_sum = error_w.sum()\n\n # fill the covariance matrix sigma\n for i in range(K):\n\n for j in range(K):\n\n # elements in sigma matrix are weighted with W\n sigma[i, j] = np.dot(error_w*errors.iloc[:, i],\n errors.iloc[:, j]) / error_w_sum\n\n # combining weights\n nominator = np.linalg.solve(sigma, np.full(K, fill_value=1))\n denominator = np.dot(np.full(K, fill_value=1), nominator)\n comb_w = nominator / denominator\n\n # censoring the combining weights\n for i in range(K):\n if comb_w[i] < 0:\n comb_w[i] = 0\n if comb_w[i] > 1:\n comb_w[i] = 1\n\n # rescale the weights so that their sum equals 1\n comb_w = comb_w/comb_w.sum()\n\n # predictions\n df_pred = pd.DataFrame({\"Bates-Granger (5)\": df_test.dot(comb_w)})\n\n return df_pred","def fd(model, EPS=0.01):\n\n N = model.N\n f = model.f\n gW_fd, gb_fd = zeros((N, N)), zeros((N))\n for x, y in model.train:\n for i in xrange(N):\n # get gW_fd\n for j in xrange(N):\n model.W[i, j] += EPS\n val1 = f(model.W, model.b)\n model.W[i, j] -= 2*EPS\n val2 = f(model.W, model.b)\n model.W[i, j] += EPS\n gW_fd[i, j] = (val1 - val2) / (2 * EPS)\n \n # get gb_fd\n model.b[i] += EPS\n val1 = f(model.W, model.b)\n model.b[i] -= 2*EPS\n val2 = f(model.W, model.b)\n model.b[i] += EPS\n gb_fd[i] = (val1 - val2) / (2 * EPS)\n\n \n return gW_fd, gb_fd","def multiple_premia_gbm(nobs=2000):\r\n sigma = .15\r\n\r\n price = 1\r\n strike = np.exp(np.linspace(-.1, .1, nobs))\r\n riskfree, maturity = 0, 30/365\r\n moneyness = np.log(strike/price) - riskfree * maturity\r\n call = np.ones_like(moneyness).astype(bool)\r\n call[moneyness < 0] = False\r\n\r\n model = GBM(GBMParam(sigma=sigma), riskfree, maturity)\r\n premium = cosmethod(model, moneyness=moneyness, call=call)\r\n plt.plot(strike, premium)\r\n plt.show()","def cal_gaussian_process(b, sigma2, X_train, y_train, X_test):\n n = X_train.shape[0]\n p = X_test.shape[0]\n\n K_n = np.array([[kernel(X_train[i], X_train[j], b) for i in range(n)] for j in range(n)])\n inv = np.linalg.inv(np.diag([sigma2] * n) + K_n)\n miu = np.zeros(p)\n Sigma = np.zeros(p)\n \n for j in range(p): # for every new point x0 in testing data.\n x0 = X_test[j]\n K_Dn = np.zeros(n) # initialize K_Dn \n for i in range(n):\n K_Dn[i] = kernel(X_train[i], x0, b) # calculate every item in K_Dn\n \n miu[j] = K_Dn.dot(inv).dot(y_train)[0] # calculate new distribution parameters\n Sigma[j] = sigma2 + kernel(x0, x0, b) - K_Dn.dot(inv).dot(K_Dn.T)\n \n return miu, Sigma","def BraggEnergy(ID,hkl,twotheta):\n ID=goodID(ID)\n d=dSpace(ID,hkl)\n l=2*d*sind(twotheta/2.0)\n E=lam2E(l)\n return E","def simulationTestGaussian2(params):\r\n x = gaussian(params[0], params[1], mu-3.5*sigma, mu+3.5*sigma)\r\n error = np.sum(np.power(optimal - x, 2))/optimal.shape[0]\r\n return 1/error","def _sigma_2(gam, eps):\n s0 = r0**2 * alpha / (3 * eps) / mec2_unit\n\n s1_1 = 16 * (1 - eps + eps**2) * np.log(gam / eps)\n s1_2 = -1 / eps**2 + 3 / eps - 4 - 4 * eps - 8 * eps**2\n s1_3 = -2 * (1 - 2 * eps) * np.log(1 - 2 * eps)\n s1_4 = 1 / (4 * eps**3) - 1 / (2 * eps**2) + 3 / eps - 2 + 4 * eps\n s1 = s1_1 + s1_2 + s1_3 * s1_4\n\n s2_1 = 2 / eps\n s2_2 = (4 - 1 / eps + 1 / (4 * eps**2)) * np.log(2 * gam)\n s2_3 = -2 + 2 / eps - 5 / (8 * eps**2)\n s2 = s2_1 * (s2_2 + s2_3)\n\n return s0 * np.where(eps <= 0.5, s1, s2) * heaviside(gam - eps)","def get_sigma_psf(self):\n pass","def energy(self):\n nocc, ntot, gmo, e = self.nocc, self.ntot, self.gmo, self.e\n\n Ec = 0.0\n for i in range(nocc):\n for j in range(nocc):\n for a in range(nocc, ntot):\n for b in range(nocc, ntot):\n Ec += gmo[i, a, j, b]*(2*gmo[i, a, j, b] - gmo[i, b, j, a])/\\\n (e[i] + e[j] - e[a] - e[b])\n\n self.Ec = Ec\n self.E_mp2 = Ec + self.E_scf\n\n print('@MP2 correlation energy: {:15.10f}\\n'.format(self.Ec))\n print('@Total MP2 energy: {:15.10f}\\n'.format(self.E_mp2))\n\n return self.E_mp2","def compute_GS(GMtcs):\n\n GS = np.mean(GMtcs,axis=0) #average over voxels\n\n return GS","def se_beta(self):\n return self._se_beta","def get_energy(edr, annealing_times, energy_type = 'Potential', out_fig = 'energy_distribution.svg'): # Could be Total-Energy\n fig, ax = plt.subplots(figsize = (16,9))\n data = pd.DataFrame()\n xvg_tmp_file = tempfile.NamedTemporaryFile(suffix='.xvg')\n energy = []\n iterator = range(0, len(annealing_times)-1, 2)\n\n for state, index in tqdm.tqdm(enumerate(iterator), total=len(iterator)):#enumerate(iterator):# # the calculation is per pair of times, beetween the first to time the temperature was keep constant, then the system was heated and repeated again.\n run = tools.run(f\"export GMX_MAXBACKUP=-1; echo {energy_type} | gmx energy -f {edr} -b {annealing_times[index]} -e {annealing_times[index + 1]} -o {xvg_tmp_file.name} | grep \\'{energy_type.replace('-',' ')}\\'\")\n energy.append(float(run.stdout.split()[-5]))\n \"\"\"\n Energy Average Err.Est. RMSD Tot-Drift\n -------------------------------------------------------------------------------\n Potential -1.30028e+06 -- 1682.1 -2422.24 (kJ/mol)\n Total Energy -952595 -- 2606.81 -3688.3 (kJ/mol)\n \"\"\"\n # Getting the histograms and checking for the same len in all intervals\n if state == 0:\n data[state] = xvg.XVG(xvg_tmp_file.name).data[:,1]\n else:\n xvg_data = xvg.XVG(xvg_tmp_file.name).data[:,1]\n if xvg_data.shape[0] > data.shape[0]:\n data[state] = xvg_data[:data.shape[0]]\n else:\n data = data.iloc[:xvg_data.shape[0]]\n data[state] = xvg_data\n\n\n print(data)\n sns.histplot(data = data, element='poly', stat = 'probability', axes = ax)\n ax.set(\n xlabel = f'{energy_type} [kJ/mol]',\n ylabel = 'Probability',\n title = f'Distribution of {energy_type}')\n # plt.show()\n fig.savefig(out_fig)\n return energy","def effective_energy_difference(self, macrostate_class: str, state1: str, state2: str) -> float:\n\n macrostate_class = str(macrostate_class)\n\n microstates_1_indices = self.states[self.states[macrostate_class] == state1].index\n microstates_2_indices = self.states[self.states[macrostate_class] == state2].index\n\n energies_1 = self.g_mle[microstates_1_indices]\n energies_2 = self.g_mle[microstates_2_indices]\n\n std_err_1 = self.std_errors[microstates_1_indices]\n std_err_2 = self.std_errors[microstates_2_indices]\n\n z1 = np.exp(-energies_1)\n z2 = np.exp(-energies_2)\n\n std_err = np.sqrt((std_err_1**2 * z1**2).sum() / z1.sum()**2 + (std_err_2**2 * z2**2).sum() / z2.sum()**2)\n diff = np.log(np.sum(z1) / np.sum(z2))\n\n return diff, std_err","def epsilon_GaAs_TISB(l_onde,n_2D,w12,f,Ts):\n ev = 1.60218e-19\n eps0 = 8.854e-12\n c = 3e8\n \n v = 1e4/l_onde\n \n epsinf = 11;\n wL = 291.2;\n wT = 267.89;\n T = 2.54;\n epsGaAs = epsinf*(1+(wL**2-wT**2)/(wT**2-v**2-1j*T*v)) \n \n meff = 9.109e-31*0.067\n ns = (n_2D/22e-7)*1e6 # passage de cm-2 ? m-2 On fixe l'epaisseur du puit a 20nm, ordre de grandeur...\n wp = np.sqrt((ns*ev**2)/(meff*eps0*epsinf)) # SI\n wp = wp*1e-2/(2*np.pi*c) #cm-1\n if w12==0:\n wp=0\n else:\n w12 = 1e4/w12 # cm-1\n \n \n eps_ssbandes = epsinf*(f*wp**2./(w12**2-v**2-1j*Ts*v))\n eps = epsGaAs + eps_ssbandes\n return eps.real + 1j*np.abs(eps.imag)","def sample_values(self, positions, gibbs):\n \"\"\"Sample important values\"\"\"\n\n if gibbs:\n self.local_energy = self.h.local_energy_gibbs(positions)\n self.accumulate_energy += self.h.local_energy_gibbs(positions)\n self.accumulate_energy_sq += self.local_energy*self.local_energy\n gradient_wf_a = 0.5*self.w.gradient_wavefunction_a(positions)\n gradient_wf_b = 0.5*self.w.gradient_wavefunction_b(positions)\n gradient_wf_W = 0.5*self.w.gradient_wavefunction_W(positions)\n else:\n self.local_energy = self.h.local_energy(positions)\n self.accumulate_energy += self.h.local_energy(positions)\n self.accumulate_energy_sq += self.local_energy*self.local_energy\n gradient_wf_a = self.w.gradient_wavefunction_a(positions)\n gradient_wf_b = self.w.gradient_wavefunction_b(positions)\n gradient_wf_W = self.w.gradient_wavefunction_W(positions)\n # self.local_energy = self.h.local_energy_numerical(positions)\n # self.accumulate_energy += self.h.local_energy_numerical(positions)\n # gradient_wf_a = np.zeros(self.w.M)\n # gradient_wf_b = np.zeros(self.w.N)\n # gradient_wf_W = np.zeros((self.w.M, self.w.N))\n\n self.accumulate_psi_term_a += gradient_wf_a\n self.accumulate_psi_term_b += gradient_wf_b\n self.accumulate_psi_term_W += gradient_wf_W\n self.accumulate_both_a += gradient_wf_a*self.local_energy\n self.accumulate_both_b += gradient_wf_b*self.local_energy\n self.accumulate_both_W += gradient_wf_W*self.local_energy","def Bo_Bosol_calc(self):\n self.Bosol = (self.g*self.alpha * self.srflx ) \n #ZEROS FOR T3W APPLICATION\n self.Bo = np.zeros([self.b.shape[0]])","def standard_deviation(self):\r\n\t\treturn self.variance()**(1/2)","def gauss_kernel(n_fwhm,sigma):\n\n x_length = int(n_fwhm * sigma + 0.5) #Add 0.5 to approximate to nearest integer\n y_length = x_length\n \n \n x, y = mgrid[-x_length:x_length+1, -y_length:y_length+1]\n g = numpy.exp(-(x**2/(2*(float(sigma)**2))+y**2/(2*(float(sigma)**2))))\n return g / g.sum()","def compute_hydration_energies(molecules, parameters):\n\n energies = dict() # energies[index] is the computed solvation energy of molecules[index]\n\n platform = openmm.Platform.getPlatformByName(\"Reference\")\n\n for molecule in molecules:\n # Create OpenMM System.\n system = openmm.System()\n for atom in molecule.GetAtoms():\n mass = OEGetDefaultMass(atom.GetAtomicNum())\n system.addParticle(mass * units.amu)\n\n # Add nonbonded term.\n # nonbonded_force = openmm.NonbondedSoftcoreForce()\n # nonbonded_force.setNonbondedMethod(openmm.NonbondedForce.NoCutoff)\n # for atom in molecule.GetAtoms():\n # charge = 0.0 * units.elementary_charge\n # sigma = 1.0 * units.angstrom\n # epsilon = 0.0 * units.kilocalories_per_mole\n # nonbonded_force.addParticle(charge, sigma, epsilon)\n # system.addForce(nonbonded_force)\n\n # Add GBVI term\n # gbvi_force = openmm.GBVISoftcoreForce()\n gbvi_force = openmm.GBVIForce() \n gbvi_force.setNonbondedMethod(openmm.GBVIForce.NoCutoff) # set no cutoff\n gbvi_force.setSoluteDielectric(1)\n gbvi_force.setSolventDielectric(78)\n\n # Use scaling method.\n # gbvi_force.setBornRadiusScalingMethod(openmm.GBVISoftcoreForce.QuinticSpline)\n # gbvi_force.setQuinticLowerLimitFactor(0.75)\n # gbvi_force.setQuinticUpperBornRadiusLimit(50.0*units.nanometers)\n\n # Build indexable list of atoms.\n atoms = [atom for atom in molecule.GetAtoms()] \n \n # Assign GB/VI parameters.\n for atom in molecule.GetAtoms(): \n atomtype = atom.GetStringData(\"gbvi_type\") # GBVI atomtype\n charge = atom.GetPartialCharge() * units.elementary_charge\n radius = parameters['%s_%s' % (atomtype, 'radius')] * units.angstroms\n gamma = parameters['%s_%s' % (atomtype, 'gamma')] * units.kilocalories_per_mole \n # gamma *= -1.0 # DEBUG\n lambda_ = 1.0 # fully interacting\n # gbvi_force.addParticle(charge, radius, gamma, lambda_) # for GBVISoftcoreForce\n gbvi_force.addParticle(charge, radius, gamma) # for GBVIForce\n\n # Add bonds.\n for bond in molecule.GetBonds():\n # Get atom indices.\n iatom = bond.GetBgnIdx()\n jatom = bond.GetEndIdx()\n # Get bond length.\n (xi, yi, zi) = molecule.GetCoords(atoms[iatom])\n (xj, yj, zj) = molecule.GetCoords(atoms[jatom])\n distance = math.sqrt((xi-xj)**2 + (yi-yj)**2 + (zi-zj)**2) * units.angstroms\n # Identify bonded atoms to GBVI.\n gbvi_force.addBond(iatom, jatom, distance)\n\n # Add the force to the system.\n system.addForce(gbvi_force)\n \n # Build coordinate array.\n natoms = len(atoms)\n coordinates = units.Quantity(numpy.zeros([natoms, 3]), units.angstroms)\n for (index,atom) in enumerate(atoms):\n (x,y,z) = molecule.GetCoords(atom)\n coordinates[index,:] = units.Quantity(numpy.array([x,y,z]),units.angstroms) \n \n # Create OpenMM Context.\n timestep = 1.0 * units.femtosecond # arbitrary\n integrator = openmm.VerletIntegrator(timestep)\n context = openmm.Context(system, integrator, platform)\n\n # Set the coordinates.\n context.setPositions(coordinates)\n \n # Get the energy\n state = context.getState(getEnergy=True)\n energies[molecule] = state.getPotentialEnergy()\n\n return energies","def hatom_energy(beta, nmax=4000):\n # calculate energy levels\n myn = np.arange(1, nmax)\n en = -1./(2*myn**2)\n # calculate Boltzmann factors\n mult = myn**2 # multiplicity due to angular states\n bfac = mult*np.exp(-beta*en)\n part = bfac.sum()\n return np.dot(bfac, en)/part","def bondEfs(self,k):\n Alpha=6.4\n b=3.0\n c=1.7\n nu=1.13\n gamma=self.om0*self.h\n q=k/gamma\n return self.bondEfsNorm/(1.0+(Alpha*q +(b*q)**1.5 + (c*q)**2)**nu)**(2.0/nu)","def calc_free_g(energies, temperatures):\n pass","def FO2(lam):\n return 1.096 + 1.385 *1e-3 *lam**(-2) + 1.448 *1e-4 *lam**(-4)","def gibbs(input_dim,variance=1., mapping=None):\r\n part = parts.gibbs.Gibbs(input_dim,variance,mapping)\r\n return kern(input_dim, [part])","def get_neb_energies_forces(self):\n energies = []\n forces = []\n for i in self.path.inner_images:\n e, f = self.model.predict_energy_and_forces(i)\n energies.append(e)\n forces.append(f)\n self.model_forces = forces\n self.energies[1:-1] = energies\n self.middle_tangents = self.path.get_unit_tangents(self.energies)\n f1 = [i-np.sum(i*j)*j for i, j in zip(self.model_forces, self.middle_tangents)]\n f2 = [i*j for i, j in zip(self.path.spring_forces, self.middle_tangents)]\n self.forces = [i+j for i, j in zip(f1, f2)]","def KsCB(fiter):\n \n fiter.mean = RooRealVar(\"mean1\",\"mean1\",490,510)#5168.)\n fiter.sigma = RooRealVar(\"sigma\",\"sigma\", 2,6)#20., 12.,40.)#35.)\n \n fiter.n = RooRealVar(\"exponent\", \"exponent\",1.)#, 0., 12 )\n \n fiter.a = RooRealVar(\"transition\",\"transition\", 0.5, 3) ## Transition point, in sigmas\n fiter.sig = RooCBShape(\"Sigmodel\",\"Sigmodel\", fiter.mass, fiter.mean, fiter.sigma, fiter.a, fiter.n) \n return 1","def _calc_Em(self):\n return (self.parameters.E0 +\n self.x * sqrt2 * self.parameters.sigma * self.mt)","def get_EG(vals):\n return get_tau_gap(vals)/2","def energy(self):\n nocc, gmo, e = self.nocc, self.gmo, self.e\n\n Ec = 0.0\n for i in range(nocc):\n for j in range(nocc):\n for a in range(nocc, len(e)):\n for b in range(nocc, len(e)):\n Ec += (1/4.0) * gmo[i, j, a, b]**2 / (e[i]+e[j]-e[a]-e[b])\n\n self.Ec = Ec\n self.E_mp2 = Ec + self.E_scf\n\n print('@MP2 correlation energy: {:15.10f}\\n'.format(self.Ec))\n print('@Total MP2 energy: {:15.10f}\\n'.format(self.E_mp2))\n\n return self.E_mp2","def estimate_wf_energy(self):\n nblocks = 50\n blocksize = int(len(self.local_energies) / nblocks)\n enmean = 0\n enmeansq = 0\n enmean_unblocked = 0\n enmeansq_unblocked = 0\n\n for b in range(nblocks):\n eblock = 0.0\n for j in range(b * blocksize, (b + 1) * blocksize):\n eblock += self.local_energies[j].real\n delta = self.local_energies[j].real - enmean_unblocked\n enmean_unblocked += delta / (j + 1)\n delta2 = self.local_energies[j].real - enmean_unblocked\n # delta != delta2 because of update to enmean_unblocked\n enmeansq_unblocked += delta * delta2\n eblock /= blocksize\n delta = eblock - enmean\n enmean += delta / (b + 1)\n delta2 = eblock - enmean\n # delta != delta2 because of update to enmean\n enmeansq += delta * delta2\n\n enmeansq /= (nblocks - 1)\n enmeansq_unblocked /= (nblocks * blocksize - 1)\n est_avg = enmean / self.n_visible\n est_error = math.sqrt(enmeansq / nblocks) / self.n_visible\n self.nqs_energy = np.squeeze(est_avg)\n self.nqs_energy_err = np.squeeze(est_error)\n\n energy_report = 'Estimated average energy per spin: {} +/- {}'\n print(energy_report.format(est_avg, est_error))\n bin_report = 'Estimate from binning analysis. ' + \\\n '{} bins of {} samples each'\n print(bin_report.format(nblocks, blocksize))\n autocorrelation = 'Estimated autocorrelation time is {}'\n self.correlation_time = 0.5 * blocksize * enmeansq / enmeansq_unblocked\n print(autocorrelation.format(self.correlation_time))","def MLE(self, svd: bool = True) -> None:\n\n N = len(self.states.name)\n\n if svd:\n self._MLE_SVD(N)\n else:\n self._MLE_NR(N)\n\n self.std_errors = np.sqrt(np.diagonal(self.covariance_matrix))\n assert np.all(self.std_errors >= 0), \"Standard errors for state free energies should be positive\"\n\n self.g_mle = self.MLE_res - self.MLE_res[0]\n self.prob_mle = pd.DataFrame(np.exp(-self.g_mle) / np.sum(np.exp(-self.g_mle)), columns=[\"probability\"])\n self.prob_mle[\"name\"] = self.states.name\n\n self.deltas = np.zeros((N, N))\n self.dGs = np.zeros((N, N))\n\n self.deltas[:] = np.nan\n\n for r in self.graph.index:\n state1, state2, _, _, _, _ = self.graph.iloc[r]\n i = self.states[self.states.name == state1].index[0]\n j = self.states[self.states.name == state2].index[0]\n\n edge_attr = self.cycle.edges()[(state1, state2)]\n deltaij = edge_attr['energy'] # measured difference\n\n self.deltas[i, j] = deltaij\n self.deltas[j, i] = -deltaij\n\n for _i, dGi in enumerate(self.g_mle):\n for _j, dGj in enumerate(self.g_mle):\n self.dGs[_i, _j] = self.g_mle[_j] - self.g_mle[_i]","def y_envelope_ef(t, args):\n anharms = args['anharms']\n couplings = args['couplings']\n e = args['e']\n g = args['g']\n couplings = [c/g for c in couplings]\n coeff = -np.sqrt(couplings[e-1]**2 \\\n + (anharms[e+2]**2/anharms[e-1]**2) \\\n * couplings[e+1]**2) / (2*anharms[e+2])\n return args['A'] * coeff * truncated_gaussian_derivative(t,\n args['sigma'],\n args['t_g']/2,\n args['t_n']/2)","def gaussian_fit(self):\r\n\r\n self.df5 = pd.DataFrame(columns=['Slit Number', 'Centre', 'Centre_err', 'Sigma', 'Sigma_err', 'FWHM', 'FWHM_err', 'Height', 'Height_err'])\r\n QDot_slits = self.QDot_detection()\r\n\r\n if len(QDot_slits) > 0: \r\n self.plot_data = pd.DataFrame(columns=[f\"{QDot_slits[0]}\"], index=self.energies)\r\n else:\r\n self.plot_data = pd.DataFrame(index=self.energies)\r\n\r\n for slit_number in QDot_slits:\r\n sel = self.df4[f'{slit_number}']\r\n self.plot_data[f'{slit_number}'] = sel\r\n \r\n # Makes a good first guess for the fit values of the gaussian\r\n max_intensity = max(sel)\r\n central_energy = sel[sel==max_intensity].index.values\r\n central_energy = central_energy[0]\r\n\r\n # Fits a gaussian model to the selected data and shows the output\r\n gauss = models.GaussianModel()\r\n fit = gauss.fit(sel, x=self.energies, weights=1 / np.sqrt(sel), center = central_energy, amplitude = max_intensity, sigma = 1, nan_policy= 'omit')\r\n \r\n self.plot_data[f'{slit_number} best fit'] = fit.best_fit\r\n\r\n # Appends the fit data for the variables to a new dataframe and shows the fit results with errors\r\n fit_variables = [slit_number]\r\n for key in fit.params:\r\n if key in ['center', 'sigma', 'fwhm', 'height']:\r\n fit_variables.append(fit.params[key].value)\r\n fit_variables.append(fit.params[key].stderr)\r\n \r\n self.df5 = self.df5.append({'Slit Number': fit_variables[0], 'Centre': fit_variables[1], 'Centre_err': fit_variables[2], 'Sigma': fit_variables[3], 'Sigma_err': fit_variables[4], 'FWHM': fit_variables[5], 'FWHM_err': fit_variables[6], 'Height': fit_variables[7], 'Height_err': fit_variables[8]}, ignore_index=True)\r\n \r\n return self.plot_data, self.df5","def compute_terms(Y, latent_means, latent_Sigmas, B1, B2, mu, g1, g2):\n # these are the 's' parameters when nu=e_q, beta=0\n ss = [-mi[-1]*si[-1] for mi, si in zip(latent_means, latent_variances)]\n\n a1 = [-np.trace(Sigma) - np.matmul(mi.T, mi) + np.log(LA.det(Sigma)) for Sigma, mi in zip(latent_Sigmas, latent_means)]\n\n B_times_mean = [np.matmul(B1+B2, mi) for mi in latent_means]\n a2 = [\n np.matmul(\n yi - mu, \n (yi - mu).T\n ).item() - np.matmul(\n yi - mu, \n Bm\n ).item() for yi, Bm in zip(Y, B_times_mean)\n ]\n\n a3_scalars = [SQRT_PI_OVER_2*erfc(si/ROOT2)+si*np.exp(-si**2/2) for si in ss]\n a3 = [\n g1*sc*np.trace(\n np.matmul(\n B1.T, \n np.matmul(B1, Sigma)\n )\n ).item() for Sigma, sc in zip(latent_Sigmas, a3_scalars)\n ]\n\n a4_scalars = [SQRT_PI_OVER_2*(erf(si/ROOT2)+1)-si*np.exp(-si**2/2) for si in ss]\n a4 = np.array([\n g2*sc*np.trace(\n np.matmul(\n B2.T, \n np.matmul(\n B2, \n Sigma\n )\n )\n ).item() for Sigma, sc in zip(latent_Sigmas, a4_scalars)\n ],\n dtype='object'\n )\n\n a5_inner = [\n erfc(si/ROOT2)*np.matmul(\n B1.T, \n B1\n ) + (erf(si/ROOT2)+1)*np.matmul(\n B2.T, \n B2\n ) for si in ss]\n a5 = [\n SQRT_PI_OVER_2*np.matmul(mi.T, np.matmul(Bi, mi)) for mi, Bi in zip(latent_means, a5_inner)\n ]\n\n # convert all list of 1d arrays to lists of floats\n a1 = [element.item()*0.5 for element in a1]\n a3 = [element.item() for element in a3]\n a4 = [element.item() for element in a4]\n a5 = [element.item() for element in a5]\n\n return a1, a2, a3, a4, a5","def _calc_msve(self):\n v = []\n for state in self._env.state_iterator():\n feature_vector = self._features.vector(state)\n v.append(utils.state_value(feature_vector, self.theta))\n\n self.msve.append(utils.rmse(v, self._true_values))","def gauss_kern(sigma,h):\n h1 = h\n h2 = h\n x, y = np.mgrid[0:h2, 0:h1]\n x = x-h2/2\n y = y-h1/2\n # sigma = 10.0\n g = np.exp( -( x**2 + y**2 ) / (2*sigma**2) )\n return g / g.sum()","def _fv(self):\n return self.beta * (self.x ** self.c)","def test_freesolv_gbsa_energies(\n self,\n gbsa_model,\n is_periodic,\n freesolv_id,\n forcefield_version,\n allow_undefined_stereo,\n ):\n import parmed as pmd\n\n from openff.toolkit._tests.utils import (\n compare_system_energies,\n create_system_from_amber,\n get_context_potential_energy,\n get_freesolv_file_path,\n )\n\n mol2_file_path, _ = get_freesolv_file_path(freesolv_id, forcefield_version)\n\n # Load molecules.\n molecule = Molecule.from_file(\n mol2_file_path, allow_undefined_stereo=allow_undefined_stereo\n )\n\n # Give each atom a unique name, otherwise OpenMM will complain\n for idx, atom in enumerate(molecule.atoms):\n atom.name = f\"{atom.symbol}{idx}\"\n positions = to_openmm(molecule.conformers[0])\n\n off_gbsas = {\n \"HCT\": \"test_forcefields/GBSA_HCT-1.0.offxml\",\n \"OBC1\": \"test_forcefields/GBSA_OBC1-1.0.offxml\",\n \"OBC2\": \"test_forcefields/GBSA_OBC2-1.0.offxml\",\n }\n # Create OpenFF System with the current toolkit.\n ff = ForceField(\n get_data_file_path(\"test_forcefields/test_forcefield.offxml\"),\n get_data_file_path(off_gbsas[gbsa_model]),\n )\n\n # OpenMM 7.7 and older don't properly handle parsing prmtop files with a GBSA model and\n # a switching function; this bug may be fixed but for testing purposes, simply turn off\n # the switching function\n ff[\"vdW\"].switch_width = 0.0 * unit.angstrom\n\n off_top = molecule.to_topology()\n if is_periodic:\n off_top.box_vectors = (\n (30.0, 0, 0),\n (0, 30.0, 0),\n (0, 0, 30.0),\n ) * unit.angstrom\n\n else:\n off_top.box_vectors = None\n\n off_omm_system = ff.create_openmm_system(\n off_top, charge_from_molecules=[molecule]\n )\n\n off_nonbonded_force = [\n force\n for force in off_omm_system.getForces()\n if isinstance(force, openmm.NonbondedForce)\n ][0]\n\n omm_top = off_top.to_openmm()\n pmd_struct = pmd.openmm.load_topology(omm_top, off_omm_system, positions)\n prmtop_file = NamedTemporaryFile(suffix=\".prmtop\")\n inpcrd_file = NamedTemporaryFile(suffix=\".inpcrd\")\n pmd_struct.save(prmtop_file.name, overwrite=True)\n pmd_struct.save(inpcrd_file.name, overwrite=True)\n\n openmm_gbsas = {\n \"HCT\": openmm.app.HCT,\n \"OBC1\": openmm.app.OBC1,\n \"OBC2\": openmm.app.OBC2,\n }\n\n # The functional form of the nonbonded force will change depending on whether the cutoff\n # is None during initialization. Therefore, we need to figure that out here.\n\n # WARNING: The NonbondedMethod enums at openmm.app.forcefield and openmm.CustomGBForce\n # aren't necessarily the same, and could be misinterpreted if the wrong one is used. For\n # create_system_from_amber, we must provide the app.forcefield version.\n\n if is_periodic:\n amber_nb_method = openmm.app.forcefield.CutoffPeriodic\n amber_cutoff = off_nonbonded_force.getCutoffDistance()\n else:\n amber_nb_method = openmm.app.forcefield.NoCutoff\n amber_cutoff = None\n\n # OpenMM will process False to mean \"don't use one\" and a quantity as \"use with this distance\"\n switching_distance = off_nonbonded_force.getUseSwitchingFunction()\n if switching_distance:\n switching_distance = off_nonbonded_force.getSwitchingDistance()\n\n (\n amber_omm_system,\n amber_omm_topology,\n amber_positions,\n ) = create_system_from_amber(\n prmtop_file.name,\n inpcrd_file.name,\n implicitSolvent=openmm_gbsas[gbsa_model],\n nonbondedMethod=amber_nb_method,\n nonbondedCutoff=amber_cutoff,\n gbsaModel=\"ACE\",\n implicitSolventKappa=0.0,\n switchDistance=switching_distance,\n )\n\n # Retrieve the GBSAForce from both the AMBER and OpenForceField systems\n off_gbsa_forces = list()\n for force in off_omm_system.getForces():\n if isinstance(force, openmm.GBSAOBCForce) or isinstance(\n force, openmm.openmm.CustomGBForce\n ):\n off_gbsa_forces.append(force)\n\n amber_gbsa_forces = list()\n for force in amber_omm_system.getForces():\n if isinstance(force, openmm.GBSAOBCForce) or isinstance(\n force, openmm.openmm.CustomGBForce\n ):\n amber_gbsa_forces.append(force)\n\n assert len(amber_gbsa_forces) == len(off_gbsa_forces) == 1\n\n off_gbsa_force = off_gbsa_forces[0]\n amber_gbsa_force = amber_gbsa_forces[0]\n\n # We get radius and screen values from each model's getStandardParameters method\n if gbsa_model == \"HCT\":\n gb_params = (\n openmm.app.internal.customgbforces.GBSAHCTForce.getStandardParameters(\n omm_top\n )\n )\n elif gbsa_model == \"OBC1\":\n gb_params = (\n openmm.app.internal.customgbforces.GBSAOBC1Force.getStandardParameters(\n omm_top\n )\n )\n elif gbsa_model == \"OBC2\":\n gb_params = (\n openmm.app.internal.customgbforces.GBSAOBC2Force.getStandardParameters(\n omm_top\n )\n )\n\n # Use GB params from OpenMM GBSA classes to populate parameters\n for idx, (radius, screen) in enumerate(gb_params):\n # Keep the charge, but throw out the old radius and screen values\n q, _, _ = amber_gbsa_force.getParticleParameters(idx)\n if isinstance(amber_gbsa_force, openmm.GBSAOBCForce):\n # Note that in GBSAOBCForce, the per-particle parameters are separate\n # arguments, while in CustomGBForce they're a single iterable\n amber_gbsa_force.setParticleParameters(idx, q, radius, screen)\n\n elif isinstance(amber_gbsa_force, openmm.CustomGBForce):\n # !!! WARNING: CustomAmberGBForceBase expects different per-particle parameters\n # depending on whether you use addParticle or setParticleParameters. In\n # setParticleParameters, we have to apply the offset and scale BEFORE setting\n # parameters, whereas in addParticle, it is applied afterwards, and the particle\n # parameters are not set until an auxillary finalize() method is called. !!!\n amber_gbsa_force.setParticleParameters(\n idx, (q, radius - 0.009, screen * (radius - 0.009))\n )\n\n # Put the GBSA force into a separate group so we can specifically compare GBSA energies\n amber_gbsa_force.setForceGroup(1)\n off_gbsa_force.setForceGroup(1)\n\n # Some manual overrides to get the OFF system's NonbondedForce matched up with the AMBER system\n if is_periodic:\n off_nonbonded_force.setNonbondedMethod(openmm.NonbondedForce.CutoffPeriodic)\n else:\n off_nonbonded_force.setNonbondedMethod(openmm.NonbondedForce.NoCutoff)\n\n off_nonbonded_force.setReactionFieldDielectric(1.0)\n\n # Create Contexts\n integrator = openmm.VerletIntegrator(1.0 * openmm_unit.femtoseconds)\n platform = Platform.getPlatformByName(\"Reference\")\n amber_context = openmm.Context(amber_omm_system, integrator, platform)\n off_context = openmm.Context(\n off_omm_system, copy.deepcopy(integrator), platform\n )\n\n # Get context energies\n amber_energy = get_context_potential_energy(amber_context, positions)\n off_energy = get_context_potential_energy(off_context, positions)\n # Very handy for debugging\n # print(openmm.XmlSerializer.serialize(off_gbsa_force))\n # print(openmm.XmlSerializer.serialize(amber_gbsa_force))\n # Ensure that the GBSA energies (which we put into ForceGroup 1) are identical\n # For Platform=OpenCL, we do get \"==\"-level identical numbers, but for \"Reference\", we don't.\n # assert amber_energy[1] == off_energy[1]\n assert (\n abs(amber_energy[1] - off_energy[1]) < 1e-5 * openmm_unit.kilojoule_per_mole\n )\n\n # Ensure that all system energies are the same\n compare_system_energies(\n off_omm_system,\n amber_omm_system,\n positions,\n by_force_type=False,\n atol=1.0e-4,\n )","def _excitonic_reorg_energy(self, SS, AG, n):\n \n # SystemBathInteraction\n sbi = AG.get_SystemBathInteraction()\n # CorrelationFunctionMatrix\n cfm = sbi.CC\n \n rg = 0.0\n \n # electronic states corresponding to single excited states\n elst = numpy.where(AG.which_band == 1)[0]\n for el1 in elst:\n reorg = cfm.get_reorganization_energy(el1-1,el1-1)\n for kk in AG.vibindices[el1]:\n rg += ((SS[kk,n]**2)*(SS[kk,n]**2)*reorg)\n return rg","def fit_gaussian(position, energy, dx=0.005, a=2, b=1.5, db=0.01, tolerance=0.05, max_iterations=1000):\n min_energy, max_energy = min(energy), max(energy)\n x_start, x_range = min(position), max(position) - min(position)\n x_gauss = np.arange(0, x_range, dx)\n f_gauss = np.exp(-a * (x_range / 2 - x_gauss) ** 2 + b)\n delta_energy = abs(max(f_gauss) - max_energy)\n b_direction = np.sign(max_energy - max(f_gauss))\n print('E_WHAM: %.3f | E_GAUSS: %.3f | b_direction: %i' % (max_energy, max(f_gauss), b_direction))\n for i in range(max_iterations):\n b = b + b_direction * db\n f_gauss_trial = np.exp(-a * (x_range / 2 - x_gauss) ** 2 + b)\n delta_energy_trial = abs(max(f_gauss_trial) - max_energy)\n if delta_energy_trial < tolerance:\n f_gauss = f_gauss_trial\n print('Found b value: %.2f with dE: %.3f within tolerance in %i iterations' % (b, delta_energy, i))\n break\n elif delta_energy_trial < delta_energy:\n f_gauss = f_gauss_trial\n delta_energy = delta_energy_trial\n print('Finished fitting. %i iterations | dE: %.3f | b_final: %.2f' % (i, delta_energy, b))\n return (x_gauss + x_start, f_gauss)","def get_SB(self):\n sr = self.sr\n Sigma = self.snu_at_1GHz / sr\n self.Sigma = Sigma\n # set the flag of SB completeness\n Sigma_in_SI = Sigma * ct._Jy_over_SI_\n if Sigma_in_SI > 1.e-20:\n self.is_complete = True\n return Sigma","def bethe_free_energy(self, potential):\n xn, xe, lpn, lpe, alpha = self(None, full_out=False)\n fn, fe = potential((xn, xe))\n bfe = -(tf.reduce_sum((fn + self.tw * lpn) * self.wn * alpha, [2, 3, 4]) +\n tf.reduce_sum((fe - lpe) * self.we * alpha, [2, 3, 4]))\n return bfe","def N_TB_EB(self, L):\n if L>2.*min(self.CMB.lMaxT, self.CMB.lMaxP):\n return 0.\n # integrand\n def integrand(x):\n theta = x[1]\n l1 = np.exp(x[0])\n l2 = self.l2(L, l1, theta)\n if l2<self.CMB.lMin or l2>min(self.CMB.lMaxT, self.CMB.lMaxP):\n return 0.\n phi = self.phi(L, l1, theta)\n result = self.F_EB(l1, l2, phi)*self.CMB.ftotalTE(l1)*self.CMB.ftotalBB(l2)\n result += 0. #self.F_EB(l2, l1, -phi)*self.CMB.ftotalTB(l1)*self.CMB.ftotalEB(l2)\n result *= self.F_TB(l1, l2, phi)\n result *= l1**2\n result /= (2.*np.pi)**2\n result *= 2.\n return result\n \n # if first time, initialize integrator\n if not hasattr(self.N_TB_EB.__func__, \"integ\"):\n self.N_TB_EB.__func__.integ = vegas.Integrator([[np.log(self.CMB.lMin), np.log(min(self.CMB.lMaxT, self.CMB.lMaxP))], [0., np.pi]])\n self.N_TB_EB.integ(integrand, nitn=8, neval=1000)\n result = self.N_TB_EB.integ(integrand, nitn=1, neval=5000)\n return result.mean","def get_sigma(self):\n sigma = np.array(np.zeros((4,8)))\n for i,c in enumerate(self.cellLines):\n for j,l in enumerate(self.ligands):\n sigma[i][j] = self.aucs[c][l]['sigma']\n return sigma","def Bates_Granger_2(df_train, df_test, nu=None):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n\n # the default length of the relevant window is equal to sample length\n if nu is None:\n nu = T\n\n if nu > T:\n raise ValueError('Parameter nu must be <= length of training sample')\n\n # check whether there is enough observations, so sigma is invertible\n if nu < K:\n raise ValueError('Parameter nu must be >= no. of individual forecasts')\n\n # forecast errors\n errors = df_train.iloc[:, 1:].subtract(df_train.iloc[:, 0], axis=0)\n\n # initialize the covariance matrix sigma\n sigma = np.full((K, K), fill_value=0, dtype=float)\n\n # fill the covariance matrix sigma\n for i in range(K):\n\n for j in range(K):\n\n sigma[i, j] = np.dot(errors.iloc[errors.shape[0]-nu:, i],\n errors.iloc[errors.shape[0]-nu:, j]) / nu\n\n # combining weights\n nominator = np.linalg.solve(sigma, np.full(K, fill_value=1))\n denominator = np.dot(np.full(K, fill_value=1), nominator)\n comb_w = nominator / denominator\n\n # censoring the combining weights\n for i in range(K):\n if comb_w[i] < 0:\n comb_w[i] = 0\n if comb_w[i] > 1:\n comb_w[i] = 1\n\n # rescale the weights so that their sum equals 1\n comb_w = comb_w/comb_w.sum()\n\n # predictions\n df_pred = pd.DataFrame({\"Bates-Granger (2)\": df_test.dot(comb_w)})\n\n return df_pred","def calculate_E0(self) -> float:\n noisy = self.kernel_eigenvectors_[-1].copy()\n np.random.shuffle(noisy)\n\n kernel_eigenvectors = self.kernel_eigenvectors_[:-1]\n kernel_eigenvectors.append(noisy)\n\n eigenvectors_matrix = scipy.sparse.csr_matrix(\n np.column_stack([eigenvector for eigenvector in kernel_eigenvectors])\n )\n\n if len(kernel_eigenvectors) == 2:\n ev0 = kernel_eigenvectors[0]\n ev1 = kernel_eigenvectors[1]\n _, Gamma, _ = scipy.sparse.linalg.svds(\n ev0.T @ ev1, k=self.n_jointly_smooth_functions, which=\"LM\"\n )\n else:\n _, Gamma, _ = scipy.sparse.linalg.svds(\n eigenvectors_matrix, k=self.n_jointly_smooth_functions, which=\"LM\"\n )\n\n Gamma.sort()\n gamma2 = Gamma[-2]\n E0 = (1 + gamma2) / 2\n return E0","def test_molecule_energy_gb_no_sa(self, zero_charges, gbsa_model):\n import parmed as pmd\n\n from openff.toolkit._tests.utils import (\n compare_system_energies,\n create_system_from_amber,\n get_context_potential_energy,\n )\n\n # Load an arbitrary molecule from the freesolv set\n molecule = Molecule.from_file(\n get_data_file_path(\"molecules/FreeSolv/mol2files_sybyl/mobley_1036761.mol2\")\n )\n\n molecule.name = (\n \"mobley_1036761\" # Name the molecule, otherwise OpenMM will complain\n )\n if zero_charges:\n molecule.partial_charges = (\n np.zeros(molecule.n_atoms) * unit.elementary_charge\n )\n\n # Give each atom a unique name, otherwise OpenMM will complain\n for idx, atom in enumerate(molecule.atoms):\n atom.name = f\"{atom.symbol}{idx}\"\n\n positions = np.concatenate(\n (molecule.conformers[0], molecule.conformers[0] + (10 * unit.angstrom))\n )\n # Create OpenFF System with the current toolkit.\n\n off_gbsas = {\n \"HCT\": \"test_forcefields/GBSA_HCT-1.0.offxml\",\n \"OBC1\": \"test_forcefields/GBSA_OBC1-1.0.offxml\",\n \"OBC2\": \"test_forcefields/GBSA_OBC2-1.0.offxml\",\n }\n\n ff = ForceField(\n get_data_file_path(\"test_forcefields/test_forcefield.offxml\"),\n get_data_file_path(off_gbsas[gbsa_model]),\n )\n ff.get_parameter_handler(\"GBSA\").sa_model = None\n off_top = Topology.from_molecules([molecule, molecule])\n off_omm_system = ff.create_openmm_system(\n off_top, charge_from_molecules=[molecule]\n )\n\n omm_top = off_top.to_openmm()\n pmd_struct = pmd.openmm.load_topology(omm_top, off_omm_system, positions)\n prmtop_file = NamedTemporaryFile(suffix=\".prmtop\")\n inpcrd_file = NamedTemporaryFile(suffix=\".inpcrd\")\n pmd_struct.save(prmtop_file.name, overwrite=True)\n pmd_struct.save(inpcrd_file.name, overwrite=True)\n\n openmm_gbsas = {\n \"HCT\": openmm.app.HCT,\n \"OBC1\": openmm.app.OBC1,\n \"OBC2\": openmm.app.OBC2,\n }\n\n (\n amber_omm_system,\n amber_omm_topology,\n amber_positions,\n ) = create_system_from_amber(\n prmtop_file.name,\n inpcrd_file.name,\n implicitSolvent=openmm_gbsas[gbsa_model],\n nonbondedMethod=openmm.app.forcefield.NoCutoff,\n nonbondedCutoff=None,\n gbsaModel=None,\n implicitSolventKappa=0.0,\n )\n\n # Retrieve the GBSAForce from both the AMBER and OpenForceField systems\n off_gbsa_forces = list()\n for force in off_omm_system.getForces():\n if isinstance(force, openmm.GBSAOBCForce) or isinstance(\n force, openmm.openmm.CustomGBForce\n ):\n off_gbsa_forces.append(force)\n\n amber_gbsa_forces = list()\n for force in amber_omm_system.getForces():\n if isinstance(force, openmm.GBSAOBCForce) or isinstance(\n force, openmm.openmm.CustomGBForce\n ):\n amber_gbsa_forces.append(force)\n\n assert len(amber_gbsa_forces) == len(off_gbsa_forces) == 1\n\n off_gbsa_force = off_gbsa_forces[0]\n amber_gbsa_force = amber_gbsa_forces[0]\n\n # We get radius and screen values from each model's getStandardParameters method\n if gbsa_model == \"HCT\":\n gb_params = (\n openmm.app.internal.customgbforces.GBSAHCTForce.getStandardParameters(\n omm_top\n )\n )\n elif gbsa_model == \"OBC1\":\n gb_params = (\n openmm.app.internal.customgbforces.GBSAOBC1Force.getStandardParameters(\n omm_top\n )\n )\n elif gbsa_model == \"OBC2\":\n gb_params = (\n openmm.app.internal.customgbforces.GBSAOBC2Force.getStandardParameters(\n omm_top\n )\n )\n # This is only necessary until https://github.com/openmm/openmm/pull/2362 is bundled into a conda release\n amber_gbsa_force.setSurfaceAreaEnergy(0)\n\n # Use GB params from OpenMM GBSA classes to populate parameters\n for idx, (radius, screen) in enumerate(gb_params):\n # Keep the charge, but throw out the old radius and screen values\n q, _, _ = amber_gbsa_force.getParticleParameters(idx)\n\n if isinstance(amber_gbsa_force, openmm.GBSAOBCForce):\n # Note that in GBSAOBCForce, the per-particle parameters are separate\n # arguments, while in CustomGBForce they're a single iterable\n amber_gbsa_force.setParticleParameters(idx, q, radius, screen)\n\n elif isinstance(amber_gbsa_force, openmm.CustomGBForce):\n # !!! WARNING: CustomAmberGBForceBase expects different per-particle parameters\n # depending on whether you use addParticle or setParticleParameters. In\n # setParticleParameters, we have to apply the offset and scale BEFORE setting\n # parameters, whereas in addParticle, it is applied afterwards, and the particle\n # parameters are not set until an auxillary finalize() method is called. !!!\n amber_gbsa_force.setParticleParameters(\n idx, (q, radius - 0.009, screen * (radius - 0.009))\n )\n\n # Put the GBSA force into a separate group so we can specifically compare GBSA energies\n amber_gbsa_force.setForceGroup(1)\n off_gbsa_force.setForceGroup(1)\n\n # Create Contexts\n integrator = openmm.VerletIntegrator(1.0 * openmm_unit.femtoseconds)\n platform = Platform.getPlatformByName(\"Reference\")\n amber_context = openmm.Context(amber_omm_system, integrator, platform)\n off_context = openmm.Context(\n off_omm_system, copy.deepcopy(integrator), platform\n )\n\n # Get context energies\n amber_energy = get_context_potential_energy(amber_context, to_openmm(positions))\n off_energy = get_context_potential_energy(off_context, to_openmm(positions))\n\n # Very handy for debugging\n # print(openmm.XmlSerializer.serialize(off_gbsa_force))\n # print(openmm.XmlSerializer.serialize(amber_gbsa_force))\n\n # Ensure that the GBSA energies (which we put into ForceGroup 1) are identical\n # For Platform=OpenCL, we do get \"==\"-level identical numbers, but for \"Reference\", we don't.\n # assert amber_energy[1] == off_energy[1]\n assert (\n abs(amber_energy[1] - off_energy[1]) < 1e-5 * openmm_unit.kilojoule_per_mole\n )\n\n # If charges are zero, the GB energy component should be 0, so the total GBSA energy should be 0\n if zero_charges:\n assert amber_energy[1]._value == 0.0\n else:\n assert amber_energy[1]._value != 0.0\n\n # Ensure that all system energies are the same\n if zero_charges or gbsa_model in [\"HCT\", \"OBC1\"]:\n compare_system_energies(\n off_omm_system,\n amber_omm_system,\n to_openmm(positions),\n by_force_type=False,\n )\n else:\n compare_system_energies(\n off_omm_system,\n amber_omm_system,\n to_openmm(positions),\n by_force_type=False,\n atol=1e-4,\n )","def bethe_free_energy(b_i, b_ij, psi_i, phi_ij, N):\n triu_idx = numpy.triu_indices(N, 1)\n # Compute Bethe energy\n bethe_E = numpy.sum(b_ij[triu_idx] * (numpy.log(b_ij[triu_idx]) -\n numpy.log(phi_ij[triu_idx]))) \\\n - ((N - 1.) - 1.) * numpy.sum(b_i * (numpy.log(b_i) -\n numpy.log(psi_i)))\n return bethe_E","def test_class():\n riskfree = .03\n maturity = 30/365\n moneyness = np.linspace(-.04, .04, 10)\n premium = np.ones_like(moneyness) * .05\n call = True\n data = {'riskfree': riskfree, 'maturity': maturity,\n 'moneyness': moneyness, 'call': call, 'premium': premium}\n\n sigma = .13\n bsm = BSmodel(sigma, data)\n\n weights = [.63]\n means = [-.01, .09]\n stds = [.16, .05]\n param = weights + means + stds\n mbs = MBSmodel(param, data)\n\n param_a, param_p, param_c = 4, 1.5, -.05\n gb2 = GB2model([param_a, param_p, param_c], data)\n print(gb2.get_pnames())\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.density(moneyness), label=model.get_name())\n plt.legend()\n plt.show()\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.premium(), label=model.get_name())\n plt.legend()\n plt.show()\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.impvol(), label=model.get_name())\n plt.legend()\n plt.show()\n\n print('BS objective function = %.4f' % bsm.objective(sigma))\n print('GB2 objective function = %.4f'\n % gb2.objective([param_a, param_p, param_c]))","def self_energy(gf_imp0, gf_imp):\n return 1/gf_imp0 - 1/gf_imp","def calculate_ft(self):\n \n # Create a function which is able to evaluate B**2\n ffunc = scipy.interpolate.interp1d(self.psigrid, self.e.getF()[self.tind])\n def b2_func(R, Z, psi):\n bt = ffunc(psi)/R\n br = -self.psifunc.ev(R, Z, dy=1)/R\n bz = self.psifunc.ev(R, Z, dx=1)/R\n \n return bt**2 + br**2 + bz**2\n \n\n def b_bmax2(R,Z,psi):\n b2 = b2_func(R,Z,psi)\n return b2 / np.max(b2)\n \n def b_bmax(R,Z,psi):\n return np.sqrt(b_bmax2(R,Z,psi))\n \n # Evaluate the flux-surface averaged h^2 and h, as required\n fsa_h2 = self.fs_average(b_bmax2)\n fsa_h = self.fs_average(b_bmax)\n \n # This is the function which gets flux-surface averaged in equation (7)\n def ftl_func(R,Z,psi):\n h = b_bmax(R,Z,psi)\n h2 = b_bmax2(R,Z,psi)\n \n return (1 - (np.sqrt(1 - h) * (1 + 0.5 * h)))/h2\n \n \n # Equation 6, 7 in Lin-Liu\n fs_ftu = 1 - fsa_h2 / fsa_h**2 * (1 - np.sqrt(1 - fsa_h) * (1 + 0.5 * fsa_h))\n fs_ftl = 1 - fsa_h2 * self.fs_average(ftl_func)\n # Equation 18, 19 \n om = 0.75\n self.fs_ft = om*fs_ftu + (1-om)*fs_ftl","def gaussian_elimination_special_case(b):\n n = len(b)\n # init new (prime) arrays\n beta_prime = np.empty(n)\n beta_prime[0] = 2\n\n b_prime = np.empty(n)\n b_prime[0] = b[0]\n\n v = np.empty(n)\n i_array = np.arange(n)\n beta_prime = (i_array+2) / (i_array+1)\n\n for i in range(1,n):\n b_prime[i] = b[i] + (b_prime[i-1] / beta_prime[i-1])\n\n v[-1] = b_prime[-1] / beta_prime[-1]\n\n for i in range(n-2, -1, -1):\n v[i] = (b_prime[i] + v[i+1])/ beta_prime[i]\n\n return v","def run_gibbs(expt, save=True, show_progress=False):\n if isinstance(expt, str):\n expt = get_experiment(expt)\n tr_expt = get_training_expt(expt)\n\n for it in tr_expt.save_after:\n for avg in AVG_VALS:\n print 'Iteration', it, avg\n try:\n rbm = load_rbm(expt, it, avg)\n except:\n continue\n log_Z = storage.load(expt.log_Z_file(it, avg)).as_numpy_array()\n final_states = storage.load(expt.final_states_file(it, avg))\n\n # sample the states proportionally to the Z estimates\n p = log_Z - np.logaddexp.reduce(log_Z)\n p /= p.sum() # not needed in theory, but numpy complains if it doesn't sum exactly to 1\n idxs = np.random.multinomial(1, p, size=expt.annealing.num_particles).argmax(1)\n states = binary_rbms.RBMState(final_states.v[idxs, :], final_states.h[idxs, :])\n\n if show_progress:\n pbar = misc.pbar(expt.gibbs_steps)\n\n for st in range(expt.gibbs_steps):\n states = rbm.step(states)\n\n if show_progress:\n pbar.update(st)\n\n if show_progress:\n pbar.finish()\n\n if save:\n storage.dump(states, expt.gibbs_states_file(it, avg))","def mbh_sigma_inv(mbh):\n _MASS = 8.32\n _SLOPE = 5.64\n _VEL = 200e5\n\n sigma = (np.log10(mbh/MSOL) - _MASS) / _SLOPE\n sigma = np.power(10.0, sigma) * _VEL\n return sigma","def _vB0(self,x):\n return 2.4e3*x**0.5/(2+x**0.5)+8.e2","def gamma(self):\r\n raise NotImplementedError('not implemented yet, will use spouge approximation')","def estimateGaussian(X):\n\tmu = np.mean(X, axis=0)\n\tsigma2 = np.std(X, axis=0) ** 2\n\treturn mu, sigma2","def get_data_term(self):\n \n if self.num_hidden == 0:\n \n data_term = -self.compute_energy(self.x, self.batch_size)\n \n else:\n \n data_term = -self.compute_free_energy(self.x)\n \n return T.sum(T.exp(-data_term))","def gbm(price: float,\n mu: float,\n sigma: float,\n dt: float,\n n: int) -> np.array:\n y = np.exp((mu - sigma ** 2 / 2) * dt + sigma * np.random.normal(0, np.sqrt(dt), size=n).T)\n y = price * y.cumprod(axis=0)\n return y","def SCB(t,init,rhoS,deltaSC,rhoC,deltaCB,rhoB):\n\n #Declaring useful parameters\n [S0,C0,B0]=init\n lambdaS=deltaSC-rhoS #Introducing global decay rates lambda.\n lambdaC=deltaCB-rhoC\n lambdaB=-rhoB\n\n #Checking for eigenvalues equalitites\n thresh=1e-5 #threshold difference for considering two eignevalues as equal\n bSC=(lambdaC-lambdaS)*(abs(lambdaC-lambdaS)>=thresh)\n bSB=(lambdaB-lambdaS)*(abs(lambdaB-lambdaS)>=thresh)\n bCB=(lambdaB-lambdaC)*(abs(lambdaB-lambdaC)>=thresh)\n\n #S has always the same expression\n S=S0*np.exp(-lambdaS*t)\n\n #there are two cases for C\n if bSC!=0:\n c2=deltaSC*S0/bSC; c1=C0-c2\n C=c1*np.exp(-lambdaC*t)+c2*np.exp(-lambdaS*t)\n\n #there are three subcases for B in that case\n if bCB==0:\n b2=deltaCB*c1; b3=deltaCB*c2/bSB; b1=B0-b3\n B=(b1+b2*t)*np.exp(-lambdaB*t)+b3*np.exp(-lambdaS*t)\n\n elif bSB==0:\n b2=deltaCB*c1/bCB; b3=deltaCB*c2; b1=B0-b2\n B=(b1+b3*t)*np.exp(-lambdaB*t)+b2*np.exp(-lambdaC*t)\n\n else:\n b2=deltaCB*c1/bCB; b3=deltaCB*c2/bSB; b1=B0-b2-b3\n B=b1*np.exp(-lambdaB*t)+b2*np.exp(-lambdaC*t)+b3*np.exp(-lambdaS*t)\n\n else:\n c2=deltaSC*S0\n c1=C0\n C=(c1+c2*t)*np.exp(-lambdaS*t)\n\n #there are two subcases for B in that case\n if bCB!=0:\n b3=deltaCB*c2/bSB; b2=(deltaCB*c1-b3)/bSB; b1=B0-b2\n B=b1*np.exp(-lambdaB*t)+(b2+b3*t)*np.exp(-lambdaC*t)\n\n else:\n b1=B0; b2=deltaCB*c1; b3=deltaCB*c2/2\n B=(b1+b2*t+b3*t**2)*np.exp(-lambdaB*t)\n\n return(np.vstack((S,C,B)))","def get_bpmf(self, kB=0.001987204134799235, temperature=300.0):\n if len(self._meaningful_energies) == 0:\n return 0.\n\n beta = 1. / temperature / kB\n V_0 = 1661.\n\n nr_samples = self.get_number_translations()\n energies = -beta * self._meaningful_energies\n e_max = energies.max()\n exp_mean = np.exp(energies - e_max).sum() / nr_samples\n\n bpmf = -temperature * kB * (np.log(exp_mean) + e_max)\n\n V_binding = self.get_box_volume()\n correction = -temperature * kB * np.log(V_binding / V_0 / 8 / np.pi**2)\n return bpmf + correction","def EI(x, gp, ndim,fMax, epsilon=0.1):\n\t#epsilon = 0.1\n\tx1=np.array(x).reshape(-1,ndim)\n\tmuNew, stdNew = gp.predict(x1, return_std=True)\n\t#fMax=max(Y_init)\n\tZ = (muNew - fMax - epsilon)/stdNew\n\treturn -((muNew - fMax - epsilon)* scipy.stats.norm.cdf(Z) + stdNew*scipy.stats.norm.pdf(Z))","def fce_re(B,E):\n return wce(B)/(2.*np.pi)/ev2gamma(E);","def calc_std(sig):\n return np.std(sig)","def mbe(self) -> float:\n return float(np.mean(self._error(self.true, self.predicted)))","def optg_xtb(self, acc='normal', nproc=1):\n for k in ['OMP_NUM_THREADS', 'MKL_NUM_THREADS']:\n os.environ[k] = '%d'%nproc\n uc = io2.Units()\n const = uc.h2e #/uc.b2a\n fmax = {'normal':1e-3, 'tight':8.e-4, 'vtight':2e-4}[acc] * const\n # 0.025 eV/A (ase force unit) ~ 0.001 Hartree/A (xtb force unit)\n print('acc = ', acc, 'fmax=', fmax)\n for cid in range(self.nconf):\n print('cid=', cid)\n c1 = self.mol.GetConformer(cid)\n zs = [ ai.GetAtomicNum() for ai in c1.GetAtoms() ]\n coords = []\n for ia in range(self.na):\n o = c1.GetAtomPosition(ia)\n coords.append( [o.x, o.y, o.z] )\n m1 = ase.Atoms(zs, coords)\n # create the calculator for GFN0-xTB under periodic boundary conditions\n calc = GFN2(print_level=2) # setting to 1 or 2 (the default) is ok, not so for 3\n m1.set_calculator(calc)\n e = m1.get_potential_energy()\n print(\"Initial energy: eV, Eh\", e, e/Hartree)\n relax = PreconFIRE(m1, precon=None, trajectory=None) # = 'tmp.traj')\n relax.run(fmax = fmax)\n for i in range(self.na):\n pt = Point3D()\n pt.x, pt.y, pt.z = coordsU[i]\n c1.SetAtomPosition(i, pt)"],"string":"[\n \"def bse_fe(self):\\n p = self.model.exog.shape[1]\\n return np.sqrt(np.diag(self.cov_params())[0:p])\",\n \"def gibbs_init(self, sigma2_s_param=None, sigma2_g_param=None):\\n #Gibbs : Initialization step\\n self.gibbs_init_step(self.nb_days, self.nb_particles, sigma2_s_param, sigma2_g_param)\\n\\n #Gibbs : step t > 0\\n for j in range(1, self.nb_particles):\\n if(j%(self.nb_particles/10)==0 or j==1):\\n print(\\\"Gibbs sampling for particle \\\" + str(j) + \\\"/\\\" + str(self.nb_particles))\\n\\n\\n self.s[:,j] = self.s[:,j-1]\\n self.g_heat[:,j] = self.g_heat[:,j-1]\\n self.sigma_s_star_2[:,j] = self.sigma_s_star_2[:,j-1]\\n self.sigma_g_star_2[:,j] = self.sigma_g_star_2[:,j-1]\\n\\n # Compute s[0] for particle j (j>0)\\n self.compute_s_0(j)\\n\\n # Compute s[n] for particle j (n>0 and j>0)\\n for i in range(1, self.nb_days):\\n self.compute_s(i,j)\\n\\n # Compute g_heat[O] for particle j (and j>0)\\n self.compute_g_0(j)\\n\\n # Compute g_heat[n] for particle j (n>0 and j>0)\\n for i in range(1, self.nb_days):\\n self.compute_g(i,j)\\n\\n shape = 0.01 + ((self.nb_days - 1)/2)\\n # Compute the new sigma_s_star2 for particle j (j>0) (follow Inverse Gamma)\\n self.sigma_s_star_2[0, j] = self.compute_sigma_star_2(shape, self.s, j)\\n\\n # Compute the new sigma_g_star2 for particle j (j>0) (follow Inverse Gamma)\\n self.sigma_s_star_2[0, j] = self.compute_sigma_star_2(shape, self.g_heat, j)\\n\\n #Compute x\\n self.compute_x()\\n #Compute w\\n self.compute_w()\",\n \"def expected_g(self):\\n raise NotImplementedError()\\n # \\\\sum_{b} \\\\gamma_b\\n trm2 = self.mf_gamma.sum(axis=2)\\n E_g = self.mf_gamma / trm2[:,:,None]\\n return E_g\",\n \"def gslb(self) :\\n try :\\n return self._gslb\\n except Exception as e:\\n raise e\",\n \"def gibbs(h, s, temp):\\n\\n return h - s * temp * R\",\n \"def _reestimate_B(self, obs_seq, gamma):\\n B_new = {\\n 'numer': [\\n np.zeros((self.n_states, self.n_features[i]))\\n for i in range(self.n_emissions)\\n ],\\n 'denom': [\\n np.zeros((self.n_states, self.n_features[i]))\\n for i in range(self.n_emissions)\\n ],\\n }\\n\\n for e in range(self.n_emissions):\\n for j in range(self.n_states):\\n for k in range(self.n_features[e]):\\n numer = 0.0\\n denom = 0.0\\n for t, obs in enumerate(obs_seq):\\n if obs[e] == k:\\n numer += gamma[t][j]\\n denom += gamma[t][j]\\n B_new['numer'][e][j][k] = numer\\n B_new['denom'][e][j][k] = denom\\n\\n return B_new\",\n \"def bsm_vega(S0, K, T, r, sigma):\\n \\n from math import log, sqrt\\n from scipy import stats\\n \\n S0 = float(S0)\\n d1 = (log(S0 / K) + (r + 0.5 * sigma ** 2) * T / (sigma * sqrt(T))\\n vega = S0 * stats.normcdf(d1, 0.0, 1.0) * sqrt(T)\\n return vega\\n \\n# Implied volatility function\\n\\ndef bsm_call_imp_vol(S0, K, T, r, C0, sigma_est, it = 100):\\n \\\"\\\"\\\" \\n Implied volatility of European call option in BSM model\\n \\n Parameters\\n ==========\\n S0 : Float\\n Initial stock/index level\\n K : Float\\n Strike Price\\n T : Float\\n Maturity Date (in year fractions)\\n r : Float\\n Constant risk-free short rate\\n sigma_est : Float\\n Estimate of impl. volatility\\n it : integer\\n Number of iterations\\n \\n Returns\\n =======\\n sigma_est : Float\\n Numerically estimated implied volatility\\n \\\"\\\"\\\"\\n for i in range(it):\\n sigma_est -= ((bsm_call_value(S0, K, T, r, sigma_est) - C0)\\n / bsm_vega(S0, K, T, r, sigma_est))\\n return sigma_est\",\n \"def _sigma_ep(self,gam,eps):\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n return self._sigma_1(gam,eps)\",\n \"def get_total_BMA_effect_size(self):\\n \\n if self.total_bma_es is None:\\n # clean up these long expressions on Isle 2\\n log_evidences = [self.results[kernel].summary(b=self.b)['evidence']['md'] \\n for kernel in self.kernel_dict.keys()] + \\\\\\n [self.results[kernel].summary(b=self.b)['evidence']['mc'] \\n for kernel in self.kernel_dict.keys()]\\n \\n M = len(log_evidences)\\n Z = logSumExp(log_evidences)\\n evidences = np.exp(log_evidences - Z)\\n disc_stats = [self.results[kernel].summary(b=self.b)['es_disc_stats'] \\n for kernel in self.kernel_dict.keys()]\\n nsamples = 50000\\n samples = list() \\n for i in range(int(M/2)):\\n samples += list(np.random.normal(loc=disc_stats[i][0], \\n scale=disc_stats[i][1], \\n size=int(nsamples*evidences[i])))\\n samples += list(np.zeros(nsamples - len(samples)))\\n \\n if np.sum(np.abs(samples))==0:\\n xrange = np.linspace(-2, 2, 500)\\n ix = np.argmin((xrange-self.b)**2)\\n es_bma = np.zeros((500))\\n es_bma[ix] = 1.0/ (xrange[1] - xrange[0])\\n else: \\n kde_fit = stats.gaussian_kde(samples, bw_method='silverman')\\n xrange = np.linspace(np.min(samples), np.max(samples), 500)\\n es_bma = kde_fit(xrange)\\n self.total_bma_es = np.sum(xrange*es_bma) * (xrange[1]-xrange[0])\\n self.total_bma_pdf = (xrange, es_bma)\\n return self.total_bma_es\",\n \"def ReadGaussian():\\n # LICHEM calculates this as the optimization energy - self energy.\\n # Self energy of the charges = {f} a.u.\\n self_line = \\\" Self energy of the charges\\\"\\n # SCF Done: E({s}) = {f} A.U. after {d} cycles\\n SCF_line = \\\" SCF Done:\\\"\\n with open('LICHM_GaussEnergy_0.log') as f:\\n for line in f:\\n if line.startswith(self_line):\\n # print(line)\\n ## Parses unmarked positive and - int and float values\\n selfE = re.findall(r'\\\\-\\\\d+\\\\.*\\\\d*', line)\\n selfE = float(selfE[0])\\n elif line.startswith(SCF_line):\\n # print(line)\\n SCFE = re.findall(r'\\\\-\\\\d+\\\\.*\\\\d*', line)\\n SCFE = float(SCFE[0])\\n f.close()\\n GaussE = SCFE - selfE\\n GaussE *= har2ev\\n return GaussE\",\n \"def bse_re(self):\\n p = self.model.exog.shape[1]\\n return np.sqrt(self.scale * np.diag(self.cov_params())[p:])\",\n \"def get_gibbs_state(self):\\n # Set the initial state and propagate\\n self.set_wignerfunction(lambda _, x, p: 0*x + 0*p + 1.)\\n return self.propagate(self.num_beta_steps)\",\n \"def expected_log_g(self):\\n raise NotImplementedError()\\n E_lng = np.zeros_like(self.mf_gamma)\\n\\n # \\\\psi(\\\\sum_{b} \\\\gamma_b)\\n trm2 = psi(self.mf_gamma.sum(axis=2))\\n for b in xrange(self.B):\\n E_lng[:,:,b] = psi(self.mf_gamma[:,:,b]) - trm2\\n\\n return E_lng\",\n \"def gibbs_init_step(self, nb_days, nb_particles, sigma2_s_param=None, sigma2_g_param=None):\\n\\n #Gibbs step 0\\n if sigma2_s_param is None:\\n self.sigma_s_star_2[0, 0] = invgamma.rvs(a=10**(-2), scale=10**(2), size = 1)[0]\\n else :\\n self.sigma_s_star_2[0, 0] = sigma2_s_param\\n if sigma2_g_param is None:\\n self.sigma_g_star_2[0, 0] = invgamma.rvs(a=10**(-2), scale=10**(2), size = 1)[0]\\n else :\\n self.sigma_g_star_2[0, 0] = sigma2_g_param\\n\\n self.s[0,0] = self.truncated_norm(0, inf, 0, 10**8)\\n self.g_heat[0,0] = self.truncated_norm(-inf, 0, 0, 10**8)\\n for i in range(1, nb_days):\\n self.s[i,0] = self.s[i-1,0] + self.truncated_norm(-self.s[i-1,0], inf, 0, self.sigma_s_star_2[0,0])\\n self.g_heat[i,0] = self.g_heat[i-1,0] + self.truncated_norm(-inf, - self.g_heat[i-1,0], 0, self.sigma_g_star_2[0,0])\",\n \"def bn(x, e=1e-7):\\n mu = np.mean(x)\\n sigma2 = np.var(x)\\n x_ = (x - mu) / np.sqrt(sigma2 + e)\\n return x_\",\n \"def _se_beta(self):\\n return _handle_ab(self._se_all, self.use_const)[1]\",\n \"def beta_Sonoi2015(self):\\n return 10.0**(-3.86*self.string_to_param(\\\"log_Teff\\\") + 0.235*self.string_to_param(\\\"log_g\\\")+14.2)\",\n \"def EGWD_fg(f):\\n A = 4.2e-47\\n res = np.zeros((len(f)))\\n for i,freq in enumerate(f): \\n if freq >=3e-3:\\n # strain \\n res[i] = A * freq**(-7/3) * np.exp(-2*(freq/5e-2)**2) \\n else:\\n res[i] = np.NaN\\n return np.array(res)\",\n \"def update(self, es, **kwargs):\\n es.sigma = self.coefficient * es.sp.weights.mueff * _norm(es.mean) / es.N / es.sp.cmean\",\n \"def f0(E, fermi, T):\\n return 1. / (1. + np.exp((E - fermi) / (k_B * T)))\",\n \"def energy(e: float) -> float:\\n\\n return (1/np.sqrt(2))*(gamma(-e/2+1/2)/(gamma(-e/2+3/4)))\",\n \"def Two_EP_B(params, ns):\\r\\n nu1,nu2,nu3,T1,T2 = params\\r\\n\\r\\n nu1_func = lambda t: nu2 * (nu3/nu2)**(t/T2)\\r\\n nu_func = lambda t: [nu1_func(t)]\\r\\n\\r\\n\\r\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0])\\r\\n fs = moments.Spectrum(sts)\\r\\n fs.integrate([nu1], T1)\\r\\n fs.integrate(nu_func, T2)\\r\\n \\r\\n return fs\",\n \"def f4():\\n n = 4\\n v = np.arange(n)**0.75 * 0.2\\n e = (np.arange(n)+1)**0.7 * 1e-1\\n\\n n = 12\\n v = np.arange(n)\\n e = np.array([0.1]*n) * 10e-0\\n\\n print(Sumb(v,e))\\n\\n f = plt.figure()\\n a = f.add_subplot(111)\\n\\n dx = 0.0001\\n x = np.arange(-1,v[-1]+1,dx)\\n y = x.copy()\\n y[:] = 0.\\n for i in range(n):\\n yx = lg(x,v[i],e[i])\\n a.plot(x,np.exp(yx),label='{:d}'.format(i))\\n y += yx\\n y = np.exp((y - np.max(y))/n**2)\\n y /= np.sum(y) * dx \\n a.plot(x,y,label='sum')\\n s = np.argsort(y)[::-1]\\n ys = np.cumsum(y[s]) * dx\\n yi = np.argwhere(ys > 0.682689492137)[0][0]\\n print('mean = {:2f}'.format(x[s[0]]))\\n print('sigma = {:2f}'.format(yi*dx/2))\\n xy = np.ndarray((yi+2,2))\\n i0,i1 = min(s[:yi]), max(s[:yi])\\n xy[:yi,0] = x[i0:i1+1]\\n xy[:yi,1] = y[i0:i1+1]\\n xy[yi:,1] = 0\\n xy[yi:,0] = x[[i1,i0]]\\n a.add_patch(Polygon(xy,fill=True,color='green',ec='none',alpha=0.25))\\n \\n leg = plt.legend()\\n plt.draw()\",\n \"def sigma2_RG(self):\\n sigma = np.sqrt(self.cosmo.gs_spectral_moment(l=2,RG=self.RG))\\n return sigma\",\n \"def verhulst(nb_init, t0, tf, eps, methode, gamma, K) :\\n f=lambda y,t : gamma*y*(1-y/K)\\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\\n return Y\",\n \"def sigma0_RG(self):\\n sigma = np.sqrt(self.cosmo.gs_spectral_moment(l=0,RG=self.RG))\\n return sigma\",\n \"def sigma_e(re, n, q, Ftot=1., c0=0.):\\n from scipy.special import gamma\\n kap = get_kappa(n)\\n return Ftot/(2*np.pi*re**2*np.exp(kap)*n*kap**(-2*n)*gamma(2*n)*q/Rc(c0)), kap\",\n \"def bergman_input(self):\\n return self.m_t\",\n \"def womgau(hop):\\n import numpy as np\\n import logging\\n import matplotlib.pyplot as plt\\n from scipy.optimize import curve_fit\\n from tmath.wombat.womwaverange import womwaverange\\n from tmath.wombat.womget_element import womget_element\\n from tmath.wombat.inputter import inputter\\n from tmath.wombat.inputter_single import inputter_single\\n from tmath.wombat.gauss import gauss\\n from tmath.wombat.gauss_cont import gauss_cont\\n from tmath.wombat.yesno import yesno\\n print(' ')\\n logging.info('Object is {}'.format(hop[0].obname))\\n print(' ')\\n print('Spectrum runs from {} to {}'.format(hop[0].wave[0],hop[0].wave[-1]))\\n print(' ')\\n print('This routine expects the spectrum to be in flambda units.')\\n print('It also expects a linear wavelength scale.')\\n print(' ')\\n print('Choose general region of spectrum\\\\n')\\n nwave,nflux,mode=womwaverange(hop[0].wave,hop[0].flux,'none')\\n print('\\\\nNow pick the exact range for the fit')\\n waveint,fluxint,mode=womwaverange(nwave,nflux,mode)\\n indexblue=womget_element(nwave, waveint[0])\\n indexred=womget_element(nwave,waveint[-1])\\n if (mode == 'w'):\\n done = False\\n while (not done):\\n print(' ')\\n wavecenter=inputter('Enter approximate center of Gaussian : ','float',False)\\n indexcenter=womget_element(waveint,wavecenter)\\n if (indexcenter <= 0) or (wavecenter > waveint[-1]):\\n print('Bad central wavelength, try again')\\n else:\\n done = True\\n else:\\n done=False\\n while (not done):\\n print('Mark the approximate center of the Gaussian')\\n pickcent=plt.ginput(1,timeout=-1)\\n indexcenter=womget_element(waveint,pickcent[0][0])\\n print('\\\\nApproximate center at {}'.format(waveint[indexcenter]))\\n print('\\\\nIs this OK?')\\n answer=yesno('y')\\n if (answer == 'y'):\\n done=True\\n weights=np.sqrt(hop[0].var[indexblue:indexred+1])\\n print(' ')\\n continuum=inputter_single('Do you want to fit gaussian with (c)ontinuum, or (n)o continuum? ','cn')\\n if (continuum == 'c'):\\n p=[fluxint[indexcenter], waveint[indexcenter],3.0,1.0,waveint[0]]\\n result=curve_fit(gauss_cont,waveint,fluxint,sigma=weights,p0=p,absolute_sigma=True,full_output=True)\\n else:\\n p=[fluxint[indexcenter], waveint[indexcenter],3.0]\\n result=curve_fit(gauss,waveint,fluxint,sigma=weights,p0=p,absolute_sigma=True,full_output=True)\\n coefferr=np.sqrt(np.diag(result[1]))\\n coeff=result[0]\\n # make 'finer-grained' version of fit, 0.2A/pix for calculations\\n wavecalc=np.arange(2*5*50*abs(coeff[2]))*0.2+coeff[1]-0.2*5*50*abs(coeff[2])\\n calccenter=womget_element(wavecalc,coeff[1])\\n if (continuum == 'c'):\\n fluxcalc=gauss_cont(wavecalc,*coeff)\\n fluxcont=wavecalc*coeff[3]+coeff[4]\\n fluxgaussian=fluxcalc-fluxcont\\n linecont=fluxcont[calccenter]\\n else:\\n fluxcalc=gauss(wavecalc,*coeff)\\n \\n \\n deltafit=wavecalc[1]-wavecalc[0]\\n calcindexblue=womget_element(wavecalc,waveint[0])\\n calcindexred=womget_element(wavecalc,waveint[-1])\\n sumfluxcalc=np.sum(fluxcalc[calcindexblue:calcindexred+1]*deltafit)\\n sumallfluxcalc=np.sum(fluxcalc*deltafit)\\n chi=(result[2]['fvec']**2).sum()\\n redchi=chi/(len(waveint)-len(coeff))\\n if (continuum == 'c'):\\n sumfluxgaussian=np.sum(fluxgaussian[calcindexblue:calcindexred+1]*deltafit)\\n sumallfluxgaussian=np.sum(fluxgaussian*deltafit)\\n sumfluxcont=np.sum(fluxcont[calcindexblue:calcindexred+1]*deltafit)\\n sumallfluxcont=np.sum(fluxcont*deltafit)\\n sumallfluxcont_test=np.sum(fluxcont)\\n # propagate uncertainty (from old version) not sure this is correct\\n height_pct=coefferr[0]/coeff[0]\\n sigma_pct=coefferr[2]/coeff[2]\\n flux_pct=np.sqrt(height_pct**2+sigma_pct**2)\\n sumfluxgaussiansig=sumfluxgaussian*flux_pct\\n sumallfluxgaussiansig=sumallfluxgaussian*flux_pct\\n plt.cla()\\n plt.plot(nwave,nflux,drawstyle='steps-mid',color='k')\\n plt.ylabel('Flux')\\n plt.xlabel('Wavelength')\\n xmin,xmax=plt.xlim()\\n ymin,ymax=plt.ylim()\\n plt.plot(wavecalc,fluxcalc,drawstyle='steps-mid',color='b')\\n if (continuum == 'c'):\\n plt.plot(wavecalc,fluxgaussian,drawstyle='steps-mid',color='r')\\n plt.plot(wavecalc,fluxcont,drawstyle='steps-mid',color='g')\\n plt.plot([waveint[0],waveint[0]],[ymin,ymax],color='k',linestyle='--')\\n plt.plot([waveint[-1],waveint[-1]],[ymin,ymax],color='k',linestyle='--')\\n plt.xlim([xmin,xmax])\\n plt.ylim([ymin,ymax])\\n logging.info('For object {} Gaussian fit'.format(hop[0].obname))\\n if (continuum == 'c'):\\n print('\\\\nData = Black, Fit = Blue, Continuum = Green, Fit-Continuum = Red\\\\n')\\n else:\\n print('\\\\nData = Black, Fit = Blue\\\\n')\\n logging.info('Height {:16.8f}+/-{:16.8f}'.format(coeff[0],coefferr[0]))\\n logging.info('Center {:16.8f}+/-{:16.8f}'.format(coeff[1],coefferr[1]))\\n logging.info('Sigma {:16.8f}+/-{:16.8f}'.format(coeff[2],coefferr[2]))\\n if (continuum == 'c'):\\n FWHM = 2.35482*np.abs(coeff[2])\\n rest_wave = input('Rest wavelength [N/A]: ') or None\\n redshift = input('Redshift [N/A]: ') or None\\n if rest_wave:\\n rest_wave = float(rest_wave)\\n w1 = (rest_wave - FWHM/2.)/(1.+float(redshift)) \\n w2 = (rest_wave + FWHM/2.)/(1.+float(redshift)) \\n c = 299792.458\\n v1 = -1.*c*((rest_wave/w1)**2. - 1)/(1+((rest_wave/w1)**2.))\\n v2 = -1.*c*((rest_wave/w2)**2. - 1)/(1+((rest_wave/w2)**2.))\\n logging.info('Slope {:16.8f}+/-{:16.8f}'.format(coeff[3],coefferr[3]))\\n logging.info('Y-intercept {:16.8f}+/-{:16.8f}'.format(coeff[4],coefferr[4]))\\n logging.info('FWHM {:16.8f}+/-{:16.8f}'.format(2.35482*np.abs(coeff[2]),2.35482*coefferr[2]))\\n logging.info('FWHM (velocity) {:16.8f} km/s'.format(v2-v1))\\n logging.info('Flux between dotted lines (Gaussian): {:16.8f}+/-{:16.8f}'.format(sumfluxgaussian, sumfluxgaussiansig))\\n logging.info('EW between dotted lines (Gaussian): {:16.8f}'.format(sumfluxgaussian/linecont))\\n logging.info('Flux for full (Gaussian): {:16.8f}+/-{:16.8f}'.format(sumallfluxgaussian, sumallfluxgaussiansig))\\n logging.info('EW for full (Gaussian): {:16.8f}'.format(sumallfluxgaussian/linecont))\\n logging.info('Continuum flux at line center: {:16.8f}'.format(linecont))\\n\\n \\n logging.info('Chi^2: {}'.format(chi))\\n logging.info('Reduced chi^2: {}'.format(redchi))\\n logging.info('All fluxes might need to be scaled by 1e-15')\\n print(' ')\\n return hop\",\n \"def gibbs_(dict_, T):\\n\\tdST = dict_['S298'] + CpT(dict_, T)[0]\\n\\t\\t\\n\\tdHT = dict_['dH298'] + CpT(dict_, T)[1]/1000\\n\\t\\t\\n\\treturn (dHT - T*dST/1000)\",\n \"def gmae(self) -> float:\\n return _geometric_mean(np.abs(self._error()))\",\n \"def EVBMF(Y, sigma2=None, H=None): \\n L,M = Y.shape #has to be L<=M\\n\\n if H is None:\\n H = L\\n\\n alpha = L/M\\n tauubar = 2.5129*np.sqrt(alpha)\\n \\n #SVD of the input matrix, max rank of H\\n _,s,_ = torch.svd(Y)\\n s = s[:H]\\n\\n #Calculate residual\\n residual = 0.\\n if H<L:\\n # residual = np.sum(np.sum(Y**2)-np.sum(s**2))\\n residual = torch.sum(torch.sum(Y**2)-torch.sum(s**2))\\n\\n #Estimation of the variance when sigma2 is unspecified\\n if sigma2 is None: \\n xubar = (1+tauubar)*(1+alpha/tauubar)\\n eH_ub = int(np.min([np.ceil(L/(1+alpha))-1, H]))-1\\n\\n upper_bound = (torch.sum(s**2)+residual)/(L*M)\\n lower_bound = np.max([s[eH_ub+1]**2/(M*xubar), torch.mean(s[eH_ub+1:]**2)/M])\\n\\n scale = 1.#/lower_bound\\n s = s*np.sqrt(scale)\\n residual = residual*scale\\n lower_bound = float(lower_bound)*scale\\n upper_bound = float(upper_bound)*scale\\n\\n sigma2_opt = minimize_scalar(EVBsigma2, args=(L,M,s,residual,xubar), bounds=[lower_bound, upper_bound], method='Bounded')\\n sigma2 = sigma2_opt.x\\n\\n #Threshold gamma term\\n threshold = np.sqrt(M*sigma2*(1+tauubar)*(1+alpha/tauubar))\\n\\n pos = torch.sum(s>threshold)\\n if pos == 0: return np.array([])\\n #Formula (15) from [2]\\n d = torch.mul(s[:pos]/2, 1-(L+M)*sigma2/s[:pos]**2 + torch.sqrt( (1-((L+M)*sigma2)/s[:pos]**2)**2 - (4*L*M*sigma2**2)/s[:pos]**4) )\\n return torch.diag(d)\",\n \"def test_Gaussian_NB_estimators():\",\n \"def _sigma_ee_nonrel(self,gam,eps):\\n s0 = 4 * r0**2 * alpha / (15 * eps)\\n x = 4 * eps / (gam**2 - 1)\\n sigma_nonrel = s0 * self._F(x,gam)\\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\\n return sigma_nonrel / mec2_unit\",\n \"def compute_derbeko_2007_gibbs_bound(empirical_gibbs_risk, m, N, KLQP, delta=0.05): \\n m_over_N = float(m)/N \\n log_complexity_term = 7 * log( m*(N+1) )\\n return empirical_gibbs_risk + sqrt( ((1.-m_over_N)/(2*m-2)) * (KLQP + log_complexity_term - log(delta) ) )\",\n \"def Bates_Granger_5(df_train, df_test, W):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n\\n # check whether there is enough observations, so sigma is invertible\\n if K > T:\\n raise ValueError('No. forecasts must be <= length of training sample')\\n\\n # forecast errors\\n errors = df_train.iloc[:, 1:].subtract(df_train.iloc[:, 0], axis=0)\\n\\n # initialize the covariance matrix sigma\\n sigma = np.full((K, K), fill_value=0, dtype=float)\\n\\n # exponential error weights\\n error_w = np.full(T, fill_value=W, dtype=float)**(np.arange(T)+1)\\n error_w_sum = error_w.sum()\\n\\n # fill the covariance matrix sigma\\n for i in range(K):\\n\\n for j in range(K):\\n\\n # elements in sigma matrix are weighted with W\\n sigma[i, j] = np.dot(error_w*errors.iloc[:, i],\\n errors.iloc[:, j]) / error_w_sum\\n\\n # combining weights\\n nominator = np.linalg.solve(sigma, np.full(K, fill_value=1))\\n denominator = np.dot(np.full(K, fill_value=1), nominator)\\n comb_w = nominator / denominator\\n\\n # censoring the combining weights\\n for i in range(K):\\n if comb_w[i] < 0:\\n comb_w[i] = 0\\n if comb_w[i] > 1:\\n comb_w[i] = 1\\n\\n # rescale the weights so that their sum equals 1\\n comb_w = comb_w/comb_w.sum()\\n\\n # predictions\\n df_pred = pd.DataFrame({\\\"Bates-Granger (5)\\\": df_test.dot(comb_w)})\\n\\n return df_pred\",\n \"def fd(model, EPS=0.01):\\n\\n N = model.N\\n f = model.f\\n gW_fd, gb_fd = zeros((N, N)), zeros((N))\\n for x, y in model.train:\\n for i in xrange(N):\\n # get gW_fd\\n for j in xrange(N):\\n model.W[i, j] += EPS\\n val1 = f(model.W, model.b)\\n model.W[i, j] -= 2*EPS\\n val2 = f(model.W, model.b)\\n model.W[i, j] += EPS\\n gW_fd[i, j] = (val1 - val2) / (2 * EPS)\\n \\n # get gb_fd\\n model.b[i] += EPS\\n val1 = f(model.W, model.b)\\n model.b[i] -= 2*EPS\\n val2 = f(model.W, model.b)\\n model.b[i] += EPS\\n gb_fd[i] = (val1 - val2) / (2 * EPS)\\n\\n \\n return gW_fd, gb_fd\",\n \"def multiple_premia_gbm(nobs=2000):\\r\\n sigma = .15\\r\\n\\r\\n price = 1\\r\\n strike = np.exp(np.linspace(-.1, .1, nobs))\\r\\n riskfree, maturity = 0, 30/365\\r\\n moneyness = np.log(strike/price) - riskfree * maturity\\r\\n call = np.ones_like(moneyness).astype(bool)\\r\\n call[moneyness < 0] = False\\r\\n\\r\\n model = GBM(GBMParam(sigma=sigma), riskfree, maturity)\\r\\n premium = cosmethod(model, moneyness=moneyness, call=call)\\r\\n plt.plot(strike, premium)\\r\\n plt.show()\",\n \"def cal_gaussian_process(b, sigma2, X_train, y_train, X_test):\\n n = X_train.shape[0]\\n p = X_test.shape[0]\\n\\n K_n = np.array([[kernel(X_train[i], X_train[j], b) for i in range(n)] for j in range(n)])\\n inv = np.linalg.inv(np.diag([sigma2] * n) + K_n)\\n miu = np.zeros(p)\\n Sigma = np.zeros(p)\\n \\n for j in range(p): # for every new point x0 in testing data.\\n x0 = X_test[j]\\n K_Dn = np.zeros(n) # initialize K_Dn \\n for i in range(n):\\n K_Dn[i] = kernel(X_train[i], x0, b) # calculate every item in K_Dn\\n \\n miu[j] = K_Dn.dot(inv).dot(y_train)[0] # calculate new distribution parameters\\n Sigma[j] = sigma2 + kernel(x0, x0, b) - K_Dn.dot(inv).dot(K_Dn.T)\\n \\n return miu, Sigma\",\n \"def BraggEnergy(ID,hkl,twotheta):\\n ID=goodID(ID)\\n d=dSpace(ID,hkl)\\n l=2*d*sind(twotheta/2.0)\\n E=lam2E(l)\\n return E\",\n \"def simulationTestGaussian2(params):\\r\\n x = gaussian(params[0], params[1], mu-3.5*sigma, mu+3.5*sigma)\\r\\n error = np.sum(np.power(optimal - x, 2))/optimal.shape[0]\\r\\n return 1/error\",\n \"def _sigma_2(gam, eps):\\n s0 = r0**2 * alpha / (3 * eps) / mec2_unit\\n\\n s1_1 = 16 * (1 - eps + eps**2) * np.log(gam / eps)\\n s1_2 = -1 / eps**2 + 3 / eps - 4 - 4 * eps - 8 * eps**2\\n s1_3 = -2 * (1 - 2 * eps) * np.log(1 - 2 * eps)\\n s1_4 = 1 / (4 * eps**3) - 1 / (2 * eps**2) + 3 / eps - 2 + 4 * eps\\n s1 = s1_1 + s1_2 + s1_3 * s1_4\\n\\n s2_1 = 2 / eps\\n s2_2 = (4 - 1 / eps + 1 / (4 * eps**2)) * np.log(2 * gam)\\n s2_3 = -2 + 2 / eps - 5 / (8 * eps**2)\\n s2 = s2_1 * (s2_2 + s2_3)\\n\\n return s0 * np.where(eps <= 0.5, s1, s2) * heaviside(gam - eps)\",\n \"def get_sigma_psf(self):\\n pass\",\n \"def energy(self):\\n nocc, ntot, gmo, e = self.nocc, self.ntot, self.gmo, self.e\\n\\n Ec = 0.0\\n for i in range(nocc):\\n for j in range(nocc):\\n for a in range(nocc, ntot):\\n for b in range(nocc, ntot):\\n Ec += gmo[i, a, j, b]*(2*gmo[i, a, j, b] - gmo[i, b, j, a])/\\\\\\n (e[i] + e[j] - e[a] - e[b])\\n\\n self.Ec = Ec\\n self.E_mp2 = Ec + self.E_scf\\n\\n print('@MP2 correlation energy: {:15.10f}\\\\n'.format(self.Ec))\\n print('@Total MP2 energy: {:15.10f}\\\\n'.format(self.E_mp2))\\n\\n return self.E_mp2\",\n \"def compute_GS(GMtcs):\\n\\n GS = np.mean(GMtcs,axis=0) #average over voxels\\n\\n return GS\",\n \"def se_beta(self):\\n return self._se_beta\",\n \"def get_energy(edr, annealing_times, energy_type = 'Potential', out_fig = 'energy_distribution.svg'): # Could be Total-Energy\\n fig, ax = plt.subplots(figsize = (16,9))\\n data = pd.DataFrame()\\n xvg_tmp_file = tempfile.NamedTemporaryFile(suffix='.xvg')\\n energy = []\\n iterator = range(0, len(annealing_times)-1, 2)\\n\\n for state, index in tqdm.tqdm(enumerate(iterator), total=len(iterator)):#enumerate(iterator):# # the calculation is per pair of times, beetween the first to time the temperature was keep constant, then the system was heated and repeated again.\\n run = tools.run(f\\\"export GMX_MAXBACKUP=-1; echo {energy_type} | gmx energy -f {edr} -b {annealing_times[index]} -e {annealing_times[index + 1]} -o {xvg_tmp_file.name} | grep \\\\'{energy_type.replace('-',' ')}\\\\'\\\")\\n energy.append(float(run.stdout.split()[-5]))\\n \\\"\\\"\\\"\\n Energy Average Err.Est. RMSD Tot-Drift\\n -------------------------------------------------------------------------------\\n Potential -1.30028e+06 -- 1682.1 -2422.24 (kJ/mol)\\n Total Energy -952595 -- 2606.81 -3688.3 (kJ/mol)\\n \\\"\\\"\\\"\\n # Getting the histograms and checking for the same len in all intervals\\n if state == 0:\\n data[state] = xvg.XVG(xvg_tmp_file.name).data[:,1]\\n else:\\n xvg_data = xvg.XVG(xvg_tmp_file.name).data[:,1]\\n if xvg_data.shape[0] > data.shape[0]:\\n data[state] = xvg_data[:data.shape[0]]\\n else:\\n data = data.iloc[:xvg_data.shape[0]]\\n data[state] = xvg_data\\n\\n\\n print(data)\\n sns.histplot(data = data, element='poly', stat = 'probability', axes = ax)\\n ax.set(\\n xlabel = f'{energy_type} [kJ/mol]',\\n ylabel = 'Probability',\\n title = f'Distribution of {energy_type}')\\n # plt.show()\\n fig.savefig(out_fig)\\n return energy\",\n \"def effective_energy_difference(self, macrostate_class: str, state1: str, state2: str) -> float:\\n\\n macrostate_class = str(macrostate_class)\\n\\n microstates_1_indices = self.states[self.states[macrostate_class] == state1].index\\n microstates_2_indices = self.states[self.states[macrostate_class] == state2].index\\n\\n energies_1 = self.g_mle[microstates_1_indices]\\n energies_2 = self.g_mle[microstates_2_indices]\\n\\n std_err_1 = self.std_errors[microstates_1_indices]\\n std_err_2 = self.std_errors[microstates_2_indices]\\n\\n z1 = np.exp(-energies_1)\\n z2 = np.exp(-energies_2)\\n\\n std_err = np.sqrt((std_err_1**2 * z1**2).sum() / z1.sum()**2 + (std_err_2**2 * z2**2).sum() / z2.sum()**2)\\n diff = np.log(np.sum(z1) / np.sum(z2))\\n\\n return diff, std_err\",\n \"def epsilon_GaAs_TISB(l_onde,n_2D,w12,f,Ts):\\n ev = 1.60218e-19\\n eps0 = 8.854e-12\\n c = 3e8\\n \\n v = 1e4/l_onde\\n \\n epsinf = 11;\\n wL = 291.2;\\n wT = 267.89;\\n T = 2.54;\\n epsGaAs = epsinf*(1+(wL**2-wT**2)/(wT**2-v**2-1j*T*v)) \\n \\n meff = 9.109e-31*0.067\\n ns = (n_2D/22e-7)*1e6 # passage de cm-2 ? m-2 On fixe l'epaisseur du puit a 20nm, ordre de grandeur...\\n wp = np.sqrt((ns*ev**2)/(meff*eps0*epsinf)) # SI\\n wp = wp*1e-2/(2*np.pi*c) #cm-1\\n if w12==0:\\n wp=0\\n else:\\n w12 = 1e4/w12 # cm-1\\n \\n \\n eps_ssbandes = epsinf*(f*wp**2./(w12**2-v**2-1j*Ts*v))\\n eps = epsGaAs + eps_ssbandes\\n return eps.real + 1j*np.abs(eps.imag)\",\n \"def sample_values(self, positions, gibbs):\\n \\\"\\\"\\\"Sample important values\\\"\\\"\\\"\\n\\n if gibbs:\\n self.local_energy = self.h.local_energy_gibbs(positions)\\n self.accumulate_energy += self.h.local_energy_gibbs(positions)\\n self.accumulate_energy_sq += self.local_energy*self.local_energy\\n gradient_wf_a = 0.5*self.w.gradient_wavefunction_a(positions)\\n gradient_wf_b = 0.5*self.w.gradient_wavefunction_b(positions)\\n gradient_wf_W = 0.5*self.w.gradient_wavefunction_W(positions)\\n else:\\n self.local_energy = self.h.local_energy(positions)\\n self.accumulate_energy += self.h.local_energy(positions)\\n self.accumulate_energy_sq += self.local_energy*self.local_energy\\n gradient_wf_a = self.w.gradient_wavefunction_a(positions)\\n gradient_wf_b = self.w.gradient_wavefunction_b(positions)\\n gradient_wf_W = self.w.gradient_wavefunction_W(positions)\\n # self.local_energy = self.h.local_energy_numerical(positions)\\n # self.accumulate_energy += self.h.local_energy_numerical(positions)\\n # gradient_wf_a = np.zeros(self.w.M)\\n # gradient_wf_b = np.zeros(self.w.N)\\n # gradient_wf_W = np.zeros((self.w.M, self.w.N))\\n\\n self.accumulate_psi_term_a += gradient_wf_a\\n self.accumulate_psi_term_b += gradient_wf_b\\n self.accumulate_psi_term_W += gradient_wf_W\\n self.accumulate_both_a += gradient_wf_a*self.local_energy\\n self.accumulate_both_b += gradient_wf_b*self.local_energy\\n self.accumulate_both_W += gradient_wf_W*self.local_energy\",\n \"def Bo_Bosol_calc(self):\\n self.Bosol = (self.g*self.alpha * self.srflx ) \\n #ZEROS FOR T3W APPLICATION\\n self.Bo = np.zeros([self.b.shape[0]])\",\n \"def standard_deviation(self):\\r\\n\\t\\treturn self.variance()**(1/2)\",\n \"def gauss_kernel(n_fwhm,sigma):\\n\\n x_length = int(n_fwhm * sigma + 0.5) #Add 0.5 to approximate to nearest integer\\n y_length = x_length\\n \\n \\n x, y = mgrid[-x_length:x_length+1, -y_length:y_length+1]\\n g = numpy.exp(-(x**2/(2*(float(sigma)**2))+y**2/(2*(float(sigma)**2))))\\n return g / g.sum()\",\n \"def compute_hydration_energies(molecules, parameters):\\n\\n energies = dict() # energies[index] is the computed solvation energy of molecules[index]\\n\\n platform = openmm.Platform.getPlatformByName(\\\"Reference\\\")\\n\\n for molecule in molecules:\\n # Create OpenMM System.\\n system = openmm.System()\\n for atom in molecule.GetAtoms():\\n mass = OEGetDefaultMass(atom.GetAtomicNum())\\n system.addParticle(mass * units.amu)\\n\\n # Add nonbonded term.\\n # nonbonded_force = openmm.NonbondedSoftcoreForce()\\n # nonbonded_force.setNonbondedMethod(openmm.NonbondedForce.NoCutoff)\\n # for atom in molecule.GetAtoms():\\n # charge = 0.0 * units.elementary_charge\\n # sigma = 1.0 * units.angstrom\\n # epsilon = 0.0 * units.kilocalories_per_mole\\n # nonbonded_force.addParticle(charge, sigma, epsilon)\\n # system.addForce(nonbonded_force)\\n\\n # Add GBVI term\\n # gbvi_force = openmm.GBVISoftcoreForce()\\n gbvi_force = openmm.GBVIForce() \\n gbvi_force.setNonbondedMethod(openmm.GBVIForce.NoCutoff) # set no cutoff\\n gbvi_force.setSoluteDielectric(1)\\n gbvi_force.setSolventDielectric(78)\\n\\n # Use scaling method.\\n # gbvi_force.setBornRadiusScalingMethod(openmm.GBVISoftcoreForce.QuinticSpline)\\n # gbvi_force.setQuinticLowerLimitFactor(0.75)\\n # gbvi_force.setQuinticUpperBornRadiusLimit(50.0*units.nanometers)\\n\\n # Build indexable list of atoms.\\n atoms = [atom for atom in molecule.GetAtoms()] \\n \\n # Assign GB/VI parameters.\\n for atom in molecule.GetAtoms(): \\n atomtype = atom.GetStringData(\\\"gbvi_type\\\") # GBVI atomtype\\n charge = atom.GetPartialCharge() * units.elementary_charge\\n radius = parameters['%s_%s' % (atomtype, 'radius')] * units.angstroms\\n gamma = parameters['%s_%s' % (atomtype, 'gamma')] * units.kilocalories_per_mole \\n # gamma *= -1.0 # DEBUG\\n lambda_ = 1.0 # fully interacting\\n # gbvi_force.addParticle(charge, radius, gamma, lambda_) # for GBVISoftcoreForce\\n gbvi_force.addParticle(charge, radius, gamma) # for GBVIForce\\n\\n # Add bonds.\\n for bond in molecule.GetBonds():\\n # Get atom indices.\\n iatom = bond.GetBgnIdx()\\n jatom = bond.GetEndIdx()\\n # Get bond length.\\n (xi, yi, zi) = molecule.GetCoords(atoms[iatom])\\n (xj, yj, zj) = molecule.GetCoords(atoms[jatom])\\n distance = math.sqrt((xi-xj)**2 + (yi-yj)**2 + (zi-zj)**2) * units.angstroms\\n # Identify bonded atoms to GBVI.\\n gbvi_force.addBond(iatom, jatom, distance)\\n\\n # Add the force to the system.\\n system.addForce(gbvi_force)\\n \\n # Build coordinate array.\\n natoms = len(atoms)\\n coordinates = units.Quantity(numpy.zeros([natoms, 3]), units.angstroms)\\n for (index,atom) in enumerate(atoms):\\n (x,y,z) = molecule.GetCoords(atom)\\n coordinates[index,:] = units.Quantity(numpy.array([x,y,z]),units.angstroms) \\n \\n # Create OpenMM Context.\\n timestep = 1.0 * units.femtosecond # arbitrary\\n integrator = openmm.VerletIntegrator(timestep)\\n context = openmm.Context(system, integrator, platform)\\n\\n # Set the coordinates.\\n context.setPositions(coordinates)\\n \\n # Get the energy\\n state = context.getState(getEnergy=True)\\n energies[molecule] = state.getPotentialEnergy()\\n\\n return energies\",\n \"def hatom_energy(beta, nmax=4000):\\n # calculate energy levels\\n myn = np.arange(1, nmax)\\n en = -1./(2*myn**2)\\n # calculate Boltzmann factors\\n mult = myn**2 # multiplicity due to angular states\\n bfac = mult*np.exp(-beta*en)\\n part = bfac.sum()\\n return np.dot(bfac, en)/part\",\n \"def bondEfs(self,k):\\n Alpha=6.4\\n b=3.0\\n c=1.7\\n nu=1.13\\n gamma=self.om0*self.h\\n q=k/gamma\\n return self.bondEfsNorm/(1.0+(Alpha*q +(b*q)**1.5 + (c*q)**2)**nu)**(2.0/nu)\",\n \"def calc_free_g(energies, temperatures):\\n pass\",\n \"def FO2(lam):\\n return 1.096 + 1.385 *1e-3 *lam**(-2) + 1.448 *1e-4 *lam**(-4)\",\n \"def gibbs(input_dim,variance=1., mapping=None):\\r\\n part = parts.gibbs.Gibbs(input_dim,variance,mapping)\\r\\n return kern(input_dim, [part])\",\n \"def get_neb_energies_forces(self):\\n energies = []\\n forces = []\\n for i in self.path.inner_images:\\n e, f = self.model.predict_energy_and_forces(i)\\n energies.append(e)\\n forces.append(f)\\n self.model_forces = forces\\n self.energies[1:-1] = energies\\n self.middle_tangents = self.path.get_unit_tangents(self.energies)\\n f1 = [i-np.sum(i*j)*j for i, j in zip(self.model_forces, self.middle_tangents)]\\n f2 = [i*j for i, j in zip(self.path.spring_forces, self.middle_tangents)]\\n self.forces = [i+j for i, j in zip(f1, f2)]\",\n \"def KsCB(fiter):\\n \\n fiter.mean = RooRealVar(\\\"mean1\\\",\\\"mean1\\\",490,510)#5168.)\\n fiter.sigma = RooRealVar(\\\"sigma\\\",\\\"sigma\\\", 2,6)#20., 12.,40.)#35.)\\n \\n fiter.n = RooRealVar(\\\"exponent\\\", \\\"exponent\\\",1.)#, 0., 12 )\\n \\n fiter.a = RooRealVar(\\\"transition\\\",\\\"transition\\\", 0.5, 3) ## Transition point, in sigmas\\n fiter.sig = RooCBShape(\\\"Sigmodel\\\",\\\"Sigmodel\\\", fiter.mass, fiter.mean, fiter.sigma, fiter.a, fiter.n) \\n return 1\",\n \"def _calc_Em(self):\\n return (self.parameters.E0 +\\n self.x * sqrt2 * self.parameters.sigma * self.mt)\",\n \"def get_EG(vals):\\n return get_tau_gap(vals)/2\",\n \"def energy(self):\\n nocc, gmo, e = self.nocc, self.gmo, self.e\\n\\n Ec = 0.0\\n for i in range(nocc):\\n for j in range(nocc):\\n for a in range(nocc, len(e)):\\n for b in range(nocc, len(e)):\\n Ec += (1/4.0) * gmo[i, j, a, b]**2 / (e[i]+e[j]-e[a]-e[b])\\n\\n self.Ec = Ec\\n self.E_mp2 = Ec + self.E_scf\\n\\n print('@MP2 correlation energy: {:15.10f}\\\\n'.format(self.Ec))\\n print('@Total MP2 energy: {:15.10f}\\\\n'.format(self.E_mp2))\\n\\n return self.E_mp2\",\n \"def estimate_wf_energy(self):\\n nblocks = 50\\n blocksize = int(len(self.local_energies) / nblocks)\\n enmean = 0\\n enmeansq = 0\\n enmean_unblocked = 0\\n enmeansq_unblocked = 0\\n\\n for b in range(nblocks):\\n eblock = 0.0\\n for j in range(b * blocksize, (b + 1) * blocksize):\\n eblock += self.local_energies[j].real\\n delta = self.local_energies[j].real - enmean_unblocked\\n enmean_unblocked += delta / (j + 1)\\n delta2 = self.local_energies[j].real - enmean_unblocked\\n # delta != delta2 because of update to enmean_unblocked\\n enmeansq_unblocked += delta * delta2\\n eblock /= blocksize\\n delta = eblock - enmean\\n enmean += delta / (b + 1)\\n delta2 = eblock - enmean\\n # delta != delta2 because of update to enmean\\n enmeansq += delta * delta2\\n\\n enmeansq /= (nblocks - 1)\\n enmeansq_unblocked /= (nblocks * blocksize - 1)\\n est_avg = enmean / self.n_visible\\n est_error = math.sqrt(enmeansq / nblocks) / self.n_visible\\n self.nqs_energy = np.squeeze(est_avg)\\n self.nqs_energy_err = np.squeeze(est_error)\\n\\n energy_report = 'Estimated average energy per spin: {} +/- {}'\\n print(energy_report.format(est_avg, est_error))\\n bin_report = 'Estimate from binning analysis. ' + \\\\\\n '{} bins of {} samples each'\\n print(bin_report.format(nblocks, blocksize))\\n autocorrelation = 'Estimated autocorrelation time is {}'\\n self.correlation_time = 0.5 * blocksize * enmeansq / enmeansq_unblocked\\n print(autocorrelation.format(self.correlation_time))\",\n \"def MLE(self, svd: bool = True) -> None:\\n\\n N = len(self.states.name)\\n\\n if svd:\\n self._MLE_SVD(N)\\n else:\\n self._MLE_NR(N)\\n\\n self.std_errors = np.sqrt(np.diagonal(self.covariance_matrix))\\n assert np.all(self.std_errors >= 0), \\\"Standard errors for state free energies should be positive\\\"\\n\\n self.g_mle = self.MLE_res - self.MLE_res[0]\\n self.prob_mle = pd.DataFrame(np.exp(-self.g_mle) / np.sum(np.exp(-self.g_mle)), columns=[\\\"probability\\\"])\\n self.prob_mle[\\\"name\\\"] = self.states.name\\n\\n self.deltas = np.zeros((N, N))\\n self.dGs = np.zeros((N, N))\\n\\n self.deltas[:] = np.nan\\n\\n for r in self.graph.index:\\n state1, state2, _, _, _, _ = self.graph.iloc[r]\\n i = self.states[self.states.name == state1].index[0]\\n j = self.states[self.states.name == state2].index[0]\\n\\n edge_attr = self.cycle.edges()[(state1, state2)]\\n deltaij = edge_attr['energy'] # measured difference\\n\\n self.deltas[i, j] = deltaij\\n self.deltas[j, i] = -deltaij\\n\\n for _i, dGi in enumerate(self.g_mle):\\n for _j, dGj in enumerate(self.g_mle):\\n self.dGs[_i, _j] = self.g_mle[_j] - self.g_mle[_i]\",\n \"def y_envelope_ef(t, args):\\n anharms = args['anharms']\\n couplings = args['couplings']\\n e = args['e']\\n g = args['g']\\n couplings = [c/g for c in couplings]\\n coeff = -np.sqrt(couplings[e-1]**2 \\\\\\n + (anharms[e+2]**2/anharms[e-1]**2) \\\\\\n * couplings[e+1]**2) / (2*anharms[e+2])\\n return args['A'] * coeff * truncated_gaussian_derivative(t,\\n args['sigma'],\\n args['t_g']/2,\\n args['t_n']/2)\",\n \"def gaussian_fit(self):\\r\\n\\r\\n self.df5 = pd.DataFrame(columns=['Slit Number', 'Centre', 'Centre_err', 'Sigma', 'Sigma_err', 'FWHM', 'FWHM_err', 'Height', 'Height_err'])\\r\\n QDot_slits = self.QDot_detection()\\r\\n\\r\\n if len(QDot_slits) > 0: \\r\\n self.plot_data = pd.DataFrame(columns=[f\\\"{QDot_slits[0]}\\\"], index=self.energies)\\r\\n else:\\r\\n self.plot_data = pd.DataFrame(index=self.energies)\\r\\n\\r\\n for slit_number in QDot_slits:\\r\\n sel = self.df4[f'{slit_number}']\\r\\n self.plot_data[f'{slit_number}'] = sel\\r\\n \\r\\n # Makes a good first guess for the fit values of the gaussian\\r\\n max_intensity = max(sel)\\r\\n central_energy = sel[sel==max_intensity].index.values\\r\\n central_energy = central_energy[0]\\r\\n\\r\\n # Fits a gaussian model to the selected data and shows the output\\r\\n gauss = models.GaussianModel()\\r\\n fit = gauss.fit(sel, x=self.energies, weights=1 / np.sqrt(sel), center = central_energy, amplitude = max_intensity, sigma = 1, nan_policy= 'omit')\\r\\n \\r\\n self.plot_data[f'{slit_number} best fit'] = fit.best_fit\\r\\n\\r\\n # Appends the fit data for the variables to a new dataframe and shows the fit results with errors\\r\\n fit_variables = [slit_number]\\r\\n for key in fit.params:\\r\\n if key in ['center', 'sigma', 'fwhm', 'height']:\\r\\n fit_variables.append(fit.params[key].value)\\r\\n fit_variables.append(fit.params[key].stderr)\\r\\n \\r\\n self.df5 = self.df5.append({'Slit Number': fit_variables[0], 'Centre': fit_variables[1], 'Centre_err': fit_variables[2], 'Sigma': fit_variables[3], 'Sigma_err': fit_variables[4], 'FWHM': fit_variables[5], 'FWHM_err': fit_variables[6], 'Height': fit_variables[7], 'Height_err': fit_variables[8]}, ignore_index=True)\\r\\n \\r\\n return self.plot_data, self.df5\",\n \"def compute_terms(Y, latent_means, latent_Sigmas, B1, B2, mu, g1, g2):\\n # these are the 's' parameters when nu=e_q, beta=0\\n ss = [-mi[-1]*si[-1] for mi, si in zip(latent_means, latent_variances)]\\n\\n a1 = [-np.trace(Sigma) - np.matmul(mi.T, mi) + np.log(LA.det(Sigma)) for Sigma, mi in zip(latent_Sigmas, latent_means)]\\n\\n B_times_mean = [np.matmul(B1+B2, mi) for mi in latent_means]\\n a2 = [\\n np.matmul(\\n yi - mu, \\n (yi - mu).T\\n ).item() - np.matmul(\\n yi - mu, \\n Bm\\n ).item() for yi, Bm in zip(Y, B_times_mean)\\n ]\\n\\n a3_scalars = [SQRT_PI_OVER_2*erfc(si/ROOT2)+si*np.exp(-si**2/2) for si in ss]\\n a3 = [\\n g1*sc*np.trace(\\n np.matmul(\\n B1.T, \\n np.matmul(B1, Sigma)\\n )\\n ).item() for Sigma, sc in zip(latent_Sigmas, a3_scalars)\\n ]\\n\\n a4_scalars = [SQRT_PI_OVER_2*(erf(si/ROOT2)+1)-si*np.exp(-si**2/2) for si in ss]\\n a4 = np.array([\\n g2*sc*np.trace(\\n np.matmul(\\n B2.T, \\n np.matmul(\\n B2, \\n Sigma\\n )\\n )\\n ).item() for Sigma, sc in zip(latent_Sigmas, a4_scalars)\\n ],\\n dtype='object'\\n )\\n\\n a5_inner = [\\n erfc(si/ROOT2)*np.matmul(\\n B1.T, \\n B1\\n ) + (erf(si/ROOT2)+1)*np.matmul(\\n B2.T, \\n B2\\n ) for si in ss]\\n a5 = [\\n SQRT_PI_OVER_2*np.matmul(mi.T, np.matmul(Bi, mi)) for mi, Bi in zip(latent_means, a5_inner)\\n ]\\n\\n # convert all list of 1d arrays to lists of floats\\n a1 = [element.item()*0.5 for element in a1]\\n a3 = [element.item() for element in a3]\\n a4 = [element.item() for element in a4]\\n a5 = [element.item() for element in a5]\\n\\n return a1, a2, a3, a4, a5\",\n \"def _calc_msve(self):\\n v = []\\n for state in self._env.state_iterator():\\n feature_vector = self._features.vector(state)\\n v.append(utils.state_value(feature_vector, self.theta))\\n\\n self.msve.append(utils.rmse(v, self._true_values))\",\n \"def gauss_kern(sigma,h):\\n h1 = h\\n h2 = h\\n x, y = np.mgrid[0:h2, 0:h1]\\n x = x-h2/2\\n y = y-h1/2\\n # sigma = 10.0\\n g = np.exp( -( x**2 + y**2 ) / (2*sigma**2) )\\n return g / g.sum()\",\n \"def _fv(self):\\n return self.beta * (self.x ** self.c)\",\n \"def test_freesolv_gbsa_energies(\\n self,\\n gbsa_model,\\n is_periodic,\\n freesolv_id,\\n forcefield_version,\\n allow_undefined_stereo,\\n ):\\n import parmed as pmd\\n\\n from openff.toolkit._tests.utils import (\\n compare_system_energies,\\n create_system_from_amber,\\n get_context_potential_energy,\\n get_freesolv_file_path,\\n )\\n\\n mol2_file_path, _ = get_freesolv_file_path(freesolv_id, forcefield_version)\\n\\n # Load molecules.\\n molecule = Molecule.from_file(\\n mol2_file_path, allow_undefined_stereo=allow_undefined_stereo\\n )\\n\\n # Give each atom a unique name, otherwise OpenMM will complain\\n for idx, atom in enumerate(molecule.atoms):\\n atom.name = f\\\"{atom.symbol}{idx}\\\"\\n positions = to_openmm(molecule.conformers[0])\\n\\n off_gbsas = {\\n \\\"HCT\\\": \\\"test_forcefields/GBSA_HCT-1.0.offxml\\\",\\n \\\"OBC1\\\": \\\"test_forcefields/GBSA_OBC1-1.0.offxml\\\",\\n \\\"OBC2\\\": \\\"test_forcefields/GBSA_OBC2-1.0.offxml\\\",\\n }\\n # Create OpenFF System with the current toolkit.\\n ff = ForceField(\\n get_data_file_path(\\\"test_forcefields/test_forcefield.offxml\\\"),\\n get_data_file_path(off_gbsas[gbsa_model]),\\n )\\n\\n # OpenMM 7.7 and older don't properly handle parsing prmtop files with a GBSA model and\\n # a switching function; this bug may be fixed but for testing purposes, simply turn off\\n # the switching function\\n ff[\\\"vdW\\\"].switch_width = 0.0 * unit.angstrom\\n\\n off_top = molecule.to_topology()\\n if is_periodic:\\n off_top.box_vectors = (\\n (30.0, 0, 0),\\n (0, 30.0, 0),\\n (0, 0, 30.0),\\n ) * unit.angstrom\\n\\n else:\\n off_top.box_vectors = None\\n\\n off_omm_system = ff.create_openmm_system(\\n off_top, charge_from_molecules=[molecule]\\n )\\n\\n off_nonbonded_force = [\\n force\\n for force in off_omm_system.getForces()\\n if isinstance(force, openmm.NonbondedForce)\\n ][0]\\n\\n omm_top = off_top.to_openmm()\\n pmd_struct = pmd.openmm.load_topology(omm_top, off_omm_system, positions)\\n prmtop_file = NamedTemporaryFile(suffix=\\\".prmtop\\\")\\n inpcrd_file = NamedTemporaryFile(suffix=\\\".inpcrd\\\")\\n pmd_struct.save(prmtop_file.name, overwrite=True)\\n pmd_struct.save(inpcrd_file.name, overwrite=True)\\n\\n openmm_gbsas = {\\n \\\"HCT\\\": openmm.app.HCT,\\n \\\"OBC1\\\": openmm.app.OBC1,\\n \\\"OBC2\\\": openmm.app.OBC2,\\n }\\n\\n # The functional form of the nonbonded force will change depending on whether the cutoff\\n # is None during initialization. Therefore, we need to figure that out here.\\n\\n # WARNING: The NonbondedMethod enums at openmm.app.forcefield and openmm.CustomGBForce\\n # aren't necessarily the same, and could be misinterpreted if the wrong one is used. For\\n # create_system_from_amber, we must provide the app.forcefield version.\\n\\n if is_periodic:\\n amber_nb_method = openmm.app.forcefield.CutoffPeriodic\\n amber_cutoff = off_nonbonded_force.getCutoffDistance()\\n else:\\n amber_nb_method = openmm.app.forcefield.NoCutoff\\n amber_cutoff = None\\n\\n # OpenMM will process False to mean \\\"don't use one\\\" and a quantity as \\\"use with this distance\\\"\\n switching_distance = off_nonbonded_force.getUseSwitchingFunction()\\n if switching_distance:\\n switching_distance = off_nonbonded_force.getSwitchingDistance()\\n\\n (\\n amber_omm_system,\\n amber_omm_topology,\\n amber_positions,\\n ) = create_system_from_amber(\\n prmtop_file.name,\\n inpcrd_file.name,\\n implicitSolvent=openmm_gbsas[gbsa_model],\\n nonbondedMethod=amber_nb_method,\\n nonbondedCutoff=amber_cutoff,\\n gbsaModel=\\\"ACE\\\",\\n implicitSolventKappa=0.0,\\n switchDistance=switching_distance,\\n )\\n\\n # Retrieve the GBSAForce from both the AMBER and OpenForceField systems\\n off_gbsa_forces = list()\\n for force in off_omm_system.getForces():\\n if isinstance(force, openmm.GBSAOBCForce) or isinstance(\\n force, openmm.openmm.CustomGBForce\\n ):\\n off_gbsa_forces.append(force)\\n\\n amber_gbsa_forces = list()\\n for force in amber_omm_system.getForces():\\n if isinstance(force, openmm.GBSAOBCForce) or isinstance(\\n force, openmm.openmm.CustomGBForce\\n ):\\n amber_gbsa_forces.append(force)\\n\\n assert len(amber_gbsa_forces) == len(off_gbsa_forces) == 1\\n\\n off_gbsa_force = off_gbsa_forces[0]\\n amber_gbsa_force = amber_gbsa_forces[0]\\n\\n # We get radius and screen values from each model's getStandardParameters method\\n if gbsa_model == \\\"HCT\\\":\\n gb_params = (\\n openmm.app.internal.customgbforces.GBSAHCTForce.getStandardParameters(\\n omm_top\\n )\\n )\\n elif gbsa_model == \\\"OBC1\\\":\\n gb_params = (\\n openmm.app.internal.customgbforces.GBSAOBC1Force.getStandardParameters(\\n omm_top\\n )\\n )\\n elif gbsa_model == \\\"OBC2\\\":\\n gb_params = (\\n openmm.app.internal.customgbforces.GBSAOBC2Force.getStandardParameters(\\n omm_top\\n )\\n )\\n\\n # Use GB params from OpenMM GBSA classes to populate parameters\\n for idx, (radius, screen) in enumerate(gb_params):\\n # Keep the charge, but throw out the old radius and screen values\\n q, _, _ = amber_gbsa_force.getParticleParameters(idx)\\n if isinstance(amber_gbsa_force, openmm.GBSAOBCForce):\\n # Note that in GBSAOBCForce, the per-particle parameters are separate\\n # arguments, while in CustomGBForce they're a single iterable\\n amber_gbsa_force.setParticleParameters(idx, q, radius, screen)\\n\\n elif isinstance(amber_gbsa_force, openmm.CustomGBForce):\\n # !!! WARNING: CustomAmberGBForceBase expects different per-particle parameters\\n # depending on whether you use addParticle or setParticleParameters. In\\n # setParticleParameters, we have to apply the offset and scale BEFORE setting\\n # parameters, whereas in addParticle, it is applied afterwards, and the particle\\n # parameters are not set until an auxillary finalize() method is called. !!!\\n amber_gbsa_force.setParticleParameters(\\n idx, (q, radius - 0.009, screen * (radius - 0.009))\\n )\\n\\n # Put the GBSA force into a separate group so we can specifically compare GBSA energies\\n amber_gbsa_force.setForceGroup(1)\\n off_gbsa_force.setForceGroup(1)\\n\\n # Some manual overrides to get the OFF system's NonbondedForce matched up with the AMBER system\\n if is_periodic:\\n off_nonbonded_force.setNonbondedMethod(openmm.NonbondedForce.CutoffPeriodic)\\n else:\\n off_nonbonded_force.setNonbondedMethod(openmm.NonbondedForce.NoCutoff)\\n\\n off_nonbonded_force.setReactionFieldDielectric(1.0)\\n\\n # Create Contexts\\n integrator = openmm.VerletIntegrator(1.0 * openmm_unit.femtoseconds)\\n platform = Platform.getPlatformByName(\\\"Reference\\\")\\n amber_context = openmm.Context(amber_omm_system, integrator, platform)\\n off_context = openmm.Context(\\n off_omm_system, copy.deepcopy(integrator), platform\\n )\\n\\n # Get context energies\\n amber_energy = get_context_potential_energy(amber_context, positions)\\n off_energy = get_context_potential_energy(off_context, positions)\\n # Very handy for debugging\\n # print(openmm.XmlSerializer.serialize(off_gbsa_force))\\n # print(openmm.XmlSerializer.serialize(amber_gbsa_force))\\n # Ensure that the GBSA energies (which we put into ForceGroup 1) are identical\\n # For Platform=OpenCL, we do get \\\"==\\\"-level identical numbers, but for \\\"Reference\\\", we don't.\\n # assert amber_energy[1] == off_energy[1]\\n assert (\\n abs(amber_energy[1] - off_energy[1]) < 1e-5 * openmm_unit.kilojoule_per_mole\\n )\\n\\n # Ensure that all system energies are the same\\n compare_system_energies(\\n off_omm_system,\\n amber_omm_system,\\n positions,\\n by_force_type=False,\\n atol=1.0e-4,\\n )\",\n \"def _excitonic_reorg_energy(self, SS, AG, n):\\n \\n # SystemBathInteraction\\n sbi = AG.get_SystemBathInteraction()\\n # CorrelationFunctionMatrix\\n cfm = sbi.CC\\n \\n rg = 0.0\\n \\n # electronic states corresponding to single excited states\\n elst = numpy.where(AG.which_band == 1)[0]\\n for el1 in elst:\\n reorg = cfm.get_reorganization_energy(el1-1,el1-1)\\n for kk in AG.vibindices[el1]:\\n rg += ((SS[kk,n]**2)*(SS[kk,n]**2)*reorg)\\n return rg\",\n \"def fit_gaussian(position, energy, dx=0.005, a=2, b=1.5, db=0.01, tolerance=0.05, max_iterations=1000):\\n min_energy, max_energy = min(energy), max(energy)\\n x_start, x_range = min(position), max(position) - min(position)\\n x_gauss = np.arange(0, x_range, dx)\\n f_gauss = np.exp(-a * (x_range / 2 - x_gauss) ** 2 + b)\\n delta_energy = abs(max(f_gauss) - max_energy)\\n b_direction = np.sign(max_energy - max(f_gauss))\\n print('E_WHAM: %.3f | E_GAUSS: %.3f | b_direction: %i' % (max_energy, max(f_gauss), b_direction))\\n for i in range(max_iterations):\\n b = b + b_direction * db\\n f_gauss_trial = np.exp(-a * (x_range / 2 - x_gauss) ** 2 + b)\\n delta_energy_trial = abs(max(f_gauss_trial) - max_energy)\\n if delta_energy_trial < tolerance:\\n f_gauss = f_gauss_trial\\n print('Found b value: %.2f with dE: %.3f within tolerance in %i iterations' % (b, delta_energy, i))\\n break\\n elif delta_energy_trial < delta_energy:\\n f_gauss = f_gauss_trial\\n delta_energy = delta_energy_trial\\n print('Finished fitting. %i iterations | dE: %.3f | b_final: %.2f' % (i, delta_energy, b))\\n return (x_gauss + x_start, f_gauss)\",\n \"def get_SB(self):\\n sr = self.sr\\n Sigma = self.snu_at_1GHz / sr\\n self.Sigma = Sigma\\n # set the flag of SB completeness\\n Sigma_in_SI = Sigma * ct._Jy_over_SI_\\n if Sigma_in_SI > 1.e-20:\\n self.is_complete = True\\n return Sigma\",\n \"def bethe_free_energy(self, potential):\\n xn, xe, lpn, lpe, alpha = self(None, full_out=False)\\n fn, fe = potential((xn, xe))\\n bfe = -(tf.reduce_sum((fn + self.tw * lpn) * self.wn * alpha, [2, 3, 4]) +\\n tf.reduce_sum((fe - lpe) * self.we * alpha, [2, 3, 4]))\\n return bfe\",\n \"def N_TB_EB(self, L):\\n if L>2.*min(self.CMB.lMaxT, self.CMB.lMaxP):\\n return 0.\\n # integrand\\n def integrand(x):\\n theta = x[1]\\n l1 = np.exp(x[0])\\n l2 = self.l2(L, l1, theta)\\n if l2<self.CMB.lMin or l2>min(self.CMB.lMaxT, self.CMB.lMaxP):\\n return 0.\\n phi = self.phi(L, l1, theta)\\n result = self.F_EB(l1, l2, phi)*self.CMB.ftotalTE(l1)*self.CMB.ftotalBB(l2)\\n result += 0. #self.F_EB(l2, l1, -phi)*self.CMB.ftotalTB(l1)*self.CMB.ftotalEB(l2)\\n result *= self.F_TB(l1, l2, phi)\\n result *= l1**2\\n result /= (2.*np.pi)**2\\n result *= 2.\\n return result\\n \\n # if first time, initialize integrator\\n if not hasattr(self.N_TB_EB.__func__, \\\"integ\\\"):\\n self.N_TB_EB.__func__.integ = vegas.Integrator([[np.log(self.CMB.lMin), np.log(min(self.CMB.lMaxT, self.CMB.lMaxP))], [0., np.pi]])\\n self.N_TB_EB.integ(integrand, nitn=8, neval=1000)\\n result = self.N_TB_EB.integ(integrand, nitn=1, neval=5000)\\n return result.mean\",\n \"def get_sigma(self):\\n sigma = np.array(np.zeros((4,8)))\\n for i,c in enumerate(self.cellLines):\\n for j,l in enumerate(self.ligands):\\n sigma[i][j] = self.aucs[c][l]['sigma']\\n return sigma\",\n \"def Bates_Granger_2(df_train, df_test, nu=None):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n\\n # the default length of the relevant window is equal to sample length\\n if nu is None:\\n nu = T\\n\\n if nu > T:\\n raise ValueError('Parameter nu must be <= length of training sample')\\n\\n # check whether there is enough observations, so sigma is invertible\\n if nu < K:\\n raise ValueError('Parameter nu must be >= no. of individual forecasts')\\n\\n # forecast errors\\n errors = df_train.iloc[:, 1:].subtract(df_train.iloc[:, 0], axis=0)\\n\\n # initialize the covariance matrix sigma\\n sigma = np.full((K, K), fill_value=0, dtype=float)\\n\\n # fill the covariance matrix sigma\\n for i in range(K):\\n\\n for j in range(K):\\n\\n sigma[i, j] = np.dot(errors.iloc[errors.shape[0]-nu:, i],\\n errors.iloc[errors.shape[0]-nu:, j]) / nu\\n\\n # combining weights\\n nominator = np.linalg.solve(sigma, np.full(K, fill_value=1))\\n denominator = np.dot(np.full(K, fill_value=1), nominator)\\n comb_w = nominator / denominator\\n\\n # censoring the combining weights\\n for i in range(K):\\n if comb_w[i] < 0:\\n comb_w[i] = 0\\n if comb_w[i] > 1:\\n comb_w[i] = 1\\n\\n # rescale the weights so that their sum equals 1\\n comb_w = comb_w/comb_w.sum()\\n\\n # predictions\\n df_pred = pd.DataFrame({\\\"Bates-Granger (2)\\\": df_test.dot(comb_w)})\\n\\n return df_pred\",\n \"def calculate_E0(self) -> float:\\n noisy = self.kernel_eigenvectors_[-1].copy()\\n np.random.shuffle(noisy)\\n\\n kernel_eigenvectors = self.kernel_eigenvectors_[:-1]\\n kernel_eigenvectors.append(noisy)\\n\\n eigenvectors_matrix = scipy.sparse.csr_matrix(\\n np.column_stack([eigenvector for eigenvector in kernel_eigenvectors])\\n )\\n\\n if len(kernel_eigenvectors) == 2:\\n ev0 = kernel_eigenvectors[0]\\n ev1 = kernel_eigenvectors[1]\\n _, Gamma, _ = scipy.sparse.linalg.svds(\\n ev0.T @ ev1, k=self.n_jointly_smooth_functions, which=\\\"LM\\\"\\n )\\n else:\\n _, Gamma, _ = scipy.sparse.linalg.svds(\\n eigenvectors_matrix, k=self.n_jointly_smooth_functions, which=\\\"LM\\\"\\n )\\n\\n Gamma.sort()\\n gamma2 = Gamma[-2]\\n E0 = (1 + gamma2) / 2\\n return E0\",\n \"def test_molecule_energy_gb_no_sa(self, zero_charges, gbsa_model):\\n import parmed as pmd\\n\\n from openff.toolkit._tests.utils import (\\n compare_system_energies,\\n create_system_from_amber,\\n get_context_potential_energy,\\n )\\n\\n # Load an arbitrary molecule from the freesolv set\\n molecule = Molecule.from_file(\\n get_data_file_path(\\\"molecules/FreeSolv/mol2files_sybyl/mobley_1036761.mol2\\\")\\n )\\n\\n molecule.name = (\\n \\\"mobley_1036761\\\" # Name the molecule, otherwise OpenMM will complain\\n )\\n if zero_charges:\\n molecule.partial_charges = (\\n np.zeros(molecule.n_atoms) * unit.elementary_charge\\n )\\n\\n # Give each atom a unique name, otherwise OpenMM will complain\\n for idx, atom in enumerate(molecule.atoms):\\n atom.name = f\\\"{atom.symbol}{idx}\\\"\\n\\n positions = np.concatenate(\\n (molecule.conformers[0], molecule.conformers[0] + (10 * unit.angstrom))\\n )\\n # Create OpenFF System with the current toolkit.\\n\\n off_gbsas = {\\n \\\"HCT\\\": \\\"test_forcefields/GBSA_HCT-1.0.offxml\\\",\\n \\\"OBC1\\\": \\\"test_forcefields/GBSA_OBC1-1.0.offxml\\\",\\n \\\"OBC2\\\": \\\"test_forcefields/GBSA_OBC2-1.0.offxml\\\",\\n }\\n\\n ff = ForceField(\\n get_data_file_path(\\\"test_forcefields/test_forcefield.offxml\\\"),\\n get_data_file_path(off_gbsas[gbsa_model]),\\n )\\n ff.get_parameter_handler(\\\"GBSA\\\").sa_model = None\\n off_top = Topology.from_molecules([molecule, molecule])\\n off_omm_system = ff.create_openmm_system(\\n off_top, charge_from_molecules=[molecule]\\n )\\n\\n omm_top = off_top.to_openmm()\\n pmd_struct = pmd.openmm.load_topology(omm_top, off_omm_system, positions)\\n prmtop_file = NamedTemporaryFile(suffix=\\\".prmtop\\\")\\n inpcrd_file = NamedTemporaryFile(suffix=\\\".inpcrd\\\")\\n pmd_struct.save(prmtop_file.name, overwrite=True)\\n pmd_struct.save(inpcrd_file.name, overwrite=True)\\n\\n openmm_gbsas = {\\n \\\"HCT\\\": openmm.app.HCT,\\n \\\"OBC1\\\": openmm.app.OBC1,\\n \\\"OBC2\\\": openmm.app.OBC2,\\n }\\n\\n (\\n amber_omm_system,\\n amber_omm_topology,\\n amber_positions,\\n ) = create_system_from_amber(\\n prmtop_file.name,\\n inpcrd_file.name,\\n implicitSolvent=openmm_gbsas[gbsa_model],\\n nonbondedMethod=openmm.app.forcefield.NoCutoff,\\n nonbondedCutoff=None,\\n gbsaModel=None,\\n implicitSolventKappa=0.0,\\n )\\n\\n # Retrieve the GBSAForce from both the AMBER and OpenForceField systems\\n off_gbsa_forces = list()\\n for force in off_omm_system.getForces():\\n if isinstance(force, openmm.GBSAOBCForce) or isinstance(\\n force, openmm.openmm.CustomGBForce\\n ):\\n off_gbsa_forces.append(force)\\n\\n amber_gbsa_forces = list()\\n for force in amber_omm_system.getForces():\\n if isinstance(force, openmm.GBSAOBCForce) or isinstance(\\n force, openmm.openmm.CustomGBForce\\n ):\\n amber_gbsa_forces.append(force)\\n\\n assert len(amber_gbsa_forces) == len(off_gbsa_forces) == 1\\n\\n off_gbsa_force = off_gbsa_forces[0]\\n amber_gbsa_force = amber_gbsa_forces[0]\\n\\n # We get radius and screen values from each model's getStandardParameters method\\n if gbsa_model == \\\"HCT\\\":\\n gb_params = (\\n openmm.app.internal.customgbforces.GBSAHCTForce.getStandardParameters(\\n omm_top\\n )\\n )\\n elif gbsa_model == \\\"OBC1\\\":\\n gb_params = (\\n openmm.app.internal.customgbforces.GBSAOBC1Force.getStandardParameters(\\n omm_top\\n )\\n )\\n elif gbsa_model == \\\"OBC2\\\":\\n gb_params = (\\n openmm.app.internal.customgbforces.GBSAOBC2Force.getStandardParameters(\\n omm_top\\n )\\n )\\n # This is only necessary until https://github.com/openmm/openmm/pull/2362 is bundled into a conda release\\n amber_gbsa_force.setSurfaceAreaEnergy(0)\\n\\n # Use GB params from OpenMM GBSA classes to populate parameters\\n for idx, (radius, screen) in enumerate(gb_params):\\n # Keep the charge, but throw out the old radius and screen values\\n q, _, _ = amber_gbsa_force.getParticleParameters(idx)\\n\\n if isinstance(amber_gbsa_force, openmm.GBSAOBCForce):\\n # Note that in GBSAOBCForce, the per-particle parameters are separate\\n # arguments, while in CustomGBForce they're a single iterable\\n amber_gbsa_force.setParticleParameters(idx, q, radius, screen)\\n\\n elif isinstance(amber_gbsa_force, openmm.CustomGBForce):\\n # !!! WARNING: CustomAmberGBForceBase expects different per-particle parameters\\n # depending on whether you use addParticle or setParticleParameters. In\\n # setParticleParameters, we have to apply the offset and scale BEFORE setting\\n # parameters, whereas in addParticle, it is applied afterwards, and the particle\\n # parameters are not set until an auxillary finalize() method is called. !!!\\n amber_gbsa_force.setParticleParameters(\\n idx, (q, radius - 0.009, screen * (radius - 0.009))\\n )\\n\\n # Put the GBSA force into a separate group so we can specifically compare GBSA energies\\n amber_gbsa_force.setForceGroup(1)\\n off_gbsa_force.setForceGroup(1)\\n\\n # Create Contexts\\n integrator = openmm.VerletIntegrator(1.0 * openmm_unit.femtoseconds)\\n platform = Platform.getPlatformByName(\\\"Reference\\\")\\n amber_context = openmm.Context(amber_omm_system, integrator, platform)\\n off_context = openmm.Context(\\n off_omm_system, copy.deepcopy(integrator), platform\\n )\\n\\n # Get context energies\\n amber_energy = get_context_potential_energy(amber_context, to_openmm(positions))\\n off_energy = get_context_potential_energy(off_context, to_openmm(positions))\\n\\n # Very handy for debugging\\n # print(openmm.XmlSerializer.serialize(off_gbsa_force))\\n # print(openmm.XmlSerializer.serialize(amber_gbsa_force))\\n\\n # Ensure that the GBSA energies (which we put into ForceGroup 1) are identical\\n # For Platform=OpenCL, we do get \\\"==\\\"-level identical numbers, but for \\\"Reference\\\", we don't.\\n # assert amber_energy[1] == off_energy[1]\\n assert (\\n abs(amber_energy[1] - off_energy[1]) < 1e-5 * openmm_unit.kilojoule_per_mole\\n )\\n\\n # If charges are zero, the GB energy component should be 0, so the total GBSA energy should be 0\\n if zero_charges:\\n assert amber_energy[1]._value == 0.0\\n else:\\n assert amber_energy[1]._value != 0.0\\n\\n # Ensure that all system energies are the same\\n if zero_charges or gbsa_model in [\\\"HCT\\\", \\\"OBC1\\\"]:\\n compare_system_energies(\\n off_omm_system,\\n amber_omm_system,\\n to_openmm(positions),\\n by_force_type=False,\\n )\\n else:\\n compare_system_energies(\\n off_omm_system,\\n amber_omm_system,\\n to_openmm(positions),\\n by_force_type=False,\\n atol=1e-4,\\n )\",\n \"def bethe_free_energy(b_i, b_ij, psi_i, phi_ij, N):\\n triu_idx = numpy.triu_indices(N, 1)\\n # Compute Bethe energy\\n bethe_E = numpy.sum(b_ij[triu_idx] * (numpy.log(b_ij[triu_idx]) -\\n numpy.log(phi_ij[triu_idx]))) \\\\\\n - ((N - 1.) - 1.) * numpy.sum(b_i * (numpy.log(b_i) -\\n numpy.log(psi_i)))\\n return bethe_E\",\n \"def test_class():\\n riskfree = .03\\n maturity = 30/365\\n moneyness = np.linspace(-.04, .04, 10)\\n premium = np.ones_like(moneyness) * .05\\n call = True\\n data = {'riskfree': riskfree, 'maturity': maturity,\\n 'moneyness': moneyness, 'call': call, 'premium': premium}\\n\\n sigma = .13\\n bsm = BSmodel(sigma, data)\\n\\n weights = [.63]\\n means = [-.01, .09]\\n stds = [.16, .05]\\n param = weights + means + stds\\n mbs = MBSmodel(param, data)\\n\\n param_a, param_p, param_c = 4, 1.5, -.05\\n gb2 = GB2model([param_a, param_p, param_c], data)\\n print(gb2.get_pnames())\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.density(moneyness), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.premium(), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.impvol(), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n print('BS objective function = %.4f' % bsm.objective(sigma))\\n print('GB2 objective function = %.4f'\\n % gb2.objective([param_a, param_p, param_c]))\",\n \"def self_energy(gf_imp0, gf_imp):\\n return 1/gf_imp0 - 1/gf_imp\",\n \"def calculate_ft(self):\\n \\n # Create a function which is able to evaluate B**2\\n ffunc = scipy.interpolate.interp1d(self.psigrid, self.e.getF()[self.tind])\\n def b2_func(R, Z, psi):\\n bt = ffunc(psi)/R\\n br = -self.psifunc.ev(R, Z, dy=1)/R\\n bz = self.psifunc.ev(R, Z, dx=1)/R\\n \\n return bt**2 + br**2 + bz**2\\n \\n\\n def b_bmax2(R,Z,psi):\\n b2 = b2_func(R,Z,psi)\\n return b2 / np.max(b2)\\n \\n def b_bmax(R,Z,psi):\\n return np.sqrt(b_bmax2(R,Z,psi))\\n \\n # Evaluate the flux-surface averaged h^2 and h, as required\\n fsa_h2 = self.fs_average(b_bmax2)\\n fsa_h = self.fs_average(b_bmax)\\n \\n # This is the function which gets flux-surface averaged in equation (7)\\n def ftl_func(R,Z,psi):\\n h = b_bmax(R,Z,psi)\\n h2 = b_bmax2(R,Z,psi)\\n \\n return (1 - (np.sqrt(1 - h) * (1 + 0.5 * h)))/h2\\n \\n \\n # Equation 6, 7 in Lin-Liu\\n fs_ftu = 1 - fsa_h2 / fsa_h**2 * (1 - np.sqrt(1 - fsa_h) * (1 + 0.5 * fsa_h))\\n fs_ftl = 1 - fsa_h2 * self.fs_average(ftl_func)\\n # Equation 18, 19 \\n om = 0.75\\n self.fs_ft = om*fs_ftu + (1-om)*fs_ftl\",\n \"def gaussian_elimination_special_case(b):\\n n = len(b)\\n # init new (prime) arrays\\n beta_prime = np.empty(n)\\n beta_prime[0] = 2\\n\\n b_prime = np.empty(n)\\n b_prime[0] = b[0]\\n\\n v = np.empty(n)\\n i_array = np.arange(n)\\n beta_prime = (i_array+2) / (i_array+1)\\n\\n for i in range(1,n):\\n b_prime[i] = b[i] + (b_prime[i-1] / beta_prime[i-1])\\n\\n v[-1] = b_prime[-1] / beta_prime[-1]\\n\\n for i in range(n-2, -1, -1):\\n v[i] = (b_prime[i] + v[i+1])/ beta_prime[i]\\n\\n return v\",\n \"def run_gibbs(expt, save=True, show_progress=False):\\n if isinstance(expt, str):\\n expt = get_experiment(expt)\\n tr_expt = get_training_expt(expt)\\n\\n for it in tr_expt.save_after:\\n for avg in AVG_VALS:\\n print 'Iteration', it, avg\\n try:\\n rbm = load_rbm(expt, it, avg)\\n except:\\n continue\\n log_Z = storage.load(expt.log_Z_file(it, avg)).as_numpy_array()\\n final_states = storage.load(expt.final_states_file(it, avg))\\n\\n # sample the states proportionally to the Z estimates\\n p = log_Z - np.logaddexp.reduce(log_Z)\\n p /= p.sum() # not needed in theory, but numpy complains if it doesn't sum exactly to 1\\n idxs = np.random.multinomial(1, p, size=expt.annealing.num_particles).argmax(1)\\n states = binary_rbms.RBMState(final_states.v[idxs, :], final_states.h[idxs, :])\\n\\n if show_progress:\\n pbar = misc.pbar(expt.gibbs_steps)\\n\\n for st in range(expt.gibbs_steps):\\n states = rbm.step(states)\\n\\n if show_progress:\\n pbar.update(st)\\n\\n if show_progress:\\n pbar.finish()\\n\\n if save:\\n storage.dump(states, expt.gibbs_states_file(it, avg))\",\n \"def mbh_sigma_inv(mbh):\\n _MASS = 8.32\\n _SLOPE = 5.64\\n _VEL = 200e5\\n\\n sigma = (np.log10(mbh/MSOL) - _MASS) / _SLOPE\\n sigma = np.power(10.0, sigma) * _VEL\\n return sigma\",\n \"def _vB0(self,x):\\n return 2.4e3*x**0.5/(2+x**0.5)+8.e2\",\n \"def gamma(self):\\r\\n raise NotImplementedError('not implemented yet, will use spouge approximation')\",\n \"def estimateGaussian(X):\\n\\tmu = np.mean(X, axis=0)\\n\\tsigma2 = np.std(X, axis=0) ** 2\\n\\treturn mu, sigma2\",\n \"def get_data_term(self):\\n \\n if self.num_hidden == 0:\\n \\n data_term = -self.compute_energy(self.x, self.batch_size)\\n \\n else:\\n \\n data_term = -self.compute_free_energy(self.x)\\n \\n return T.sum(T.exp(-data_term))\",\n \"def gbm(price: float,\\n mu: float,\\n sigma: float,\\n dt: float,\\n n: int) -> np.array:\\n y = np.exp((mu - sigma ** 2 / 2) * dt + sigma * np.random.normal(0, np.sqrt(dt), size=n).T)\\n y = price * y.cumprod(axis=0)\\n return y\",\n \"def SCB(t,init,rhoS,deltaSC,rhoC,deltaCB,rhoB):\\n\\n #Declaring useful parameters\\n [S0,C0,B0]=init\\n lambdaS=deltaSC-rhoS #Introducing global decay rates lambda.\\n lambdaC=deltaCB-rhoC\\n lambdaB=-rhoB\\n\\n #Checking for eigenvalues equalitites\\n thresh=1e-5 #threshold difference for considering two eignevalues as equal\\n bSC=(lambdaC-lambdaS)*(abs(lambdaC-lambdaS)>=thresh)\\n bSB=(lambdaB-lambdaS)*(abs(lambdaB-lambdaS)>=thresh)\\n bCB=(lambdaB-lambdaC)*(abs(lambdaB-lambdaC)>=thresh)\\n\\n #S has always the same expression\\n S=S0*np.exp(-lambdaS*t)\\n\\n #there are two cases for C\\n if bSC!=0:\\n c2=deltaSC*S0/bSC; c1=C0-c2\\n C=c1*np.exp(-lambdaC*t)+c2*np.exp(-lambdaS*t)\\n\\n #there are three subcases for B in that case\\n if bCB==0:\\n b2=deltaCB*c1; b3=deltaCB*c2/bSB; b1=B0-b3\\n B=(b1+b2*t)*np.exp(-lambdaB*t)+b3*np.exp(-lambdaS*t)\\n\\n elif bSB==0:\\n b2=deltaCB*c1/bCB; b3=deltaCB*c2; b1=B0-b2\\n B=(b1+b3*t)*np.exp(-lambdaB*t)+b2*np.exp(-lambdaC*t)\\n\\n else:\\n b2=deltaCB*c1/bCB; b3=deltaCB*c2/bSB; b1=B0-b2-b3\\n B=b1*np.exp(-lambdaB*t)+b2*np.exp(-lambdaC*t)+b3*np.exp(-lambdaS*t)\\n\\n else:\\n c2=deltaSC*S0\\n c1=C0\\n C=(c1+c2*t)*np.exp(-lambdaS*t)\\n\\n #there are two subcases for B in that case\\n if bCB!=0:\\n b3=deltaCB*c2/bSB; b2=(deltaCB*c1-b3)/bSB; b1=B0-b2\\n B=b1*np.exp(-lambdaB*t)+(b2+b3*t)*np.exp(-lambdaC*t)\\n\\n else:\\n b1=B0; b2=deltaCB*c1; b3=deltaCB*c2/2\\n B=(b1+b2*t+b3*t**2)*np.exp(-lambdaB*t)\\n\\n return(np.vstack((S,C,B)))\",\n \"def get_bpmf(self, kB=0.001987204134799235, temperature=300.0):\\n if len(self._meaningful_energies) == 0:\\n return 0.\\n\\n beta = 1. / temperature / kB\\n V_0 = 1661.\\n\\n nr_samples = self.get_number_translations()\\n energies = -beta * self._meaningful_energies\\n e_max = energies.max()\\n exp_mean = np.exp(energies - e_max).sum() / nr_samples\\n\\n bpmf = -temperature * kB * (np.log(exp_mean) + e_max)\\n\\n V_binding = self.get_box_volume()\\n correction = -temperature * kB * np.log(V_binding / V_0 / 8 / np.pi**2)\\n return bpmf + correction\",\n \"def EI(x, gp, ndim,fMax, epsilon=0.1):\\n\\t#epsilon = 0.1\\n\\tx1=np.array(x).reshape(-1,ndim)\\n\\tmuNew, stdNew = gp.predict(x1, return_std=True)\\n\\t#fMax=max(Y_init)\\n\\tZ = (muNew - fMax - epsilon)/stdNew\\n\\treturn -((muNew - fMax - epsilon)* scipy.stats.norm.cdf(Z) + stdNew*scipy.stats.norm.pdf(Z))\",\n \"def fce_re(B,E):\\n return wce(B)/(2.*np.pi)/ev2gamma(E);\",\n \"def calc_std(sig):\\n return np.std(sig)\",\n \"def mbe(self) -> float:\\n return float(np.mean(self._error(self.true, self.predicted)))\",\n \"def optg_xtb(self, acc='normal', nproc=1):\\n for k in ['OMP_NUM_THREADS', 'MKL_NUM_THREADS']:\\n os.environ[k] = '%d'%nproc\\n uc = io2.Units()\\n const = uc.h2e #/uc.b2a\\n fmax = {'normal':1e-3, 'tight':8.e-4, 'vtight':2e-4}[acc] * const\\n # 0.025 eV/A (ase force unit) ~ 0.001 Hartree/A (xtb force unit)\\n print('acc = ', acc, 'fmax=', fmax)\\n for cid in range(self.nconf):\\n print('cid=', cid)\\n c1 = self.mol.GetConformer(cid)\\n zs = [ ai.GetAtomicNum() for ai in c1.GetAtoms() ]\\n coords = []\\n for ia in range(self.na):\\n o = c1.GetAtomPosition(ia)\\n coords.append( [o.x, o.y, o.z] )\\n m1 = ase.Atoms(zs, coords)\\n # create the calculator for GFN0-xTB under periodic boundary conditions\\n calc = GFN2(print_level=2) # setting to 1 or 2 (the default) is ok, not so for 3\\n m1.set_calculator(calc)\\n e = m1.get_potential_energy()\\n print(\\\"Initial energy: eV, Eh\\\", e, e/Hartree)\\n relax = PreconFIRE(m1, precon=None, trajectory=None) # = 'tmp.traj')\\n relax.run(fmax = fmax)\\n for i in range(self.na):\\n pt = Point3D()\\n pt.x, pt.y, pt.z = coordsU[i]\\n c1.SetAtomPosition(i, pt)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6456396","0.6389554","0.6329877","0.62599355","0.6148615","0.60690296","0.60667825","0.60188264","0.5966948","0.59658873","0.5957548","0.595523","0.5916523","0.5912427","0.58843017","0.58783764","0.58764386","0.5825013","0.5818016","0.57995355","0.5766157","0.57576686","0.5757597","0.57069707","0.5703186","0.5696178","0.569614","0.56610787","0.56511086","0.5650665","0.5646676","0.564552","0.5644698","0.5641289","0.56377715","0.56314695","0.5628645","0.5627959","0.5627153","0.56257755","0.56165284","0.56043863","0.55814755","0.5581366","0.5562171","0.55507","0.5550098","0.55499136","0.5547658","0.5544328","0.5544304","0.554319","0.55428576","0.55404484","0.5536002","0.5513518","0.5500452","0.5497516","0.549522","0.549397","0.54917634","0.54862654","0.54802716","0.5475604","0.5473827","0.54699713","0.54682916","0.5466036","0.5458746","0.54578185","0.54452986","0.54447293","0.54404795","0.5439543","0.5433345","0.54323804","0.54302245","0.5428888","0.5424975","0.54246444","0.5422955","0.54200506","0.5415558","0.5406912","0.5402189","0.54005265","0.53988427","0.5393183","0.53911114","0.53793085","0.5373351","0.5373158","0.5369649","0.53685105","0.536471","0.5362105","0.536143","0.53573966","0.53539777","0.53521305","0.53504467"],"string":"[\n \"0.6456396\",\n \"0.6389554\",\n \"0.6329877\",\n \"0.62599355\",\n \"0.6148615\",\n \"0.60690296\",\n \"0.60667825\",\n \"0.60188264\",\n \"0.5966948\",\n \"0.59658873\",\n \"0.5957548\",\n \"0.595523\",\n \"0.5916523\",\n \"0.5912427\",\n \"0.58843017\",\n \"0.58783764\",\n \"0.58764386\",\n \"0.5825013\",\n \"0.5818016\",\n \"0.57995355\",\n \"0.5766157\",\n \"0.57576686\",\n \"0.5757597\",\n \"0.57069707\",\n \"0.5703186\",\n \"0.5696178\",\n \"0.569614\",\n \"0.56610787\",\n \"0.56511086\",\n \"0.5650665\",\n \"0.5646676\",\n \"0.564552\",\n \"0.5644698\",\n \"0.5641289\",\n \"0.56377715\",\n \"0.56314695\",\n \"0.5628645\",\n \"0.5627959\",\n \"0.5627153\",\n \"0.56257755\",\n \"0.56165284\",\n \"0.56043863\",\n \"0.55814755\",\n \"0.5581366\",\n \"0.5562171\",\n \"0.55507\",\n \"0.5550098\",\n \"0.55499136\",\n \"0.5547658\",\n \"0.5544328\",\n \"0.5544304\",\n \"0.554319\",\n \"0.55428576\",\n \"0.55404484\",\n \"0.5536002\",\n \"0.5513518\",\n \"0.5500452\",\n \"0.5497516\",\n \"0.549522\",\n \"0.549397\",\n \"0.54917634\",\n \"0.54862654\",\n \"0.54802716\",\n \"0.5475604\",\n \"0.5473827\",\n \"0.54699713\",\n \"0.54682916\",\n \"0.5466036\",\n \"0.5458746\",\n \"0.54578185\",\n \"0.54452986\",\n \"0.54447293\",\n \"0.54404795\",\n \"0.5439543\",\n \"0.5433345\",\n \"0.54323804\",\n \"0.54302245\",\n \"0.5428888\",\n \"0.5424975\",\n \"0.54246444\",\n \"0.5422955\",\n \"0.54200506\",\n \"0.5415558\",\n \"0.5406912\",\n \"0.5402189\",\n \"0.54005265\",\n \"0.53988427\",\n \"0.5393183\",\n \"0.53911114\",\n \"0.53793085\",\n \"0.5373351\",\n \"0.5373158\",\n \"0.5369649\",\n \"0.53685105\",\n \"0.536471\",\n \"0.5362105\",\n \"0.536143\",\n \"0.53573966\",\n \"0.53539777\",\n \"0.53521305\",\n \"0.53504467\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93785,"cells":{"query":{"kind":"string","value":"Test for correct instantiation given a set of players."},"document":{"kind":"string","value":"def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setup_players(self, players):\n\t\tself.players.clear()\n\t\tids = set([p.get_player_id() for p in players])\n\t\tfor p in self.state.get_players():\n\t\t\tif p not in ids:\n\t\t\t\traise PlayerException(p)\n\t\tfor p in players:\n\t\t\tself.players[p.get_player_id()] = p","def test_valid_game_setup(self):\n self.assertEqual(self._game.active_players(), 1)\n for x in xrange(1, 4):\n self._game.add_player(self._users[x], x)\n self.assertEqual(self._game.active_players(), 4)\n self.assertIsREADY(self._game)","def test_set_player_names(self):\n\n # Setup new games and attempt to set their players' names\n valid_players = [\n [\"Bob\", \"Sam\", \"Cal\", \"Kris\"],\n [\"Player 1\", \"Player 2\", \"Player 3\", \"Player 4\", \"Player 5\"],\n [\"Bot\"],\n [\"P1\", \"P2\", \"P3\"],\n ]\n for players in valid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(len(players))\n game.set_player_names(players)\n self.assertEqual(game.player_names, players, msg=\"The game's player names were not correctly set with: \" + str(players))\n\n # Make sure that the new game state is corectly set\n self.assertEqual(game.state.name, \"start_game\", msg=\"The game's state was not correctly set after setting the player names.\")\n\n # Try to set invalid players\n invalid_players = [\n None,\n [None, None],\n [123, 456, 789],\n [\"Bob\", \"Sam\", 123],\n [\"John\", \"\"],\n ]\n for players in invalid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(len(players or \"1\"))\n success = False\n try:\n game.set_player_names(players)\n except InvalidGamePlayerNames:\n success = True\n self.assertTrue(success, msg=\"The following invalid series of player names was able to be set: \" + str(players))\n\n # Test the case where the number of players given is not the same as the number of names given\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(2)\n success = False\n try:\n game.set_player_names([\"P1\", \"P2\", \"P3\"])\n except InvalidGamePlayerNames:\n success = True\n self.assertTrue(success, msg=\"A number of player names unequal to the number to the number of players in the game was able to be set.\")\n\n # Try to reset the names of the players to throw an error\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(3)\n game.set_player_names([\"P1\", \"P2\", \"P3\"])\n success = False\n try:\n game.set_player_names([\"P01\", \"P02\", \"P03\"])\n except InvalidGameMethodOrder:\n success = True\n self.assertTrue(success, msg=\"The names of the players was incorrectly able to be reset.\")","def test_invalid_game_setup(self):\n with self.assertRaises(ValueError):\n self._game.add_player(self._creator, 1)\n with self.assertRaises(ValueError):\n self._game.add_player(self._users[1], 0)\n for x in xrange(1, 4):\n self._game.add_player(self._users[x], x)\n with self.assertRaises(ValueError):\n self._game.add_player(self._users[4], 1)","def test_create_player(self):\n self.assertIsInstance(self.player, ship.Ship)\n self.assertEqual(self.player.position, constants.PLAYER_START_PLACE)\n self.assertEqual(self.player.width, constants.PLAYER_WIDTH)\n self.assertEqual(self.player.height, constants.PLAYER_HEIGHT)\n self.assertEqual(self.player.img, constants.PLAYER_IMG)\n self.assertEqual(self.player.health, constants.PLAYER_HEALTH)","def __init__(self, players):\n\n self._players = players\n self._game = None","def test_init(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n students, projects, supervisors, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n for student, game_student in zip(students, game.students):\n assert student.name == game_student.name\n assert student._pref_names == game_student._pref_names\n\n for project, game_project in zip(projects, game.projects):\n assert project.name == game_project.name\n assert project._pref_names == game_project._pref_names\n assert project.capacity == game_project.capacity\n assert project.supervisor.name == game_project.supervisor.name\n\n for supervisor, game_supervisor in zip(supervisors, game.supervisors):\n assert supervisor.name == game_supervisor.name\n assert supervisor._pref_names == game_supervisor._pref_names\n assert supervisor.capacity == game_supervisor.capacity\n\n supervisor_projects = [p.name for p in supervisor.projects]\n game_supervisor_projects = [p.name for p in game_supervisor.projects]\n assert supervisor_projects == game_supervisor_projects\n\n assert all([student.matching is None for student in game.students])\n assert all([project.matching == [] for project in game.projects])\n assert all([supervisor.matching == [] for supervisor in game.supervisors])\n assert game.matching is None\n assert game.blocking_pairs is None\n assert game.clean is clean","def __init__(self, players=None):\n self.game = Game()\n if players:\n self.player1 = players[0]\n self.player2 = players[1]\n else:\n self.player1 = Player('X')\n self.player2 = Player('O')\n self.record = Record()\n self.winning_moves = []","def seed_players(self):\n raise NotImplementedError()","def test_set_players_number(self):\n\n # Setup new games and attempt to set thier number of players\n valid_players = [\n 1,\n 2,\n 10,\n 999,\n ]\n for players in valid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(players)\n self.assertEqual(game.players_number, players, msg=\"The number of players was not correctly set with \" + str(players) + \" players.\")\n\n # Make sure that the new game state is correctly set\n self.assertEqual(game.state.name, \"get_player_names\", msg=\"The game state was not correctly set after setting the number of players in the game.\")\n\n # Try to set invalid player numbers\n invalid_players = [\n 0,\n -1,\n -100,\n 1.5,\n ]\n for players in invalid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n success = False\n try:\n game.set_players_number(players)\n except InvalidGamePlayersNumber:\n success = True\n self.assertTrue(success, msg=\"An invalid number of players \" + str(players) + \" was able to be set.\")\n\n # Try to reset the number of players to throw an error\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(3)\n success = False\n try:\n game.set_players_number(2)\n except InvalidGameMethodOrder:\n success = True\n self.assertTrue(success, msg=\"The number of players was incorrectly able to be reset.\")","def test_player_creator():\n filename = os.path.abspath(\"data/data.csv\")\n creator = PlayerCreator(filename)\n creator.parse_csv()\n assert isinstance(creator.csv_data, pd.DataFrame)\n tuples = creator.create_object_tuples()\n assert isinstance(tuples, list)\n creator.create_players(tuples[:100], 10)","def test_legit_player(self):\n board = Board()\n player1 = LegitPlayer()\n player2 = LegitPlayer()\n player_guard1 = PlayerGuard(player1)\n player_guard2 = PlayerGuard(player2)\n\n # set ids\n p1id = uuid.uuid4()\n p2id = uuid.uuid4()\n player_guard1.set_id(p1id)\n player_guard2.set_id(p2id)\n\n # test methods don't error out\n player_guard1.start_of_game()\n player_guard2.start_of_game()\n board.place_worker(*player_guard1.place_worker(board))\n board.place_worker(*player_guard2.place_worker(board))\n board.place_worker(*player_guard2.place_worker(board))\n board.place_worker(*player_guard1.place_worker(board))\n player_guard1.play_turn(board)\n player_guard2.play_turn(board)\n player_guard1.end_of_game(\"legit player\")\n player_guard2.end_of_game(\"legit player\")","def test_players_per_type_num_players_less(self):\n type_of_player = [ss.Player, ss.LazyPlayer, ss.Player]\n sim = ss.Simulation(player_field=type_of_player)\n run = sim.players_per_type()\n assert list(run.keys()) == ['Player', 'LazyPlayer']","def __init__(self, players, piles=None):\n self.players = players\n self.piles = piles if (piles != None) else [5, 5, 5, 5]\n self.nplayer = 1 # player 1 starts.","def test_init(self):\r\n\r\n a_players = [Player(1), Player(2)]\r\n a_x_dist = 10\r\n a_y_dist = 3\r\n a_num_to_win = 3\r\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n #Check to be sure the board is properly set up\r\n self.assertEqual(a_game.board, Board(grid=[[0 for _ in range(a_y_dist)] for _ in range(a_x_dist)]))","def testPlayer():\n\n print(\"\\n ---------- Test Player ---------\")\n\n failure = False\n hand = {'c':1, 'a':1, 'b':1 ,'d':1, 'o':1, 'e':1}\n\n p1 = Player(1, Hand(6, hand))\n p2 = Player(2, Hand(6, hand))\n\n if not p1.getHand() == hand and p2.getHand() == hand:\n failure = True\n print(\"FAILURE: Se esperaría la mano que se ingresó:\", hand,\"y se está regresando:\", p1.getHand())\n\n if not p1.getIdNum() == '1' and p2.getIdNum() == '2':\n failure = True\n print(\"FAILURE: Se espera que p1 sea el jugador 1 y p2 sea el jugador 2, y se está obteniendo:\", p1.getIdNum(),\n p2.getIdNum())\n\n print(\"Jugador 1\")\n print(\"\\t\", p1.addPoints(5))\n print(\"\\t\", p1.addPoints(12))\n if not p1.getScore() == 17:\n failure = True\n print(\"FAILURE: Se esperan 17 puntos, y se están obteniendo:\", p1.getScore())\n print(p1)\n\n print(\"Jugador 2\")\n print(\"\\t\", p2.addPoints(3))\n print(\"\\t\", p2.addPoints(10))\n if not p2.getScore() == 13:\n failure = True\n print(\"FAILURE: Se esperan 13 puntos, y se están obteniendo:\", p1.getScore())\n print(p2)\n\n if not (p1 > p2) == 1:\n failure = True\n print(\"FAILURE: Se esperaba un 1, indicando que el puntaje del P1 es mayor al del P2. Se está regresando:\",\n p1 > p2)\n if not (p1 < p2) == -1:\n failure = True\n print(\"FAILURE: Se esperaba un -1, indicando que el puntaje del P2 es menor al del P1. Se está regresando:\",\n p2 < p1)\n if (p1 == p2):\n failure = True\n print(\"FAILURE: Se esperaba un valor falso y se está obteniendo:\",\n p2 < p1)\n\n if not failure:\n print(\"SUCCESS: testPlayer()\")\n else:\n print(\"FAILURE: testPlayer()\")","def __init__(self, players: list, max_size=0):\n\t\tself.id = uuid.uuid4()\n\t\tself.max_size = max_size\n\t\tif not len(players) > max_size:\n\t\t\tlogging.error('Size mismatch')\n\t\tself.players = players","def __init__(self, number_players=1000):\n self.player_list = []\n for i in range(number_players):\n self.player_list.append(Player())","def test_loop_player(self):\n board = Board()\n player1 = LoopPlayer()\n player_guard1 = PlayerGuard(player1, timeout=1)\n\n # set ids\n p1id = uuid.uuid4() \n self.assertRaises(PlayerTimeout, player_guard1.set_id, p1id)\n\n self.assertRaises(PlayerTimeout, player_guard1.start_of_game)\n self.assertRaises(PlayerTimeout, player_guard1.place_worker, board)\n self.assertRaises(PlayerTimeout, player_guard1.play_turn, board)\n self.assertRaises(PlayerTimeout, player_guard1.end_of_game, \"player\")","def test_init_with_existing_game(self):\n pass\n # Ensure judge is the same","def __init__(self, player_control, players=None):\r\n self.player_control = player_control\r\n self.players = {} # copy for restoration\r\n if players is not None:\r\n for player in players.values():\r\n self.add_player(player)","def test_equal(self):\r\n\r\n a_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n a_x_dist = 3\r\n a_y_dist = 3\r\n a_num_to_win = 1\r\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n b_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n b_x_dist = 3\r\n b_y_dist = 3\r\n b_num_to_win = 1\r\n b_game = Game(b_players, b_x_dist, b_y_dist, b_num_to_win)\r\n\r\n c_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n c_x_dist = 3\r\n c_y_dist = 3\r\n c_num_to_win = 1\r\n c_game = Game(c_players, c_x_dist, c_y_dist, c_num_to_win)\r\n\r\n self.assertTrue(b_game == a_game == c_game)\r\n\r\n a_game.play_game()\r\n b_game.play_game()\r\n\r\n self.assertTrue(a_game == b_game)\r\n self.assertFalse(c_game == a_game)\r\n\r\n c_game.play_game()\r\n\r\n self.assertTrue(b_game == a_game == c_game)","def test_suitor_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.suitors[0].matching = Player(name=\"foo\", pref_names=[])\n\n with pytest.raises(Exception):\n match._check_suitor_matching()","def initialize_round(list_of_players):\n Card.rejected_cards = []\n Card.initialize_deck_from_json()\n for player in list_of_players:\n player.initialize_cards()\n player.thalom = False","def test_get_players(self):\n\n test_player_lines = (\n '\"Player stacks: '\n '#1 \"\"Ert @ 9z1zzoqiIt\"\" (2000) | '\n '#2 \"\"`~!@#$%^& @ 9z1zzoqiIt\"\" (2000) | '\n '#3 \"\"Paulie. D @ TfZNpyIPhD\"\" (2000) | '\n '#4 \"\"Russ @ PjBYO_8gbf\"\" (2000) | '\n '#6 \"\"Chon @ bcp1N58-1M\"\" (2000) | '\n '#8 \"\"Benny @ eSbnubU-KP\"\" (2000) | '\n '#9 \"\"Gargs @ izsy1Zibpi\"\" (2000)\"'\n \",2021-01-09T18:13:11.491Z,161021599150607\"\n )\n\n expected = {\n Player(id_=\"9z1zzoqiIt\", name=\"Ert\"),\n Player(id_=\"9z1zzoqiIt\", name=\"`~!@#$%^&\"),\n Player(id_=\"TfZNpyIPhD\", name=\"Paulie. D\"),\n Player(id_=\"PjBYO_8gbf\", name=\"Russ\"),\n Player(id_=\"bcp1N58-1M\", name=\"Chon\"),\n Player(id_=\"eSbnubU-KP\", name=\"Benny\"),\n Player(id_=\"izsy1Zibpi\", name=\"Gargs\"),\n }\n\n actual, _ = parser.parse_players(test_player_lines)\n self.assertEqual(actual, expected)","def createWithPlayers(cls, players, **kwargs):\n game = TicTacToe()\n return cls(game, players, **kwargs)","def test_players_per_type_num_players(self):\n type_of_player = [ss.Player, ss.LazyPlayer, ss.ResilientPlayer]\n sim = ss.Simulation(player_field=type_of_player)\n run = sim.players_per_type()\n assert list(run.keys()) == ['Player', 'LazyPlayer', 'ResilientPlayer']","def test_winners_per_type_num_players_less(self):\n type_of_player = [ss.Player, ss.LazyPlayer, ss.Player]\n sim = ss.Simulation(player_field=type_of_player)\n run = sim.winners_per_type()\n assert list(run.keys()) == ['Player', 'LazyPlayer']","def spawn_players(self) -> None:\n #Create the player\n self.player1 = Player(self.sensitivity, self.screen_width, self.screen_height, self.screen_width//(3/2), self.screen_height-50, self.player_lives, self.fps, self.player1_bullet, Direction.UP, self.debug)\n\n #Create the AI\n self.player2 = AIPlayer(self.sensitivity, self.screen_width, self.screen_height, self.screen_width//3, self.screen_height-50, self.player_lives, self.fps, self.player2_bullet, Direction.UP, 1, True, self.debug)","def enough_players():\n return True","def test_gridironfootballplayers_get(self):\n pass","def __init__(\r\n self,\r\n player_count=4,\r\n strategy=[HumanRandom(), HumanRandom(), HumanRandom(), HumanRandom()],\r\n rules=None,\r\n ):\r\n # shuffle the cards\r\n shuffle(self.community_cards)\r\n shuffle(self.chance_cards)\r\n\r\n self.player_positions = [0] * player_count\r\n self.current_player = randint(0, player_count - 1)\r\n self.player_list = []\r\n for i in range(player_count):\r\n self.player_list.append(\r\n Player(uid=i, token=self.token[i], strategy=strategy[i])\r\n )\r\n self.full_turn_count = 1","def __init__(self, player_list):\n\n self.tournament_depth = 0\n self.winner_state = 0\n self.winner_list = []\n self.winner_list_temp = []\n\n if (len(player_list) < 3) | (len(player_list) > 8):\n raise Exception(\"Error: It has to be between 3 - 8 players!\")\n if not all(isinstance(s, str) for s in player_list):\n raise Exception(\"Error: The player names has to be in the form of a string!\")\n self.waiting_players = player_list.copy()\n self.start_player_list = player_list.copy()\n player_list_copy = player_list.copy()\n if ((len(player_list) % 2) == 0) | (len(player_list) == 3):\n self.opponents_queue = make_opponents(player_list_copy)\n self.all_opponents = [self.opponents_queue.copy()]\n self.opponents = self.opponents_queue.pop(0)\n self.waiting_players.remove(self.opponents[0])\n self.waiting_players.remove(self.opponents[1])\n for i in range(len(self.opponents_queue)):\n self.waiting_players.remove(self.opponents_queue[i][0])\n self.waiting_players.remove(self.opponents_queue[i][1])\n else:\n self.opponents_queue = make_opponents(player_list_copy)\n self.all_opponents = [self.opponents_queue.copy()]\n self.opponents = self.opponents_queue.pop(0)\n self.waiting_players.remove(self.opponents[0])\n self.waiting_players.remove(self.opponents[1])","def __init__(self, players):\n\n # Instantiate a Players object with the players queue\n self._players = Players(players)\n # Instantiate the Die to be used for the current game\n self._die = Die()\n # Track the game status\n self._active_turn = True\n self._end_game = False","def test_players_per_type(self):\n sim = ss.Simulation()\n assert type(sim.players_per_type()) == dict","def test_start_game_no_players(self):\n self.assertRaises(Exception, self.game.start_game)","def __init__(self, noPlayers, noLaps):\n\t\tself.noPlayers = noPlayers\n\t\tself.noLaps = noLaps\n\t\tself.players = []\n\t\tself.laps = dict()\n\t\tfor pNo in range(0, noPlayers-1):\n\t\t\tplayer = Player()\n\t\t\tself.players.append(player)\n\t\t\tself.laps[id(player)] = 0","def initialize_players():\n return [Player(name, Hand([]), 0) for name in PLAYER_NAMES]","def initialize_players():\n while True:\n nb_of_players = input(\"\\nEntrez le nombre de joueurs : \")\n if not nb_of_players.isdigit():\n print(\"You have to enter a number!\")\n else:\n nb_of_players = int(nb_of_players)\n if nb_of_players < 2:\n print(\"You have to enter at least two!\")\n else:\n break\n nb_of_players = int(nb_of_players)\n list_of_players = [] #This list is going to be returned\n names_secure = [] #stores player's names in lower mode for security\n for index in range(1, nb_of_players+1):\n while True:\n player_name = input(\"Entrer le nom du joueur {} \".format(index))\n if (player_name.lower() == 'end' or player_name.lower() in names_secure):\n print(\"Incorrect Name\")\n else:\n names_secure.append(player_name.lower())\n new_player = Player(player_name)\n list_of_players.append(new_player)\n break\n return list_of_players","def __init__(self, player_id = 0):\n all_players = ['adiumy', 'amanda', 'beastie', 'emule', 'gavroche', 'hexley', 'kiki', 'konqi', 'nolok', 'pidgin', 'puffy', 'sara_the_racer', 'sara_the_wizard', 'suzanne', 'tux', 'wilber', 'xue']\n self.kart = all_players[np.random.choice(len(all_players))]","def make_players(player_names, seed):\n\n np.random.seed(seed)\n players = [Player(name) for name in player_names]\n\n for player in players:\n player.set_prefs(\n np.random.permutation([p for p in players if p != player]).tolist()\n )\n\n return players","def test_add_players(self):\n campaign = self.campaign\n\n campaign.players.add(self.player.id)\n campaign.players.add(self.gm.id)\n\n self.assertQuerysetEqual(campaign.players.all().order_by(\"username\"), [self.gm, self.player], transform=lambda x: x)","def test_malformed_player(self):\n board = Board()\n player1 = MalformedDataPlayer()\n player_guard1 = PlayerGuard(player1, timeout=3)\n\n p1id = uuid.uuid4()\n\n player_guard1.set_id(uuid.uuid4())\n\n self.assertRaises(PlayerMalformedData, player_guard1.place_worker, board)\n self.assertRaises(PlayerMalformedData, player_guard1.play_turn, board)","def add_player(players):\n markers = [player.mark for player in players]\n while True:\n init_args = Player.prompt_init()\n # Check valid player.\n if init_args['mark'] in markers:\n print(\"Sorry someone already chose marker {}, please choose another.\".format(\n init_args['mark']))\n continue\n # Exit if valid player.\n break\n # If we get here then we have a valid player to add.\n return Player(**init_args)","def setup_game(num_players=1, num_decks=1):\n players = []\n for i in range (num_players):\n players.append(player(i+1))\n new_dealer = dealer(\"Dealer\")\n play_shoe = shoe(num_decks).shuffle_shoe()\n return players, new_dealer, play_shoe","def __init__(self, players, turns, game, deterministic_cache):\n self.players = players\n self.turns = turns\n self.game = game\n self.deterministic_cache = deterministic_cache\n self.opponents = players","def test_new_player(new_player, new_room):\n\n try:\n uuid.UUID(str(new_player.id), version=4)\n except ValueError:\n raise ValueError('new_player id is not valid uuid4')\n assert new_player.order_of_turn == 1\n assert new_player.score == 0\n assert new_player.token_presence is False\n assert new_player.username == 'Victor'\n assert new_player.ready is False\n assert new_player.playing is False\n assert new_player.surrender is False\n assert new_player.dice_has_rolled is False\n assert json.loads(new_player.last_dice_values) == [0, 0]\n assert new_player.room_id == new_room.id","def _make_players(resident_prefs, hospital_prefs, capacities):\n\n resident_dict, hospital_dict = _make_instances(\n resident_prefs, hospital_prefs, capacities\n )\n\n for resident_name, resident in resident_dict.items():\n prefs = [hospital_dict[name] for name in resident_prefs[resident_name]]\n resident.set_prefs(prefs)\n\n for hospital_name, hospital in hospital_dict.items():\n prefs = [resident_dict[name] for name in hospital_prefs[hospital_name]]\n hospital.set_prefs(prefs)\n\n residents = list(resident_dict.values())\n hospitals = list(hospital_dict.values())\n\n return residents, hospitals","def init_players(self):\n complain = \"\"\n players_turn = random.sample(range(self.n_players), self.n_players)\n players_created = {}\n picked_colors = []\n for x in range(self.n_players):\n while True:\n clear_output()\n try:\n color = input(\n f\"{complain}Player {x+1}, please type in one of the following colors: ({', '.join([x.capitalize() for x in self.world.player_colors if x not in picked_colors])}):\\n\").lower()\n if color in self.world.player_colors and color not in picked_colors:\n picked_colors.append(color)\n players_created[players_turn[x]] = Player(\n color.capitalize(), self.start_troops)\n break\n else:\n complain = \"Please enter a valid color\\n\"\n except:\n pass\n\n self.players = [players_created[y] for x in range(\n self.n_players) for y in players_created.keys() if int(y) == x]","def test_add_player(self):\n user = User.objects.create_user('Luca', 'luca@bongo.cat', 'bongo_cat')\n profile = models.Profile.objects.get(user=user)\n game = models.Game.objects.all()[0]\n number_of_piles = len(settings.HYDROCARBON_STOCKS_PER_PLAYER)\n for i_pile in range(number_of_piles):\n self.assertEqual(game.hydrocarbon_piles.get(index=i_pile).stock_amount, 0)\n game.add_player(profile=profile)\n for i_pile in range(number_of_piles):\n self.assertEqual(game.hydrocarbon_piles.get(index=i_pile).stock_amount,\n settings.HYDROCARBON_STOCKS_PER_PLAYER[i_pile][0])","def test_exception_player(self):\n board = Board()\n player1 = ExceptionPlayer()\n player_guard1 = PlayerGuard(player1, timeout=3)\n\n # set ids\n p1id = uuid.uuid4() \n self.assertRaises(PlayerRaisedException, player_guard1.set_id, p1id)\n\n self.assertRaises(PlayerRaisedException, player_guard1.start_of_game)\n self.assertRaises(PlayerRaisedException, player_guard1.place_worker, board)\n self.assertRaises(PlayerRaisedException, player_guard1.play_turn, board)\n self.assertRaises(PlayerRaisedException, player_guard1.end_of_game, \"player\")","def createWithSamePlayersClass(cls, players_cls, **kwargs):\n players = [players_cls(), players_cls()]\n game = TicTacToe()\n return cls(game, players, **kwargs)","def createPlayers(self, board):\n tempVar = 0\n while tempVar == 0:\n try:\n numPlayers = int(input(\"How many players do you want to have?\\n\"))\n if numPlayers >= 2 and numPlayers <= 6:\n tempVar = 1\n else:\n print(\"Thats not the right amount of players please have 2-6 players.\")\n except:\n print(\"That number wont work please enter a number between 2 and 6.\")\n\n for i in range(numPlayers):\n tempState = False\n os.system('clear')\n name = input(\"What is the name of player \" + str(i + 1) + \"?\\n\")\n if i == 0:\n player1 = playerClass.player(board, name)\n while tempState == False:\n print(player1, \"has an initial roll of\", player1.getIR(), \"\\n\")\n time.sleep(0.1)\n tempState = self.addPlayers(player1)\n if tempState == False:\n print(\"Thats a tie. Rerolling ...\\n\")\n time.sleep(0.5)\n os.system('clear')\n else:\n input(\"Press enter to continue\")\n elif i == 1:\n player2 = playerClass.player(board, name)\n while tempState == False:\n print(player2, \"has an initial roll of\", player2.getIR(), \"\\n\")\n time.sleep(0.1)\n tempState = self.addPlayers(player2)\n if tempState == False:\n print(\"Thats a tie. Rerolling ...\\n\")\n time.sleep(0.5)\n os.system('clear')\n else:\n input(\"Press enter to continue\")\n elif i == 2:\n player3 = playerClass.player(board, name)\n while tempState == False:\n print(player3, \"has an initial roll of\", player3.getIR(), \"\\n\")\n time.sleep(0.1)\n tempState = self.addPlayers(player3)\n if tempState == False:\n print(\"Thats a tie. Rerolling ...\\n\")\n time.sleep(0.5)\n os.system('clear')\n else:\n input(\"Press enter to continue\")\n elif i == 3:\n player4 = playerClass.player(board, name)\n while tempState == False:\n print(player4, \"has an initial roll of\", player4.getIR(), \"\\n\")\n time.sleep(0.1)\n tempState = self.addPlayers(player4)\n if tempState == False:\n print(\"Thats a tie. Rerolling ...\\n\")\n time.sleep(0.5)\n os.system('clear')\n else:\n input(\"Press enter to continue\")\n elif i == 4:\n player5 = playerClass.player(board, name)\n while tempState == False:\n print(player5, \"has an initial roll of\", player5.getIR(), \"\\n\")\n time.sleep(0.1)\n tempState = self.addPlayers(player5)\n if tempState == False:\n print(\"Thats a tie. Rerolling ...\\n\")\n time.sleep(0.5)\n os.system('clear')\n else:\n input(\"Press enter to continue\")\n elif i == 5:\n player6 = playerClass.player(board, name)\n while tempState == False:\n print(player6, \"has an initial roll of\", player6.getIR(), \"\\n\")\n time.sleep(0.1)\n tempState = self.addPlayers(player6)\n if tempState == False:\n print(\"Thats a tie. Rerolling ...\\n\")\n time.sleep(0.5)\n os.system('clear')\n else:\n input(\"Press enter to continue\")","def test_winners_per_type_num_players(self):\n type_of_player = [ss.Player, ss.LazyPlayer, ss.ResilientPlayer]\n sim = ss.Simulation(player_field=type_of_player)\n run = sim.winners_per_type()\n assert list(run.keys()) == ['Player', 'LazyPlayer', 'ResilientPlayer']","def test_constructor_positive():\n\n\n file1=\"/home/unit_test_grids/test_game_grid.txt\"\n my_game=Game(file1)\n \n assert my_game.filename==file1,\"The filename is not correct \"\\\n \"in the constructor.\"\n \n \n assert my_game.listOfMoves==[],\"The list of moves is not correct \"\\\n \"in the constructor.\"\n \n \n assert my_game.numberOfMoves==0,\"The number of moves is not correct \"\\\n \"in the constructor.\"\n \n \n assert my_game.my_player.row==0,\"The constructor is not creating the my_player\"\\\n \" object correctly.The row attribute of my_player \"\\\n \" object is not initialised correctly.\"\n \n \n assert my_game.my_player.col==1,\"The constructor is not creating the my_player\"\\\n \" object correctly.The column attribute (col) of my_player \"\\\n \" object is not initialised correctly.\"\n \n \n assert my_game.my_player.num_water_buckets==0,\"The constructor is not creating the my_player\"\\\n \" object correctly.The number of water buckets\"\\\n \" attribute (num_water_buckets) of my_player \"\\\n \" object is not initialised correctly.\"","def create_players(num_players):\n players = []\n for player_number in xrange(1,num_players+1):\n # create new players\n players.append(\n Player(player_number, get_new_deck(), deque()))\n\n return players","def test_play_game_hard(self):\r\n wins = [0,0,0]\r\n\r\n for i in range(1,10):\r\n a_player_1_id = 1\r\n a_player_2_id = 2\r\n a_players = [RandomPlayer(a_player_1_id), RandomPlayer(a_player_2_id)]\r\n a_x_dist = i\r\n a_y_dist = i\r\n a_num_to_win = 3\r\n a_game = Game(a_players,a_x_dist,a_y_dist,a_num_to_win)\r\n a_game.play_game()\r\n\r\n wins[a_game.winner] += 1\r\n\r\n print(wins)","def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching.append(Player(name=\"foo\", pref_names=[]))\n\n with pytest.raises(Exception):\n match._check_reviewer_matching()","def get_players(n_players):\n\n if n_players < 2 or 8 < n_players:\n raise ValueError('A game must have between 2 to 8 players. You input {} players.'.format(n_players))\n\n return {classes.Player(p) for p in range(n_players)}","def test_too_many_players_join(self):\n self.game.join_game(self.user1)\n self.game.join_game(self.user2)\n self.game.join_game(self.user3)\n self.game.join_game(self.user4)\n\n self.assertRaises(Exception, self.game.join_game, self.user1)","def test_pasture_intercon():\n a = Pasture((0, 1))\n b = Pasture((1, 1))\n c = Pasture((1, 2))\n d = Pasture([(0, 3), (0, 4)])\n e = Pasture((0, 2))\n f = Pasture([(1, 2), (1, 3)])\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures(a)\n p.build_pastures(b)\n p.build_pastures(c)\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures([a, b, c])\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures([a, b])\n p.build_pastures(c)\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures([b, c])\n p.build_pastures(a)\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures(b)\n p.build_pastures([a, c])\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures(c)\n with pytest.raises(AgricolaLogicError):\n p.build_pastures(a)\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures(a)\n with pytest.raises(AgricolaLogicError):\n p.build_pastures(c)\n\n p = Player(\"p0\", wood=20, rooms=[])\n with pytest.raises(AgricolaLogicError):\n p.build_pastures([a, c])\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures(d)\n p.build_pastures(e)\n\n p = Player(\"p0\", wood=20, rooms=[])\n p.build_pastures(d)\n p.build_pastures(f)\n\n p = Player(\"p0\", wood=20, fences_avail=17, rooms=[])\n p.build_pastures([a, b, c, d, e])\n\n p = Player(\"p0\", wood=20, fences_avail=17, rooms=[])\n with pytest.raises(AgricolaLogicError):\n p.build_pastures([a, b, c, d])","def prepare_game(\n decks_count: int, auto_mode: bool, player_one_name: str, players_count: int\n) -> List[Player]:\n # define list of random names for bot-players\n random_names = RANDOM_NAMES\n shuffle(random_names)\n if player_one_name:\n names = [player_one_name] + random_names[: players_count - 1]\n else:\n names = random_names[:players_count]\n\n # generate cards and split all cards to the even chunks\n all_cards = generate_deck(decks_count)\n chunked_deck = chunk_deck(all_cards, len(all_cards) // players_count)\n\n # initialize players\n players: List[Player] = []\n for player, hand in zip(names, chunked_deck):\n players.append(Player(name=player, hand=Deck(hand)))\n # set auto_mode flag to the main player, because it's enabled by default\n players[0].auto_mode = auto_mode\n return players","def test_join(self):\n pig = game.pig.Pig('PlayerA', 'PlayerB', 'PlayerC')\n self.assertEqual(pig.get_players(), ('PlayerA', 'PlayerB', 'PlayerC'))","def __init__(self):\r\n self.playerPreparers = [WhitePlayerPreparer(), BlackPlayerPreparer()]","def test_init(resident_names, hospital_names, capacities, seed):\n\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n assert match.suitors == residents\n assert match.reviewers == hospitals\n assert all([suitor.matching is None for suitor in match.suitors])\n assert all([reviewer.matching == [] for reviewer in match.reviewers])\n assert match.matching is None","def test_get_player(self):\n pass","def test_poker_one_sf(self):\n self.assertEqual(poker([self.sf1, self.fk, self.fh]), [self.sf1])","def __init__(self, players_number: int, marbles: int) -> None:\n self.turn = 0\n self.player = 0\n self.marbles = marbles\n self.players = [Player(pk=i) for i in range(players_number)]\n self.current_marble = Marble.get_zero_marble()","def __init__(self, n_human_players=1, n_ai_players=1, n_cards=10):\n\n self.n_cards = n_cards\n\n # Load the answers\n with open(\"cards-against-humanity/answers.pickle\", 'rb') as ans:\n self.answer_cards = pickle.load(ans)\n shuffle(self.answer_cards)\n self.used_answers = 0\n\n # Load the questions\n with open(\"cards-against-humanity/questions.pickle\", 'rb') as qs:\n self.question_cards = pickle.load(qs)\n shuffle(self.question_cards)\n self.used_questions = 0\n\n # Make the human players\n self.human_players = []\n for _ in range(n_human_players):\n self.human_players.append(HumanPlayer(Player(self, n_cards)))\n\n # Make the AI players\n self.ai_players = []\n for _ in range(n_ai_players):\n self.ai_players.append(AIPlayer(Player(self, n_cards)))","def test_decision():\n player = sequence_threshold.Player(threshold=10)\n\n player.cards = SortedSet()\n assert player.play(20, 11) == True\n assert player.play(20, 10) == True\n assert player.play(20, 9) == False\n\n player.cards = SortedSet([30])\n assert player.play(20, 11) == True\n assert player.play(20, 10) == True\n assert player.play(20, 9) == False\n assert player.play(31, 1) == True\n assert player.play(29, 1) == True\n assert player.play(32, 1) == False\n with pytest.raises(Exception):\n player.play(30, 1)\n\n player.threshold = 5\n\n player.cards = SortedSet()\n assert player.play(20, 16) == True\n assert player.play(20, 15) == True\n assert player.play(20, 14) == False\n\n player.cards = SortedSet([30])\n assert player.play(20, 16) == True\n assert player.play(20, 15) == True\n assert player.play(20, 14) == False\n assert player.play(31, 1) == True\n assert player.play(29, 1) == True\n assert player.play(32, 1) == False\n with pytest.raises(Exception):\n player.play(30, 1)","def __init__(self, players, num_of_players):\r\n self.players = players\r\n self.num_of_players = num_of_players\r\n self.active_players = num_of_players\r\n self.dealer = Dealer()\r\n self.card_stack = CardStack()\r\n self.money_stack = MoneyStack()\r\n self.cur_player = 0\r\n self.round_num = 0\r\n self.round_player_money = 0","def test_pasture_overlap():\n a = Pasture([(0, 1)])\n b = Pasture([(0, 1), (0, 2)])\n c = Pasture([(0, 2), (0, 3)])\n\n assert a.overlaps(b)\n assert b.overlaps(a)\n\n assert b.overlaps(c)\n assert c.overlaps(b)\n\n assert not a.overlaps(c)\n assert not c.overlaps(a)\n\n p = Player(\"p0\", wood=20)\n p.build_pastures(a)\n p.build_pastures(c)\n\n p = Player(\"p0\", wood=20)\n p.build_pastures(c)\n p.build_pastures(a)\n\n p = Player(\"p0\", wood=20)\n p.build_pastures([a, c])\n\n p = Player(\"p0\", wood=20)\n p.build_pastures([c, a])\n\n p = Player(\"p0\", wood=20)\n p.build_pastures(b)\n\n p = Player(\"p0\", wood=20)\n with pytest.raises(AgricolaLogicError):\n p.build_pastures([a, b])\n\n p = Player(\"p0\", wood=20)\n p.build_pastures(a)\n with pytest.raises(AgricolaLogicError):\n p.build_pastures(b)\n\n p = Player(\"p0\", wood=20)\n p.build_pastures(b)\n with pytest.raises(AgricolaLogicError):\n p.build_pastures(a)\n\n p = Player(\"p0\", wood=20)\n with pytest.raises(AgricolaLogicError):\n p.build_pastures([c, b])\n\n p = Player(\"p0\", wood=20)\n p.build_pastures(c)\n with pytest.raises(AgricolaLogicError):\n p.build_pastures(b)\n\n p = Player(\"p0\", wood=20)\n p.build_pastures(b)\n with pytest.raises(AgricolaLogicError):\n p.build_pastures(c)","def create_player(self, players, player_no, start_tile):\n if player_no in players.keys():\n raise ValueError(f\"Invalid map layout. Can't create player no {player_no} as it already exists.\")\n players[player_no] = Player(player_no, start_tile, self)\n return players[player_no]","def __init__(self, players):\n self.players = players\n self.board = Board()","def test_demo_ai_game_finishes(self):\n player1 = Player(ai=BattleshipsAI())\n player2 = Player(opponent=player1, ai=BattleshipsAI())\n player1.opponent = player2\n\n game = GameRunner(player1, player2)\n self.assertIsNot(game.play(), None)","def testRegistration():\n deleteMatches()\n deletePlayers()\n registerPlayer(\"Red Ranger\")\n registerPlayer(\"Blue Ranger\")\n registerPlayer(\"Yellow Ranger\")\n registerPlayer(\"Pink Ranger\")\n registerPlayer(\"Green Ranger\")\n standings = playerStandings()\n [id1, id2, id3, id4, id5] = [row[0] for row in standings]\n reportMatch(id1, id2)\n reportMatch(id3, id4)\n completeTournament('Battle Royale')\n currentPlayers = countPlayers()\n oldPlayers = countPlayers('Battle Royale')\n if not (currentPlayers==0 and oldPlayers==5):\n raise ValueError(\n \"Players' status were not updated correctly when tournament\"\n \" completes\"\n )\n\n print \"6. Keeping of completed tournaments in database is checked.\"","def __init__(self, lobby_id, players):\n self.card_wished = None\n self.wait_for_card_wish = False\n self.current_player = None\n self.direction_clock_wise = True\n self.current_draw_punishment = 1\n\n self.players = []\n self.lobby_id = lobby_id\n if isinstance(players, list):\n for player in players:\n self.players.append(player)\n else:\n self.players.append(players)\n\n players_len = len(self.players)\n if players_len > 0:\n self.current_player = self.players[random.randint(0, players_len) - 1]\n\n # Get the cards of the game\n game = Game.objects.get(name=\"maumau\")\n card_set = CardSet.objects.get(id=game.card_set_id)\n self.cards = list(card_set.cards.all())\n\n self.__shuffle_cards()\n for player in self.players:\n self.draw_cards(player, 5)\n\n self.discard_pile = []\n self.discard_pile.append(self.cards.pop())","def __init__(self, players):\n\n self._players = players\n self._current_player = players.get()","def create_players(self):\n for i in range(self.number_of_players):\n self.players_names.append(pyip.inputStr(\n prompt=f'\\nEnter name of player {i + 1}:\\n'))","def assign_points(players):\n pass","def test_strategy(self):\n\n axelrod.seed(8)\n opponent = axelrod.Cooperator()\n player = axelrod.WorseAndWorse()\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'D'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C')])\n\n # Test that behaviour does not depend on opponent\n opponent = axelrod.Defector()\n player = axelrod.WorseAndWorse()\n axelrod.seed(8)\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'D'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C')])","def test_initial_limit(self):\n\n g = test_setup.simple_two_player()\n\n p1, p2 = g.players\n\n self.assertEqual(g._clientele_limit(p1), 2)\n self.assertEqual(g._clientele_limit(p2), 2)","def test_SetPlayerPuzzleScores_manyplayers(self):\r\n puzzle_id = ['1']\r\n player1_score = 0.08\r\n player2_score = 0.02\r\n response1 = self.make_puzzle_score_request(\r\n puzzle_id, player1_score, self.user\r\n )\r\n\r\n # There should now be a score in the db.\r\n top_10 = Score.get_tops_n(10, puzzle_id)\r\n self.assertEqual(len(top_10), 1)\r\n self.assertEqual(top_10[0]['score'], Score.display_score(player1_score))\r\n\r\n response2 = self.make_puzzle_score_request(\r\n puzzle_id, player2_score, self.user2\r\n )\r\n\r\n # There should now be two scores in the db\r\n top_10 = Score.get_tops_n(10, puzzle_id)\r\n self.assertEqual(len(top_10), 2)\r\n\r\n # Top score should be player2_score. Second should be player1_score\r\n self.assertAlmostEqual(\r\n top_10[0]['score'],\r\n Score.display_score(player2_score),\r\n delta=0.5\r\n )\r\n self.assertAlmostEqual(\r\n top_10[1]['score'],\r\n Score.display_score(player1_score),\r\n delta=0.5\r\n )\r\n\r\n # Top score user should be self.user2.username\r\n self.assertEqual(top_10[0]['username'], self.user2.username)","def initialise_players(update=False, file_object=None):\n # do not run if there are existing Player records\n players = Player.objects.all()\n if players.exists():\n # flag that this is an update performed to get new players before the auction\n update = True\n\n codes = get_player_codes()\n print(\"codes:\", codes)\n\n # get a lookup dict of PremTeams, key = web_code\n prem_team_dict = get_prem_team_dict(key='web_code')\n print(prem_team_dict)\n\n # get the rows from the provided stats file\n if file_object:\n rows = json.load(file_object)\n file_object.close()\n else:\n print(\"No file object provided\")\n return\n print(\"No. of player table rows = \", len(rows))\n\n # track new players *during update only*\n new_players = []\n\n print(\"****\")\n print(\"New Players\")\n print(\"****\")\n\n for row in rows:\n\n first_name = row.get('first_name')\n if first_name:\n name = \"{}. {}\".format(first_name[0], row['last_name'])\n else:\n name = row['last_name']\n\n web_code = row['id']\n prem_team_code = row['squad_id']\n prem_team = prem_team_dict[prem_team_code]\n value = float(row['cost'] / 1000000.0)\n position = str(row['position'])\n\n # stats for new players can be an empty list\n stats = row['stats'] or {}\n\n last_years_total = stats.get('total_points', 0)\n\n # create (or update) a Player instance\n p, created = Player.objects.update_or_create(\n name=name, web_code=web_code,\n defaults={\n 'prem_team': prem_team,\n 'value': value,\n 'last_years_total': last_years_total,\n 'position': position,\n }\n )\n\n # assign the player codes if this is an update (i.e. pre-auction)\n if update and created:\n codes[p.position] += 1\n p.code = codes[p.position]\n p.is_new = True\n p.save()\n new_players.append(p)\n print(p.code, p.name, p.prem_team, p.value, created)\n\n print(\"****\")\n print('All players saved')\n print(\"****\")\n\n if not update:\n # Makes ure players are in the correct order before calculating tha AZFF player code\n # get each group of players (by position), order by decreasing value then\n # assign an incrementing code for AZFF\n # Note that we can't use the custom manager at this stage (it uses code not web_code)\n\n # GKP\n gkps = Player.objects.filter(position=Player.GKP).order_by('-value')\n print('gkps.count() = ', gkps.count())\n for g in gkps:\n codes[Player.GKP] += 1\n g.code = codes[Player.GKP]\n g.save()\n\n # DEF\n defs = Player.objects.filter(position=Player.DEF).order_by('-value')\n print('defs.count() = ', defs.count())\n for d in defs:\n codes[Player.DEF] += 1\n d.code = codes[Player.DEF]\n d.save()\n\n # MID\n mids = Player.objects.filter(position=Player.MID).order_by('-value')\n print('mids.count() = ', mids.count())\n for m in mids:\n codes[Player.MID] += 1\n m.code = codes[Player.MID]\n m.save()\n\n # STR\n strs = Player.objects.filter(position=Player.STR).order_by('-value')\n print('strs.count() = ', strs.count())\n for s in strs:\n codes[Player.STR] += 1\n s.code = codes[Player.STR]\n s.save()","def create_players_list(self):\n for p in self.players_names:\n self._players_list.append(Player(p))","def __init__(self, num_agents, min_buy_in, max_buy_in, buy_ins):\n\t\tself.deck = Deck()\n\t\tself.deck.shuffle()\n\n\t\tself.player_count = num_agents\n\t\tself.players = []\n\t\tself.minBuyIn = min_buy_in\n\t\tself.maxBuyIn = max_buy_in\n\n\t\tfor i in range(num_agents):\n\t\t\tplayers.append(TexasPlayer(buy_ins[i], i + 1))","def __init__(self, players, frames_nbr=10, pins_nbr=10, throws_nbr=2):\n self.pins_nbr = pins_nbr\n self.throws = []\n self.throws_nbr = throws_nbr\n self.frames_nbr = frames_nbr\n self.players = players","def __init__(self, game: int) -> None:\n self.game = game\n self.players: List[object] = []","def test_instantiate_valid_target(self):\n # create test configs\n test_configs = [\n {\"_target_\": \"collections.deque\"},\n {\"_target_\": \"collections.UserString\", \"seq\": \"test string\"}\n ]\n\n # create truth objects\n truth_objs = [deque(), UserString(\"test string\")]\n\n # check that instantiate returns truth object for each config\n for truth_obj, test_config in zip(truth_objs, test_configs):\n self.assertEqual(truth_obj, instantiate(test_config))","def spawn_players(self) -> None:\n # Initialise the players\n self.player1 = Player(self.sensitivity, self.screen_width, self.screen_height, self.screen_width // 2, 50,\n self.player_lives, self.fps, self.player1_bullet, Direction.DOWN, self.debug)\n self.player2 = Player(self.sensitivity, self.screen_width, self.screen_height, self.screen_width // 2,\n self.screen_height - 50, self.player_lives, self.fps, self.player2_bullet, Direction.UP,\n self.debug)\n\n # Rotate the image of the player at the top\n self.player1.rotate(180)","def test_pasture_cost():\n a = Pasture((0, 1))\n b = Pasture((1, 1))\n c = Pasture((1, 2))\n d = Pasture([(0, 3), (0, 4)])\n e = Pasture((0, 2))\n f = Pasture([(1, 2), (1, 3)])\n\n player = Player(\"p0\", wood=0, rooms=[])\n for p in [a, b, c, d, e, f]:\n with pytest.raises(AgricolaNotEnoughResources):\n player.build_pastures(p)\n\n player = Player(\"p0\", wood=4, rooms=[])\n player.build_pastures(a)\n assert player.wood == 0\n\n player = Player(\"p0\", wood=4, rooms=[])\n player.build_pastures(b)\n assert player.wood == 0\n\n player = Player(\"p0\", wood=4, rooms=[])\n player.build_pastures(c)\n assert player.wood == 0\n\n player = Player(\"p0\", wood=6, rooms=[])\n player.build_pastures(d)\n assert player.wood == 0\n\n player = Player(\"p0\", wood=4, rooms=[])\n player.build_pastures(e)\n assert player.wood == 0\n\n player = Player(\"p0\", wood=6, rooms=[])\n player.build_pastures(f)\n assert player.wood == 0\n\n player = Player(\"p0\", wood=6, rooms=[])\n player.build_pastures(f)\n assert player.wood == 0\n\n player = Player(\"p0\", wood=10, rooms=[])\n player.build_pastures([a, b, c])\n assert player.wood == 0\n\n player = Player(\"p0\", wood=11, rooms=[])\n player.build_pastures([d, f])\n assert player.wood == 0\n\n player = Player(\"p0\", wood=9, rooms=[])\n player.build_pastures([d, e])\n assert player.wood == 0\n\n player = Player(\"p0\", wood=13, rooms=[])\n player.build_pastures([d, e, f])\n assert player.wood == 0","def setUp(self):\n d = self.deck = TestDeck()\n self.game = test_setup.two_player_lead('Laborer', deck=d)\n self.p1, self.p2 = self.game.players","def __initPicks(self):\n picks = self.teamparser.getPlayerPicks()\n try:\n self.picks = picks[(self.team, self.position)] #players picks\n except KeyError, err:\n self.picks = []\n raise TypeError, \"Invalid Team/Position: \" + self.team","def set_players(self, player_min: int, player_max: int):\n name: str = ' '\n print('Please give between %i and %i names for your players'\n % (player_min, player_max))\n while (name != '') and (len(self.players) < player_max):\n name = input('Players {}: '.format(len(self.players)+1))\n if name != '':\n self.players.append(Player(name))\n elif len(self.players) < player_min:\n name = ' '\n\n print()\n print('{} players registered.'.format(len(self.players)))\n print()","def __init__(self, *names):\n deck = Deck.create(shuffle=True)\n self.names = (list(names) + \"P1 P2 P3 P4 \".split())[:4]\n self.hands = {\n n: Player(n, h) for n, h in zip(self.names, deck.deal(4))\n }","def test_create_player_successful(self):\n payload = {'name': 'Mayita', 'victories': 0, 'defeats': 0}\n self.client.post(PLAYERS_URL, payload)\n\n print('PLAYERS_URL: ',PLAYERS_URL)\n exists = Player.objects.filter(\n name=payload['name']\n ).exists()\n self.assertTrue(exists)","def test_new(self):\n obj = Game.new(self._creator, self._ds)\n self.assertIsInstance(obj, Game, \"Game instance not initialized.\")\n self.assertHasAttribute(obj, 'uid', \"Game has no unique ID.\")\n self.assertHasAttributes(obj, [\n 'players', 'spectators', 'state', 'points', 'options', 'table'])\n self.assertIsCREATED(obj)","def __init__(self, initial=[1, 3, 5, 7]):\n self.piles = initial.copy()\n self.player = 0\n self.winner = None","def __init__(self, initial=[1, 3, 5, 7]):\n self.piles = initial.copy()\n self.player = 0\n self.winner = None","def create_players(tournament):\n enter_player = 1\n while enter_player <= 8:\n player_list = select_players()\n selection = view.menu_create_player(player_list)\n if selection == 1:\n # ---------------------------------------------------------------------------------\n # Joueur existant\n view.print_actors(player_list)\n data = create_existing_player()\n player_db = Player(data['Nom'], data['Prenom'], data['Date de Naissance'],\n data['Sexe'], data['Classement'])\n # add the player id to the list of tournament players_id\n tournament.players_index_list.append(data.doc_id)\n # add the player to the list of tournament players with the tournament method\n tournament.add_player(player_db)\n # ---------------------------------------------------------------------------------\n elif selection == 2:\n # ---------------------------------------------------------------------------------\n # Nouveau joueur\n data = view.create_player_view(enter_player)\n player = Player(data['name'], data['first_name'], data['birth_day'], data['sex'],\n data['ranking'])\n # add the player to the list of tournament players with the tournament method\n tournament.add_player(player)\n # players are registered in the database\n save_player(player)\n # prendre l'identifiantiant du joueur\n for id_player in get_players_id(1):\n tournament.players_index_list.append(id_player)\n # ---------------------------------------------------------------------------------\n enter_player += 1"],"string":"[\n \"def setup_players(self, players):\\n\\t\\tself.players.clear()\\n\\t\\tids = set([p.get_player_id() for p in players])\\n\\t\\tfor p in self.state.get_players():\\n\\t\\t\\tif p not in ids:\\n\\t\\t\\t\\traise PlayerException(p)\\n\\t\\tfor p in players:\\n\\t\\t\\tself.players[p.get_player_id()] = p\",\n \"def test_valid_game_setup(self):\\n self.assertEqual(self._game.active_players(), 1)\\n for x in xrange(1, 4):\\n self._game.add_player(self._users[x], x)\\n self.assertEqual(self._game.active_players(), 4)\\n self.assertIsREADY(self._game)\",\n \"def test_set_player_names(self):\\n\\n # Setup new games and attempt to set their players' names\\n valid_players = [\\n [\\\"Bob\\\", \\\"Sam\\\", \\\"Cal\\\", \\\"Kris\\\"],\\n [\\\"Player 1\\\", \\\"Player 2\\\", \\\"Player 3\\\", \\\"Player 4\\\", \\\"Player 5\\\"],\\n [\\\"Bot\\\"],\\n [\\\"P1\\\", \\\"P2\\\", \\\"P3\\\"],\\n ]\\n for players in valid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(len(players))\\n game.set_player_names(players)\\n self.assertEqual(game.player_names, players, msg=\\\"The game's player names were not correctly set with: \\\" + str(players))\\n\\n # Make sure that the new game state is corectly set\\n self.assertEqual(game.state.name, \\\"start_game\\\", msg=\\\"The game's state was not correctly set after setting the player names.\\\")\\n\\n # Try to set invalid players\\n invalid_players = [\\n None,\\n [None, None],\\n [123, 456, 789],\\n [\\\"Bob\\\", \\\"Sam\\\", 123],\\n [\\\"John\\\", \\\"\\\"],\\n ]\\n for players in invalid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(len(players or \\\"1\\\"))\\n success = False\\n try:\\n game.set_player_names(players)\\n except InvalidGamePlayerNames:\\n success = True\\n self.assertTrue(success, msg=\\\"The following invalid series of player names was able to be set: \\\" + str(players))\\n\\n # Test the case where the number of players given is not the same as the number of names given\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(2)\\n success = False\\n try:\\n game.set_player_names([\\\"P1\\\", \\\"P2\\\", \\\"P3\\\"])\\n except InvalidGamePlayerNames:\\n success = True\\n self.assertTrue(success, msg=\\\"A number of player names unequal to the number to the number of players in the game was able to be set.\\\")\\n\\n # Try to reset the names of the players to throw an error\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(3)\\n game.set_player_names([\\\"P1\\\", \\\"P2\\\", \\\"P3\\\"])\\n success = False\\n try:\\n game.set_player_names([\\\"P01\\\", \\\"P02\\\", \\\"P03\\\"])\\n except InvalidGameMethodOrder:\\n success = True\\n self.assertTrue(success, msg=\\\"The names of the players was incorrectly able to be reset.\\\")\",\n \"def test_invalid_game_setup(self):\\n with self.assertRaises(ValueError):\\n self._game.add_player(self._creator, 1)\\n with self.assertRaises(ValueError):\\n self._game.add_player(self._users[1], 0)\\n for x in xrange(1, 4):\\n self._game.add_player(self._users[x], x)\\n with self.assertRaises(ValueError):\\n self._game.add_player(self._users[4], 1)\",\n \"def test_create_player(self):\\n self.assertIsInstance(self.player, ship.Ship)\\n self.assertEqual(self.player.position, constants.PLAYER_START_PLACE)\\n self.assertEqual(self.player.width, constants.PLAYER_WIDTH)\\n self.assertEqual(self.player.height, constants.PLAYER_HEIGHT)\\n self.assertEqual(self.player.img, constants.PLAYER_IMG)\\n self.assertEqual(self.player.health, constants.PLAYER_HEALTH)\",\n \"def __init__(self, players):\\n\\n self._players = players\\n self._game = None\",\n \"def test_init(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n students, projects, supervisors, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n for student, game_student in zip(students, game.students):\\n assert student.name == game_student.name\\n assert student._pref_names == game_student._pref_names\\n\\n for project, game_project in zip(projects, game.projects):\\n assert project.name == game_project.name\\n assert project._pref_names == game_project._pref_names\\n assert project.capacity == game_project.capacity\\n assert project.supervisor.name == game_project.supervisor.name\\n\\n for supervisor, game_supervisor in zip(supervisors, game.supervisors):\\n assert supervisor.name == game_supervisor.name\\n assert supervisor._pref_names == game_supervisor._pref_names\\n assert supervisor.capacity == game_supervisor.capacity\\n\\n supervisor_projects = [p.name for p in supervisor.projects]\\n game_supervisor_projects = [p.name for p in game_supervisor.projects]\\n assert supervisor_projects == game_supervisor_projects\\n\\n assert all([student.matching is None for student in game.students])\\n assert all([project.matching == [] for project in game.projects])\\n assert all([supervisor.matching == [] for supervisor in game.supervisors])\\n assert game.matching is None\\n assert game.blocking_pairs is None\\n assert game.clean is clean\",\n \"def __init__(self, players=None):\\n self.game = Game()\\n if players:\\n self.player1 = players[0]\\n self.player2 = players[1]\\n else:\\n self.player1 = Player('X')\\n self.player2 = Player('O')\\n self.record = Record()\\n self.winning_moves = []\",\n \"def seed_players(self):\\n raise NotImplementedError()\",\n \"def test_set_players_number(self):\\n\\n # Setup new games and attempt to set thier number of players\\n valid_players = [\\n 1,\\n 2,\\n 10,\\n 999,\\n ]\\n for players in valid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(players)\\n self.assertEqual(game.players_number, players, msg=\\\"The number of players was not correctly set with \\\" + str(players) + \\\" players.\\\")\\n\\n # Make sure that the new game state is correctly set\\n self.assertEqual(game.state.name, \\\"get_player_names\\\", msg=\\\"The game state was not correctly set after setting the number of players in the game.\\\")\\n\\n # Try to set invalid player numbers\\n invalid_players = [\\n 0,\\n -1,\\n -100,\\n 1.5,\\n ]\\n for players in invalid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n success = False\\n try:\\n game.set_players_number(players)\\n except InvalidGamePlayersNumber:\\n success = True\\n self.assertTrue(success, msg=\\\"An invalid number of players \\\" + str(players) + \\\" was able to be set.\\\")\\n\\n # Try to reset the number of players to throw an error\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(3)\\n success = False\\n try:\\n game.set_players_number(2)\\n except InvalidGameMethodOrder:\\n success = True\\n self.assertTrue(success, msg=\\\"The number of players was incorrectly able to be reset.\\\")\",\n \"def test_player_creator():\\n filename = os.path.abspath(\\\"data/data.csv\\\")\\n creator = PlayerCreator(filename)\\n creator.parse_csv()\\n assert isinstance(creator.csv_data, pd.DataFrame)\\n tuples = creator.create_object_tuples()\\n assert isinstance(tuples, list)\\n creator.create_players(tuples[:100], 10)\",\n \"def test_legit_player(self):\\n board = Board()\\n player1 = LegitPlayer()\\n player2 = LegitPlayer()\\n player_guard1 = PlayerGuard(player1)\\n player_guard2 = PlayerGuard(player2)\\n\\n # set ids\\n p1id = uuid.uuid4()\\n p2id = uuid.uuid4()\\n player_guard1.set_id(p1id)\\n player_guard2.set_id(p2id)\\n\\n # test methods don't error out\\n player_guard1.start_of_game()\\n player_guard2.start_of_game()\\n board.place_worker(*player_guard1.place_worker(board))\\n board.place_worker(*player_guard2.place_worker(board))\\n board.place_worker(*player_guard2.place_worker(board))\\n board.place_worker(*player_guard1.place_worker(board))\\n player_guard1.play_turn(board)\\n player_guard2.play_turn(board)\\n player_guard1.end_of_game(\\\"legit player\\\")\\n player_guard2.end_of_game(\\\"legit player\\\")\",\n \"def test_players_per_type_num_players_less(self):\\n type_of_player = [ss.Player, ss.LazyPlayer, ss.Player]\\n sim = ss.Simulation(player_field=type_of_player)\\n run = sim.players_per_type()\\n assert list(run.keys()) == ['Player', 'LazyPlayer']\",\n \"def __init__(self, players, piles=None):\\n self.players = players\\n self.piles = piles if (piles != None) else [5, 5, 5, 5]\\n self.nplayer = 1 # player 1 starts.\",\n \"def test_init(self):\\r\\n\\r\\n a_players = [Player(1), Player(2)]\\r\\n a_x_dist = 10\\r\\n a_y_dist = 3\\r\\n a_num_to_win = 3\\r\\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\\r\\n\\r\\n #Check to be sure the board is properly set up\\r\\n self.assertEqual(a_game.board, Board(grid=[[0 for _ in range(a_y_dist)] for _ in range(a_x_dist)]))\",\n \"def testPlayer():\\n\\n print(\\\"\\\\n ---------- Test Player ---------\\\")\\n\\n failure = False\\n hand = {'c':1, 'a':1, 'b':1 ,'d':1, 'o':1, 'e':1}\\n\\n p1 = Player(1, Hand(6, hand))\\n p2 = Player(2, Hand(6, hand))\\n\\n if not p1.getHand() == hand and p2.getHand() == hand:\\n failure = True\\n print(\\\"FAILURE: Se esperaría la mano que se ingresó:\\\", hand,\\\"y se está regresando:\\\", p1.getHand())\\n\\n if not p1.getIdNum() == '1' and p2.getIdNum() == '2':\\n failure = True\\n print(\\\"FAILURE: Se espera que p1 sea el jugador 1 y p2 sea el jugador 2, y se está obteniendo:\\\", p1.getIdNum(),\\n p2.getIdNum())\\n\\n print(\\\"Jugador 1\\\")\\n print(\\\"\\\\t\\\", p1.addPoints(5))\\n print(\\\"\\\\t\\\", p1.addPoints(12))\\n if not p1.getScore() == 17:\\n failure = True\\n print(\\\"FAILURE: Se esperan 17 puntos, y se están obteniendo:\\\", p1.getScore())\\n print(p1)\\n\\n print(\\\"Jugador 2\\\")\\n print(\\\"\\\\t\\\", p2.addPoints(3))\\n print(\\\"\\\\t\\\", p2.addPoints(10))\\n if not p2.getScore() == 13:\\n failure = True\\n print(\\\"FAILURE: Se esperan 13 puntos, y se están obteniendo:\\\", p1.getScore())\\n print(p2)\\n\\n if not (p1 > p2) == 1:\\n failure = True\\n print(\\\"FAILURE: Se esperaba un 1, indicando que el puntaje del P1 es mayor al del P2. Se está regresando:\\\",\\n p1 > p2)\\n if not (p1 < p2) == -1:\\n failure = True\\n print(\\\"FAILURE: Se esperaba un -1, indicando que el puntaje del P2 es menor al del P1. Se está regresando:\\\",\\n p2 < p1)\\n if (p1 == p2):\\n failure = True\\n print(\\\"FAILURE: Se esperaba un valor falso y se está obteniendo:\\\",\\n p2 < p1)\\n\\n if not failure:\\n print(\\\"SUCCESS: testPlayer()\\\")\\n else:\\n print(\\\"FAILURE: testPlayer()\\\")\",\n \"def __init__(self, players: list, max_size=0):\\n\\t\\tself.id = uuid.uuid4()\\n\\t\\tself.max_size = max_size\\n\\t\\tif not len(players) > max_size:\\n\\t\\t\\tlogging.error('Size mismatch')\\n\\t\\tself.players = players\",\n \"def __init__(self, number_players=1000):\\n self.player_list = []\\n for i in range(number_players):\\n self.player_list.append(Player())\",\n \"def test_loop_player(self):\\n board = Board()\\n player1 = LoopPlayer()\\n player_guard1 = PlayerGuard(player1, timeout=1)\\n\\n # set ids\\n p1id = uuid.uuid4() \\n self.assertRaises(PlayerTimeout, player_guard1.set_id, p1id)\\n\\n self.assertRaises(PlayerTimeout, player_guard1.start_of_game)\\n self.assertRaises(PlayerTimeout, player_guard1.place_worker, board)\\n self.assertRaises(PlayerTimeout, player_guard1.play_turn, board)\\n self.assertRaises(PlayerTimeout, player_guard1.end_of_game, \\\"player\\\")\",\n \"def test_init_with_existing_game(self):\\n pass\\n # Ensure judge is the same\",\n \"def __init__(self, player_control, players=None):\\r\\n self.player_control = player_control\\r\\n self.players = {} # copy for restoration\\r\\n if players is not None:\\r\\n for player in players.values():\\r\\n self.add_player(player)\",\n \"def test_equal(self):\\r\\n\\r\\n a_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n a_x_dist = 3\\r\\n a_y_dist = 3\\r\\n a_num_to_win = 1\\r\\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\\r\\n\\r\\n b_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n b_x_dist = 3\\r\\n b_y_dist = 3\\r\\n b_num_to_win = 1\\r\\n b_game = Game(b_players, b_x_dist, b_y_dist, b_num_to_win)\\r\\n\\r\\n c_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n c_x_dist = 3\\r\\n c_y_dist = 3\\r\\n c_num_to_win = 1\\r\\n c_game = Game(c_players, c_x_dist, c_y_dist, c_num_to_win)\\r\\n\\r\\n self.assertTrue(b_game == a_game == c_game)\\r\\n\\r\\n a_game.play_game()\\r\\n b_game.play_game()\\r\\n\\r\\n self.assertTrue(a_game == b_game)\\r\\n self.assertFalse(c_game == a_game)\\r\\n\\r\\n c_game.play_game()\\r\\n\\r\\n self.assertTrue(b_game == a_game == c_game)\",\n \"def test_suitor_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.suitors[0].matching = Player(name=\\\"foo\\\", pref_names=[])\\n\\n with pytest.raises(Exception):\\n match._check_suitor_matching()\",\n \"def initialize_round(list_of_players):\\n Card.rejected_cards = []\\n Card.initialize_deck_from_json()\\n for player in list_of_players:\\n player.initialize_cards()\\n player.thalom = False\",\n \"def test_get_players(self):\\n\\n test_player_lines = (\\n '\\\"Player stacks: '\\n '#1 \\\"\\\"Ert @ 9z1zzoqiIt\\\"\\\" (2000) | '\\n '#2 \\\"\\\"`~!@#$%^& @ 9z1zzoqiIt\\\"\\\" (2000) | '\\n '#3 \\\"\\\"Paulie. D @ TfZNpyIPhD\\\"\\\" (2000) | '\\n '#4 \\\"\\\"Russ @ PjBYO_8gbf\\\"\\\" (2000) | '\\n '#6 \\\"\\\"Chon @ bcp1N58-1M\\\"\\\" (2000) | '\\n '#8 \\\"\\\"Benny @ eSbnubU-KP\\\"\\\" (2000) | '\\n '#9 \\\"\\\"Gargs @ izsy1Zibpi\\\"\\\" (2000)\\\"'\\n \\\",2021-01-09T18:13:11.491Z,161021599150607\\\"\\n )\\n\\n expected = {\\n Player(id_=\\\"9z1zzoqiIt\\\", name=\\\"Ert\\\"),\\n Player(id_=\\\"9z1zzoqiIt\\\", name=\\\"`~!@#$%^&\\\"),\\n Player(id_=\\\"TfZNpyIPhD\\\", name=\\\"Paulie. D\\\"),\\n Player(id_=\\\"PjBYO_8gbf\\\", name=\\\"Russ\\\"),\\n Player(id_=\\\"bcp1N58-1M\\\", name=\\\"Chon\\\"),\\n Player(id_=\\\"eSbnubU-KP\\\", name=\\\"Benny\\\"),\\n Player(id_=\\\"izsy1Zibpi\\\", name=\\\"Gargs\\\"),\\n }\\n\\n actual, _ = parser.parse_players(test_player_lines)\\n self.assertEqual(actual, expected)\",\n \"def createWithPlayers(cls, players, **kwargs):\\n game = TicTacToe()\\n return cls(game, players, **kwargs)\",\n \"def test_players_per_type_num_players(self):\\n type_of_player = [ss.Player, ss.LazyPlayer, ss.ResilientPlayer]\\n sim = ss.Simulation(player_field=type_of_player)\\n run = sim.players_per_type()\\n assert list(run.keys()) == ['Player', 'LazyPlayer', 'ResilientPlayer']\",\n \"def test_winners_per_type_num_players_less(self):\\n type_of_player = [ss.Player, ss.LazyPlayer, ss.Player]\\n sim = ss.Simulation(player_field=type_of_player)\\n run = sim.winners_per_type()\\n assert list(run.keys()) == ['Player', 'LazyPlayer']\",\n \"def spawn_players(self) -> None:\\n #Create the player\\n self.player1 = Player(self.sensitivity, self.screen_width, self.screen_height, self.screen_width//(3/2), self.screen_height-50, self.player_lives, self.fps, self.player1_bullet, Direction.UP, self.debug)\\n\\n #Create the AI\\n self.player2 = AIPlayer(self.sensitivity, self.screen_width, self.screen_height, self.screen_width//3, self.screen_height-50, self.player_lives, self.fps, self.player2_bullet, Direction.UP, 1, True, self.debug)\",\n \"def enough_players():\\n return True\",\n \"def test_gridironfootballplayers_get(self):\\n pass\",\n \"def __init__(\\r\\n self,\\r\\n player_count=4,\\r\\n strategy=[HumanRandom(), HumanRandom(), HumanRandom(), HumanRandom()],\\r\\n rules=None,\\r\\n ):\\r\\n # shuffle the cards\\r\\n shuffle(self.community_cards)\\r\\n shuffle(self.chance_cards)\\r\\n\\r\\n self.player_positions = [0] * player_count\\r\\n self.current_player = randint(0, player_count - 1)\\r\\n self.player_list = []\\r\\n for i in range(player_count):\\r\\n self.player_list.append(\\r\\n Player(uid=i, token=self.token[i], strategy=strategy[i])\\r\\n )\\r\\n self.full_turn_count = 1\",\n \"def __init__(self, player_list):\\n\\n self.tournament_depth = 0\\n self.winner_state = 0\\n self.winner_list = []\\n self.winner_list_temp = []\\n\\n if (len(player_list) < 3) | (len(player_list) > 8):\\n raise Exception(\\\"Error: It has to be between 3 - 8 players!\\\")\\n if not all(isinstance(s, str) for s in player_list):\\n raise Exception(\\\"Error: The player names has to be in the form of a string!\\\")\\n self.waiting_players = player_list.copy()\\n self.start_player_list = player_list.copy()\\n player_list_copy = player_list.copy()\\n if ((len(player_list) % 2) == 0) | (len(player_list) == 3):\\n self.opponents_queue = make_opponents(player_list_copy)\\n self.all_opponents = [self.opponents_queue.copy()]\\n self.opponents = self.opponents_queue.pop(0)\\n self.waiting_players.remove(self.opponents[0])\\n self.waiting_players.remove(self.opponents[1])\\n for i in range(len(self.opponents_queue)):\\n self.waiting_players.remove(self.opponents_queue[i][0])\\n self.waiting_players.remove(self.opponents_queue[i][1])\\n else:\\n self.opponents_queue = make_opponents(player_list_copy)\\n self.all_opponents = [self.opponents_queue.copy()]\\n self.opponents = self.opponents_queue.pop(0)\\n self.waiting_players.remove(self.opponents[0])\\n self.waiting_players.remove(self.opponents[1])\",\n \"def __init__(self, players):\\n\\n # Instantiate a Players object with the players queue\\n self._players = Players(players)\\n # Instantiate the Die to be used for the current game\\n self._die = Die()\\n # Track the game status\\n self._active_turn = True\\n self._end_game = False\",\n \"def test_players_per_type(self):\\n sim = ss.Simulation()\\n assert type(sim.players_per_type()) == dict\",\n \"def test_start_game_no_players(self):\\n self.assertRaises(Exception, self.game.start_game)\",\n \"def __init__(self, noPlayers, noLaps):\\n\\t\\tself.noPlayers = noPlayers\\n\\t\\tself.noLaps = noLaps\\n\\t\\tself.players = []\\n\\t\\tself.laps = dict()\\n\\t\\tfor pNo in range(0, noPlayers-1):\\n\\t\\t\\tplayer = Player()\\n\\t\\t\\tself.players.append(player)\\n\\t\\t\\tself.laps[id(player)] = 0\",\n \"def initialize_players():\\n return [Player(name, Hand([]), 0) for name in PLAYER_NAMES]\",\n \"def initialize_players():\\n while True:\\n nb_of_players = input(\\\"\\\\nEntrez le nombre de joueurs : \\\")\\n if not nb_of_players.isdigit():\\n print(\\\"You have to enter a number!\\\")\\n else:\\n nb_of_players = int(nb_of_players)\\n if nb_of_players < 2:\\n print(\\\"You have to enter at least two!\\\")\\n else:\\n break\\n nb_of_players = int(nb_of_players)\\n list_of_players = [] #This list is going to be returned\\n names_secure = [] #stores player's names in lower mode for security\\n for index in range(1, nb_of_players+1):\\n while True:\\n player_name = input(\\\"Entrer le nom du joueur {} \\\".format(index))\\n if (player_name.lower() == 'end' or player_name.lower() in names_secure):\\n print(\\\"Incorrect Name\\\")\\n else:\\n names_secure.append(player_name.lower())\\n new_player = Player(player_name)\\n list_of_players.append(new_player)\\n break\\n return list_of_players\",\n \"def __init__(self, player_id = 0):\\n all_players = ['adiumy', 'amanda', 'beastie', 'emule', 'gavroche', 'hexley', 'kiki', 'konqi', 'nolok', 'pidgin', 'puffy', 'sara_the_racer', 'sara_the_wizard', 'suzanne', 'tux', 'wilber', 'xue']\\n self.kart = all_players[np.random.choice(len(all_players))]\",\n \"def make_players(player_names, seed):\\n\\n np.random.seed(seed)\\n players = [Player(name) for name in player_names]\\n\\n for player in players:\\n player.set_prefs(\\n np.random.permutation([p for p in players if p != player]).tolist()\\n )\\n\\n return players\",\n \"def test_add_players(self):\\n campaign = self.campaign\\n\\n campaign.players.add(self.player.id)\\n campaign.players.add(self.gm.id)\\n\\n self.assertQuerysetEqual(campaign.players.all().order_by(\\\"username\\\"), [self.gm, self.player], transform=lambda x: x)\",\n \"def test_malformed_player(self):\\n board = Board()\\n player1 = MalformedDataPlayer()\\n player_guard1 = PlayerGuard(player1, timeout=3)\\n\\n p1id = uuid.uuid4()\\n\\n player_guard1.set_id(uuid.uuid4())\\n\\n self.assertRaises(PlayerMalformedData, player_guard1.place_worker, board)\\n self.assertRaises(PlayerMalformedData, player_guard1.play_turn, board)\",\n \"def add_player(players):\\n markers = [player.mark for player in players]\\n while True:\\n init_args = Player.prompt_init()\\n # Check valid player.\\n if init_args['mark'] in markers:\\n print(\\\"Sorry someone already chose marker {}, please choose another.\\\".format(\\n init_args['mark']))\\n continue\\n # Exit if valid player.\\n break\\n # If we get here then we have a valid player to add.\\n return Player(**init_args)\",\n \"def setup_game(num_players=1, num_decks=1):\\n players = []\\n for i in range (num_players):\\n players.append(player(i+1))\\n new_dealer = dealer(\\\"Dealer\\\")\\n play_shoe = shoe(num_decks).shuffle_shoe()\\n return players, new_dealer, play_shoe\",\n \"def __init__(self, players, turns, game, deterministic_cache):\\n self.players = players\\n self.turns = turns\\n self.game = game\\n self.deterministic_cache = deterministic_cache\\n self.opponents = players\",\n \"def test_new_player(new_player, new_room):\\n\\n try:\\n uuid.UUID(str(new_player.id), version=4)\\n except ValueError:\\n raise ValueError('new_player id is not valid uuid4')\\n assert new_player.order_of_turn == 1\\n assert new_player.score == 0\\n assert new_player.token_presence is False\\n assert new_player.username == 'Victor'\\n assert new_player.ready is False\\n assert new_player.playing is False\\n assert new_player.surrender is False\\n assert new_player.dice_has_rolled is False\\n assert json.loads(new_player.last_dice_values) == [0, 0]\\n assert new_player.room_id == new_room.id\",\n \"def _make_players(resident_prefs, hospital_prefs, capacities):\\n\\n resident_dict, hospital_dict = _make_instances(\\n resident_prefs, hospital_prefs, capacities\\n )\\n\\n for resident_name, resident in resident_dict.items():\\n prefs = [hospital_dict[name] for name in resident_prefs[resident_name]]\\n resident.set_prefs(prefs)\\n\\n for hospital_name, hospital in hospital_dict.items():\\n prefs = [resident_dict[name] for name in hospital_prefs[hospital_name]]\\n hospital.set_prefs(prefs)\\n\\n residents = list(resident_dict.values())\\n hospitals = list(hospital_dict.values())\\n\\n return residents, hospitals\",\n \"def init_players(self):\\n complain = \\\"\\\"\\n players_turn = random.sample(range(self.n_players), self.n_players)\\n players_created = {}\\n picked_colors = []\\n for x in range(self.n_players):\\n while True:\\n clear_output()\\n try:\\n color = input(\\n f\\\"{complain}Player {x+1}, please type in one of the following colors: ({', '.join([x.capitalize() for x in self.world.player_colors if x not in picked_colors])}):\\\\n\\\").lower()\\n if color in self.world.player_colors and color not in picked_colors:\\n picked_colors.append(color)\\n players_created[players_turn[x]] = Player(\\n color.capitalize(), self.start_troops)\\n break\\n else:\\n complain = \\\"Please enter a valid color\\\\n\\\"\\n except:\\n pass\\n\\n self.players = [players_created[y] for x in range(\\n self.n_players) for y in players_created.keys() if int(y) == x]\",\n \"def test_add_player(self):\\n user = User.objects.create_user('Luca', 'luca@bongo.cat', 'bongo_cat')\\n profile = models.Profile.objects.get(user=user)\\n game = models.Game.objects.all()[0]\\n number_of_piles = len(settings.HYDROCARBON_STOCKS_PER_PLAYER)\\n for i_pile in range(number_of_piles):\\n self.assertEqual(game.hydrocarbon_piles.get(index=i_pile).stock_amount, 0)\\n game.add_player(profile=profile)\\n for i_pile in range(number_of_piles):\\n self.assertEqual(game.hydrocarbon_piles.get(index=i_pile).stock_amount,\\n settings.HYDROCARBON_STOCKS_PER_PLAYER[i_pile][0])\",\n \"def test_exception_player(self):\\n board = Board()\\n player1 = ExceptionPlayer()\\n player_guard1 = PlayerGuard(player1, timeout=3)\\n\\n # set ids\\n p1id = uuid.uuid4() \\n self.assertRaises(PlayerRaisedException, player_guard1.set_id, p1id)\\n\\n self.assertRaises(PlayerRaisedException, player_guard1.start_of_game)\\n self.assertRaises(PlayerRaisedException, player_guard1.place_worker, board)\\n self.assertRaises(PlayerRaisedException, player_guard1.play_turn, board)\\n self.assertRaises(PlayerRaisedException, player_guard1.end_of_game, \\\"player\\\")\",\n \"def createWithSamePlayersClass(cls, players_cls, **kwargs):\\n players = [players_cls(), players_cls()]\\n game = TicTacToe()\\n return cls(game, players, **kwargs)\",\n \"def createPlayers(self, board):\\n tempVar = 0\\n while tempVar == 0:\\n try:\\n numPlayers = int(input(\\\"How many players do you want to have?\\\\n\\\"))\\n if numPlayers >= 2 and numPlayers <= 6:\\n tempVar = 1\\n else:\\n print(\\\"Thats not the right amount of players please have 2-6 players.\\\")\\n except:\\n print(\\\"That number wont work please enter a number between 2 and 6.\\\")\\n\\n for i in range(numPlayers):\\n tempState = False\\n os.system('clear')\\n name = input(\\\"What is the name of player \\\" + str(i + 1) + \\\"?\\\\n\\\")\\n if i == 0:\\n player1 = playerClass.player(board, name)\\n while tempState == False:\\n print(player1, \\\"has an initial roll of\\\", player1.getIR(), \\\"\\\\n\\\")\\n time.sleep(0.1)\\n tempState = self.addPlayers(player1)\\n if tempState == False:\\n print(\\\"Thats a tie. Rerolling ...\\\\n\\\")\\n time.sleep(0.5)\\n os.system('clear')\\n else:\\n input(\\\"Press enter to continue\\\")\\n elif i == 1:\\n player2 = playerClass.player(board, name)\\n while tempState == False:\\n print(player2, \\\"has an initial roll of\\\", player2.getIR(), \\\"\\\\n\\\")\\n time.sleep(0.1)\\n tempState = self.addPlayers(player2)\\n if tempState == False:\\n print(\\\"Thats a tie. Rerolling ...\\\\n\\\")\\n time.sleep(0.5)\\n os.system('clear')\\n else:\\n input(\\\"Press enter to continue\\\")\\n elif i == 2:\\n player3 = playerClass.player(board, name)\\n while tempState == False:\\n print(player3, \\\"has an initial roll of\\\", player3.getIR(), \\\"\\\\n\\\")\\n time.sleep(0.1)\\n tempState = self.addPlayers(player3)\\n if tempState == False:\\n print(\\\"Thats a tie. Rerolling ...\\\\n\\\")\\n time.sleep(0.5)\\n os.system('clear')\\n else:\\n input(\\\"Press enter to continue\\\")\\n elif i == 3:\\n player4 = playerClass.player(board, name)\\n while tempState == False:\\n print(player4, \\\"has an initial roll of\\\", player4.getIR(), \\\"\\\\n\\\")\\n time.sleep(0.1)\\n tempState = self.addPlayers(player4)\\n if tempState == False:\\n print(\\\"Thats a tie. Rerolling ...\\\\n\\\")\\n time.sleep(0.5)\\n os.system('clear')\\n else:\\n input(\\\"Press enter to continue\\\")\\n elif i == 4:\\n player5 = playerClass.player(board, name)\\n while tempState == False:\\n print(player5, \\\"has an initial roll of\\\", player5.getIR(), \\\"\\\\n\\\")\\n time.sleep(0.1)\\n tempState = self.addPlayers(player5)\\n if tempState == False:\\n print(\\\"Thats a tie. Rerolling ...\\\\n\\\")\\n time.sleep(0.5)\\n os.system('clear')\\n else:\\n input(\\\"Press enter to continue\\\")\\n elif i == 5:\\n player6 = playerClass.player(board, name)\\n while tempState == False:\\n print(player6, \\\"has an initial roll of\\\", player6.getIR(), \\\"\\\\n\\\")\\n time.sleep(0.1)\\n tempState = self.addPlayers(player6)\\n if tempState == False:\\n print(\\\"Thats a tie. Rerolling ...\\\\n\\\")\\n time.sleep(0.5)\\n os.system('clear')\\n else:\\n input(\\\"Press enter to continue\\\")\",\n \"def test_winners_per_type_num_players(self):\\n type_of_player = [ss.Player, ss.LazyPlayer, ss.ResilientPlayer]\\n sim = ss.Simulation(player_field=type_of_player)\\n run = sim.winners_per_type()\\n assert list(run.keys()) == ['Player', 'LazyPlayer', 'ResilientPlayer']\",\n \"def test_constructor_positive():\\n\\n\\n file1=\\\"/home/unit_test_grids/test_game_grid.txt\\\"\\n my_game=Game(file1)\\n \\n assert my_game.filename==file1,\\\"The filename is not correct \\\"\\\\\\n \\\"in the constructor.\\\"\\n \\n \\n assert my_game.listOfMoves==[],\\\"The list of moves is not correct \\\"\\\\\\n \\\"in the constructor.\\\"\\n \\n \\n assert my_game.numberOfMoves==0,\\\"The number of moves is not correct \\\"\\\\\\n \\\"in the constructor.\\\"\\n \\n \\n assert my_game.my_player.row==0,\\\"The constructor is not creating the my_player\\\"\\\\\\n \\\" object correctly.The row attribute of my_player \\\"\\\\\\n \\\" object is not initialised correctly.\\\"\\n \\n \\n assert my_game.my_player.col==1,\\\"The constructor is not creating the my_player\\\"\\\\\\n \\\" object correctly.The column attribute (col) of my_player \\\"\\\\\\n \\\" object is not initialised correctly.\\\"\\n \\n \\n assert my_game.my_player.num_water_buckets==0,\\\"The constructor is not creating the my_player\\\"\\\\\\n \\\" object correctly.The number of water buckets\\\"\\\\\\n \\\" attribute (num_water_buckets) of my_player \\\"\\\\\\n \\\" object is not initialised correctly.\\\"\",\n \"def create_players(num_players):\\n players = []\\n for player_number in xrange(1,num_players+1):\\n # create new players\\n players.append(\\n Player(player_number, get_new_deck(), deque()))\\n\\n return players\",\n \"def test_play_game_hard(self):\\r\\n wins = [0,0,0]\\r\\n\\r\\n for i in range(1,10):\\r\\n a_player_1_id = 1\\r\\n a_player_2_id = 2\\r\\n a_players = [RandomPlayer(a_player_1_id), RandomPlayer(a_player_2_id)]\\r\\n a_x_dist = i\\r\\n a_y_dist = i\\r\\n a_num_to_win = 3\\r\\n a_game = Game(a_players,a_x_dist,a_y_dist,a_num_to_win)\\r\\n a_game.play_game()\\r\\n\\r\\n wins[a_game.winner] += 1\\r\\n\\r\\n print(wins)\",\n \"def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.reviewers[0].matching.append(Player(name=\\\"foo\\\", pref_names=[]))\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_matching()\",\n \"def get_players(n_players):\\n\\n if n_players < 2 or 8 < n_players:\\n raise ValueError('A game must have between 2 to 8 players. You input {} players.'.format(n_players))\\n\\n return {classes.Player(p) for p in range(n_players)}\",\n \"def test_too_many_players_join(self):\\n self.game.join_game(self.user1)\\n self.game.join_game(self.user2)\\n self.game.join_game(self.user3)\\n self.game.join_game(self.user4)\\n\\n self.assertRaises(Exception, self.game.join_game, self.user1)\",\n \"def test_pasture_intercon():\\n a = Pasture((0, 1))\\n b = Pasture((1, 1))\\n c = Pasture((1, 2))\\n d = Pasture([(0, 3), (0, 4)])\\n e = Pasture((0, 2))\\n f = Pasture([(1, 2), (1, 3)])\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures(a)\\n p.build_pastures(b)\\n p.build_pastures(c)\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures([a, b, c])\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures([a, b])\\n p.build_pastures(c)\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures([b, c])\\n p.build_pastures(a)\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures(b)\\n p.build_pastures([a, c])\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures(c)\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures(a)\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures(a)\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures(c)\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures([a, c])\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures(d)\\n p.build_pastures(e)\\n\\n p = Player(\\\"p0\\\", wood=20, rooms=[])\\n p.build_pastures(d)\\n p.build_pastures(f)\\n\\n p = Player(\\\"p0\\\", wood=20, fences_avail=17, rooms=[])\\n p.build_pastures([a, b, c, d, e])\\n\\n p = Player(\\\"p0\\\", wood=20, fences_avail=17, rooms=[])\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures([a, b, c, d])\",\n \"def prepare_game(\\n decks_count: int, auto_mode: bool, player_one_name: str, players_count: int\\n) -> List[Player]:\\n # define list of random names for bot-players\\n random_names = RANDOM_NAMES\\n shuffle(random_names)\\n if player_one_name:\\n names = [player_one_name] + random_names[: players_count - 1]\\n else:\\n names = random_names[:players_count]\\n\\n # generate cards and split all cards to the even chunks\\n all_cards = generate_deck(decks_count)\\n chunked_deck = chunk_deck(all_cards, len(all_cards) // players_count)\\n\\n # initialize players\\n players: List[Player] = []\\n for player, hand in zip(names, chunked_deck):\\n players.append(Player(name=player, hand=Deck(hand)))\\n # set auto_mode flag to the main player, because it's enabled by default\\n players[0].auto_mode = auto_mode\\n return players\",\n \"def test_join(self):\\n pig = game.pig.Pig('PlayerA', 'PlayerB', 'PlayerC')\\n self.assertEqual(pig.get_players(), ('PlayerA', 'PlayerB', 'PlayerC'))\",\n \"def __init__(self):\\r\\n self.playerPreparers = [WhitePlayerPreparer(), BlackPlayerPreparer()]\",\n \"def test_init(resident_names, hospital_names, capacities, seed):\\n\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n assert match.suitors == residents\\n assert match.reviewers == hospitals\\n assert all([suitor.matching is None for suitor in match.suitors])\\n assert all([reviewer.matching == [] for reviewer in match.reviewers])\\n assert match.matching is None\",\n \"def test_get_player(self):\\n pass\",\n \"def test_poker_one_sf(self):\\n self.assertEqual(poker([self.sf1, self.fk, self.fh]), [self.sf1])\",\n \"def __init__(self, players_number: int, marbles: int) -> None:\\n self.turn = 0\\n self.player = 0\\n self.marbles = marbles\\n self.players = [Player(pk=i) for i in range(players_number)]\\n self.current_marble = Marble.get_zero_marble()\",\n \"def __init__(self, n_human_players=1, n_ai_players=1, n_cards=10):\\n\\n self.n_cards = n_cards\\n\\n # Load the answers\\n with open(\\\"cards-against-humanity/answers.pickle\\\", 'rb') as ans:\\n self.answer_cards = pickle.load(ans)\\n shuffle(self.answer_cards)\\n self.used_answers = 0\\n\\n # Load the questions\\n with open(\\\"cards-against-humanity/questions.pickle\\\", 'rb') as qs:\\n self.question_cards = pickle.load(qs)\\n shuffle(self.question_cards)\\n self.used_questions = 0\\n\\n # Make the human players\\n self.human_players = []\\n for _ in range(n_human_players):\\n self.human_players.append(HumanPlayer(Player(self, n_cards)))\\n\\n # Make the AI players\\n self.ai_players = []\\n for _ in range(n_ai_players):\\n self.ai_players.append(AIPlayer(Player(self, n_cards)))\",\n \"def test_decision():\\n player = sequence_threshold.Player(threshold=10)\\n\\n player.cards = SortedSet()\\n assert player.play(20, 11) == True\\n assert player.play(20, 10) == True\\n assert player.play(20, 9) == False\\n\\n player.cards = SortedSet([30])\\n assert player.play(20, 11) == True\\n assert player.play(20, 10) == True\\n assert player.play(20, 9) == False\\n assert player.play(31, 1) == True\\n assert player.play(29, 1) == True\\n assert player.play(32, 1) == False\\n with pytest.raises(Exception):\\n player.play(30, 1)\\n\\n player.threshold = 5\\n\\n player.cards = SortedSet()\\n assert player.play(20, 16) == True\\n assert player.play(20, 15) == True\\n assert player.play(20, 14) == False\\n\\n player.cards = SortedSet([30])\\n assert player.play(20, 16) == True\\n assert player.play(20, 15) == True\\n assert player.play(20, 14) == False\\n assert player.play(31, 1) == True\\n assert player.play(29, 1) == True\\n assert player.play(32, 1) == False\\n with pytest.raises(Exception):\\n player.play(30, 1)\",\n \"def __init__(self, players, num_of_players):\\r\\n self.players = players\\r\\n self.num_of_players = num_of_players\\r\\n self.active_players = num_of_players\\r\\n self.dealer = Dealer()\\r\\n self.card_stack = CardStack()\\r\\n self.money_stack = MoneyStack()\\r\\n self.cur_player = 0\\r\\n self.round_num = 0\\r\\n self.round_player_money = 0\",\n \"def test_pasture_overlap():\\n a = Pasture([(0, 1)])\\n b = Pasture([(0, 1), (0, 2)])\\n c = Pasture([(0, 2), (0, 3)])\\n\\n assert a.overlaps(b)\\n assert b.overlaps(a)\\n\\n assert b.overlaps(c)\\n assert c.overlaps(b)\\n\\n assert not a.overlaps(c)\\n assert not c.overlaps(a)\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures(a)\\n p.build_pastures(c)\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures(c)\\n p.build_pastures(a)\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures([a, c])\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures([c, a])\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures(b)\\n\\n p = Player(\\\"p0\\\", wood=20)\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures([a, b])\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures(a)\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures(b)\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures(b)\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures(a)\\n\\n p = Player(\\\"p0\\\", wood=20)\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures([c, b])\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures(c)\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures(b)\\n\\n p = Player(\\\"p0\\\", wood=20)\\n p.build_pastures(b)\\n with pytest.raises(AgricolaLogicError):\\n p.build_pastures(c)\",\n \"def create_player(self, players, player_no, start_tile):\\n if player_no in players.keys():\\n raise ValueError(f\\\"Invalid map layout. Can't create player no {player_no} as it already exists.\\\")\\n players[player_no] = Player(player_no, start_tile, self)\\n return players[player_no]\",\n \"def __init__(self, players):\\n self.players = players\\n self.board = Board()\",\n \"def test_demo_ai_game_finishes(self):\\n player1 = Player(ai=BattleshipsAI())\\n player2 = Player(opponent=player1, ai=BattleshipsAI())\\n player1.opponent = player2\\n\\n game = GameRunner(player1, player2)\\n self.assertIsNot(game.play(), None)\",\n \"def testRegistration():\\n deleteMatches()\\n deletePlayers()\\n registerPlayer(\\\"Red Ranger\\\")\\n registerPlayer(\\\"Blue Ranger\\\")\\n registerPlayer(\\\"Yellow Ranger\\\")\\n registerPlayer(\\\"Pink Ranger\\\")\\n registerPlayer(\\\"Green Ranger\\\")\\n standings = playerStandings()\\n [id1, id2, id3, id4, id5] = [row[0] for row in standings]\\n reportMatch(id1, id2)\\n reportMatch(id3, id4)\\n completeTournament('Battle Royale')\\n currentPlayers = countPlayers()\\n oldPlayers = countPlayers('Battle Royale')\\n if not (currentPlayers==0 and oldPlayers==5):\\n raise ValueError(\\n \\\"Players' status were not updated correctly when tournament\\\"\\n \\\" completes\\\"\\n )\\n\\n print \\\"6. Keeping of completed tournaments in database is checked.\\\"\",\n \"def __init__(self, lobby_id, players):\\n self.card_wished = None\\n self.wait_for_card_wish = False\\n self.current_player = None\\n self.direction_clock_wise = True\\n self.current_draw_punishment = 1\\n\\n self.players = []\\n self.lobby_id = lobby_id\\n if isinstance(players, list):\\n for player in players:\\n self.players.append(player)\\n else:\\n self.players.append(players)\\n\\n players_len = len(self.players)\\n if players_len > 0:\\n self.current_player = self.players[random.randint(0, players_len) - 1]\\n\\n # Get the cards of the game\\n game = Game.objects.get(name=\\\"maumau\\\")\\n card_set = CardSet.objects.get(id=game.card_set_id)\\n self.cards = list(card_set.cards.all())\\n\\n self.__shuffle_cards()\\n for player in self.players:\\n self.draw_cards(player, 5)\\n\\n self.discard_pile = []\\n self.discard_pile.append(self.cards.pop())\",\n \"def __init__(self, players):\\n\\n self._players = players\\n self._current_player = players.get()\",\n \"def create_players(self):\\n for i in range(self.number_of_players):\\n self.players_names.append(pyip.inputStr(\\n prompt=f'\\\\nEnter name of player {i + 1}:\\\\n'))\",\n \"def assign_points(players):\\n pass\",\n \"def test_strategy(self):\\n\\n axelrod.seed(8)\\n opponent = axelrod.Cooperator()\\n player = axelrod.WorseAndWorse()\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'D'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C')])\\n\\n # Test that behaviour does not depend on opponent\\n opponent = axelrod.Defector()\\n player = axelrod.WorseAndWorse()\\n axelrod.seed(8)\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'D'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C')])\",\n \"def test_initial_limit(self):\\n\\n g = test_setup.simple_two_player()\\n\\n p1, p2 = g.players\\n\\n self.assertEqual(g._clientele_limit(p1), 2)\\n self.assertEqual(g._clientele_limit(p2), 2)\",\n \"def test_SetPlayerPuzzleScores_manyplayers(self):\\r\\n puzzle_id = ['1']\\r\\n player1_score = 0.08\\r\\n player2_score = 0.02\\r\\n response1 = self.make_puzzle_score_request(\\r\\n puzzle_id, player1_score, self.user\\r\\n )\\r\\n\\r\\n # There should now be a score in the db.\\r\\n top_10 = Score.get_tops_n(10, puzzle_id)\\r\\n self.assertEqual(len(top_10), 1)\\r\\n self.assertEqual(top_10[0]['score'], Score.display_score(player1_score))\\r\\n\\r\\n response2 = self.make_puzzle_score_request(\\r\\n puzzle_id, player2_score, self.user2\\r\\n )\\r\\n\\r\\n # There should now be two scores in the db\\r\\n top_10 = Score.get_tops_n(10, puzzle_id)\\r\\n self.assertEqual(len(top_10), 2)\\r\\n\\r\\n # Top score should be player2_score. Second should be player1_score\\r\\n self.assertAlmostEqual(\\r\\n top_10[0]['score'],\\r\\n Score.display_score(player2_score),\\r\\n delta=0.5\\r\\n )\\r\\n self.assertAlmostEqual(\\r\\n top_10[1]['score'],\\r\\n Score.display_score(player1_score),\\r\\n delta=0.5\\r\\n )\\r\\n\\r\\n # Top score user should be self.user2.username\\r\\n self.assertEqual(top_10[0]['username'], self.user2.username)\",\n \"def initialise_players(update=False, file_object=None):\\n # do not run if there are existing Player records\\n players = Player.objects.all()\\n if players.exists():\\n # flag that this is an update performed to get new players before the auction\\n update = True\\n\\n codes = get_player_codes()\\n print(\\\"codes:\\\", codes)\\n\\n # get a lookup dict of PremTeams, key = web_code\\n prem_team_dict = get_prem_team_dict(key='web_code')\\n print(prem_team_dict)\\n\\n # get the rows from the provided stats file\\n if file_object:\\n rows = json.load(file_object)\\n file_object.close()\\n else:\\n print(\\\"No file object provided\\\")\\n return\\n print(\\\"No. of player table rows = \\\", len(rows))\\n\\n # track new players *during update only*\\n new_players = []\\n\\n print(\\\"****\\\")\\n print(\\\"New Players\\\")\\n print(\\\"****\\\")\\n\\n for row in rows:\\n\\n first_name = row.get('first_name')\\n if first_name:\\n name = \\\"{}. {}\\\".format(first_name[0], row['last_name'])\\n else:\\n name = row['last_name']\\n\\n web_code = row['id']\\n prem_team_code = row['squad_id']\\n prem_team = prem_team_dict[prem_team_code]\\n value = float(row['cost'] / 1000000.0)\\n position = str(row['position'])\\n\\n # stats for new players can be an empty list\\n stats = row['stats'] or {}\\n\\n last_years_total = stats.get('total_points', 0)\\n\\n # create (or update) a Player instance\\n p, created = Player.objects.update_or_create(\\n name=name, web_code=web_code,\\n defaults={\\n 'prem_team': prem_team,\\n 'value': value,\\n 'last_years_total': last_years_total,\\n 'position': position,\\n }\\n )\\n\\n # assign the player codes if this is an update (i.e. pre-auction)\\n if update and created:\\n codes[p.position] += 1\\n p.code = codes[p.position]\\n p.is_new = True\\n p.save()\\n new_players.append(p)\\n print(p.code, p.name, p.prem_team, p.value, created)\\n\\n print(\\\"****\\\")\\n print('All players saved')\\n print(\\\"****\\\")\\n\\n if not update:\\n # Makes ure players are in the correct order before calculating tha AZFF player code\\n # get each group of players (by position), order by decreasing value then\\n # assign an incrementing code for AZFF\\n # Note that we can't use the custom manager at this stage (it uses code not web_code)\\n\\n # GKP\\n gkps = Player.objects.filter(position=Player.GKP).order_by('-value')\\n print('gkps.count() = ', gkps.count())\\n for g in gkps:\\n codes[Player.GKP] += 1\\n g.code = codes[Player.GKP]\\n g.save()\\n\\n # DEF\\n defs = Player.objects.filter(position=Player.DEF).order_by('-value')\\n print('defs.count() = ', defs.count())\\n for d in defs:\\n codes[Player.DEF] += 1\\n d.code = codes[Player.DEF]\\n d.save()\\n\\n # MID\\n mids = Player.objects.filter(position=Player.MID).order_by('-value')\\n print('mids.count() = ', mids.count())\\n for m in mids:\\n codes[Player.MID] += 1\\n m.code = codes[Player.MID]\\n m.save()\\n\\n # STR\\n strs = Player.objects.filter(position=Player.STR).order_by('-value')\\n print('strs.count() = ', strs.count())\\n for s in strs:\\n codes[Player.STR] += 1\\n s.code = codes[Player.STR]\\n s.save()\",\n \"def create_players_list(self):\\n for p in self.players_names:\\n self._players_list.append(Player(p))\",\n \"def __init__(self, num_agents, min_buy_in, max_buy_in, buy_ins):\\n\\t\\tself.deck = Deck()\\n\\t\\tself.deck.shuffle()\\n\\n\\t\\tself.player_count = num_agents\\n\\t\\tself.players = []\\n\\t\\tself.minBuyIn = min_buy_in\\n\\t\\tself.maxBuyIn = max_buy_in\\n\\n\\t\\tfor i in range(num_agents):\\n\\t\\t\\tplayers.append(TexasPlayer(buy_ins[i], i + 1))\",\n \"def __init__(self, players, frames_nbr=10, pins_nbr=10, throws_nbr=2):\\n self.pins_nbr = pins_nbr\\n self.throws = []\\n self.throws_nbr = throws_nbr\\n self.frames_nbr = frames_nbr\\n self.players = players\",\n \"def __init__(self, game: int) -> None:\\n self.game = game\\n self.players: List[object] = []\",\n \"def test_instantiate_valid_target(self):\\n # create test configs\\n test_configs = [\\n {\\\"_target_\\\": \\\"collections.deque\\\"},\\n {\\\"_target_\\\": \\\"collections.UserString\\\", \\\"seq\\\": \\\"test string\\\"}\\n ]\\n\\n # create truth objects\\n truth_objs = [deque(), UserString(\\\"test string\\\")]\\n\\n # check that instantiate returns truth object for each config\\n for truth_obj, test_config in zip(truth_objs, test_configs):\\n self.assertEqual(truth_obj, instantiate(test_config))\",\n \"def spawn_players(self) -> None:\\n # Initialise the players\\n self.player1 = Player(self.sensitivity, self.screen_width, self.screen_height, self.screen_width // 2, 50,\\n self.player_lives, self.fps, self.player1_bullet, Direction.DOWN, self.debug)\\n self.player2 = Player(self.sensitivity, self.screen_width, self.screen_height, self.screen_width // 2,\\n self.screen_height - 50, self.player_lives, self.fps, self.player2_bullet, Direction.UP,\\n self.debug)\\n\\n # Rotate the image of the player at the top\\n self.player1.rotate(180)\",\n \"def test_pasture_cost():\\n a = Pasture((0, 1))\\n b = Pasture((1, 1))\\n c = Pasture((1, 2))\\n d = Pasture([(0, 3), (0, 4)])\\n e = Pasture((0, 2))\\n f = Pasture([(1, 2), (1, 3)])\\n\\n player = Player(\\\"p0\\\", wood=0, rooms=[])\\n for p in [a, b, c, d, e, f]:\\n with pytest.raises(AgricolaNotEnoughResources):\\n player.build_pastures(p)\\n\\n player = Player(\\\"p0\\\", wood=4, rooms=[])\\n player.build_pastures(a)\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=4, rooms=[])\\n player.build_pastures(b)\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=4, rooms=[])\\n player.build_pastures(c)\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=6, rooms=[])\\n player.build_pastures(d)\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=4, rooms=[])\\n player.build_pastures(e)\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=6, rooms=[])\\n player.build_pastures(f)\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=6, rooms=[])\\n player.build_pastures(f)\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=10, rooms=[])\\n player.build_pastures([a, b, c])\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=11, rooms=[])\\n player.build_pastures([d, f])\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=9, rooms=[])\\n player.build_pastures([d, e])\\n assert player.wood == 0\\n\\n player = Player(\\\"p0\\\", wood=13, rooms=[])\\n player.build_pastures([d, e, f])\\n assert player.wood == 0\",\n \"def setUp(self):\\n d = self.deck = TestDeck()\\n self.game = test_setup.two_player_lead('Laborer', deck=d)\\n self.p1, self.p2 = self.game.players\",\n \"def __initPicks(self):\\n picks = self.teamparser.getPlayerPicks()\\n try:\\n self.picks = picks[(self.team, self.position)] #players picks\\n except KeyError, err:\\n self.picks = []\\n raise TypeError, \\\"Invalid Team/Position: \\\" + self.team\",\n \"def set_players(self, player_min: int, player_max: int):\\n name: str = ' '\\n print('Please give between %i and %i names for your players'\\n % (player_min, player_max))\\n while (name != '') and (len(self.players) < player_max):\\n name = input('Players {}: '.format(len(self.players)+1))\\n if name != '':\\n self.players.append(Player(name))\\n elif len(self.players) < player_min:\\n name = ' '\\n\\n print()\\n print('{} players registered.'.format(len(self.players)))\\n print()\",\n \"def __init__(self, *names):\\n deck = Deck.create(shuffle=True)\\n self.names = (list(names) + \\\"P1 P2 P3 P4 \\\".split())[:4]\\n self.hands = {\\n n: Player(n, h) for n, h in zip(self.names, deck.deal(4))\\n }\",\n \"def test_create_player_successful(self):\\n payload = {'name': 'Mayita', 'victories': 0, 'defeats': 0}\\n self.client.post(PLAYERS_URL, payload)\\n\\n print('PLAYERS_URL: ',PLAYERS_URL)\\n exists = Player.objects.filter(\\n name=payload['name']\\n ).exists()\\n self.assertTrue(exists)\",\n \"def test_new(self):\\n obj = Game.new(self._creator, self._ds)\\n self.assertIsInstance(obj, Game, \\\"Game instance not initialized.\\\")\\n self.assertHasAttribute(obj, 'uid', \\\"Game has no unique ID.\\\")\\n self.assertHasAttributes(obj, [\\n 'players', 'spectators', 'state', 'points', 'options', 'table'])\\n self.assertIsCREATED(obj)\",\n \"def __init__(self, initial=[1, 3, 5, 7]):\\n self.piles = initial.copy()\\n self.player = 0\\n self.winner = None\",\n \"def __init__(self, initial=[1, 3, 5, 7]):\\n self.piles = initial.copy()\\n self.player = 0\\n self.winner = None\",\n \"def create_players(tournament):\\n enter_player = 1\\n while enter_player <= 8:\\n player_list = select_players()\\n selection = view.menu_create_player(player_list)\\n if selection == 1:\\n # ---------------------------------------------------------------------------------\\n # Joueur existant\\n view.print_actors(player_list)\\n data = create_existing_player()\\n player_db = Player(data['Nom'], data['Prenom'], data['Date de Naissance'],\\n data['Sexe'], data['Classement'])\\n # add the player id to the list of tournament players_id\\n tournament.players_index_list.append(data.doc_id)\\n # add the player to the list of tournament players with the tournament method\\n tournament.add_player(player_db)\\n # ---------------------------------------------------------------------------------\\n elif selection == 2:\\n # ---------------------------------------------------------------------------------\\n # Nouveau joueur\\n data = view.create_player_view(enter_player)\\n player = Player(data['name'], data['first_name'], data['birth_day'], data['sex'],\\n data['ranking'])\\n # add the player to the list of tournament players with the tournament method\\n tournament.add_player(player)\\n # players are registered in the database\\n save_player(player)\\n # prendre l'identifiantiant du joueur\\n for id_player in get_players_id(1):\\n tournament.players_index_list.append(id_player)\\n # ---------------------------------------------------------------------------------\\n enter_player += 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68950206","0.68166816","0.6779744","0.6726286","0.6673796","0.6636629","0.65848154","0.6571941","0.6493531","0.64693403","0.6440882","0.64096856","0.6371468","0.63653827","0.6354527","0.6353987","0.6349084","0.63259184","0.6288197","0.6285662","0.6261972","0.62616354","0.6260546","0.62135726","0.620951","0.6208894","0.61785704","0.6178015","0.6169811","0.615494","0.6145165","0.6135045","0.6118676","0.6115899","0.6110253","0.61097693","0.609806","0.60954225","0.60901976","0.6077055","0.6074221","0.6060983","0.6057688","0.604408","0.60437024","0.6038838","0.60266984","0.6013018","0.6001301","0.59938663","0.59929323","0.59861106","0.59785753","0.59750366","0.5959303","0.593365","0.59245306","0.5921753","0.5915652","0.59127444","0.5907536","0.58946073","0.5886481","0.5882716","0.5877295","0.58702195","0.5868329","0.58614904","0.5858375","0.58551604","0.58505076","0.58411616","0.58276594","0.58251876","0.5824504","0.5824444","0.5814156","0.581207","0.5810052","0.5807625","0.5804791","0.5799211","0.5796672","0.57832205","0.57748985","0.57744765","0.57637304","0.5760382","0.57588446","0.57462144","0.5745886","0.57442087","0.5741184","0.57398033","0.57340246","0.5729501","0.57289","0.5726517","0.5726517","0.5722785"],"string":"[\n \"0.68950206\",\n \"0.68166816\",\n \"0.6779744\",\n \"0.6726286\",\n \"0.6673796\",\n \"0.6636629\",\n \"0.65848154\",\n \"0.6571941\",\n \"0.6493531\",\n \"0.64693403\",\n \"0.6440882\",\n \"0.64096856\",\n \"0.6371468\",\n \"0.63653827\",\n \"0.6354527\",\n \"0.6353987\",\n \"0.6349084\",\n \"0.63259184\",\n \"0.6288197\",\n \"0.6285662\",\n \"0.6261972\",\n \"0.62616354\",\n \"0.6260546\",\n \"0.62135726\",\n \"0.620951\",\n \"0.6208894\",\n \"0.61785704\",\n \"0.6178015\",\n \"0.6169811\",\n \"0.615494\",\n \"0.6145165\",\n \"0.6135045\",\n \"0.6118676\",\n \"0.6115899\",\n \"0.6110253\",\n \"0.61097693\",\n \"0.609806\",\n \"0.60954225\",\n \"0.60901976\",\n \"0.6077055\",\n \"0.6074221\",\n \"0.6060983\",\n \"0.6057688\",\n \"0.604408\",\n \"0.60437024\",\n \"0.6038838\",\n \"0.60266984\",\n \"0.6013018\",\n \"0.6001301\",\n \"0.59938663\",\n \"0.59929323\",\n \"0.59861106\",\n \"0.59785753\",\n \"0.59750366\",\n \"0.5959303\",\n \"0.593365\",\n \"0.59245306\",\n \"0.5921753\",\n \"0.5915652\",\n \"0.59127444\",\n \"0.5907536\",\n \"0.58946073\",\n \"0.5886481\",\n \"0.5882716\",\n \"0.5877295\",\n \"0.58702195\",\n \"0.5868329\",\n \"0.58614904\",\n \"0.5858375\",\n \"0.58551604\",\n \"0.58505076\",\n \"0.58411616\",\n \"0.58276594\",\n \"0.58251876\",\n \"0.5824504\",\n \"0.5824444\",\n \"0.5814156\",\n \"0.581207\",\n \"0.5810052\",\n \"0.5807625\",\n \"0.5804791\",\n \"0.5799211\",\n \"0.5796672\",\n \"0.57832205\",\n \"0.57748985\",\n \"0.57744765\",\n \"0.57637304\",\n \"0.5760382\",\n \"0.57588446\",\n \"0.57462144\",\n \"0.5745886\",\n \"0.57442087\",\n \"0.5741184\",\n \"0.57398033\",\n \"0.57340246\",\n \"0.5729501\",\n \"0.57289\",\n \"0.5726517\",\n \"0.5726517\",\n \"0.5722785\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7017496"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93786,"cells":{"query":{"kind":"string","value":"Test for correct instantiation given a set of dictionaries."},"document":{"kind":"string","value":"def test_create_from_dictionaries(connections, clean):\n\n resident_prefs, hospital_prefs, capacities = connections\n\n game = HospitalResident.create_from_dictionaries(\n resident_prefs, hospital_prefs, capacities, clean\n )\n\n for resident in game.residents:\n assert resident._pref_names == resident_prefs[resident.name]\n assert resident.matching is None\n\n for hospital in game.hospitals:\n assert hospital._pref_names == hospital_prefs[hospital.name]\n assert hospital.capacity == capacities[hospital.name]\n assert hospital.matching == []\n\n assert game.matching is None\n assert game.clean is clean"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_create_from_dictionaries(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n stud_prefs, sup_prefs, proj_sups, proj_caps, sup_caps = make_connections(\n student_names, project_names, supervisor_names, capacities, seed\n )\n\n game = StudentAllocation.create_from_dictionaries(\n stud_prefs, sup_prefs, proj_sups, proj_caps, sup_caps, clean\n )\n\n for student in game.students:\n assert student._pref_names == stud_prefs[student.name]\n assert student.matching is None\n\n for project in game.projects:\n assert project.supervisor.name == proj_sups[project.name]\n assert project.matching == []\n\n for supervisor in game.supervisors:\n assert supervisor._pref_names == sup_prefs[supervisor.name]\n assert supervisor.matching == []\n\n assert game.matching is None\n assert game.clean is clean","def test_set_creation():\r\n test_list = [1, 2, 1, 3] # it is alist\r\n set1 = set(test_list)\r\n assert {1,2,3} == set1 # randomly arranged and don't allow duplicates\r\n\r\n test_string = \"apple\"\r\n set2 = set(test_string)\r\n assert {'a','p','l','e'} == set2\r\n\r\n test_dict = { 1: \"one\", 2 : \"two\"}\r\n set3 = set(test_dict)\r\n assert {1,2} == set3 #only keys are considered\r\n\r\n set4 = set(test_dict.values())\r\n assert {\"one\",\"two\"} == set4\r\n\r\n set5 = set(test_dict.items())\r\n assert set([(2, 'two'), (1, 'one')]) == set5 ######################DOUBT\r","def testfromjson(self):\n dicty = {\"id\": 5, \"class\": \"string\", \"list\": [], \"set\": {}}\n self.assertEqual([dicty, dicty],\n Base.from_json_string(json.dumps([dicty, dicty])))","def test_fromkeys(self):\n d = SplayDict.fromkeys(['a', 'b', 'c'], 1)\n self.assertIn('a' , d)\n self.assertIn('b' , d)\n self.assertIn('c' , d)\n self.assertEqual(d['a'] , 1)\n self.assertEqual(d['b'] , 1)\n self.assertEqual(d['c'] , 1)","def test_build_format_map(all_sets: dict, expected: dict) -> None:\n result = build_format_map(all_sets, regular=False)\n assert result == expected","def check_for_dict(check):","def test_create_from_dicts(self):\n cols = list(zip(*self.dtypes))[0]\n dicts = [dict([(cols[i], d[i]) for i in xrange(len(d))])\n for d in self.idata]\n\n tbl = Table.create(\n ':memory:', \"Bar\", dicts, verbose=True,\n primary_key='id', autoincrement=True)\n\n self.check_index(self.idata, tbl.select())\n for idx, col in enumerate(cols):\n if col == 'id':\n continue\n self.check_data(self.idata[:, [0, idx]], tbl[col])","def testfromjson(self):\n dicty = {\"id\": 5, \"class\": \"string\", \"list\": [], \"set\": {}}\n self.assertEqual([dicty], Base.from_json_string(json.dumps([dicty])))","def test_dictionary(self):\n self.assertIsInstance(self.test1json, dict)","def check_dict(dic, validator, messages):\n check_dict_alg(dic, validator, [], messages, validator, \"NoObject\")","def test_validation(self):\n\n with self.assertRaises(ValidationError):\n Dictionary().validate([1,2,3])\n\n with self.assertRaises(ValidationError):\n Dictionary().validate('stringy')\n\n with self.assertRaises(ValidationError):\n Dictionary().validate(1)","def assert_template_dicts(obj1: Any, obj2: Any) -> None:\n assert to_template_dict(obj1) == to_template_dict(obj2)","def test_create_obj_by_type_from_dict(self):\n test_obj = {}\n returned_obj = self.tested_class._create_obj_by_type(test_obj)\n self.assertIsInstance(returned_obj, self.tested_class)","def testInitFromDict():\n conf = naiveConf.NaiveConf({})\n conf = naiveConf.NaiveConf({'a':5})\n assert conf.a == 5","def test_get_cases_for_dict(self):\n pass","def test_instantiate_valid_target(self):\n # create test configs\n test_configs = [\n {\"_target_\": \"collections.deque\"},\n {\"_target_\": \"collections.UserString\", \"seq\": \"test string\"}\n ]\n\n # create truth objects\n truth_objs = [deque(), UserString(\"test string\")]\n\n # check that instantiate returns truth object for each config\n for truth_obj, test_config in zip(truth_objs, test_configs):\n self.assertEqual(truth_obj, instantiate(test_config))","def test_validation(self):\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate([1,2,3])\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate('stringy')\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate(1)","def verifyDictTypes( template, dictToCheck ):\n for key in dictToCheck:\n if not ( ( isinstance( dictToCheck[ key ], list ) and\n isinstance( template[ key ], list ) ) or\n ( isinstance( dictToCheck[ key ], dict ) and\n isinstance( template[ key ], dict ) ) or\n ( isinstance( dictToCheck[ key ], template[ key ] ) ) ):\n return False\n\n return True","def test_isadict(self):\n # It is a dict-subclass, so this kind of pointless, but it doen't hurt.\n d, m = dict(a=5), ConfigDict(a=5)\n d['key'], m['key'] = 'value', 'value'\n d['k2'], m['k2'] = 'v1', 'v1'\n d['k2'], m['k2'] = 'v2', 'v2'\n self.assertEqual(d.keys(), m.keys())\n self.assertEqual(list(d.values()), list(m.values()))\n self.assertEqual(d.get('key'), m.get('key'))\n self.assertEqual(d.get('cay'), m.get('cay'))\n self.assertEqual(list(iter(d)), list(iter(m)))\n self.assertEqual([k for k in d], [k for k in m])\n self.assertEqual(len(d), len(m))\n self.assertEqual('key' in d, 'key' in m)\n self.assertEqual('cay' in d, 'cay' in m)\n self.assertRaises(KeyError, lambda: m['cay'])","def test_init_with_nested_dicts(self):\n regex = 'mappings can not be nested'\n with self.assertRaisesRegex(ValueError, regex):\n query = DataQuery({'A': {'B': 'C'}}, D='x')","def test_if_it_accepts_dictionary(self):\n with self.assertRaises(TypeError):\n prime_numbers({})","def test_construct_tickets_2(self):\n dict_list = [self.ticket_dict1, self.ticket_dict2]\n eval_data_dict = {\"eval_mode\": \"custom_eval_mode\",\n \"eval_flag_dict\": {}}\n list_of_tickets = tickets.construct_tickets(dict_list,\n eval_data_dict, \"function\", self.required_keys,\n self.optional_keys, self.keywords)\n with self.subTest():\n self.assertEqual(len(list_of_tickets), 1)","def test_comparing(self):\n for test in self.test_dict_data:\n self.assertEqual(dottedDict(test[0]), test[1])","def test_valid_analysis_request(analysis_request_dict: JSONDict) -> None:\n\n request = AnalysisRequest(**analysis_request_dict)\n\n assert request.dict() == analysis_request_dict","def create_dicts():\n load_data_for_dict('data/atis/train/seq.in', 'data/atis/voc/vocabulary.json')\n load_data_for_dict('data/atis/valid/seq.in', 'data/atis/voc/vocabulary.json')\n load_data_for_dict('data/atis/test/seq.in', 'data/atis/voc/vocabulary.json') \n load_data_for_dict('data/atis/train/seq.out', 'data/atis/voc/slot_vocabulary.json')","def test_unique_train_items(train_items: List[JSONDict]) -> None:\n validated = TrainCollection(items=train_items)\n assert validated.dict() == train_items","def test_15_dict_test(self):\n with self.assertRaises(TypeError) as x:\n r = Rectangle(10, {})\n self.assertEqual(\n \"height must be an integer\",\n str(x.exception))\n with self.assertRaises(TypeError) as x:\n r = Rectangle({\"a\": 1, \"b\": 2, \"c\": 3}, 2)\n self.assertEqual(\n \"width must be an integer\",\n str(x.exception))\n with self.assertRaises(TypeError) as x:\n r = Rectangle(10, 2, {\"a\": 1})\n self.assertEqual(\n \"x must be an integer\",\n str(x.exception))\n with self.assertRaises(TypeError) as x:\n r = Rectangle(10, 2, 0, {\"hi\": None})\n self.assertEqual(\n \"y must be an integer\",\n str(x.exception))","def _dict_assert(actual_dict, expected_dict):\n for key in set(actual_dict) & set(expected_dict):\n _value_assert(key, actual_dict[key], expected_dict[key])","def test_jurisdictions_instances(self):\n obj = Jurisdiction()\n self.assertIsInstance(obj, Jurisdiction)","def test_init(resident_names, hospital_names, capacities, seed):\n\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n assert match.suitors == residents\n assert match.reviewers == hospitals\n assert all([suitor.matching is None for suitor in match.suitors])\n assert all([reviewer.matching == [] for reviewer in match.reviewers])\n assert match.matching is None","def test_init_hash(self):\n bill = Bill(self.input_hash)\n for key, value in self.input_hash.iteritems():\n self.assertEqual(value, bill.__dict__[key])","def test_make_compatible_taxa_summaries_sample_id_map_incomplete_map(self):\r\n self.assertRaises(ValueError, _make_compatible_taxa_summaries,\r\n self.taxa_summary3, self.taxa_summary4, self.sample_id_map3)","def test_correct_train_item(train_items: List[JSONDict]) -> None:\n validated = TrainItem(**train_items[0])\n assert validated.dict() == train_items[0]","def test_output_named_tuple_vs_dictionary_2():\n assert oldest_person_age == oldest_person_age_d, \"Max age cannot be different for Named Tuple and Dictionary list\"","def __init__(self, other):\n if type(other) == dict:\n for k in other:\n if type(other[k]) == dict:\n other[k] = JsonFactory(other[k])\n self.__dict__ = other\n else:\n raise PJFInvalidType(other, dict)","def test_1():\n results = base_tests()\n assert type(results) is list\n assert type(results[0]) is dict\n assert len(results) == 3","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def test_dumps_set(self):\n try:\n _build_test_dirs()\n dicti = {\n 'set': set([1, 2, 4, 4, 2]),\n 'array': [1, 2, 3],\n 'string': 'trololo',\n 'int': 1,\n 'float': 4.32,\n 'true': True,\n 'false': False,\n 'null': None\n }\n with open(_TEST_FILE, 'w+') as fileobj:\n morejson.dump(dicti, fileobj)\n with open(_TEST_FILE, 'r') as fileobj:\n self.assertEqual(dicti, morejson.load(fileobj))\n finally:\n _dismantle_test_dirs()","def testMapConstructor(self):\n # We only use one map type since they all share the same implementation for\n # this logic.\n with self.assertRaises(AssertionError):\n data_types.StepBuildStatsMap({1: 2})\n m = data_types.StepBuildStatsMap({'step': data_types.BuildStats()})\n self.assertEqual(m, {'step': data_types.BuildStats()})","def test_26_create(self):\n r1 = Rectangle(3, 5, 1)\n r1_dictionary = r1.to_dictionary()\n r2 = Rectangle.create(**r1_dictionary)\n self.assertEqual((r1 == r2), False)\n self.assertEqual((r1 is r2), False)","def test_dict(self, dictionary: dict) -> None:\r\n if not isinstance(dictionary, dict):\r\n raise ValueError(f'Expected dictionary, but received {type(dictionary)}')\r\n for key, value in dictionary.items():\r\n conditional_check(key, self.case_check, self.ignored_keys)\r\n if isinstance(value, dict):\r\n self.test_dict(dictionary=value)\r\n elif isinstance(value, list):\r\n self.test_list(items=value)","def test_empty_dict_by_name():\n\n @type_checked\n def _run_test(thing:dict):\n assert isinstance(thing, dict)\n\n _run_test({\"baz\": True})","def test_class_kwargs(self):\n dictonary = {\n 'id': '662a23b3-abc7-4f43-81dc-64c000001c00', 'score': 100}\n review1 = Review(**dictonary)\n self.assertTrue(hasattr(review1, \"id\"))\n self.assertEqual(review1.id, '662a23b3-abc7-4f43-81dc-64c000001c00')\n self.assertTrue(hasattr(review1, \"score\"))\n self.assertEqual(review1.score, 100)\n self.assertTrue(hasattr(review1, \"created_at\"))\n self.assertTrue(type(review1.updated_at), datetime)\n self.assertTrue(hasattr(review1, \"updated_at\"))\n self.assertTrue(type(review1.created_at), datetime)\n self.assertEqual(review1.__class__.__name__, \"Review\")","def test_init(self, testdata: TestData) -> None:\n for data in testdata['observation_type']:\n observation_type = ObservationType(**data)\n for key, value in data.items():\n assert getattr(observation_type, key) == value","def test_class_kwargs(self):\n dictonary = {\n 'id': '662a23b3-abc7-4f43-81dc-64c000001c00', 'score': 100}\n state1 = State(**dictonary)\n self.assertTrue(hasattr(state1, \"id\"))\n self.assertEqual(state1.id, '662a23b3-abc7-4f43-81dc-64c000001c00')\n self.assertTrue(hasattr(state1, \"score\"))\n self.assertEqual(state1.score, 100)\n self.assertTrue(hasattr(state1, \"created_at\"))\n self.assertTrue(type(state1.updated_at), datetime)\n self.assertTrue(hasattr(state1, \"updated_at\"))\n self.assertTrue(type(state1.created_at), datetime)\n self.assertEqual(state1.__class__.__name__, \"State\")","def _is_dict_in_one_of_dicts(d, dicts):\n\n for vd in dicts:\n if Settings._is_in_dict(d, vd):\n return True\n return False","def test_init(self):\n some_key_value1 = mock.MagicMock()\n some_key_value2 = \"some_str\"\n some_nested_value1 = mock.MagicMock()\n some_nested_value2 = [100, \"test_strt\", mock.MagicMock()]\n test_data = {\n \"some_key1\": some_key_value1,\n \"some_key2\": some_key_value2,\n \"some_key3\": {\n \"some_nested_key1\": some_nested_value1,\n \"some_nested_key2\": some_nested_value2\n }\n }\n data_holder = self.tested_class(test_data)\n\n self.assertIsInstance(data_holder, self.tested_class)\n self.assertEqual(data_holder.some_key1, some_key_value1)\n self.assertEqual(data_holder.some_key2, some_key_value2)\n self.assertIsInstance(data_holder.some_key3, self.tested_class)\n self.assertEqual(data_holder.some_key3.some_nested_key1, some_nested_value1)\n self.assertEqual(data_holder.some_key3.some_nested_key2, some_nested_value2)","def test_create(self):\n B._Base__nb_objects = 0\n\n d = {'id': 5, 'width': 3, 'height': 7, 'x': 2, 'y': 1}\n r1 = Rectangle.create(**d)\n self.assertEqual(r1.to_dictionary(), d)\n self.assertEqual(B._Base__nb_objects, 1)\n\n s2 = Square(5)\n d4 = s2.to_dictionary()\n s5 = Square.create(**d4)\n self.assertEqual(s5.to_dictionary(), d4)\n self.assertEqual(B._Base__nb_objects, 3)","def test_deep_set_create(self):\n mdict = copy.deepcopy(self.dict1)\n res = dictupdate.set_dict_key_value(mdict, \"K:L:M\", \"Q\")\n self.assertEqual(\n {\n \"A\": \"B\",\n \"C\": {\"D\": \"E\", \"F\": {\"G\": \"H\", \"I\": \"J\"}},\n \"K\": {\"L\": {\"M\": \"Q\"}},\n },\n res,\n )","def test_jurisdiction_attributes(self):\n obj = Jurisdiction(**{'name': 'Antioquia', 'victims': 1})\n self.assertIsInstance(obj.name, str)\n self.assertIsInstance(obj.victims, int)","def test_init(self):\n space = Space()\n assert isinstance(space, dict)","def test_from_dict(self):\n\n class Person(Model):\n name = StringField()\n age = IntegralField(bounds = (0, None))\n siblings = ListField(of = StringField())\n\n # In the normal case where all the data coincides with fields\n # correctly.\n person1 = Person.from_dict({\n \"name\": \"Joe Shmoe\",\n \"age\": 21,\n \"siblings\": [\"Dick Shmoe\", \"Jane Shmoe\"]\n })\n assert person1.name == \"Joe Shmoe\"\n assert person1.age == 21\n assert person1.siblings == [\"Dick Shmoe\", \"Jane Shmoe\"]\n\n # In the less normal case where the data does not coincide with fields\n person2 = Person.from_dict({\n \"notaname\": 2,\n \"age\": \"lots\"\n })\n assert person2.notaname == 2\n assert person2.age == \"lots\"\n assert person2.name is None\n assert person2.siblings is None\n\n # In the even less normal case where no data exists at all\n person3 = Person.from_dict({})\n assert person3.name is None\n assert person3.age is None\n assert person3.siblings is None","def verifyDicts(cityCodeDict, aircraftCodeDict, flightDict):\n print 'verify dictionary creation by length'\n print 'length of cityCodeDict', len(cityCodeDict)\n print 'length of aircraftCodeDict', len(aircraftCodeDict)\n print 'length of flightDict', len(flightDict)\n print 'display a row from each dictionary to verify contents'\n for key in cityCodeDict.keys():\n print 'for key', key, 'the row is', cityCodeDict[key].cityCode, cityCodeDict[key].city\n break\n for key in aircraftCodeDict.keys():\n print 'for key', key, 'the row is', aircraftCodeDict[key].aircraftCode, aircraftCodeDict[key].name\n break \n for key in flightDict.keys():\n print 'for key', key, 'the row is', flightDict[key].flightnum, flightDict[key].departCity,flightDict[key].arriveCity\n break","def verifyDicts(cityCodeDict, aircraftCodeDict, flightDict):\n print 'verify dictionary creation by length'\n print 'length of cityCodeDict', len(cityCodeDict)\n print 'length of aircraftCodeDict', len(aircraftCodeDict)\n print 'length of flightDict', len(flightDict)\n print 'display a row from each dictionary to verify contents'\n for key in cityCodeDict.keys():\n print 'for key', key, 'the row is', cityCodeDict[key].cityCode, cityCodeDict[key].city\n break\n for key in aircraftCodeDict.keys():\n print 'for key', key, 'the row is', aircraftCodeDict[key].aircraftCode, aircraftCodeDict[key].name\n break \n for key in flightDict.keys():\n print 'for key', key, 'the row is', flightDict[key].flightnum, flightDict[key].departCity,flightDict[key].arriveCity\n break","def test_init_with_kwargs(self):\n self.base1.name = \"Holberton\"\n model_json = self.base1.to_dict()\n new_model = BaseModel(**model_json)\n self.assertDictEqual(model_json, new_model.to_dict())\n self.assertIn(\"name\", new_model.to_dict())\n self.assertIsNot(self.base1, new_model)","def verify_exploration_dict(exploration_dict):\n EXPLORATION_SCHEMA = [\n ('default_skin', basestring), ('param_changes', list),\n ('param_specs', dict), ('schema_version', int), ('states', list)\n ]\n utils.verify_dict_keys_and_types(exploration_dict, EXPLORATION_SCHEMA)\n\n # Each param spec value should be a dict of the form {obj_type: [STRING]}.\n for param_key in exploration_dict['param_specs']:\n ps_value = exploration_dict['param_specs'][param_key]\n if len(ps_value) != 1 or ps_value.keys()[0] != 'obj_type':\n raise Exception('Invalid param_spec dict: %s' % ps_value)\n\n obj_class = obj_services.get_object_class(ps_value['obj_type'])\n if obj_class is None:\n raise Exception('No object class specified.')\n\n # Verify there is at least one state.\n if not exploration_dict['states']:\n raise Exception('Each exploration should have at least one state.')\n\n state_name_list = []\n for state_desc in exploration_dict['states']:\n state_name = state_desc['name']\n if state_name in state_name_list:\n raise Exception('Duplicate state name: %s' % state_name)\n state_name_list.append(state_name)\n\n for state_desc in exploration_dict['states']:\n verify_state_dict(\n state_desc, state_name_list, exploration_dict['param_specs'])\n\n _verify_all_states_reachable(exploration_dict['states'])\n _verify_no_dead_ends(exploration_dict['states'])","def test_get_unique_drug_list(self):\n dict1 = self.test_dict\n dict2 = get_unique_drug_list(self.test_sorted_tuple)\n self.assertEqual(dict1, dict2)","def test_items(self):\n obs = self.tester.items()\n self.assertTrue(isinstance(obs, Iterable))\n exp = {('physical_location', 'ANL'), ('has_physical_specimen', True),\n ('has_extracted_data', True), ('sample_type', 'ENVO:soil'),\n ('required_sample_info_status', 'completed'),\n ('collection_timestamp', datetime(2011, 11, 11, 13, 00, 00)),\n ('host_subject_id', '1001:M7'),\n ('description', 'Cannabis Soil Microbiome'),\n ('season_environment', 'winter'), ('assigned_from_geo', 'n'),\n ('texture', '64.6 sand, 17.6 silt, 17.8 clay'),\n ('taxon_id', '1118232'), ('depth', 0.15),\n ('host_taxid', '3483'), ('common_name', 'root metagenome'),\n ('water_content_soil', 0.164), ('elevation', 114), ('temp', 15),\n ('tot_nitro', 1.41), ('samp_salinity', 7.15), ('altitude', 0),\n ('env_biome',\n 'ENVO:Temperate grasslands, savannas, and shrubland biome'),\n ('country', 'GAZ:United States of America'), ('ph', 6.94),\n ('anonymized_name', 'SKB8'), ('tot_org_carb', 5),\n ('description_duplicate', 'Burmese root'),\n ('env_feature', 'ENVO:plant-associated habitat'),\n ('latitude', 74.0894932572),\n ('longitude', 65.3283470202)}\n self.assertEqual(set(obs), exp)","def test_check_xyz_dict(self):\n xyz1 = converter.check_xyz_dict(self.xyz1['str'])\n self.assertEqual(xyz1, self.xyz1['dict'])\n\n xyz2 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\n 'coords': ((0.0, 0.0, 0.0),\n (0.6300326, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))}\n xyz2 = converter.check_xyz_dict(xyz2)\n expected_xyz2 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\n 'isotopes': (12, 1, 1, 1, 1),\n 'coords': ((0.0, 0.0, 0.0),\n (0.6300326, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))}\n self.assertEqual(xyz2, expected_xyz2)\n\n xyz3 = 3.0\n with self.assertRaises(ConverterError):\n converter.check_xyz_dict(xyz3)\n\n xyz4 = {'coords': ((0.0, 0.0, 0.0),\n (0.6300326, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))}\n with self.assertRaises(ConverterError):\n converter.check_xyz_dict(xyz4)\n\n xyz5 = {'symbols': ('C', 'H', 'H', 'H', 'H', 'S', 'S', 'S'),\n 'coords': ((0.0, 0.0, 0.0),\n (0.6300326, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))}\n with self.assertRaises(ConverterError):\n converter.check_xyz_dict(xyz5)\n\n # test a zmat input\n zmat6 = {'symbols': ('N', 'N', 'H', 'H'),\n 'coords': ((None, None, None), ('R_1_0', None, None), ('R_2_1', 'A_2_1_0', None),\n ('R_3_2', 'A_3_2_0', 'D_3_2_0_1')),\n 'vars': {'R_1_0': 1.2451214479859707, 'R_2_1': 1.8953164901754294, 'A_2_1_0': 30.18165946689929,\n 'R_3_2': 2.785552137148173, 'A_3_2_0': 24.405141545817347,\n 'D_3_2_0_1': 3.6222548091772e-06}, 'map': {0: 0, 1: 1, 2: 2, 3: 3}}\n xyz6 = converter.check_xyz_dict(zmat6)\n expected_xyz6 = {'symbols': ('N', 'N', 'H', 'H'),\n 'isotopes': (14, 14, 1, 1),\n 'coords': ((-2.4426534384901547e-09, -4.375090750708016e-09, -0.622560729110669),\n (-2.4426534384901547e-09, -4.375090750708016e-09, 0.6225607188753017),\n (-2.4426534384901547e-09, 0.9528575945413793, -1.015818661524137),\n (7.032081834243086e-08, -0.9528574729632926, 1.015818803737915))}\n\n self.assertEqual(xyz6, expected_xyz6)","def test_creation_dict():\n with pytest.raises(ValueError) as __:\n value = dict()\n __ = param.Integer(value=value)","def test_addEntryByDict(self):\n self.g.entryFormat = ['term', 'tags', 'value']\n b = self.g.add_entry({'term': 'foo', 'tags': 'a', 'value': '1'})\n self.assertTrue(b)","def __init__(self, dictionary: dict[str, t.Any]) -> None:\n missing_keys = self.__annotations__.keys() - dictionary.keys()\n if missing_keys:\n raise KeyError(f\"Fetched object lacks expected keys: {missing_keys}\")\n for annotation in self.__annotations__:\n setattr(self, annotation, dictionary[annotation])","def test_instantiate_no_target(self):\n # create test configs\n test_configs = [\n {},\n {\"a\": 1, \"b\": 2}\n ]\n\n # check that instantiate raises ValueError for each test config\n for test_conf in test_configs:\n self.assertRaises(ValueError, instantiate, test_conf)","def test_minimal_input():\n transactions = []\n itemsets, rules = apriori(transactions, 0.2, 0.2)\n assert itemsets == {} and rules == []\n\n with pytest.raises(ValueError):\n itemsets, rules = apriori(transactions, -0.2, 0.2)\n\n with pytest.raises(ValueError):\n itemsets, rules = apriori(transactions, 0.2, -0.2)\n\n with pytest.raises(ValueError):\n itemsets, rules = apriori(transactions, \"asdf\", 1)\n\n itemsets, rules = apriori([(1, 2), (1, 2), (1, 3)], 1, 1)\n itemsets, rules = apriori([(1, 2), (1, 2), (1, 3)], 1.0, 1.0)","def test_constructor_canonizes_dict_to_io(\n self, input_list_with_dict, output_list_with_dict, dict_list\n ):\n\n A = MsgMultiSend(inputs=input_list_with_dict, outputs=output_list_with_dict)\n B = MsgMultiSend(inputs=dict_list, outputs=dict_list)\n\n assert all(isinstance(i, Input) for i in A.inputs)\n assert all(isinstance(o, Output) for o in A.outputs)","def test_items(self):\n obs = self.tester.items()\n self.assertTrue(isinstance(obs, Iterable))\n exp = {('center_name', 'ANL'), ('center_project_name', None),\n ('emp_status', 'EMP'), ('barcodesequence', 'AGCGCTCACATC'),\n ('library_construction_protocol',\n 'This analysis was done as in Caporaso et al 2011 Genome '\n 'research. The PCR primers (F515/R806) were developed against '\n 'the V4 region of the 16S rRNA (both bacteria and archaea), '\n 'which we determined would yield optimal community clustering '\n 'with reads of this length using a procedure similar to that '\n 'of ref. 15. [For reference, this primer pair amplifies the '\n 'region 533_786 in the Escherichia coli strain 83972 sequence '\n '(greengenes accession no. prokMSA_id:470367).] The reverse '\n 'PCR primer is barcoded with a 12-base error-correcting Golay '\n 'code to facilitate multiplexing of up to 1,500 samples per '\n 'lane, and both PCR primers contain sequencer adapter '\n 'regions.'), ('linkerprimersequence', 'GTGCCAGCMGCCGCGGTAA'),\n ('target_subfragment', 'V4'), ('target_gene', '16S rRNA'),\n ('run_center', 'ANL'), ('run_prefix', 's_G1_L001_sequences'),\n ('run_date', '8/1/12'), ('experiment_center', 'ANL'),\n ('experiment_design_description',\n 'micro biome of soil and rhizosphere of cannabis plants '\n 'from CA'), ('experiment_title', 'Cannabis Soil Microbiome'),\n ('platform', 'Illumina'), ('samp_size', '.25,g'),\n ('sequencing_meth', 'Sequencing by synthesis'),\n ('illumina_technology', 'MiSeq'), ('sample_center', 'ANL'),\n ('pcr_primers',\n 'FWD:GTGCCAGCMGCCGCGGTAA; REV:GGACTACHVGGGTWTCTAAT'),\n ('study_center', 'CCME')}\n self.assertEqual(set(obs), exp)","def test_empty_dict_coerce():\n\n @type_checked\n def _run_test(thing:{}):\n assert isinstance(thing, dict)\n\n _run_test([(\"something\", \"is_true\")])","def __init__(self, schema: dict, **kwargs) -> None:\r\n self.case_check = case_check(settings.CASE)\r\n self.ignored_keys = set_ignored_keys(**kwargs)\r\n if read_type(schema) == 'object':\r\n logger.debug('root -> dict')\r\n self.test_dict(schema)\r\n elif read_type(schema) == 'array':\r\n logger.debug('root -> list')\r\n self.test_list(schema)\r\n else:\r\n logger.debug('Skipping case check')","def test_exceptional_cases(self):\n # autogeneratearing strings throws a type error\n args = ('foo', 'bar')\n self.assertRaises(TypeError, autogenerate, args)\n # UNLESS(?) they're the same\n args = ('foo', 'foo')\n self.assertEqual(autogenerate(*args), [])\n # raise type error if classes aren't the same\n args = (['foo'], {'bar': 1})\n self.assertRaises(TypeError, autogenerate, args)\n # raises an error if it doesn't know about what classes they are\n class Flub(object):\n def __init__(self, val):\n self.val = val\n self.assertRaises(TypeError, autogenerate, (Flub('hi'), Flub('bye')))\n # but it doesn't if cmp works on the object and they're the same...\n class Flub2(Flub):\n def __cmp__(self, other):\n return cmp(self.val, other.val)\n self.assertEqual(autogenerate(Flub2('hi'), Flub2('hi')), [])","def test_instantiate_non_existent_class(self):\n # create test configs\n test_configs = [\n {\"_target_\": \"collections.NonExistentClass\"},\n {\"_target_\": \"collections.OtherNonExistentClass\", \"a\": 1, \"b\": 2}\n ]\n\n # check that instantiate raises AttributeError for each test config\n for test_conf in test_configs:\n self.assertRaises(AttributeError, instantiate, test_conf)","def test_list_facet_dictionaries(self):\n pass","def test_empty_dict():\n\n @type_checked\n def _run_test(thing:{}):\n assert isinstance(thing, dict)\n\n _run_test({\"foo\": \"bar\"})","def test_init(attributes):\n instance = Participant(**attributes)\n for attr, value in attributes.items():\n assert getattr(instance, attr) == value","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_categories)","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_categories)","def test_dict_data_reversible(self):\n from tests.sampletest.datatest import DataTest\n\n tests = self.loader.load(\"tests.sampletest.datatest.DataTest\")\n self.assertEqual(len(tests), 3)\n self.assertEqual(type(tests[0]), DataTest)\n\n test_set = \" \".join(map(lambda x: x.test_name, tests)).split()\n loader = TestLoader()\n new_tests = loader.load(test_set)\n self.assertEqual(len(new_tests), len(tests))","def initializeFromDict(self, inputDict):\n pass","def test_create_from_dict(self):\n b1 = BaseModel()\n b1.name = \"Holberton\"\n b1.my_number = 89\n my_model_json = b1.to_dict()\n b2 = BaseModel(**my_model_json)\n self.assertEqual(b1.my_number, b2.my_number)\n self.assertEqual(b1.id, b2.id)\n self.assertEqual(b1.name, b2.name)\n self.assertEqual(b1.created_at, b2.created_at)\n self.assertEqual(b1.updated_at, b2.updated_at)\n self.assertNotEqual(b1, b2)","def test_construct_tickets_3(self):\n\n tkt_dict1 = {}\n tkt_dict1[\"type\"] = \"add\"\n tkt_dict1[\"phage_id\"] = \"Trixie\"\n tkt_dict1[\"description_field\"] = \"product\"\n tkt_dict1[\"eval_mode\"] = \"final\"\n\n tkt_dict2 = {}\n tkt_dict2[\"type\"] = \"add\"\n tkt_dict2[\"phage_id\"] = \"L5\"\n tkt_dict2[\"description_field\"] = \"product\"\n tkt_dict2[\"eval_mode\"] = \"final\"\n\n tkt_dict3 = {}\n tkt_dict3[\"type\"] = \"add\"\n tkt_dict3[\"phage_id\"] = \"RedRock\"\n\n tkt_dict4 = {}\n tkt_dict4[\"type\"] = \"add\"\n tkt_dict4[\"phage_id\"] = \"Bxb1\"\n\n dict_list = [tkt_dict1, tkt_dict2, tkt_dict3, tkt_dict4]\n eval_data_dict = {\"eval_mode\": \"custom_eval_mode\",\n \"eval_flag_dict\": {\"check_locus_tag\": False}}\n tkt_list = tickets.construct_tickets(dict_list,\n eval_data_dict, \"function\", self.required_keys,\n self.optional_keys, self.keywords)\n\n tkt_list[0].eval_flags[\"check_locus_tag\"] = 0\n tkt_list[1].eval_flags[\"check_locus_tag\"] = 1\n tkt_list[2].eval_flags[\"check_locus_tag\"] = 2\n tkt_list[3].eval_flags[\"check_locus_tag\"] = 3\n\n with self.subTest():\n self.assertEqual(tkt_list[0].eval_flags[\"check_locus_tag\"], 0)\n with self.subTest():\n self.assertEqual(tkt_list[1].eval_flags[\"check_locus_tag\"], 1)\n with self.subTest():\n self.assertEqual(tkt_list[2].eval_flags[\"check_locus_tag\"], 2)\n with self.subTest():\n self.assertEqual(tkt_list[3].eval_flags[\"check_locus_tag\"], 3)","def test_construct_tickets_1(self):\n dict_list = [self.ticket_dict1, self.ticket_dict4]\n eval_data_dict = {\"eval_mode\": \"custom_eval_mode\",\n \"eval_flag_dict\": {\"check_locus_tag\": False}}\n list_of_tickets = tickets.construct_tickets(dict_list,\n eval_data_dict, \"function\", self.required_keys,\n self.optional_keys, self.keywords)\n with self.subTest():\n self.assertEqual(len(list_of_tickets), 2)\n with self.subTest():\n self.assertEqual(list_of_tickets[0].id, 1)\n with self.subTest():\n self.assertEqual(list_of_tickets[0].eval_mode, \"final\")\n with self.subTest():\n self.assertEqual(list_of_tickets[0].description_field, \"product\")\n with self.subTest():\n self.assertTrue(list_of_tickets[0].eval_flags[\"check_locus_tag\"])\n with self.subTest():\n self.assertEqual(list_of_tickets[1].id, 2)\n with self.subTest():\n self.assertEqual(list_of_tickets[1].eval_mode, \"custom_eval_mode\")\n with self.subTest():\n self.assertEqual(list_of_tickets[1].description_field, \"function\")\n with self.subTest():\n self.assertFalse(list_of_tickets[1].eval_flags[\"check_locus_tag\"])","def test_values(self):\n obs = self.tester.values()\n self.assertTrue(isinstance(obs, Iterable))\n exp = {PrepSample('1.SKB1.640202', self.tester),\n PrepSample('1.SKB2.640194', self.tester),\n PrepSample('1.SKB3.640195', self.tester),\n PrepSample('1.SKB4.640189', self.tester),\n PrepSample('1.SKB5.640181', self.tester),\n PrepSample('1.SKB6.640176', self.tester),\n PrepSample('1.SKB7.640196', self.tester),\n PrepSample('1.SKB8.640193', self.tester),\n PrepSample('1.SKB9.640200', self.tester),\n PrepSample('1.SKD1.640179', self.tester),\n PrepSample('1.SKD2.640178', self.tester),\n PrepSample('1.SKD3.640198', self.tester),\n PrepSample('1.SKD4.640185', self.tester),\n PrepSample('1.SKD5.640186', self.tester),\n PrepSample('1.SKD6.640190', self.tester),\n PrepSample('1.SKD7.640191', self.tester),\n PrepSample('1.SKD8.640184', self.tester),\n PrepSample('1.SKD9.640182', self.tester),\n PrepSample('1.SKM1.640183', self.tester),\n PrepSample('1.SKM2.640199', self.tester),\n PrepSample('1.SKM3.640197', self.tester),\n PrepSample('1.SKM4.640180', self.tester),\n PrepSample('1.SKM5.640177', self.tester),\n PrepSample('1.SKM6.640187', self.tester),\n PrepSample('1.SKM7.640188', self.tester),\n PrepSample('1.SKM8.640201', self.tester),\n PrepSample('1.SKM9.640192', self.tester)}\n # Creating a list and looping over it since unittest does not call\n # the __eq__ function on the objects\n for o, e in zip(sorted(list(obs), key=lambda x: x.id),\n sorted(exp, key=lambda x: x.id)):\n self.assertEqual(o, e)","def test_fn_call_with_dict():\n l = [1, 2, 3, 4, 5]\n ds = [defaultdict(int), defaultdict(int), defaultdict(int)]\n for d in ds:\n for fn in [s7.div, s7.mul, s7.add, \"abcd\", 1234]:\n try:\n f = s7.count_fn_called_with_dict(dict_=d, fn=fn)\n for i in range(0, random.randint(2, 10)):\n f(*l)\n assert fn in d.keys() and d[fn] == (i + 1)\n except Exception as e:\n assert e.__class__.__name__ == TypeError.__name__","def test_27_create_list(self):\n r = Rectangle(1, 1)\n r_dictionary = r.to_dictionary()\n with self.assertRaises(TypeError) as e:\n r = Rectangle.create([1, 2, 3])\n self.assertEqual(\n \"create() takes 1 positional argument but 2 were given\", str(\n e.exception))","def setUp(self):\n cwd = Path(__file__).parent.absolute()\n with open(f'{cwd}/test.json', 'r') as f:\n default = json.load(f)\n\n for data in default['results']:\n set_id = data.pop('set_id')\n products_data = data.pop('products')\n\n set_obj = Set.objects.create(id=set_id)\n\n spl_obj = set_obj.spls.create(**data)\n\n for product_data in products_data:\n product_data.pop('name')\n packages_data = product_data.pop('packages')\n if 'inactive_ingredients' in product_data:\n inactive_ingredients_data = product_data\\\n .pop('inactive_ingredients')\n\n inactive_ingredients_list = []\n for inactive_ingredient_data in inactive_ingredients_data:\n try:\n ingredient = InactiveIngredient.objects.get(\n **inactive_ingredient_data\n )\n inactive_ingredients_list.append(ingredient)\n except Exception:\n ingredient = InactiveIngredient.objects.create(\n **inactive_ingredient_data\n )\n inactive_ingredients_list.append(ingredient)\n\n product_obj = spl_obj.products.create(**product_data)\n product_obj.inactive_ingredients\\\n .add(*inactive_ingredients_list)\n\n for package_data in packages_data:\n product_obj.packages.create(**package_data)","def assertDictSubset(self, subset, superset):\n if not set(subset.items()).issubset(set(superset.items())):\n raise AssertionError(\"{0} is not subset of {1}\".format(subset, superset))","def __init__(self, map):\n self.map = map\n self._map = _map = {}\n for key in map.keys():\n _map[key] = key\n self.fast_validate = (10, _map, self.validate)","def testMapSetdefault(self):\n # We only use one map type since they all share the same implementation for\n # this logic.\n m = data_types.StepBuildStatsMap()\n with self.assertRaises(AssertionError):\n m.setdefault(1, data_types.BuildStats())\n with self.assertRaises(AssertionError):\n m.setdefault('1', 2)\n m.setdefault('1', data_types.BuildStats())\n self.assertEqual(m, {'1': data_types.BuildStats()})","def test_dict_type(self):\n\n expected = TypeError\n input_ = []\n with self.assertRaises(expected):\n math.factorial(input_)","def __init__(self, only=[], deactivate=[]):\n self._tests = OrderedDict()\n deactivate = set(deactivate)\n for name, test in self._known.iteritems():\n if (len(only) == 0 or name in only) and deactivate.isdisjoint(test.keywords):\n self.addTest(self._known[name])","def test_attr_dict(self):\n obj = awstats_reader.AttrDict([('this','that'), ('thus','those')])\n self.assertEqual(obj.thus, 'those')","def test_create_cds_translation_dict_1(self):\n t1 = \"ABCDE\"\n t2 = \"FGHIJ\"\n t3 = \"RSTUV\"\n self.gene_data_1[\"Translation\"] = t1\n self.gene_data_2[\"Translation\"] = t2\n self.gene_data_3[\"Translation\"] = t3\n cdd_list = [self.gene_data_1, self.gene_data_2, self.gene_data_3]\n dict = find_domains.create_cds_translation_dict(cdd_list)\n\n exp_keys = {t1, t2, t3}\n with self.subTest():\n self.assertEqual(dict.keys(), exp_keys)\n with self.subTest():\n self.assertEqual(dict[t1], {self.gene_data_1[\"GeneID\"]})\n with self.subTest():\n self.assertEqual(dict[t2], {self.gene_data_2[\"GeneID\"]})\n with self.subTest():\n self.assertEqual(dict[t3], {self.gene_data_3[\"GeneID\"]})","def test_dict(self, testdata: TestData) -> None:\n for data in testdata['observation_type']:\n observation_type = ObservationType.from_dict(data)\n assert data == observation_type.to_dict()","def __init__(self, response_data: Any, **kwargs) -> None:\r\n self.case_check = case_check(settings.CASE)\r\n self.ignored_keys = set_ignored_keys(**kwargs)\r\n if isinstance(response_data, dict):\r\n self.test_dict(response_data)\r\n elif isinstance(response_data, list):\r\n self.test_list(response_data)\r\n else:\r\n logger.debug('Skipping case check')","def test_can_instantiate(self):\n\n exc_thrown = False\n\n try:\n self.klass(*self.instantiate_args)\n except Exception:\n exc_thrown = True\n\n self.assertFalse(exc_thrown)","def testDictContains(self):\n self.Check(\"\"\"\n d1 = {\"x\": 42}\n if \"x\" in d1:\n print d1[\"x\"]\n else:\n print d1[\"nonsense\"] # Dead code\n\n d2 = {}\n if \"x\" in d2:\n print d2[\"nonsense\"] # Dead code\n\n d3 = {__any_object__: __any_object__}\n if \"x\" in d3:\n print d3[\"x\"]\n else:\n print d3[\"y\"]\n \"\"\")","def test_individual_valid(self, kwargs):\n # defaults\n final_kwargs = {'script': 'echo \"hello world\"', 'title': '', 'debug': False, 'strict': False,\n 'dry_run': False, 'item': None, 'env': {}, 'model': {}, 'variables': {}}\n final_kwargs.update(kwargs)\n\n config = ShellConfig(**final_kwargs)\n for key, value in final_kwargs.items():\n assert_that(key in config.__dict__, equal_to(True))\n assert_that(config.__dict__[key], equal_to(value))","def test_dict(self, obj: dict) -> None:\r\n properties = read_properties(obj)\r\n for key, value in properties.items():\r\n conditional_check(key, self.case_check, self.ignored_keys)\r\n if read_type(value) == 'object':\r\n logger.debug('dict -> dict')\r\n self.test_dict(obj=value)\r\n elif read_type(value) == 'array':\r\n logger.debug('dict -> list')\r\n self.test_list(array=value)","def dict_check(*args, func=None):\n func = func or inspect.stack()[2][3]\n for var in args:\n if not isinstance(var, (dict, collections.UserDict, collections.abc.MutableMapping)):\n name = type(var).__name__\n raise DictError(\n 'Function {} expected dict, {} got instead.'.format(func, name))","def __init__(self, *values):\n if (len(values) == 1) and (type(values[0]) in SequenceTypes):\n values = values[0]\n self.values = values[:]\n self.values_ = values_ = {}\n for key in values:\n values_[key] = key\n self.fast_validate = (10, values_, self.validate)","def test_18_0_create(self):\n\n r1_dict = self.r1.to_dictionary()\n r = Rectangle.create(**r1_dict)\n self.assertFalse(self.r1 == r)\n self.assertFalse(self.r1 is r)"],"string":"[\n \"def test_create_from_dictionaries(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n stud_prefs, sup_prefs, proj_sups, proj_caps, sup_caps = make_connections(\\n student_names, project_names, supervisor_names, capacities, seed\\n )\\n\\n game = StudentAllocation.create_from_dictionaries(\\n stud_prefs, sup_prefs, proj_sups, proj_caps, sup_caps, clean\\n )\\n\\n for student in game.students:\\n assert student._pref_names == stud_prefs[student.name]\\n assert student.matching is None\\n\\n for project in game.projects:\\n assert project.supervisor.name == proj_sups[project.name]\\n assert project.matching == []\\n\\n for supervisor in game.supervisors:\\n assert supervisor._pref_names == sup_prefs[supervisor.name]\\n assert supervisor.matching == []\\n\\n assert game.matching is None\\n assert game.clean is clean\",\n \"def test_set_creation():\\r\\n test_list = [1, 2, 1, 3] # it is alist\\r\\n set1 = set(test_list)\\r\\n assert {1,2,3} == set1 # randomly arranged and don't allow duplicates\\r\\n\\r\\n test_string = \\\"apple\\\"\\r\\n set2 = set(test_string)\\r\\n assert {'a','p','l','e'} == set2\\r\\n\\r\\n test_dict = { 1: \\\"one\\\", 2 : \\\"two\\\"}\\r\\n set3 = set(test_dict)\\r\\n assert {1,2} == set3 #only keys are considered\\r\\n\\r\\n set4 = set(test_dict.values())\\r\\n assert {\\\"one\\\",\\\"two\\\"} == set4\\r\\n\\r\\n set5 = set(test_dict.items())\\r\\n assert set([(2, 'two'), (1, 'one')]) == set5 ######################DOUBT\\r\",\n \"def testfromjson(self):\\n dicty = {\\\"id\\\": 5, \\\"class\\\": \\\"string\\\", \\\"list\\\": [], \\\"set\\\": {}}\\n self.assertEqual([dicty, dicty],\\n Base.from_json_string(json.dumps([dicty, dicty])))\",\n \"def test_fromkeys(self):\\n d = SplayDict.fromkeys(['a', 'b', 'c'], 1)\\n self.assertIn('a' , d)\\n self.assertIn('b' , d)\\n self.assertIn('c' , d)\\n self.assertEqual(d['a'] , 1)\\n self.assertEqual(d['b'] , 1)\\n self.assertEqual(d['c'] , 1)\",\n \"def test_build_format_map(all_sets: dict, expected: dict) -> None:\\n result = build_format_map(all_sets, regular=False)\\n assert result == expected\",\n \"def check_for_dict(check):\",\n \"def test_create_from_dicts(self):\\n cols = list(zip(*self.dtypes))[0]\\n dicts = [dict([(cols[i], d[i]) for i in xrange(len(d))])\\n for d in self.idata]\\n\\n tbl = Table.create(\\n ':memory:', \\\"Bar\\\", dicts, verbose=True,\\n primary_key='id', autoincrement=True)\\n\\n self.check_index(self.idata, tbl.select())\\n for idx, col in enumerate(cols):\\n if col == 'id':\\n continue\\n self.check_data(self.idata[:, [0, idx]], tbl[col])\",\n \"def testfromjson(self):\\n dicty = {\\\"id\\\": 5, \\\"class\\\": \\\"string\\\", \\\"list\\\": [], \\\"set\\\": {}}\\n self.assertEqual([dicty], Base.from_json_string(json.dumps([dicty])))\",\n \"def test_dictionary(self):\\n self.assertIsInstance(self.test1json, dict)\",\n \"def check_dict(dic, validator, messages):\\n check_dict_alg(dic, validator, [], messages, validator, \\\"NoObject\\\")\",\n \"def test_validation(self):\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate([1,2,3])\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate('stringy')\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate(1)\",\n \"def assert_template_dicts(obj1: Any, obj2: Any) -> None:\\n assert to_template_dict(obj1) == to_template_dict(obj2)\",\n \"def test_create_obj_by_type_from_dict(self):\\n test_obj = {}\\n returned_obj = self.tested_class._create_obj_by_type(test_obj)\\n self.assertIsInstance(returned_obj, self.tested_class)\",\n \"def testInitFromDict():\\n conf = naiveConf.NaiveConf({})\\n conf = naiveConf.NaiveConf({'a':5})\\n assert conf.a == 5\",\n \"def test_get_cases_for_dict(self):\\n pass\",\n \"def test_instantiate_valid_target(self):\\n # create test configs\\n test_configs = [\\n {\\\"_target_\\\": \\\"collections.deque\\\"},\\n {\\\"_target_\\\": \\\"collections.UserString\\\", \\\"seq\\\": \\\"test string\\\"}\\n ]\\n\\n # create truth objects\\n truth_objs = [deque(), UserString(\\\"test string\\\")]\\n\\n # check that instantiate returns truth object for each config\\n for truth_obj, test_config in zip(truth_objs, test_configs):\\n self.assertEqual(truth_obj, instantiate(test_config))\",\n \"def test_validation(self):\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate([1,2,3])\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate('stringy')\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate(1)\",\n \"def verifyDictTypes( template, dictToCheck ):\\n for key in dictToCheck:\\n if not ( ( isinstance( dictToCheck[ key ], list ) and\\n isinstance( template[ key ], list ) ) or\\n ( isinstance( dictToCheck[ key ], dict ) and\\n isinstance( template[ key ], dict ) ) or\\n ( isinstance( dictToCheck[ key ], template[ key ] ) ) ):\\n return False\\n\\n return True\",\n \"def test_isadict(self):\\n # It is a dict-subclass, so this kind of pointless, but it doen't hurt.\\n d, m = dict(a=5), ConfigDict(a=5)\\n d['key'], m['key'] = 'value', 'value'\\n d['k2'], m['k2'] = 'v1', 'v1'\\n d['k2'], m['k2'] = 'v2', 'v2'\\n self.assertEqual(d.keys(), m.keys())\\n self.assertEqual(list(d.values()), list(m.values()))\\n self.assertEqual(d.get('key'), m.get('key'))\\n self.assertEqual(d.get('cay'), m.get('cay'))\\n self.assertEqual(list(iter(d)), list(iter(m)))\\n self.assertEqual([k for k in d], [k for k in m])\\n self.assertEqual(len(d), len(m))\\n self.assertEqual('key' in d, 'key' in m)\\n self.assertEqual('cay' in d, 'cay' in m)\\n self.assertRaises(KeyError, lambda: m['cay'])\",\n \"def test_init_with_nested_dicts(self):\\n regex = 'mappings can not be nested'\\n with self.assertRaisesRegex(ValueError, regex):\\n query = DataQuery({'A': {'B': 'C'}}, D='x')\",\n \"def test_if_it_accepts_dictionary(self):\\n with self.assertRaises(TypeError):\\n prime_numbers({})\",\n \"def test_construct_tickets_2(self):\\n dict_list = [self.ticket_dict1, self.ticket_dict2]\\n eval_data_dict = {\\\"eval_mode\\\": \\\"custom_eval_mode\\\",\\n \\\"eval_flag_dict\\\": {}}\\n list_of_tickets = tickets.construct_tickets(dict_list,\\n eval_data_dict, \\\"function\\\", self.required_keys,\\n self.optional_keys, self.keywords)\\n with self.subTest():\\n self.assertEqual(len(list_of_tickets), 1)\",\n \"def test_comparing(self):\\n for test in self.test_dict_data:\\n self.assertEqual(dottedDict(test[0]), test[1])\",\n \"def test_valid_analysis_request(analysis_request_dict: JSONDict) -> None:\\n\\n request = AnalysisRequest(**analysis_request_dict)\\n\\n assert request.dict() == analysis_request_dict\",\n \"def create_dicts():\\n load_data_for_dict('data/atis/train/seq.in', 'data/atis/voc/vocabulary.json')\\n load_data_for_dict('data/atis/valid/seq.in', 'data/atis/voc/vocabulary.json')\\n load_data_for_dict('data/atis/test/seq.in', 'data/atis/voc/vocabulary.json') \\n load_data_for_dict('data/atis/train/seq.out', 'data/atis/voc/slot_vocabulary.json')\",\n \"def test_unique_train_items(train_items: List[JSONDict]) -> None:\\n validated = TrainCollection(items=train_items)\\n assert validated.dict() == train_items\",\n \"def test_15_dict_test(self):\\n with self.assertRaises(TypeError) as x:\\n r = Rectangle(10, {})\\n self.assertEqual(\\n \\\"height must be an integer\\\",\\n str(x.exception))\\n with self.assertRaises(TypeError) as x:\\n r = Rectangle({\\\"a\\\": 1, \\\"b\\\": 2, \\\"c\\\": 3}, 2)\\n self.assertEqual(\\n \\\"width must be an integer\\\",\\n str(x.exception))\\n with self.assertRaises(TypeError) as x:\\n r = Rectangle(10, 2, {\\\"a\\\": 1})\\n self.assertEqual(\\n \\\"x must be an integer\\\",\\n str(x.exception))\\n with self.assertRaises(TypeError) as x:\\n r = Rectangle(10, 2, 0, {\\\"hi\\\": None})\\n self.assertEqual(\\n \\\"y must be an integer\\\",\\n str(x.exception))\",\n \"def _dict_assert(actual_dict, expected_dict):\\n for key in set(actual_dict) & set(expected_dict):\\n _value_assert(key, actual_dict[key], expected_dict[key])\",\n \"def test_jurisdictions_instances(self):\\n obj = Jurisdiction()\\n self.assertIsInstance(obj, Jurisdiction)\",\n \"def test_init(resident_names, hospital_names, capacities, seed):\\n\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n assert match.suitors == residents\\n assert match.reviewers == hospitals\\n assert all([suitor.matching is None for suitor in match.suitors])\\n assert all([reviewer.matching == [] for reviewer in match.reviewers])\\n assert match.matching is None\",\n \"def test_init_hash(self):\\n bill = Bill(self.input_hash)\\n for key, value in self.input_hash.iteritems():\\n self.assertEqual(value, bill.__dict__[key])\",\n \"def test_make_compatible_taxa_summaries_sample_id_map_incomplete_map(self):\\r\\n self.assertRaises(ValueError, _make_compatible_taxa_summaries,\\r\\n self.taxa_summary3, self.taxa_summary4, self.sample_id_map3)\",\n \"def test_correct_train_item(train_items: List[JSONDict]) -> None:\\n validated = TrainItem(**train_items[0])\\n assert validated.dict() == train_items[0]\",\n \"def test_output_named_tuple_vs_dictionary_2():\\n assert oldest_person_age == oldest_person_age_d, \\\"Max age cannot be different for Named Tuple and Dictionary list\\\"\",\n \"def __init__(self, other):\\n if type(other) == dict:\\n for k in other:\\n if type(other[k]) == dict:\\n other[k] = JsonFactory(other[k])\\n self.__dict__ = other\\n else:\\n raise PJFInvalidType(other, dict)\",\n \"def test_1():\\n results = base_tests()\\n assert type(results) is list\\n assert type(results[0]) is dict\\n assert len(results) == 3\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def test_dumps_set(self):\\n try:\\n _build_test_dirs()\\n dicti = {\\n 'set': set([1, 2, 4, 4, 2]),\\n 'array': [1, 2, 3],\\n 'string': 'trololo',\\n 'int': 1,\\n 'float': 4.32,\\n 'true': True,\\n 'false': False,\\n 'null': None\\n }\\n with open(_TEST_FILE, 'w+') as fileobj:\\n morejson.dump(dicti, fileobj)\\n with open(_TEST_FILE, 'r') as fileobj:\\n self.assertEqual(dicti, morejson.load(fileobj))\\n finally:\\n _dismantle_test_dirs()\",\n \"def testMapConstructor(self):\\n # We only use one map type since they all share the same implementation for\\n # this logic.\\n with self.assertRaises(AssertionError):\\n data_types.StepBuildStatsMap({1: 2})\\n m = data_types.StepBuildStatsMap({'step': data_types.BuildStats()})\\n self.assertEqual(m, {'step': data_types.BuildStats()})\",\n \"def test_26_create(self):\\n r1 = Rectangle(3, 5, 1)\\n r1_dictionary = r1.to_dictionary()\\n r2 = Rectangle.create(**r1_dictionary)\\n self.assertEqual((r1 == r2), False)\\n self.assertEqual((r1 is r2), False)\",\n \"def test_dict(self, dictionary: dict) -> None:\\r\\n if not isinstance(dictionary, dict):\\r\\n raise ValueError(f'Expected dictionary, but received {type(dictionary)}')\\r\\n for key, value in dictionary.items():\\r\\n conditional_check(key, self.case_check, self.ignored_keys)\\r\\n if isinstance(value, dict):\\r\\n self.test_dict(dictionary=value)\\r\\n elif isinstance(value, list):\\r\\n self.test_list(items=value)\",\n \"def test_empty_dict_by_name():\\n\\n @type_checked\\n def _run_test(thing:dict):\\n assert isinstance(thing, dict)\\n\\n _run_test({\\\"baz\\\": True})\",\n \"def test_class_kwargs(self):\\n dictonary = {\\n 'id': '662a23b3-abc7-4f43-81dc-64c000001c00', 'score': 100}\\n review1 = Review(**dictonary)\\n self.assertTrue(hasattr(review1, \\\"id\\\"))\\n self.assertEqual(review1.id, '662a23b3-abc7-4f43-81dc-64c000001c00')\\n self.assertTrue(hasattr(review1, \\\"score\\\"))\\n self.assertEqual(review1.score, 100)\\n self.assertTrue(hasattr(review1, \\\"created_at\\\"))\\n self.assertTrue(type(review1.updated_at), datetime)\\n self.assertTrue(hasattr(review1, \\\"updated_at\\\"))\\n self.assertTrue(type(review1.created_at), datetime)\\n self.assertEqual(review1.__class__.__name__, \\\"Review\\\")\",\n \"def test_init(self, testdata: TestData) -> None:\\n for data in testdata['observation_type']:\\n observation_type = ObservationType(**data)\\n for key, value in data.items():\\n assert getattr(observation_type, key) == value\",\n \"def test_class_kwargs(self):\\n dictonary = {\\n 'id': '662a23b3-abc7-4f43-81dc-64c000001c00', 'score': 100}\\n state1 = State(**dictonary)\\n self.assertTrue(hasattr(state1, \\\"id\\\"))\\n self.assertEqual(state1.id, '662a23b3-abc7-4f43-81dc-64c000001c00')\\n self.assertTrue(hasattr(state1, \\\"score\\\"))\\n self.assertEqual(state1.score, 100)\\n self.assertTrue(hasattr(state1, \\\"created_at\\\"))\\n self.assertTrue(type(state1.updated_at), datetime)\\n self.assertTrue(hasattr(state1, \\\"updated_at\\\"))\\n self.assertTrue(type(state1.created_at), datetime)\\n self.assertEqual(state1.__class__.__name__, \\\"State\\\")\",\n \"def _is_dict_in_one_of_dicts(d, dicts):\\n\\n for vd in dicts:\\n if Settings._is_in_dict(d, vd):\\n return True\\n return False\",\n \"def test_init(self):\\n some_key_value1 = mock.MagicMock()\\n some_key_value2 = \\\"some_str\\\"\\n some_nested_value1 = mock.MagicMock()\\n some_nested_value2 = [100, \\\"test_strt\\\", mock.MagicMock()]\\n test_data = {\\n \\\"some_key1\\\": some_key_value1,\\n \\\"some_key2\\\": some_key_value2,\\n \\\"some_key3\\\": {\\n \\\"some_nested_key1\\\": some_nested_value1,\\n \\\"some_nested_key2\\\": some_nested_value2\\n }\\n }\\n data_holder = self.tested_class(test_data)\\n\\n self.assertIsInstance(data_holder, self.tested_class)\\n self.assertEqual(data_holder.some_key1, some_key_value1)\\n self.assertEqual(data_holder.some_key2, some_key_value2)\\n self.assertIsInstance(data_holder.some_key3, self.tested_class)\\n self.assertEqual(data_holder.some_key3.some_nested_key1, some_nested_value1)\\n self.assertEqual(data_holder.some_key3.some_nested_key2, some_nested_value2)\",\n \"def test_create(self):\\n B._Base__nb_objects = 0\\n\\n d = {'id': 5, 'width': 3, 'height': 7, 'x': 2, 'y': 1}\\n r1 = Rectangle.create(**d)\\n self.assertEqual(r1.to_dictionary(), d)\\n self.assertEqual(B._Base__nb_objects, 1)\\n\\n s2 = Square(5)\\n d4 = s2.to_dictionary()\\n s5 = Square.create(**d4)\\n self.assertEqual(s5.to_dictionary(), d4)\\n self.assertEqual(B._Base__nb_objects, 3)\",\n \"def test_deep_set_create(self):\\n mdict = copy.deepcopy(self.dict1)\\n res = dictupdate.set_dict_key_value(mdict, \\\"K:L:M\\\", \\\"Q\\\")\\n self.assertEqual(\\n {\\n \\\"A\\\": \\\"B\\\",\\n \\\"C\\\": {\\\"D\\\": \\\"E\\\", \\\"F\\\": {\\\"G\\\": \\\"H\\\", \\\"I\\\": \\\"J\\\"}},\\n \\\"K\\\": {\\\"L\\\": {\\\"M\\\": \\\"Q\\\"}},\\n },\\n res,\\n )\",\n \"def test_jurisdiction_attributes(self):\\n obj = Jurisdiction(**{'name': 'Antioquia', 'victims': 1})\\n self.assertIsInstance(obj.name, str)\\n self.assertIsInstance(obj.victims, int)\",\n \"def test_init(self):\\n space = Space()\\n assert isinstance(space, dict)\",\n \"def test_from_dict(self):\\n\\n class Person(Model):\\n name = StringField()\\n age = IntegralField(bounds = (0, None))\\n siblings = ListField(of = StringField())\\n\\n # In the normal case where all the data coincides with fields\\n # correctly.\\n person1 = Person.from_dict({\\n \\\"name\\\": \\\"Joe Shmoe\\\",\\n \\\"age\\\": 21,\\n \\\"siblings\\\": [\\\"Dick Shmoe\\\", \\\"Jane Shmoe\\\"]\\n })\\n assert person1.name == \\\"Joe Shmoe\\\"\\n assert person1.age == 21\\n assert person1.siblings == [\\\"Dick Shmoe\\\", \\\"Jane Shmoe\\\"]\\n\\n # In the less normal case where the data does not coincide with fields\\n person2 = Person.from_dict({\\n \\\"notaname\\\": 2,\\n \\\"age\\\": \\\"lots\\\"\\n })\\n assert person2.notaname == 2\\n assert person2.age == \\\"lots\\\"\\n assert person2.name is None\\n assert person2.siblings is None\\n\\n # In the even less normal case where no data exists at all\\n person3 = Person.from_dict({})\\n assert person3.name is None\\n assert person3.age is None\\n assert person3.siblings is None\",\n \"def verifyDicts(cityCodeDict, aircraftCodeDict, flightDict):\\n print 'verify dictionary creation by length'\\n print 'length of cityCodeDict', len(cityCodeDict)\\n print 'length of aircraftCodeDict', len(aircraftCodeDict)\\n print 'length of flightDict', len(flightDict)\\n print 'display a row from each dictionary to verify contents'\\n for key in cityCodeDict.keys():\\n print 'for key', key, 'the row is', cityCodeDict[key].cityCode, cityCodeDict[key].city\\n break\\n for key in aircraftCodeDict.keys():\\n print 'for key', key, 'the row is', aircraftCodeDict[key].aircraftCode, aircraftCodeDict[key].name\\n break \\n for key in flightDict.keys():\\n print 'for key', key, 'the row is', flightDict[key].flightnum, flightDict[key].departCity,flightDict[key].arriveCity\\n break\",\n \"def verifyDicts(cityCodeDict, aircraftCodeDict, flightDict):\\n print 'verify dictionary creation by length'\\n print 'length of cityCodeDict', len(cityCodeDict)\\n print 'length of aircraftCodeDict', len(aircraftCodeDict)\\n print 'length of flightDict', len(flightDict)\\n print 'display a row from each dictionary to verify contents'\\n for key in cityCodeDict.keys():\\n print 'for key', key, 'the row is', cityCodeDict[key].cityCode, cityCodeDict[key].city\\n break\\n for key in aircraftCodeDict.keys():\\n print 'for key', key, 'the row is', aircraftCodeDict[key].aircraftCode, aircraftCodeDict[key].name\\n break \\n for key in flightDict.keys():\\n print 'for key', key, 'the row is', flightDict[key].flightnum, flightDict[key].departCity,flightDict[key].arriveCity\\n break\",\n \"def test_init_with_kwargs(self):\\n self.base1.name = \\\"Holberton\\\"\\n model_json = self.base1.to_dict()\\n new_model = BaseModel(**model_json)\\n self.assertDictEqual(model_json, new_model.to_dict())\\n self.assertIn(\\\"name\\\", new_model.to_dict())\\n self.assertIsNot(self.base1, new_model)\",\n \"def verify_exploration_dict(exploration_dict):\\n EXPLORATION_SCHEMA = [\\n ('default_skin', basestring), ('param_changes', list),\\n ('param_specs', dict), ('schema_version', int), ('states', list)\\n ]\\n utils.verify_dict_keys_and_types(exploration_dict, EXPLORATION_SCHEMA)\\n\\n # Each param spec value should be a dict of the form {obj_type: [STRING]}.\\n for param_key in exploration_dict['param_specs']:\\n ps_value = exploration_dict['param_specs'][param_key]\\n if len(ps_value) != 1 or ps_value.keys()[0] != 'obj_type':\\n raise Exception('Invalid param_spec dict: %s' % ps_value)\\n\\n obj_class = obj_services.get_object_class(ps_value['obj_type'])\\n if obj_class is None:\\n raise Exception('No object class specified.')\\n\\n # Verify there is at least one state.\\n if not exploration_dict['states']:\\n raise Exception('Each exploration should have at least one state.')\\n\\n state_name_list = []\\n for state_desc in exploration_dict['states']:\\n state_name = state_desc['name']\\n if state_name in state_name_list:\\n raise Exception('Duplicate state name: %s' % state_name)\\n state_name_list.append(state_name)\\n\\n for state_desc in exploration_dict['states']:\\n verify_state_dict(\\n state_desc, state_name_list, exploration_dict['param_specs'])\\n\\n _verify_all_states_reachable(exploration_dict['states'])\\n _verify_no_dead_ends(exploration_dict['states'])\",\n \"def test_get_unique_drug_list(self):\\n dict1 = self.test_dict\\n dict2 = get_unique_drug_list(self.test_sorted_tuple)\\n self.assertEqual(dict1, dict2)\",\n \"def test_items(self):\\n obs = self.tester.items()\\n self.assertTrue(isinstance(obs, Iterable))\\n exp = {('physical_location', 'ANL'), ('has_physical_specimen', True),\\n ('has_extracted_data', True), ('sample_type', 'ENVO:soil'),\\n ('required_sample_info_status', 'completed'),\\n ('collection_timestamp', datetime(2011, 11, 11, 13, 00, 00)),\\n ('host_subject_id', '1001:M7'),\\n ('description', 'Cannabis Soil Microbiome'),\\n ('season_environment', 'winter'), ('assigned_from_geo', 'n'),\\n ('texture', '64.6 sand, 17.6 silt, 17.8 clay'),\\n ('taxon_id', '1118232'), ('depth', 0.15),\\n ('host_taxid', '3483'), ('common_name', 'root metagenome'),\\n ('water_content_soil', 0.164), ('elevation', 114), ('temp', 15),\\n ('tot_nitro', 1.41), ('samp_salinity', 7.15), ('altitude', 0),\\n ('env_biome',\\n 'ENVO:Temperate grasslands, savannas, and shrubland biome'),\\n ('country', 'GAZ:United States of America'), ('ph', 6.94),\\n ('anonymized_name', 'SKB8'), ('tot_org_carb', 5),\\n ('description_duplicate', 'Burmese root'),\\n ('env_feature', 'ENVO:plant-associated habitat'),\\n ('latitude', 74.0894932572),\\n ('longitude', 65.3283470202)}\\n self.assertEqual(set(obs), exp)\",\n \"def test_check_xyz_dict(self):\\n xyz1 = converter.check_xyz_dict(self.xyz1['str'])\\n self.assertEqual(xyz1, self.xyz1['dict'])\\n\\n xyz2 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\\n 'coords': ((0.0, 0.0, 0.0),\\n (0.6300326, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))}\\n xyz2 = converter.check_xyz_dict(xyz2)\\n expected_xyz2 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\\n 'isotopes': (12, 1, 1, 1, 1),\\n 'coords': ((0.0, 0.0, 0.0),\\n (0.6300326, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))}\\n self.assertEqual(xyz2, expected_xyz2)\\n\\n xyz3 = 3.0\\n with self.assertRaises(ConverterError):\\n converter.check_xyz_dict(xyz3)\\n\\n xyz4 = {'coords': ((0.0, 0.0, 0.0),\\n (0.6300326, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))}\\n with self.assertRaises(ConverterError):\\n converter.check_xyz_dict(xyz4)\\n\\n xyz5 = {'symbols': ('C', 'H', 'H', 'H', 'H', 'S', 'S', 'S'),\\n 'coords': ((0.0, 0.0, 0.0),\\n (0.6300326, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))}\\n with self.assertRaises(ConverterError):\\n converter.check_xyz_dict(xyz5)\\n\\n # test a zmat input\\n zmat6 = {'symbols': ('N', 'N', 'H', 'H'),\\n 'coords': ((None, None, None), ('R_1_0', None, None), ('R_2_1', 'A_2_1_0', None),\\n ('R_3_2', 'A_3_2_0', 'D_3_2_0_1')),\\n 'vars': {'R_1_0': 1.2451214479859707, 'R_2_1': 1.8953164901754294, 'A_2_1_0': 30.18165946689929,\\n 'R_3_2': 2.785552137148173, 'A_3_2_0': 24.405141545817347,\\n 'D_3_2_0_1': 3.6222548091772e-06}, 'map': {0: 0, 1: 1, 2: 2, 3: 3}}\\n xyz6 = converter.check_xyz_dict(zmat6)\\n expected_xyz6 = {'symbols': ('N', 'N', 'H', 'H'),\\n 'isotopes': (14, 14, 1, 1),\\n 'coords': ((-2.4426534384901547e-09, -4.375090750708016e-09, -0.622560729110669),\\n (-2.4426534384901547e-09, -4.375090750708016e-09, 0.6225607188753017),\\n (-2.4426534384901547e-09, 0.9528575945413793, -1.015818661524137),\\n (7.032081834243086e-08, -0.9528574729632926, 1.015818803737915))}\\n\\n self.assertEqual(xyz6, expected_xyz6)\",\n \"def test_creation_dict():\\n with pytest.raises(ValueError) as __:\\n value = dict()\\n __ = param.Integer(value=value)\",\n \"def test_addEntryByDict(self):\\n self.g.entryFormat = ['term', 'tags', 'value']\\n b = self.g.add_entry({'term': 'foo', 'tags': 'a', 'value': '1'})\\n self.assertTrue(b)\",\n \"def __init__(self, dictionary: dict[str, t.Any]) -> None:\\n missing_keys = self.__annotations__.keys() - dictionary.keys()\\n if missing_keys:\\n raise KeyError(f\\\"Fetched object lacks expected keys: {missing_keys}\\\")\\n for annotation in self.__annotations__:\\n setattr(self, annotation, dictionary[annotation])\",\n \"def test_instantiate_no_target(self):\\n # create test configs\\n test_configs = [\\n {},\\n {\\\"a\\\": 1, \\\"b\\\": 2}\\n ]\\n\\n # check that instantiate raises ValueError for each test config\\n for test_conf in test_configs:\\n self.assertRaises(ValueError, instantiate, test_conf)\",\n \"def test_minimal_input():\\n transactions = []\\n itemsets, rules = apriori(transactions, 0.2, 0.2)\\n assert itemsets == {} and rules == []\\n\\n with pytest.raises(ValueError):\\n itemsets, rules = apriori(transactions, -0.2, 0.2)\\n\\n with pytest.raises(ValueError):\\n itemsets, rules = apriori(transactions, 0.2, -0.2)\\n\\n with pytest.raises(ValueError):\\n itemsets, rules = apriori(transactions, \\\"asdf\\\", 1)\\n\\n itemsets, rules = apriori([(1, 2), (1, 2), (1, 3)], 1, 1)\\n itemsets, rules = apriori([(1, 2), (1, 2), (1, 3)], 1.0, 1.0)\",\n \"def test_constructor_canonizes_dict_to_io(\\n self, input_list_with_dict, output_list_with_dict, dict_list\\n ):\\n\\n A = MsgMultiSend(inputs=input_list_with_dict, outputs=output_list_with_dict)\\n B = MsgMultiSend(inputs=dict_list, outputs=dict_list)\\n\\n assert all(isinstance(i, Input) for i in A.inputs)\\n assert all(isinstance(o, Output) for o in A.outputs)\",\n \"def test_items(self):\\n obs = self.tester.items()\\n self.assertTrue(isinstance(obs, Iterable))\\n exp = {('center_name', 'ANL'), ('center_project_name', None),\\n ('emp_status', 'EMP'), ('barcodesequence', 'AGCGCTCACATC'),\\n ('library_construction_protocol',\\n 'This analysis was done as in Caporaso et al 2011 Genome '\\n 'research. The PCR primers (F515/R806) were developed against '\\n 'the V4 region of the 16S rRNA (both bacteria and archaea), '\\n 'which we determined would yield optimal community clustering '\\n 'with reads of this length using a procedure similar to that '\\n 'of ref. 15. [For reference, this primer pair amplifies the '\\n 'region 533_786 in the Escherichia coli strain 83972 sequence '\\n '(greengenes accession no. prokMSA_id:470367).] The reverse '\\n 'PCR primer is barcoded with a 12-base error-correcting Golay '\\n 'code to facilitate multiplexing of up to 1,500 samples per '\\n 'lane, and both PCR primers contain sequencer adapter '\\n 'regions.'), ('linkerprimersequence', 'GTGCCAGCMGCCGCGGTAA'),\\n ('target_subfragment', 'V4'), ('target_gene', '16S rRNA'),\\n ('run_center', 'ANL'), ('run_prefix', 's_G1_L001_sequences'),\\n ('run_date', '8/1/12'), ('experiment_center', 'ANL'),\\n ('experiment_design_description',\\n 'micro biome of soil and rhizosphere of cannabis plants '\\n 'from CA'), ('experiment_title', 'Cannabis Soil Microbiome'),\\n ('platform', 'Illumina'), ('samp_size', '.25,g'),\\n ('sequencing_meth', 'Sequencing by synthesis'),\\n ('illumina_technology', 'MiSeq'), ('sample_center', 'ANL'),\\n ('pcr_primers',\\n 'FWD:GTGCCAGCMGCCGCGGTAA; REV:GGACTACHVGGGTWTCTAAT'),\\n ('study_center', 'CCME')}\\n self.assertEqual(set(obs), exp)\",\n \"def test_empty_dict_coerce():\\n\\n @type_checked\\n def _run_test(thing:{}):\\n assert isinstance(thing, dict)\\n\\n _run_test([(\\\"something\\\", \\\"is_true\\\")])\",\n \"def __init__(self, schema: dict, **kwargs) -> None:\\r\\n self.case_check = case_check(settings.CASE)\\r\\n self.ignored_keys = set_ignored_keys(**kwargs)\\r\\n if read_type(schema) == 'object':\\r\\n logger.debug('root -> dict')\\r\\n self.test_dict(schema)\\r\\n elif read_type(schema) == 'array':\\r\\n logger.debug('root -> list')\\r\\n self.test_list(schema)\\r\\n else:\\r\\n logger.debug('Skipping case check')\",\n \"def test_exceptional_cases(self):\\n # autogeneratearing strings throws a type error\\n args = ('foo', 'bar')\\n self.assertRaises(TypeError, autogenerate, args)\\n # UNLESS(?) they're the same\\n args = ('foo', 'foo')\\n self.assertEqual(autogenerate(*args), [])\\n # raise type error if classes aren't the same\\n args = (['foo'], {'bar': 1})\\n self.assertRaises(TypeError, autogenerate, args)\\n # raises an error if it doesn't know about what classes they are\\n class Flub(object):\\n def __init__(self, val):\\n self.val = val\\n self.assertRaises(TypeError, autogenerate, (Flub('hi'), Flub('bye')))\\n # but it doesn't if cmp works on the object and they're the same...\\n class Flub2(Flub):\\n def __cmp__(self, other):\\n return cmp(self.val, other.val)\\n self.assertEqual(autogenerate(Flub2('hi'), Flub2('hi')), [])\",\n \"def test_instantiate_non_existent_class(self):\\n # create test configs\\n test_configs = [\\n {\\\"_target_\\\": \\\"collections.NonExistentClass\\\"},\\n {\\\"_target_\\\": \\\"collections.OtherNonExistentClass\\\", \\\"a\\\": 1, \\\"b\\\": 2}\\n ]\\n\\n # check that instantiate raises AttributeError for each test config\\n for test_conf in test_configs:\\n self.assertRaises(AttributeError, instantiate, test_conf)\",\n \"def test_list_facet_dictionaries(self):\\n pass\",\n \"def test_empty_dict():\\n\\n @type_checked\\n def _run_test(thing:{}):\\n assert isinstance(thing, dict)\\n\\n _run_test({\\\"foo\\\": \\\"bar\\\"})\",\n \"def test_init(attributes):\\n instance = Participant(**attributes)\\n for attr, value in attributes.items():\\n assert getattr(instance, attr) == value\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_categories)\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_categories)\",\n \"def test_dict_data_reversible(self):\\n from tests.sampletest.datatest import DataTest\\n\\n tests = self.loader.load(\\\"tests.sampletest.datatest.DataTest\\\")\\n self.assertEqual(len(tests), 3)\\n self.assertEqual(type(tests[0]), DataTest)\\n\\n test_set = \\\" \\\".join(map(lambda x: x.test_name, tests)).split()\\n loader = TestLoader()\\n new_tests = loader.load(test_set)\\n self.assertEqual(len(new_tests), len(tests))\",\n \"def initializeFromDict(self, inputDict):\\n pass\",\n \"def test_create_from_dict(self):\\n b1 = BaseModel()\\n b1.name = \\\"Holberton\\\"\\n b1.my_number = 89\\n my_model_json = b1.to_dict()\\n b2 = BaseModel(**my_model_json)\\n self.assertEqual(b1.my_number, b2.my_number)\\n self.assertEqual(b1.id, b2.id)\\n self.assertEqual(b1.name, b2.name)\\n self.assertEqual(b1.created_at, b2.created_at)\\n self.assertEqual(b1.updated_at, b2.updated_at)\\n self.assertNotEqual(b1, b2)\",\n \"def test_construct_tickets_3(self):\\n\\n tkt_dict1 = {}\\n tkt_dict1[\\\"type\\\"] = \\\"add\\\"\\n tkt_dict1[\\\"phage_id\\\"] = \\\"Trixie\\\"\\n tkt_dict1[\\\"description_field\\\"] = \\\"product\\\"\\n tkt_dict1[\\\"eval_mode\\\"] = \\\"final\\\"\\n\\n tkt_dict2 = {}\\n tkt_dict2[\\\"type\\\"] = \\\"add\\\"\\n tkt_dict2[\\\"phage_id\\\"] = \\\"L5\\\"\\n tkt_dict2[\\\"description_field\\\"] = \\\"product\\\"\\n tkt_dict2[\\\"eval_mode\\\"] = \\\"final\\\"\\n\\n tkt_dict3 = {}\\n tkt_dict3[\\\"type\\\"] = \\\"add\\\"\\n tkt_dict3[\\\"phage_id\\\"] = \\\"RedRock\\\"\\n\\n tkt_dict4 = {}\\n tkt_dict4[\\\"type\\\"] = \\\"add\\\"\\n tkt_dict4[\\\"phage_id\\\"] = \\\"Bxb1\\\"\\n\\n dict_list = [tkt_dict1, tkt_dict2, tkt_dict3, tkt_dict4]\\n eval_data_dict = {\\\"eval_mode\\\": \\\"custom_eval_mode\\\",\\n \\\"eval_flag_dict\\\": {\\\"check_locus_tag\\\": False}}\\n tkt_list = tickets.construct_tickets(dict_list,\\n eval_data_dict, \\\"function\\\", self.required_keys,\\n self.optional_keys, self.keywords)\\n\\n tkt_list[0].eval_flags[\\\"check_locus_tag\\\"] = 0\\n tkt_list[1].eval_flags[\\\"check_locus_tag\\\"] = 1\\n tkt_list[2].eval_flags[\\\"check_locus_tag\\\"] = 2\\n tkt_list[3].eval_flags[\\\"check_locus_tag\\\"] = 3\\n\\n with self.subTest():\\n self.assertEqual(tkt_list[0].eval_flags[\\\"check_locus_tag\\\"], 0)\\n with self.subTest():\\n self.assertEqual(tkt_list[1].eval_flags[\\\"check_locus_tag\\\"], 1)\\n with self.subTest():\\n self.assertEqual(tkt_list[2].eval_flags[\\\"check_locus_tag\\\"], 2)\\n with self.subTest():\\n self.assertEqual(tkt_list[3].eval_flags[\\\"check_locus_tag\\\"], 3)\",\n \"def test_construct_tickets_1(self):\\n dict_list = [self.ticket_dict1, self.ticket_dict4]\\n eval_data_dict = {\\\"eval_mode\\\": \\\"custom_eval_mode\\\",\\n \\\"eval_flag_dict\\\": {\\\"check_locus_tag\\\": False}}\\n list_of_tickets = tickets.construct_tickets(dict_list,\\n eval_data_dict, \\\"function\\\", self.required_keys,\\n self.optional_keys, self.keywords)\\n with self.subTest():\\n self.assertEqual(len(list_of_tickets), 2)\\n with self.subTest():\\n self.assertEqual(list_of_tickets[0].id, 1)\\n with self.subTest():\\n self.assertEqual(list_of_tickets[0].eval_mode, \\\"final\\\")\\n with self.subTest():\\n self.assertEqual(list_of_tickets[0].description_field, \\\"product\\\")\\n with self.subTest():\\n self.assertTrue(list_of_tickets[0].eval_flags[\\\"check_locus_tag\\\"])\\n with self.subTest():\\n self.assertEqual(list_of_tickets[1].id, 2)\\n with self.subTest():\\n self.assertEqual(list_of_tickets[1].eval_mode, \\\"custom_eval_mode\\\")\\n with self.subTest():\\n self.assertEqual(list_of_tickets[1].description_field, \\\"function\\\")\\n with self.subTest():\\n self.assertFalse(list_of_tickets[1].eval_flags[\\\"check_locus_tag\\\"])\",\n \"def test_values(self):\\n obs = self.tester.values()\\n self.assertTrue(isinstance(obs, Iterable))\\n exp = {PrepSample('1.SKB1.640202', self.tester),\\n PrepSample('1.SKB2.640194', self.tester),\\n PrepSample('1.SKB3.640195', self.tester),\\n PrepSample('1.SKB4.640189', self.tester),\\n PrepSample('1.SKB5.640181', self.tester),\\n PrepSample('1.SKB6.640176', self.tester),\\n PrepSample('1.SKB7.640196', self.tester),\\n PrepSample('1.SKB8.640193', self.tester),\\n PrepSample('1.SKB9.640200', self.tester),\\n PrepSample('1.SKD1.640179', self.tester),\\n PrepSample('1.SKD2.640178', self.tester),\\n PrepSample('1.SKD3.640198', self.tester),\\n PrepSample('1.SKD4.640185', self.tester),\\n PrepSample('1.SKD5.640186', self.tester),\\n PrepSample('1.SKD6.640190', self.tester),\\n PrepSample('1.SKD7.640191', self.tester),\\n PrepSample('1.SKD8.640184', self.tester),\\n PrepSample('1.SKD9.640182', self.tester),\\n PrepSample('1.SKM1.640183', self.tester),\\n PrepSample('1.SKM2.640199', self.tester),\\n PrepSample('1.SKM3.640197', self.tester),\\n PrepSample('1.SKM4.640180', self.tester),\\n PrepSample('1.SKM5.640177', self.tester),\\n PrepSample('1.SKM6.640187', self.tester),\\n PrepSample('1.SKM7.640188', self.tester),\\n PrepSample('1.SKM8.640201', self.tester),\\n PrepSample('1.SKM9.640192', self.tester)}\\n # Creating a list and looping over it since unittest does not call\\n # the __eq__ function on the objects\\n for o, e in zip(sorted(list(obs), key=lambda x: x.id),\\n sorted(exp, key=lambda x: x.id)):\\n self.assertEqual(o, e)\",\n \"def test_fn_call_with_dict():\\n l = [1, 2, 3, 4, 5]\\n ds = [defaultdict(int), defaultdict(int), defaultdict(int)]\\n for d in ds:\\n for fn in [s7.div, s7.mul, s7.add, \\\"abcd\\\", 1234]:\\n try:\\n f = s7.count_fn_called_with_dict(dict_=d, fn=fn)\\n for i in range(0, random.randint(2, 10)):\\n f(*l)\\n assert fn in d.keys() and d[fn] == (i + 1)\\n except Exception as e:\\n assert e.__class__.__name__ == TypeError.__name__\",\n \"def test_27_create_list(self):\\n r = Rectangle(1, 1)\\n r_dictionary = r.to_dictionary()\\n with self.assertRaises(TypeError) as e:\\n r = Rectangle.create([1, 2, 3])\\n self.assertEqual(\\n \\\"create() takes 1 positional argument but 2 were given\\\", str(\\n e.exception))\",\n \"def setUp(self):\\n cwd = Path(__file__).parent.absolute()\\n with open(f'{cwd}/test.json', 'r') as f:\\n default = json.load(f)\\n\\n for data in default['results']:\\n set_id = data.pop('set_id')\\n products_data = data.pop('products')\\n\\n set_obj = Set.objects.create(id=set_id)\\n\\n spl_obj = set_obj.spls.create(**data)\\n\\n for product_data in products_data:\\n product_data.pop('name')\\n packages_data = product_data.pop('packages')\\n if 'inactive_ingredients' in product_data:\\n inactive_ingredients_data = product_data\\\\\\n .pop('inactive_ingredients')\\n\\n inactive_ingredients_list = []\\n for inactive_ingredient_data in inactive_ingredients_data:\\n try:\\n ingredient = InactiveIngredient.objects.get(\\n **inactive_ingredient_data\\n )\\n inactive_ingredients_list.append(ingredient)\\n except Exception:\\n ingredient = InactiveIngredient.objects.create(\\n **inactive_ingredient_data\\n )\\n inactive_ingredients_list.append(ingredient)\\n\\n product_obj = spl_obj.products.create(**product_data)\\n product_obj.inactive_ingredients\\\\\\n .add(*inactive_ingredients_list)\\n\\n for package_data in packages_data:\\n product_obj.packages.create(**package_data)\",\n \"def assertDictSubset(self, subset, superset):\\n if not set(subset.items()).issubset(set(superset.items())):\\n raise AssertionError(\\\"{0} is not subset of {1}\\\".format(subset, superset))\",\n \"def __init__(self, map):\\n self.map = map\\n self._map = _map = {}\\n for key in map.keys():\\n _map[key] = key\\n self.fast_validate = (10, _map, self.validate)\",\n \"def testMapSetdefault(self):\\n # We only use one map type since they all share the same implementation for\\n # this logic.\\n m = data_types.StepBuildStatsMap()\\n with self.assertRaises(AssertionError):\\n m.setdefault(1, data_types.BuildStats())\\n with self.assertRaises(AssertionError):\\n m.setdefault('1', 2)\\n m.setdefault('1', data_types.BuildStats())\\n self.assertEqual(m, {'1': data_types.BuildStats()})\",\n \"def test_dict_type(self):\\n\\n expected = TypeError\\n input_ = []\\n with self.assertRaises(expected):\\n math.factorial(input_)\",\n \"def __init__(self, only=[], deactivate=[]):\\n self._tests = OrderedDict()\\n deactivate = set(deactivate)\\n for name, test in self._known.iteritems():\\n if (len(only) == 0 or name in only) and deactivate.isdisjoint(test.keywords):\\n self.addTest(self._known[name])\",\n \"def test_attr_dict(self):\\n obj = awstats_reader.AttrDict([('this','that'), ('thus','those')])\\n self.assertEqual(obj.thus, 'those')\",\n \"def test_create_cds_translation_dict_1(self):\\n t1 = \\\"ABCDE\\\"\\n t2 = \\\"FGHIJ\\\"\\n t3 = \\\"RSTUV\\\"\\n self.gene_data_1[\\\"Translation\\\"] = t1\\n self.gene_data_2[\\\"Translation\\\"] = t2\\n self.gene_data_3[\\\"Translation\\\"] = t3\\n cdd_list = [self.gene_data_1, self.gene_data_2, self.gene_data_3]\\n dict = find_domains.create_cds_translation_dict(cdd_list)\\n\\n exp_keys = {t1, t2, t3}\\n with self.subTest():\\n self.assertEqual(dict.keys(), exp_keys)\\n with self.subTest():\\n self.assertEqual(dict[t1], {self.gene_data_1[\\\"GeneID\\\"]})\\n with self.subTest():\\n self.assertEqual(dict[t2], {self.gene_data_2[\\\"GeneID\\\"]})\\n with self.subTest():\\n self.assertEqual(dict[t3], {self.gene_data_3[\\\"GeneID\\\"]})\",\n \"def test_dict(self, testdata: TestData) -> None:\\n for data in testdata['observation_type']:\\n observation_type = ObservationType.from_dict(data)\\n assert data == observation_type.to_dict()\",\n \"def __init__(self, response_data: Any, **kwargs) -> None:\\r\\n self.case_check = case_check(settings.CASE)\\r\\n self.ignored_keys = set_ignored_keys(**kwargs)\\r\\n if isinstance(response_data, dict):\\r\\n self.test_dict(response_data)\\r\\n elif isinstance(response_data, list):\\r\\n self.test_list(response_data)\\r\\n else:\\r\\n logger.debug('Skipping case check')\",\n \"def test_can_instantiate(self):\\n\\n exc_thrown = False\\n\\n try:\\n self.klass(*self.instantiate_args)\\n except Exception:\\n exc_thrown = True\\n\\n self.assertFalse(exc_thrown)\",\n \"def testDictContains(self):\\n self.Check(\\\"\\\"\\\"\\n d1 = {\\\"x\\\": 42}\\n if \\\"x\\\" in d1:\\n print d1[\\\"x\\\"]\\n else:\\n print d1[\\\"nonsense\\\"] # Dead code\\n\\n d2 = {}\\n if \\\"x\\\" in d2:\\n print d2[\\\"nonsense\\\"] # Dead code\\n\\n d3 = {__any_object__: __any_object__}\\n if \\\"x\\\" in d3:\\n print d3[\\\"x\\\"]\\n else:\\n print d3[\\\"y\\\"]\\n \\\"\\\"\\\")\",\n \"def test_individual_valid(self, kwargs):\\n # defaults\\n final_kwargs = {'script': 'echo \\\"hello world\\\"', 'title': '', 'debug': False, 'strict': False,\\n 'dry_run': False, 'item': None, 'env': {}, 'model': {}, 'variables': {}}\\n final_kwargs.update(kwargs)\\n\\n config = ShellConfig(**final_kwargs)\\n for key, value in final_kwargs.items():\\n assert_that(key in config.__dict__, equal_to(True))\\n assert_that(config.__dict__[key], equal_to(value))\",\n \"def test_dict(self, obj: dict) -> None:\\r\\n properties = read_properties(obj)\\r\\n for key, value in properties.items():\\r\\n conditional_check(key, self.case_check, self.ignored_keys)\\r\\n if read_type(value) == 'object':\\r\\n logger.debug('dict -> dict')\\r\\n self.test_dict(obj=value)\\r\\n elif read_type(value) == 'array':\\r\\n logger.debug('dict -> list')\\r\\n self.test_list(array=value)\",\n \"def dict_check(*args, func=None):\\n func = func or inspect.stack()[2][3]\\n for var in args:\\n if not isinstance(var, (dict, collections.UserDict, collections.abc.MutableMapping)):\\n name = type(var).__name__\\n raise DictError(\\n 'Function {} expected dict, {} got instead.'.format(func, name))\",\n \"def __init__(self, *values):\\n if (len(values) == 1) and (type(values[0]) in SequenceTypes):\\n values = values[0]\\n self.values = values[:]\\n self.values_ = values_ = {}\\n for key in values:\\n values_[key] = key\\n self.fast_validate = (10, values_, self.validate)\",\n \"def test_18_0_create(self):\\n\\n r1_dict = self.r1.to_dictionary()\\n r = Rectangle.create(**r1_dict)\\n self.assertFalse(self.r1 == r)\\n self.assertFalse(self.r1 is r)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64251405","0.61764544","0.599205","0.5967179","0.5964097","0.59410644","0.57628375","0.5736472","0.57099533","0.56787115","0.56783843","0.56684375","0.56655174","0.5664207","0.5655415","0.5654512","0.5649937","0.5634078","0.5615306","0.55827117","0.5577973","0.5564169","0.55490935","0.5526454","0.55127645","0.5507093","0.5496994","0.54942226","0.54825926","0.54651326","0.5463219","0.54537135","0.54487014","0.5446305","0.54434526","0.5431411","0.5426532","0.54229325","0.5406871","0.5399971","0.53992194","0.5395621","0.5394846","0.53886765","0.53850067","0.5379946","0.5377561","0.53713596","0.53705746","0.53651965","0.53574353","0.5353787","0.53504","0.53504","0.53400594","0.5332866","0.53319806","0.53207433","0.5308183","0.5306311","0.5302535","0.5302111","0.5283172","0.5279783","0.52768123","0.52757573","0.5267553","0.52606857","0.5256366","0.52563256","0.5255479","0.52524954","0.5246923","0.5243535","0.5243535","0.52425665","0.5231909","0.52098566","0.5203633","0.5202971","0.52028155","0.52012163","0.51902145","0.51891464","0.51875335","0.51864487","0.51859117","0.5182207","0.5181662","0.5174168","0.51735836","0.51681507","0.5167668","0.51667446","0.51622784","0.5152037","0.5148206","0.51474637","0.5147417","0.51448345"],"string":"[\n \"0.64251405\",\n \"0.61764544\",\n \"0.599205\",\n \"0.5967179\",\n \"0.5964097\",\n \"0.59410644\",\n \"0.57628375\",\n \"0.5736472\",\n \"0.57099533\",\n \"0.56787115\",\n \"0.56783843\",\n \"0.56684375\",\n \"0.56655174\",\n \"0.5664207\",\n \"0.5655415\",\n \"0.5654512\",\n \"0.5649937\",\n \"0.5634078\",\n \"0.5615306\",\n \"0.55827117\",\n \"0.5577973\",\n \"0.5564169\",\n \"0.55490935\",\n \"0.5526454\",\n \"0.55127645\",\n \"0.5507093\",\n \"0.5496994\",\n \"0.54942226\",\n \"0.54825926\",\n \"0.54651326\",\n \"0.5463219\",\n \"0.54537135\",\n \"0.54487014\",\n \"0.5446305\",\n \"0.54434526\",\n \"0.5431411\",\n \"0.5426532\",\n \"0.54229325\",\n \"0.5406871\",\n \"0.5399971\",\n \"0.53992194\",\n \"0.5395621\",\n \"0.5394846\",\n \"0.53886765\",\n \"0.53850067\",\n \"0.5379946\",\n \"0.5377561\",\n \"0.53713596\",\n \"0.53705746\",\n \"0.53651965\",\n \"0.53574353\",\n \"0.5353787\",\n \"0.53504\",\n \"0.53504\",\n \"0.53400594\",\n \"0.5332866\",\n \"0.53319806\",\n \"0.53207433\",\n \"0.5308183\",\n \"0.5306311\",\n \"0.5302535\",\n \"0.5302111\",\n \"0.5283172\",\n \"0.5279783\",\n \"0.52768123\",\n \"0.52757573\",\n \"0.5267553\",\n \"0.52606857\",\n \"0.5256366\",\n \"0.52563256\",\n \"0.5255479\",\n \"0.52524954\",\n \"0.5246923\",\n \"0.5243535\",\n \"0.5243535\",\n \"0.52425665\",\n \"0.5231909\",\n \"0.52098566\",\n \"0.5203633\",\n \"0.5202971\",\n \"0.52028155\",\n \"0.52012163\",\n \"0.51902145\",\n \"0.51891464\",\n \"0.51875335\",\n \"0.51864487\",\n \"0.51859117\",\n \"0.5182207\",\n \"0.5181662\",\n \"0.5174168\",\n \"0.51735836\",\n \"0.51681507\",\n \"0.5167668\",\n \"0.51667446\",\n \"0.51622784\",\n \"0.5152037\",\n \"0.5148206\",\n \"0.51474637\",\n \"0.5147417\",\n \"0.51448345\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6566189"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93787,"cells":{"query":{"kind":"string","value":"Test that inputs to an instance of HR can be verified."},"document":{"kind":"string","value":"def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_validParameters(self):\n # stupid/simple inputs\n h = Helix(\"thing\", \"Cu\", 0, 0, 1, 1, 1)\n self.assertEqual(h.getDimension(\"axialPitch\"), 1)\n\n # standard case / inputs ordered well\n h = Helix(\n \"what\",\n \"Cu\",\n Tinput=25.0,\n Thot=425.0,\n id=0.1,\n od=0.35,\n mult=1.0,\n axialPitch=1.123,\n helixDiameter=1.5,\n )\n self.assertTrue(1.123 < h.getDimension(\"axialPitch\") < 1.15)\n\n # inputs ordered crazy\n h = Helix(\n material=\"Cu\",\n id=0.1,\n mult=1.0,\n Tinput=25.0,\n Thot=425.0,\n axialPitch=1.123,\n name=\"stuff\",\n od=0.35,\n helixDiameter=1.5,\n )\n self.assertTrue(1.123 < h.getDimension(\"axialPitch\") < 1.15)\n\n # missing helixDiameter input\n with self.assertRaises(TypeError):\n h = Helix(\n name=\"helix\",\n material=\"Cu\",\n Tinput=25.0,\n Thot=425.0,\n id=0.1,\n od=0.35,\n mult=1.0,\n axialPitch=1.123,\n )","def test_input_type_hhm(self, _run_mock):\n hhblits = self.tool(input_type=hhsuite.QueryType.HMM)\n self.assertEqual(hhblits.REQUIRED, [\"hhm\"])\n hhblits.run({\"hhm\": self.IN_HHM_FILE})\n self.verify_common(\"hhblits\", hhblits)\n _, kw_args = hhblits.tool.call_args\n self.assertEqual(kw_args[\"options\"][\"input\"], self.IN_HHM_FILE)","def test_check_validity(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n assert match.check_validity()","def test_validate(self):\n pass","def test_is_active_of_homework_positive():\n assert oop_hw.is_active()","def input_check(self):\n\n if self.species == 'He': assert self.line_model == 'voigt'\n n_upper_range, e_dens_range, temp_range, b_field_range = get_param_ranges(self.line_model)\n\n if np.isnan(n_upper_range).sum() <= 1:\n assert (self.n_upper in range(n_upper_range[0], n_upper_range[1]))\n if np.isnan(e_dens_range).sum() <= 1:\n assert (e_dens_range[0] <= self.e_dens <= e_dens_range[1])\n if np.isnan(temp_range).sum() <= 1:\n assert (temp_range[0] <= self.temp <= temp_range[1])\n if np.isnan(b_field_range).sum() <= 1:\n assert (b_field_range[0] <= self.b_field <= b_field_range[1])","def testHealthAssessHosp(self):\n attr = self.session.create_visit_attr()\n\n self.util.boolTypeTest(self, attr, \"hosp\")\n\n self.util.boolPropertyTest(self, attr, \"hosp\")","def test_teacher_check_homework_positive():\n assert opp_teacher.check_homework(result_1)","def test_kyc_get_validation_legal(self):\n pass","def test_input1(self):\n in1 =\"elephant\"\n result = options.check(in1)\n self.assertEqual(result,True)","def test_holidays_validate(self):\n self.request.sudo(self.user_1.id).holidays_validate()\n\n accrual = self.employee.get_leave_accrual(self.leave_type.id)\n self.assertEqual(accrual.total_hours, 22.5)","def test_check_validity(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n game.solve()\n assert game.check_validity()","def test_birth_validation(self):","def test_negative_is_active_of_homework():\n assert not expired_hw.is_active()","def test_validation_passes_with_valid_input(self):\n\n result = LandCompensationLandSoldValidator.validate(VALID_LAND_SOLD, VALID_WORK_DONE)\n self.assertEqual(0, len(result.errors))","def test_enforcement_boundary_create_command_human_readable(\n enforcement_boundary_success, enforcement_boundary_success_hr\n):\n hr_output = prepare_enforcement_boundary_create_output(enforcement_boundary_success)\n\n assert hr_output == enforcement_boundary_success_hr","def test_validate_answer(self):\r\n problem = self.build_problem(answer=\"42\")\r\n responder = problem.responders.values()[0]\r\n self.assertTrue(responder.validate_answer('23.5'))\r\n self.assertFalse(responder.validate_answer('fish'))","def check_validity(self):","def _expected_inputs():","def test_check_args_working_hours(self):\n test_date = dt.datetime(2021, 6, 18, 7, 0, 0)\n with self.assertRaises(ValueError) as context:\n self.duedate.check_args(test_date, self.test_turn_time)\n self.assertTrue(\n \"You can submit requests from 9AM till 5PM.\" in str(context.exception))","def test_teacher_create_homework():\n assert isinstance(oop_hw, Homework)","def test_input2(self):\n in1 =\"aple\"\n result = options.checkw(in1)\n self.assertEqual(result,False)","def test_check_inputs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n with warnings.catch_warnings(record=True):\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.students == game._all_students\n assert game.projects == game._all_projects\n assert game.supervisors == game._all_supervisors","def test_initialization_of_homework_result_homework():\n assert result_1.homework == oop_hw","def test_valid_user_input(self):\n\n test_name = sys._getframe().f_code.co_name\n\n log.info(\"###### TEST EXECUTION STARTED :: \" + test_name + \" ######\")\n\n user_input = data_reader.get_data(test_name, \"Text_Message\")\n\n with allure.step(\"Verify User Input\"):\n result = self.main_page.verify_valid_user_input(user_input)\n self.exe_status.mark_final(test_step=test_name, result=result)","def assert_rh(instance: machine_learning.StationMachineLearning,\n model_rh: float, timestamp: datetime, bias_adjusted_rh: float):\n result = instance.predict_rh(model_rh, timestamp)\n assert result == bias_adjusted_rh","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def test_hav_bad_input(self):\n self.assertRaises(ValueError, lambda: latlongcalcs.hav('$'))\n self.assertRaises(ValueError, lambda: latlongcalcs.hav('ge'))","def test_rules():","def validate():","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_validate_input_good_input(self):\r\n _validate_input(self.dist_matrix_header, self.dist_matrix,\r\n self.mapping_header, self.mapping, \"Treatment\")","def test_kyc_get_validation(self):\n pass","def isInputValid(self, input):\r\n pass","def test__validate_status__1():\n for input_value in (\n 12.6,\n ):\n with vampytest.assert_raises(TypeError):\n validate_status(input_value)","def test_check_validity(game):\n\n game.solve()\n assert game.check_validity()","def test_validators():","async def test_form_validate_input(hass):\n result = await hass.config_entries.flow.async_init(\n DOMAIN, context={\"source\": config_entries.SOURCE_USER}\n )\n\n with requests_mock.Mocker() as mock_request:\n mock_request.get(\n \"https://api.wall-box.com/auth/token/user\",\n json=authorisation_response,\n status_code=HTTPStatus.OK,\n )\n mock_request.get(\n \"https://api.wall-box.com/chargers/status/12345\",\n json=test_response,\n status_code=HTTPStatus.OK,\n )\n result2 = await hass.config_entries.flow.async_configure(\n result[\"flow_id\"],\n {\n \"station\": \"12345\",\n \"username\": \"test-username\",\n \"password\": \"test-password\",\n },\n )\n\n assert result2[\"title\"] == \"Wallbox Portal\"\n assert result2[\"data\"][\"station\"] == \"12345\"","def test_valid_input(helper):\r\n output = auth_register_v1(email='harrypotter@gmail.com',\r\n password='qw3rtyAppl3s@99',\r\n name_first='Harry',\r\n name_last='Potter')\r\n\r\n expected = auth_login_v1(email='harrypotter@gmail.com',\r\n password='qw3rtyAppl3s@99')\r\n assert output.get('auth_user_id') == expected.get('auth_user_id') == 1\r\n\r\n output = auth_register_v1(email='harrypotter3@gmail.com',\r\n password='qw3rtyAppl3s@04',\r\n name_first='Harrrrry',\r\n name_last='Pottttter')\r\n \r\n expected = auth_login_v1(email='harrypotter3@gmail.com',\r\n password='qw3rtyAppl3s@04')\r\n assert output.get('auth_user_id') == expected.get('auth_user_id') == 2","def test_custom_rise(self):\n self.employee.give_raise(self.custom_rise)\n self.assertEqual(self.employee.anual_salary, self.salary + self.custom_rise)","def test_validates(if_statement_validator):\n test = {\n 'condition': 'is',\n 'target': 'bob',\n 'then': 'arne',\n }\n assert if_statement_validator(test).unwrap() == test","def test_generating(resp):\n errors = []\n if not check_int(resp[\"tightness\"]):\n errors.append(\"Invalid type for Itinerary response's 'tightness' field.\")\n\n if not isinstance(resp, bool):\n errors.append(\"Invalid type for Itinerary response's 'start_from_airport' field.\")","def test__validate_title__1():\n for input_value in (\n 12.6,\n ):\n with vampytest.assert_raises(TypeError):\n validate_title(input_value)","def test_student_do_homework_positive():\n assert isinstance(result_1, HomeworkResult)","def test_teacher_check_homework_raises_type_error_if_not_a_homework_result_instance():\n with pytest.raises(TypeError):\n opp_teacher.check_homework(\"done this\")","def test_task_conforms_to_spec(self, domain, task):\n is_benchmark = (domain, task) in suite.BENCHMARKING\n env = suite.load(domain, task)\n observation_spec = env.observation_spec()\n action_spec = env.action_spec()\n\n # Check action bounds.\n if is_benchmark:\n self._validate_control_range(action_spec.minimum, action_spec.maximum)\n\n # Step through the environment, applying random actions sampled within the\n # valid range and check the observations, rewards, and discounts.\n policy = uniform_random_policy(action_spec)\n for time_step in step_environment(env, policy):\n self._validate_observation(time_step.observation, observation_spec)\n self._validate_discount(time_step)\n if is_benchmark:\n self._validate_reward_range(time_step)","def test_initialization_of_homework_deadline():\n expected = datetime.timedelta(days=1)\n assert oop_hw.deadline_days == expected","def test_emirp_check():\r\n pass","def test_field_rules():","def test_is(self):\n invalid = self.TDTT()\n self.check_invalid_is(invalid)\n\n valid = self.TDTT(when=self.txt_when)\n self.check_valid_is(valid)","def test_validate_ok(self, args, value):\n sch = scheme.Scheme(*args)\n sch.validate(value)","def test_init(self):\n self.assertEqual(self.ing_mgr.ingredient_list, [])\n self.assertEqual(self.ing_mgr.user_input, True)","def test_teacher_check_homework_negative_if_solution_is_not_ok():\n assert not opp_teacher.check_homework(result_3)","def test_validate_goals():\n assert (validate_goals([\"max\", \"min\"], 2) == np.array([1, -1])).all()\n with pytest.raises(ValueError):\n validate_goals([2, 3], 2)\n with pytest.raises(ValueError):\n validate_goals([1], 2)\n with pytest.raises(ValueError):\n validate_goals(1, 1)\n\n assert (validate_goals(None, 3) == np.array([1, 1, 1])).all()","def test_special_case(self):\n cases = [\n ('3467875434578764345789654', False),\n ('AAAAAAAAAAA', False),\n ('', False),\n ]\n for titulo_eleitoral, is_valid in cases:\n self.assertEqual(self.titulo_eleitoral.validate(titulo_eleitoral), is_valid)","def _check_validity(self):\n pass","def test_validate_input_bad_input(self):\r\n self.assertRaises(ValueError, _validate_input,\r\n None, None, None, None, None)\r\n self.assertRaises(ValueError, _validate_input,\r\n self.dist_matrix_header, self.dist_matrix,\r\n self.mapping_header, self.mapping, None)\r\n self.assertRaises(ValueError, _validate_input,\r\n self.dist_matrix_header, 12,\r\n self.mapping_header, self.mapping, None)\r\n self.assertRaises(ValueError, _validate_input,\r\n self.dist_matrix_header, self.dist_matrix,\r\n self.mapping_header, self.mapping, 42)\r\n self.assertRaises(ValueError, _validate_input,\r\n self.dist_matrix_header, self.dist_matrix,\r\n self.mapping_header, self.mapping, \"aeiou\")","def test_check_inputs_hospital_capacity(game):\n\n hospital = game.hospitals[0]\n capacity = hospital.capacity\n hospital.capacity = 0\n\n assert hospital._original_capacity == capacity\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_capacity(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def check():\n hokusai.check()","def validate_input(self, *args):\n return","def test_parameters(self):\n assert self.hll.p == 8\n assert self.hll.m == 256\n assert round(self.hll.alpha - 0.7182725932495458, 5) == 0\n assert round(self.hll.error - 0.065, 5) == 0\n assert self.hll64.treshold == 120000","def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_suitor_prefs()\n\n match.suitors[0].pref_names = [1, 2, 3]\n\n with pytest.raises(Exception):\n match._check_suitor_prefs()","def test_mypy_validations(arguments, assertions):\n assert_mypy_output(\n template.format('spec_components', 'tc', 'TestComponent', arguments),\n **assertions\n )","def test_validate(self):\n # Instances of datetime.datetime simply pass through as-is.\n self.assertEquals(self.dt_when,\n self.TDTT.validate_when(self.dt_when))\n\n # Date/time in string form should be in ISO-8601 format.\n self.assertEquals(self.dt_when,\n self.TDTT.validate_when(self.txt_when))\n\n self.assertEquals(None,\n self.TDTT.validate_when(self.NOT_DATE_AND_TIME))\n\n encoded = {'when': self.txt_when, 'unused': 'ignored'}\n decoded_props = {'when': self.dt_when}\n self.check_validate(encoded, decoded_props, self.TDTT.validate)","def validate_input(helper, definition):\n\n start_time_start = definition.parameters.get('start_time_start', None)\n interval = definition.parameters.get('interval', None)\n\n if int(interval) < 86400:\n raise ValueError(\n \"Interval should be 86400 or more for historical data, not {}.\".format(interval))\n\n try:\n # validate start_time_start format:\n if start_time_start:\n datetime.strptime(\n start_time_start, '%m/%d/%Y %H:%M:%S')\n except ValueError:\n raise ValueError(\n \"Incorrect data format, time should be MM/DD/YYYY hh:mm:ss\")\n\n enddt = datetime.utcnow().date() - timedelta(3)\n end_time = datetime.combine(enddt, datetime.max.time())\n start_time_start = datetime.strptime(start_time_start, '%m/%d/%Y %H:%M:%S')\n if start_time_start >= end_time:\n raise ValueError(\n \"Begin Date must be at least 3 days ago. Please enter a time before {}.\".format(end_time.strftime('%m/%d/%Y %H:%M:%S')))\n pass","def test_validate_input_rejection(self):\n with nose.assert_raises(exceptions.RejectionError):\n self.dtm1.validate_input('000011')","def test_validation(self):\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate([1,2,3])\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate('stringy')\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate(1)","def test_validation(self):\n\n with self.assertRaises(ValidationError):\n Dictionary().validate([1,2,3])\n\n with self.assertRaises(ValidationError):\n Dictionary().validate('stringy')\n\n with self.assertRaises(ValidationError):\n Dictionary().validate(1)","def test_rule_create_command_success_hr(rule_create_success, rule_create_success_hr):\n resp = prepare_rule_create_output(rule_create_success)\n assert resp == rule_create_success_hr","def test_init_inputs(self):\n initial_year = 2017\n gross_income = 500\n inputs = {\n 'gross_income': {\n initial_year: Money(1000), initial_year + 2: Money(0)\n }\n }\n person = Person(\n initial_year, 'Name', 2000, retirement_date=2065,\n gross_income=gross_income, raise_rate=1, inputs=inputs)\n # We've gross income for the first and third years; the second\n # year should be set programmatically based on a 100% raise.\n self.assertEqual(person.gross_income, Money(1000))\n person.next_year()\n self.assertEqual(person.gross_income, Money(2000))\n person.next_year()\n self.assertEqual(person.gross_income, Money(0))","def test_emp_man_hours(self):\n start = timezone.make_aware(dt.datetime(2016, 6, 3, 6, 30))\n stop = timezone.make_aware(dt.datetime(2016, 6, 3, 10, 30))\n emp_hours = 0\n\n expected_emp_hours = 20.95\n\n # getting employee objects that are clocked in\n clocked_in_emp = get_clocked_in(start)\n emp_that_left = get_emp_who_left_during_shift(start, stop)\n emp_that_breaked = get_emp_who_left_on_break(start, stop)\n\n # testing return of number of hours\n for employee in clocked_in_emp:\n print(\"EMP= \", employee.PRSN_NBR_TXT)\n emp_hour = get_emp_man_hours(employee, start, stop)\n print(\"EMP HOUR= \", emp_hour)\n emp_hours += emp_hour\n\n for employee in emp_that_left:\n print(\"EMP= \", employee.PRSN_NBR_TXT)\n emp_hour = get_emp_man_hours(employee, start, stop)\n print(\"EMP HOUR= \", emp_hour)\n emp_hours += emp_hour\n\n for employee in emp_that_breaked:\n print(\"EMP= \", employee.PRSN_NBR_TXT)\n emp_hour = get_emp_man_hours(employee, start, stop)\n print(\"EMP HOUR= \", emp_hour)\n emp_hours += emp_hour\n\n self.assertAlmostEqual(emp_hours, expected_emp_hours)","def test_check_is_required(fake_check):\n assert fake_check.is_required()","def verify():","def validate_user_input(user_input):\n responses = ['t', 'r', 'q']\n return user_input in responses","def test_validate_fl_fa_fs_hn_wl(session, desc, valid, reg_type, message_content):\n # setup\n json_data = copy.deepcopy(FINANCING)\n json_data['type'] = reg_type\n del json_data['trustIndenture']\n del json_data['lifeYears']\n json_data['lifeInfinite'] = True\n if desc == DESC_MISSING_GC:\n del json_data['generalCollateral']\n if desc != DESC_INCLUDES_VC:\n del json_data['vehicleCollateral']\n\n error_msg = validator.validate(json_data)\n if valid:\n assert error_msg == ''\n elif message_content:\n # print(error_msg)\n assert error_msg != ''\n assert error_msg.find(message_content) != -1","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def test_validate_answer(self):\r\n sample_dict = {'x': (1, 2)}\r\n problem = self.build_problem(\r\n sample_dict=sample_dict,\r\n num_samples=10,\r\n tolerance=\"1%\",\r\n answer=\"x\"\r\n )\r\n self.assertTrue(problem.responders.values()[0].validate_answer('14*x'))\r\n self.assertFalse(problem.responders.values()[0].validate_answer('3*y+2*x'))","def test_it_exists():\n dan = Hobbit(\"Dan\")\n assert type(dan) == Hobbit\n assert dan.name == \"Dan\"\n assert dan.disposition == \"adventurous\"\n assert dan.age == 0","def test_check_input_id(self):\n r3 = Rectangle(10, 2, 0, 0, 12)\n self.assertEqual(r3.id, 12)","def test_validation(self):\n self.validationFails()","def CanRunHidlHalTest(test_instance, dut, shell=None):\n if shell is None:\n dut.shell.InvokeTerminal(\"check_hal_preconditions\")\n shell = dut.shell.check_hal_preconditions\n\n opt_params = [\n keys.ConfigKeys.IKEY_ABI_BITNESS,\n keys.ConfigKeys.IKEY_PRECONDITION_HWBINDER_SERVICE,\n keys.ConfigKeys.IKEY_PRECONDITION_FEATURE,\n keys.ConfigKeys.IKEY_PRECONDITION_FILE_PATH_PREFIX,\n keys.ConfigKeys.IKEY_PRECONDITION_LSHAL,\n keys.ConfigKeys.IKEY_PRECONDITION_VINTF,\n ]\n test_instance.getUserParams(opt_param_names=opt_params)\n\n hwbinder_service_name = str(getattr(test_instance,\n keys.ConfigKeys.IKEY_PRECONDITION_HWBINDER_SERVICE, \"\"))\n if hwbinder_service_name:\n if not hwbinder_service_name.startswith(\"android.hardware.\"):\n logging.error(\"The given hwbinder service name %s is invalid.\",\n hwbinder_service_name)\n else:\n cmd_results = shell.Execute(\"ps -A\")\n hwbinder_service_name += \"@\"\n if (any(cmd_results[const.EXIT_CODE]) or\n hwbinder_service_name not in cmd_results[const.STDOUT][0]):\n logging.warn(\"The required hwbinder service %s not found.\",\n hwbinder_service_name)\n return False\n\n feature = str(getattr(test_instance,\n keys.ConfigKeys.IKEY_PRECONDITION_FEATURE, \"\"))\n if feature:\n if not feature.startswith(\"android.hardware.\"):\n logging.error(\n \"The given feature name %s is invalid for HIDL HAL.\",\n feature)\n else:\n cmd_results = shell.Execute(\"pm list features\")\n if (any(cmd_results[const.EXIT_CODE]) or\n feature not in cmd_results[const.STDOUT][0]):\n logging.warn(\"The required feature %s not found.\",\n feature)\n return False\n\n file_path_prefix = str(getattr(test_instance,\n keys.ConfigKeys.IKEY_PRECONDITION_FILE_PATH_PREFIX, \"\"))\n if file_path_prefix:\n cmd_results = shell.Execute(\"ls %s*\" % file_path_prefix)\n if any(cmd_results[const.EXIT_CODE]):\n logging.warn(\"The required file (prefix: %s) not found.\",\n file_path_prefix)\n return False\n\n hal = str(getattr(test_instance,\n keys.ConfigKeys.IKEY_PRECONDITION_VINTF, \"\"))\n vintf_xml = None\n if hal:\n use_lshal = False\n vintf_xml = dut.getVintfXml(use_lshal=use_lshal)\n logging.debug(\"precondition-vintf used to retrieve VINTF xml.\")\n else:\n use_lshal = True\n hal = str(getattr(test_instance,\n keys.ConfigKeys.IKEY_PRECONDITION_LSHAL, \"\"))\n if hal:\n vintf_xml = dut.getVintfXml(use_lshal=use_lshal)\n logging.debug(\"precondition-lshal used to retrieve VINTF xml.\")\n\n if vintf_xml:\n result = IsHalRegisteredInVintfXml(hal, vintf_xml,\n test_instance.abi_bitness)\n if not result and use_lshal:\n # this is for when a test is configured to use the runtime HAL\n # service availability (the default mode for HIDL tests).\n # if a HAL is in vendor/manifest.xml, test is supposed to fail\n # even though a respective HIDL HAL service is not running.\n vintf_xml = dut.getVintfXml(use_lshal=False)\n return IsHalRegisteredInVintfXml(hal, vintf_xml,\n test_instance.abi_bitness)\n return result\n\n return True","def test_create_Student_param_validate(self):\n school_ids = self.create_School(1,20)\n url = '/students'\n\n \"\"\"Normal request\"\"\"\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': 20, 'nationality': 'Thailand', 'school': school_ids[0]}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n \"\"\"first_name, last_name more than 20 characters and nationality more than 30 character\"\"\"\n data = {'first_name': 'A'*21, 'last_name': 'A'*21,'age': 20, 'nationality': 'A'*31, 'school': school_ids[0]}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(response.data['first_name'][0].code, 'max_length')\n self.assertEqual(str(response.data['first_name'][0]), 'Ensure this field has no more than 20 characters.')\n self.assertEqual(response.data['last_name'][0].code, 'max_length')\n self.assertEqual(str(response.data['last_name'][0]), 'Ensure this field has no more than 20 characters.')\n self.assertEqual(response.data['nationality'][0].code, 'max_length')\n self.assertEqual(str(response.data['nationality'][0]), 'Ensure this field has no more than 30 characters.')\n\n \"\"\"-1,0,150,151 age\"\"\"\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': 0, 'nationality': 'Thailand', 'school': school_ids[0]}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': 150, 'nationality': 'Thailand', 'school': school_ids[0]}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': -1, 'nationality': 'Thailand', 'school': school_ids[0]}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(response.data['age'][0].code, 'min_value')\n self.assertEqual(str(response.data['age'][0]), 'Ensure this value is greater than or equal to 0.')\n\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': 151, 'nationality': 'Thailand', 'school': school_ids[0]}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(response.data['age'][0].code, 'max_value')\n self.assertEqual(str(response.data['age'][0]), 'Ensure this value is less than or equal to 150.')","def test__validate_archived_at__1():\n for input_parameter in (\n 12.6,\n ):\n with vampytest.assert_raises(TypeError):\n validate_archived_at(input_parameter)","def test_student_do_homework_raise_deadline_error_if_expired():\n with pytest.raises(DeadlineError):\n lazy_student.do_homework(expired_hw, \"done\")","def test_init(self):\n nt.assert_raises(Exception, CisInterface.CisInput, 'error')","def test_non_integral_validation(self):","def test_non_integral_validation(self):","def check(self, description: Description) -> bool:","def validate(self):","def validate(self):","def Check(self, parameters):","def test_check_sun_above_horizon():\n pass","def _validate_input(self):\n\n try:\n expected_type(str, self.venue_id, \"venue_id\")\n expected_type(datetime.datetime, self.timestamp_utc, \"timestamp_utc\")\n\n expected_type(VenueStreamType, self.measurement_type, \"measurement_type\")\n\n expected_type(int, self.number_of_people, \"number_of_people\")\n\n if self.measurement_type is VenueStreamType.ABSOLUTE:\n if self.operator:\n raise ValueError(\"The stream type for the venue doesn't allow passing an Event operator\")\n\n elif self.measurement_type is VenueStreamType.EVENT:\n expected_type(EventStreamOperator, self.operator, \"operator\")\n else:\n raise ValueError(\"Unsupported member of the VenueStreamType enum\")\n\n if self.metadata:\n expected_type(dict, self.metadata, \"metadata\")\n\n except Exception as ex:\n raise ValueError(\"Validation of input failed. Reason: %s\" % str(ex))","def test__validate_title__0():\n for input_value, expected_output in (\n (None, ''),\n ('a', 'a'),\n ):\n output = validate_title(input_value)\n vampytest.assert_eq(output, expected_output)","def test_traffic_analysis_human_readable(\n traffic_analysis_success, traffic_analysis_success_hr\n):\n resp = prepare_traffic_analysis_output(traffic_analysis_success)\n assert resp == traffic_analysis_success_hr","def test_createItinerary(self):\n rv = self.json_post('/createItinerary/alex', dict(\n name = 'New Day',\n date = '2015-08-21T00:00:00.000Z'\n ))\n itinHash = str('alex' + \"_\" + '2015-08-21T00:00:00.000Z')\n assert itinHash in str(rv.data)\n\n rv = self.json_post('/createItinerary/alex', dict(\n name = 'New Day',\n date= '2015-08-21T00:00:00.000Z'\n ))\n assert 'Itinerary date already in use' in str(rv.data)\n\n rv = self.json_post('/createItinerary/bbbb', dict(\n name = 'New Day',\n date= '2015-08-21T00:00:00.000Z'\n ))\n assert 'Invalid username' in str(rv.data)","def verify(self):","def test_allowed(self):\n user_input = [\"1\",\"1\",\"0\"]\n with patch(\"builtins.input\", side_effect=user_input) as input_call:\n with patch(\"sys.stdout\", new=StringIO()) as output:\n import attempt\n self.assertEqual(output.getvalue().strip(),\"You can take this course.\")","def test_number_of_hours_util(self):\n self.assertEqual(20, number_of_hours(50, 40))","def _expected_inputs():\n return 1"],"string":"[\n \"def test_validParameters(self):\\n # stupid/simple inputs\\n h = Helix(\\\"thing\\\", \\\"Cu\\\", 0, 0, 1, 1, 1)\\n self.assertEqual(h.getDimension(\\\"axialPitch\\\"), 1)\\n\\n # standard case / inputs ordered well\\n h = Helix(\\n \\\"what\\\",\\n \\\"Cu\\\",\\n Tinput=25.0,\\n Thot=425.0,\\n id=0.1,\\n od=0.35,\\n mult=1.0,\\n axialPitch=1.123,\\n helixDiameter=1.5,\\n )\\n self.assertTrue(1.123 < h.getDimension(\\\"axialPitch\\\") < 1.15)\\n\\n # inputs ordered crazy\\n h = Helix(\\n material=\\\"Cu\\\",\\n id=0.1,\\n mult=1.0,\\n Tinput=25.0,\\n Thot=425.0,\\n axialPitch=1.123,\\n name=\\\"stuff\\\",\\n od=0.35,\\n helixDiameter=1.5,\\n )\\n self.assertTrue(1.123 < h.getDimension(\\\"axialPitch\\\") < 1.15)\\n\\n # missing helixDiameter input\\n with self.assertRaises(TypeError):\\n h = Helix(\\n name=\\\"helix\\\",\\n material=\\\"Cu\\\",\\n Tinput=25.0,\\n Thot=425.0,\\n id=0.1,\\n od=0.35,\\n mult=1.0,\\n axialPitch=1.123,\\n )\",\n \"def test_input_type_hhm(self, _run_mock):\\n hhblits = self.tool(input_type=hhsuite.QueryType.HMM)\\n self.assertEqual(hhblits.REQUIRED, [\\\"hhm\\\"])\\n hhblits.run({\\\"hhm\\\": self.IN_HHM_FILE})\\n self.verify_common(\\\"hhblits\\\", hhblits)\\n _, kw_args = hhblits.tool.call_args\\n self.assertEqual(kw_args[\\\"options\\\"][\\\"input\\\"], self.IN_HHM_FILE)\",\n \"def test_check_validity(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n assert match.check_validity()\",\n \"def test_validate(self):\\n pass\",\n \"def test_is_active_of_homework_positive():\\n assert oop_hw.is_active()\",\n \"def input_check(self):\\n\\n if self.species == 'He': assert self.line_model == 'voigt'\\n n_upper_range, e_dens_range, temp_range, b_field_range = get_param_ranges(self.line_model)\\n\\n if np.isnan(n_upper_range).sum() <= 1:\\n assert (self.n_upper in range(n_upper_range[0], n_upper_range[1]))\\n if np.isnan(e_dens_range).sum() <= 1:\\n assert (e_dens_range[0] <= self.e_dens <= e_dens_range[1])\\n if np.isnan(temp_range).sum() <= 1:\\n assert (temp_range[0] <= self.temp <= temp_range[1])\\n if np.isnan(b_field_range).sum() <= 1:\\n assert (b_field_range[0] <= self.b_field <= b_field_range[1])\",\n \"def testHealthAssessHosp(self):\\n attr = self.session.create_visit_attr()\\n\\n self.util.boolTypeTest(self, attr, \\\"hosp\\\")\\n\\n self.util.boolPropertyTest(self, attr, \\\"hosp\\\")\",\n \"def test_teacher_check_homework_positive():\\n assert opp_teacher.check_homework(result_1)\",\n \"def test_kyc_get_validation_legal(self):\\n pass\",\n \"def test_input1(self):\\n in1 =\\\"elephant\\\"\\n result = options.check(in1)\\n self.assertEqual(result,True)\",\n \"def test_holidays_validate(self):\\n self.request.sudo(self.user_1.id).holidays_validate()\\n\\n accrual = self.employee.get_leave_accrual(self.leave_type.id)\\n self.assertEqual(accrual.total_hours, 22.5)\",\n \"def test_check_validity(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n game.solve()\\n assert game.check_validity()\",\n \"def test_birth_validation(self):\",\n \"def test_negative_is_active_of_homework():\\n assert not expired_hw.is_active()\",\n \"def test_validation_passes_with_valid_input(self):\\n\\n result = LandCompensationLandSoldValidator.validate(VALID_LAND_SOLD, VALID_WORK_DONE)\\n self.assertEqual(0, len(result.errors))\",\n \"def test_enforcement_boundary_create_command_human_readable(\\n enforcement_boundary_success, enforcement_boundary_success_hr\\n):\\n hr_output = prepare_enforcement_boundary_create_output(enforcement_boundary_success)\\n\\n assert hr_output == enforcement_boundary_success_hr\",\n \"def test_validate_answer(self):\\r\\n problem = self.build_problem(answer=\\\"42\\\")\\r\\n responder = problem.responders.values()[0]\\r\\n self.assertTrue(responder.validate_answer('23.5'))\\r\\n self.assertFalse(responder.validate_answer('fish'))\",\n \"def check_validity(self):\",\n \"def _expected_inputs():\",\n \"def test_check_args_working_hours(self):\\n test_date = dt.datetime(2021, 6, 18, 7, 0, 0)\\n with self.assertRaises(ValueError) as context:\\n self.duedate.check_args(test_date, self.test_turn_time)\\n self.assertTrue(\\n \\\"You can submit requests from 9AM till 5PM.\\\" in str(context.exception))\",\n \"def test_teacher_create_homework():\\n assert isinstance(oop_hw, Homework)\",\n \"def test_input2(self):\\n in1 =\\\"aple\\\"\\n result = options.checkw(in1)\\n self.assertEqual(result,False)\",\n \"def test_check_inputs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n with warnings.catch_warnings(record=True):\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.students == game._all_students\\n assert game.projects == game._all_projects\\n assert game.supervisors == game._all_supervisors\",\n \"def test_initialization_of_homework_result_homework():\\n assert result_1.homework == oop_hw\",\n \"def test_valid_user_input(self):\\n\\n test_name = sys._getframe().f_code.co_name\\n\\n log.info(\\\"###### TEST EXECUTION STARTED :: \\\" + test_name + \\\" ######\\\")\\n\\n user_input = data_reader.get_data(test_name, \\\"Text_Message\\\")\\n\\n with allure.step(\\\"Verify User Input\\\"):\\n result = self.main_page.verify_valid_user_input(user_input)\\n self.exe_status.mark_final(test_step=test_name, result=result)\",\n \"def assert_rh(instance: machine_learning.StationMachineLearning,\\n model_rh: float, timestamp: datetime, bias_adjusted_rh: float):\\n result = instance.predict_rh(model_rh, timestamp)\\n assert result == bias_adjusted_rh\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def test_hav_bad_input(self):\\n self.assertRaises(ValueError, lambda: latlongcalcs.hav('$'))\\n self.assertRaises(ValueError, lambda: latlongcalcs.hav('ge'))\",\n \"def test_rules():\",\n \"def validate():\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_validate_input_good_input(self):\\r\\n _validate_input(self.dist_matrix_header, self.dist_matrix,\\r\\n self.mapping_header, self.mapping, \\\"Treatment\\\")\",\n \"def test_kyc_get_validation(self):\\n pass\",\n \"def isInputValid(self, input):\\r\\n pass\",\n \"def test__validate_status__1():\\n for input_value in (\\n 12.6,\\n ):\\n with vampytest.assert_raises(TypeError):\\n validate_status(input_value)\",\n \"def test_check_validity(game):\\n\\n game.solve()\\n assert game.check_validity()\",\n \"def test_validators():\",\n \"async def test_form_validate_input(hass):\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN, context={\\\"source\\\": config_entries.SOURCE_USER}\\n )\\n\\n with requests_mock.Mocker() as mock_request:\\n mock_request.get(\\n \\\"https://api.wall-box.com/auth/token/user\\\",\\n json=authorisation_response,\\n status_code=HTTPStatus.OK,\\n )\\n mock_request.get(\\n \\\"https://api.wall-box.com/chargers/status/12345\\\",\\n json=test_response,\\n status_code=HTTPStatus.OK,\\n )\\n result2 = await hass.config_entries.flow.async_configure(\\n result[\\\"flow_id\\\"],\\n {\\n \\\"station\\\": \\\"12345\\\",\\n \\\"username\\\": \\\"test-username\\\",\\n \\\"password\\\": \\\"test-password\\\",\\n },\\n )\\n\\n assert result2[\\\"title\\\"] == \\\"Wallbox Portal\\\"\\n assert result2[\\\"data\\\"][\\\"station\\\"] == \\\"12345\\\"\",\n \"def test_valid_input(helper):\\r\\n output = auth_register_v1(email='harrypotter@gmail.com',\\r\\n password='qw3rtyAppl3s@99',\\r\\n name_first='Harry',\\r\\n name_last='Potter')\\r\\n\\r\\n expected = auth_login_v1(email='harrypotter@gmail.com',\\r\\n password='qw3rtyAppl3s@99')\\r\\n assert output.get('auth_user_id') == expected.get('auth_user_id') == 1\\r\\n\\r\\n output = auth_register_v1(email='harrypotter3@gmail.com',\\r\\n password='qw3rtyAppl3s@04',\\r\\n name_first='Harrrrry',\\r\\n name_last='Pottttter')\\r\\n \\r\\n expected = auth_login_v1(email='harrypotter3@gmail.com',\\r\\n password='qw3rtyAppl3s@04')\\r\\n assert output.get('auth_user_id') == expected.get('auth_user_id') == 2\",\n \"def test_custom_rise(self):\\n self.employee.give_raise(self.custom_rise)\\n self.assertEqual(self.employee.anual_salary, self.salary + self.custom_rise)\",\n \"def test_validates(if_statement_validator):\\n test = {\\n 'condition': 'is',\\n 'target': 'bob',\\n 'then': 'arne',\\n }\\n assert if_statement_validator(test).unwrap() == test\",\n \"def test_generating(resp):\\n errors = []\\n if not check_int(resp[\\\"tightness\\\"]):\\n errors.append(\\\"Invalid type for Itinerary response's 'tightness' field.\\\")\\n\\n if not isinstance(resp, bool):\\n errors.append(\\\"Invalid type for Itinerary response's 'start_from_airport' field.\\\")\",\n \"def test__validate_title__1():\\n for input_value in (\\n 12.6,\\n ):\\n with vampytest.assert_raises(TypeError):\\n validate_title(input_value)\",\n \"def test_student_do_homework_positive():\\n assert isinstance(result_1, HomeworkResult)\",\n \"def test_teacher_check_homework_raises_type_error_if_not_a_homework_result_instance():\\n with pytest.raises(TypeError):\\n opp_teacher.check_homework(\\\"done this\\\")\",\n \"def test_task_conforms_to_spec(self, domain, task):\\n is_benchmark = (domain, task) in suite.BENCHMARKING\\n env = suite.load(domain, task)\\n observation_spec = env.observation_spec()\\n action_spec = env.action_spec()\\n\\n # Check action bounds.\\n if is_benchmark:\\n self._validate_control_range(action_spec.minimum, action_spec.maximum)\\n\\n # Step through the environment, applying random actions sampled within the\\n # valid range and check the observations, rewards, and discounts.\\n policy = uniform_random_policy(action_spec)\\n for time_step in step_environment(env, policy):\\n self._validate_observation(time_step.observation, observation_spec)\\n self._validate_discount(time_step)\\n if is_benchmark:\\n self._validate_reward_range(time_step)\",\n \"def test_initialization_of_homework_deadline():\\n expected = datetime.timedelta(days=1)\\n assert oop_hw.deadline_days == expected\",\n \"def test_emirp_check():\\r\\n pass\",\n \"def test_field_rules():\",\n \"def test_is(self):\\n invalid = self.TDTT()\\n self.check_invalid_is(invalid)\\n\\n valid = self.TDTT(when=self.txt_when)\\n self.check_valid_is(valid)\",\n \"def test_validate_ok(self, args, value):\\n sch = scheme.Scheme(*args)\\n sch.validate(value)\",\n \"def test_init(self):\\n self.assertEqual(self.ing_mgr.ingredient_list, [])\\n self.assertEqual(self.ing_mgr.user_input, True)\",\n \"def test_teacher_check_homework_negative_if_solution_is_not_ok():\\n assert not opp_teacher.check_homework(result_3)\",\n \"def test_validate_goals():\\n assert (validate_goals([\\\"max\\\", \\\"min\\\"], 2) == np.array([1, -1])).all()\\n with pytest.raises(ValueError):\\n validate_goals([2, 3], 2)\\n with pytest.raises(ValueError):\\n validate_goals([1], 2)\\n with pytest.raises(ValueError):\\n validate_goals(1, 1)\\n\\n assert (validate_goals(None, 3) == np.array([1, 1, 1])).all()\",\n \"def test_special_case(self):\\n cases = [\\n ('3467875434578764345789654', False),\\n ('AAAAAAAAAAA', False),\\n ('', False),\\n ]\\n for titulo_eleitoral, is_valid in cases:\\n self.assertEqual(self.titulo_eleitoral.validate(titulo_eleitoral), is_valid)\",\n \"def _check_validity(self):\\n pass\",\n \"def test_validate_input_bad_input(self):\\r\\n self.assertRaises(ValueError, _validate_input,\\r\\n None, None, None, None, None)\\r\\n self.assertRaises(ValueError, _validate_input,\\r\\n self.dist_matrix_header, self.dist_matrix,\\r\\n self.mapping_header, self.mapping, None)\\r\\n self.assertRaises(ValueError, _validate_input,\\r\\n self.dist_matrix_header, 12,\\r\\n self.mapping_header, self.mapping, None)\\r\\n self.assertRaises(ValueError, _validate_input,\\r\\n self.dist_matrix_header, self.dist_matrix,\\r\\n self.mapping_header, self.mapping, 42)\\r\\n self.assertRaises(ValueError, _validate_input,\\r\\n self.dist_matrix_header, self.dist_matrix,\\r\\n self.mapping_header, self.mapping, \\\"aeiou\\\")\",\n \"def test_check_inputs_hospital_capacity(game):\\n\\n hospital = game.hospitals[0]\\n capacity = hospital.capacity\\n hospital.capacity = 0\\n\\n assert hospital._original_capacity == capacity\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_capacity(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def check():\\n hokusai.check()\",\n \"def validate_input(self, *args):\\n return\",\n \"def test_parameters(self):\\n assert self.hll.p == 8\\n assert self.hll.m == 256\\n assert round(self.hll.alpha - 0.7182725932495458, 5) == 0\\n assert round(self.hll.error - 0.065, 5) == 0\\n assert self.hll64.treshold == 120000\",\n \"def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_suitor_prefs()\\n\\n match.suitors[0].pref_names = [1, 2, 3]\\n\\n with pytest.raises(Exception):\\n match._check_suitor_prefs()\",\n \"def test_mypy_validations(arguments, assertions):\\n assert_mypy_output(\\n template.format('spec_components', 'tc', 'TestComponent', arguments),\\n **assertions\\n )\",\n \"def test_validate(self):\\n # Instances of datetime.datetime simply pass through as-is.\\n self.assertEquals(self.dt_when,\\n self.TDTT.validate_when(self.dt_when))\\n\\n # Date/time in string form should be in ISO-8601 format.\\n self.assertEquals(self.dt_when,\\n self.TDTT.validate_when(self.txt_when))\\n\\n self.assertEquals(None,\\n self.TDTT.validate_when(self.NOT_DATE_AND_TIME))\\n\\n encoded = {'when': self.txt_when, 'unused': 'ignored'}\\n decoded_props = {'when': self.dt_when}\\n self.check_validate(encoded, decoded_props, self.TDTT.validate)\",\n \"def validate_input(helper, definition):\\n\\n start_time_start = definition.parameters.get('start_time_start', None)\\n interval = definition.parameters.get('interval', None)\\n\\n if int(interval) < 86400:\\n raise ValueError(\\n \\\"Interval should be 86400 or more for historical data, not {}.\\\".format(interval))\\n\\n try:\\n # validate start_time_start format:\\n if start_time_start:\\n datetime.strptime(\\n start_time_start, '%m/%d/%Y %H:%M:%S')\\n except ValueError:\\n raise ValueError(\\n \\\"Incorrect data format, time should be MM/DD/YYYY hh:mm:ss\\\")\\n\\n enddt = datetime.utcnow().date() - timedelta(3)\\n end_time = datetime.combine(enddt, datetime.max.time())\\n start_time_start = datetime.strptime(start_time_start, '%m/%d/%Y %H:%M:%S')\\n if start_time_start >= end_time:\\n raise ValueError(\\n \\\"Begin Date must be at least 3 days ago. Please enter a time before {}.\\\".format(end_time.strftime('%m/%d/%Y %H:%M:%S')))\\n pass\",\n \"def test_validate_input_rejection(self):\\n with nose.assert_raises(exceptions.RejectionError):\\n self.dtm1.validate_input('000011')\",\n \"def test_validation(self):\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate([1,2,3])\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate('stringy')\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate(1)\",\n \"def test_validation(self):\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate([1,2,3])\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate('stringy')\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate(1)\",\n \"def test_rule_create_command_success_hr(rule_create_success, rule_create_success_hr):\\n resp = prepare_rule_create_output(rule_create_success)\\n assert resp == rule_create_success_hr\",\n \"def test_init_inputs(self):\\n initial_year = 2017\\n gross_income = 500\\n inputs = {\\n 'gross_income': {\\n initial_year: Money(1000), initial_year + 2: Money(0)\\n }\\n }\\n person = Person(\\n initial_year, 'Name', 2000, retirement_date=2065,\\n gross_income=gross_income, raise_rate=1, inputs=inputs)\\n # We've gross income for the first and third years; the second\\n # year should be set programmatically based on a 100% raise.\\n self.assertEqual(person.gross_income, Money(1000))\\n person.next_year()\\n self.assertEqual(person.gross_income, Money(2000))\\n person.next_year()\\n self.assertEqual(person.gross_income, Money(0))\",\n \"def test_emp_man_hours(self):\\n start = timezone.make_aware(dt.datetime(2016, 6, 3, 6, 30))\\n stop = timezone.make_aware(dt.datetime(2016, 6, 3, 10, 30))\\n emp_hours = 0\\n\\n expected_emp_hours = 20.95\\n\\n # getting employee objects that are clocked in\\n clocked_in_emp = get_clocked_in(start)\\n emp_that_left = get_emp_who_left_during_shift(start, stop)\\n emp_that_breaked = get_emp_who_left_on_break(start, stop)\\n\\n # testing return of number of hours\\n for employee in clocked_in_emp:\\n print(\\\"EMP= \\\", employee.PRSN_NBR_TXT)\\n emp_hour = get_emp_man_hours(employee, start, stop)\\n print(\\\"EMP HOUR= \\\", emp_hour)\\n emp_hours += emp_hour\\n\\n for employee in emp_that_left:\\n print(\\\"EMP= \\\", employee.PRSN_NBR_TXT)\\n emp_hour = get_emp_man_hours(employee, start, stop)\\n print(\\\"EMP HOUR= \\\", emp_hour)\\n emp_hours += emp_hour\\n\\n for employee in emp_that_breaked:\\n print(\\\"EMP= \\\", employee.PRSN_NBR_TXT)\\n emp_hour = get_emp_man_hours(employee, start, stop)\\n print(\\\"EMP HOUR= \\\", emp_hour)\\n emp_hours += emp_hour\\n\\n self.assertAlmostEqual(emp_hours, expected_emp_hours)\",\n \"def test_check_is_required(fake_check):\\n assert fake_check.is_required()\",\n \"def verify():\",\n \"def validate_user_input(user_input):\\n responses = ['t', 'r', 'q']\\n return user_input in responses\",\n \"def test_validate_fl_fa_fs_hn_wl(session, desc, valid, reg_type, message_content):\\n # setup\\n json_data = copy.deepcopy(FINANCING)\\n json_data['type'] = reg_type\\n del json_data['trustIndenture']\\n del json_data['lifeYears']\\n json_data['lifeInfinite'] = True\\n if desc == DESC_MISSING_GC:\\n del json_data['generalCollateral']\\n if desc != DESC_INCLUDES_VC:\\n del json_data['vehicleCollateral']\\n\\n error_msg = validator.validate(json_data)\\n if valid:\\n assert error_msg == ''\\n elif message_content:\\n # print(error_msg)\\n assert error_msg != ''\\n assert error_msg.find(message_content) != -1\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def test_validate_answer(self):\\r\\n sample_dict = {'x': (1, 2)}\\r\\n problem = self.build_problem(\\r\\n sample_dict=sample_dict,\\r\\n num_samples=10,\\r\\n tolerance=\\\"1%\\\",\\r\\n answer=\\\"x\\\"\\r\\n )\\r\\n self.assertTrue(problem.responders.values()[0].validate_answer('14*x'))\\r\\n self.assertFalse(problem.responders.values()[0].validate_answer('3*y+2*x'))\",\n \"def test_it_exists():\\n dan = Hobbit(\\\"Dan\\\")\\n assert type(dan) == Hobbit\\n assert dan.name == \\\"Dan\\\"\\n assert dan.disposition == \\\"adventurous\\\"\\n assert dan.age == 0\",\n \"def test_check_input_id(self):\\n r3 = Rectangle(10, 2, 0, 0, 12)\\n self.assertEqual(r3.id, 12)\",\n \"def test_validation(self):\\n self.validationFails()\",\n \"def CanRunHidlHalTest(test_instance, dut, shell=None):\\n if shell is None:\\n dut.shell.InvokeTerminal(\\\"check_hal_preconditions\\\")\\n shell = dut.shell.check_hal_preconditions\\n\\n opt_params = [\\n keys.ConfigKeys.IKEY_ABI_BITNESS,\\n keys.ConfigKeys.IKEY_PRECONDITION_HWBINDER_SERVICE,\\n keys.ConfigKeys.IKEY_PRECONDITION_FEATURE,\\n keys.ConfigKeys.IKEY_PRECONDITION_FILE_PATH_PREFIX,\\n keys.ConfigKeys.IKEY_PRECONDITION_LSHAL,\\n keys.ConfigKeys.IKEY_PRECONDITION_VINTF,\\n ]\\n test_instance.getUserParams(opt_param_names=opt_params)\\n\\n hwbinder_service_name = str(getattr(test_instance,\\n keys.ConfigKeys.IKEY_PRECONDITION_HWBINDER_SERVICE, \\\"\\\"))\\n if hwbinder_service_name:\\n if not hwbinder_service_name.startswith(\\\"android.hardware.\\\"):\\n logging.error(\\\"The given hwbinder service name %s is invalid.\\\",\\n hwbinder_service_name)\\n else:\\n cmd_results = shell.Execute(\\\"ps -A\\\")\\n hwbinder_service_name += \\\"@\\\"\\n if (any(cmd_results[const.EXIT_CODE]) or\\n hwbinder_service_name not in cmd_results[const.STDOUT][0]):\\n logging.warn(\\\"The required hwbinder service %s not found.\\\",\\n hwbinder_service_name)\\n return False\\n\\n feature = str(getattr(test_instance,\\n keys.ConfigKeys.IKEY_PRECONDITION_FEATURE, \\\"\\\"))\\n if feature:\\n if not feature.startswith(\\\"android.hardware.\\\"):\\n logging.error(\\n \\\"The given feature name %s is invalid for HIDL HAL.\\\",\\n feature)\\n else:\\n cmd_results = shell.Execute(\\\"pm list features\\\")\\n if (any(cmd_results[const.EXIT_CODE]) or\\n feature not in cmd_results[const.STDOUT][0]):\\n logging.warn(\\\"The required feature %s not found.\\\",\\n feature)\\n return False\\n\\n file_path_prefix = str(getattr(test_instance,\\n keys.ConfigKeys.IKEY_PRECONDITION_FILE_PATH_PREFIX, \\\"\\\"))\\n if file_path_prefix:\\n cmd_results = shell.Execute(\\\"ls %s*\\\" % file_path_prefix)\\n if any(cmd_results[const.EXIT_CODE]):\\n logging.warn(\\\"The required file (prefix: %s) not found.\\\",\\n file_path_prefix)\\n return False\\n\\n hal = str(getattr(test_instance,\\n keys.ConfigKeys.IKEY_PRECONDITION_VINTF, \\\"\\\"))\\n vintf_xml = None\\n if hal:\\n use_lshal = False\\n vintf_xml = dut.getVintfXml(use_lshal=use_lshal)\\n logging.debug(\\\"precondition-vintf used to retrieve VINTF xml.\\\")\\n else:\\n use_lshal = True\\n hal = str(getattr(test_instance,\\n keys.ConfigKeys.IKEY_PRECONDITION_LSHAL, \\\"\\\"))\\n if hal:\\n vintf_xml = dut.getVintfXml(use_lshal=use_lshal)\\n logging.debug(\\\"precondition-lshal used to retrieve VINTF xml.\\\")\\n\\n if vintf_xml:\\n result = IsHalRegisteredInVintfXml(hal, vintf_xml,\\n test_instance.abi_bitness)\\n if not result and use_lshal:\\n # this is for when a test is configured to use the runtime HAL\\n # service availability (the default mode for HIDL tests).\\n # if a HAL is in vendor/manifest.xml, test is supposed to fail\\n # even though a respective HIDL HAL service is not running.\\n vintf_xml = dut.getVintfXml(use_lshal=False)\\n return IsHalRegisteredInVintfXml(hal, vintf_xml,\\n test_instance.abi_bitness)\\n return result\\n\\n return True\",\n \"def test_create_Student_param_validate(self):\\n school_ids = self.create_School(1,20)\\n url = '/students'\\n\\n \\\"\\\"\\\"Normal request\\\"\\\"\\\"\\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': 20, 'nationality': 'Thailand', 'school': school_ids[0]}\\n response = self.client.post(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n\\n \\\"\\\"\\\"first_name, last_name more than 20 characters and nationality more than 30 character\\\"\\\"\\\"\\n data = {'first_name': 'A'*21, 'last_name': 'A'*21,'age': 20, 'nationality': 'A'*31, 'school': school_ids[0]}\\n response = self.client.post(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(response.data['first_name'][0].code, 'max_length')\\n self.assertEqual(str(response.data['first_name'][0]), 'Ensure this field has no more than 20 characters.')\\n self.assertEqual(response.data['last_name'][0].code, 'max_length')\\n self.assertEqual(str(response.data['last_name'][0]), 'Ensure this field has no more than 20 characters.')\\n self.assertEqual(response.data['nationality'][0].code, 'max_length')\\n self.assertEqual(str(response.data['nationality'][0]), 'Ensure this field has no more than 30 characters.')\\n\\n \\\"\\\"\\\"-1,0,150,151 age\\\"\\\"\\\"\\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': 0, 'nationality': 'Thailand', 'school': school_ids[0]}\\n response = self.client.post(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n\\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': 150, 'nationality': 'Thailand', 'school': school_ids[0]}\\n response = self.client.post(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n\\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': -1, 'nationality': 'Thailand', 'school': school_ids[0]}\\n response = self.client.post(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(response.data['age'][0].code, 'min_value')\\n self.assertEqual(str(response.data['age'][0]), 'Ensure this value is greater than or equal to 0.')\\n\\n data = {'first_name': 'Poompatai', 'last_name': 'Puntitpong','age': 151, 'nationality': 'Thailand', 'school': school_ids[0]}\\n response = self.client.post(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(response.data['age'][0].code, 'max_value')\\n self.assertEqual(str(response.data['age'][0]), 'Ensure this value is less than or equal to 150.')\",\n \"def test__validate_archived_at__1():\\n for input_parameter in (\\n 12.6,\\n ):\\n with vampytest.assert_raises(TypeError):\\n validate_archived_at(input_parameter)\",\n \"def test_student_do_homework_raise_deadline_error_if_expired():\\n with pytest.raises(DeadlineError):\\n lazy_student.do_homework(expired_hw, \\\"done\\\")\",\n \"def test_init(self):\\n nt.assert_raises(Exception, CisInterface.CisInput, 'error')\",\n \"def test_non_integral_validation(self):\",\n \"def test_non_integral_validation(self):\",\n \"def check(self, description: Description) -> bool:\",\n \"def validate(self):\",\n \"def validate(self):\",\n \"def Check(self, parameters):\",\n \"def test_check_sun_above_horizon():\\n pass\",\n \"def _validate_input(self):\\n\\n try:\\n expected_type(str, self.venue_id, \\\"venue_id\\\")\\n expected_type(datetime.datetime, self.timestamp_utc, \\\"timestamp_utc\\\")\\n\\n expected_type(VenueStreamType, self.measurement_type, \\\"measurement_type\\\")\\n\\n expected_type(int, self.number_of_people, \\\"number_of_people\\\")\\n\\n if self.measurement_type is VenueStreamType.ABSOLUTE:\\n if self.operator:\\n raise ValueError(\\\"The stream type for the venue doesn't allow passing an Event operator\\\")\\n\\n elif self.measurement_type is VenueStreamType.EVENT:\\n expected_type(EventStreamOperator, self.operator, \\\"operator\\\")\\n else:\\n raise ValueError(\\\"Unsupported member of the VenueStreamType enum\\\")\\n\\n if self.metadata:\\n expected_type(dict, self.metadata, \\\"metadata\\\")\\n\\n except Exception as ex:\\n raise ValueError(\\\"Validation of input failed. Reason: %s\\\" % str(ex))\",\n \"def test__validate_title__0():\\n for input_value, expected_output in (\\n (None, ''),\\n ('a', 'a'),\\n ):\\n output = validate_title(input_value)\\n vampytest.assert_eq(output, expected_output)\",\n \"def test_traffic_analysis_human_readable(\\n traffic_analysis_success, traffic_analysis_success_hr\\n):\\n resp = prepare_traffic_analysis_output(traffic_analysis_success)\\n assert resp == traffic_analysis_success_hr\",\n \"def test_createItinerary(self):\\n rv = self.json_post('/createItinerary/alex', dict(\\n name = 'New Day',\\n date = '2015-08-21T00:00:00.000Z'\\n ))\\n itinHash = str('alex' + \\\"_\\\" + '2015-08-21T00:00:00.000Z')\\n assert itinHash in str(rv.data)\\n\\n rv = self.json_post('/createItinerary/alex', dict(\\n name = 'New Day',\\n date= '2015-08-21T00:00:00.000Z'\\n ))\\n assert 'Itinerary date already in use' in str(rv.data)\\n\\n rv = self.json_post('/createItinerary/bbbb', dict(\\n name = 'New Day',\\n date= '2015-08-21T00:00:00.000Z'\\n ))\\n assert 'Invalid username' in str(rv.data)\",\n \"def verify(self):\",\n \"def test_allowed(self):\\n user_input = [\\\"1\\\",\\\"1\\\",\\\"0\\\"]\\n with patch(\\\"builtins.input\\\", side_effect=user_input) as input_call:\\n with patch(\\\"sys.stdout\\\", new=StringIO()) as output:\\n import attempt\\n self.assertEqual(output.getvalue().strip(),\\\"You can take this course.\\\")\",\n \"def test_number_of_hours_util(self):\\n self.assertEqual(20, number_of_hours(50, 40))\",\n \"def _expected_inputs():\\n return 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6665209","0.6371906","0.627438","0.62270224","0.6090711","0.6064556","0.6048889","0.603993","0.6019733","0.5987716","0.59819835","0.5966513","0.5957849","0.5943022","0.5917704","0.5901547","0.5900447","0.5894026","0.5893552","0.58609515","0.58448863","0.58367103","0.5833359","0.5823029","0.5800815","0.57525337","0.57288396","0.5725204","0.57018447","0.5698631","0.5692854","0.5680305","0.56495893","0.5646645","0.5646117","0.5643146","0.5636517","0.56362844","0.5635594","0.5624414","0.560959","0.5605836","0.56047934","0.55917555","0.5589009","0.558784","0.55843973","0.5573403","0.55587935","0.55480456","0.5542232","0.55361986","0.55277234","0.55044997","0.54986364","0.5487452","0.54853415","0.54807216","0.5475537","0.54605293","0.5459507","0.5454404","0.5453645","0.5446569","0.54439527","0.54410446","0.5437195","0.54337484","0.5431509","0.54206854","0.5417364","0.5415775","0.5404465","0.5404324","0.53990275","0.5392182","0.53825134","0.5382252","0.53766876","0.5364249","0.53618824","0.5360888","0.53548735","0.5353832","0.5335209","0.5332899","0.5332899","0.5331962","0.5331031","0.5331031","0.53283805","0.53283733","0.5318078","0.53079784","0.53069973","0.5306078","0.53059375","0.5303047","0.530119","0.5299877"],"string":"[\n \"0.6665209\",\n \"0.6371906\",\n \"0.627438\",\n \"0.62270224\",\n \"0.6090711\",\n \"0.6064556\",\n \"0.6048889\",\n \"0.603993\",\n \"0.6019733\",\n \"0.5987716\",\n \"0.59819835\",\n \"0.5966513\",\n \"0.5957849\",\n \"0.5943022\",\n \"0.5917704\",\n \"0.5901547\",\n \"0.5900447\",\n \"0.5894026\",\n \"0.5893552\",\n \"0.58609515\",\n \"0.58448863\",\n \"0.58367103\",\n \"0.5833359\",\n \"0.5823029\",\n \"0.5800815\",\n \"0.57525337\",\n \"0.57288396\",\n \"0.5725204\",\n \"0.57018447\",\n \"0.5698631\",\n \"0.5692854\",\n \"0.5680305\",\n \"0.56495893\",\n \"0.5646645\",\n \"0.5646117\",\n \"0.5643146\",\n \"0.5636517\",\n \"0.56362844\",\n \"0.5635594\",\n \"0.5624414\",\n \"0.560959\",\n \"0.5605836\",\n \"0.56047934\",\n \"0.55917555\",\n \"0.5589009\",\n \"0.558784\",\n \"0.55843973\",\n \"0.5573403\",\n \"0.55587935\",\n \"0.55480456\",\n \"0.5542232\",\n \"0.55361986\",\n \"0.55277234\",\n \"0.55044997\",\n \"0.54986364\",\n \"0.5487452\",\n \"0.54853415\",\n \"0.54807216\",\n \"0.5475537\",\n \"0.54605293\",\n \"0.5459507\",\n \"0.5454404\",\n \"0.5453645\",\n \"0.5446569\",\n \"0.54439527\",\n \"0.54410446\",\n \"0.5437195\",\n \"0.54337484\",\n \"0.5431509\",\n \"0.54206854\",\n \"0.5417364\",\n \"0.5415775\",\n \"0.5404465\",\n \"0.5404324\",\n \"0.53990275\",\n \"0.5392182\",\n \"0.53825134\",\n \"0.5382252\",\n \"0.53766876\",\n \"0.5364249\",\n \"0.53618824\",\n \"0.5360888\",\n \"0.53548735\",\n \"0.5353832\",\n \"0.5335209\",\n \"0.5332899\",\n \"0.5332899\",\n \"0.5331962\",\n \"0.5331031\",\n \"0.5331031\",\n \"0.53283805\",\n \"0.53283733\",\n \"0.5318078\",\n \"0.53079784\",\n \"0.53069973\",\n \"0.5306078\",\n \"0.53059375\",\n \"0.5303047\",\n \"0.530119\",\n \"0.5299877\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62387663"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":93788,"cells":{"query":{"kind":"string","value":"Test each resident has only hospitals in its preference list. If not, check that a warning is caught and the resident's preferences are changed."},"document":{"kind":"string","value":"def test_check_inputs_resident_prefs_all_hospitals(game):\n\n resident = game.residents[0]\n resident.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert resident.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert resident.prefs == []"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_check_inputs_hospital_prefs_all_residents(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert hospital.prefs == []","def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.append(\n ValueError(\n f\"{hospital} has not ranked all the residents that \"\n f\"ranked it: {set(hospital.prefs)} != \"\n f\"{set(residents_that_ranked)}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_hospital_reciprocated_all_prefs(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n hospital._forget(resident)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"hospitals\", \"residents\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert hospital not in resident.prefs","def _check_resident_prefs(self):\n\n errors = []\n for resident in self.residents:\n if not set(resident.prefs).issubset(set(self.hospitals)):\n errors.append(\n ValueError(\n f\"{resident} has ranked a non-hospital: \"\n f\"{set(resident.prefs)} != {set(self.hospitals)}\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert resident not in hospital.prefs","def test_check_inputs_resident_prefs_all_nonempty(game):\n\n resident = game.residents[0]\n resident.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n assert resident.name in str(record[0].message)\n\n if game.clean:\n assert resident not in game.residents","def _check_hospital_matching(self):\n\n errors = []\n for hospital in self.hospitals:\n for resident in hospital.matching:\n if resident not in hospital.prefs:\n errors.append(\n ValueError(\n f\"{hospital} has {resident} in their matching but \"\n \"they do not appear in their preference list: \"\n f\"{hospital.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_hospital_prefs_all_nonempty(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def _check_mutual_preference(resident, hospital):\n\n return resident in hospital.prefs and hospital in resident.prefs","def _check_resident_matching(self):\n\n errors = []\n for resident in self.residents:\n if (\n resident.matching is not None\n and resident.matching not in resident.prefs\n ):\n errors.append(\n ValueError(\n f\"{resident} is matched to {resident.matching} but \"\n \"they do not appear in their preference list: \"\n f\"{resident.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def _check_resident_unhappy(resident, hospital):\n\n return resident.matching is None or resident.prefers(\n hospital, resident.matching\n )","def _check_hospital_unhappy(resident, hospital):\n\n return len(hospital.matching) < hospital.capacity or any(\n [hospital.prefers(resident, match) for match in hospital.matching]\n )","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_check_inputs_supervisor_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n supervisor.prefs.remove(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"supervisors\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert supervisor.name in message\n\n if clean:\n assert supervisor not in student.prefs\n assert all(\n project not in student.prefs for project in supervisor.projects\n )","def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_suitor_prefs()\n\n match.suitors[0].pref_names = [1, 2, 3]\n\n with pytest.raises(Exception):\n match._check_suitor_prefs()","def check_stability(self):\n\n blocking_pairs = []\n for resident in self.residents:\n for hospital in self.hospitals:\n if (\n _check_mutual_preference(resident, hospital)\n and _check_resident_unhappy(resident, hospital)\n and _check_hospital_unhappy(resident, hospital)\n ):\n blocking_pairs.append((resident, hospital))\n\n self.blocking_pairs = blocking_pairs\n return not any(blocking_pairs)","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def test_check_inputs_supervisor_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n projects = supervisor.projects\n for project in student.prefs:\n if project in projects:\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"supervisors\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(supervisor.name)\n assert student.name in message\n\n if clean:\n assert student not in supervisor.prefs\n assert all(\n student not in project.prefs for project in supervisor.projects\n )","def test_check_stability():\n\n residents = [Player(\"A\", [1, 2]), Player(\"B\", [2]), Player(\"C\", [2, 1])]\n hospitals = [Player(1, [\"C\", \"A\"], 3), Player(2, [\"A\", \"B\", \"C\"], 3)]\n match = HospitalResident(residents, hospitals)\n\n matching = match.solve()\n assert match.check_stability()\n\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\n matching[P_1] = [P_C]\n matching[P_2] = [P_A, P_B]\n\n assert not match.check_stability()","def test_resident_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"resident\")\n\n assert set(hospitals) == set(matching.keys())\n assert all(\n [\n r in set(residents)\n for r in set(\n [\n r_match\n for matches in matching.values()\n for r_match in matches\n ]\n )\n ]\n )\n\n for resident in residents:\n if resident.matching:\n assert resident.pref_names.index(resident.matching.name) == 0","def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"hospital\")\n\n assert set(hospitals) == set(matching.keys())\n\n for hospital, matches in matching.items():\n old_idx = -np.infty\n for resident in matches:\n idx = hospital.pref_names.index(resident.name)\n assert idx >= old_idx\n old_idx = idx","def test_check_for_unacceptable_matches_residents(game):\n\n resident = game.residents[0]\n hospital = Hospital(name=\"foo\", capacity=1)\n resident.matching = hospital\n\n issues = game._check_for_unacceptable_matches(\"residents\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(resident.name)\n assert issue.endswith(f\"{resident.prefs}.\")\n assert hospital.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def testHealthAssessHosp(self):\n attr = self.session.create_visit_attr()\n\n self.util.boolTypeTest(self, attr, \"hosp\")\n\n self.util.boolPropertyTest(self, attr, \"hosp\")","def test_check_inputs_project_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n student = project.prefs[0]\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"projects\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(project.name)\n assert student.name in message\n\n if clean:\n assert student not in project.prefs","def test_check_inputs_hospital_capacity(game):\n\n hospital = game.hospitals[0]\n capacity = hospital.capacity\n hospital.capacity = 0\n\n assert hospital._original_capacity == capacity\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_capacity(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def test_check_inputs_project_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n student = project.prefs[0]\n project._forget(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"projects\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert project.name in message\n\n if clean:\n assert project not in student.prefs","def test_inputs_reviewer_prefs(\n resident_names, hospital_names, capacities, seed\n):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_reviewer_prefs()\n\n match.reviewers[0].pref_names.pop()\n\n with pytest.raises(Exception):\n match._check_reviewer_prefs()","def _find_homeless_mps(self):\n mps = Person.objects.filter(\n active=True,\n house__name=HOUSE_OF_COMMONS,\n constituency=None,\n )\n\n self.stdout('MPs with missing constituency:')\n for mp in mps:\n self.stdout(f' [{mp.parliamentdotuk}] {mp.name} has no constituency')","def test_check_for_oversubscribed_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n supervisor.matching = range(supervisor.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.capacity))\n assert str(supervisor.matching) in e","def _pareto_dominance(self, party1: int, party2: int, profile: list[set[int]]) -> bool:\n strict_preference = False\n for ballot in profile:\n if party2 in ballot and party1 not in ballot:\n return False\n if party1 in ballot and party2 not in ballot:\n strict_preference = True\n return strict_preference","def _check_personal_pronouns(pronoun: str, last_nouns: list) -> list:\n pronoun_details = []\n pronoun_lower = pronoun.lower()\n if pronoun == 'I' or pronoun_lower in ('me', 'myself', 'my'):\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\n elif pronoun_lower in ('we', 'us', 'ourselves', 'our'):\n # Find singular or plural person nouns (any gender)\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, True))\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\n elif pronoun_lower in ('they', 'them', 'themselves', 'their'):\n # Give preference to persons (any gender or number)\n noun_list = _check_criteria(pronoun_lower, last_nouns, None, None, True)\n if noun_list:\n pronoun_details.extend(noun_list)\n else:\n # Check for non-persons\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, False))\n elif pronoun_lower in ('she', 'herself', 'her'):\n # Find singular, feminine, person nouns\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, True, True))\n elif pronoun_lower in ('he', 'himself', 'him'):\n # Find singular, masculine, person nouns\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, False, True))\n elif pronoun_lower in ('it', 'itself', 'its'):\n # Find singular, non-person nouns (no gender)\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, None, False))\n final_details = [] # May be duplicates in the list due to duplicates in last_nouns\n for pronoun_detail in pronoun_details:\n if pronoun_detail in final_details:\n continue\n final_details.append(pronoun_detail)\n return final_details","def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals","def testhospital_vs_confirmed(self):\n data = load_covid_data(file)\n aim_day = data['evolution']['2020-03-16']\n # Artificial cut one value , it supposed to be 4 number\n aim_day['epidemiology']['confirmed']['total']['age'] = [10, 11, 12]\n try:\n cases_population = cases_per_population_by_age(data)\n except Exception as e:\n raise Exception","def checkonly(self):\n OTHER_WSREP.append(socket.gethostbyname(socket.gethostname()))\n for hostitem in ALL_NODES:\n checkhost(hostitem)\n if OTHER_WSREP:\n for wsrepitem in OTHER_WSREP:\n REMAINING_NODES.append(wsrepitem)\n if REMAINING_NODES:\n for wsrephost in OTHER_WSREP:\n checkwsrep(wsrephost)\n print ''","def get_possible_preferences():\n df = pd.read_csv(\"../data/restaurant_info.csv\")\n foods = set(list(df[\"food\"]))\n areas = set(list(df[\"area\"].dropna()))\n price_ranges = set(list(df[\"pricerange\"]))\n\n return foods, areas, price_ranges","def check_all_server_power_state(self, state):\n api_data = request(\"get\", \"/serviceProfile\")\n self.assertEqual(api_data['status'], 200,\n 'Incorrect HTTP return code, expected 200, got:' + str(api_data['status']))\n total_elements = 0\n for server in api_data[\"json\"][\"ServiceProfile\"][\"members\"]:\n if server[\"assoc_state\"] == \"associated\":\n api_data_c = request(\"get\", \"/power\",\n query={\"identifier\": str(server[\"path\"])})\n self.assertEqual(api_data_c['status'], 200,\n 'Incorrect HTTP return code, expected 200, got:' +\n str(api_data_c['status']))\n self.assertEqual(api_data_c[\"json\"][\"serverState\"], state,\n 'Server ' + str(server[\"path\"]) + ' reported power state ' +\n str(api_data_c[\"json\"][\"serverState\"]) + ' expected: ' + state)\n total_elements += 1\n self.assertGreater(total_elements, 0, \"Found zero elements\")","def death_check(self):\r\n chance = random.random()\r\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\r\n if self.unique_id in head_of_household_list:\r\n try:\r\n head_of_household_list[self.hh_id] = 0\r\n except TypeError: # head of household migrated\r\n head_of_household_list[self.past_hh_id] = 0\r\n self.model.number_of_humans -= 1\r\n if self.unique_id in labor_list:\r\n labor_list.remove(self.unique_id)\r\n if self.work_status == 1:\r\n try:\r\n num_labor_list[self.hh_id] -= 1\r\n except TypeError:\r\n num_labor_list[self.past_hh_id] -= 1\r\n if self.unique_id in former_hoh_list:\r\n try:\r\n former_hoh_list[self.hh_id] = 0\r\n except:\r\n former_hoh_list[self.past_hh_id] = 0\r\n if [self.unique_id, self.hh_id] in single_male_list:\r\n single_male_list.remove([self.unique_id, self.hh_id])\r\n if self.unique_id in married_male_list:\r\n married_male_list.remove(self.unique_id)\r\n human_death_list.append(self.unique_id)\r\n try:\r\n hh_size_list[self.hh_id] -= 1\r\n except:\r\n hh_size_list[self.past_hh_id] -= 1\r\n human_demographic_structure_list[self.age_category] -= 1\r\n\r\n self.model.schedule.remove(self)\r\n if self in self.model.grid:\r\n self.model.grid.remove_agent(self)","def test_check_stability():\n\n students = [Student(\"A\"), Student(\"B\"), Student(\"C\")]\n projects = [Project(\"P\", 2), Project(\"Q\", 2)]\n supervisors = [Supervisor(\"X\", 2), Supervisor(\"Y\", 2)]\n\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\n\n p.set_supervisor(x)\n q.set_supervisor(y)\n\n a.set_prefs([p, q])\n b.set_prefs([q])\n c.set_prefs([q, p])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = StudentAllocation(students, projects, supervisors)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (p, q) = game.students, game.projects\n\n matching[p] = [c]\n matching[q] = [a, b]\n\n assert not game.check_stability()","def validate_loans(loans):\n def validate_loan(loan):\n return (type(loan)==list or type(loan)==tuple) and len(loan)==3 \\\n and type(loan[0])==str and type(loan[1])==str and loan[1] in database.LOANABLE_RESOURCES and type(loan[2])==int and loan[2]>=0\n return type(loans)==list and False not in [validate_loan(load) for loan in loans]","def test_check_ess_settings(self):\n ess_settings1 = {'gaussian': [self.servers[0]], 'molpro': [self.servers[1], self.servers[0]],\n 'qchem': [self.servers[0]]}\n ess_settings2 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\n ess_settings3 = {'gaussian': self.servers[0], 'molpro': [self.servers[1], self.servers[0]],\n 'qchem': self.servers[0]}\n ess_settings4 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\n ess_settings5 = {'gaussian': 'local', 'molpro': self.servers[1], 'qchem': self.servers[0]}\n\n ess_settings1 = check_ess_settings(ess_settings1)\n ess_settings2 = check_ess_settings(ess_settings2)\n ess_settings3 = check_ess_settings(ess_settings3)\n ess_settings4 = check_ess_settings(ess_settings4)\n ess_settings5 = check_ess_settings(ess_settings5)\n\n ess_list = [ess_settings1, ess_settings2, ess_settings3, ess_settings4, ess_settings5]\n\n for ess in ess_list:\n for soft, server_list in ess.items():\n self.assertTrue(soft in ['gaussian', 'molpro', 'qchem'])\n self.assertIsInstance(server_list, list)\n\n with self.assertRaises(SettingsError):\n ess_settings6 = {'nosoft': ['server1']}\n check_ess_settings(ess_settings6)\n with self.assertRaises(SettingsError):\n ess_settings7 = {'gaussian': ['noserver']}\n check_ess_settings(ess_settings7)","def _check_inputs(self):\n\n self._check_resident_prefs()\n self._check_hospital_prefs()","def isProteic(self):\n from MolKit.PDBresidueNames import AAnames\n\n self.AARes = [x for x in self.residues if x.type in AAnames]\n\n water = [x for x in self.residues if x.type in ['HOH', 'WAT']]\n\n if len(self.AARes) and len(self.AARes)+len(water) == len(self.residues):\n return True\n else:\n return False","def test_preferences_properties(self):\n with pytest.raises(AssertionError):\n self.preferences.exchange_params_by_currency_id\n with pytest.raises(AssertionError):\n self.preferences.utility_params_by_good_id","def cr_check_population_availability(unit_id, table):\n\n T = current.T\n tablename = table._tablename\n\n if tablename == \"cr_shelter\":\n query = (table.site_id == site_id)\n #elif tablename == \"cr_shelter_unit\":\n else:\n query = (table.id == unit_id)\n\n record = current.db(query).select(table.capacity_day,\n table.population_day,\n table.capacity_night,\n table.population_night,\n limitby = (0, 1),\n ).first()\n\n day_and_night = current.deployment_settings.get_cr_day_and_night()\n\n warning = None\n full_day = full_night = False\n\n capacity_day = record.capacity_day\n population_day = record.population_day\n if capacity_day is not None and \\\n population_day and \\\n population_day >= capacity_day:\n full_day = True\n\n if day_and_night:\n capacity_night = record.capacity_night\n population_night = record.population_night\n if capacity_night is not None and \\\n population_night and \\\n population_night >= capacity_night:\n full_night = True\n\n if not day_and_night and full_day or full_day and full_night:\n if tablename == \"cr_shelter\":\n warning = T(\"Warning: this shelter is full\")\n elif tablename == \"cr_shelter_unit\":\n warning = T(\"Warning: this housing unit is full\")\n elif full_day:\n if tablename == \"cr_shelter\":\n warning = T(\"Warning: this shelter is full for daytime\")\n elif tablename == \"cr_shelter_unit\":\n warning = T(\"Warning: this housing unit is full for daytime\")\n elif full_night:\n if tablename == \"cr_shelter\":\n warning = T(\"Warning: this shelter is full for the night\")\n elif tablename == \"cr_shelter_unit\":\n warning = T(\"Warning: this housing unit is full for the night\")\n\n if warning:\n response = current.response\n response_warning = response.warning\n if response_warning:\n response.warning = \"%s - %s\" % (response_warning, warning)\n else:\n response.warning = warning","def resident_optimal(residents, hospitals):\n\n free_residents = residents[:]\n while free_residents:\n\n resident = free_residents.pop()\n hospital = resident.get_favourite()\n\n match_pair(resident, hospital)\n\n if len(hospital.matching) > hospital.capacity:\n worst = hospital.get_worst_match()\n unmatch_pair(worst, hospital)\n free_residents.append(worst)\n\n if len(hospital.matching) == hospital.capacity:\n successors = hospital.get_successors()\n for successor in successors:\n delete_pair(hospital, successor)\n if not successor.prefs:\n free_residents.remove(successor)\n\n return {r: r.matching for r in hospitals}","def testNoSpecialties(self):\n self.failUnlessEqual(self.person.getSpecialties(), [])","def check_all_neighbors_present_local(duthosts, per_host, asic, neighbors, all_cfg_facts,\n nbrhosts, nbr_macs, check_nbr_state=True):\n cfg_facts = all_cfg_facts[per_host.hostname][asic.asic_index]['ansible_facts']\n neighs = cfg_facts['BGP_NEIGHBOR']\n\n fail_cnt = 0\n\n # Grab dumps of the asicdb, appdb, voqdb, and arp table\n asicdb = AsicDbCli(asic)\n asic_dump = asicdb.dump_neighbor_table()\n\n appdb = AppDbCli(asic)\n app_dump = appdb.dump_neighbor_table()\n\n encaps = {}\n\n if per_host.is_multi_asic:\n arptable = per_host.switch_arptable(namespace=asic.namespace)['ansible_facts']\n else:\n arptable = per_host.switch_arptable()['ansible_facts']\n\n if len(duthosts.supervisor_nodes) == 1:\n voqdb = VoqDbCli(duthosts.supervisor_nodes[0])\n voq_dump = voqdb.dump_neighbor_table()\n elif per_host.is_multi_asic:\n # look on linecard for pizzabox multiasic\n voqdb = VoqDbCli(per_host)\n voq_dump = voqdb.dump_neighbor_table()\n else:\n voq_dump = {}\n\n for neighbor in neighbors:\n nbr_vm = get_vm_with_ip(neighbor, nbrhosts)\n neigh_mac = nbr_macs[nbr_vm['vm']][nbr_vm['port']]\n local_ip = neighs[neighbor]['local_addr']\n local_port = get_port_by_ip(cfg_facts, local_ip)\n\n sysport_info = {'slot': cfg_facts['DEVICE_METADATA']['localhost']['hostname'],\n 'asic': cfg_facts['DEVICE_METADATA']['localhost']['asic_name']}\n\n # Validate the asic db entries\n for entry in asic_dump:\n matchstr = '\"%s\",' % neighbor\n if matchstr in entry:\n\n if neigh_mac.lower() != asic_dump[entry]['value']['SAI_NEIGHBOR_ENTRY_ATTR_DST_MAC_ADDRESS'].lower():\n logger.error(\"Asic neighbor macs for %s do not match: %s != %s\", neighbor, neigh_mac.lower(),\n asic_dump[entry]['value']['SAI_NEIGHBOR_ENTRY_ATTR_DST_MAC_ADDRESS'].lower())\n fail_cnt += 1\n else:\n logger.debug(\"Asic neighbor macs for %s match: %s == %s\", neighbor, neigh_mac.lower(),\n asic_dump[entry]['value']['SAI_NEIGHBOR_ENTRY_ATTR_DST_MAC_ADDRESS'].lower())\n encaps[neighbor] = asic_dump[entry]['value']['SAI_NEIGHBOR_ENTRY_ATTR_ENCAP_INDEX']\n break\n else:\n logger.error(\"Did not find neighbor in asictable for IP: %s\" % neighbor)\n fail_cnt += 1\n\n # Validate the APP db entries\n for entry in app_dump:\n matchstr = ':%s' % neighbor\n if entry.endswith(matchstr):\n if neigh_mac.lower() != app_dump[entry]['value']['neigh'].lower():\n logger.error(\"App neighbor macs for %s do not match: %s != %s\", neighbor, neigh_mac.lower(),\n app_dump[entry]['value']['neigh'].lower())\n fail_cnt += 1\n else:\n logger.debug(\"App neighbor macs for %s match: %s == %s\", neighbor, neigh_mac.lower(),\n app_dump[entry]['value']['neigh'].lower())\n\n pytest_assert(\":{}:\".format(local_port) in entry, \"Port for %s does not match\" % entry)\n break\n else:\n logger.error(\"Did not find neighbor in app for IP: %s\" % neighbor)\n fail_cnt += 1\n\n # Validate the arp table entries\n if check_nbr_state:\n check_host_arp_table(per_host, asic, neighbor, neigh_mac, local_port, 'REACHABLE', arptable=arptable)\n else:\n check_host_arp_table(per_host, asic, neighbor, neigh_mac, local_port, None, arptable=arptable)\n\n # supervisor checks\n for entry in voq_dump:\n if entry.endswith('|%s' % neighbor) or entry.endswith(':%s' % neighbor):\n\n if \"portchannel\" in local_port.lower():\n slotname = cfg_facts['DEVICE_METADATA']['localhost']['hostname']\n asicname = cfg_facts['DEVICE_METADATA']['localhost']['asic_name']\n else:\n slotname = sysport_info['slot']\n asicname = sysport_info['asic']\n\n logger.debug(\"Neigh key: %s, slotnum: %s\", entry, slotname)\n pytest_assert(\"|%s|\" % slotname in entry,\n \"Slot for %s does not match %s\" % (entry, slotname))\n pytest_assert(\"|%s:\" % local_port in entry or \"|%s|\" % local_port in entry,\n \"Port for %s does not match %s\" % (entry, local_port))\n pytest_assert(\"|%s|\" % asicname in entry,\n \"Asic for %s does not match %s\" % (entry, asicname))\n\n pytest_assert(voq_dump[entry]['value']['neigh'].lower() == neigh_mac.lower(),\n \"Voq: neighbor: %s mac does not match: %s\" %\n (neighbor, voq_dump[entry]['value']['neigh'].lower()))\n pytest_assert(voq_dump[entry]['value']['encap_index'].lower() == encaps[neighbor],\n \"Voq: encap: %s mac does not match: %s\" %\n (neighbor, voq_dump[entry]['value']['encap_index'].lower()))\n break\n else:\n logger.error(\"Neighbor: %s on slot: %s, asic: %s not present in voq\",\n neighbor, sysport_info['slot'], sysport_info['asic'])\n fail_cnt += 1\n\n logger.info(\"Local %s/%s and chassisdb neighbor validation of %s is successful (mac: %s, idx: %s)\",\n per_host.hostname, asic.asic_index, neighbor, neigh_mac, encaps[neighbor])\n\n return {'encaps': encaps, 'fail_cnt': fail_cnt}","def seesSuggestions(self):\n return self.floor.owner == self.user and self.floor.permissiveness == \"permissive\"","def _acceptable(self, team):\r\n current = [c for c in self.configurations if self._validateNoSpies(c, team)]\r\n return bool(len(current) > 0)","def hospital_optimal(hospitals):\n\n free_hospitals = hospitals[:]\n while free_hospitals:\n\n hospital = free_hospitals.pop()\n resident = hospital.get_favourite()\n\n if resident.matching:\n curr_match = resident.matching\n unmatch_pair(resident, curr_match)\n if curr_match not in free_hospitals:\n free_hospitals.append(curr_match)\n\n match_pair(resident, hospital)\n if len(hospital.matching) < hospital.capacity and [\n res for res in hospital.prefs if res not in hospital.matching\n ]:\n free_hospitals.append(hospital)\n\n successors = resident.get_successors()\n for successor in successors:\n delete_pair(resident, successor)\n if (\n not [\n res\n for res in successor.prefs\n if res not in successor.matching\n ]\n and successor in free_hospitals\n ):\n free_hospitals.remove(successor)\n\n return {r: r.matching for r in hospitals}","def test_check_for_unacceptable_matches_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n student = Student(name=\"foo\")\n supervisor.matching.append(student)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.prefs))\n assert student.name in error","def hospital_resident(residents, hospitals, optimal=\"resident\"):\n\n if optimal == \"resident\":\n return resident_optimal(residents, hospitals)\n if optimal == \"hospital\":\n return hospital_optimal(hospitals)","def _list_validity_check(l, valid_l):\n\n if not Settings._is_in_list(l, valid_l):\n raise InvalidSettingError()","def check_hint_condition(self, hxml_set, student_answers):\r\n pass","def _validateSpies(self, config, team, sabotaged):\r\n spies = [s for s in team if s in self.getSpies(config)]\r\n \"\"\"If there are more spies in our config than the number of sabotages made \r\n then return True, because this config is compatible with the sabotages made. \r\n Otherwise it is not compatible, so return False.\"\"\"\r\n return len(spies) >= sabotaged","def test_pre_assigned(self):\n shift1 = RegularWorkshift.objects.create(\n workshift_type=self.wtype1,\n pool=self.p1,\n hours=5,\n )\n shift2 = RegularWorkshift.objects.create(\n workshift_type=self.wtype3,\n pool=self.p1,\n hours=1,\n )\n shift2.current_assignees = [self.profile]\n\n unfinished = utils.auto_assign_shifts(self.semester)\n self.assertEqual([self.profile], unfinished)\n self.assertNotIn(self.profile, shift1.current_assignees.all())\n\n pool_hours = self.profile.pool_hours.get(pool=self.p1)\n self.assertEqual(\n pool_hours.assigned_hours,\n 1,\n )","def test_homeland_defense(self):\n self.conf[\"game\"][\"homeland_defense\"] = \"100/50/25\"\n self.assertEqual(None, self.sapphire.owner)\n battle = self.battle\n sess = self.sess\n\n # Skirmish 1\n s1 = battle.create_skirmish(self.alice, 10) # Attack 10\n s1a = s1.react(self.carol, 4, hinder=False) # --Support 4\n s1a.react(self.bob, 3) # ----Attack 3\n s1.react(self.dave, 8) # --Attack 8\n # Winner will be team orangered, 11 VP\n\n # Skirmish 2\n battle.create_skirmish(self.bob, 5) # Attack 5\n # Winner will be team periwinkle, 10 VP for unopposed\n\n # End fight\n self.battle.ends = self.battle.begins\n sess.commit()\n self.assert_(battle.past_end_time())\n\n updates = Battle.update_all(sess, conf=self.conf)\n sess.commit()\n\n # Overall winner would ordinarily be orangered, but this should get\n # modified by the homeland defense buffs - Sapphire is right next to\n # the test capital, and so will get an extra 50%\n self.assertEqual(battle.victor, 1)\n self.assertEqual(battle.score1, 15)\n # OR should get 25% bonus\n self.assertEqual(battle.score0, 13)","def test_check_full_house_false(self):\n not_full_house_fixtures = [[1, 1, 1, 1, 2],\n [2, 1, 1, 1, 1],\n [1, 1, 2, 2, 3],\n [1, 2, 3, 4, 5],\n ]\n\n for fixture in not_full_house_fixtures:\n score = self.roll.check_full_house(fixture)\n\n self.assertNotEqual(score, 25)\n self.assertEqual(score, 0)\n self.assertEqual(len(fixture), 5)","def test_positive_electrode_potential_profile(self):\n\n # TODO: add these when have averages","def check_site_equiv(conn, general_site_type_pkey, specific_site_type_pkey):\n cur = conn.cursor()\n\n cur.execute(\n \"SELECT name FROM site_pin WHERE site_type_pkey = ?\",\n (general_site_type_pkey, )\n )\n general_site_pins = set(name for (name, ) in cur)\n\n cur.execute(\n \"SELECT name FROM site_pin WHERE site_type_pkey = ?\",\n (specific_site_type_pkey, )\n )\n for (name, ) in cur:\n assert name in general_site_pins, (\n general_site_pins, name, specific_site_type_pkey\n )","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def power_off_all_server_profiles():\n FusionUIBase.navigate_to_section(SectionType.SERVER_PROFILES, time_for_loading=5)\n profile_name_list = CommonOperationServerProfile.get_server_profile_list()\n\n total = len(profile_name_list)\n already_off_or_not_supported = 0\n powered_off = 0\n\n for n, profile_name in enumerate(profile_name_list):\n logger.info(\"{2} No: {0} --- Total: {1} {2}\".format((n + 1), total, '-' * 14))\n logger.info(\"powering off a server profile named '%s'\" % profile_name)\n CommonOperationServerProfile.click_server_profile(profile_name=profile_name, time_for_loading=4)\n # check if already powered off\n FusionUIBase.select_view_by_name(view_name='General', timeout=5, fail_if_false=False)\n if VerifyServerProfile.verify_general_server_power(expect_value='On', timeout=5, fail_if_false=False) is False:\n logger.warn(\"power state of server profile '%s' is not 'On' (it's Off, or not supported due to being managed by another system), 'Power Off' action is unavailable.\" % profile_name)\n already_off_or_not_supported += 1\n else:\n if power_off_server_profile_by_name(profile_name) is False:\n logger.warn(\"server profile '%s' is NOT powered off successfully\" % profile_name)\n continue\n else:\n powered_off += 1\n\n logger.info(\"{0} == Summary == {0}\".format('-' * 14))\n if total - already_off_or_not_supported == 0:\n logger.warn(\"no server profile to power off! all %s server profile(s) is NOT applicable to power off (already powered off, or unknown power state), test is considered PASS\" % already_off_or_not_supported)\n return True\n else:\n if powered_off < total:\n logger.warn(\"not all of the server profile(s) is successfully powered off - %s out of %s powered off \" % (powered_off, total))\n if powered_off + already_off_or_not_supported == total:\n logger.warn(\"%s already-off-or-not-supported server profile(s) is skipped being powered off, test is considered PASS\" % already_off_or_not_supported)\n return True\n else:\n logger.warn(\"%s already-off-or-not-supported server profile(s) is skipped being powered off, \"\n \"%s server profile(s) left is failed being powered off \" % (already_off_or_not_supported, total - powered_off - already_off_or_not_supported))\n return False\n\n logger.info(\"all of the server profile(s) is successfully powered off - %s out of %s \" % (powered_off, total))\n return True","def negative_test_for_check_local_storage(profile_obj):\n status = True\n for profile in profile_obj:\n elements_test_list = [profile.emptyChar, profile.invalidChars]\n\n \"\"\"\n Setting invalid values for: \"Name\" and \"Number of physical drivers\"\n \"\"\"\n for element in elements_test_list:\n # Fill Logical Drive form\n if __fill_logical_drive_form(element):\n # Click Logical Drive Create button\n if ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_BTN_CREATE_LOGICAL_DRIVE_FORM):\n if element == '':\n # Check warning messages\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_NAME_FORM) and __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\n logger.warn(\"At least one of the warning messages was NOT found \")\n status = False\n\n else:\n # Check warning messages\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\n logger.warn(\"Warning message NOT found\")\n status = False\n else:\n logger.warn(\"Logical Drive Form 'Button' returns an error. Please, check the test report for more details\")\n status = False\n else:\n logger.warn(\"Logical Drive Form returns an error. Please, check the test report for more details\")\n status = False\n\n # Click 'Cancel' form button\n if not ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_CANCEL_LOCAL_STORAGE_FORM):\n logger.warn(\"Cancel button element NOT found\")\n status = False\n\n return status","def is_unhappy(self):\n #checked!#\n ###your code here###\n same=0\n for i in self.home.neighbors:\n if i.occupant!=None:\n if i.occupant.group==self.group:\n same+=1\n happniess=float(same)/len(self.home.neighbors)\n if happniess<self.happiness_threshold:\n return True\n else:\n return False","def test_verification_status_invisible(self):\r\n self.client.login(username=\"jack\", password=\"test\")\r\n self.check_verification_status_off('verified', 'You\\'re enrolled as a verified student')\r\n self.check_verification_status_off('honor', 'You\\'re enrolled as an honor code student')\r\n self.check_verification_status_off('audit', 'You\\'re auditing this course')","def check_risk(self):\r\n gms = self.cleaned_data.get(\"gms\", [])\r\n risk = self.cleaned_data.get(\"risk\", RPEvent.NORMAL_RISK)\r\n if not any(\r\n gm\r\n for gm in gms\r\n if gm.player.is_staff or gm.player.check_permstring(\"builders\")\r\n ):\r\n if risk != RPEvent.NORMAL_RISK:\r\n self.add_error(\r\n \"risk\",\r\n \"Risk cannot be altered without a staff member as GM. Set to: %r\"\r\n % risk,\r\n )","def get_everyone_denied(self):","def testAssistantDoesNotGetUserPrefRole(self):\n self._testAssistantOwnershipAfter(task=\"create\")\n self.failIf('def456' in self.person.users_with_local_role('User Preferences Editor'), \"Assistant can edit user prefs after create\")\n self.simulateATGUIInteraction(task=\"edit\")\n self.failIf('def456' in self.person.users_with_local_role('User Preferences Editor'), \"Assistant can edit user prefs after edit\")","def test_calculates_risk_points_with_one_house(self):\n\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\n\n for house in self.risk_profile[\"home\"]:\n self.assertEquals(house[\"risk_points\"], 2)","def test_open_no_setup(restaurant_only, hall_only, kitchen_only, delivery_only):\n # Here checks not all variants, cause restaurant_only is not isolated\n # object. They were previously check and working alongside\n # but affects result if together.\n\n # no setups\n with pytest.raises(CustomWarning):\n restaurant_only.open()\n assert restaurant_only.is_working is False, \"You need to setup Kitchen, Delivery and Hall\"\n\n # only kitchen\n with pytest.raises(CustomWarning):\n restaurant_only.set_kitchen(kitchen_only)\n restaurant_only.open()\n assert restaurant_only.is_working is False, \"You need to setup Kitchen, Delivery and Hall\"\n\n # only delivery and kitchen\n with pytest.raises(CustomWarning):\n restaurant_only.set_delivery(delivery_only)\n restaurant_only.set_kitchen(kitchen_only)\n restaurant_only.open()\n assert restaurant_only.is_working is False, \"You need to setup Kitchen, Delivery and Hall\"","def test_anvil_first_patient_everything(caplog, anvil_server, anvil_research_studies_with_observations):\n research_studies = {r.relativePath(): r for r in anvil_research_studies_with_observations if\n r.__class__.__name__ == 'ResearchStudy'}\n resource_counts = _validate_patient_everything(caplog, anvil_server,\n [research_studies['ResearchStudy/1000G-high-coverage-2019']])\n assert False, resource_counts","def healthcare():","def checks(self, all=False):\n if all:\n warn_states = [\"unknown\", \"passing\", \"warning\", \"critical\"]\n else:\n warn_states = [\"unknown\", \"warning\", \"critical\"]\n checks = {}\n for warn_state in warn_states:\n for state in self.consul.health.state(warn_state):\n if not state['Node'] in checks:\n checks[state['Node']] = dict()\n if not state['ServiceID'] in checks[state['Node']]:\n checks[state['Node']][state['ServiceID']] = {\n 'checks': [],\n 'name': state['ServiceName']\n }\n checks[state['Node']][state['ServiceID']]['checks'].append(\n (state['Name'], state['Status'], state['Output'])\n )\n return checks","def violated(self) -> bool:\n ...","def check_items(self, items):\n for item in items:\n if item['user'] in self.required_list and item['user'] not in self.approved_required_list:\n if item['result'] is True or item['result'] == 'approved':\n logger.info(\"%s approved\", item['user'])\n self.approved_required_list.append(item['user'])\n else:\n logger.info(\"%s declined\", item['user'])\n error_msg = \"Since {} is a required member for approval, this approval step fails. \".format(item['user'])\n logger.error(error_msg)\n self.exit(rc=2, detail=error_msg)\n if item['user'] in self.optional_list and item['user'] not in self.approved_optional_list:\n if item['result'] is True or item['result'] == 'approved':\n logger.info(\"%s approved\", item['user'])\n if item['user'] in self.declined_optional_list:\n self.declined_optional_list.remove(item['user'])\n self.approved_optional_list.append(item['user'])\n else:\n logger.info(\"%s declined\", item['user'])\n self.declined_optional_list.append(item['user'])\n if len(self.declined_optional_list) >= (len(self.optional_list) - self.number_optional):\n error_msg = \"Not be able to fulfill requirement that {} optional approvals, since {} declined request.\".format(\n self.number_optional, self.declined_optional_list)\n logger.error(error_msg)\n self.exit(rc=2, detail=error_msg)\n\n if len(self.approved_required_list) >= len(self.required_list) \\\n and len(self.approved_optional_list) >= self.number_optional:\n logger.info(\"Approval requirements are fully met. Exit gracefully.\")\n self.exit(0)","def _checkstate(self, queryset):\n for presup in queryset.filter(estado='borrador').exclude(fecha_realizado=None):\n if ((presup.fecha_realizado + timedelta(days=21) < datetime.now().date() and presup.tipo != 'asistencia') or\n (presup.fecha_realizado + timedelta(days=60) < datetime.now().date() and presup.tipo == 'asistencia')):\n presup._toState_cancelado(obs=\"Registro automático: Presupuesto vencido\")","def check_proof(self):\n for gene in self.population:\n if gene.is_proof:\n print(gene.chromosome)\n for state in gene.coq_states:\n print(state)\n self.proofs.append(Gene(chromosome=gene.valid_tactics))","def test_tally_no_candidates(self):\n self.init_elect_types()\n\n userA = models.User(\n name = \"UserA\",\n email = \"userA@eLect.com\",\n password = \"asdf\")\n\n session.add(userA)\n session.commit()\n\n electionA = models.Election(\n title = \"Election A\",\n admin_id = userA.id)\n\n session.add(electionA)\n session.commit()\n\n raceA = models.Race(\n title = \"Race A\",\n election_id = electionA.id\n )\n\n session.add(raceA)\n session.commit()\n\n with self.assertRaises(NoCandidates):\n self.wta.check_race(raceA.id)\n\n with self.assertRaises(NoCandidates):\n self.proportional.check_race(raceA.id)\n\n with self.assertRaises(NoCandidates):\n self.schulze.check_race(raceA.id)","def isStable(couples,mPref,wPref) -> bool:\n couples_dict = {woman:man for man,woman in couples}\n \n for m,w in couples:\n\n # w's (m's partner) rank in m's preference list\n w_rank = mPref[m].index(w)\n \n # list of all women that m prefers over w\n preferred_over_w = mPref[m][:w_rank]\n \n\n for hotter in preferred_over_w:\n\n # get each woman's current partner (in preferred list)\n hotters_man = couples_dict[hotter]\n\n # if she prefers m over her current partner then marriage is not stable\n if wPref[hotter].index(m) < wPref[hotter].index(hotters_man): \n msg = \"{}'s marriage to {} is unstable:\\n\\t\" + \\\n \"{} prefers {} > {} (his current wife)\\n\\t\" +\\\n \"{} prefers {} > {} (her current husband)\"\n print(msg.format(m, w, m, hotter, w, hotter, m, hotters_man) )\n return False\n return True","def check_puzzle_unicity(puzzle_list: list) -> None:\n if len(set(puzzle_list)) != len(puzzle_list):\n raise ParsingError(\"Puzzle numbers must be unique.\")","def _full_check_and_validate(self, p_map):\n if PitchConstraintSolver.pmap_full(p_map):\n for p in self._policies:\n if not p.verify(p_map):\n return False\n return True\n else:\n return False","def test_statuses_exist(self):\n\n assert hasattr(Partner, 'AVAILABLE')\n assert hasattr(Partner, 'NOT_AVAILABLE')\n assert hasattr(Partner, 'WAITLIST')\n\n assert hasattr(Partner, 'STATUS_CHOICES')\n\n assert len(Partner.STATUS_CHOICES) == 3\n\n database_statuses = [x[0] for x in Partner.STATUS_CHOICES]\n\n assert Partner.AVAILABLE in database_statuses\n assert Partner.NOT_AVAILABLE in database_statuses\n assert Partner.WAITLIST in database_statuses","def test_ap_hs20_roaming_partner_preference(dev, apdev):\n params = hs20_ap_params()\n params['domain_name'] = \"roaming.example.org\"\n hostapd.add_ap(apdev[0]['ifname'], params)\n\n params = hs20_ap_params()\n params['ssid'] = \"test-hs20-other\"\n params['domain_name'] = \"roaming.example.net\"\n hostapd.add_ap(apdev[1]['ifname'], params)\n\n logger.info(\"Verify default vs. specified preference\")\n values = default_cred()\n values['roaming_partner'] = \"roaming.example.net,1,127,*\"\n policy_test(dev[0], apdev[1], values, only_one=False)\n values['roaming_partner'] = \"roaming.example.net,1,129,*\"\n policy_test(dev[0], apdev[0], values, only_one=False)\n\n logger.info(\"Verify partial FQDN match\")\n values['roaming_partner'] = \"example.net,0,0,*\"\n policy_test(dev[0], apdev[1], values, only_one=False)\n values['roaming_partner'] = \"example.net,0,255,*\"\n policy_test(dev[0], apdev[0], values, only_one=False)","def factor_in_multiple_professors(self):\n professors = [professor for professor in self.course.professors if professor.lower() != \"none\"]\n number_professors = len(set(professors))\n if number_professors > 1:\n self.score = self.score + number_professors","def how_eligible(essay):\n eligibility_requirements = ['?', '\"', ',', '!']\n return len(set(filter((lambda x: x in eligibility_requirements), essay)))","def lookup(self,user_preferences):\n res = list()\n \n fit_area = set()\n fit_price = set()\n fit_food = set()\n \n if user_preferences[0] == \"any\" or user_preferences[0] == 0:\n fit_area = set(range(len(self.area)))\n else:\n for i,a in enumerate(self.area):\n if a == user_preferences[0]:\n fit_area.add(i)\n if user_preferences[1] == \"any\" or user_preferences[1] == 0:\n fit_price = set(range(len(self.price_range)))\n else:\n for j,p in enumerate(self.price_range):\n if p == user_preferences[1]:\n fit_price.add(j)\n if user_preferences[2] == \"any\" or user_preferences[2] == 0:\n fit_food = set(range(len(self.food_types)))\n else:\n for k,f in enumerate(self.food_types):\n if f == user_preferences[2]:\n fit_food.add(k)\n option_numbers = fit_area.intersection(fit_price, fit_food)\n if option_numbers:\n for i in option_numbers:\n res.append(self.restaurant_names[i])\n \n return res","def test_half_life_unstable_isotopes():\n for isotope in data_about_isotopes:\n if (\n \"half_life\" not in data_about_isotopes[isotope]\n and not data_about_isotopes[isotope]\n ):\n with pytest.raises(MissingParticleDataError):\n half_life(isotope)","def test_auto_assign_two_preferred(self):\n shift1 = RegularWorkshift.objects.create(\n workshift_type=self.wtype1,\n pool=self.p1,\n hours=5,\n )\n shift2 = RegularWorkshift.objects.create(\n workshift_type=self.wtype2,\n pool=self.p1,\n hours=5,\n )\n unfinished = utils.auto_assign_shifts(self.semester)\n self.assertEqual([], unfinished)\n self.assertIn(self.profile, shift1.current_assignees.all())\n self.assertNotIn(self.profile, shift2.current_assignees.all())\n\n instances = WorkshiftInstance.objects.filter(weekly_workshift=shift1)\n self.assertGreater(instances.count(), 0)\n self.assertTrue(all(\n instance.workshifter == self.profile\n for instance in instances\n ))\n\n instances = WorkshiftInstance.objects.filter(weekly_workshift=shift2)\n self.assertGreater(instances.count(), 0)\n self.assertTrue(all(\n instance.workshifter is None\n for instance in instances\n ))\n\n pool_hours = self.profile.pool_hours.get(pool=self.p1)\n self.assertEqual(\n pool_hours.assigned_hours,\n pool_hours.hours,\n )","def __check_all_config_values_set(cfg: __Config):\n unset_values = [key for key, val in cfg.__dict__.items() if val is None]\n if len(unset_values) > 0:\n print(f\"WARNING: The following config variables have not been set: {unset_values}\")","def feasible(individual):\n val=0;\n for i in individual:\n if viajes[val][6]==True and False==vehiculos_esp[i]:\n return False\n val+=1\n return True","def test_for_criteria(self):\n ignore = ['interpreter_method', 'average_by_sample_or_site', 'include_nrm']\n values = ([dic['value'] for dic in self.acceptance_criteria.values() if (dic['criterion_name'] not in ignore and dic['value'] != -999)])\n return values","def _assert_problem_is_valid(self) -> None:\n # Will throw error if any of these are not set\n specified = self.ignore + self.silence + self.target\n if not all(\n [\n pr in specified\n for pr in self.observer.photoreceptors\n if pr is not None\n ]\n ):\n raise SilSubProblemError(\n \"At least one photoreceptor is not accounted for by the \"\n + \"*ignore*, *silence* and *target* properties.\"\n )","def check_neighbors_are_gone(duthosts, all_cfg_facts, per_host, asic, neighbors):\n asicdb = AsicDbCli(asic)\n appdb = AppDbCli(asic)\n\n # Check that the arp entry is deleted for all the neighbors on all the asics.\n poll_neighbor_table_delete(duthosts, neighbors, poll_time=30)\n\n asicdb_neigh_table = asicdb.dump_neighbor_table()\n app_neigh_table = appdb.dump_neighbor_table()\n voqdb = VoqDbCli(duthosts.supervisor_nodes[0])\n voq_dump = voqdb.dump_neighbor_table()\n\n for neighbor in neighbors:\n logger.info(\"Checking neighbor entry for %s is deleted from host: %s, asic: %s\",\n neighbor, per_host.hostname, asic.asic_index)\n\n for entry in list(asicdb_neigh_table.keys()):\n search = '\"ip\":\"%s\"' % neighbor\n if search in entry:\n raise AssertionError(\"Found neighbor %s in asicdb: %s\", search, entry)\n\n for entry in list(app_neigh_table.keys()):\n if entry.endswith(\":\" + neighbor):\n raise AssertionError(\"Found neighbor %s in app: %s\", neighbor, entry)\n\n # check supervisor\n for entry in list(voq_dump.keys()):\n if entry.endswith(\":\" + neighbor):\n raise AssertionError(\"Found neighbor %s in voq: %s\", neighbor, entry)\n\n # check remote hosts\n for rem_host in duthosts.frontend_nodes:\n for rem_asic in rem_host.asics:\n if rem_host == per_host and rem_asic == asic:\n # skip remote check on local host\n continue\n for neighbor in neighbors:\n logger.info(\"Remote host checks of ARP/NDP for %s are deleted from host: %s, asic: %s\", neighbor,\n rem_host.hostname,\n rem_asic.asic_index)\n rem_cfg_facts = all_cfg_facts[rem_host.hostname][rem_asic.asic_index]['ansible_facts']\n remote_inband_info = get_inband_info(rem_cfg_facts)\n if remote_inband_info == {}:\n logger.info(\"No inband configuration on this ASIC: %s/%s, skipping\", rem_host.hostname,\n rem_asic.asic_index)\n continue\n asicdb = AsicDbCli(rem_asic)\n appdb = AppDbCli(rem_asic)\n asicdb_neigh_table = asicdb.dump_neighbor_table()\n app_neigh_table = appdb.dump_neighbor_table()\n\n for entry in list(asicdb_neigh_table.keys()):\n search = '\"ip\":\"%s\"' % neighbor\n if search in entry:\n raise AssertionError(\"Found neighbor %s in asicdb: %s\", search, entry)\n\n for entry in list(app_neigh_table.keys()):\n if entry.endswith(\":\" + neighbor):\n raise AssertionError(\"Found neighbor %s in app: %s\", neighbor, entry)\n\n check_neighbor_kernel_route(rem_host, rem_asic.asic_index, neighbor, remote_inband_info['port'],\n present=False)","def verify_can_edit_server_profile_general_info_when_server_power_on(profile_obj):\n\n FusionUIBase.navigate_to_section(SectionType.SERVER_PROFILES, time_for_loading=5)\n\n total = len(profile_obj)\n not_exists = 0\n edited = 0\n\n for n, profile in enumerate(profile_obj):\n logger.info(\"{2} No: {0} --- Total: {1} {2}\".format((n + 1), total, '-' * 14))\n logger.info(\"editing a server profile with name '%s' ...\" % profile.name)\n # checking if the profile is not existing for editing\n if not VerifyServerProfile.verify_server_profile_exist(profile.name, fail_if_false=False):\n logger.warn(\"server profile '%s' does not exist\" % profile.name)\n not_exists += 1\n continue\n CommonOperationServerProfile.click_server_profile(profile.name)\n\n EditServerProfile.select_action_edit()\n EditServerProfile.wait_edit_server_profile_dialog_shown()\n BuiltIn().sleep(2)\n EditServerProfile.input_name(profile.newName) if getattr(profile, 'newName', None) is not None else None\n EditServerProfile.input_description(profile.desc) if getattr(profile, 'desc', None) is not None else None\n\n # Server hardware must be \"power\" on status\n if not VerifyServerProfile.is_power_on_error_visible_when_edit_server_profile(profile.server, 10):\n logger.warn(\"Server hardware '%s' is not 'Powered on, please power on it\" % profile.server)\n continue\n\n sht_selected = EditServerProfile.get_selected_server_hardware_type(profile.server)\n if getattr(profile, 'hardwareType', None) is not None:\n if profile.hardwareType not in sht_selected:\n logger.warn(\"server hardware type '%s' of server '%s' is NOT consistent with test data '%s'\" % (sht_selected, profile.server, profile.hardwareType))\n EditServerProfile.ChangeServerHardwareTypeAndEnclosureGroup.change_server_hardware_type(profile.hardwareType, timeout=5, fail_if_false=False)\n\n eg_selected = EditServerProfile.get_selected_enclosure_group(profile.server)\n if profile.enclgroup not in eg_selected:\n logger.warn(\"enclosure group '%s' of server '%s' is NOT consistent with test data '%s'\" % (eg_selected, profile.server, profile.enclgroup))\n EditServerProfile.ChangeServerHardwareTypeAndEnclosureGroup.change_enclosure_group(profile.enclgroup, timeout=5, fail_if_false=False)\n\n # EditServerProfile.input_select_server_hardware(profile.server, auto_power_off=False)\n\n if getattr(profile, 'Affinity', None) is not None:\n logger.info(\"test data for 'Affinity' is found: <%s>, start setting Affinity ...\" % profile.Affinity)\n EditServerProfile.select_affinity_by_text(profile.Affinity)\n\n if getattr(profile, 'Firmware', None) is not None:\n logger.info(\"test data for 'Firmware' is found: <%s>, start setting Firmware Baseline ...\" % profile.Firmware)\n logger.debug(\"test data for 'Firmware' is found: <%s>\" % profile.Firmware, also_console=False)\n # set Firmware Baseline and force-installation option\n CommonOperationServerProfile.Firmware.set(profile.Firmware)\n\n if getattr(profile, 'Connections', None) is not None:\n logger.debug(\"test data for 'Connections' is found: <%s>\" % profile.Connections, also_console=False)\n logger.info(\"test data for 'Connections' is found, start adding connections ...\")\n logger.warn(\"Only connection name is allowed to modification\")\n # add connections\n CommonOperationServerProfile.Connection().set(profile.Connections)\n\n if getattr(profile, 'LocalStorage', None) is not None:\n logger.warn(\"Modify the 'BootSettings' section will return error when server power on, so ignore this setting\")\n\n if getattr(profile, 'SANStorage', None) is not None:\n logger.warn(\"Modify the 'BootSettings' section will return error when server power on, so ignore this setting\")\n\n if getattr(profile, 'BootSettings', None) is not None:\n logger.warn(\"Modify the 'BootSettings' section will return error when server power on, so ignore this setting\")\n\n if getattr(profile, 'Advanced', None) is not None:\n logger.warn(\"Modify the 'Advanced' section will return error when server power on, so ignore this setting\")\n\n EditServerProfile.click_ok_button()\n if CommonOperationServerProfile.BootSettings.get_error_message_from_boot_mode() is not None:\n logger.warn(\"test data may be wrongly defined for 'Boot mode', which caused an error that blocks profile being created. \"\n \"test will skip this profile '%s' and continue to create other server profiles\" % profile.name)\n continue\n\n BuiltIn().sleep(2)\n status, msg = FusionUIBase.get_error_message_from_dialog(timeout=10)\n if status is True:\n logger.warn(\"unexpected error occurred: %s\" % msg)\n ui_lib.fail_test(msg)\n\n EditServerProfile.wait_edit_server_profile_dialog_disappear(timeout=300)\n FusionUIBase.show_activity_sidebar()\n profile_name = profile.newName if getattr(profile, 'newName', None) is not None else profile.name\n FusionUIBase.wait_activity_action_ok(profile_name, 'Update', timeout=300, fail_if_false=True)\n FusionUIBase.show_activity_sidebar()\n CommonOperationServerProfile.wait_server_profile_status_ok(profile_name, timeout=300, fail_if_false=True)\n logger.info(\"edited server profile '%s' successfully\" % profile_name)\n edited += 1\n\n logger.info(\"{0} == Summary == {0}\".format('-' * 14))\n if total - not_exists == 0:\n logger.warn(\"no server profile to edit! all %s server profile(s) is NOT existing, test is considered FAILED\" % not_exists)\n return False\n else:\n if edited < total:\n logger.warn(\"not all of the server profile(s) is successfully edited - %s out of %s edited \" % (edited, total))\n if edited + not_exists == total:\n logger.warn(\"%s not-existing server profile(s) is skipped being edited, test is considered FAILED\" % not_exists)\n return False\n else:\n logger.warn(\"%s not-existing server profile(s) is skipped being edited, %s profile(s) left is failed being edited \" % (not_exists, total - edited - not_exists))\n return False\n\n logger.info(\"all of the server profile(s) is successfully edited - %s out of %s \" % (edited, total))\n return True","def check_relevant_forms(L):\n L2 = list()\n for F in L.values():\n # needed_ev=(\n S = F.shim_corr\n # print \"S=\",S\n ok_ev = 0\n for g in S:\n if g.atkin_lehner_eigenvalue() == -1:\n ok_ev = ok_ev + 1\n if ok_ev > 0:\n print(\"Number of ok forms on \", F.space.WR.N, \" :\", ok_ev)\n F.list_coefficents('D', fd=True, neg=False, nmin=0, nmax=1000, latex=False, nd=50, prime=True)\n L2.append(F)\n return L2","def _check_approval_update(self, state):\n\t\tcurrent_employee = self.env['hr.employee'].search([('user_id', '=', self.env.uid)], limit=1)\n\t\t# is_officer = self.env.user.has_group('hr_holidays.group_hr_holidays_user')\n\t\tis_manager = self.env.user.has_group('hr_holidays.group_hr_holidays_manager')\n\t\tfor holiday in self:\n\t\t\tval_type = holiday.holiday_status_id.validation_type\n\t\t\tif state == 'confirm':\n\t\t\t\tcontinue\n\n\t\t\tif state == 'draft':\n\t\t\t\tif holiday.employee_id != current_employee and not is_manager:\n\t\t\t\t\traise UserError(_('Only a Leave Manager can reset other people leaves.'))\n\t\t\t\tcontinue\n\n\t\t\t# if not is_officer:\n\t\t\t# \traise UserError(_('Only a Leave Officer or Manager can approve or refuse leave requests.'))\n\n\t\t\t# if is_officer:\n\t\t\t# \t# use ir.rule based first access check: department, members, ... (see security.xml)\n\t\t\tholiday.check_access_rule('write')\n\n\t\t\tif holiday.employee_id == current_employee and not is_manager:\n\t\t\t\traise UserError(_('Only a Leave Manager can approve its own requests.'))\n\n\t\t\tif (state == 'validate1' and val_type == 'both') or (state == 'validate' and val_type == 'manager'):\n\t\t\t\tmanager = holiday.employee_id.parent_id or holiday.employee_id.department_id.manager_id\n\t\t\t\tif (manager and manager != current_employee) and not self.env.user.has_group('hr_holidays.group_hr_holidays_manager'):\n\t\t\t\t\traise UserError(_('You must be either %s\\'s manager or Leave manager to approve this leave') % (holiday.employee_id.name))\n\n\t\t\tif state == 'validate' and val_type == 'both':\n\t\t\t\tif not self.env.user.has_group('hr_holidays.group_hr_holidays_manager'):\n\t\t\t\t\traise UserError(_('Only an Leave Manager can apply the second approval on leave requests.'))"],"string":"[\n \"def test_check_inputs_hospital_prefs_all_residents(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert hospital.prefs == []\",\n \"def _check_hospital_prefs(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n residents_that_ranked = [\\n res for res in self.residents if hospital in res.prefs\\n ]\\n if set(hospital.prefs) != set(residents_that_ranked):\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has not ranked all the residents that \\\"\\n f\\\"ranked it: {set(hospital.prefs)} != \\\"\\n f\\\"{set(residents_that_ranked)}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_hospital_reciprocated_all_prefs(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n hospital._forget(resident)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"hospitals\\\", \\\"residents\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert hospital not in resident.prefs\",\n \"def _check_resident_prefs(self):\\n\\n errors = []\\n for resident in self.residents:\\n if not set(resident.prefs).issubset(set(self.hospitals)):\\n errors.append(\\n ValueError(\\n f\\\"{resident} has ranked a non-hospital: \\\"\\n f\\\"{set(resident.prefs)} != {set(self.hospitals)}\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_hospital_prefs_all_reciprocated(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n resident._forget(hospital)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert resident not in hospital.prefs\",\n \"def test_check_inputs_resident_prefs_all_nonempty(game):\\n\\n resident = game.residents[0]\\n resident.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n assert resident.name in str(record[0].message)\\n\\n if game.clean:\\n assert resident not in game.residents\",\n \"def _check_hospital_matching(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n for resident in hospital.matching:\\n if resident not in hospital.prefs:\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has {resident} in their matching but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{hospital.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_hospital_prefs_all_nonempty(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def _check_mutual_preference(resident, hospital):\\n\\n return resident in hospital.prefs and hospital in resident.prefs\",\n \"def _check_resident_matching(self):\\n\\n errors = []\\n for resident in self.residents:\\n if (\\n resident.matching is not None\\n and resident.matching not in resident.prefs\\n ):\\n errors.append(\\n ValueError(\\n f\\\"{resident} is matched to {resident.matching} but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{resident.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def _check_resident_unhappy(resident, hospital):\\n\\n return resident.matching is None or resident.prefers(\\n hospital, resident.matching\\n )\",\n \"def _check_hospital_unhappy(resident, hospital):\\n\\n return len(hospital.matching) < hospital.capacity or any(\\n [hospital.prefers(resident, match) for match in hospital.matching]\\n )\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_check_inputs_supervisor_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n supervisor.prefs.remove(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"supervisors\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert supervisor.name in message\\n\\n if clean:\\n assert supervisor not in student.prefs\\n assert all(\\n project not in student.prefs for project in supervisor.projects\\n )\",\n \"def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_suitor_prefs()\\n\\n match.suitors[0].pref_names = [1, 2, 3]\\n\\n with pytest.raises(Exception):\\n match._check_suitor_prefs()\",\n \"def check_stability(self):\\n\\n blocking_pairs = []\\n for resident in self.residents:\\n for hospital in self.hospitals:\\n if (\\n _check_mutual_preference(resident, hospital)\\n and _check_resident_unhappy(resident, hospital)\\n and _check_hospital_unhappy(resident, hospital)\\n ):\\n blocking_pairs.append((resident, hospital))\\n\\n self.blocking_pairs = blocking_pairs\\n return not any(blocking_pairs)\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def test_check_inputs_supervisor_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n projects = supervisor.projects\\n for project in student.prefs:\\n if project in projects:\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"supervisors\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(supervisor.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in supervisor.prefs\\n assert all(\\n student not in project.prefs for project in supervisor.projects\\n )\",\n \"def test_check_stability():\\n\\n residents = [Player(\\\"A\\\", [1, 2]), Player(\\\"B\\\", [2]), Player(\\\"C\\\", [2, 1])]\\n hospitals = [Player(1, [\\\"C\\\", \\\"A\\\"], 3), Player(2, [\\\"A\\\", \\\"B\\\", \\\"C\\\"], 3)]\\n match = HospitalResident(residents, hospitals)\\n\\n matching = match.solve()\\n assert match.check_stability()\\n\\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\\n matching[P_1] = [P_C]\\n matching[P_2] = [P_A, P_B]\\n\\n assert not match.check_stability()\",\n \"def test_resident_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"resident\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n assert all(\\n [\\n r in set(residents)\\n for r in set(\\n [\\n r_match\\n for matches in matching.values()\\n for r_match in matches\\n ]\\n )\\n ]\\n )\\n\\n for resident in residents:\\n if resident.matching:\\n assert resident.pref_names.index(resident.matching.name) == 0\",\n \"def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"hospital\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n\\n for hospital, matches in matching.items():\\n old_idx = -np.infty\\n for resident in matches:\\n idx = hospital.pref_names.index(resident.name)\\n assert idx >= old_idx\\n old_idx = idx\",\n \"def test_check_for_unacceptable_matches_residents(game):\\n\\n resident = game.residents[0]\\n hospital = Hospital(name=\\\"foo\\\", capacity=1)\\n resident.matching = hospital\\n\\n issues = game._check_for_unacceptable_matches(\\\"residents\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(resident.name)\\n assert issue.endswith(f\\\"{resident.prefs}.\\\")\\n assert hospital.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def testHealthAssessHosp(self):\\n attr = self.session.create_visit_attr()\\n\\n self.util.boolTypeTest(self, attr, \\\"hosp\\\")\\n\\n self.util.boolPropertyTest(self, attr, \\\"hosp\\\")\",\n \"def test_check_inputs_project_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n student = project.prefs[0]\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"projects\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(project.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in project.prefs\",\n \"def test_check_inputs_hospital_capacity(game):\\n\\n hospital = game.hospitals[0]\\n capacity = hospital.capacity\\n hospital.capacity = 0\\n\\n assert hospital._original_capacity == capacity\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_capacity(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def test_check_inputs_project_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n student = project.prefs[0]\\n project._forget(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"projects\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert project.name in message\\n\\n if clean:\\n assert project not in student.prefs\",\n \"def test_inputs_reviewer_prefs(\\n resident_names, hospital_names, capacities, seed\\n):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_reviewer_prefs()\\n\\n match.reviewers[0].pref_names.pop()\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_prefs()\",\n \"def _find_homeless_mps(self):\\n mps = Person.objects.filter(\\n active=True,\\n house__name=HOUSE_OF_COMMONS,\\n constituency=None,\\n )\\n\\n self.stdout('MPs with missing constituency:')\\n for mp in mps:\\n self.stdout(f' [{mp.parliamentdotuk}] {mp.name} has no constituency')\",\n \"def test_check_for_oversubscribed_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n supervisor.matching = range(supervisor.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.capacity))\\n assert str(supervisor.matching) in e\",\n \"def _pareto_dominance(self, party1: int, party2: int, profile: list[set[int]]) -> bool:\\n strict_preference = False\\n for ballot in profile:\\n if party2 in ballot and party1 not in ballot:\\n return False\\n if party1 in ballot and party2 not in ballot:\\n strict_preference = True\\n return strict_preference\",\n \"def _check_personal_pronouns(pronoun: str, last_nouns: list) -> list:\\n pronoun_details = []\\n pronoun_lower = pronoun.lower()\\n if pronoun == 'I' or pronoun_lower in ('me', 'myself', 'my'):\\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\\n elif pronoun_lower in ('we', 'us', 'ourselves', 'our'):\\n # Find singular or plural person nouns (any gender)\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, True))\\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\\n elif pronoun_lower in ('they', 'them', 'themselves', 'their'):\\n # Give preference to persons (any gender or number)\\n noun_list = _check_criteria(pronoun_lower, last_nouns, None, None, True)\\n if noun_list:\\n pronoun_details.extend(noun_list)\\n else:\\n # Check for non-persons\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, False))\\n elif pronoun_lower in ('she', 'herself', 'her'):\\n # Find singular, feminine, person nouns\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, True, True))\\n elif pronoun_lower in ('he', 'himself', 'him'):\\n # Find singular, masculine, person nouns\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, False, True))\\n elif pronoun_lower in ('it', 'itself', 'its'):\\n # Find singular, non-person nouns (no gender)\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, None, False))\\n final_details = [] # May be duplicates in the list due to duplicates in last_nouns\\n for pronoun_detail in pronoun_details:\\n if pronoun_detail in final_details:\\n continue\\n final_details.append(pronoun_detail)\\n return final_details\",\n \"def test_check_inputs(game):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.residents == game._all_residents\\n assert game.hospitals == game._all_hospitals\",\n \"def testhospital_vs_confirmed(self):\\n data = load_covid_data(file)\\n aim_day = data['evolution']['2020-03-16']\\n # Artificial cut one value , it supposed to be 4 number\\n aim_day['epidemiology']['confirmed']['total']['age'] = [10, 11, 12]\\n try:\\n cases_population = cases_per_population_by_age(data)\\n except Exception as e:\\n raise Exception\",\n \"def checkonly(self):\\n OTHER_WSREP.append(socket.gethostbyname(socket.gethostname()))\\n for hostitem in ALL_NODES:\\n checkhost(hostitem)\\n if OTHER_WSREP:\\n for wsrepitem in OTHER_WSREP:\\n REMAINING_NODES.append(wsrepitem)\\n if REMAINING_NODES:\\n for wsrephost in OTHER_WSREP:\\n checkwsrep(wsrephost)\\n print ''\",\n \"def get_possible_preferences():\\n df = pd.read_csv(\\\"../data/restaurant_info.csv\\\")\\n foods = set(list(df[\\\"food\\\"]))\\n areas = set(list(df[\\\"area\\\"].dropna()))\\n price_ranges = set(list(df[\\\"pricerange\\\"]))\\n\\n return foods, areas, price_ranges\",\n \"def check_all_server_power_state(self, state):\\n api_data = request(\\\"get\\\", \\\"/serviceProfile\\\")\\n self.assertEqual(api_data['status'], 200,\\n 'Incorrect HTTP return code, expected 200, got:' + str(api_data['status']))\\n total_elements = 0\\n for server in api_data[\\\"json\\\"][\\\"ServiceProfile\\\"][\\\"members\\\"]:\\n if server[\\\"assoc_state\\\"] == \\\"associated\\\":\\n api_data_c = request(\\\"get\\\", \\\"/power\\\",\\n query={\\\"identifier\\\": str(server[\\\"path\\\"])})\\n self.assertEqual(api_data_c['status'], 200,\\n 'Incorrect HTTP return code, expected 200, got:' +\\n str(api_data_c['status']))\\n self.assertEqual(api_data_c[\\\"json\\\"][\\\"serverState\\\"], state,\\n 'Server ' + str(server[\\\"path\\\"]) + ' reported power state ' +\\n str(api_data_c[\\\"json\\\"][\\\"serverState\\\"]) + ' expected: ' + state)\\n total_elements += 1\\n self.assertGreater(total_elements, 0, \\\"Found zero elements\\\")\",\n \"def death_check(self):\\r\\n chance = random.random()\\r\\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\\r\\n if self.unique_id in head_of_household_list:\\r\\n try:\\r\\n head_of_household_list[self.hh_id] = 0\\r\\n except TypeError: # head of household migrated\\r\\n head_of_household_list[self.past_hh_id] = 0\\r\\n self.model.number_of_humans -= 1\\r\\n if self.unique_id in labor_list:\\r\\n labor_list.remove(self.unique_id)\\r\\n if self.work_status == 1:\\r\\n try:\\r\\n num_labor_list[self.hh_id] -= 1\\r\\n except TypeError:\\r\\n num_labor_list[self.past_hh_id] -= 1\\r\\n if self.unique_id in former_hoh_list:\\r\\n try:\\r\\n former_hoh_list[self.hh_id] = 0\\r\\n except:\\r\\n former_hoh_list[self.past_hh_id] = 0\\r\\n if [self.unique_id, self.hh_id] in single_male_list:\\r\\n single_male_list.remove([self.unique_id, self.hh_id])\\r\\n if self.unique_id in married_male_list:\\r\\n married_male_list.remove(self.unique_id)\\r\\n human_death_list.append(self.unique_id)\\r\\n try:\\r\\n hh_size_list[self.hh_id] -= 1\\r\\n except:\\r\\n hh_size_list[self.past_hh_id] -= 1\\r\\n human_demographic_structure_list[self.age_category] -= 1\\r\\n\\r\\n self.model.schedule.remove(self)\\r\\n if self in self.model.grid:\\r\\n self.model.grid.remove_agent(self)\",\n \"def test_check_stability():\\n\\n students = [Student(\\\"A\\\"), Student(\\\"B\\\"), Student(\\\"C\\\")]\\n projects = [Project(\\\"P\\\", 2), Project(\\\"Q\\\", 2)]\\n supervisors = [Supervisor(\\\"X\\\", 2), Supervisor(\\\"Y\\\", 2)]\\n\\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\\n\\n p.set_supervisor(x)\\n q.set_supervisor(y)\\n\\n a.set_prefs([p, q])\\n b.set_prefs([q])\\n c.set_prefs([q, p])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = StudentAllocation(students, projects, supervisors)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (p, q) = game.students, game.projects\\n\\n matching[p] = [c]\\n matching[q] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def validate_loans(loans):\\n def validate_loan(loan):\\n return (type(loan)==list or type(loan)==tuple) and len(loan)==3 \\\\\\n and type(loan[0])==str and type(loan[1])==str and loan[1] in database.LOANABLE_RESOURCES and type(loan[2])==int and loan[2]>=0\\n return type(loans)==list and False not in [validate_loan(load) for loan in loans]\",\n \"def test_check_ess_settings(self):\\n ess_settings1 = {'gaussian': [self.servers[0]], 'molpro': [self.servers[1], self.servers[0]],\\n 'qchem': [self.servers[0]]}\\n ess_settings2 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n ess_settings3 = {'gaussian': self.servers[0], 'molpro': [self.servers[1], self.servers[0]],\\n 'qchem': self.servers[0]}\\n ess_settings4 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n ess_settings5 = {'gaussian': 'local', 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n\\n ess_settings1 = check_ess_settings(ess_settings1)\\n ess_settings2 = check_ess_settings(ess_settings2)\\n ess_settings3 = check_ess_settings(ess_settings3)\\n ess_settings4 = check_ess_settings(ess_settings4)\\n ess_settings5 = check_ess_settings(ess_settings5)\\n\\n ess_list = [ess_settings1, ess_settings2, ess_settings3, ess_settings4, ess_settings5]\\n\\n for ess in ess_list:\\n for soft, server_list in ess.items():\\n self.assertTrue(soft in ['gaussian', 'molpro', 'qchem'])\\n self.assertIsInstance(server_list, list)\\n\\n with self.assertRaises(SettingsError):\\n ess_settings6 = {'nosoft': ['server1']}\\n check_ess_settings(ess_settings6)\\n with self.assertRaises(SettingsError):\\n ess_settings7 = {'gaussian': ['noserver']}\\n check_ess_settings(ess_settings7)\",\n \"def _check_inputs(self):\\n\\n self._check_resident_prefs()\\n self._check_hospital_prefs()\",\n \"def isProteic(self):\\n from MolKit.PDBresidueNames import AAnames\\n\\n self.AARes = [x for x in self.residues if x.type in AAnames]\\n\\n water = [x for x in self.residues if x.type in ['HOH', 'WAT']]\\n\\n if len(self.AARes) and len(self.AARes)+len(water) == len(self.residues):\\n return True\\n else:\\n return False\",\n \"def test_preferences_properties(self):\\n with pytest.raises(AssertionError):\\n self.preferences.exchange_params_by_currency_id\\n with pytest.raises(AssertionError):\\n self.preferences.utility_params_by_good_id\",\n \"def cr_check_population_availability(unit_id, table):\\n\\n T = current.T\\n tablename = table._tablename\\n\\n if tablename == \\\"cr_shelter\\\":\\n query = (table.site_id == site_id)\\n #elif tablename == \\\"cr_shelter_unit\\\":\\n else:\\n query = (table.id == unit_id)\\n\\n record = current.db(query).select(table.capacity_day,\\n table.population_day,\\n table.capacity_night,\\n table.population_night,\\n limitby = (0, 1),\\n ).first()\\n\\n day_and_night = current.deployment_settings.get_cr_day_and_night()\\n\\n warning = None\\n full_day = full_night = False\\n\\n capacity_day = record.capacity_day\\n population_day = record.population_day\\n if capacity_day is not None and \\\\\\n population_day and \\\\\\n population_day >= capacity_day:\\n full_day = True\\n\\n if day_and_night:\\n capacity_night = record.capacity_night\\n population_night = record.population_night\\n if capacity_night is not None and \\\\\\n population_night and \\\\\\n population_night >= capacity_night:\\n full_night = True\\n\\n if not day_and_night and full_day or full_day and full_night:\\n if tablename == \\\"cr_shelter\\\":\\n warning = T(\\\"Warning: this shelter is full\\\")\\n elif tablename == \\\"cr_shelter_unit\\\":\\n warning = T(\\\"Warning: this housing unit is full\\\")\\n elif full_day:\\n if tablename == \\\"cr_shelter\\\":\\n warning = T(\\\"Warning: this shelter is full for daytime\\\")\\n elif tablename == \\\"cr_shelter_unit\\\":\\n warning = T(\\\"Warning: this housing unit is full for daytime\\\")\\n elif full_night:\\n if tablename == \\\"cr_shelter\\\":\\n warning = T(\\\"Warning: this shelter is full for the night\\\")\\n elif tablename == \\\"cr_shelter_unit\\\":\\n warning = T(\\\"Warning: this housing unit is full for the night\\\")\\n\\n if warning:\\n response = current.response\\n response_warning = response.warning\\n if response_warning:\\n response.warning = \\\"%s - %s\\\" % (response_warning, warning)\\n else:\\n response.warning = warning\",\n \"def resident_optimal(residents, hospitals):\\n\\n free_residents = residents[:]\\n while free_residents:\\n\\n resident = free_residents.pop()\\n hospital = resident.get_favourite()\\n\\n match_pair(resident, hospital)\\n\\n if len(hospital.matching) > hospital.capacity:\\n worst = hospital.get_worst_match()\\n unmatch_pair(worst, hospital)\\n free_residents.append(worst)\\n\\n if len(hospital.matching) == hospital.capacity:\\n successors = hospital.get_successors()\\n for successor in successors:\\n delete_pair(hospital, successor)\\n if not successor.prefs:\\n free_residents.remove(successor)\\n\\n return {r: r.matching for r in hospitals}\",\n \"def testNoSpecialties(self):\\n self.failUnlessEqual(self.person.getSpecialties(), [])\",\n \"def check_all_neighbors_present_local(duthosts, per_host, asic, neighbors, all_cfg_facts,\\n nbrhosts, nbr_macs, check_nbr_state=True):\\n cfg_facts = all_cfg_facts[per_host.hostname][asic.asic_index]['ansible_facts']\\n neighs = cfg_facts['BGP_NEIGHBOR']\\n\\n fail_cnt = 0\\n\\n # Grab dumps of the asicdb, appdb, voqdb, and arp table\\n asicdb = AsicDbCli(asic)\\n asic_dump = asicdb.dump_neighbor_table()\\n\\n appdb = AppDbCli(asic)\\n app_dump = appdb.dump_neighbor_table()\\n\\n encaps = {}\\n\\n if per_host.is_multi_asic:\\n arptable = per_host.switch_arptable(namespace=asic.namespace)['ansible_facts']\\n else:\\n arptable = per_host.switch_arptable()['ansible_facts']\\n\\n if len(duthosts.supervisor_nodes) == 1:\\n voqdb = VoqDbCli(duthosts.supervisor_nodes[0])\\n voq_dump = voqdb.dump_neighbor_table()\\n elif per_host.is_multi_asic:\\n # look on linecard for pizzabox multiasic\\n voqdb = VoqDbCli(per_host)\\n voq_dump = voqdb.dump_neighbor_table()\\n else:\\n voq_dump = {}\\n\\n for neighbor in neighbors:\\n nbr_vm = get_vm_with_ip(neighbor, nbrhosts)\\n neigh_mac = nbr_macs[nbr_vm['vm']][nbr_vm['port']]\\n local_ip = neighs[neighbor]['local_addr']\\n local_port = get_port_by_ip(cfg_facts, local_ip)\\n\\n sysport_info = {'slot': cfg_facts['DEVICE_METADATA']['localhost']['hostname'],\\n 'asic': cfg_facts['DEVICE_METADATA']['localhost']['asic_name']}\\n\\n # Validate the asic db entries\\n for entry in asic_dump:\\n matchstr = '\\\"%s\\\",' % neighbor\\n if matchstr in entry:\\n\\n if neigh_mac.lower() != asic_dump[entry]['value']['SAI_NEIGHBOR_ENTRY_ATTR_DST_MAC_ADDRESS'].lower():\\n logger.error(\\\"Asic neighbor macs for %s do not match: %s != %s\\\", neighbor, neigh_mac.lower(),\\n asic_dump[entry]['value']['SAI_NEIGHBOR_ENTRY_ATTR_DST_MAC_ADDRESS'].lower())\\n fail_cnt += 1\\n else:\\n logger.debug(\\\"Asic neighbor macs for %s match: %s == %s\\\", neighbor, neigh_mac.lower(),\\n asic_dump[entry]['value']['SAI_NEIGHBOR_ENTRY_ATTR_DST_MAC_ADDRESS'].lower())\\n encaps[neighbor] = asic_dump[entry]['value']['SAI_NEIGHBOR_ENTRY_ATTR_ENCAP_INDEX']\\n break\\n else:\\n logger.error(\\\"Did not find neighbor in asictable for IP: %s\\\" % neighbor)\\n fail_cnt += 1\\n\\n # Validate the APP db entries\\n for entry in app_dump:\\n matchstr = ':%s' % neighbor\\n if entry.endswith(matchstr):\\n if neigh_mac.lower() != app_dump[entry]['value']['neigh'].lower():\\n logger.error(\\\"App neighbor macs for %s do not match: %s != %s\\\", neighbor, neigh_mac.lower(),\\n app_dump[entry]['value']['neigh'].lower())\\n fail_cnt += 1\\n else:\\n logger.debug(\\\"App neighbor macs for %s match: %s == %s\\\", neighbor, neigh_mac.lower(),\\n app_dump[entry]['value']['neigh'].lower())\\n\\n pytest_assert(\\\":{}:\\\".format(local_port) in entry, \\\"Port for %s does not match\\\" % entry)\\n break\\n else:\\n logger.error(\\\"Did not find neighbor in app for IP: %s\\\" % neighbor)\\n fail_cnt += 1\\n\\n # Validate the arp table entries\\n if check_nbr_state:\\n check_host_arp_table(per_host, asic, neighbor, neigh_mac, local_port, 'REACHABLE', arptable=arptable)\\n else:\\n check_host_arp_table(per_host, asic, neighbor, neigh_mac, local_port, None, arptable=arptable)\\n\\n # supervisor checks\\n for entry in voq_dump:\\n if entry.endswith('|%s' % neighbor) or entry.endswith(':%s' % neighbor):\\n\\n if \\\"portchannel\\\" in local_port.lower():\\n slotname = cfg_facts['DEVICE_METADATA']['localhost']['hostname']\\n asicname = cfg_facts['DEVICE_METADATA']['localhost']['asic_name']\\n else:\\n slotname = sysport_info['slot']\\n asicname = sysport_info['asic']\\n\\n logger.debug(\\\"Neigh key: %s, slotnum: %s\\\", entry, slotname)\\n pytest_assert(\\\"|%s|\\\" % slotname in entry,\\n \\\"Slot for %s does not match %s\\\" % (entry, slotname))\\n pytest_assert(\\\"|%s:\\\" % local_port in entry or \\\"|%s|\\\" % local_port in entry,\\n \\\"Port for %s does not match %s\\\" % (entry, local_port))\\n pytest_assert(\\\"|%s|\\\" % asicname in entry,\\n \\\"Asic for %s does not match %s\\\" % (entry, asicname))\\n\\n pytest_assert(voq_dump[entry]['value']['neigh'].lower() == neigh_mac.lower(),\\n \\\"Voq: neighbor: %s mac does not match: %s\\\" %\\n (neighbor, voq_dump[entry]['value']['neigh'].lower()))\\n pytest_assert(voq_dump[entry]['value']['encap_index'].lower() == encaps[neighbor],\\n \\\"Voq: encap: %s mac does not match: %s\\\" %\\n (neighbor, voq_dump[entry]['value']['encap_index'].lower()))\\n break\\n else:\\n logger.error(\\\"Neighbor: %s on slot: %s, asic: %s not present in voq\\\",\\n neighbor, sysport_info['slot'], sysport_info['asic'])\\n fail_cnt += 1\\n\\n logger.info(\\\"Local %s/%s and chassisdb neighbor validation of %s is successful (mac: %s, idx: %s)\\\",\\n per_host.hostname, asic.asic_index, neighbor, neigh_mac, encaps[neighbor])\\n\\n return {'encaps': encaps, 'fail_cnt': fail_cnt}\",\n \"def seesSuggestions(self):\\n return self.floor.owner == self.user and self.floor.permissiveness == \\\"permissive\\\"\",\n \"def _acceptable(self, team):\\r\\n current = [c for c in self.configurations if self._validateNoSpies(c, team)]\\r\\n return bool(len(current) > 0)\",\n \"def hospital_optimal(hospitals):\\n\\n free_hospitals = hospitals[:]\\n while free_hospitals:\\n\\n hospital = free_hospitals.pop()\\n resident = hospital.get_favourite()\\n\\n if resident.matching:\\n curr_match = resident.matching\\n unmatch_pair(resident, curr_match)\\n if curr_match not in free_hospitals:\\n free_hospitals.append(curr_match)\\n\\n match_pair(resident, hospital)\\n if len(hospital.matching) < hospital.capacity and [\\n res for res in hospital.prefs if res not in hospital.matching\\n ]:\\n free_hospitals.append(hospital)\\n\\n successors = resident.get_successors()\\n for successor in successors:\\n delete_pair(resident, successor)\\n if (\\n not [\\n res\\n for res in successor.prefs\\n if res not in successor.matching\\n ]\\n and successor in free_hospitals\\n ):\\n free_hospitals.remove(successor)\\n\\n return {r: r.matching for r in hospitals}\",\n \"def test_check_for_unacceptable_matches_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n student = Student(name=\\\"foo\\\")\\n supervisor.matching.append(student)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.prefs))\\n assert student.name in error\",\n \"def hospital_resident(residents, hospitals, optimal=\\\"resident\\\"):\\n\\n if optimal == \\\"resident\\\":\\n return resident_optimal(residents, hospitals)\\n if optimal == \\\"hospital\\\":\\n return hospital_optimal(hospitals)\",\n \"def _list_validity_check(l, valid_l):\\n\\n if not Settings._is_in_list(l, valid_l):\\n raise InvalidSettingError()\",\n \"def check_hint_condition(self, hxml_set, student_answers):\\r\\n pass\",\n \"def _validateSpies(self, config, team, sabotaged):\\r\\n spies = [s for s in team if s in self.getSpies(config)]\\r\\n \\\"\\\"\\\"If there are more spies in our config than the number of sabotages made \\r\\n then return True, because this config is compatible with the sabotages made. \\r\\n Otherwise it is not compatible, so return False.\\\"\\\"\\\"\\r\\n return len(spies) >= sabotaged\",\n \"def test_pre_assigned(self):\\n shift1 = RegularWorkshift.objects.create(\\n workshift_type=self.wtype1,\\n pool=self.p1,\\n hours=5,\\n )\\n shift2 = RegularWorkshift.objects.create(\\n workshift_type=self.wtype3,\\n pool=self.p1,\\n hours=1,\\n )\\n shift2.current_assignees = [self.profile]\\n\\n unfinished = utils.auto_assign_shifts(self.semester)\\n self.assertEqual([self.profile], unfinished)\\n self.assertNotIn(self.profile, shift1.current_assignees.all())\\n\\n pool_hours = self.profile.pool_hours.get(pool=self.p1)\\n self.assertEqual(\\n pool_hours.assigned_hours,\\n 1,\\n )\",\n \"def test_homeland_defense(self):\\n self.conf[\\\"game\\\"][\\\"homeland_defense\\\"] = \\\"100/50/25\\\"\\n self.assertEqual(None, self.sapphire.owner)\\n battle = self.battle\\n sess = self.sess\\n\\n # Skirmish 1\\n s1 = battle.create_skirmish(self.alice, 10) # Attack 10\\n s1a = s1.react(self.carol, 4, hinder=False) # --Support 4\\n s1a.react(self.bob, 3) # ----Attack 3\\n s1.react(self.dave, 8) # --Attack 8\\n # Winner will be team orangered, 11 VP\\n\\n # Skirmish 2\\n battle.create_skirmish(self.bob, 5) # Attack 5\\n # Winner will be team periwinkle, 10 VP for unopposed\\n\\n # End fight\\n self.battle.ends = self.battle.begins\\n sess.commit()\\n self.assert_(battle.past_end_time())\\n\\n updates = Battle.update_all(sess, conf=self.conf)\\n sess.commit()\\n\\n # Overall winner would ordinarily be orangered, but this should get\\n # modified by the homeland defense buffs - Sapphire is right next to\\n # the test capital, and so will get an extra 50%\\n self.assertEqual(battle.victor, 1)\\n self.assertEqual(battle.score1, 15)\\n # OR should get 25% bonus\\n self.assertEqual(battle.score0, 13)\",\n \"def test_check_full_house_false(self):\\n not_full_house_fixtures = [[1, 1, 1, 1, 2],\\n [2, 1, 1, 1, 1],\\n [1, 1, 2, 2, 3],\\n [1, 2, 3, 4, 5],\\n ]\\n\\n for fixture in not_full_house_fixtures:\\n score = self.roll.check_full_house(fixture)\\n\\n self.assertNotEqual(score, 25)\\n self.assertEqual(score, 0)\\n self.assertEqual(len(fixture), 5)\",\n \"def test_positive_electrode_potential_profile(self):\\n\\n # TODO: add these when have averages\",\n \"def check_site_equiv(conn, general_site_type_pkey, specific_site_type_pkey):\\n cur = conn.cursor()\\n\\n cur.execute(\\n \\\"SELECT name FROM site_pin WHERE site_type_pkey = ?\\\",\\n (general_site_type_pkey, )\\n )\\n general_site_pins = set(name for (name, ) in cur)\\n\\n cur.execute(\\n \\\"SELECT name FROM site_pin WHERE site_type_pkey = ?\\\",\\n (specific_site_type_pkey, )\\n )\\n for (name, ) in cur:\\n assert name in general_site_pins, (\\n general_site_pins, name, specific_site_type_pkey\\n )\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def power_off_all_server_profiles():\\n FusionUIBase.navigate_to_section(SectionType.SERVER_PROFILES, time_for_loading=5)\\n profile_name_list = CommonOperationServerProfile.get_server_profile_list()\\n\\n total = len(profile_name_list)\\n already_off_or_not_supported = 0\\n powered_off = 0\\n\\n for n, profile_name in enumerate(profile_name_list):\\n logger.info(\\\"{2} No: {0} --- Total: {1} {2}\\\".format((n + 1), total, '-' * 14))\\n logger.info(\\\"powering off a server profile named '%s'\\\" % profile_name)\\n CommonOperationServerProfile.click_server_profile(profile_name=profile_name, time_for_loading=4)\\n # check if already powered off\\n FusionUIBase.select_view_by_name(view_name='General', timeout=5, fail_if_false=False)\\n if VerifyServerProfile.verify_general_server_power(expect_value='On', timeout=5, fail_if_false=False) is False:\\n logger.warn(\\\"power state of server profile '%s' is not 'On' (it's Off, or not supported due to being managed by another system), 'Power Off' action is unavailable.\\\" % profile_name)\\n already_off_or_not_supported += 1\\n else:\\n if power_off_server_profile_by_name(profile_name) is False:\\n logger.warn(\\\"server profile '%s' is NOT powered off successfully\\\" % profile_name)\\n continue\\n else:\\n powered_off += 1\\n\\n logger.info(\\\"{0} == Summary == {0}\\\".format('-' * 14))\\n if total - already_off_or_not_supported == 0:\\n logger.warn(\\\"no server profile to power off! all %s server profile(s) is NOT applicable to power off (already powered off, or unknown power state), test is considered PASS\\\" % already_off_or_not_supported)\\n return True\\n else:\\n if powered_off < total:\\n logger.warn(\\\"not all of the server profile(s) is successfully powered off - %s out of %s powered off \\\" % (powered_off, total))\\n if powered_off + already_off_or_not_supported == total:\\n logger.warn(\\\"%s already-off-or-not-supported server profile(s) is skipped being powered off, test is considered PASS\\\" % already_off_or_not_supported)\\n return True\\n else:\\n logger.warn(\\\"%s already-off-or-not-supported server profile(s) is skipped being powered off, \\\"\\n \\\"%s server profile(s) left is failed being powered off \\\" % (already_off_or_not_supported, total - powered_off - already_off_or_not_supported))\\n return False\\n\\n logger.info(\\\"all of the server profile(s) is successfully powered off - %s out of %s \\\" % (powered_off, total))\\n return True\",\n \"def negative_test_for_check_local_storage(profile_obj):\\n status = True\\n for profile in profile_obj:\\n elements_test_list = [profile.emptyChar, profile.invalidChars]\\n\\n \\\"\\\"\\\"\\n Setting invalid values for: \\\"Name\\\" and \\\"Number of physical drivers\\\"\\n \\\"\\\"\\\"\\n for element in elements_test_list:\\n # Fill Logical Drive form\\n if __fill_logical_drive_form(element):\\n # Click Logical Drive Create button\\n if ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_BTN_CREATE_LOGICAL_DRIVE_FORM):\\n if element == '':\\n # Check warning messages\\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_NAME_FORM) and __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\\n logger.warn(\\\"At least one of the warning messages was NOT found \\\")\\n status = False\\n\\n else:\\n # Check warning messages\\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\\n logger.warn(\\\"Warning message NOT found\\\")\\n status = False\\n else:\\n logger.warn(\\\"Logical Drive Form 'Button' returns an error. Please, check the test report for more details\\\")\\n status = False\\n else:\\n logger.warn(\\\"Logical Drive Form returns an error. Please, check the test report for more details\\\")\\n status = False\\n\\n # Click 'Cancel' form button\\n if not ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_CANCEL_LOCAL_STORAGE_FORM):\\n logger.warn(\\\"Cancel button element NOT found\\\")\\n status = False\\n\\n return status\",\n \"def is_unhappy(self):\\n #checked!#\\n ###your code here###\\n same=0\\n for i in self.home.neighbors:\\n if i.occupant!=None:\\n if i.occupant.group==self.group:\\n same+=1\\n happniess=float(same)/len(self.home.neighbors)\\n if happniess<self.happiness_threshold:\\n return True\\n else:\\n return False\",\n \"def test_verification_status_invisible(self):\\r\\n self.client.login(username=\\\"jack\\\", password=\\\"test\\\")\\r\\n self.check_verification_status_off('verified', 'You\\\\'re enrolled as a verified student')\\r\\n self.check_verification_status_off('honor', 'You\\\\'re enrolled as an honor code student')\\r\\n self.check_verification_status_off('audit', 'You\\\\'re auditing this course')\",\n \"def check_risk(self):\\r\\n gms = self.cleaned_data.get(\\\"gms\\\", [])\\r\\n risk = self.cleaned_data.get(\\\"risk\\\", RPEvent.NORMAL_RISK)\\r\\n if not any(\\r\\n gm\\r\\n for gm in gms\\r\\n if gm.player.is_staff or gm.player.check_permstring(\\\"builders\\\")\\r\\n ):\\r\\n if risk != RPEvent.NORMAL_RISK:\\r\\n self.add_error(\\r\\n \\\"risk\\\",\\r\\n \\\"Risk cannot be altered without a staff member as GM. Set to: %r\\\"\\r\\n % risk,\\r\\n )\",\n \"def get_everyone_denied(self):\",\n \"def testAssistantDoesNotGetUserPrefRole(self):\\n self._testAssistantOwnershipAfter(task=\\\"create\\\")\\n self.failIf('def456' in self.person.users_with_local_role('User Preferences Editor'), \\\"Assistant can edit user prefs after create\\\")\\n self.simulateATGUIInteraction(task=\\\"edit\\\")\\n self.failIf('def456' in self.person.users_with_local_role('User Preferences Editor'), \\\"Assistant can edit user prefs after edit\\\")\",\n \"def test_calculates_risk_points_with_one_house(self):\\n\\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\\n\\n for house in self.risk_profile[\\\"home\\\"]:\\n self.assertEquals(house[\\\"risk_points\\\"], 2)\",\n \"def test_open_no_setup(restaurant_only, hall_only, kitchen_only, delivery_only):\\n # Here checks not all variants, cause restaurant_only is not isolated\\n # object. They were previously check and working alongside\\n # but affects result if together.\\n\\n # no setups\\n with pytest.raises(CustomWarning):\\n restaurant_only.open()\\n assert restaurant_only.is_working is False, \\\"You need to setup Kitchen, Delivery and Hall\\\"\\n\\n # only kitchen\\n with pytest.raises(CustomWarning):\\n restaurant_only.set_kitchen(kitchen_only)\\n restaurant_only.open()\\n assert restaurant_only.is_working is False, \\\"You need to setup Kitchen, Delivery and Hall\\\"\\n\\n # only delivery and kitchen\\n with pytest.raises(CustomWarning):\\n restaurant_only.set_delivery(delivery_only)\\n restaurant_only.set_kitchen(kitchen_only)\\n restaurant_only.open()\\n assert restaurant_only.is_working is False, \\\"You need to setup Kitchen, Delivery and Hall\\\"\",\n \"def test_anvil_first_patient_everything(caplog, anvil_server, anvil_research_studies_with_observations):\\n research_studies = {r.relativePath(): r for r in anvil_research_studies_with_observations if\\n r.__class__.__name__ == 'ResearchStudy'}\\n resource_counts = _validate_patient_everything(caplog, anvil_server,\\n [research_studies['ResearchStudy/1000G-high-coverage-2019']])\\n assert False, resource_counts\",\n \"def healthcare():\",\n \"def checks(self, all=False):\\n if all:\\n warn_states = [\\\"unknown\\\", \\\"passing\\\", \\\"warning\\\", \\\"critical\\\"]\\n else:\\n warn_states = [\\\"unknown\\\", \\\"warning\\\", \\\"critical\\\"]\\n checks = {}\\n for warn_state in warn_states:\\n for state in self.consul.health.state(warn_state):\\n if not state['Node'] in checks:\\n checks[state['Node']] = dict()\\n if not state['ServiceID'] in checks[state['Node']]:\\n checks[state['Node']][state['ServiceID']] = {\\n 'checks': [],\\n 'name': state['ServiceName']\\n }\\n checks[state['Node']][state['ServiceID']]['checks'].append(\\n (state['Name'], state['Status'], state['Output'])\\n )\\n return checks\",\n \"def violated(self) -> bool:\\n ...\",\n \"def check_items(self, items):\\n for item in items:\\n if item['user'] in self.required_list and item['user'] not in self.approved_required_list:\\n if item['result'] is True or item['result'] == 'approved':\\n logger.info(\\\"%s approved\\\", item['user'])\\n self.approved_required_list.append(item['user'])\\n else:\\n logger.info(\\\"%s declined\\\", item['user'])\\n error_msg = \\\"Since {} is a required member for approval, this approval step fails. \\\".format(item['user'])\\n logger.error(error_msg)\\n self.exit(rc=2, detail=error_msg)\\n if item['user'] in self.optional_list and item['user'] not in self.approved_optional_list:\\n if item['result'] is True or item['result'] == 'approved':\\n logger.info(\\\"%s approved\\\", item['user'])\\n if item['user'] in self.declined_optional_list:\\n self.declined_optional_list.remove(item['user'])\\n self.approved_optional_list.append(item['user'])\\n else:\\n logger.info(\\\"%s declined\\\", item['user'])\\n self.declined_optional_list.append(item['user'])\\n if len(self.declined_optional_list) >= (len(self.optional_list) - self.number_optional):\\n error_msg = \\\"Not be able to fulfill requirement that {} optional approvals, since {} declined request.\\\".format(\\n self.number_optional, self.declined_optional_list)\\n logger.error(error_msg)\\n self.exit(rc=2, detail=error_msg)\\n\\n if len(self.approved_required_list) >= len(self.required_list) \\\\\\n and len(self.approved_optional_list) >= self.number_optional:\\n logger.info(\\\"Approval requirements are fully met. Exit gracefully.\\\")\\n self.exit(0)\",\n \"def _checkstate(self, queryset):\\n for presup in queryset.filter(estado='borrador').exclude(fecha_realizado=None):\\n if ((presup.fecha_realizado + timedelta(days=21) < datetime.now().date() and presup.tipo != 'asistencia') or\\n (presup.fecha_realizado + timedelta(days=60) < datetime.now().date() and presup.tipo == 'asistencia')):\\n presup._toState_cancelado(obs=\\\"Registro automático: Presupuesto vencido\\\")\",\n \"def check_proof(self):\\n for gene in self.population:\\n if gene.is_proof:\\n print(gene.chromosome)\\n for state in gene.coq_states:\\n print(state)\\n self.proofs.append(Gene(chromosome=gene.valid_tactics))\",\n \"def test_tally_no_candidates(self):\\n self.init_elect_types()\\n\\n userA = models.User(\\n name = \\\"UserA\\\",\\n email = \\\"userA@eLect.com\\\",\\n password = \\\"asdf\\\")\\n\\n session.add(userA)\\n session.commit()\\n\\n electionA = models.Election(\\n title = \\\"Election A\\\",\\n admin_id = userA.id)\\n\\n session.add(electionA)\\n session.commit()\\n\\n raceA = models.Race(\\n title = \\\"Race A\\\",\\n election_id = electionA.id\\n )\\n\\n session.add(raceA)\\n session.commit()\\n\\n with self.assertRaises(NoCandidates):\\n self.wta.check_race(raceA.id)\\n\\n with self.assertRaises(NoCandidates):\\n self.proportional.check_race(raceA.id)\\n\\n with self.assertRaises(NoCandidates):\\n self.schulze.check_race(raceA.id)\",\n \"def isStable(couples,mPref,wPref) -> bool:\\n couples_dict = {woman:man for man,woman in couples}\\n \\n for m,w in couples:\\n\\n # w's (m's partner) rank in m's preference list\\n w_rank = mPref[m].index(w)\\n \\n # list of all women that m prefers over w\\n preferred_over_w = mPref[m][:w_rank]\\n \\n\\n for hotter in preferred_over_w:\\n\\n # get each woman's current partner (in preferred list)\\n hotters_man = couples_dict[hotter]\\n\\n # if she prefers m over her current partner then marriage is not stable\\n if wPref[hotter].index(m) < wPref[hotter].index(hotters_man): \\n msg = \\\"{}'s marriage to {} is unstable:\\\\n\\\\t\\\" + \\\\\\n \\\"{} prefers {} > {} (his current wife)\\\\n\\\\t\\\" +\\\\\\n \\\"{} prefers {} > {} (her current husband)\\\"\\n print(msg.format(m, w, m, hotter, w, hotter, m, hotters_man) )\\n return False\\n return True\",\n \"def check_puzzle_unicity(puzzle_list: list) -> None:\\n if len(set(puzzle_list)) != len(puzzle_list):\\n raise ParsingError(\\\"Puzzle numbers must be unique.\\\")\",\n \"def _full_check_and_validate(self, p_map):\\n if PitchConstraintSolver.pmap_full(p_map):\\n for p in self._policies:\\n if not p.verify(p_map):\\n return False\\n return True\\n else:\\n return False\",\n \"def test_statuses_exist(self):\\n\\n assert hasattr(Partner, 'AVAILABLE')\\n assert hasattr(Partner, 'NOT_AVAILABLE')\\n assert hasattr(Partner, 'WAITLIST')\\n\\n assert hasattr(Partner, 'STATUS_CHOICES')\\n\\n assert len(Partner.STATUS_CHOICES) == 3\\n\\n database_statuses = [x[0] for x in Partner.STATUS_CHOICES]\\n\\n assert Partner.AVAILABLE in database_statuses\\n assert Partner.NOT_AVAILABLE in database_statuses\\n assert Partner.WAITLIST in database_statuses\",\n \"def test_ap_hs20_roaming_partner_preference(dev, apdev):\\n params = hs20_ap_params()\\n params['domain_name'] = \\\"roaming.example.org\\\"\\n hostapd.add_ap(apdev[0]['ifname'], params)\\n\\n params = hs20_ap_params()\\n params['ssid'] = \\\"test-hs20-other\\\"\\n params['domain_name'] = \\\"roaming.example.net\\\"\\n hostapd.add_ap(apdev[1]['ifname'], params)\\n\\n logger.info(\\\"Verify default vs. specified preference\\\")\\n values = default_cred()\\n values['roaming_partner'] = \\\"roaming.example.net,1,127,*\\\"\\n policy_test(dev[0], apdev[1], values, only_one=False)\\n values['roaming_partner'] = \\\"roaming.example.net,1,129,*\\\"\\n policy_test(dev[0], apdev[0], values, only_one=False)\\n\\n logger.info(\\\"Verify partial FQDN match\\\")\\n values['roaming_partner'] = \\\"example.net,0,0,*\\\"\\n policy_test(dev[0], apdev[1], values, only_one=False)\\n values['roaming_partner'] = \\\"example.net,0,255,*\\\"\\n policy_test(dev[0], apdev[0], values, only_one=False)\",\n \"def factor_in_multiple_professors(self):\\n professors = [professor for professor in self.course.professors if professor.lower() != \\\"none\\\"]\\n number_professors = len(set(professors))\\n if number_professors > 1:\\n self.score = self.score + number_professors\",\n \"def how_eligible(essay):\\n eligibility_requirements = ['?', '\\\"', ',', '!']\\n return len(set(filter((lambda x: x in eligibility_requirements), essay)))\",\n \"def lookup(self,user_preferences):\\n res = list()\\n \\n fit_area = set()\\n fit_price = set()\\n fit_food = set()\\n \\n if user_preferences[0] == \\\"any\\\" or user_preferences[0] == 0:\\n fit_area = set(range(len(self.area)))\\n else:\\n for i,a in enumerate(self.area):\\n if a == user_preferences[0]:\\n fit_area.add(i)\\n if user_preferences[1] == \\\"any\\\" or user_preferences[1] == 0:\\n fit_price = set(range(len(self.price_range)))\\n else:\\n for j,p in enumerate(self.price_range):\\n if p == user_preferences[1]:\\n fit_price.add(j)\\n if user_preferences[2] == \\\"any\\\" or user_preferences[2] == 0:\\n fit_food = set(range(len(self.food_types)))\\n else:\\n for k,f in enumerate(self.food_types):\\n if f == user_preferences[2]:\\n fit_food.add(k)\\n option_numbers = fit_area.intersection(fit_price, fit_food)\\n if option_numbers:\\n for i in option_numbers:\\n res.append(self.restaurant_names[i])\\n \\n return res\",\n \"def test_half_life_unstable_isotopes():\\n for isotope in data_about_isotopes:\\n if (\\n \\\"half_life\\\" not in data_about_isotopes[isotope]\\n and not data_about_isotopes[isotope]\\n ):\\n with pytest.raises(MissingParticleDataError):\\n half_life(isotope)\",\n \"def test_auto_assign_two_preferred(self):\\n shift1 = RegularWorkshift.objects.create(\\n workshift_type=self.wtype1,\\n pool=self.p1,\\n hours=5,\\n )\\n shift2 = RegularWorkshift.objects.create(\\n workshift_type=self.wtype2,\\n pool=self.p1,\\n hours=5,\\n )\\n unfinished = utils.auto_assign_shifts(self.semester)\\n self.assertEqual([], unfinished)\\n self.assertIn(self.profile, shift1.current_assignees.all())\\n self.assertNotIn(self.profile, shift2.current_assignees.all())\\n\\n instances = WorkshiftInstance.objects.filter(weekly_workshift=shift1)\\n self.assertGreater(instances.count(), 0)\\n self.assertTrue(all(\\n instance.workshifter == self.profile\\n for instance in instances\\n ))\\n\\n instances = WorkshiftInstance.objects.filter(weekly_workshift=shift2)\\n self.assertGreater(instances.count(), 0)\\n self.assertTrue(all(\\n instance.workshifter is None\\n for instance in instances\\n ))\\n\\n pool_hours = self.profile.pool_hours.get(pool=self.p1)\\n self.assertEqual(\\n pool_hours.assigned_hours,\\n pool_hours.hours,\\n )\",\n \"def __check_all_config_values_set(cfg: __Config):\\n unset_values = [key for key, val in cfg.__dict__.items() if val is None]\\n if len(unset_values) > 0:\\n print(f\\\"WARNING: The following config variables have not been set: {unset_values}\\\")\",\n \"def feasible(individual):\\n val=0;\\n for i in individual:\\n if viajes[val][6]==True and False==vehiculos_esp[i]:\\n return False\\n val+=1\\n return True\",\n \"def test_for_criteria(self):\\n ignore = ['interpreter_method', 'average_by_sample_or_site', 'include_nrm']\\n values = ([dic['value'] for dic in self.acceptance_criteria.values() if (dic['criterion_name'] not in ignore and dic['value'] != -999)])\\n return values\",\n \"def _assert_problem_is_valid(self) -> None:\\n # Will throw error if any of these are not set\\n specified = self.ignore + self.silence + self.target\\n if not all(\\n [\\n pr in specified\\n for pr in self.observer.photoreceptors\\n if pr is not None\\n ]\\n ):\\n raise SilSubProblemError(\\n \\\"At least one photoreceptor is not accounted for by the \\\"\\n + \\\"*ignore*, *silence* and *target* properties.\\\"\\n )\",\n \"def check_neighbors_are_gone(duthosts, all_cfg_facts, per_host, asic, neighbors):\\n asicdb = AsicDbCli(asic)\\n appdb = AppDbCli(asic)\\n\\n # Check that the arp entry is deleted for all the neighbors on all the asics.\\n poll_neighbor_table_delete(duthosts, neighbors, poll_time=30)\\n\\n asicdb_neigh_table = asicdb.dump_neighbor_table()\\n app_neigh_table = appdb.dump_neighbor_table()\\n voqdb = VoqDbCli(duthosts.supervisor_nodes[0])\\n voq_dump = voqdb.dump_neighbor_table()\\n\\n for neighbor in neighbors:\\n logger.info(\\\"Checking neighbor entry for %s is deleted from host: %s, asic: %s\\\",\\n neighbor, per_host.hostname, asic.asic_index)\\n\\n for entry in list(asicdb_neigh_table.keys()):\\n search = '\\\"ip\\\":\\\"%s\\\"' % neighbor\\n if search in entry:\\n raise AssertionError(\\\"Found neighbor %s in asicdb: %s\\\", search, entry)\\n\\n for entry in list(app_neigh_table.keys()):\\n if entry.endswith(\\\":\\\" + neighbor):\\n raise AssertionError(\\\"Found neighbor %s in app: %s\\\", neighbor, entry)\\n\\n # check supervisor\\n for entry in list(voq_dump.keys()):\\n if entry.endswith(\\\":\\\" + neighbor):\\n raise AssertionError(\\\"Found neighbor %s in voq: %s\\\", neighbor, entry)\\n\\n # check remote hosts\\n for rem_host in duthosts.frontend_nodes:\\n for rem_asic in rem_host.asics:\\n if rem_host == per_host and rem_asic == asic:\\n # skip remote check on local host\\n continue\\n for neighbor in neighbors:\\n logger.info(\\\"Remote host checks of ARP/NDP for %s are deleted from host: %s, asic: %s\\\", neighbor,\\n rem_host.hostname,\\n rem_asic.asic_index)\\n rem_cfg_facts = all_cfg_facts[rem_host.hostname][rem_asic.asic_index]['ansible_facts']\\n remote_inband_info = get_inband_info(rem_cfg_facts)\\n if remote_inband_info == {}:\\n logger.info(\\\"No inband configuration on this ASIC: %s/%s, skipping\\\", rem_host.hostname,\\n rem_asic.asic_index)\\n continue\\n asicdb = AsicDbCli(rem_asic)\\n appdb = AppDbCli(rem_asic)\\n asicdb_neigh_table = asicdb.dump_neighbor_table()\\n app_neigh_table = appdb.dump_neighbor_table()\\n\\n for entry in list(asicdb_neigh_table.keys()):\\n search = '\\\"ip\\\":\\\"%s\\\"' % neighbor\\n if search in entry:\\n raise AssertionError(\\\"Found neighbor %s in asicdb: %s\\\", search, entry)\\n\\n for entry in list(app_neigh_table.keys()):\\n if entry.endswith(\\\":\\\" + neighbor):\\n raise AssertionError(\\\"Found neighbor %s in app: %s\\\", neighbor, entry)\\n\\n check_neighbor_kernel_route(rem_host, rem_asic.asic_index, neighbor, remote_inband_info['port'],\\n present=False)\",\n \"def verify_can_edit_server_profile_general_info_when_server_power_on(profile_obj):\\n\\n FusionUIBase.navigate_to_section(SectionType.SERVER_PROFILES, time_for_loading=5)\\n\\n total = len(profile_obj)\\n not_exists = 0\\n edited = 0\\n\\n for n, profile in enumerate(profile_obj):\\n logger.info(\\\"{2} No: {0} --- Total: {1} {2}\\\".format((n + 1), total, '-' * 14))\\n logger.info(\\\"editing a server profile with name '%s' ...\\\" % profile.name)\\n # checking if the profile is not existing for editing\\n if not VerifyServerProfile.verify_server_profile_exist(profile.name, fail_if_false=False):\\n logger.warn(\\\"server profile '%s' does not exist\\\" % profile.name)\\n not_exists += 1\\n continue\\n CommonOperationServerProfile.click_server_profile(profile.name)\\n\\n EditServerProfile.select_action_edit()\\n EditServerProfile.wait_edit_server_profile_dialog_shown()\\n BuiltIn().sleep(2)\\n EditServerProfile.input_name(profile.newName) if getattr(profile, 'newName', None) is not None else None\\n EditServerProfile.input_description(profile.desc) if getattr(profile, 'desc', None) is not None else None\\n\\n # Server hardware must be \\\"power\\\" on status\\n if not VerifyServerProfile.is_power_on_error_visible_when_edit_server_profile(profile.server, 10):\\n logger.warn(\\\"Server hardware '%s' is not 'Powered on, please power on it\\\" % profile.server)\\n continue\\n\\n sht_selected = EditServerProfile.get_selected_server_hardware_type(profile.server)\\n if getattr(profile, 'hardwareType', None) is not None:\\n if profile.hardwareType not in sht_selected:\\n logger.warn(\\\"server hardware type '%s' of server '%s' is NOT consistent with test data '%s'\\\" % (sht_selected, profile.server, profile.hardwareType))\\n EditServerProfile.ChangeServerHardwareTypeAndEnclosureGroup.change_server_hardware_type(profile.hardwareType, timeout=5, fail_if_false=False)\\n\\n eg_selected = EditServerProfile.get_selected_enclosure_group(profile.server)\\n if profile.enclgroup not in eg_selected:\\n logger.warn(\\\"enclosure group '%s' of server '%s' is NOT consistent with test data '%s'\\\" % (eg_selected, profile.server, profile.enclgroup))\\n EditServerProfile.ChangeServerHardwareTypeAndEnclosureGroup.change_enclosure_group(profile.enclgroup, timeout=5, fail_if_false=False)\\n\\n # EditServerProfile.input_select_server_hardware(profile.server, auto_power_off=False)\\n\\n if getattr(profile, 'Affinity', None) is not None:\\n logger.info(\\\"test data for 'Affinity' is found: <%s>, start setting Affinity ...\\\" % profile.Affinity)\\n EditServerProfile.select_affinity_by_text(profile.Affinity)\\n\\n if getattr(profile, 'Firmware', None) is not None:\\n logger.info(\\\"test data for 'Firmware' is found: <%s>, start setting Firmware Baseline ...\\\" % profile.Firmware)\\n logger.debug(\\\"test data for 'Firmware' is found: <%s>\\\" % profile.Firmware, also_console=False)\\n # set Firmware Baseline and force-installation option\\n CommonOperationServerProfile.Firmware.set(profile.Firmware)\\n\\n if getattr(profile, 'Connections', None) is not None:\\n logger.debug(\\\"test data for 'Connections' is found: <%s>\\\" % profile.Connections, also_console=False)\\n logger.info(\\\"test data for 'Connections' is found, start adding connections ...\\\")\\n logger.warn(\\\"Only connection name is allowed to modification\\\")\\n # add connections\\n CommonOperationServerProfile.Connection().set(profile.Connections)\\n\\n if getattr(profile, 'LocalStorage', None) is not None:\\n logger.warn(\\\"Modify the 'BootSettings' section will return error when server power on, so ignore this setting\\\")\\n\\n if getattr(profile, 'SANStorage', None) is not None:\\n logger.warn(\\\"Modify the 'BootSettings' section will return error when server power on, so ignore this setting\\\")\\n\\n if getattr(profile, 'BootSettings', None) is not None:\\n logger.warn(\\\"Modify the 'BootSettings' section will return error when server power on, so ignore this setting\\\")\\n\\n if getattr(profile, 'Advanced', None) is not None:\\n logger.warn(\\\"Modify the 'Advanced' section will return error when server power on, so ignore this setting\\\")\\n\\n EditServerProfile.click_ok_button()\\n if CommonOperationServerProfile.BootSettings.get_error_message_from_boot_mode() is not None:\\n logger.warn(\\\"test data may be wrongly defined for 'Boot mode', which caused an error that blocks profile being created. \\\"\\n \\\"test will skip this profile '%s' and continue to create other server profiles\\\" % profile.name)\\n continue\\n\\n BuiltIn().sleep(2)\\n status, msg = FusionUIBase.get_error_message_from_dialog(timeout=10)\\n if status is True:\\n logger.warn(\\\"unexpected error occurred: %s\\\" % msg)\\n ui_lib.fail_test(msg)\\n\\n EditServerProfile.wait_edit_server_profile_dialog_disappear(timeout=300)\\n FusionUIBase.show_activity_sidebar()\\n profile_name = profile.newName if getattr(profile, 'newName', None) is not None else profile.name\\n FusionUIBase.wait_activity_action_ok(profile_name, 'Update', timeout=300, fail_if_false=True)\\n FusionUIBase.show_activity_sidebar()\\n CommonOperationServerProfile.wait_server_profile_status_ok(profile_name, timeout=300, fail_if_false=True)\\n logger.info(\\\"edited server profile '%s' successfully\\\" % profile_name)\\n edited += 1\\n\\n logger.info(\\\"{0} == Summary == {0}\\\".format('-' * 14))\\n if total - not_exists == 0:\\n logger.warn(\\\"no server profile to edit! all %s server profile(s) is NOT existing, test is considered FAILED\\\" % not_exists)\\n return False\\n else:\\n if edited < total:\\n logger.warn(\\\"not all of the server profile(s) is successfully edited - %s out of %s edited \\\" % (edited, total))\\n if edited + not_exists == total:\\n logger.warn(\\\"%s not-existing server profile(s) is skipped being edited, test is considered FAILED\\\" % not_exists)\\n return False\\n else:\\n logger.warn(\\\"%s not-existing server profile(s) is skipped being edited, %s profile(s) left is failed being edited \\\" % (not_exists, total - edited - not_exists))\\n return False\\n\\n logger.info(\\\"all of the server profile(s) is successfully edited - %s out of %s \\\" % (edited, total))\\n return True\",\n \"def check_relevant_forms(L):\\n L2 = list()\\n for F in L.values():\\n # needed_ev=(\\n S = F.shim_corr\\n # print \\\"S=\\\",S\\n ok_ev = 0\\n for g in S:\\n if g.atkin_lehner_eigenvalue() == -1:\\n ok_ev = ok_ev + 1\\n if ok_ev > 0:\\n print(\\\"Number of ok forms on \\\", F.space.WR.N, \\\" :\\\", ok_ev)\\n F.list_coefficents('D', fd=True, neg=False, nmin=0, nmax=1000, latex=False, nd=50, prime=True)\\n L2.append(F)\\n return L2\",\n \"def _check_approval_update(self, state):\\n\\t\\tcurrent_employee = self.env['hr.employee'].search([('user_id', '=', self.env.uid)], limit=1)\\n\\t\\t# is_officer = self.env.user.has_group('hr_holidays.group_hr_holidays_user')\\n\\t\\tis_manager = self.env.user.has_group('hr_holidays.group_hr_holidays_manager')\\n\\t\\tfor holiday in self:\\n\\t\\t\\tval_type = holiday.holiday_status_id.validation_type\\n\\t\\t\\tif state == 'confirm':\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tif state == 'draft':\\n\\t\\t\\t\\tif holiday.employee_id != current_employee and not is_manager:\\n\\t\\t\\t\\t\\traise UserError(_('Only a Leave Manager can reset other people leaves.'))\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t# if not is_officer:\\n\\t\\t\\t# \\traise UserError(_('Only a Leave Officer or Manager can approve or refuse leave requests.'))\\n\\n\\t\\t\\t# if is_officer:\\n\\t\\t\\t# \\t# use ir.rule based first access check: department, members, ... (see security.xml)\\n\\t\\t\\tholiday.check_access_rule('write')\\n\\n\\t\\t\\tif holiday.employee_id == current_employee and not is_manager:\\n\\t\\t\\t\\traise UserError(_('Only a Leave Manager can approve its own requests.'))\\n\\n\\t\\t\\tif (state == 'validate1' and val_type == 'both') or (state == 'validate' and val_type == 'manager'):\\n\\t\\t\\t\\tmanager = holiday.employee_id.parent_id or holiday.employee_id.department_id.manager_id\\n\\t\\t\\t\\tif (manager and manager != current_employee) and not self.env.user.has_group('hr_holidays.group_hr_holidays_manager'):\\n\\t\\t\\t\\t\\traise UserError(_('You must be either %s\\\\'s manager or Leave manager to approve this leave') % (holiday.employee_id.name))\\n\\n\\t\\t\\tif state == 'validate' and val_type == 'both':\\n\\t\\t\\t\\tif not self.env.user.has_group('hr_holidays.group_hr_holidays_manager'):\\n\\t\\t\\t\\t\\traise UserError(_('Only an Leave Manager can apply the second approval on leave requests.'))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76461375","0.7579265","0.75171703","0.75103134","0.7466917","0.71831375","0.70516497","0.7038354","0.64109606","0.62843776","0.6155221","0.6151795","0.608665","0.6074421","0.59615344","0.5961042","0.587259","0.5870157","0.5802495","0.5735709","0.5553829","0.5484349","0.5469888","0.5390564","0.53609776","0.53537416","0.5333613","0.53064215","0.52196866","0.51940364","0.51776034","0.51393","0.510626","0.5063039","0.49437043","0.49292797","0.49129447","0.49004573","0.48984492","0.48936072","0.48919055","0.4873328","0.48636937","0.4863515","0.4860488","0.48596987","0.48429814","0.48298532","0.48278052","0.48272103","0.4819877","0.48013103","0.47993138","0.4792817","0.47885016","0.4760007","0.47595707","0.4754753","0.4753547","0.4752549","0.47380126","0.47365642","0.4730235","0.4730235","0.4730235","0.47268856","0.47249925","0.47233915","0.47229192","0.47201234","0.47193515","0.47013754","0.4697544","0.46966517","0.46926358","0.46897772","0.4683583","0.46810442","0.468059","0.46805835","0.46793053","0.4678678","0.46763536","0.46756396","0.46714514","0.46690187","0.46674392","0.46665326","0.46656317","0.466073","0.4650567","0.4650151","0.46477643","0.46465993","0.46465266","0.46462038","0.4643783","0.46383956","0.46343705","0.46306664"],"string":"[\n \"0.76461375\",\n \"0.7579265\",\n \"0.75171703\",\n \"0.75103134\",\n \"0.7466917\",\n \"0.71831375\",\n \"0.70516497\",\n \"0.7038354\",\n \"0.64109606\",\n \"0.62843776\",\n \"0.6155221\",\n \"0.6151795\",\n \"0.608665\",\n \"0.6074421\",\n \"0.59615344\",\n \"0.5961042\",\n \"0.587259\",\n \"0.5870157\",\n \"0.5802495\",\n \"0.5735709\",\n \"0.5553829\",\n \"0.5484349\",\n \"0.5469888\",\n \"0.5390564\",\n \"0.53609776\",\n \"0.53537416\",\n \"0.5333613\",\n \"0.53064215\",\n \"0.52196866\",\n \"0.51940364\",\n \"0.51776034\",\n \"0.51393\",\n \"0.510626\",\n \"0.5063039\",\n \"0.49437043\",\n \"0.49292797\",\n \"0.49129447\",\n \"0.49004573\",\n \"0.48984492\",\n \"0.48936072\",\n \"0.48919055\",\n \"0.4873328\",\n \"0.48636937\",\n \"0.4863515\",\n \"0.4860488\",\n \"0.48596987\",\n \"0.48429814\",\n \"0.48298532\",\n \"0.48278052\",\n \"0.48272103\",\n \"0.4819877\",\n \"0.48013103\",\n \"0.47993138\",\n \"0.4792817\",\n \"0.47885016\",\n \"0.4760007\",\n \"0.47595707\",\n \"0.4754753\",\n \"0.4753547\",\n \"0.4752549\",\n \"0.47380126\",\n \"0.47365642\",\n \"0.4730235\",\n \"0.4730235\",\n \"0.4730235\",\n \"0.47268856\",\n \"0.47249925\",\n \"0.47233915\",\n \"0.47229192\",\n \"0.47201234\",\n \"0.47193515\",\n \"0.47013754\",\n \"0.4697544\",\n \"0.46966517\",\n \"0.46926358\",\n \"0.46897772\",\n \"0.4683583\",\n \"0.46810442\",\n \"0.468059\",\n \"0.46805835\",\n \"0.46793053\",\n \"0.4678678\",\n \"0.46763536\",\n \"0.46756396\",\n \"0.46714514\",\n \"0.46690187\",\n \"0.46674392\",\n \"0.46665326\",\n \"0.46656317\",\n \"0.466073\",\n \"0.4650567\",\n \"0.4650151\",\n \"0.46477643\",\n \"0.46465993\",\n \"0.46465266\",\n \"0.46462038\",\n \"0.4643783\",\n \"0.46383956\",\n \"0.46343705\",\n \"0.46306664\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7821524"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93789,"cells":{"query":{"kind":"string","value":"Test each hospital has only residents in its preference list. If not, check that a warning is caught and the hospitals's preferences are changed."},"document":{"kind":"string","value":"def test_check_inputs_hospital_prefs_all_residents(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert hospital.prefs == []"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.append(\n ValueError(\n f\"{hospital} has not ranked all the residents that \"\n f\"ranked it: {set(hospital.prefs)} != \"\n f\"{set(residents_that_ranked)}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_hospital_reciprocated_all_prefs(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n hospital._forget(resident)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"hospitals\", \"residents\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert hospital not in resident.prefs","def test_check_inputs_resident_prefs_all_hospitals(game):\n\n resident = game.residents[0]\n resident.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert resident.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert resident.prefs == []","def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert resident not in hospital.prefs","def _check_resident_prefs(self):\n\n errors = []\n for resident in self.residents:\n if not set(resident.prefs).issubset(set(self.hospitals)):\n errors.append(\n ValueError(\n f\"{resident} has ranked a non-hospital: \"\n f\"{set(resident.prefs)} != {set(self.hospitals)}\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_resident_prefs_all_nonempty(game):\n\n resident = game.residents[0]\n resident.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n assert resident.name in str(record[0].message)\n\n if game.clean:\n assert resident not in game.residents","def test_check_inputs_hospital_prefs_all_nonempty(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def _check_hospital_matching(self):\n\n errors = []\n for hospital in self.hospitals:\n for resident in hospital.matching:\n if resident not in hospital.prefs:\n errors.append(\n ValueError(\n f\"{hospital} has {resident} in their matching but \"\n \"they do not appear in their preference list: \"\n f\"{hospital.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def _check_mutual_preference(resident, hospital):\n\n return resident in hospital.prefs and hospital in resident.prefs","def test_check_inputs_supervisor_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n supervisor.prefs.remove(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"supervisors\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert supervisor.name in message\n\n if clean:\n assert supervisor not in student.prefs\n assert all(\n project not in student.prefs for project in supervisor.projects\n )","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def _check_resident_matching(self):\n\n errors = []\n for resident in self.residents:\n if (\n resident.matching is not None\n and resident.matching not in resident.prefs\n ):\n errors.append(\n ValueError(\n f\"{resident} is matched to {resident.matching} but \"\n \"they do not appear in their preference list: \"\n f\"{resident.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_supervisor_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n projects = supervisor.projects\n for project in student.prefs:\n if project in projects:\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"supervisors\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(supervisor.name)\n assert student.name in message\n\n if clean:\n assert student not in supervisor.prefs\n assert all(\n student not in project.prefs for project in supervisor.projects\n )","def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_suitor_prefs()\n\n match.suitors[0].pref_names = [1, 2, 3]\n\n with pytest.raises(Exception):\n match._check_suitor_prefs()","def _check_hospital_unhappy(resident, hospital):\n\n return len(hospital.matching) < hospital.capacity or any(\n [hospital.prefers(resident, match) for match in hospital.matching]\n )","def _check_resident_unhappy(resident, hospital):\n\n return resident.matching is None or resident.prefers(\n hospital, resident.matching\n )","def test_check_stability():\n\n residents = [Player(\"A\", [1, 2]), Player(\"B\", [2]), Player(\"C\", [2, 1])]\n hospitals = [Player(1, [\"C\", \"A\"], 3), Player(2, [\"A\", \"B\", \"C\"], 3)]\n match = HospitalResident(residents, hospitals)\n\n matching = match.solve()\n assert match.check_stability()\n\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\n matching[P_1] = [P_C]\n matching[P_2] = [P_A, P_B]\n\n assert not match.check_stability()","def check_stability(self):\n\n blocking_pairs = []\n for resident in self.residents:\n for hospital in self.hospitals:\n if (\n _check_mutual_preference(resident, hospital)\n and _check_resident_unhappy(resident, hospital)\n and _check_hospital_unhappy(resident, hospital)\n ):\n blocking_pairs.append((resident, hospital))\n\n self.blocking_pairs = blocking_pairs\n return not any(blocking_pairs)","def test_check_inputs_project_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n student = project.prefs[0]\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"projects\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(project.name)\n assert student.name in message\n\n if clean:\n assert student not in project.prefs","def test_check_inputs_project_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n student = project.prefs[0]\n project._forget(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"projects\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert project.name in message\n\n if clean:\n assert project not in student.prefs","def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_resident_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"resident\")\n\n assert set(hospitals) == set(matching.keys())\n assert all(\n [\n r in set(residents)\n for r in set(\n [\n r_match\n for matches in matching.values()\n for r_match in matches\n ]\n )\n ]\n )\n\n for resident in residents:\n if resident.matching:\n assert resident.pref_names.index(resident.matching.name) == 0","def test_check_inputs_hospital_capacity(game):\n\n hospital = game.hospitals[0]\n capacity = hospital.capacity\n hospital.capacity = 0\n\n assert hospital._original_capacity == capacity\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_capacity(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def isProteic(self):\n from MolKit.PDBresidueNames import AAnames\n\n self.AARes = [x for x in self.residues if x.type in AAnames]\n\n water = [x for x in self.residues if x.type in ['HOH', 'WAT']]\n\n if len(self.AARes) and len(self.AARes)+len(water) == len(self.residues):\n return True\n else:\n return False","def test_check_for_unacceptable_matches_residents(game):\n\n resident = game.residents[0]\n hospital = Hospital(name=\"foo\", capacity=1)\n resident.matching = hospital\n\n issues = game._check_for_unacceptable_matches(\"residents\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(resident.name)\n assert issue.endswith(f\"{resident.prefs}.\")\n assert hospital.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def get_possible_preferences():\n df = pd.read_csv(\"../data/restaurant_info.csv\")\n foods = set(list(df[\"food\"]))\n areas = set(list(df[\"area\"].dropna()))\n price_ranges = set(list(df[\"pricerange\"]))\n\n return foods, areas, price_ranges","def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"hospital\")\n\n assert set(hospitals) == set(matching.keys())\n\n for hospital, matches in matching.items():\n old_idx = -np.infty\n for resident in matches:\n idx = hospital.pref_names.index(resident.name)\n assert idx >= old_idx\n old_idx = idx","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def _pareto_dominance(self, party1: int, party2: int, profile: list[set[int]]) -> bool:\n strict_preference = False\n for ballot in profile:\n if party2 in ballot and party1 not in ballot:\n return False\n if party1 in ballot and party2 not in ballot:\n strict_preference = True\n return strict_preference","def _find_homeless_mps(self):\n mps = Person.objects.filter(\n active=True,\n house__name=HOUSE_OF_COMMONS,\n constituency=None,\n )\n\n self.stdout('MPs with missing constituency:')\n for mp in mps:\n self.stdout(f' [{mp.parliamentdotuk}] {mp.name} has no constituency')","def test_inputs_reviewer_prefs(\n resident_names, hospital_names, capacities, seed\n):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_reviewer_prefs()\n\n match.reviewers[0].pref_names.pop()\n\n with pytest.raises(Exception):\n match._check_reviewer_prefs()","def check_relevant_forms(L):\n L2 = list()\n for F in L.values():\n # needed_ev=(\n S = F.shim_corr\n # print \"S=\",S\n ok_ev = 0\n for g in S:\n if g.atkin_lehner_eigenvalue() == -1:\n ok_ev = ok_ev + 1\n if ok_ev > 0:\n print(\"Number of ok forms on \", F.space.WR.N, \" :\", ok_ev)\n F.list_coefficents('D', fd=True, neg=False, nmin=0, nmax=1000, latex=False, nd=50, prime=True)\n L2.append(F)\n return L2","def isResidueAssigned(residue):\n\n for atom in residue.atoms:\n if atom.atomSet:\n if atom.atomSet.resonanceSets:\n return True\n \n return False","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def check_primers(self):\n primer_keys=['sequence','description']\n if self.data.has_key('primer_dict'):\n for primer in self.data['primer_dict'].keys():\n for key in primer_keys:\n if not self.data['primer_dict'][primer].has_key(key):\n self.data['primer_dict'][primer][key]='None'","def test_check_stability():\n\n students = [Student(\"A\"), Student(\"B\"), Student(\"C\")]\n projects = [Project(\"P\", 2), Project(\"Q\", 2)]\n supervisors = [Supervisor(\"X\", 2), Supervisor(\"Y\", 2)]\n\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\n\n p.set_supervisor(x)\n q.set_supervisor(y)\n\n a.set_prefs([p, q])\n b.set_prefs([q])\n c.set_prefs([q, p])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = StudentAllocation(students, projects, supervisors)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (p, q) = game.students, game.projects\n\n matching[p] = [c]\n matching[q] = [a, b]\n\n assert not game.check_stability()","def _check_inputs(self):\n\n self._check_resident_prefs()\n self._check_hospital_prefs()","def _check_personal_pronouns(pronoun: str, last_nouns: list) -> list:\n pronoun_details = []\n pronoun_lower = pronoun.lower()\n if pronoun == 'I' or pronoun_lower in ('me', 'myself', 'my'):\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\n elif pronoun_lower in ('we', 'us', 'ourselves', 'our'):\n # Find singular or plural person nouns (any gender)\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, True))\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\n elif pronoun_lower in ('they', 'them', 'themselves', 'their'):\n # Give preference to persons (any gender or number)\n noun_list = _check_criteria(pronoun_lower, last_nouns, None, None, True)\n if noun_list:\n pronoun_details.extend(noun_list)\n else:\n # Check for non-persons\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, False))\n elif pronoun_lower in ('she', 'herself', 'her'):\n # Find singular, feminine, person nouns\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, True, True))\n elif pronoun_lower in ('he', 'himself', 'him'):\n # Find singular, masculine, person nouns\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, False, True))\n elif pronoun_lower in ('it', 'itself', 'its'):\n # Find singular, non-person nouns (no gender)\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, None, False))\n final_details = [] # May be duplicates in the list due to duplicates in last_nouns\n for pronoun_detail in pronoun_details:\n if pronoun_detail in final_details:\n continue\n final_details.append(pronoun_detail)\n return final_details","def check_hint_condition(self, hxml_set, student_answers):\r\n pass","def validate_loans(loans):\n def validate_loan(loan):\n return (type(loan)==list or type(loan)==tuple) and len(loan)==3 \\\n and type(loan[0])==str and type(loan[1])==str and loan[1] in database.LOANABLE_RESOURCES and type(loan[2])==int and loan[2]>=0\n return type(loans)==list and False not in [validate_loan(load) for loan in loans]","def feasible(individual):\n val=0;\n for i in individual:\n if viajes[val][6]==True and False==vehiculos_esp[i]:\n return False\n val+=1\n return True","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def _filter_satisfied(self, update_setd=False):\n\n model = self.oracle.get_model()\n setd = set()\n\n for i, cl in enumerate(self.soft):\n if not self.satc[i]:\n if self._satisfied(cl, model):\n self.satc[i] = True\n self.ss_assumps.append(self.sels[i])\n else:\n setd = setd.union(set(cl))\n\n if update_setd:\n self.setd = list(setd)","def isStable(couples,mPref,wPref) -> bool:\n couples_dict = {woman:man for man,woman in couples}\n \n for m,w in couples:\n\n # w's (m's partner) rank in m's preference list\n w_rank = mPref[m].index(w)\n \n # list of all women that m prefers over w\n preferred_over_w = mPref[m][:w_rank]\n \n\n for hotter in preferred_over_w:\n\n # get each woman's current partner (in preferred list)\n hotters_man = couples_dict[hotter]\n\n # if she prefers m over her current partner then marriage is not stable\n if wPref[hotter].index(m) < wPref[hotter].index(hotters_man): \n msg = \"{}'s marriage to {} is unstable:\\n\\t\" + \\\n \"{} prefers {} > {} (his current wife)\\n\\t\" +\\\n \"{} prefers {} > {} (her current husband)\"\n print(msg.format(m, w, m, hotter, w, hotter, m, hotters_man) )\n return False\n return True","def _check_all_valence_terms_assigned(\n handler,\n assigned_terms,\n topology,\n valence_terms,\n):\n if len(assigned_terms) == len(valence_terms):\n return\n\n # Convert the valence term to a valence dictionary to make sure\n # the order of atom indices doesn't matter for comparison.\n valence_terms_dict = assigned_terms.__class__()\n for atoms in valence_terms:\n atom_indices = (topology.atom_index(a) for a in atoms)\n valence_terms_dict[atom_indices] = atoms\n\n # Check that both valence dictionaries have the same keys (i.e. terms).\n assigned_terms_set = set(assigned_terms.keys())\n valence_terms_set = set(valence_terms_dict.keys())\n unassigned_terms = valence_terms_set.difference(assigned_terms_set)\n not_found_terms = assigned_terms_set.difference(valence_terms_set)\n\n # Raise an error if there are unassigned terms.\n err_msg = \"\"\n\n if len(unassigned_terms) > 0:\n unassigned_atom_tuples = []\n\n unassigned_str = \"\"\n for unassigned_tuple in unassigned_terms:\n unassigned_str += \"\\n- Topology indices \" + str(unassigned_tuple)\n unassigned_str += \": names and elements \"\n\n unassigned_atoms = []\n\n # Pull and add additional helpful info on missing terms\n for atom_idx in unassigned_tuple:\n atom = topology.atom(atom_idx)\n unassigned_atoms.append(atom)\n unassigned_str += f\"({atom.name} {atom.symbol}), \"\n unassigned_atom_tuples.append(tuple(unassigned_atoms))\n err_msg += (\n \"{parameter_handler} was not able to find parameters for the following valence terms:\\n\"\n \"{unassigned_str}\"\n ).format(\n parameter_handler=handler.__class__.__name__,\n unassigned_str=unassigned_str,\n )\n if len(not_found_terms) > 0:\n if err_msg != \"\":\n err_msg += \"\\n\"\n not_found_str = \"\\n- \".join([str(x) for x in not_found_terms])\n err_msg += (\n \"{parameter_handler} assigned terms that were not found in the topology:\\n\"\n \"- {not_found_str}\"\n ).format(\n parameter_handler=handler.__class__.__name__,\n not_found_str=not_found_str,\n )\n if err_msg:\n err_msg += \"\\n\"\n\n if isinstance(handler, BondHandler):\n exception_class = UnassignedBondError\n elif isinstance(handler, AngleHandler):\n exception_class = UnassignedAngleError\n elif isinstance(handler, (ProperTorsionHandler, ImproperTorsionHandler)):\n exception_class = UnassignedTorsionError\n else:\n raise RuntimeError(\n f\"Could not find an exception class for handler {handler}\",\n )\n\n exception = exception_class(err_msg)\n exception.unassigned_topology_atom_tuples = unassigned_atom_tuples\n exception.handler_class = handler.__class__\n raise exception","def check_proof(self):\n for gene in self.population:\n if gene.is_proof:\n print(gene.chromosome)\n for state in gene.coq_states:\n print(state)\n self.proofs.append(Gene(chromosome=gene.valid_tactics))","def listErrors(individual): #TODO: add other errors implemented elsewhere\n\n results = [birthBeforeMarriage(individual), birthBeforeDeath(individual), marriageBeforeDeath(individual), \n datesBeforeCurrentDate(individual), noBigamy(individual)]\n results = [x for x in results if x is not None]\n return not all(results)","def testhospital_vs_confirmed(self):\n data = load_covid_data(file)\n aim_day = data['evolution']['2020-03-16']\n # Artificial cut one value , it supposed to be 4 number\n aim_day['epidemiology']['confirmed']['total']['age'] = [10, 11, 12]\n try:\n cases_population = cases_per_population_by_age(data)\n except Exception as e:\n raise Exception","def test_preferences_properties(self):\n with pytest.raises(AssertionError):\n self.preferences.exchange_params_by_currency_id\n with pytest.raises(AssertionError):\n self.preferences.utility_params_by_good_id","def testConditionReasons(self):\n \n state = State.from_problem(self.prob)\n\n relevantVars = []\n drive = self.dom.get_action(\"drive\")\n with drive.instantiate([\"agent\", \"tru1\", \"apt1\"], self.prob):\n self.assert_(state.is_satisfied(drive.precondition, relevantVars))\n\n relevantVars = set(relevantVars)\n \n s1 = StateVariable(self.prob.functions[\"city-of\"][0], [self.prob[\"pos1\"]])\n s2 = StateVariable(self.prob.functions[\"city-of\"][0], [self.prob[\"apt1\"]])\n s3 = StateVariable(self.prob.functions[\"location-of\"][0], [self.prob[\"tru1\"]])\n \n self.assertEqual(len(relevantVars), 3)\n self.assert_(s1 in relevantVars)\n self.assert_(s2 in relevantVars)\n self.assert_(s3 in relevantVars)","def _clean_politician_set(self, cleaned_data, geoname_ids):\n\n field_name = 'politician_set'\n charge_ids = [int(elem) for elem in cleaned_data[field_name].strip('|').split('|')]\n charge_ids_copy = list(charge_ids)\n\n if charge_ids:\n\n politician_ids = []\n\n for cityrep_id in geoname_ids:\n if not len(charge_ids_copy):\n break\n found_ids, pol_ids = self.get_politicians_from_cityrep(\n charge_ids_copy,\n cityrep_id\n )\n for pol_id in pol_ids:\n politician_ids.append(pol_id)\n #print(\"\\ncityrep_id: %s\\ncharge_ids_copy: %s\\nfound_ids: %s\\n\" % (cityrep_id, charge_ids_copy, found_ids))\n for charge_id in found_ids:\n charge_ids_copy.remove(charge_id)\n \n if len(charge_ids_copy):\n raise exceptions.ValidationError(u\"Non tutti i politici sono stati recuperati. Sono rimasti fuori i politici con id: %s\" % charge_ids_copy)\n\n lookup = get_lookup(MAP_FIELD_NAME_TO_CHANNEL[field_name])\n values = lookup.get_objects(politician_ids)\n\n else:\n values = []\n\n return values","def testProtractedNSESanityChecks(self):\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))","def test_anvil_first_patient_everything(caplog, anvil_server, anvil_research_studies_with_observations):\n research_studies = {r.relativePath(): r for r in anvil_research_studies_with_observations if\n r.__class__.__name__ == 'ResearchStudy'}\n resource_counts = _validate_patient_everything(caplog, anvil_server,\n [research_studies['ResearchStudy/1000G-high-coverage-2019']])\n assert False, resource_counts","def get_sellability_report(melons):\n\n # Fill in the rest \n for melon in melons:\n\n if melon.is_sellable():\n sellability = \"CAN BE SOLD\"\n else:\n sellability = \"NOT SELLABLE\"\n\n print(\"Harvested by {} from Field {} ({})\".format(melon.harvester,melon.field,sellability))","def check_primal_and_dual_solutions(self):\r\n\r\n # Solve primal and dual models\r\n self.primal.solve_model(self.m_p)\r\n self.dual.solve_model(self.m_d)\r\n\r\n # Elements to check\r\n elements = {\r\n 'power - existing thermal':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_THERMAL', 'is_dual': True}\r\n },\r\n 'power - existing wind':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_WIND', 'is_dual': True}\r\n },\r\n 'power - existing solar':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_SOLAR', 'is_dual': True}\r\n },\r\n 'power - existing hydro':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_HYDRO', 'is_dual': True}\r\n },\r\n # 'charging power - existing storage':\r\n # {\r\n # 'primal': {'id': 'p_in', 'is_dual': False},\r\n # 'dual': {'id': 'CHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\r\n # },\r\n # 'discharging power - existing storage':\r\n # {\r\n # 'primal': {'id': 'p_out', 'is_dual': False},\r\n # 'dual': {'id': 'DISCHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\r\n # },\r\n 'power - candidate thermal':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_THERMAL', 'is_dual': True}\r\n },\r\n 'power - candidate wind':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_WIND', 'is_dual': True}\r\n },\r\n 'power - candidate solar':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_SOLAR', 'is_dual': True}\r\n },\r\n 'charging power - candidate storage':\r\n {\r\n 'primal': {'id': 'p_in', 'is_dual': False},\r\n 'dual': {'id': 'CHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\r\n },\r\n 'discharging power - candidate storage':\r\n {\r\n 'primal': {'id': 'p_out', 'is_dual': False},\r\n 'dual': {'id': 'DISCHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\r\n },\r\n 'locational marginal prices':\r\n {\r\n 'primal': {'id': 'POWER_BALANCE', 'is_dual': True},\r\n 'dual': {'id': 'lamb', 'is_dual': False}\r\n },\r\n 'load shedding':\r\n {\r\n 'primal': {'id': 'p_V', 'is_dual': False},\r\n 'dual': {'id': 'LOAD_SHEDDING_POWER', 'is_dual': True}\r\n },\r\n }\r\n\r\n print(f\"Checking primal and dual solutions\")\r\n for el_id, e in elements.items():\r\n # Absolute, non-zero and max absolute difference\r\n absolute_diff, non_zero_diff, max_abs_diff = self.compare_primal_and_dual_components(e)\r\n\r\n print('---------------------------------------------------------------------------')\r\n print(f\"Primal component: {e['primal']['id']}, Dual component: {e['dual']['id']}')\")\r\n print(f'Total keys: {len(absolute_diff)}')\r\n print(f\"Max absolute difference: {max_abs_diff}\")\r\n print(f\"Non-zero diff: {non_zero_diff}\")","def testAxiomReasoning(self):\n \n state = State.from_problem(self.prob)\n extstate, reasons, universalReasons = state.get_extended_state(getReasons=True)\n\n relevantVars = []\n relevantReplanVars = []\n load = self.dom.get_action(\"a_load\")\n with load.instantiate([\"agent\", \"obj11\", \"tru1\"], self.prob):\n self.assert_(extstate.is_satisfied(load.precondition, relevantVars))\n self.assert_(extstate.is_satisfied(load.replan, relevantReplanVars))\n\n all_reasons = set(relevantVars)\n for v in relevantVars:\n all_reasons |= reasons[v]\n\n all_replan_reasons = set(relevantReplanVars)\n for v in relevantReplanVars:\n all_replan_reasons |= reasons[v]\n \n s1 = StateVariable(self.prob.functions[\"location-of\"][0], [self.prob[\"tru1\"]])\n s2 = StateVariable(self.prob.functions[\"location-of\"][0], [self.prob[\"obj11\"]])\n\n self.assert_(s1 in all_reasons)\n self.assert_(s2 in all_reasons)\n self.assertFalse(s1 in all_replan_reasons)\n self.assert_(s2 in all_replan_reasons)","def violated(self) -> bool:\n ...","def forwardcheck(var, val, assignment, user_dict):\n\t\tif curr_domains:\n\t\t\tfor (meal, restaurant) in curr_deleted[var]:\n\t\t\t\tcurr_domains[meal].append(restaurant)\n\t\t\tcurr_deleted[var] = []\n\n\t\t\tfor meal in neighbors[var]:\n\t\t\t\tif meal not in assignment:\n\t\t\t\t\tfor restaurant in curr_domains[meal][:]:\n\t\t\t\t\t\tnum_cats = count_categories(assignment.values())\n\t\t\t\t\t\tif not constraints_match(num_cats, user_dict):\n\t\t\t\t\t\t#if not user_solution_checker(user_dict, meal, restaurant, assignment):\n\t\t\t\t\t\t\tcurr_domains[meal].remove(restaurant)\n\t\t\t\t\t\t\tcurr_deleted[var].append((meal, restaurant))","def cleanse_priest_list(priests_list):","def factor_in_multiple_professors(self):\n professors = [professor for professor in self.course.professors if professor.lower() != \"none\"]\n number_professors = len(set(professors))\n if number_professors > 1:\n self.score = self.score + number_professors","def check_all(self):\n return self.check_rs() + self.check_hgvs()","def test_check_for_oversubscribed_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n supervisor.matching = range(supervisor.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.capacity))\n assert str(supervisor.matching) in e","def test_open_no_setup(restaurant_only, hall_only, kitchen_only, delivery_only):\n # Here checks not all variants, cause restaurant_only is not isolated\n # object. They were previously check and working alongside\n # but affects result if together.\n\n # no setups\n with pytest.raises(CustomWarning):\n restaurant_only.open()\n assert restaurant_only.is_working is False, \"You need to setup Kitchen, Delivery and Hall\"\n\n # only kitchen\n with pytest.raises(CustomWarning):\n restaurant_only.set_kitchen(kitchen_only)\n restaurant_only.open()\n assert restaurant_only.is_working is False, \"You need to setup Kitchen, Delivery and Hall\"\n\n # only delivery and kitchen\n with pytest.raises(CustomWarning):\n restaurant_only.set_delivery(delivery_only)\n restaurant_only.set_kitchen(kitchen_only)\n restaurant_only.open()\n assert restaurant_only.is_working is False, \"You need to setup Kitchen, Delivery and Hall\"","def _check_primary(self, old_list):\n try:\n self._get_primary(self._elements)\n except PrimaryElementViolation:\n self._elements = copy.copy(old_list)\n raise","def testNoSpecialties(self):\n self.failUnlessEqual(self.person.getSpecialties(), [])","def test_for_criteria(self):\n ignore = ['interpreter_method', 'average_by_sample_or_site', 'include_nrm']\n values = ([dic['value'] for dic in self.acceptance_criteria.values() if (dic['criterion_name'] not in ignore and dic['value'] != -999)])\n return values","def _check_evidence_output(out):\n list_out = [\"evidence_code\", \"evidence_category\"]\n if out not in list_out:\n raise ValueError(\"{} not in {}\".format(out, list_out))","def _check_family(self):\n for (s, (b, c)), (cond, ref) in families.items():\n if s != self.SYMBOL or len(b) != self._.d:\n continue\n vars = tuple(set(sum(map(variables, b + c), ())))\n sols = _solve([SR(l) == r for l, r\n in zip(self._.b[:-1] + self._.c[1:], b + c)],\n vars)\n if any(checkConditions(cond, sol) for sol in sols\n if is_integral(sol)):\n raise InfeasibleError(refs=ref)","def cr_check_population_availability(unit_id, table):\n\n T = current.T\n tablename = table._tablename\n\n if tablename == \"cr_shelter\":\n query = (table.site_id == site_id)\n #elif tablename == \"cr_shelter_unit\":\n else:\n query = (table.id == unit_id)\n\n record = current.db(query).select(table.capacity_day,\n table.population_day,\n table.capacity_night,\n table.population_night,\n limitby = (0, 1),\n ).first()\n\n day_and_night = current.deployment_settings.get_cr_day_and_night()\n\n warning = None\n full_day = full_night = False\n\n capacity_day = record.capacity_day\n population_day = record.population_day\n if capacity_day is not None and \\\n population_day and \\\n population_day >= capacity_day:\n full_day = True\n\n if day_and_night:\n capacity_night = record.capacity_night\n population_night = record.population_night\n if capacity_night is not None and \\\n population_night and \\\n population_night >= capacity_night:\n full_night = True\n\n if not day_and_night and full_day or full_day and full_night:\n if tablename == \"cr_shelter\":\n warning = T(\"Warning: this shelter is full\")\n elif tablename == \"cr_shelter_unit\":\n warning = T(\"Warning: this housing unit is full\")\n elif full_day:\n if tablename == \"cr_shelter\":\n warning = T(\"Warning: this shelter is full for daytime\")\n elif tablename == \"cr_shelter_unit\":\n warning = T(\"Warning: this housing unit is full for daytime\")\n elif full_night:\n if tablename == \"cr_shelter\":\n warning = T(\"Warning: this shelter is full for the night\")\n elif tablename == \"cr_shelter_unit\":\n warning = T(\"Warning: this housing unit is full for the night\")\n\n if warning:\n response = current.response\n response_warning = response.warning\n if response_warning:\n response.warning = \"%s - %s\" % (response_warning, warning)\n else:\n response.warning = warning","def _list_validity_check(l, valid_l):\n\n if not Settings._is_in_list(l, valid_l):\n raise InvalidSettingError()","def _assert_problem_is_valid(self) -> None:\n # Will throw error if any of these are not set\n specified = self.ignore + self.silence + self.target\n if not all(\n [\n pr in specified\n for pr in self.observer.photoreceptors\n if pr is not None\n ]\n ):\n raise SilSubProblemError(\n \"At least one photoreceptor is not accounted for by the \"\n + \"*ignore*, *silence* and *target* properties.\"\n )","def test_kids_first_patient_everything(caplog, kids_first_server, kids_first_research_studies_with_observations):\n resource_counts = _validate_patient_everything(caplog, kids_first_server,\n kids_first_research_studies_with_observations)\n assert False, resource_counts","def how_eligible(essay):\n eligibility_requirements = ['?', '\"', ',', '!']\n return len(set(filter((lambda x: x in eligibility_requirements), essay)))","def consistency_check(self):\n for opt, opt_def in self.opt_dict.iteritems():\n selected_imp = opt_def['selected_imp']\n if selected_imp == 'none':\n continue\n else:\n mapped_opt = self.imp2opt_dict[selected_imp]\n\n if mapped_opt != opt:\n emsg = 'ERROR: imp2opt_dict fails to map {i} to {o}'\n print emsg.format(i=selected_imp, o=opt)\n\n for imp, opt in self.imp2opt_dict.iteritems():\n if imp == 'none':\n print 'ERROR: Found \"none\" as a keyword in imp2opt_dict'\n continue\n elif opt == 'none':\n continue\n else:\n selected_imp = self.opt_dict[opt]['selected_imp']\n\n if imp != selected_imp:\n emsg = 'ERROR: imp2opt_dict fails to map {i} to {o}'\n print emsg.format(i=selected_imp, o=opt)","def addAllergies(self):\n if int(self.pid)%100 < 85: # no allergies for ~ 85%\n exclusion = NO_ALLERGY.sub({\n 'exclusion':\"no known allergies\",\n 'exclusion_id':\"160244002\",\n }).done()\n self.data.append(SDMX.sub({'models':exclusion}, escape=False).done())\n else: # Sprinkle in some sulfa allergies\n al = DRUG_CLASS_ALLERGY.sub({\n 'reaction': \"skin rash\",\n 'reaction_id': \"271807003\",\n 'category': \"drug allergy\",\n 'category_id': \"416098002\",\n 'allergen': \"sulfonamide antibacterial\",\n 'allergen_id': \"N0000175503\",\n 'severity': \"mild\",\n 'severity_id': \"255604002\",\n }).done()\n self.data.append(SDMX.sub({'models':al}, escape=False).done())\n \n if int(self.pid)%2: # and throw in peanut allergies for every other patient\n al = FOOD_ALLERGY.sub({\n 'reaction': \"anaphylaxis\",\n 'reaction_id': \"39579001\",\n 'category': \"food allergy\",\n 'category_id': \"414285001\",\n 'allergen': \"peanut\",\n 'allergen_id': \"QE1QX6B99R\",\n 'severity': \"severe\",\n 'severity_id': \"24484000\",\n }).done()\n self.data.append(SDMX.sub({'models':al}, escape=False).done())","def check_risk(self):\r\n gms = self.cleaned_data.get(\"gms\", [])\r\n risk = self.cleaned_data.get(\"risk\", RPEvent.NORMAL_RISK)\r\n if not any(\r\n gm\r\n for gm in gms\r\n if gm.player.is_staff or gm.player.check_permstring(\"builders\")\r\n ):\r\n if risk != RPEvent.NORMAL_RISK:\r\n self.add_error(\r\n \"risk\",\r\n \"Risk cannot be altered without a staff member as GM. Set to: %r\"\r\n % risk,\r\n )","def check_auto_reject(self):\r\n for pr in self:\r\n if not pr.line_ids.filtered(lambda l: l.cancelled is False):\r\n pr.write({'state': 'rejected'})","def check_usability(hdulist):\n status = True\n\n if 'INSTRUME' in hdulist[0].header:\n if change_style(hdulist[0].header['INSTRUME']):\n pass\n else:\n print (\"Not a valid value for INSTRUME: {}\".format(hdulist[0].header['INSTRUME']))\n status = False\n else:\n print (\"Missing INSTRUME header in file \")\n status = False\n if 'REFTYPE' in hdulist[0].header:\n pass\n else:\n print (\"Missing REFTYPE header in file \")\n status = False\n\n return status","def resident_optimal(residents, hospitals):\n\n free_residents = residents[:]\n while free_residents:\n\n resident = free_residents.pop()\n hospital = resident.get_favourite()\n\n match_pair(resident, hospital)\n\n if len(hospital.matching) > hospital.capacity:\n worst = hospital.get_worst_match()\n unmatch_pair(worst, hospital)\n free_residents.append(worst)\n\n if len(hospital.matching) == hospital.capacity:\n successors = hospital.get_successors()\n for successor in successors:\n delete_pair(hospital, successor)\n if not successor.prefs:\n free_residents.remove(successor)\n\n return {r: r.matching for r in hospitals}","def score_professor_conflicts(self):\n prof_conflict_score = 0\n multiplier = 4\n \n for day_num in range(self.num_days):\n \n current_day = self.days[ day_num ]\n num_conflicts = 0\n \n for prof_name in current_day.keys():\n if not self.get_prof_by_name[prof_name].available( day_num ):\n num_conflicts += 1\n \n prof_conflict_score += multiplier * ( num_conflicts ** 2 )\n \n self.prof_conflict_score = prof_conflict_score\n return self.prof_conflict_score","def check_values_available(pCountry):\n # Creer un vecteur qui compte le nombre de valeurs different de zero\n vector = employment.query('country == \"{}\"'.format(pCountry)).sex.value_counts().values\n\n # Retourne vrai s'il n'y a pas que des zeros\n return np.sum(np.nonzero(vector)) != 0","def hospital_resident(residents, hospitals, optimal=\"resident\"):\n\n if optimal == \"resident\":\n return resident_optimal(residents, hospitals)\n if optimal == \"hospital\":\n return hospital_optimal(hospitals)","def get_sellability_report(melons):\n\n # Fill in the rest \n sellable = None\n for melon in melons:\n if melon.is_sellable():\n sellable = 'CAN BE SOLD'\n else:\n sellable = 'NOT SELLABLE'\n print(f'Harvested by {melon.harvester} from Field {melon.field_number} {sellable}')","def es_satisfecho_por(self, candidata):","def test_dropping_all_correct(self):\r\n self.dropping_setup()\r\n\r\n self.dropping_homework_stage1()\r\n for name in self.hw3_names:\r\n self.submit_question_answer(name, {'2_1': 'Correct'})\r\n\r\n self.check_grade_percent(1.0)\r\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 2.0]) # Order matters\r\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 1.0])","def test_AnvilResearchStudy_Condition(anvil_research_studies_with_observations):\n _validate_ResearchStudy_Condition(anvil_research_studies_with_observations)","def checkAndUpdateAllPossibilities(self, G, PD, prevPat):\n ### removing candidate if any other action was performed on it ###\n if 'shrink' not in prevPat.pat_type:\n if prevPat.pat_type in ['merge']:\n for p in prevPat.prev_order:\n if p in self.Data:\n del self.Data[p]\n elif prevPat.pat_type in ['update', 'split', 'remove']:\n if prevPat.prev_order in self.Data:\n del self.Data[prevPat.prev_order]\n if self.gtype == 'U':\n for k,v in self.Data.items():\n if len(set(v['Pat'].NL).intersection(set(prevPat.NL))) > 1:\n self.updateConstraintEvaluation(G, PD, k, 2)\n else:\n self.updateConstraintEvaluation(G, PD, k, 1)\n else:\n for k,v in self.Data.items():\n inInt = len(set(v['Pat'].inNL).intersection(set(prevPat.inNL)))\n outInt = len(set(v['Pat'].outNL).intersection(set(prevPat.outNL)))\n if inInt > 1 and outInt > 1:\n self.updateConstraintEvaluation(G, PD, k, 2)\n else:\n self.updateConstraintEvaluation(G, PD, k, 1)\n return","def _checkstate(self, queryset):\n for presup in queryset.filter(estado='borrador').exclude(fecha_realizado=None):\n if ((presup.fecha_realizado + timedelta(days=21) < datetime.now().date() and presup.tipo != 'asistencia') or\n (presup.fecha_realizado + timedelta(days=60) < datetime.now().date() and presup.tipo == 'asistencia')):\n presup._toState_cancelado(obs=\"Registro automático: Presupuesto vencido\")","def check_completeness(ISM):\n for item in ISM:\n if item not in ['A', 'T', 'C', 'G', '-']:\n return False\n return True","def __check_all_config_values_set(cfg: __Config):\n unset_values = [key for key, val in cfg.__dict__.items() if val is None]\n if len(unset_values) > 0:\n print(f\"WARNING: The following config variables have not been set: {unset_values}\")","def test_vote_badpklist(self):\r\n mock_module = CHModuleFactory.create()\r\n json_in = {'answer': '24.0', 'hint': '0', 'pk_list': json.dumps([['24.0', 0], ['24.0', 12]])}\r\n hint_and_votes = mock_module.tally_vote(json_in)['hint_and_votes']\r\n self.assertTrue(['Best hint', 41] in hint_and_votes)\r\n self.assertTrue(len(hint_and_votes) == 1)","def test_half_life_unstable_isotopes():\n for isotope in data_about_isotopes:\n if (\n \"half_life\" not in data_about_isotopes[isotope]\n and not data_about_isotopes[isotope]\n ):\n with pytest.raises(MissingParticleDataError):\n half_life(isotope)","def test_check_ess_settings(self):\n ess_settings1 = {'gaussian': [self.servers[0]], 'molpro': [self.servers[1], self.servers[0]],\n 'qchem': [self.servers[0]]}\n ess_settings2 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\n ess_settings3 = {'gaussian': self.servers[0], 'molpro': [self.servers[1], self.servers[0]],\n 'qchem': self.servers[0]}\n ess_settings4 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\n ess_settings5 = {'gaussian': 'local', 'molpro': self.servers[1], 'qchem': self.servers[0]}\n\n ess_settings1 = check_ess_settings(ess_settings1)\n ess_settings2 = check_ess_settings(ess_settings2)\n ess_settings3 = check_ess_settings(ess_settings3)\n ess_settings4 = check_ess_settings(ess_settings4)\n ess_settings5 = check_ess_settings(ess_settings5)\n\n ess_list = [ess_settings1, ess_settings2, ess_settings3, ess_settings4, ess_settings5]\n\n for ess in ess_list:\n for soft, server_list in ess.items():\n self.assertTrue(soft in ['gaussian', 'molpro', 'qchem'])\n self.assertIsInstance(server_list, list)\n\n with self.assertRaises(SettingsError):\n ess_settings6 = {'nosoft': ['server1']}\n check_ess_settings(ess_settings6)\n with self.assertRaises(SettingsError):\n ess_settings7 = {'gaussian': ['noserver']}\n check_ess_settings(ess_settings7)","def filter_residues(residues, biomolecule='PROTEIN'):\n biomolecule = biomolecule.strip().upper()\n standard_residues = []\n for res in residues:\n if res.get_resname().strip() in STANDARD_RESIDUES[biomolecule]:\n if not res.id[0].strip(): standard_residues.append(res) # filter out hetro residues\n return standard_residues","def check_to_Done(self,cr,uid,ids,context=None):\n\n for rec in self.browse(cr, uid, ids, context):\n if not rec.maintenance_id: \n raise osv.except_osv(_('ValidateError'), _(\"There Is NO maintenace request refrence to this accident.\"))\n return False\n return True","def post_check(self, dataset, resources=None):\r\n infidx = where(isinf(dataset.get_attribute(\"residential_land_value\")))[0]\r\n\r\n if infidx.size > 0:\r\n logger.log_warning(\"Infinite values in residential_land_value.\")\r\n dataset.set_values_of_one_attribute(\"residential_land_value\", self.maxfloat32,\r\n infidx)\r\n infidx = where(isinf(dataset.get_attribute(\"nonresidential_land_value\")))[0]\r\n if infidx.size > 0:\r\n logger.log_warning(\"Infinite values in nonresidential_land_value.\")\r\n dataset.set_values_of_one_attribute(\"nonresidential_land_value\", self.maxfloat32,\r\n infidx)","def test_check_for_unacceptable_matches_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n student = Student(name=\"foo\")\n supervisor.matching.append(student)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.prefs))\n assert student.name in error"],"string":"[\n \"def _check_hospital_prefs(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n residents_that_ranked = [\\n res for res in self.residents if hospital in res.prefs\\n ]\\n if set(hospital.prefs) != set(residents_that_ranked):\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has not ranked all the residents that \\\"\\n f\\\"ranked it: {set(hospital.prefs)} != \\\"\\n f\\\"{set(residents_that_ranked)}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_hospital_reciprocated_all_prefs(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n hospital._forget(resident)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"hospitals\\\", \\\"residents\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert hospital not in resident.prefs\",\n \"def test_check_inputs_resident_prefs_all_hospitals(game):\\n\\n resident = game.residents[0]\\n resident.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert resident.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert resident.prefs == []\",\n \"def test_check_inputs_hospital_prefs_all_reciprocated(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n resident._forget(hospital)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert resident not in hospital.prefs\",\n \"def _check_resident_prefs(self):\\n\\n errors = []\\n for resident in self.residents:\\n if not set(resident.prefs).issubset(set(self.hospitals)):\\n errors.append(\\n ValueError(\\n f\\\"{resident} has ranked a non-hospital: \\\"\\n f\\\"{set(resident.prefs)} != {set(self.hospitals)}\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_resident_prefs_all_nonempty(game):\\n\\n resident = game.residents[0]\\n resident.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n assert resident.name in str(record[0].message)\\n\\n if game.clean:\\n assert resident not in game.residents\",\n \"def test_check_inputs_hospital_prefs_all_nonempty(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def _check_hospital_matching(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n for resident in hospital.matching:\\n if resident not in hospital.prefs:\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has {resident} in their matching but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{hospital.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def _check_mutual_preference(resident, hospital):\\n\\n return resident in hospital.prefs and hospital in resident.prefs\",\n \"def test_check_inputs_supervisor_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n supervisor.prefs.remove(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"supervisors\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert supervisor.name in message\\n\\n if clean:\\n assert supervisor not in student.prefs\\n assert all(\\n project not in student.prefs for project in supervisor.projects\\n )\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def _check_resident_matching(self):\\n\\n errors = []\\n for resident in self.residents:\\n if (\\n resident.matching is not None\\n and resident.matching not in resident.prefs\\n ):\\n errors.append(\\n ValueError(\\n f\\\"{resident} is matched to {resident.matching} but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{resident.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_supervisor_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n projects = supervisor.projects\\n for project in student.prefs:\\n if project in projects:\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"supervisors\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(supervisor.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in supervisor.prefs\\n assert all(\\n student not in project.prefs for project in supervisor.projects\\n )\",\n \"def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_suitor_prefs()\\n\\n match.suitors[0].pref_names = [1, 2, 3]\\n\\n with pytest.raises(Exception):\\n match._check_suitor_prefs()\",\n \"def _check_hospital_unhappy(resident, hospital):\\n\\n return len(hospital.matching) < hospital.capacity or any(\\n [hospital.prefers(resident, match) for match in hospital.matching]\\n )\",\n \"def _check_resident_unhappy(resident, hospital):\\n\\n return resident.matching is None or resident.prefers(\\n hospital, resident.matching\\n )\",\n \"def test_check_stability():\\n\\n residents = [Player(\\\"A\\\", [1, 2]), Player(\\\"B\\\", [2]), Player(\\\"C\\\", [2, 1])]\\n hospitals = [Player(1, [\\\"C\\\", \\\"A\\\"], 3), Player(2, [\\\"A\\\", \\\"B\\\", \\\"C\\\"], 3)]\\n match = HospitalResident(residents, hospitals)\\n\\n matching = match.solve()\\n assert match.check_stability()\\n\\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\\n matching[P_1] = [P_C]\\n matching[P_2] = [P_A, P_B]\\n\\n assert not match.check_stability()\",\n \"def check_stability(self):\\n\\n blocking_pairs = []\\n for resident in self.residents:\\n for hospital in self.hospitals:\\n if (\\n _check_mutual_preference(resident, hospital)\\n and _check_resident_unhappy(resident, hospital)\\n and _check_hospital_unhappy(resident, hospital)\\n ):\\n blocking_pairs.append((resident, hospital))\\n\\n self.blocking_pairs = blocking_pairs\\n return not any(blocking_pairs)\",\n \"def test_check_inputs_project_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n student = project.prefs[0]\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"projects\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(project.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in project.prefs\",\n \"def test_check_inputs_project_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n student = project.prefs[0]\\n project._forget(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"projects\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert project.name in message\\n\\n if clean:\\n assert project not in student.prefs\",\n \"def test_check_inputs(game):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.residents == game._all_residents\\n assert game.hospitals == game._all_hospitals\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_resident_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"resident\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n assert all(\\n [\\n r in set(residents)\\n for r in set(\\n [\\n r_match\\n for matches in matching.values()\\n for r_match in matches\\n ]\\n )\\n ]\\n )\\n\\n for resident in residents:\\n if resident.matching:\\n assert resident.pref_names.index(resident.matching.name) == 0\",\n \"def test_check_inputs_hospital_capacity(game):\\n\\n hospital = game.hospitals[0]\\n capacity = hospital.capacity\\n hospital.capacity = 0\\n\\n assert hospital._original_capacity == capacity\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_capacity(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def isProteic(self):\\n from MolKit.PDBresidueNames import AAnames\\n\\n self.AARes = [x for x in self.residues if x.type in AAnames]\\n\\n water = [x for x in self.residues if x.type in ['HOH', 'WAT']]\\n\\n if len(self.AARes) and len(self.AARes)+len(water) == len(self.residues):\\n return True\\n else:\\n return False\",\n \"def test_check_for_unacceptable_matches_residents(game):\\n\\n resident = game.residents[0]\\n hospital = Hospital(name=\\\"foo\\\", capacity=1)\\n resident.matching = hospital\\n\\n issues = game._check_for_unacceptable_matches(\\\"residents\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(resident.name)\\n assert issue.endswith(f\\\"{resident.prefs}.\\\")\\n assert hospital.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def get_possible_preferences():\\n df = pd.read_csv(\\\"../data/restaurant_info.csv\\\")\\n foods = set(list(df[\\\"food\\\"]))\\n areas = set(list(df[\\\"area\\\"].dropna()))\\n price_ranges = set(list(df[\\\"pricerange\\\"]))\\n\\n return foods, areas, price_ranges\",\n \"def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"hospital\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n\\n for hospital, matches in matching.items():\\n old_idx = -np.infty\\n for resident in matches:\\n idx = hospital.pref_names.index(resident.name)\\n assert idx >= old_idx\\n old_idx = idx\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def _pareto_dominance(self, party1: int, party2: int, profile: list[set[int]]) -> bool:\\n strict_preference = False\\n for ballot in profile:\\n if party2 in ballot and party1 not in ballot:\\n return False\\n if party1 in ballot and party2 not in ballot:\\n strict_preference = True\\n return strict_preference\",\n \"def _find_homeless_mps(self):\\n mps = Person.objects.filter(\\n active=True,\\n house__name=HOUSE_OF_COMMONS,\\n constituency=None,\\n )\\n\\n self.stdout('MPs with missing constituency:')\\n for mp in mps:\\n self.stdout(f' [{mp.parliamentdotuk}] {mp.name} has no constituency')\",\n \"def test_inputs_reviewer_prefs(\\n resident_names, hospital_names, capacities, seed\\n):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_reviewer_prefs()\\n\\n match.reviewers[0].pref_names.pop()\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_prefs()\",\n \"def check_relevant_forms(L):\\n L2 = list()\\n for F in L.values():\\n # needed_ev=(\\n S = F.shim_corr\\n # print \\\"S=\\\",S\\n ok_ev = 0\\n for g in S:\\n if g.atkin_lehner_eigenvalue() == -1:\\n ok_ev = ok_ev + 1\\n if ok_ev > 0:\\n print(\\\"Number of ok forms on \\\", F.space.WR.N, \\\" :\\\", ok_ev)\\n F.list_coefficents('D', fd=True, neg=False, nmin=0, nmax=1000, latex=False, nd=50, prime=True)\\n L2.append(F)\\n return L2\",\n \"def isResidueAssigned(residue):\\n\\n for atom in residue.atoms:\\n if atom.atomSet:\\n if atom.atomSet.resonanceSets:\\n return True\\n \\n return False\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def check_primers(self):\\n primer_keys=['sequence','description']\\n if self.data.has_key('primer_dict'):\\n for primer in self.data['primer_dict'].keys():\\n for key in primer_keys:\\n if not self.data['primer_dict'][primer].has_key(key):\\n self.data['primer_dict'][primer][key]='None'\",\n \"def test_check_stability():\\n\\n students = [Student(\\\"A\\\"), Student(\\\"B\\\"), Student(\\\"C\\\")]\\n projects = [Project(\\\"P\\\", 2), Project(\\\"Q\\\", 2)]\\n supervisors = [Supervisor(\\\"X\\\", 2), Supervisor(\\\"Y\\\", 2)]\\n\\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\\n\\n p.set_supervisor(x)\\n q.set_supervisor(y)\\n\\n a.set_prefs([p, q])\\n b.set_prefs([q])\\n c.set_prefs([q, p])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = StudentAllocation(students, projects, supervisors)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (p, q) = game.students, game.projects\\n\\n matching[p] = [c]\\n matching[q] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def _check_inputs(self):\\n\\n self._check_resident_prefs()\\n self._check_hospital_prefs()\",\n \"def _check_personal_pronouns(pronoun: str, last_nouns: list) -> list:\\n pronoun_details = []\\n pronoun_lower = pronoun.lower()\\n if pronoun == 'I' or pronoun_lower in ('me', 'myself', 'my'):\\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\\n elif pronoun_lower in ('we', 'us', 'ourselves', 'our'):\\n # Find singular or plural person nouns (any gender)\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, True))\\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\\n elif pronoun_lower in ('they', 'them', 'themselves', 'their'):\\n # Give preference to persons (any gender or number)\\n noun_list = _check_criteria(pronoun_lower, last_nouns, None, None, True)\\n if noun_list:\\n pronoun_details.extend(noun_list)\\n else:\\n # Check for non-persons\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, False))\\n elif pronoun_lower in ('she', 'herself', 'her'):\\n # Find singular, feminine, person nouns\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, True, True))\\n elif pronoun_lower in ('he', 'himself', 'him'):\\n # Find singular, masculine, person nouns\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, False, True))\\n elif pronoun_lower in ('it', 'itself', 'its'):\\n # Find singular, non-person nouns (no gender)\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, None, False))\\n final_details = [] # May be duplicates in the list due to duplicates in last_nouns\\n for pronoun_detail in pronoun_details:\\n if pronoun_detail in final_details:\\n continue\\n final_details.append(pronoun_detail)\\n return final_details\",\n \"def check_hint_condition(self, hxml_set, student_answers):\\r\\n pass\",\n \"def validate_loans(loans):\\n def validate_loan(loan):\\n return (type(loan)==list or type(loan)==tuple) and len(loan)==3 \\\\\\n and type(loan[0])==str and type(loan[1])==str and loan[1] in database.LOANABLE_RESOURCES and type(loan[2])==int and loan[2]>=0\\n return type(loans)==list and False not in [validate_loan(load) for loan in loans]\",\n \"def feasible(individual):\\n val=0;\\n for i in individual:\\n if viajes[val][6]==True and False==vehiculos_esp[i]:\\n return False\\n val+=1\\n return True\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def _filter_satisfied(self, update_setd=False):\\n\\n model = self.oracle.get_model()\\n setd = set()\\n\\n for i, cl in enumerate(self.soft):\\n if not self.satc[i]:\\n if self._satisfied(cl, model):\\n self.satc[i] = True\\n self.ss_assumps.append(self.sels[i])\\n else:\\n setd = setd.union(set(cl))\\n\\n if update_setd:\\n self.setd = list(setd)\",\n \"def isStable(couples,mPref,wPref) -> bool:\\n couples_dict = {woman:man for man,woman in couples}\\n \\n for m,w in couples:\\n\\n # w's (m's partner) rank in m's preference list\\n w_rank = mPref[m].index(w)\\n \\n # list of all women that m prefers over w\\n preferred_over_w = mPref[m][:w_rank]\\n \\n\\n for hotter in preferred_over_w:\\n\\n # get each woman's current partner (in preferred list)\\n hotters_man = couples_dict[hotter]\\n\\n # if she prefers m over her current partner then marriage is not stable\\n if wPref[hotter].index(m) < wPref[hotter].index(hotters_man): \\n msg = \\\"{}'s marriage to {} is unstable:\\\\n\\\\t\\\" + \\\\\\n \\\"{} prefers {} > {} (his current wife)\\\\n\\\\t\\\" +\\\\\\n \\\"{} prefers {} > {} (her current husband)\\\"\\n print(msg.format(m, w, m, hotter, w, hotter, m, hotters_man) )\\n return False\\n return True\",\n \"def _check_all_valence_terms_assigned(\\n handler,\\n assigned_terms,\\n topology,\\n valence_terms,\\n):\\n if len(assigned_terms) == len(valence_terms):\\n return\\n\\n # Convert the valence term to a valence dictionary to make sure\\n # the order of atom indices doesn't matter for comparison.\\n valence_terms_dict = assigned_terms.__class__()\\n for atoms in valence_terms:\\n atom_indices = (topology.atom_index(a) for a in atoms)\\n valence_terms_dict[atom_indices] = atoms\\n\\n # Check that both valence dictionaries have the same keys (i.e. terms).\\n assigned_terms_set = set(assigned_terms.keys())\\n valence_terms_set = set(valence_terms_dict.keys())\\n unassigned_terms = valence_terms_set.difference(assigned_terms_set)\\n not_found_terms = assigned_terms_set.difference(valence_terms_set)\\n\\n # Raise an error if there are unassigned terms.\\n err_msg = \\\"\\\"\\n\\n if len(unassigned_terms) > 0:\\n unassigned_atom_tuples = []\\n\\n unassigned_str = \\\"\\\"\\n for unassigned_tuple in unassigned_terms:\\n unassigned_str += \\\"\\\\n- Topology indices \\\" + str(unassigned_tuple)\\n unassigned_str += \\\": names and elements \\\"\\n\\n unassigned_atoms = []\\n\\n # Pull and add additional helpful info on missing terms\\n for atom_idx in unassigned_tuple:\\n atom = topology.atom(atom_idx)\\n unassigned_atoms.append(atom)\\n unassigned_str += f\\\"({atom.name} {atom.symbol}), \\\"\\n unassigned_atom_tuples.append(tuple(unassigned_atoms))\\n err_msg += (\\n \\\"{parameter_handler} was not able to find parameters for the following valence terms:\\\\n\\\"\\n \\\"{unassigned_str}\\\"\\n ).format(\\n parameter_handler=handler.__class__.__name__,\\n unassigned_str=unassigned_str,\\n )\\n if len(not_found_terms) > 0:\\n if err_msg != \\\"\\\":\\n err_msg += \\\"\\\\n\\\"\\n not_found_str = \\\"\\\\n- \\\".join([str(x) for x in not_found_terms])\\n err_msg += (\\n \\\"{parameter_handler} assigned terms that were not found in the topology:\\\\n\\\"\\n \\\"- {not_found_str}\\\"\\n ).format(\\n parameter_handler=handler.__class__.__name__,\\n not_found_str=not_found_str,\\n )\\n if err_msg:\\n err_msg += \\\"\\\\n\\\"\\n\\n if isinstance(handler, BondHandler):\\n exception_class = UnassignedBondError\\n elif isinstance(handler, AngleHandler):\\n exception_class = UnassignedAngleError\\n elif isinstance(handler, (ProperTorsionHandler, ImproperTorsionHandler)):\\n exception_class = UnassignedTorsionError\\n else:\\n raise RuntimeError(\\n f\\\"Could not find an exception class for handler {handler}\\\",\\n )\\n\\n exception = exception_class(err_msg)\\n exception.unassigned_topology_atom_tuples = unassigned_atom_tuples\\n exception.handler_class = handler.__class__\\n raise exception\",\n \"def check_proof(self):\\n for gene in self.population:\\n if gene.is_proof:\\n print(gene.chromosome)\\n for state in gene.coq_states:\\n print(state)\\n self.proofs.append(Gene(chromosome=gene.valid_tactics))\",\n \"def listErrors(individual): #TODO: add other errors implemented elsewhere\\n\\n results = [birthBeforeMarriage(individual), birthBeforeDeath(individual), marriageBeforeDeath(individual), \\n datesBeforeCurrentDate(individual), noBigamy(individual)]\\n results = [x for x in results if x is not None]\\n return not all(results)\",\n \"def testhospital_vs_confirmed(self):\\n data = load_covid_data(file)\\n aim_day = data['evolution']['2020-03-16']\\n # Artificial cut one value , it supposed to be 4 number\\n aim_day['epidemiology']['confirmed']['total']['age'] = [10, 11, 12]\\n try:\\n cases_population = cases_per_population_by_age(data)\\n except Exception as e:\\n raise Exception\",\n \"def test_preferences_properties(self):\\n with pytest.raises(AssertionError):\\n self.preferences.exchange_params_by_currency_id\\n with pytest.raises(AssertionError):\\n self.preferences.utility_params_by_good_id\",\n \"def testConditionReasons(self):\\n \\n state = State.from_problem(self.prob)\\n\\n relevantVars = []\\n drive = self.dom.get_action(\\\"drive\\\")\\n with drive.instantiate([\\\"agent\\\", \\\"tru1\\\", \\\"apt1\\\"], self.prob):\\n self.assert_(state.is_satisfied(drive.precondition, relevantVars))\\n\\n relevantVars = set(relevantVars)\\n \\n s1 = StateVariable(self.prob.functions[\\\"city-of\\\"][0], [self.prob[\\\"pos1\\\"]])\\n s2 = StateVariable(self.prob.functions[\\\"city-of\\\"][0], [self.prob[\\\"apt1\\\"]])\\n s3 = StateVariable(self.prob.functions[\\\"location-of\\\"][0], [self.prob[\\\"tru1\\\"]])\\n \\n self.assertEqual(len(relevantVars), 3)\\n self.assert_(s1 in relevantVars)\\n self.assert_(s2 in relevantVars)\\n self.assert_(s3 in relevantVars)\",\n \"def _clean_politician_set(self, cleaned_data, geoname_ids):\\n\\n field_name = 'politician_set'\\n charge_ids = [int(elem) for elem in cleaned_data[field_name].strip('|').split('|')]\\n charge_ids_copy = list(charge_ids)\\n\\n if charge_ids:\\n\\n politician_ids = []\\n\\n for cityrep_id in geoname_ids:\\n if not len(charge_ids_copy):\\n break\\n found_ids, pol_ids = self.get_politicians_from_cityrep(\\n charge_ids_copy,\\n cityrep_id\\n )\\n for pol_id in pol_ids:\\n politician_ids.append(pol_id)\\n #print(\\\"\\\\ncityrep_id: %s\\\\ncharge_ids_copy: %s\\\\nfound_ids: %s\\\\n\\\" % (cityrep_id, charge_ids_copy, found_ids))\\n for charge_id in found_ids:\\n charge_ids_copy.remove(charge_id)\\n \\n if len(charge_ids_copy):\\n raise exceptions.ValidationError(u\\\"Non tutti i politici sono stati recuperati. Sono rimasti fuori i politici con id: %s\\\" % charge_ids_copy)\\n\\n lookup = get_lookup(MAP_FIELD_NAME_TO_CHANNEL[field_name])\\n values = lookup.get_objects(politician_ids)\\n\\n else:\\n values = []\\n\\n return values\",\n \"def testProtractedNSESanityChecks(self):\\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))\",\n \"def test_anvil_first_patient_everything(caplog, anvil_server, anvil_research_studies_with_observations):\\n research_studies = {r.relativePath(): r for r in anvil_research_studies_with_observations if\\n r.__class__.__name__ == 'ResearchStudy'}\\n resource_counts = _validate_patient_everything(caplog, anvil_server,\\n [research_studies['ResearchStudy/1000G-high-coverage-2019']])\\n assert False, resource_counts\",\n \"def get_sellability_report(melons):\\n\\n # Fill in the rest \\n for melon in melons:\\n\\n if melon.is_sellable():\\n sellability = \\\"CAN BE SOLD\\\"\\n else:\\n sellability = \\\"NOT SELLABLE\\\"\\n\\n print(\\\"Harvested by {} from Field {} ({})\\\".format(melon.harvester,melon.field,sellability))\",\n \"def check_primal_and_dual_solutions(self):\\r\\n\\r\\n # Solve primal and dual models\\r\\n self.primal.solve_model(self.m_p)\\r\\n self.dual.solve_model(self.m_d)\\r\\n\\r\\n # Elements to check\\r\\n elements = {\\r\\n 'power - existing thermal':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_THERMAL', 'is_dual': True}\\r\\n },\\r\\n 'power - existing wind':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_WIND', 'is_dual': True}\\r\\n },\\r\\n 'power - existing solar':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_SOLAR', 'is_dual': True}\\r\\n },\\r\\n 'power - existing hydro':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_HYDRO', 'is_dual': True}\\r\\n },\\r\\n # 'charging power - existing storage':\\r\\n # {\\r\\n # 'primal': {'id': 'p_in', 'is_dual': False},\\r\\n # 'dual': {'id': 'CHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\\r\\n # },\\r\\n # 'discharging power - existing storage':\\r\\n # {\\r\\n # 'primal': {'id': 'p_out', 'is_dual': False},\\r\\n # 'dual': {'id': 'DISCHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\\r\\n # },\\r\\n 'power - candidate thermal':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_THERMAL', 'is_dual': True}\\r\\n },\\r\\n 'power - candidate wind':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_WIND', 'is_dual': True}\\r\\n },\\r\\n 'power - candidate solar':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_SOLAR', 'is_dual': True}\\r\\n },\\r\\n 'charging power - candidate storage':\\r\\n {\\r\\n 'primal': {'id': 'p_in', 'is_dual': False},\\r\\n 'dual': {'id': 'CHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\\r\\n },\\r\\n 'discharging power - candidate storage':\\r\\n {\\r\\n 'primal': {'id': 'p_out', 'is_dual': False},\\r\\n 'dual': {'id': 'DISCHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\\r\\n },\\r\\n 'locational marginal prices':\\r\\n {\\r\\n 'primal': {'id': 'POWER_BALANCE', 'is_dual': True},\\r\\n 'dual': {'id': 'lamb', 'is_dual': False}\\r\\n },\\r\\n 'load shedding':\\r\\n {\\r\\n 'primal': {'id': 'p_V', 'is_dual': False},\\r\\n 'dual': {'id': 'LOAD_SHEDDING_POWER', 'is_dual': True}\\r\\n },\\r\\n }\\r\\n\\r\\n print(f\\\"Checking primal and dual solutions\\\")\\r\\n for el_id, e in elements.items():\\r\\n # Absolute, non-zero and max absolute difference\\r\\n absolute_diff, non_zero_diff, max_abs_diff = self.compare_primal_and_dual_components(e)\\r\\n\\r\\n print('---------------------------------------------------------------------------')\\r\\n print(f\\\"Primal component: {e['primal']['id']}, Dual component: {e['dual']['id']}')\\\")\\r\\n print(f'Total keys: {len(absolute_diff)}')\\r\\n print(f\\\"Max absolute difference: {max_abs_diff}\\\")\\r\\n print(f\\\"Non-zero diff: {non_zero_diff}\\\")\",\n \"def testAxiomReasoning(self):\\n \\n state = State.from_problem(self.prob)\\n extstate, reasons, universalReasons = state.get_extended_state(getReasons=True)\\n\\n relevantVars = []\\n relevantReplanVars = []\\n load = self.dom.get_action(\\\"a_load\\\")\\n with load.instantiate([\\\"agent\\\", \\\"obj11\\\", \\\"tru1\\\"], self.prob):\\n self.assert_(extstate.is_satisfied(load.precondition, relevantVars))\\n self.assert_(extstate.is_satisfied(load.replan, relevantReplanVars))\\n\\n all_reasons = set(relevantVars)\\n for v in relevantVars:\\n all_reasons |= reasons[v]\\n\\n all_replan_reasons = set(relevantReplanVars)\\n for v in relevantReplanVars:\\n all_replan_reasons |= reasons[v]\\n \\n s1 = StateVariable(self.prob.functions[\\\"location-of\\\"][0], [self.prob[\\\"tru1\\\"]])\\n s2 = StateVariable(self.prob.functions[\\\"location-of\\\"][0], [self.prob[\\\"obj11\\\"]])\\n\\n self.assert_(s1 in all_reasons)\\n self.assert_(s2 in all_reasons)\\n self.assertFalse(s1 in all_replan_reasons)\\n self.assert_(s2 in all_replan_reasons)\",\n \"def violated(self) -> bool:\\n ...\",\n \"def forwardcheck(var, val, assignment, user_dict):\\n\\t\\tif curr_domains:\\n\\t\\t\\tfor (meal, restaurant) in curr_deleted[var]:\\n\\t\\t\\t\\tcurr_domains[meal].append(restaurant)\\n\\t\\t\\tcurr_deleted[var] = []\\n\\n\\t\\t\\tfor meal in neighbors[var]:\\n\\t\\t\\t\\tif meal not in assignment:\\n\\t\\t\\t\\t\\tfor restaurant in curr_domains[meal][:]:\\n\\t\\t\\t\\t\\t\\tnum_cats = count_categories(assignment.values())\\n\\t\\t\\t\\t\\t\\tif not constraints_match(num_cats, user_dict):\\n\\t\\t\\t\\t\\t\\t#if not user_solution_checker(user_dict, meal, restaurant, assignment):\\n\\t\\t\\t\\t\\t\\t\\tcurr_domains[meal].remove(restaurant)\\n\\t\\t\\t\\t\\t\\t\\tcurr_deleted[var].append((meal, restaurant))\",\n \"def cleanse_priest_list(priests_list):\",\n \"def factor_in_multiple_professors(self):\\n professors = [professor for professor in self.course.professors if professor.lower() != \\\"none\\\"]\\n number_professors = len(set(professors))\\n if number_professors > 1:\\n self.score = self.score + number_professors\",\n \"def check_all(self):\\n return self.check_rs() + self.check_hgvs()\",\n \"def test_check_for_oversubscribed_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n supervisor.matching = range(supervisor.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.capacity))\\n assert str(supervisor.matching) in e\",\n \"def test_open_no_setup(restaurant_only, hall_only, kitchen_only, delivery_only):\\n # Here checks not all variants, cause restaurant_only is not isolated\\n # object. They were previously check and working alongside\\n # but affects result if together.\\n\\n # no setups\\n with pytest.raises(CustomWarning):\\n restaurant_only.open()\\n assert restaurant_only.is_working is False, \\\"You need to setup Kitchen, Delivery and Hall\\\"\\n\\n # only kitchen\\n with pytest.raises(CustomWarning):\\n restaurant_only.set_kitchen(kitchen_only)\\n restaurant_only.open()\\n assert restaurant_only.is_working is False, \\\"You need to setup Kitchen, Delivery and Hall\\\"\\n\\n # only delivery and kitchen\\n with pytest.raises(CustomWarning):\\n restaurant_only.set_delivery(delivery_only)\\n restaurant_only.set_kitchen(kitchen_only)\\n restaurant_only.open()\\n assert restaurant_only.is_working is False, \\\"You need to setup Kitchen, Delivery and Hall\\\"\",\n \"def _check_primary(self, old_list):\\n try:\\n self._get_primary(self._elements)\\n except PrimaryElementViolation:\\n self._elements = copy.copy(old_list)\\n raise\",\n \"def testNoSpecialties(self):\\n self.failUnlessEqual(self.person.getSpecialties(), [])\",\n \"def test_for_criteria(self):\\n ignore = ['interpreter_method', 'average_by_sample_or_site', 'include_nrm']\\n values = ([dic['value'] for dic in self.acceptance_criteria.values() if (dic['criterion_name'] not in ignore and dic['value'] != -999)])\\n return values\",\n \"def _check_evidence_output(out):\\n list_out = [\\\"evidence_code\\\", \\\"evidence_category\\\"]\\n if out not in list_out:\\n raise ValueError(\\\"{} not in {}\\\".format(out, list_out))\",\n \"def _check_family(self):\\n for (s, (b, c)), (cond, ref) in families.items():\\n if s != self.SYMBOL or len(b) != self._.d:\\n continue\\n vars = tuple(set(sum(map(variables, b + c), ())))\\n sols = _solve([SR(l) == r for l, r\\n in zip(self._.b[:-1] + self._.c[1:], b + c)],\\n vars)\\n if any(checkConditions(cond, sol) for sol in sols\\n if is_integral(sol)):\\n raise InfeasibleError(refs=ref)\",\n \"def cr_check_population_availability(unit_id, table):\\n\\n T = current.T\\n tablename = table._tablename\\n\\n if tablename == \\\"cr_shelter\\\":\\n query = (table.site_id == site_id)\\n #elif tablename == \\\"cr_shelter_unit\\\":\\n else:\\n query = (table.id == unit_id)\\n\\n record = current.db(query).select(table.capacity_day,\\n table.population_day,\\n table.capacity_night,\\n table.population_night,\\n limitby = (0, 1),\\n ).first()\\n\\n day_and_night = current.deployment_settings.get_cr_day_and_night()\\n\\n warning = None\\n full_day = full_night = False\\n\\n capacity_day = record.capacity_day\\n population_day = record.population_day\\n if capacity_day is not None and \\\\\\n population_day and \\\\\\n population_day >= capacity_day:\\n full_day = True\\n\\n if day_and_night:\\n capacity_night = record.capacity_night\\n population_night = record.population_night\\n if capacity_night is not None and \\\\\\n population_night and \\\\\\n population_night >= capacity_night:\\n full_night = True\\n\\n if not day_and_night and full_day or full_day and full_night:\\n if tablename == \\\"cr_shelter\\\":\\n warning = T(\\\"Warning: this shelter is full\\\")\\n elif tablename == \\\"cr_shelter_unit\\\":\\n warning = T(\\\"Warning: this housing unit is full\\\")\\n elif full_day:\\n if tablename == \\\"cr_shelter\\\":\\n warning = T(\\\"Warning: this shelter is full for daytime\\\")\\n elif tablename == \\\"cr_shelter_unit\\\":\\n warning = T(\\\"Warning: this housing unit is full for daytime\\\")\\n elif full_night:\\n if tablename == \\\"cr_shelter\\\":\\n warning = T(\\\"Warning: this shelter is full for the night\\\")\\n elif tablename == \\\"cr_shelter_unit\\\":\\n warning = T(\\\"Warning: this housing unit is full for the night\\\")\\n\\n if warning:\\n response = current.response\\n response_warning = response.warning\\n if response_warning:\\n response.warning = \\\"%s - %s\\\" % (response_warning, warning)\\n else:\\n response.warning = warning\",\n \"def _list_validity_check(l, valid_l):\\n\\n if not Settings._is_in_list(l, valid_l):\\n raise InvalidSettingError()\",\n \"def _assert_problem_is_valid(self) -> None:\\n # Will throw error if any of these are not set\\n specified = self.ignore + self.silence + self.target\\n if not all(\\n [\\n pr in specified\\n for pr in self.observer.photoreceptors\\n if pr is not None\\n ]\\n ):\\n raise SilSubProblemError(\\n \\\"At least one photoreceptor is not accounted for by the \\\"\\n + \\\"*ignore*, *silence* and *target* properties.\\\"\\n )\",\n \"def test_kids_first_patient_everything(caplog, kids_first_server, kids_first_research_studies_with_observations):\\n resource_counts = _validate_patient_everything(caplog, kids_first_server,\\n kids_first_research_studies_with_observations)\\n assert False, resource_counts\",\n \"def how_eligible(essay):\\n eligibility_requirements = ['?', '\\\"', ',', '!']\\n return len(set(filter((lambda x: x in eligibility_requirements), essay)))\",\n \"def consistency_check(self):\\n for opt, opt_def in self.opt_dict.iteritems():\\n selected_imp = opt_def['selected_imp']\\n if selected_imp == 'none':\\n continue\\n else:\\n mapped_opt = self.imp2opt_dict[selected_imp]\\n\\n if mapped_opt != opt:\\n emsg = 'ERROR: imp2opt_dict fails to map {i} to {o}'\\n print emsg.format(i=selected_imp, o=opt)\\n\\n for imp, opt in self.imp2opt_dict.iteritems():\\n if imp == 'none':\\n print 'ERROR: Found \\\"none\\\" as a keyword in imp2opt_dict'\\n continue\\n elif opt == 'none':\\n continue\\n else:\\n selected_imp = self.opt_dict[opt]['selected_imp']\\n\\n if imp != selected_imp:\\n emsg = 'ERROR: imp2opt_dict fails to map {i} to {o}'\\n print emsg.format(i=selected_imp, o=opt)\",\n \"def addAllergies(self):\\n if int(self.pid)%100 < 85: # no allergies for ~ 85%\\n exclusion = NO_ALLERGY.sub({\\n 'exclusion':\\\"no known allergies\\\",\\n 'exclusion_id':\\\"160244002\\\",\\n }).done()\\n self.data.append(SDMX.sub({'models':exclusion}, escape=False).done())\\n else: # Sprinkle in some sulfa allergies\\n al = DRUG_CLASS_ALLERGY.sub({\\n 'reaction': \\\"skin rash\\\",\\n 'reaction_id': \\\"271807003\\\",\\n 'category': \\\"drug allergy\\\",\\n 'category_id': \\\"416098002\\\",\\n 'allergen': \\\"sulfonamide antibacterial\\\",\\n 'allergen_id': \\\"N0000175503\\\",\\n 'severity': \\\"mild\\\",\\n 'severity_id': \\\"255604002\\\",\\n }).done()\\n self.data.append(SDMX.sub({'models':al}, escape=False).done())\\n \\n if int(self.pid)%2: # and throw in peanut allergies for every other patient\\n al = FOOD_ALLERGY.sub({\\n 'reaction': \\\"anaphylaxis\\\",\\n 'reaction_id': \\\"39579001\\\",\\n 'category': \\\"food allergy\\\",\\n 'category_id': \\\"414285001\\\",\\n 'allergen': \\\"peanut\\\",\\n 'allergen_id': \\\"QE1QX6B99R\\\",\\n 'severity': \\\"severe\\\",\\n 'severity_id': \\\"24484000\\\",\\n }).done()\\n self.data.append(SDMX.sub({'models':al}, escape=False).done())\",\n \"def check_risk(self):\\r\\n gms = self.cleaned_data.get(\\\"gms\\\", [])\\r\\n risk = self.cleaned_data.get(\\\"risk\\\", RPEvent.NORMAL_RISK)\\r\\n if not any(\\r\\n gm\\r\\n for gm in gms\\r\\n if gm.player.is_staff or gm.player.check_permstring(\\\"builders\\\")\\r\\n ):\\r\\n if risk != RPEvent.NORMAL_RISK:\\r\\n self.add_error(\\r\\n \\\"risk\\\",\\r\\n \\\"Risk cannot be altered without a staff member as GM. Set to: %r\\\"\\r\\n % risk,\\r\\n )\",\n \"def check_auto_reject(self):\\r\\n for pr in self:\\r\\n if not pr.line_ids.filtered(lambda l: l.cancelled is False):\\r\\n pr.write({'state': 'rejected'})\",\n \"def check_usability(hdulist):\\n status = True\\n\\n if 'INSTRUME' in hdulist[0].header:\\n if change_style(hdulist[0].header['INSTRUME']):\\n pass\\n else:\\n print (\\\"Not a valid value for INSTRUME: {}\\\".format(hdulist[0].header['INSTRUME']))\\n status = False\\n else:\\n print (\\\"Missing INSTRUME header in file \\\")\\n status = False\\n if 'REFTYPE' in hdulist[0].header:\\n pass\\n else:\\n print (\\\"Missing REFTYPE header in file \\\")\\n status = False\\n\\n return status\",\n \"def resident_optimal(residents, hospitals):\\n\\n free_residents = residents[:]\\n while free_residents:\\n\\n resident = free_residents.pop()\\n hospital = resident.get_favourite()\\n\\n match_pair(resident, hospital)\\n\\n if len(hospital.matching) > hospital.capacity:\\n worst = hospital.get_worst_match()\\n unmatch_pair(worst, hospital)\\n free_residents.append(worst)\\n\\n if len(hospital.matching) == hospital.capacity:\\n successors = hospital.get_successors()\\n for successor in successors:\\n delete_pair(hospital, successor)\\n if not successor.prefs:\\n free_residents.remove(successor)\\n\\n return {r: r.matching for r in hospitals}\",\n \"def score_professor_conflicts(self):\\n prof_conflict_score = 0\\n multiplier = 4\\n \\n for day_num in range(self.num_days):\\n \\n current_day = self.days[ day_num ]\\n num_conflicts = 0\\n \\n for prof_name in current_day.keys():\\n if not self.get_prof_by_name[prof_name].available( day_num ):\\n num_conflicts += 1\\n \\n prof_conflict_score += multiplier * ( num_conflicts ** 2 )\\n \\n self.prof_conflict_score = prof_conflict_score\\n return self.prof_conflict_score\",\n \"def check_values_available(pCountry):\\n # Creer un vecteur qui compte le nombre de valeurs different de zero\\n vector = employment.query('country == \\\"{}\\\"'.format(pCountry)).sex.value_counts().values\\n\\n # Retourne vrai s'il n'y a pas que des zeros\\n return np.sum(np.nonzero(vector)) != 0\",\n \"def hospital_resident(residents, hospitals, optimal=\\\"resident\\\"):\\n\\n if optimal == \\\"resident\\\":\\n return resident_optimal(residents, hospitals)\\n if optimal == \\\"hospital\\\":\\n return hospital_optimal(hospitals)\",\n \"def get_sellability_report(melons):\\n\\n # Fill in the rest \\n sellable = None\\n for melon in melons:\\n if melon.is_sellable():\\n sellable = 'CAN BE SOLD'\\n else:\\n sellable = 'NOT SELLABLE'\\n print(f'Harvested by {melon.harvester} from Field {melon.field_number} {sellable}')\",\n \"def es_satisfecho_por(self, candidata):\",\n \"def test_dropping_all_correct(self):\\r\\n self.dropping_setup()\\r\\n\\r\\n self.dropping_homework_stage1()\\r\\n for name in self.hw3_names:\\r\\n self.submit_question_answer(name, {'2_1': 'Correct'})\\r\\n\\r\\n self.check_grade_percent(1.0)\\r\\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 2.0]) # Order matters\\r\\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 1.0])\",\n \"def test_AnvilResearchStudy_Condition(anvil_research_studies_with_observations):\\n _validate_ResearchStudy_Condition(anvil_research_studies_with_observations)\",\n \"def checkAndUpdateAllPossibilities(self, G, PD, prevPat):\\n ### removing candidate if any other action was performed on it ###\\n if 'shrink' not in prevPat.pat_type:\\n if prevPat.pat_type in ['merge']:\\n for p in prevPat.prev_order:\\n if p in self.Data:\\n del self.Data[p]\\n elif prevPat.pat_type in ['update', 'split', 'remove']:\\n if prevPat.prev_order in self.Data:\\n del self.Data[prevPat.prev_order]\\n if self.gtype == 'U':\\n for k,v in self.Data.items():\\n if len(set(v['Pat'].NL).intersection(set(prevPat.NL))) > 1:\\n self.updateConstraintEvaluation(G, PD, k, 2)\\n else:\\n self.updateConstraintEvaluation(G, PD, k, 1)\\n else:\\n for k,v in self.Data.items():\\n inInt = len(set(v['Pat'].inNL).intersection(set(prevPat.inNL)))\\n outInt = len(set(v['Pat'].outNL).intersection(set(prevPat.outNL)))\\n if inInt > 1 and outInt > 1:\\n self.updateConstraintEvaluation(G, PD, k, 2)\\n else:\\n self.updateConstraintEvaluation(G, PD, k, 1)\\n return\",\n \"def _checkstate(self, queryset):\\n for presup in queryset.filter(estado='borrador').exclude(fecha_realizado=None):\\n if ((presup.fecha_realizado + timedelta(days=21) < datetime.now().date() and presup.tipo != 'asistencia') or\\n (presup.fecha_realizado + timedelta(days=60) < datetime.now().date() and presup.tipo == 'asistencia')):\\n presup._toState_cancelado(obs=\\\"Registro automático: Presupuesto vencido\\\")\",\n \"def check_completeness(ISM):\\n for item in ISM:\\n if item not in ['A', 'T', 'C', 'G', '-']:\\n return False\\n return True\",\n \"def __check_all_config_values_set(cfg: __Config):\\n unset_values = [key for key, val in cfg.__dict__.items() if val is None]\\n if len(unset_values) > 0:\\n print(f\\\"WARNING: The following config variables have not been set: {unset_values}\\\")\",\n \"def test_vote_badpklist(self):\\r\\n mock_module = CHModuleFactory.create()\\r\\n json_in = {'answer': '24.0', 'hint': '0', 'pk_list': json.dumps([['24.0', 0], ['24.0', 12]])}\\r\\n hint_and_votes = mock_module.tally_vote(json_in)['hint_and_votes']\\r\\n self.assertTrue(['Best hint', 41] in hint_and_votes)\\r\\n self.assertTrue(len(hint_and_votes) == 1)\",\n \"def test_half_life_unstable_isotopes():\\n for isotope in data_about_isotopes:\\n if (\\n \\\"half_life\\\" not in data_about_isotopes[isotope]\\n and not data_about_isotopes[isotope]\\n ):\\n with pytest.raises(MissingParticleDataError):\\n half_life(isotope)\",\n \"def test_check_ess_settings(self):\\n ess_settings1 = {'gaussian': [self.servers[0]], 'molpro': [self.servers[1], self.servers[0]],\\n 'qchem': [self.servers[0]]}\\n ess_settings2 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n ess_settings3 = {'gaussian': self.servers[0], 'molpro': [self.servers[1], self.servers[0]],\\n 'qchem': self.servers[0]}\\n ess_settings4 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n ess_settings5 = {'gaussian': 'local', 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n\\n ess_settings1 = check_ess_settings(ess_settings1)\\n ess_settings2 = check_ess_settings(ess_settings2)\\n ess_settings3 = check_ess_settings(ess_settings3)\\n ess_settings4 = check_ess_settings(ess_settings4)\\n ess_settings5 = check_ess_settings(ess_settings5)\\n\\n ess_list = [ess_settings1, ess_settings2, ess_settings3, ess_settings4, ess_settings5]\\n\\n for ess in ess_list:\\n for soft, server_list in ess.items():\\n self.assertTrue(soft in ['gaussian', 'molpro', 'qchem'])\\n self.assertIsInstance(server_list, list)\\n\\n with self.assertRaises(SettingsError):\\n ess_settings6 = {'nosoft': ['server1']}\\n check_ess_settings(ess_settings6)\\n with self.assertRaises(SettingsError):\\n ess_settings7 = {'gaussian': ['noserver']}\\n check_ess_settings(ess_settings7)\",\n \"def filter_residues(residues, biomolecule='PROTEIN'):\\n biomolecule = biomolecule.strip().upper()\\n standard_residues = []\\n for res in residues:\\n if res.get_resname().strip() in STANDARD_RESIDUES[biomolecule]:\\n if not res.id[0].strip(): standard_residues.append(res) # filter out hetro residues\\n return standard_residues\",\n \"def check_to_Done(self,cr,uid,ids,context=None):\\n\\n for rec in self.browse(cr, uid, ids, context):\\n if not rec.maintenance_id: \\n raise osv.except_osv(_('ValidateError'), _(\\\"There Is NO maintenace request refrence to this accident.\\\"))\\n return False\\n return True\",\n \"def post_check(self, dataset, resources=None):\\r\\n infidx = where(isinf(dataset.get_attribute(\\\"residential_land_value\\\")))[0]\\r\\n\\r\\n if infidx.size > 0:\\r\\n logger.log_warning(\\\"Infinite values in residential_land_value.\\\")\\r\\n dataset.set_values_of_one_attribute(\\\"residential_land_value\\\", self.maxfloat32,\\r\\n infidx)\\r\\n infidx = where(isinf(dataset.get_attribute(\\\"nonresidential_land_value\\\")))[0]\\r\\n if infidx.size > 0:\\r\\n logger.log_warning(\\\"Infinite values in nonresidential_land_value.\\\")\\r\\n dataset.set_values_of_one_attribute(\\\"nonresidential_land_value\\\", self.maxfloat32,\\r\\n infidx)\",\n \"def test_check_for_unacceptable_matches_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n student = Student(name=\\\"foo\\\")\\n supervisor.matching.append(student)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.prefs))\\n assert student.name in error\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7820969","0.76278716","0.75781184","0.74795306","0.73008245","0.70282596","0.69267523","0.67586416","0.6459601","0.62566453","0.6184107","0.6075766","0.6060278","0.600085","0.5896375","0.5779385","0.5753072","0.5702106","0.56878966","0.5652447","0.5528952","0.5509873","0.5469307","0.54074794","0.53781444","0.53507334","0.5333747","0.53287673","0.53205","0.52729064","0.5237624","0.52249444","0.5206237","0.51650137","0.5165012","0.5162032","0.5150089","0.5134867","0.5127164","0.5100895","0.5062756","0.5055119","0.50335866","0.50335866","0.50335866","0.5003659","0.49869528","0.4975668","0.49606422","0.49525604","0.49427417","0.49053022","0.49048594","0.48952863","0.4891462","0.48745456","0.4863616","0.48611465","0.48608154","0.4853903","0.48454598","0.48356795","0.48313943","0.48208892","0.4815107","0.4809486","0.48079193","0.48037618","0.48003343","0.47984925","0.47970578","0.47879857","0.4785177","0.47581196","0.47574285","0.4748574","0.47446886","0.47444943","0.47443634","0.47443092","0.47396958","0.47381246","0.47369504","0.47357938","0.47290838","0.47222897","0.4721665","0.4718207","0.47115234","0.47104958","0.47024032","0.4699019","0.46986258","0.46965647","0.4693878","0.46910882","0.46825662","0.46817535","0.46792075","0.46709627"],"string":"[\n \"0.7820969\",\n \"0.76278716\",\n \"0.75781184\",\n \"0.74795306\",\n \"0.73008245\",\n \"0.70282596\",\n \"0.69267523\",\n \"0.67586416\",\n \"0.6459601\",\n \"0.62566453\",\n \"0.6184107\",\n \"0.6075766\",\n \"0.6060278\",\n \"0.600085\",\n \"0.5896375\",\n \"0.5779385\",\n \"0.5753072\",\n \"0.5702106\",\n \"0.56878966\",\n \"0.5652447\",\n \"0.5528952\",\n \"0.5509873\",\n \"0.5469307\",\n \"0.54074794\",\n \"0.53781444\",\n \"0.53507334\",\n \"0.5333747\",\n \"0.53287673\",\n \"0.53205\",\n \"0.52729064\",\n \"0.5237624\",\n \"0.52249444\",\n \"0.5206237\",\n \"0.51650137\",\n \"0.5165012\",\n \"0.5162032\",\n \"0.5150089\",\n \"0.5134867\",\n \"0.5127164\",\n \"0.5100895\",\n \"0.5062756\",\n \"0.5055119\",\n \"0.50335866\",\n \"0.50335866\",\n \"0.50335866\",\n \"0.5003659\",\n \"0.49869528\",\n \"0.4975668\",\n \"0.49606422\",\n \"0.49525604\",\n \"0.49427417\",\n \"0.49053022\",\n \"0.49048594\",\n \"0.48952863\",\n \"0.4891462\",\n \"0.48745456\",\n \"0.4863616\",\n \"0.48611465\",\n \"0.48608154\",\n \"0.4853903\",\n \"0.48454598\",\n \"0.48356795\",\n \"0.48313943\",\n \"0.48208892\",\n \"0.4815107\",\n \"0.4809486\",\n \"0.48079193\",\n \"0.48037618\",\n \"0.48003343\",\n \"0.47984925\",\n \"0.47970578\",\n \"0.47879857\",\n \"0.4785177\",\n \"0.47581196\",\n \"0.47574285\",\n \"0.4748574\",\n \"0.47446886\",\n \"0.47444943\",\n \"0.47443634\",\n \"0.47443092\",\n \"0.47396958\",\n \"0.47381246\",\n \"0.47369504\",\n \"0.47357938\",\n \"0.47290838\",\n \"0.47222897\",\n \"0.4721665\",\n \"0.4718207\",\n \"0.47115234\",\n \"0.47104958\",\n \"0.47024032\",\n \"0.4699019\",\n \"0.46986258\",\n \"0.46965647\",\n \"0.4693878\",\n \"0.46910882\",\n \"0.46825662\",\n \"0.46817535\",\n \"0.46792075\",\n \"0.46709627\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77083474"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":93790,"cells":{"query":{"kind":"string","value":"Test each hospital has ranked only residents that have ranked it. If not, check that a warning is caught and the hospital has forgotten any such residents."},"document":{"kind":"string","value":"def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert resident not in hospital.prefs"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.append(\n ValueError(\n f\"{hospital} has not ranked all the residents that \"\n f\"ranked it: {set(hospital.prefs)} != \"\n f\"{set(residents_that_ranked)}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_stability():\n\n residents = [Player(\"A\", [1, 2]), Player(\"B\", [2]), Player(\"C\", [2, 1])]\n hospitals = [Player(1, [\"C\", \"A\"], 3), Player(2, [\"A\", \"B\", \"C\"], 3)]\n match = HospitalResident(residents, hospitals)\n\n matching = match.solve()\n assert match.check_stability()\n\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\n matching[P_1] = [P_C]\n matching[P_2] = [P_A, P_B]\n\n assert not match.check_stability()","def _check_ties(self,rank):\n for k in self._run:\n curr_dict = defaultdict(list)\n qid = self._run[k][0].get_qid()\n for t in self._run[k]:\n # print t.get_str()\n curr_dict[t.get_score()].append(t)\n curr_dict = OrderedDict(sorted(curr_dict.iteritems(),reverse=True))\n cnt = 0\n for score in curr_dict:\n cnt = cnt + 1\n if len(curr_dict[score]) > 1 and cnt<rank:\n self._ties[qid] = 1","def _check_resident_prefs(self):\n\n errors = []\n for resident in self.residents:\n if not set(resident.prefs).issubset(set(self.hospitals)):\n errors.append(\n ValueError(\n f\"{resident} has ranked a non-hospital: \"\n f\"{set(resident.prefs)} != {set(self.hospitals)}\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_lsr_rankings():\n for case in iter_testcases('rankings'):\n n_items = case[\"n_items\"]\n data = case[\"data\"]\n assert np.allclose(\n case[\"lsr_est\"], lsr_rankings(n_items, data),\n atol=ATOL, rtol=RTOL)","def test_ilsr_rankings():\n for case in iter_testcases('rankings'):\n n_items = case[\"n_items\"]\n data = case[\"data\"]\n assert np.allclose(\n case[\"ml_est\"], ilsr_rankings(n_items, data),\n atol=ATOL, rtol=RTOL)","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def test_check_for_unacceptable_matches_residents(game):\n\n resident = game.residents[0]\n hospital = Hospital(name=\"foo\", capacity=1)\n resident.matching = hospital\n\n issues = game._check_for_unacceptable_matches(\"residents\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(resident.name)\n assert issue.endswith(f\"{resident.prefs}.\")\n assert hospital.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_check_inputs_hospital_reciprocated_all_prefs(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n hospital._forget(resident)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"hospitals\", \"residents\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert hospital not in resident.prefs","def test_resident_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"resident\")\n\n assert set(hospitals) == set(matching.keys())\n assert all(\n [\n r in set(residents)\n for r in set(\n [\n r_match\n for matches in matching.values()\n for r_match in matches\n ]\n )\n ]\n )\n\n for resident in residents:\n if resident.matching:\n assert resident.pref_names.index(resident.matching.name) == 0","def fit_score(self, solution):\r\n illegal_neighbours = 0\r\n legal_neighbours = 0\r\n for polygon in solution.genetic_units.values():\r\n for neighbour_id in polygon.neighbours_ids:\r\n if polygon.color is solution.genetic_units[neighbour_id].color:\r\n illegal_neighbours += 1\r\n else:\r\n legal_neighbours += 1\r\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\r\n return illegal_neighbours\r\n else:\r\n return legal_neighbours","def _check_resident_unhappy(resident, hospital):\n\n return resident.matching is None or resident.prefers(\n hospital, resident.matching\n )","def testProtractedNSESanityChecks(self):\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))","def testSpeciesRichnessCorrect(self):\n self.assertEqual(136, self.coal.get_species_richness(1))\n self.assertNotEqual(self.coal.get_species_richness(2), self.coal.get_species_richness(1))\n self.assertNotEqual(self.coal.get_species_richness(3), self.coal.get_species_richness(2))","def test_check_for_oversubscribed_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n supervisor.matching = range(supervisor.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.capacity))\n assert str(supervisor.matching) in e","def has_standard_residues(self):\n for frag in self.iter_standard_residues():\n return True\n return False","def is_error(ranking, references):\n return 1 if average_precision(ranking, references) < 1 else 0","def has_non_standard_residues(self):\n for frag in self.iter_non_standard_residues():\n return True\n return False","def test_check_inputs_resident_prefs_all_nonempty(game):\n\n resident = game.residents[0]\n resident.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n assert resident.name in str(record[0].message)\n\n if game.clean:\n assert resident not in game.residents","def get_ranks(d): \n raise NotImplementedError(\"Problem 3 Incomplete\")","def test_hand_has_all_unique_ranks(hand, card_list, expected):\n hand.add_cards(card_list)\n assert hand.has_all_unique_ranks() == expected","def has_straight(self):\n res = []\n self.suit_hist()\n for val in self.ranks.keys():\n res.append(val)\n res.sort()\n self.is_sequence(res, 3)","def test_check_inputs_hospital_prefs_all_residents(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert hospital.prefs == []","def assess_risk(black_marbles: list, white_marbles: list):\n danger = Evaluator.danger_positions\n black_risk = 0\n white_risk = 0\n for black_marble in black_marbles:\n letter, num = Board.convert_to_nums(black_marble)\n left = Board.convert_to_string(letter, num - 1)\n right = Board.convert_to_string(letter, num + 1)\n upL = Board.convert_to_string(letter + 1, num)\n upR = Board.convert_to_string(letter + 1, num + 1)\n downL = Board.convert_to_string(letter - 1, num - 1)\n downR = Board.convert_to_string(letter - 1, num)\n test_spots = [left, right, upL, upR, downL, downR]\n if black_marble in danger:\n black_risk += 2\n for spot in test_spots:\n if spot in white_marbles:\n black_risk += 5\n\n for white_marble in white_marbles:\n letter, num = Board.convert_to_nums(white_marble)\n left = Board.convert_to_string(letter, num - 1)\n right = Board.convert_to_string(letter, num + 1)\n upL = Board.convert_to_string(letter + 1, num)\n upR = Board.convert_to_string(letter + 1, num + 1)\n downL = Board.convert_to_string(letter - 1, num - 1)\n downR = Board.convert_to_string(letter - 1, num)\n test_spots = [left, right, upL, upR, downL, downR]\n if white_marble in danger:\n white_risk += 2\n for spot in test_spots:\n if spot in black_marbles:\n white_risk += 5\n return black_risk, white_risk","def test_residues(parse_mmtf):\n parse_mmtf.assertEqual(len(parse_mmtf.mmcif_res), len(parse_mmtf.mmtf_res))\n for i, e in enumerate(parse_mmtf.mmcif_res):\n mmcif_r = parse_mmtf.mmcif_res[i]\n mmtf_r = parse_mmtf.mmtf_res[i]\n parse_mmtf.assertEqual(mmtf_r.level, mmcif_r.level)\n parse_mmtf.assertEqual(mmtf_r.disordered, mmcif_r.disordered)\n parse_mmtf.assertEqual(mmtf_r.resname, mmcif_r.resname)\n parse_mmtf.assertEqual(mmtf_r.segid, mmcif_r.segid)\n parse_mmtf.mmcif_atoms = [x for x in mmcif_r.get_atom()]\n parse_mmtf.mmtf_atoms = [x for x in mmtf_r.get_atom()]\n test_atoms(parse_mmtf=parse_mmtf)","def test_check_inputs_resident_prefs_all_hospitals(game):\n\n resident = game.residents[0]\n resident.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert resident.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert resident.prefs == []","def _check_hospital_unhappy(resident, hospital):\n\n return len(hospital.matching) < hospital.capacity or any(\n [hospital.prefers(resident, match) for match in hospital.matching]\n )","def check_order(racers):\n racers_orderd = sorted(racers, key=lambda x: (x.group, x.time))\n a = 1\n b = 1\n for r in racers_orderd:\n if r.group == \"ALL\":\n r.rank = str(a)\n a += 1\n else:\n r.rank = str(b)\n b += 1\n return racers_orderd","def listErrors(individual): #TODO: add other errors implemented elsewhere\n\n results = [birthBeforeMarriage(individual), birthBeforeDeath(individual), marriageBeforeDeath(individual), \n datesBeforeCurrentDate(individual), noBigamy(individual)]\n results = [x for x in results if x is not None]\n return not all(results)","def test_check_for_unacceptable_matches_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n student = Student(name=\"foo\")\n supervisor.matching.append(student)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.prefs))\n assert student.name in error","def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals","def testNumberIndividualsCorrect(self):\n for ref in self.coal.get_community_references():\n self.assertEqual(1000, self.coal.get_number_individuals(community_reference=ref))","def arsenalResults(dat):\n arsScore = int(dat[0])\n othScore = int(dat[2])\n if arsScore > othScore:\n res = 1\n elif arsScore == othScore:\n res = 2\n else:\n res = 0\n return res","def _check_resident_matching(self):\n\n errors = []\n for resident in self.residents:\n if (\n resident.matching is not None\n and resident.matching not in resident.prefs\n ):\n errors.append(\n ValueError(\n f\"{resident} is matched to {resident.matching} but \"\n \"they do not appear in their preference list: \"\n f\"{resident.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def __statistics_students_failing(self):\n students_list = self.__grade_controller.get_list_of_failing_students()\n if len(students_list) == 0:\n print(\"There is no student failing!\")\n return\n\n for student in students_list:\n print(str(student) + \"\\n\")","def count_standard_residues(self):\n n = 0\n for na in self.iter_standard_residues():\n n += 1\n return n","def rank_err(data_mod, mod_dict):\n # checks that order of patients is identical for both models, and that correct data dictionary is passed\n np.testing.assert_array_equal(mod_dict['linear'].Y, mod_dict['rbf'].Y, err_msg='lin and rbf models not trained on same data')\n np.testing.assert_array_equal(mod_dict['linear'].X, mod_dict['rbf'].X, err_msg='lin and rbf models not trained on same data')\n np.testing.assert_array_equal(mod_dict['rbf'].X, data_mod['XA'], err_msg='mogp models and data dictionary not trained on same data') \n\n err_dict = {}\n for key in mod_dict.keys():\n err_dict[key] = calc_rmse_full_mod(data=data_mod, model_type=key, mod=mod_dict[key])\n\n return err_dict","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def is_unhappy(self):\n #checked!#\n ###your code here###\n same=0\n for i in self.home.neighbors:\n if i.occupant!=None:\n if i.occupant.group==self.group:\n same+=1\n happniess=float(same)/len(self.home.neighbors)\n if happniess<self.happiness_threshold:\n return True\n else:\n return False","def isranked(self):\n rank = self.console.getCvar('sv_ranked')\n return rank and rank.getInt() == 2","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def test_something(self):\n\n allure.dynamic.title(\"Testing compute_ranks\")\n allure.dynamic.severity(allure.severity_level.NORMAL)\n allure.dynamic.description_html('<h3>Codewars badge:</h3>'\n '<img src=\"https://www.codewars.com/users/myFirstCode'\n '/badges/large\">'\n '<h3>Test Description:</h3>'\n \"<p>Test the function taht organizes a sports league in a \"\n \"round-robin-system. Each team meets all other teams. \"\n \"In your league a win gives a team 2 points, a draw gives \"\n \"both teams 1 point. After some games you have to compute \"\n \"the order of the teams in your league. You use the following \"\n \"criteria to arrange the teams:</p>\"\n \"<ul><li>- Points</li>\"\n \"<li>- Scoring differential (the difference between goals \"\n \"scored and those conceded)</li>\"\n \"<li>- Goals scored</li></ul>\")\n\n test_data = [\n (6,\n [[0, 5, 2, 2],\n [1, 4, 0, 2],\n [2, 3, 1, 2],\n [1, 5, 2, 2],\n [2, 0, 1, 1],\n [3, 4, 1, 1],\n [2, 5, 0, 2],\n [3, 1, 1, 1],\n [4, 0, 2, 0]],\n [4, 4, 6, 3, 1, 2]),\n (6,\n [[0, 5, 2, 0],\n [1, 4, 2, 2],\n [2, 3, 1, 3],\n [1, 5, 0, 0],\n [2, 0, 2, 1],\n [3, 4, 3, 1]],\n [2, 3, 4, 1, 5, 6]),\n (4,\n [[0, 3, 1, 1],\n [1, 2, 2, 2],\n [1, 3, 2, 0],\n [2, 0, 2, 0]],\n [3, 1, 1, 3]),\n (10,\n [],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n (8,\n [[0, 7, 2, 0]],\n [1, 2, 2, 2, 2, 2, 2, 8])\n ]\n\n for data in test_data:\n number = data[0]\n games = data[1]\n expected = data[2]\n actual_result = compute_ranks(number, games)\n print_log(number=number,\n games=games,\n expected=expected,\n actual_result=actual_result)\n\n with allure.step(\"Enter a test data and verify the result:\"):\n self.assertEqual(expected, actual_result)","def test_resids(self):\n cr = CaseReader(self.filename)\n last_case = cr.get_case(-1)\n last_case_id = cr.list_cases()[-1]\n n = cr.num_cases\n with h5py.File(self.filename, 'r') as f:\n for key in f[last_case_id]['Residuals'].keys():\n val = f[last_case_id]['Residuals'][key][()]\n np.testing.assert_almost_equal(last_case.resids[key], val,\n err_msg='Case reader gives'\n ' incorrect Unknown'\n ' value for'\n ' {0}'.format(key))","def death_check(self):\r\n chance = random.random()\r\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\r\n if self.unique_id in head_of_household_list:\r\n try:\r\n head_of_household_list[self.hh_id] = 0\r\n except TypeError: # head of household migrated\r\n head_of_household_list[self.past_hh_id] = 0\r\n self.model.number_of_humans -= 1\r\n if self.unique_id in labor_list:\r\n labor_list.remove(self.unique_id)\r\n if self.work_status == 1:\r\n try:\r\n num_labor_list[self.hh_id] -= 1\r\n except TypeError:\r\n num_labor_list[self.past_hh_id] -= 1\r\n if self.unique_id in former_hoh_list:\r\n try:\r\n former_hoh_list[self.hh_id] = 0\r\n except:\r\n former_hoh_list[self.past_hh_id] = 0\r\n if [self.unique_id, self.hh_id] in single_male_list:\r\n single_male_list.remove([self.unique_id, self.hh_id])\r\n if self.unique_id in married_male_list:\r\n married_male_list.remove(self.unique_id)\r\n human_death_list.append(self.unique_id)\r\n try:\r\n hh_size_list[self.hh_id] -= 1\r\n except:\r\n hh_size_list[self.past_hh_id] -= 1\r\n human_demographic_structure_list[self.age_category] -= 1\r\n\r\n self.model.schedule.remove(self)\r\n if self in self.model.grid:\r\n self.model.grid.remove_agent(self)","def isResidueAssigned(residue):\n\n for atom in residue.atoms:\n if atom.atomSet:\n if atom.atomSet.resonanceSets:\n return True\n \n return False","def _check_RS(self,suits_frequency):\n if len(np.array(suits_frequency.loc[suits_frequency==5])) == 1:\n RS=1\n else:\n RS=0\n\n return RS","def _check_unstake_result(self) -> None:\n balance_score = self.icx.get_balance(self.address) - self._daily_reward.get()\n if balance_score > 0:\n unstake_info_list = self.getUserUnstakeInfo()\n for each_info in unstake_info_list:\n value_to_transfer = each_info[0]\n if value_to_transfer <= balance_score:\n self._send_ICX(each_info[1], value_to_transfer)\n self._linked_list_var.remove(self._linked_list_var._head_id.get())\n break","def test_check_stability():\n\n students = [Student(\"A\"), Student(\"B\"), Student(\"C\")]\n projects = [Project(\"P\", 2), Project(\"Q\", 2)]\n supervisors = [Supervisor(\"X\", 2), Supervisor(\"Y\", 2)]\n\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\n\n p.set_supervisor(x)\n q.set_supervisor(y)\n\n a.set_prefs([p, q])\n b.set_prefs([q])\n c.set_prefs([q, p])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = StudentAllocation(students, projects, supervisors)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (p, q) = game.students, game.projects\n\n matching[p] = [c]\n matching[q] = [a, b]\n\n assert not game.check_stability()","def evaluate_ranks(articles, rank_tuples):\n rank_tp=defaultdict(int)\n rank_fn=defaultdict(int)\n rank_fp=defaultdict(int)\n \n for article in articles:\n for mention in article.entity_mentions:\n form=mention.mention\n meaning=mention.gold_link\n sys_meaning=mention.sys_link\n t_gold=(form, meaning)\n t_sys=(form, sys_meaning)\n for rank, r_tuples in rank_tuples.items():\n if t_gold in r_tuples and t_sys in r_tuples:\n rank_tp[rank]+=1\n break\n elif t_gold in r_tuples:\n rank_fn[rank]+=1\n elif t_sys in r_tuples:\n rank_fp[rank]+=1\n print('tp', rank_tp)\n print('fp', rank_fp)\n print('fn', rank_fn)\n \n rank_prec={}\n rank_recall={}\n rank_f1={}\n \n for rank in range(1,13):\n if rank_tp[rank]+rank_fp[rank]>0:\n rank_prec[rank]=rank_tp[rank]/(rank_tp[rank]+rank_fp[rank])\n else:\n rank_prec[rank]=0.0\n if rank_tp[rank]+rank_fn[rank]>0:\n rank_recall[rank]=rank_tp[rank]/(rank_tp[rank]+rank_fn[rank])\n else:\n rank_recall[rank]=0.0\n if rank_prec[rank]+rank_recall[rank]>0:\n rank_f1[rank]=2*rank_prec[rank]*rank_recall[rank]/(rank_prec[rank]+rank_recall[rank])\n else:\n rank_f1[rank]=0.0\n print('precision', rank_prec)\n print()\n print('recall', rank_recall)\n print()\n print('f1', rank_f1)\n print()\n return rank_prec, rank_recall, rank_f1","def assignRanks(self):\r\n\t\trank = 0\r\n\t\tscores = list(self._playerScores)\r\n\t\tscores.reverse()\r\n\t\tfor playerScore in scores:\r\n\t\t\tif not playerScore.has(NOT_MET) or not playerScore.value(NOT_MET):\r\n\t\t\t\trank += 1\r\n\t\t\t\tplayerScore.set(RANK, smallText(BugUtil.colorText(u\"%d\" % rank, ScoreOpt.getRankColor())))\r\n\t\tif rank > 0:\r\n\t\t\tself._anyHas[RANK] = True","def test_solve(resident_names, hospital_names, capacities, seed):\n\n for optimal in [\"suitor\", \"resident\", \"reviewer\", \"hospital\"]:\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n matching = match.solve(optimal)\n assert isinstance(matching, Matching)\n assert set(matching.keys()) == set(hospitals)\n matched_residents = [\n res for match in matching.values() for res in match\n ]\n assert matched_residents != [] and set(matched_residents).issubset(\n set(residents)\n )\n\n for resident in set(residents) - set(matched_residents):\n assert resident.matching is None","def rlWorkout(gameInfo):\n \n if gameInfo['myScore'] >= 0 and gameInfo['opponentScore'] >= 0:\n if gameInfo['myScore'] > gameInfo['opponentScore']:\n determineRocketLeagueWin(gameInfo['goals'], gameInfo['assist'], gameInfo['saves'])\n else:\n determineRocketLeagueLoss(gameInfo['goals'], gameInfo['assist'], gameInfo['saves'])\n else:\n print(\"Doesn't make sense dude\")\n return workoutInfo","def tie_exists(self):\n return len(self.marks) == 9","def testNSESanityChecks(self):\n self.assertEqual(100, self.c.get_species_richness())\n self.assertEqual(67, self.c2.get_species_richness())","def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"hospital\")\n\n assert set(hospitals) == set(matching.keys())\n\n for hospital, matches in matching.items():\n old_idx = -np.infty\n for resident in matches:\n idx = hospital.pref_names.index(resident.name)\n assert idx >= old_idx\n old_idx = idx","def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching.append(Player(name=\"foo\", pref_names=[]))\n\n with pytest.raises(Exception):\n match._check_reviewer_matching()","def check_stability(self):\n\n blocking_pairs = []\n for resident in self.residents:\n for hospital in self.hospitals:\n if (\n _check_mutual_preference(resident, hospital)\n and _check_resident_unhappy(resident, hospital)\n and _check_hospital_unhappy(resident, hospital)\n ):\n blocking_pairs.append((resident, hospital))\n\n self.blocking_pairs = blocking_pairs\n return not any(blocking_pairs)","def test_check_inputs_supervisor_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n supervisor.prefs.remove(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"supervisors\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert supervisor.name in message\n\n if clean:\n assert supervisor not in student.prefs\n assert all(\n project not in student.prefs for project in supervisor.projects\n )","def rerank_candidates(s, pred2sub_rank, all_predictions, rerank_top=20):\n predicted_smiles = []\n model_input = []\n for (predict_smi, label), _ in Counter(all_predictions).most_common(rerank_top):\n if predict_smi == s:\n continue\n features = get_all_features(\n get_all_ranking_info(pred2sub_rank[predict_smi]))\n predicted_smiles.append((predict_smi, label))\n model_input.append(features)\n\n model = RankingModel()\n model.load_state_dict(torch.load('./models/ranker/rank_model.pt', map_location='cpu'))\n model.eval()\n\n test_loader = DataLoader(RankingTestDataset(\n model_input), batch_size=1000, shuffle=False, num_workers=2)\n ranking_scores = []\n for data in test_loader:\n outputs = model(data)[0]\n ranking_scores.extend(outputs.detach().cpu().numpy())\n\n assert len(predicted_smiles) == len(ranking_scores)\n pred_smi2score = {k: v[1]\n for k, v in zip(predicted_smiles, ranking_scores)}\n return pred_smi2score","def test_enter_score_cleanup(self):\n tournament_scores = len(TournamentScore.query.all())\n scores = len(ScoreDAO.query.all())\n\n self.enter_score_bad_values()\n\n compare(tournament_scores, len(TournamentScore.query.all()))\n compare(scores, len(ScoreDAO.query.all()))","def test_calculates_risk_points_with_mortgaged_house(self):\n\n self.user[\"houses\"].insert(0, {\n \"id\": 123,\n \"ownership_status\": \"mortgaged\"\n })\n\n self.risk_profile[\"home\"].insert(0, {\n \"id\": 123,\n \"is_eligible\": True,\n \"risk_points\": 1\n })\n\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\n\n for house in self.risk_profile[\"home\"]:\n if house[\"id\"] == 123:\n self.assertEquals(house[\"risk_points\"], 2)\n else: \n self.assertEquals(house[\"risk_points\"], 1)\n \n self.assertEquals(self.risk_profile[\"disability\"][\"risk_points\"], 2)","def recip_rank(recs, truth):\n good = recs['item'].isin(truth.index)\n npz, = np.nonzero(good)\n if len(npz):\n return 1.0 / (npz[0] + 1.0)\n else:\n return 0.0","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def error_output_rank(self):\n rank_method_string = ''\n for i in range(len(methods_of_ranking)):\n if i == 0:\n continue\n elif i != len(methods_of_ranking) - 1:\n rank_method_string += (methods_of_ranking[i] + '() or ')\n else:\n rank_method_string += (methods_of_ranking[i] + '()')\n print(\"please rank first by \" + rank_method_string)\n sys.exit(0)","def is_legal_solution(self, solution):\r\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\r\n return self.fit_score(solution) == 0\r\n else:\r\n return self.fit_score(solution) == sum(x for x in range(1, 12))","def test_mirror_ranking(self):\n updater = AptMirrorUpdater()\n # Make sure that multiple discovered mirrors are available.\n assert sum(m.is_available for m in updater.ranked_mirrors) > 10","def test_check_inputs_supervisor_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n projects = supervisor.projects\n for project in student.prefs:\n if project in projects:\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"supervisors\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(supervisor.name)\n assert student.name in message\n\n if clean:\n assert student not in supervisor.prefs\n assert all(\n student not in project.prefs for project in supervisor.projects\n )","def rank_sensitvity_bis(dsys, X, Y, n_test=100):\n\n optdmd_train_error, optdmd_test_error = list(), list()\n\n # Fit a DMD model for each possible rank.\n for rank in range(1, dsys.A.shape[0] + 1):\n # Fit the DMD model (optimal closed-form solution)\n optdmd = OptDMD(svd_rank=rank, factorization=\"svd\").fit(X, Y)\n\n # One-step ahead prediction using both DMD models.\n y_predict_opt = optdmd.predict(X)\n\n # Compute the one-step ahead prediction error.\n optdmd_train_error.append(norm(y_predict_opt - Y) / norm(Y))\n\n # Evaluate the error on test data.\n optdmd_error = list()\n for _ in range(n_test):\n # Test initial condition.\n x0_test = normal(loc=0.0, scale=1.0, size=(dsys.A.shape[1]))\n\n # Run simulation to generate dataset.\n t, _, x_test = dlsim(dsys, np.zeros((250, dsys.inputs)), x0=x0_test)\n\n # Split the training data into input/output snapshots.\n y_test, X_test = x_test.T[:, 1:], x_test.T[:, :-1]\n\n # One-step ahead prediction using both DMD models.\n y_predict_opt = optdmd.predict(X_test)\n\n # Compute the one-step ahead prediction error.\n optdmd_error.append(norm(y_predict_opt - y_test) / norm(y_test))\n\n # Store the error for rank i DMD.\n optdmd_test_error.append(np.asarray(optdmd_error))\n\n # Complete rank-sensitivity.\n optdmd_test_error = np.asarray(optdmd_test_error)\n optdmd_train_error = np.asarray(optdmd_train_error)\n\n return optdmd_train_error, optdmd_test_error","def test_rescoring_randomized_problem(self):\r\n # First define the custom response problem:\r\n problem_url_name = 'H1P1'\r\n self.define_randomized_custom_response_problem(problem_url_name)\r\n location = InstructorTaskModuleTestCase.problem_location(problem_url_name)\r\n descriptor = self.module_store.get_item(location)\r\n # run with more than one user\r\n userlist = ['u1', 'u2', 'u3', 'u4']\r\n for username in userlist:\r\n # first render the problem, so that a seed will be created for this user\r\n self.render_problem(username, problem_url_name)\r\n # submit a bogus answer, in order to get the problem to tell us its real answer\r\n dummy_answer = \"1000\"\r\n self.submit_student_answer(username, problem_url_name, [dummy_answer, dummy_answer])\r\n # we should have gotten the problem wrong, since we're way out of range:\r\n self.check_state(username, descriptor, 0, 1, 1)\r\n # dig the correct answer out of the problem's message\r\n module = self.get_student_module(username, descriptor)\r\n state = json.loads(module.state)\r\n correct_map = state['correct_map']\r\n log.info(\"Correct Map: %s\", correct_map)\r\n # only one response, so pull it out:\r\n answer = correct_map.values()[0]['msg']\r\n self.submit_student_answer(username, problem_url_name, [answer, answer])\r\n # we should now get the problem right, with a second attempt:\r\n self.check_state(username, descriptor, 1, 1, 2)\r\n\r\n # redefine the problem (as stored in Mongo) so that the definition of correct changes\r\n self.define_randomized_custom_response_problem(problem_url_name, redefine=True)\r\n # confirm that simply rendering the problem again does not result in a change\r\n # in the grade (or the attempts):\r\n self.render_problem('u1', problem_url_name)\r\n self.check_state('u1', descriptor, 1, 1, 2)\r\n\r\n # rescore the problem for only one student -- only that student's grade should change\r\n # (and none of the attempts):\r\n self.submit_rescore_one_student_answer('instructor', problem_url_name, User.objects.get(username='u1'))\r\n for username in userlist:\r\n self.check_state(username, descriptor, 0 if username == 'u1' else 1, 1, 2)\r\n\r\n # rescore the problem for all students\r\n self.submit_rescore_all_student_answers('instructor', problem_url_name)\r\n\r\n # all grades should change to being wrong (with no change in attempts)\r\n for username in userlist:\r\n self.check_state(username, descriptor, 0, 1, 2)","def testDispersalSimulation(self):\n self.assertEqual(715, self.c.get_species_richness(1))","def find_qualifying_loans(bank_data, credit_score, debt, income, loan, home_value):\n\n # Calculate the monthly debt ratio\n monthly_debt_ratio = calculate_monthly_debt_ratio(debt, income)\n print(\"\\n\", \"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\")\n print(\"\\n\", \"\\u001b[34;1m\")\n print(f\"------->>> Monthly debt-to-income ratio is: {monthly_debt_ratio:.02f}.\")\n print(\"\\u001b[0m\")\n\n # Calculate loan to value ratio\n loan_to_value_ratio = calculate_loan_to_value_ratio(loan, home_value)\n print(\"\\n\", \"\\u001b[34;1m\")\n print(f\"------->>> Loan-to-value ratio is {loan_to_value_ratio:.02f}.\")\n print(\"\\n\", \"\\u001b[0m\")\n print(\" * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\", \"\\n\")\n\n # Run qualification filters\n bank_data_filtered = filter_max_loan_size(loan, bank_data)\n bank_data_filtered = filter_credit_score(credit_score, bank_data_filtered)\n bank_data_filtered = filter_debt_to_income(monthly_debt_ratio, bank_data_filtered)\n bank_data_filtered = filter_loan_to_value(loan_to_value_ratio, bank_data_filtered)\n number_of_loans_available = len(bank_data_filtered)\n if number_of_loans_available <= 0:\n print(\"I'm sorry. We have no loans to offer at this time.\"), \"\\n\"\n exit()\n else: \n print(\"\\u001b[32m\", f\"Congratulations! You qualify for: {number_of_loans_available} loan(s).\", \"\\u001b[0m\", \"\\n\")\n\n return bank_data_filtered","def test_residue_loop_no_selection(self):\n\n # Spin data.\n num = [1, 2, 4, -5, -4]\n name = [None, 'Glu', 'Pro', None, None]\n\n # Loop over the residues.\n i = 0\n for res in mol_res_spin.residue_loop():\n # Test the residue numbers.\n self.assertEqual(res.num, num[i])\n\n # Test the residue names.\n self.assertEqual(res.name, name[i])\n\n # Increment i.\n i = i + 1\n\n # Test loop length.\n self.assertEqual(i, 5)","def check_evaluations(session, dates):\n user_ids = get_campus_students(session, 13)\n bad_evals = []\n\n print(f'found {len(user_ids)} students !')\n for user_id in user_ids:\n # Get all evaluations from student id in selected timeframe\n evaluations = get_all_pages(session, f'/users/{user_id}/scale_teams/as_corrected', 100, params=dates)\n if evaluations == '[]':\n continue\n # Check each returned evaluation if bad or not\n for evaluation in evaluations:\n # Object BadEvaluation is returned if rules come back True\n bad = detect_bad_eval(evaluation)\n if isinstance(bad, BadEvaluation):\n bad_evals.append(bad)\n\n return bad_evals","def __statistics_best_situation(self):\n students_list = self.__grade_controller.get_list_of_students_with_best_situation()\n if len(students_list) == 0:\n print(\"There is no student with a grade!\")\n return\n\n for student in students_list:\n print(str(student) + \"\\n\")","def test_avalanche_warning_by_region_obs(self):\n pass","def test_score(self, n_neighbors, weights):\n _check_knn_score(\n KNeighborsPartialLabelRanker(n_neighbors, weights), self.X, self.Y)","def test_check_yahtzee_bonus_false(self):\n\n not_yahtzee_bonus_fixtures = [[1, 1, 1, 1, 2],\n [1, 1, 1, 2, 1],\n [1, 1, 2, 1, 1],\n [1, 2, 1, 1, 1],\n [2, 1, 1, 1, 1],\n ]\n\n for fixture in not_yahtzee_bonus_fixtures:\n score = self.roll.check_yahtzee_bonus(fixture)\n\n self.assertNotEqual(score, 50)\n self.assertEqual(score, 0)\n self.assertEqual(len(fixture), 5)","def test_score(self, n_neighbors, weights):\n _check_knn_score(\n KNeighborsLabelRanker(n_neighbors, weights), self.X, self.Y)","def test_solve(game, optimal):\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_hospital, hospital in zip(matching_keys, hospitals):\n assert game_hospital.name == hospital.name\n assert game_hospital._pref_names == hospital._pref_names\n assert game_hospital.capacity == hospital.capacity\n\n matched_residents = [\n resident for match in matching.values() for resident in match\n ]\n\n assert matched_residents != [] and set(matched_residents).issubset(\n set(game.residents)\n )\n\n for resident in set(game.residents) - set(matched_residents):\n assert resident.matching is None","def validate_rank_data(R):\n if not isinstance(R, np.ndarray):\n raise TypeError(\"Data must be an ndarray.\")\n\n if R.size == 0:\n raise ValueError(\"Input data is empty\")\n\n if R.ndim == 1:\n\n true_r = np.arange(1, R.size+1)\n\n if not _is_disjoint(true_r, R):\n raise ValueError((\"Base classifier predictions must be an ndarray of \"\n \"sample rank values.\"))\n elif R.ndim == 2:\n\n true_r = np.arange(1, R.shape[1] + 1)\n\n for i in range(R.shape[0]):\n if not _is_disjoint(true_r, R[i, :]):\n raise ValueError((\"Base classifier predictions must \"\n \"be sample ranks.\"))\n else:\n raise ValueError(\"Data must be a 1-d or 2-d ndarray.\")","def testAxiomReasoning(self):\n \n state = State.from_problem(self.prob)\n extstate, reasons, universalReasons = state.get_extended_state(getReasons=True)\n\n relevantVars = []\n relevantReplanVars = []\n load = self.dom.get_action(\"a_load\")\n with load.instantiate([\"agent\", \"obj11\", \"tru1\"], self.prob):\n self.assert_(extstate.is_satisfied(load.precondition, relevantVars))\n self.assert_(extstate.is_satisfied(load.replan, relevantReplanVars))\n\n all_reasons = set(relevantVars)\n for v in relevantVars:\n all_reasons |= reasons[v]\n\n all_replan_reasons = set(relevantReplanVars)\n for v in relevantReplanVars:\n all_replan_reasons |= reasons[v]\n \n s1 = StateVariable(self.prob.functions[\"location-of\"][0], [self.prob[\"tru1\"]])\n s2 = StateVariable(self.prob.functions[\"location-of\"][0], [self.prob[\"obj11\"]])\n\n self.assert_(s1 in all_reasons)\n self.assert_(s2 in all_reasons)\n self.assertFalse(s1 in all_replan_reasons)\n self.assert_(s2 in all_replan_reasons)","def user_story_4(self):\n for family in self.families.values():\n if family.married != 'NA':\n if family.wife_id != 'NA' and family.husb_id != 'NA' and family.divorced != 'NA':\n if family.divorced < family.married:\n print(\n f'US04 - {self.individuals[family.wife_id].name} and {self.individuals[family.husb_id].name} married after divorce on line {family._married_line}')","def test_anvil_first_patient_everything(caplog, anvil_server, anvil_research_studies_with_observations):\n research_studies = {r.relativePath(): r for r in anvil_research_studies_with_observations if\n r.__class__.__name__ == 'ResearchStudy'}\n resource_counts = _validate_patient_everything(caplog, anvil_server,\n [research_studies['ResearchStudy/1000G-high-coverage-2019']])\n assert False, resource_counts","def likely_regressions(self):\n return set([label for label, count in self.regressions.items() if count == 0])","def test_roster(self):\n self.assertIsNone(self.model.get_issue_number((\"test\",), \"test\"),\n \"Did not return None even though roster entry doesn't exist\")\n\n test_tuples = ( ((\"cs61a-ab\",), \"proj1\", 1000),\n ((\"cs61a-ab\",), \"proj2\", 1001),\n ((\"cs61a-bc\", \"cs61a-cd\"), \"proj1\", 2000),\n ((\"cs61a-bc\", \"cs61a-cd\"), \"proj2\", 2001) )\n\n # pylint: disable=W0142\n for test_tuple in test_tuples:\n self.model.set_issue_number(*test_tuple)\n\n for students, assign, issue_num in test_tuples:\n self.assertEquals(issue_num,\n self.model.get_issue_number(students, assign),\n \"Got wrong issue number\")","def test_verify_non_existing_rank(self):\n data = {'rank_id': 5}\n res = self.post(url='/verify/4', data=data, role='admin')\n self.assertEqual(res.status_code, 401)\n self.assertException(res, exc.EntryNotFound)","def check_all(self):\n return self.check_rs() + self.check_hgvs()","def test_non_zero_sum_profile_welfare():\n game = matgame.matgame([[[3.5, 2.5]]])\n assert np.isclose(\n regret.pure_social_welfare(game, [1, 1]), 6\n ), \"didn't properly sum welfare\"","def validate_step(self,\n x,\n y):\n\n statistics = dict()\n\n for i in range(self.bootstraps):\n fm = self.forward_models[i]\n\n # calculate the prediction error and accuracy of the model\n d = fm.get_distribution(x, training=False)\n nll = -d.log_prob(y)[:, 0]\n\n # evaluate how correct the rank fo the model predictions are\n rank_correlation = spearman(y[:, 0], d.mean()[:, 0])\n\n statistics[f'oracle_{i}/validate/nll'] = nll\n statistics[f'oracle_{i}/validate/rank_corr'] = rank_correlation\n\n return statistics","def straight(ranks):\n return (max(ranks)-min(ranks) == 4) and len(set(ranks)) == 5","def _identify_anomalies(self, X, yhat, prob):\n\n y = DTA_LSTM._shift_traces_left(X)\n\n for i in range(len(X)):\n\n rp = self._compare_traces(y[i], yhat[i])\n self.rank_prob.append(rp)\n\n if self.explain:\n print('X[{}]: {} = {}'.format(i, X[i], rp))\n\n return self.rank_prob","def testDispersalSimulation(self):\n self.assertEqual(1172, self.c.get_species_richness(1))","def validate_perturbed_residues(molecule, perturbed):\n length = len(molecule)\n numPerturbed = len(perturbed)\n #if ","def __count_errors(node, testSet, res):\n training_results = __get_results(node) #Get a dictionary of labels and counts for the *training* data which made it to this node\n leaf_label = None #Initialize a label for this leaf\n majority_count = 0 #Initialize a variable to track the number of observations for the label with the most observations\n #Note that the steps below do not handle ties of the majority count in a nice way.\n for label, count in training_results.items(): #iterate through each pair of labels and counts from the training set\n if count > majority_count: #find the label with the highest count\n leaf_label = label #the label for the leaf is the label with the highest count\n majority_count = count #keep track of the count for the leaf_label\n \n wrong_labels = testSet[res].unique().tolist() #initialize wrong_labels to be all labels in the testSet\n if leaf_label in wrong_labels: #If the leaf label is in the list of labels for the part of the test set that got to this node\n wrong_labels.remove(leaf_label) #remove the leaf_label so that all which remains are incorrect labels\n \n wrong_count = 0 #Initialize a count of how many testSet observations will be classified incorrectly\n testCounts = testSet.groupby(res).size() #Get a series of the testSet labels and how many observations pertain to each label\n for label in wrong_labels: #Iterate over all the labels not equal to the leaf_label\n wrong_count += testCounts[label] #Sum up all of the observations with a label not equal to the leaf_label\n return wrong_count","def testNoSpecialties(self):\n self.failUnlessEqual(self.person.getSpecialties(), [])","def test_special_RR(self):\n self.check_oneq_special_cases(U3Gate(0.0, 0.1, -0.1).to_matrix(), \"RR\", {})\n self.check_oneq_special_cases(U3Gate(0.0, 0.1, 0.2).to_matrix(), \"RR\", {\"r\": 2})\n self.check_oneq_special_cases(U3Gate(-np.pi, 0.2, 0.0).to_matrix(), \"RR\", {\"r\": 1})\n self.check_oneq_special_cases(U3Gate(np.pi, 0.0, 0.2).to_matrix(), \"RR\", {\"r\": 1})\n self.check_oneq_special_cases(U3Gate(0.1, 0.2, 0.3).to_matrix(), \"RR\", {\"r\": 2})","def count_non_standard_residues(self):\n n = 0\n for frag in self.iter_non_standard_residues():\n n += 1\n return n"],"string":"[\n \"def _check_hospital_prefs(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n residents_that_ranked = [\\n res for res in self.residents if hospital in res.prefs\\n ]\\n if set(hospital.prefs) != set(residents_that_ranked):\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has not ranked all the residents that \\\"\\n f\\\"ranked it: {set(hospital.prefs)} != \\\"\\n f\\\"{set(residents_that_ranked)}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_stability():\\n\\n residents = [Player(\\\"A\\\", [1, 2]), Player(\\\"B\\\", [2]), Player(\\\"C\\\", [2, 1])]\\n hospitals = [Player(1, [\\\"C\\\", \\\"A\\\"], 3), Player(2, [\\\"A\\\", \\\"B\\\", \\\"C\\\"], 3)]\\n match = HospitalResident(residents, hospitals)\\n\\n matching = match.solve()\\n assert match.check_stability()\\n\\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\\n matching[P_1] = [P_C]\\n matching[P_2] = [P_A, P_B]\\n\\n assert not match.check_stability()\",\n \"def _check_ties(self,rank):\\n for k in self._run:\\n curr_dict = defaultdict(list)\\n qid = self._run[k][0].get_qid()\\n for t in self._run[k]:\\n # print t.get_str()\\n curr_dict[t.get_score()].append(t)\\n curr_dict = OrderedDict(sorted(curr_dict.iteritems(),reverse=True))\\n cnt = 0\\n for score in curr_dict:\\n cnt = cnt + 1\\n if len(curr_dict[score]) > 1 and cnt<rank:\\n self._ties[qid] = 1\",\n \"def _check_resident_prefs(self):\\n\\n errors = []\\n for resident in self.residents:\\n if not set(resident.prefs).issubset(set(self.hospitals)):\\n errors.append(\\n ValueError(\\n f\\\"{resident} has ranked a non-hospital: \\\"\\n f\\\"{set(resident.prefs)} != {set(self.hospitals)}\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_lsr_rankings():\\n for case in iter_testcases('rankings'):\\n n_items = case[\\\"n_items\\\"]\\n data = case[\\\"data\\\"]\\n assert np.allclose(\\n case[\\\"lsr_est\\\"], lsr_rankings(n_items, data),\\n atol=ATOL, rtol=RTOL)\",\n \"def test_ilsr_rankings():\\n for case in iter_testcases('rankings'):\\n n_items = case[\\\"n_items\\\"]\\n data = case[\\\"data\\\"]\\n assert np.allclose(\\n case[\\\"ml_est\\\"], ilsr_rankings(n_items, data),\\n atol=ATOL, rtol=RTOL)\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def test_check_for_unacceptable_matches_residents(game):\\n\\n resident = game.residents[0]\\n hospital = Hospital(name=\\\"foo\\\", capacity=1)\\n resident.matching = hospital\\n\\n issues = game._check_for_unacceptable_matches(\\\"residents\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(resident.name)\\n assert issue.endswith(f\\\"{resident.prefs}.\\\")\\n assert hospital.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_check_inputs_hospital_reciprocated_all_prefs(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n hospital._forget(resident)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"hospitals\\\", \\\"residents\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert hospital not in resident.prefs\",\n \"def test_resident_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"resident\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n assert all(\\n [\\n r in set(residents)\\n for r in set(\\n [\\n r_match\\n for matches in matching.values()\\n for r_match in matches\\n ]\\n )\\n ]\\n )\\n\\n for resident in residents:\\n if resident.matching:\\n assert resident.pref_names.index(resident.matching.name) == 0\",\n \"def fit_score(self, solution):\\r\\n illegal_neighbours = 0\\r\\n legal_neighbours = 0\\r\\n for polygon in solution.genetic_units.values():\\r\\n for neighbour_id in polygon.neighbours_ids:\\r\\n if polygon.color is solution.genetic_units[neighbour_id].color:\\r\\n illegal_neighbours += 1\\r\\n else:\\r\\n legal_neighbours += 1\\r\\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\\r\\n return illegal_neighbours\\r\\n else:\\r\\n return legal_neighbours\",\n \"def _check_resident_unhappy(resident, hospital):\\n\\n return resident.matching is None or resident.prefers(\\n hospital, resident.matching\\n )\",\n \"def testProtractedNSESanityChecks(self):\\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))\",\n \"def testSpeciesRichnessCorrect(self):\\n self.assertEqual(136, self.coal.get_species_richness(1))\\n self.assertNotEqual(self.coal.get_species_richness(2), self.coal.get_species_richness(1))\\n self.assertNotEqual(self.coal.get_species_richness(3), self.coal.get_species_richness(2))\",\n \"def test_check_for_oversubscribed_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n supervisor.matching = range(supervisor.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.capacity))\\n assert str(supervisor.matching) in e\",\n \"def has_standard_residues(self):\\n for frag in self.iter_standard_residues():\\n return True\\n return False\",\n \"def is_error(ranking, references):\\n return 1 if average_precision(ranking, references) < 1 else 0\",\n \"def has_non_standard_residues(self):\\n for frag in self.iter_non_standard_residues():\\n return True\\n return False\",\n \"def test_check_inputs_resident_prefs_all_nonempty(game):\\n\\n resident = game.residents[0]\\n resident.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n assert resident.name in str(record[0].message)\\n\\n if game.clean:\\n assert resident not in game.residents\",\n \"def get_ranks(d): \\n raise NotImplementedError(\\\"Problem 3 Incomplete\\\")\",\n \"def test_hand_has_all_unique_ranks(hand, card_list, expected):\\n hand.add_cards(card_list)\\n assert hand.has_all_unique_ranks() == expected\",\n \"def has_straight(self):\\n res = []\\n self.suit_hist()\\n for val in self.ranks.keys():\\n res.append(val)\\n res.sort()\\n self.is_sequence(res, 3)\",\n \"def test_check_inputs_hospital_prefs_all_residents(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert hospital.prefs == []\",\n \"def assess_risk(black_marbles: list, white_marbles: list):\\n danger = Evaluator.danger_positions\\n black_risk = 0\\n white_risk = 0\\n for black_marble in black_marbles:\\n letter, num = Board.convert_to_nums(black_marble)\\n left = Board.convert_to_string(letter, num - 1)\\n right = Board.convert_to_string(letter, num + 1)\\n upL = Board.convert_to_string(letter + 1, num)\\n upR = Board.convert_to_string(letter + 1, num + 1)\\n downL = Board.convert_to_string(letter - 1, num - 1)\\n downR = Board.convert_to_string(letter - 1, num)\\n test_spots = [left, right, upL, upR, downL, downR]\\n if black_marble in danger:\\n black_risk += 2\\n for spot in test_spots:\\n if spot in white_marbles:\\n black_risk += 5\\n\\n for white_marble in white_marbles:\\n letter, num = Board.convert_to_nums(white_marble)\\n left = Board.convert_to_string(letter, num - 1)\\n right = Board.convert_to_string(letter, num + 1)\\n upL = Board.convert_to_string(letter + 1, num)\\n upR = Board.convert_to_string(letter + 1, num + 1)\\n downL = Board.convert_to_string(letter - 1, num - 1)\\n downR = Board.convert_to_string(letter - 1, num)\\n test_spots = [left, right, upL, upR, downL, downR]\\n if white_marble in danger:\\n white_risk += 2\\n for spot in test_spots:\\n if spot in black_marbles:\\n white_risk += 5\\n return black_risk, white_risk\",\n \"def test_residues(parse_mmtf):\\n parse_mmtf.assertEqual(len(parse_mmtf.mmcif_res), len(parse_mmtf.mmtf_res))\\n for i, e in enumerate(parse_mmtf.mmcif_res):\\n mmcif_r = parse_mmtf.mmcif_res[i]\\n mmtf_r = parse_mmtf.mmtf_res[i]\\n parse_mmtf.assertEqual(mmtf_r.level, mmcif_r.level)\\n parse_mmtf.assertEqual(mmtf_r.disordered, mmcif_r.disordered)\\n parse_mmtf.assertEqual(mmtf_r.resname, mmcif_r.resname)\\n parse_mmtf.assertEqual(mmtf_r.segid, mmcif_r.segid)\\n parse_mmtf.mmcif_atoms = [x for x in mmcif_r.get_atom()]\\n parse_mmtf.mmtf_atoms = [x for x in mmtf_r.get_atom()]\\n test_atoms(parse_mmtf=parse_mmtf)\",\n \"def test_check_inputs_resident_prefs_all_hospitals(game):\\n\\n resident = game.residents[0]\\n resident.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert resident.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert resident.prefs == []\",\n \"def _check_hospital_unhappy(resident, hospital):\\n\\n return len(hospital.matching) < hospital.capacity or any(\\n [hospital.prefers(resident, match) for match in hospital.matching]\\n )\",\n \"def check_order(racers):\\n racers_orderd = sorted(racers, key=lambda x: (x.group, x.time))\\n a = 1\\n b = 1\\n for r in racers_orderd:\\n if r.group == \\\"ALL\\\":\\n r.rank = str(a)\\n a += 1\\n else:\\n r.rank = str(b)\\n b += 1\\n return racers_orderd\",\n \"def listErrors(individual): #TODO: add other errors implemented elsewhere\\n\\n results = [birthBeforeMarriage(individual), birthBeforeDeath(individual), marriageBeforeDeath(individual), \\n datesBeforeCurrentDate(individual), noBigamy(individual)]\\n results = [x for x in results if x is not None]\\n return not all(results)\",\n \"def test_check_for_unacceptable_matches_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n student = Student(name=\\\"foo\\\")\\n supervisor.matching.append(student)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.prefs))\\n assert student.name in error\",\n \"def test_check_inputs(game):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.residents == game._all_residents\\n assert game.hospitals == game._all_hospitals\",\n \"def testNumberIndividualsCorrect(self):\\n for ref in self.coal.get_community_references():\\n self.assertEqual(1000, self.coal.get_number_individuals(community_reference=ref))\",\n \"def arsenalResults(dat):\\n arsScore = int(dat[0])\\n othScore = int(dat[2])\\n if arsScore > othScore:\\n res = 1\\n elif arsScore == othScore:\\n res = 2\\n else:\\n res = 0\\n return res\",\n \"def _check_resident_matching(self):\\n\\n errors = []\\n for resident in self.residents:\\n if (\\n resident.matching is not None\\n and resident.matching not in resident.prefs\\n ):\\n errors.append(\\n ValueError(\\n f\\\"{resident} is matched to {resident.matching} but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{resident.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def __statistics_students_failing(self):\\n students_list = self.__grade_controller.get_list_of_failing_students()\\n if len(students_list) == 0:\\n print(\\\"There is no student failing!\\\")\\n return\\n\\n for student in students_list:\\n print(str(student) + \\\"\\\\n\\\")\",\n \"def count_standard_residues(self):\\n n = 0\\n for na in self.iter_standard_residues():\\n n += 1\\n return n\",\n \"def rank_err(data_mod, mod_dict):\\n # checks that order of patients is identical for both models, and that correct data dictionary is passed\\n np.testing.assert_array_equal(mod_dict['linear'].Y, mod_dict['rbf'].Y, err_msg='lin and rbf models not trained on same data')\\n np.testing.assert_array_equal(mod_dict['linear'].X, mod_dict['rbf'].X, err_msg='lin and rbf models not trained on same data')\\n np.testing.assert_array_equal(mod_dict['rbf'].X, data_mod['XA'], err_msg='mogp models and data dictionary not trained on same data') \\n\\n err_dict = {}\\n for key in mod_dict.keys():\\n err_dict[key] = calc_rmse_full_mod(data=data_mod, model_type=key, mod=mod_dict[key])\\n\\n return err_dict\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def is_unhappy(self):\\n #checked!#\\n ###your code here###\\n same=0\\n for i in self.home.neighbors:\\n if i.occupant!=None:\\n if i.occupant.group==self.group:\\n same+=1\\n happniess=float(same)/len(self.home.neighbors)\\n if happniess<self.happiness_threshold:\\n return True\\n else:\\n return False\",\n \"def isranked(self):\\n rank = self.console.getCvar('sv_ranked')\\n return rank and rank.getInt() == 2\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def test_something(self):\\n\\n allure.dynamic.title(\\\"Testing compute_ranks\\\")\\n allure.dynamic.severity(allure.severity_level.NORMAL)\\n allure.dynamic.description_html('<h3>Codewars badge:</h3>'\\n '<img src=\\\"https://www.codewars.com/users/myFirstCode'\\n '/badges/large\\\">'\\n '<h3>Test Description:</h3>'\\n \\\"<p>Test the function taht organizes a sports league in a \\\"\\n \\\"round-robin-system. Each team meets all other teams. \\\"\\n \\\"In your league a win gives a team 2 points, a draw gives \\\"\\n \\\"both teams 1 point. After some games you have to compute \\\"\\n \\\"the order of the teams in your league. You use the following \\\"\\n \\\"criteria to arrange the teams:</p>\\\"\\n \\\"<ul><li>- Points</li>\\\"\\n \\\"<li>- Scoring differential (the difference between goals \\\"\\n \\\"scored and those conceded)</li>\\\"\\n \\\"<li>- Goals scored</li></ul>\\\")\\n\\n test_data = [\\n (6,\\n [[0, 5, 2, 2],\\n [1, 4, 0, 2],\\n [2, 3, 1, 2],\\n [1, 5, 2, 2],\\n [2, 0, 1, 1],\\n [3, 4, 1, 1],\\n [2, 5, 0, 2],\\n [3, 1, 1, 1],\\n [4, 0, 2, 0]],\\n [4, 4, 6, 3, 1, 2]),\\n (6,\\n [[0, 5, 2, 0],\\n [1, 4, 2, 2],\\n [2, 3, 1, 3],\\n [1, 5, 0, 0],\\n [2, 0, 2, 1],\\n [3, 4, 3, 1]],\\n [2, 3, 4, 1, 5, 6]),\\n (4,\\n [[0, 3, 1, 1],\\n [1, 2, 2, 2],\\n [1, 3, 2, 0],\\n [2, 0, 2, 0]],\\n [3, 1, 1, 3]),\\n (10,\\n [],\\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\\n (8,\\n [[0, 7, 2, 0]],\\n [1, 2, 2, 2, 2, 2, 2, 8])\\n ]\\n\\n for data in test_data:\\n number = data[0]\\n games = data[1]\\n expected = data[2]\\n actual_result = compute_ranks(number, games)\\n print_log(number=number,\\n games=games,\\n expected=expected,\\n actual_result=actual_result)\\n\\n with allure.step(\\\"Enter a test data and verify the result:\\\"):\\n self.assertEqual(expected, actual_result)\",\n \"def test_resids(self):\\n cr = CaseReader(self.filename)\\n last_case = cr.get_case(-1)\\n last_case_id = cr.list_cases()[-1]\\n n = cr.num_cases\\n with h5py.File(self.filename, 'r') as f:\\n for key in f[last_case_id]['Residuals'].keys():\\n val = f[last_case_id]['Residuals'][key][()]\\n np.testing.assert_almost_equal(last_case.resids[key], val,\\n err_msg='Case reader gives'\\n ' incorrect Unknown'\\n ' value for'\\n ' {0}'.format(key))\",\n \"def death_check(self):\\r\\n chance = random.random()\\r\\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\\r\\n if self.unique_id in head_of_household_list:\\r\\n try:\\r\\n head_of_household_list[self.hh_id] = 0\\r\\n except TypeError: # head of household migrated\\r\\n head_of_household_list[self.past_hh_id] = 0\\r\\n self.model.number_of_humans -= 1\\r\\n if self.unique_id in labor_list:\\r\\n labor_list.remove(self.unique_id)\\r\\n if self.work_status == 1:\\r\\n try:\\r\\n num_labor_list[self.hh_id] -= 1\\r\\n except TypeError:\\r\\n num_labor_list[self.past_hh_id] -= 1\\r\\n if self.unique_id in former_hoh_list:\\r\\n try:\\r\\n former_hoh_list[self.hh_id] = 0\\r\\n except:\\r\\n former_hoh_list[self.past_hh_id] = 0\\r\\n if [self.unique_id, self.hh_id] in single_male_list:\\r\\n single_male_list.remove([self.unique_id, self.hh_id])\\r\\n if self.unique_id in married_male_list:\\r\\n married_male_list.remove(self.unique_id)\\r\\n human_death_list.append(self.unique_id)\\r\\n try:\\r\\n hh_size_list[self.hh_id] -= 1\\r\\n except:\\r\\n hh_size_list[self.past_hh_id] -= 1\\r\\n human_demographic_structure_list[self.age_category] -= 1\\r\\n\\r\\n self.model.schedule.remove(self)\\r\\n if self in self.model.grid:\\r\\n self.model.grid.remove_agent(self)\",\n \"def isResidueAssigned(residue):\\n\\n for atom in residue.atoms:\\n if atom.atomSet:\\n if atom.atomSet.resonanceSets:\\n return True\\n \\n return False\",\n \"def _check_RS(self,suits_frequency):\\n if len(np.array(suits_frequency.loc[suits_frequency==5])) == 1:\\n RS=1\\n else:\\n RS=0\\n\\n return RS\",\n \"def _check_unstake_result(self) -> None:\\n balance_score = self.icx.get_balance(self.address) - self._daily_reward.get()\\n if balance_score > 0:\\n unstake_info_list = self.getUserUnstakeInfo()\\n for each_info in unstake_info_list:\\n value_to_transfer = each_info[0]\\n if value_to_transfer <= balance_score:\\n self._send_ICX(each_info[1], value_to_transfer)\\n self._linked_list_var.remove(self._linked_list_var._head_id.get())\\n break\",\n \"def test_check_stability():\\n\\n students = [Student(\\\"A\\\"), Student(\\\"B\\\"), Student(\\\"C\\\")]\\n projects = [Project(\\\"P\\\", 2), Project(\\\"Q\\\", 2)]\\n supervisors = [Supervisor(\\\"X\\\", 2), Supervisor(\\\"Y\\\", 2)]\\n\\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\\n\\n p.set_supervisor(x)\\n q.set_supervisor(y)\\n\\n a.set_prefs([p, q])\\n b.set_prefs([q])\\n c.set_prefs([q, p])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = StudentAllocation(students, projects, supervisors)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (p, q) = game.students, game.projects\\n\\n matching[p] = [c]\\n matching[q] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def evaluate_ranks(articles, rank_tuples):\\n rank_tp=defaultdict(int)\\n rank_fn=defaultdict(int)\\n rank_fp=defaultdict(int)\\n \\n for article in articles:\\n for mention in article.entity_mentions:\\n form=mention.mention\\n meaning=mention.gold_link\\n sys_meaning=mention.sys_link\\n t_gold=(form, meaning)\\n t_sys=(form, sys_meaning)\\n for rank, r_tuples in rank_tuples.items():\\n if t_gold in r_tuples and t_sys in r_tuples:\\n rank_tp[rank]+=1\\n break\\n elif t_gold in r_tuples:\\n rank_fn[rank]+=1\\n elif t_sys in r_tuples:\\n rank_fp[rank]+=1\\n print('tp', rank_tp)\\n print('fp', rank_fp)\\n print('fn', rank_fn)\\n \\n rank_prec={}\\n rank_recall={}\\n rank_f1={}\\n \\n for rank in range(1,13):\\n if rank_tp[rank]+rank_fp[rank]>0:\\n rank_prec[rank]=rank_tp[rank]/(rank_tp[rank]+rank_fp[rank])\\n else:\\n rank_prec[rank]=0.0\\n if rank_tp[rank]+rank_fn[rank]>0:\\n rank_recall[rank]=rank_tp[rank]/(rank_tp[rank]+rank_fn[rank])\\n else:\\n rank_recall[rank]=0.0\\n if rank_prec[rank]+rank_recall[rank]>0:\\n rank_f1[rank]=2*rank_prec[rank]*rank_recall[rank]/(rank_prec[rank]+rank_recall[rank])\\n else:\\n rank_f1[rank]=0.0\\n print('precision', rank_prec)\\n print()\\n print('recall', rank_recall)\\n print()\\n print('f1', rank_f1)\\n print()\\n return rank_prec, rank_recall, rank_f1\",\n \"def assignRanks(self):\\r\\n\\t\\trank = 0\\r\\n\\t\\tscores = list(self._playerScores)\\r\\n\\t\\tscores.reverse()\\r\\n\\t\\tfor playerScore in scores:\\r\\n\\t\\t\\tif not playerScore.has(NOT_MET) or not playerScore.value(NOT_MET):\\r\\n\\t\\t\\t\\trank += 1\\r\\n\\t\\t\\t\\tplayerScore.set(RANK, smallText(BugUtil.colorText(u\\\"%d\\\" % rank, ScoreOpt.getRankColor())))\\r\\n\\t\\tif rank > 0:\\r\\n\\t\\t\\tself._anyHas[RANK] = True\",\n \"def test_solve(resident_names, hospital_names, capacities, seed):\\n\\n for optimal in [\\\"suitor\\\", \\\"resident\\\", \\\"reviewer\\\", \\\"hospital\\\"]:\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n matching = match.solve(optimal)\\n assert isinstance(matching, Matching)\\n assert set(matching.keys()) == set(hospitals)\\n matched_residents = [\\n res for match in matching.values() for res in match\\n ]\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(residents)\\n )\\n\\n for resident in set(residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def rlWorkout(gameInfo):\\n \\n if gameInfo['myScore'] >= 0 and gameInfo['opponentScore'] >= 0:\\n if gameInfo['myScore'] > gameInfo['opponentScore']:\\n determineRocketLeagueWin(gameInfo['goals'], gameInfo['assist'], gameInfo['saves'])\\n else:\\n determineRocketLeagueLoss(gameInfo['goals'], gameInfo['assist'], gameInfo['saves'])\\n else:\\n print(\\\"Doesn't make sense dude\\\")\\n return workoutInfo\",\n \"def tie_exists(self):\\n return len(self.marks) == 9\",\n \"def testNSESanityChecks(self):\\n self.assertEqual(100, self.c.get_species_richness())\\n self.assertEqual(67, self.c2.get_species_richness())\",\n \"def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"hospital\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n\\n for hospital, matches in matching.items():\\n old_idx = -np.infty\\n for resident in matches:\\n idx = hospital.pref_names.index(resident.name)\\n assert idx >= old_idx\\n old_idx = idx\",\n \"def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.reviewers[0].matching.append(Player(name=\\\"foo\\\", pref_names=[]))\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_matching()\",\n \"def check_stability(self):\\n\\n blocking_pairs = []\\n for resident in self.residents:\\n for hospital in self.hospitals:\\n if (\\n _check_mutual_preference(resident, hospital)\\n and _check_resident_unhappy(resident, hospital)\\n and _check_hospital_unhappy(resident, hospital)\\n ):\\n blocking_pairs.append((resident, hospital))\\n\\n self.blocking_pairs = blocking_pairs\\n return not any(blocking_pairs)\",\n \"def test_check_inputs_supervisor_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n supervisor.prefs.remove(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"supervisors\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert supervisor.name in message\\n\\n if clean:\\n assert supervisor not in student.prefs\\n assert all(\\n project not in student.prefs for project in supervisor.projects\\n )\",\n \"def rerank_candidates(s, pred2sub_rank, all_predictions, rerank_top=20):\\n predicted_smiles = []\\n model_input = []\\n for (predict_smi, label), _ in Counter(all_predictions).most_common(rerank_top):\\n if predict_smi == s:\\n continue\\n features = get_all_features(\\n get_all_ranking_info(pred2sub_rank[predict_smi]))\\n predicted_smiles.append((predict_smi, label))\\n model_input.append(features)\\n\\n model = RankingModel()\\n model.load_state_dict(torch.load('./models/ranker/rank_model.pt', map_location='cpu'))\\n model.eval()\\n\\n test_loader = DataLoader(RankingTestDataset(\\n model_input), batch_size=1000, shuffle=False, num_workers=2)\\n ranking_scores = []\\n for data in test_loader:\\n outputs = model(data)[0]\\n ranking_scores.extend(outputs.detach().cpu().numpy())\\n\\n assert len(predicted_smiles) == len(ranking_scores)\\n pred_smi2score = {k: v[1]\\n for k, v in zip(predicted_smiles, ranking_scores)}\\n return pred_smi2score\",\n \"def test_enter_score_cleanup(self):\\n tournament_scores = len(TournamentScore.query.all())\\n scores = len(ScoreDAO.query.all())\\n\\n self.enter_score_bad_values()\\n\\n compare(tournament_scores, len(TournamentScore.query.all()))\\n compare(scores, len(ScoreDAO.query.all()))\",\n \"def test_calculates_risk_points_with_mortgaged_house(self):\\n\\n self.user[\\\"houses\\\"].insert(0, {\\n \\\"id\\\": 123,\\n \\\"ownership_status\\\": \\\"mortgaged\\\"\\n })\\n\\n self.risk_profile[\\\"home\\\"].insert(0, {\\n \\\"id\\\": 123,\\n \\\"is_eligible\\\": True,\\n \\\"risk_points\\\": 1\\n })\\n\\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\\n\\n for house in self.risk_profile[\\\"home\\\"]:\\n if house[\\\"id\\\"] == 123:\\n self.assertEquals(house[\\\"risk_points\\\"], 2)\\n else: \\n self.assertEquals(house[\\\"risk_points\\\"], 1)\\n \\n self.assertEquals(self.risk_profile[\\\"disability\\\"][\\\"risk_points\\\"], 2)\",\n \"def recip_rank(recs, truth):\\n good = recs['item'].isin(truth.index)\\n npz, = np.nonzero(good)\\n if len(npz):\\n return 1.0 / (npz[0] + 1.0)\\n else:\\n return 0.0\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def error_output_rank(self):\\n rank_method_string = ''\\n for i in range(len(methods_of_ranking)):\\n if i == 0:\\n continue\\n elif i != len(methods_of_ranking) - 1:\\n rank_method_string += (methods_of_ranking[i] + '() or ')\\n else:\\n rank_method_string += (methods_of_ranking[i] + '()')\\n print(\\\"please rank first by \\\" + rank_method_string)\\n sys.exit(0)\",\n \"def is_legal_solution(self, solution):\\r\\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\\r\\n return self.fit_score(solution) == 0\\r\\n else:\\r\\n return self.fit_score(solution) == sum(x for x in range(1, 12))\",\n \"def test_mirror_ranking(self):\\n updater = AptMirrorUpdater()\\n # Make sure that multiple discovered mirrors are available.\\n assert sum(m.is_available for m in updater.ranked_mirrors) > 10\",\n \"def test_check_inputs_supervisor_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n projects = supervisor.projects\\n for project in student.prefs:\\n if project in projects:\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"supervisors\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(supervisor.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in supervisor.prefs\\n assert all(\\n student not in project.prefs for project in supervisor.projects\\n )\",\n \"def rank_sensitvity_bis(dsys, X, Y, n_test=100):\\n\\n optdmd_train_error, optdmd_test_error = list(), list()\\n\\n # Fit a DMD model for each possible rank.\\n for rank in range(1, dsys.A.shape[0] + 1):\\n # Fit the DMD model (optimal closed-form solution)\\n optdmd = OptDMD(svd_rank=rank, factorization=\\\"svd\\\").fit(X, Y)\\n\\n # One-step ahead prediction using both DMD models.\\n y_predict_opt = optdmd.predict(X)\\n\\n # Compute the one-step ahead prediction error.\\n optdmd_train_error.append(norm(y_predict_opt - Y) / norm(Y))\\n\\n # Evaluate the error on test data.\\n optdmd_error = list()\\n for _ in range(n_test):\\n # Test initial condition.\\n x0_test = normal(loc=0.0, scale=1.0, size=(dsys.A.shape[1]))\\n\\n # Run simulation to generate dataset.\\n t, _, x_test = dlsim(dsys, np.zeros((250, dsys.inputs)), x0=x0_test)\\n\\n # Split the training data into input/output snapshots.\\n y_test, X_test = x_test.T[:, 1:], x_test.T[:, :-1]\\n\\n # One-step ahead prediction using both DMD models.\\n y_predict_opt = optdmd.predict(X_test)\\n\\n # Compute the one-step ahead prediction error.\\n optdmd_error.append(norm(y_predict_opt - y_test) / norm(y_test))\\n\\n # Store the error for rank i DMD.\\n optdmd_test_error.append(np.asarray(optdmd_error))\\n\\n # Complete rank-sensitivity.\\n optdmd_test_error = np.asarray(optdmd_test_error)\\n optdmd_train_error = np.asarray(optdmd_train_error)\\n\\n return optdmd_train_error, optdmd_test_error\",\n \"def test_rescoring_randomized_problem(self):\\r\\n # First define the custom response problem:\\r\\n problem_url_name = 'H1P1'\\r\\n self.define_randomized_custom_response_problem(problem_url_name)\\r\\n location = InstructorTaskModuleTestCase.problem_location(problem_url_name)\\r\\n descriptor = self.module_store.get_item(location)\\r\\n # run with more than one user\\r\\n userlist = ['u1', 'u2', 'u3', 'u4']\\r\\n for username in userlist:\\r\\n # first render the problem, so that a seed will be created for this user\\r\\n self.render_problem(username, problem_url_name)\\r\\n # submit a bogus answer, in order to get the problem to tell us its real answer\\r\\n dummy_answer = \\\"1000\\\"\\r\\n self.submit_student_answer(username, problem_url_name, [dummy_answer, dummy_answer])\\r\\n # we should have gotten the problem wrong, since we're way out of range:\\r\\n self.check_state(username, descriptor, 0, 1, 1)\\r\\n # dig the correct answer out of the problem's message\\r\\n module = self.get_student_module(username, descriptor)\\r\\n state = json.loads(module.state)\\r\\n correct_map = state['correct_map']\\r\\n log.info(\\\"Correct Map: %s\\\", correct_map)\\r\\n # only one response, so pull it out:\\r\\n answer = correct_map.values()[0]['msg']\\r\\n self.submit_student_answer(username, problem_url_name, [answer, answer])\\r\\n # we should now get the problem right, with a second attempt:\\r\\n self.check_state(username, descriptor, 1, 1, 2)\\r\\n\\r\\n # redefine the problem (as stored in Mongo) so that the definition of correct changes\\r\\n self.define_randomized_custom_response_problem(problem_url_name, redefine=True)\\r\\n # confirm that simply rendering the problem again does not result in a change\\r\\n # in the grade (or the attempts):\\r\\n self.render_problem('u1', problem_url_name)\\r\\n self.check_state('u1', descriptor, 1, 1, 2)\\r\\n\\r\\n # rescore the problem for only one student -- only that student's grade should change\\r\\n # (and none of the attempts):\\r\\n self.submit_rescore_one_student_answer('instructor', problem_url_name, User.objects.get(username='u1'))\\r\\n for username in userlist:\\r\\n self.check_state(username, descriptor, 0 if username == 'u1' else 1, 1, 2)\\r\\n\\r\\n # rescore the problem for all students\\r\\n self.submit_rescore_all_student_answers('instructor', problem_url_name)\\r\\n\\r\\n # all grades should change to being wrong (with no change in attempts)\\r\\n for username in userlist:\\r\\n self.check_state(username, descriptor, 0, 1, 2)\",\n \"def testDispersalSimulation(self):\\n self.assertEqual(715, self.c.get_species_richness(1))\",\n \"def find_qualifying_loans(bank_data, credit_score, debt, income, loan, home_value):\\n\\n # Calculate the monthly debt ratio\\n monthly_debt_ratio = calculate_monthly_debt_ratio(debt, income)\\n print(\\\"\\\\n\\\", \\\"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\\\")\\n print(\\\"\\\\n\\\", \\\"\\\\u001b[34;1m\\\")\\n print(f\\\"------->>> Monthly debt-to-income ratio is: {monthly_debt_ratio:.02f}.\\\")\\n print(\\\"\\\\u001b[0m\\\")\\n\\n # Calculate loan to value ratio\\n loan_to_value_ratio = calculate_loan_to_value_ratio(loan, home_value)\\n print(\\\"\\\\n\\\", \\\"\\\\u001b[34;1m\\\")\\n print(f\\\"------->>> Loan-to-value ratio is {loan_to_value_ratio:.02f}.\\\")\\n print(\\\"\\\\n\\\", \\\"\\\\u001b[0m\\\")\\n print(\\\" * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\\\", \\\"\\\\n\\\")\\n\\n # Run qualification filters\\n bank_data_filtered = filter_max_loan_size(loan, bank_data)\\n bank_data_filtered = filter_credit_score(credit_score, bank_data_filtered)\\n bank_data_filtered = filter_debt_to_income(monthly_debt_ratio, bank_data_filtered)\\n bank_data_filtered = filter_loan_to_value(loan_to_value_ratio, bank_data_filtered)\\n number_of_loans_available = len(bank_data_filtered)\\n if number_of_loans_available <= 0:\\n print(\\\"I'm sorry. We have no loans to offer at this time.\\\"), \\\"\\\\n\\\"\\n exit()\\n else: \\n print(\\\"\\\\u001b[32m\\\", f\\\"Congratulations! You qualify for: {number_of_loans_available} loan(s).\\\", \\\"\\\\u001b[0m\\\", \\\"\\\\n\\\")\\n\\n return bank_data_filtered\",\n \"def test_residue_loop_no_selection(self):\\n\\n # Spin data.\\n num = [1, 2, 4, -5, -4]\\n name = [None, 'Glu', 'Pro', None, None]\\n\\n # Loop over the residues.\\n i = 0\\n for res in mol_res_spin.residue_loop():\\n # Test the residue numbers.\\n self.assertEqual(res.num, num[i])\\n\\n # Test the residue names.\\n self.assertEqual(res.name, name[i])\\n\\n # Increment i.\\n i = i + 1\\n\\n # Test loop length.\\n self.assertEqual(i, 5)\",\n \"def check_evaluations(session, dates):\\n user_ids = get_campus_students(session, 13)\\n bad_evals = []\\n\\n print(f'found {len(user_ids)} students !')\\n for user_id in user_ids:\\n # Get all evaluations from student id in selected timeframe\\n evaluations = get_all_pages(session, f'/users/{user_id}/scale_teams/as_corrected', 100, params=dates)\\n if evaluations == '[]':\\n continue\\n # Check each returned evaluation if bad or not\\n for evaluation in evaluations:\\n # Object BadEvaluation is returned if rules come back True\\n bad = detect_bad_eval(evaluation)\\n if isinstance(bad, BadEvaluation):\\n bad_evals.append(bad)\\n\\n return bad_evals\",\n \"def __statistics_best_situation(self):\\n students_list = self.__grade_controller.get_list_of_students_with_best_situation()\\n if len(students_list) == 0:\\n print(\\\"There is no student with a grade!\\\")\\n return\\n\\n for student in students_list:\\n print(str(student) + \\\"\\\\n\\\")\",\n \"def test_avalanche_warning_by_region_obs(self):\\n pass\",\n \"def test_score(self, n_neighbors, weights):\\n _check_knn_score(\\n KNeighborsPartialLabelRanker(n_neighbors, weights), self.X, self.Y)\",\n \"def test_check_yahtzee_bonus_false(self):\\n\\n not_yahtzee_bonus_fixtures = [[1, 1, 1, 1, 2],\\n [1, 1, 1, 2, 1],\\n [1, 1, 2, 1, 1],\\n [1, 2, 1, 1, 1],\\n [2, 1, 1, 1, 1],\\n ]\\n\\n for fixture in not_yahtzee_bonus_fixtures:\\n score = self.roll.check_yahtzee_bonus(fixture)\\n\\n self.assertNotEqual(score, 50)\\n self.assertEqual(score, 0)\\n self.assertEqual(len(fixture), 5)\",\n \"def test_score(self, n_neighbors, weights):\\n _check_knn_score(\\n KNeighborsLabelRanker(n_neighbors, weights), self.X, self.Y)\",\n \"def test_solve(game, optimal):\\n\\n matching = game.solve(optimal)\\n assert isinstance(matching, MultipleMatching)\\n\\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\\n for game_hospital, hospital in zip(matching_keys, hospitals):\\n assert game_hospital.name == hospital.name\\n assert game_hospital._pref_names == hospital._pref_names\\n assert game_hospital.capacity == hospital.capacity\\n\\n matched_residents = [\\n resident for match in matching.values() for resident in match\\n ]\\n\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(game.residents)\\n )\\n\\n for resident in set(game.residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def validate_rank_data(R):\\n if not isinstance(R, np.ndarray):\\n raise TypeError(\\\"Data must be an ndarray.\\\")\\n\\n if R.size == 0:\\n raise ValueError(\\\"Input data is empty\\\")\\n\\n if R.ndim == 1:\\n\\n true_r = np.arange(1, R.size+1)\\n\\n if not _is_disjoint(true_r, R):\\n raise ValueError((\\\"Base classifier predictions must be an ndarray of \\\"\\n \\\"sample rank values.\\\"))\\n elif R.ndim == 2:\\n\\n true_r = np.arange(1, R.shape[1] + 1)\\n\\n for i in range(R.shape[0]):\\n if not _is_disjoint(true_r, R[i, :]):\\n raise ValueError((\\\"Base classifier predictions must \\\"\\n \\\"be sample ranks.\\\"))\\n else:\\n raise ValueError(\\\"Data must be a 1-d or 2-d ndarray.\\\")\",\n \"def testAxiomReasoning(self):\\n \\n state = State.from_problem(self.prob)\\n extstate, reasons, universalReasons = state.get_extended_state(getReasons=True)\\n\\n relevantVars = []\\n relevantReplanVars = []\\n load = self.dom.get_action(\\\"a_load\\\")\\n with load.instantiate([\\\"agent\\\", \\\"obj11\\\", \\\"tru1\\\"], self.prob):\\n self.assert_(extstate.is_satisfied(load.precondition, relevantVars))\\n self.assert_(extstate.is_satisfied(load.replan, relevantReplanVars))\\n\\n all_reasons = set(relevantVars)\\n for v in relevantVars:\\n all_reasons |= reasons[v]\\n\\n all_replan_reasons = set(relevantReplanVars)\\n for v in relevantReplanVars:\\n all_replan_reasons |= reasons[v]\\n \\n s1 = StateVariable(self.prob.functions[\\\"location-of\\\"][0], [self.prob[\\\"tru1\\\"]])\\n s2 = StateVariable(self.prob.functions[\\\"location-of\\\"][0], [self.prob[\\\"obj11\\\"]])\\n\\n self.assert_(s1 in all_reasons)\\n self.assert_(s2 in all_reasons)\\n self.assertFalse(s1 in all_replan_reasons)\\n self.assert_(s2 in all_replan_reasons)\",\n \"def user_story_4(self):\\n for family in self.families.values():\\n if family.married != 'NA':\\n if family.wife_id != 'NA' and family.husb_id != 'NA' and family.divorced != 'NA':\\n if family.divorced < family.married:\\n print(\\n f'US04 - {self.individuals[family.wife_id].name} and {self.individuals[family.husb_id].name} married after divorce on line {family._married_line}')\",\n \"def test_anvil_first_patient_everything(caplog, anvil_server, anvil_research_studies_with_observations):\\n research_studies = {r.relativePath(): r for r in anvil_research_studies_with_observations if\\n r.__class__.__name__ == 'ResearchStudy'}\\n resource_counts = _validate_patient_everything(caplog, anvil_server,\\n [research_studies['ResearchStudy/1000G-high-coverage-2019']])\\n assert False, resource_counts\",\n \"def likely_regressions(self):\\n return set([label for label, count in self.regressions.items() if count == 0])\",\n \"def test_roster(self):\\n self.assertIsNone(self.model.get_issue_number((\\\"test\\\",), \\\"test\\\"),\\n \\\"Did not return None even though roster entry doesn't exist\\\")\\n\\n test_tuples = ( ((\\\"cs61a-ab\\\",), \\\"proj1\\\", 1000),\\n ((\\\"cs61a-ab\\\",), \\\"proj2\\\", 1001),\\n ((\\\"cs61a-bc\\\", \\\"cs61a-cd\\\"), \\\"proj1\\\", 2000),\\n ((\\\"cs61a-bc\\\", \\\"cs61a-cd\\\"), \\\"proj2\\\", 2001) )\\n\\n # pylint: disable=W0142\\n for test_tuple in test_tuples:\\n self.model.set_issue_number(*test_tuple)\\n\\n for students, assign, issue_num in test_tuples:\\n self.assertEquals(issue_num,\\n self.model.get_issue_number(students, assign),\\n \\\"Got wrong issue number\\\")\",\n \"def test_verify_non_existing_rank(self):\\n data = {'rank_id': 5}\\n res = self.post(url='/verify/4', data=data, role='admin')\\n self.assertEqual(res.status_code, 401)\\n self.assertException(res, exc.EntryNotFound)\",\n \"def check_all(self):\\n return self.check_rs() + self.check_hgvs()\",\n \"def test_non_zero_sum_profile_welfare():\\n game = matgame.matgame([[[3.5, 2.5]]])\\n assert np.isclose(\\n regret.pure_social_welfare(game, [1, 1]), 6\\n ), \\\"didn't properly sum welfare\\\"\",\n \"def validate_step(self,\\n x,\\n y):\\n\\n statistics = dict()\\n\\n for i in range(self.bootstraps):\\n fm = self.forward_models[i]\\n\\n # calculate the prediction error and accuracy of the model\\n d = fm.get_distribution(x, training=False)\\n nll = -d.log_prob(y)[:, 0]\\n\\n # evaluate how correct the rank fo the model predictions are\\n rank_correlation = spearman(y[:, 0], d.mean()[:, 0])\\n\\n statistics[f'oracle_{i}/validate/nll'] = nll\\n statistics[f'oracle_{i}/validate/rank_corr'] = rank_correlation\\n\\n return statistics\",\n \"def straight(ranks):\\n return (max(ranks)-min(ranks) == 4) and len(set(ranks)) == 5\",\n \"def _identify_anomalies(self, X, yhat, prob):\\n\\n y = DTA_LSTM._shift_traces_left(X)\\n\\n for i in range(len(X)):\\n\\n rp = self._compare_traces(y[i], yhat[i])\\n self.rank_prob.append(rp)\\n\\n if self.explain:\\n print('X[{}]: {} = {}'.format(i, X[i], rp))\\n\\n return self.rank_prob\",\n \"def testDispersalSimulation(self):\\n self.assertEqual(1172, self.c.get_species_richness(1))\",\n \"def validate_perturbed_residues(molecule, perturbed):\\n length = len(molecule)\\n numPerturbed = len(perturbed)\\n #if \",\n \"def __count_errors(node, testSet, res):\\n training_results = __get_results(node) #Get a dictionary of labels and counts for the *training* data which made it to this node\\n leaf_label = None #Initialize a label for this leaf\\n majority_count = 0 #Initialize a variable to track the number of observations for the label with the most observations\\n #Note that the steps below do not handle ties of the majority count in a nice way.\\n for label, count in training_results.items(): #iterate through each pair of labels and counts from the training set\\n if count > majority_count: #find the label with the highest count\\n leaf_label = label #the label for the leaf is the label with the highest count\\n majority_count = count #keep track of the count for the leaf_label\\n \\n wrong_labels = testSet[res].unique().tolist() #initialize wrong_labels to be all labels in the testSet\\n if leaf_label in wrong_labels: #If the leaf label is in the list of labels for the part of the test set that got to this node\\n wrong_labels.remove(leaf_label) #remove the leaf_label so that all which remains are incorrect labels\\n \\n wrong_count = 0 #Initialize a count of how many testSet observations will be classified incorrectly\\n testCounts = testSet.groupby(res).size() #Get a series of the testSet labels and how many observations pertain to each label\\n for label in wrong_labels: #Iterate over all the labels not equal to the leaf_label\\n wrong_count += testCounts[label] #Sum up all of the observations with a label not equal to the leaf_label\\n return wrong_count\",\n \"def testNoSpecialties(self):\\n self.failUnlessEqual(self.person.getSpecialties(), [])\",\n \"def test_special_RR(self):\\n self.check_oneq_special_cases(U3Gate(0.0, 0.1, -0.1).to_matrix(), \\\"RR\\\", {})\\n self.check_oneq_special_cases(U3Gate(0.0, 0.1, 0.2).to_matrix(), \\\"RR\\\", {\\\"r\\\": 2})\\n self.check_oneq_special_cases(U3Gate(-np.pi, 0.2, 0.0).to_matrix(), \\\"RR\\\", {\\\"r\\\": 1})\\n self.check_oneq_special_cases(U3Gate(np.pi, 0.0, 0.2).to_matrix(), \\\"RR\\\", {\\\"r\\\": 1})\\n self.check_oneq_special_cases(U3Gate(0.1, 0.2, 0.3).to_matrix(), \\\"RR\\\", {\\\"r\\\": 2})\",\n \"def count_non_standard_residues(self):\\n n = 0\\n for frag in self.iter_non_standard_residues():\\n n += 1\\n return n\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65927255","0.62228805","0.6033156","0.5816217","0.57702166","0.5740605","0.569085","0.56001204","0.552595","0.5460149","0.54297745","0.53983676","0.5360775","0.5351841","0.5335599","0.5331168","0.528455","0.5271201","0.52700424","0.52670234","0.5262435","0.52570313","0.5243496","0.52413565","0.522431","0.52108073","0.5210444","0.5199926","0.5196297","0.5195278","0.5185289","0.5172189","0.51659626","0.5151994","0.51497763","0.5127692","0.51248354","0.51113564","0.5110612","0.51104903","0.5106363","0.5106363","0.5106363","0.51023674","0.5100745","0.5090541","0.508377","0.50830424","0.5066824","0.5063194","0.5057819","0.50566125","0.5054471","0.5051484","0.50506586","0.5047803","0.5043544","0.5035888","0.50357974","0.5035169","0.5028475","0.5021771","0.50109816","0.5010768","0.5008151","0.49986488","0.49974254","0.49885964","0.49858373","0.49833435","0.49690023","0.49650696","0.49553373","0.49495357","0.49351","0.49348852","0.49302918","0.49253523","0.4923055","0.49183127","0.49177325","0.49176353","0.49135777","0.49078837","0.49074233","0.49037588","0.49027357","0.49005204","0.48995152","0.4899003","0.48979735","0.48974532","0.48930535","0.48924702","0.4889447","0.48832256","0.48815918","0.48797318","0.4873946","0.48729882"],"string":"[\n \"0.65927255\",\n \"0.62228805\",\n \"0.6033156\",\n \"0.5816217\",\n \"0.57702166\",\n \"0.5740605\",\n \"0.569085\",\n \"0.56001204\",\n \"0.552595\",\n \"0.5460149\",\n \"0.54297745\",\n \"0.53983676\",\n \"0.5360775\",\n \"0.5351841\",\n \"0.5335599\",\n \"0.5331168\",\n \"0.528455\",\n \"0.5271201\",\n \"0.52700424\",\n \"0.52670234\",\n \"0.5262435\",\n \"0.52570313\",\n \"0.5243496\",\n \"0.52413565\",\n \"0.522431\",\n \"0.52108073\",\n \"0.5210444\",\n \"0.5199926\",\n \"0.5196297\",\n \"0.5195278\",\n \"0.5185289\",\n \"0.5172189\",\n \"0.51659626\",\n \"0.5151994\",\n \"0.51497763\",\n \"0.5127692\",\n \"0.51248354\",\n \"0.51113564\",\n \"0.5110612\",\n \"0.51104903\",\n \"0.5106363\",\n \"0.5106363\",\n \"0.5106363\",\n \"0.51023674\",\n \"0.5100745\",\n \"0.5090541\",\n \"0.508377\",\n \"0.50830424\",\n \"0.5066824\",\n \"0.5063194\",\n \"0.5057819\",\n \"0.50566125\",\n \"0.5054471\",\n \"0.5051484\",\n \"0.50506586\",\n \"0.5047803\",\n \"0.5043544\",\n \"0.5035888\",\n \"0.50357974\",\n \"0.5035169\",\n \"0.5028475\",\n \"0.5021771\",\n \"0.50109816\",\n \"0.5010768\",\n \"0.5008151\",\n \"0.49986488\",\n \"0.49974254\",\n \"0.49885964\",\n \"0.49858373\",\n \"0.49833435\",\n \"0.49690023\",\n \"0.49650696\",\n \"0.49553373\",\n \"0.49495357\",\n \"0.49351\",\n \"0.49348852\",\n \"0.49302918\",\n \"0.49253523\",\n \"0.4923055\",\n \"0.49183127\",\n \"0.49177325\",\n \"0.49176353\",\n \"0.49135777\",\n \"0.49078837\",\n \"0.49074233\",\n \"0.49037588\",\n \"0.49027357\",\n \"0.49005204\",\n \"0.48995152\",\n \"0.4899003\",\n \"0.48979735\",\n \"0.48974532\",\n \"0.48930535\",\n \"0.48924702\",\n \"0.4889447\",\n \"0.48832256\",\n \"0.48815918\",\n \"0.48797318\",\n \"0.4873946\",\n \"0.48729882\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5287138"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":93791,"cells":{"query":{"kind":"string","value":"Test each hospital has ranked all residents that have ranked it. If not, check that a warning is caught and any such resident has forgotten the hospital."},"document":{"kind":"string","value":"def test_check_inputs_hospital_reciprocated_all_prefs(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n hospital._forget(resident)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"hospitals\", \"residents\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert hospital not in resident.prefs"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.append(\n ValueError(\n f\"{hospital} has not ranked all the residents that \"\n f\"ranked it: {set(hospital.prefs)} != \"\n f\"{set(residents_that_ranked)}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_stability():\n\n residents = [Player(\"A\", [1, 2]), Player(\"B\", [2]), Player(\"C\", [2, 1])]\n hospitals = [Player(1, [\"C\", \"A\"], 3), Player(2, [\"A\", \"B\", \"C\"], 3)]\n match = HospitalResident(residents, hospitals)\n\n matching = match.solve()\n assert match.check_stability()\n\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\n matching[P_1] = [P_C]\n matching[P_2] = [P_A, P_B]\n\n assert not match.check_stability()","def _check_ties(self,rank):\n for k in self._run:\n curr_dict = defaultdict(list)\n qid = self._run[k][0].get_qid()\n for t in self._run[k]:\n # print t.get_str()\n curr_dict[t.get_score()].append(t)\n curr_dict = OrderedDict(sorted(curr_dict.iteritems(),reverse=True))\n cnt = 0\n for score in curr_dict:\n cnt = cnt + 1\n if len(curr_dict[score]) > 1 and cnt<rank:\n self._ties[qid] = 1","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def _check_resident_prefs(self):\n\n errors = []\n for resident in self.residents:\n if not set(resident.prefs).issubset(set(self.hospitals)):\n errors.append(\n ValueError(\n f\"{resident} has ranked a non-hospital: \"\n f\"{set(resident.prefs)} != {set(self.hospitals)}\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert resident not in hospital.prefs","def test_check_for_unacceptable_matches_residents(game):\n\n resident = game.residents[0]\n hospital = Hospital(name=\"foo\", capacity=1)\n resident.matching = hospital\n\n issues = game._check_for_unacceptable_matches(\"residents\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(resident.name)\n assert issue.endswith(f\"{resident.prefs}.\")\n assert hospital.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def _check_hospital_unhappy(resident, hospital):\n\n return len(hospital.matching) < hospital.capacity or any(\n [hospital.prefers(resident, match) for match in hospital.matching]\n )","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def test_check_inputs_hospital_prefs_all_residents(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert hospital.prefs == []","def test_lsr_rankings():\n for case in iter_testcases('rankings'):\n n_items = case[\"n_items\"]\n data = case[\"data\"]\n assert np.allclose(\n case[\"lsr_est\"], lsr_rankings(n_items, data),\n atol=ATOL, rtol=RTOL)","def _check_resident_unhappy(resident, hospital):\n\n return resident.matching is None or resident.prefers(\n hospital, resident.matching\n )","def test_ilsr_rankings():\n for case in iter_testcases('rankings'):\n n_items = case[\"n_items\"]\n data = case[\"data\"]\n assert np.allclose(\n case[\"ml_est\"], ilsr_rankings(n_items, data),\n atol=ATOL, rtol=RTOL)","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_check_inputs_resident_prefs_all_hospitals(game):\n\n resident = game.residents[0]\n resident.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert resident.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert resident.prefs == []","def test_resident_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"resident\")\n\n assert set(hospitals) == set(matching.keys())\n assert all(\n [\n r in set(residents)\n for r in set(\n [\n r_match\n for matches in matching.values()\n for r_match in matches\n ]\n )\n ]\n )\n\n for resident in residents:\n if resident.matching:\n assert resident.pref_names.index(resident.matching.name) == 0","def death_check(self):\r\n chance = random.random()\r\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\r\n if self.unique_id in head_of_household_list:\r\n try:\r\n head_of_household_list[self.hh_id] = 0\r\n except TypeError: # head of household migrated\r\n head_of_household_list[self.past_hh_id] = 0\r\n self.model.number_of_humans -= 1\r\n if self.unique_id in labor_list:\r\n labor_list.remove(self.unique_id)\r\n if self.work_status == 1:\r\n try:\r\n num_labor_list[self.hh_id] -= 1\r\n except TypeError:\r\n num_labor_list[self.past_hh_id] -= 1\r\n if self.unique_id in former_hoh_list:\r\n try:\r\n former_hoh_list[self.hh_id] = 0\r\n except:\r\n former_hoh_list[self.past_hh_id] = 0\r\n if [self.unique_id, self.hh_id] in single_male_list:\r\n single_male_list.remove([self.unique_id, self.hh_id])\r\n if self.unique_id in married_male_list:\r\n married_male_list.remove(self.unique_id)\r\n human_death_list.append(self.unique_id)\r\n try:\r\n hh_size_list[self.hh_id] -= 1\r\n except:\r\n hh_size_list[self.past_hh_id] -= 1\r\n human_demographic_structure_list[self.age_category] -= 1\r\n\r\n self.model.schedule.remove(self)\r\n if self in self.model.grid:\r\n self.model.grid.remove_agent(self)","def test_check_for_oversubscribed_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n supervisor.matching = range(supervisor.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.capacity))\n assert str(supervisor.matching) in e","def fit_score(self, solution):\r\n illegal_neighbours = 0\r\n legal_neighbours = 0\r\n for polygon in solution.genetic_units.values():\r\n for neighbour_id in polygon.neighbours_ids:\r\n if polygon.color is solution.genetic_units[neighbour_id].color:\r\n illegal_neighbours += 1\r\n else:\r\n legal_neighbours += 1\r\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\r\n return illegal_neighbours\r\n else:\r\n return legal_neighbours","def __statistics_students_failing(self):\n students_list = self.__grade_controller.get_list_of_failing_students()\n if len(students_list) == 0:\n print(\"There is no student failing!\")\n return\n\n for student in students_list:\n print(str(student) + \"\\n\")","def listErrors(individual): #TODO: add other errors implemented elsewhere\n\n results = [birthBeforeMarriage(individual), birthBeforeDeath(individual), marriageBeforeDeath(individual), \n datesBeforeCurrentDate(individual), noBigamy(individual)]\n results = [x for x in results if x is not None]\n return not all(results)","def check_stability(self):\n\n blocking_pairs = []\n for resident in self.residents:\n for hospital in self.hospitals:\n if (\n _check_mutual_preference(resident, hospital)\n and _check_resident_unhappy(resident, hospital)\n and _check_hospital_unhappy(resident, hospital)\n ):\n blocking_pairs.append((resident, hospital))\n\n self.blocking_pairs = blocking_pairs\n return not any(blocking_pairs)","def test_check_inputs_resident_prefs_all_nonempty(game):\n\n resident = game.residents[0]\n resident.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n assert resident.name in str(record[0].message)\n\n if game.clean:\n assert resident not in game.residents","def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals","def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"hospital\")\n\n assert set(hospitals) == set(matching.keys())\n\n for hospital, matches in matching.items():\n old_idx = -np.infty\n for resident in matches:\n idx = hospital.pref_names.index(resident.name)\n assert idx >= old_idx\n old_idx = idx","def is_unhappy(self):\n #checked!#\n ###your code here###\n same=0\n for i in self.home.neighbors:\n if i.occupant!=None:\n if i.occupant.group==self.group:\n same+=1\n happniess=float(same)/len(self.home.neighbors)\n if happniess<self.happiness_threshold:\n return True\n else:\n return False","def _check_hospital_matching(self):\n\n errors = []\n for hospital in self.hospitals:\n for resident in hospital.matching:\n if resident not in hospital.prefs:\n errors.append(\n ValueError(\n f\"{hospital} has {resident} in their matching but \"\n \"they do not appear in their preference list: \"\n f\"{hospital.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def assess_risk(black_marbles: list, white_marbles: list):\n danger = Evaluator.danger_positions\n black_risk = 0\n white_risk = 0\n for black_marble in black_marbles:\n letter, num = Board.convert_to_nums(black_marble)\n left = Board.convert_to_string(letter, num - 1)\n right = Board.convert_to_string(letter, num + 1)\n upL = Board.convert_to_string(letter + 1, num)\n upR = Board.convert_to_string(letter + 1, num + 1)\n downL = Board.convert_to_string(letter - 1, num - 1)\n downR = Board.convert_to_string(letter - 1, num)\n test_spots = [left, right, upL, upR, downL, downR]\n if black_marble in danger:\n black_risk += 2\n for spot in test_spots:\n if spot in white_marbles:\n black_risk += 5\n\n for white_marble in white_marbles:\n letter, num = Board.convert_to_nums(white_marble)\n left = Board.convert_to_string(letter, num - 1)\n right = Board.convert_to_string(letter, num + 1)\n upL = Board.convert_to_string(letter + 1, num)\n upR = Board.convert_to_string(letter + 1, num + 1)\n downL = Board.convert_to_string(letter - 1, num - 1)\n downR = Board.convert_to_string(letter - 1, num)\n test_spots = [left, right, upL, upR, downL, downR]\n if white_marble in danger:\n white_risk += 2\n for spot in test_spots:\n if spot in black_marbles:\n white_risk += 5\n return black_risk, white_risk","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def test_solve(resident_names, hospital_names, capacities, seed):\n\n for optimal in [\"suitor\", \"resident\", \"reviewer\", \"hospital\"]:\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n matching = match.solve(optimal)\n assert isinstance(matching, Matching)\n assert set(matching.keys()) == set(hospitals)\n matched_residents = [\n res for match in matching.values() for res in match\n ]\n assert matched_residents != [] and set(matched_residents).issubset(\n set(residents)\n )\n\n for resident in set(residents) - set(matched_residents):\n assert resident.matching is None","def test_calculates_risk_points_with_mortgaged_house(self):\n\n self.user[\"houses\"].insert(0, {\n \"id\": 123,\n \"ownership_status\": \"mortgaged\"\n })\n\n self.risk_profile[\"home\"].insert(0, {\n \"id\": 123,\n \"is_eligible\": True,\n \"risk_points\": 1\n })\n\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\n\n for house in self.risk_profile[\"home\"]:\n if house[\"id\"] == 123:\n self.assertEquals(house[\"risk_points\"], 2)\n else: \n self.assertEquals(house[\"risk_points\"], 1)\n \n self.assertEquals(self.risk_profile[\"disability\"][\"risk_points\"], 2)","def test_hand_has_all_unique_ranks(hand, card_list, expected):\n hand.add_cards(card_list)\n assert hand.has_all_unique_ranks() == expected","def test_solve(game, optimal):\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_hospital, hospital in zip(matching_keys, hospitals):\n assert game_hospital.name == hospital.name\n assert game_hospital._pref_names == hospital._pref_names\n assert game_hospital.capacity == hospital.capacity\n\n matched_residents = [\n resident for match in matching.values() for resident in match\n ]\n\n assert matched_residents != [] and set(matched_residents).issubset(\n set(game.residents)\n )\n\n for resident in set(game.residents) - set(matched_residents):\n assert resident.matching is None","def _check_unstake_result(self) -> None:\n balance_score = self.icx.get_balance(self.address) - self._daily_reward.get()\n if balance_score > 0:\n unstake_info_list = self.getUserUnstakeInfo()\n for each_info in unstake_info_list:\n value_to_transfer = each_info[0]\n if value_to_transfer <= balance_score:\n self._send_ICX(each_info[1], value_to_transfer)\n self._linked_list_var.remove(self._linked_list_var._head_id.get())\n break","def test_check_stability():\n\n students = [Student(\"A\"), Student(\"B\"), Student(\"C\")]\n projects = [Project(\"P\", 2), Project(\"Q\", 2)]\n supervisors = [Supervisor(\"X\", 2), Supervisor(\"Y\", 2)]\n\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\n\n p.set_supervisor(x)\n q.set_supervisor(y)\n\n a.set_prefs([p, q])\n b.set_prefs([q])\n c.set_prefs([q, p])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = StudentAllocation(students, projects, supervisors)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (p, q) = game.students, game.projects\n\n matching[p] = [c]\n matching[q] = [a, b]\n\n assert not game.check_stability()","def test_get_state_comparison_stats_ailments(self):\n\n # AilmentType and Ailment breakdown per AilmentType should be in returned stats if present\n ailment_type_headache = AilmentTypeFactory(name='Headache')\n ailment_migraine_headache = AilmentFactory(name='Migraine Headache', type=ailment_type_headache)\n ailment_tension_headache = AilmentFactory(name='Tension Headache', type=ailment_type_headache)\n ailment_type_sprain_or_bruise = AilmentTypeFactory(name='Sprain or Bruise')\n ailment_sprain = AilmentFactory(name='Sprain', type=ailment_type_sprain_or_bruise)\n\n # Texas: 2 with sprain and 2 with nothing\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_sprain)\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 1 with migraine headache, 1 with sprain, 1 with tension headache, and 1 with nothing\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_migraine_headache)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_sprain)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_tension_headache)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n # Mississippi: 2 with migraine headache, 2 with tension headache\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_migraine_headache)\n employee.bureau_states.add(self.mississippi)\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_tension_headache)\n employee.bureau_states.add(self.mississippi)\n\n stats = get_state_comparison_stats(number=2)\n\n # With ailment types, ailments should be grouped together\n key = '% With Headache'\n expected_output = [('Mississippi', 100), ('Kentucky', 50)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with headache\")\n\n key = '% With Sprain or Bruise'\n expected_output = [('Texas', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with sprain or bruise\"\n )\n\n # Ailments should be treated individually\n key = '% With Migraine Headache'\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with migraine headache\"\n )\n\n key = '% With Tension Headache'\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with tension headache\"\n )\n\n # There should be no breakdown for sprain, because it's the only ailment of that type\n key = '% With Sprain'\n self.assertEqual(len([item for item in stats if key in item]), 0,\n \"There should be no breakdown for an ailment if it's the only one of its type\")","def rlWorkout(gameInfo):\n \n if gameInfo['myScore'] >= 0 and gameInfo['opponentScore'] >= 0:\n if gameInfo['myScore'] > gameInfo['opponentScore']:\n determineRocketLeagueWin(gameInfo['goals'], gameInfo['assist'], gameInfo['saves'])\n else:\n determineRocketLeagueLoss(gameInfo['goals'], gameInfo['assist'], gameInfo['saves'])\n else:\n print(\"Doesn't make sense dude\")\n return workoutInfo","def test_check_for_unacceptable_matches_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n student = Student(name=\"foo\")\n supervisor.matching.append(student)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.prefs))\n assert student.name in error","def has_straight(self):\n res = []\n self.suit_hist()\n for val in self.ranks.keys():\n res.append(val)\n res.sort()\n self.is_sequence(res, 3)","def testSpeciesRichnessCorrect(self):\n self.assertEqual(136, self.coal.get_species_richness(1))\n self.assertNotEqual(self.coal.get_species_richness(2), self.coal.get_species_richness(1))\n self.assertNotEqual(self.coal.get_species_richness(3), self.coal.get_species_richness(2))","def test_enter_score_cleanup(self):\n tournament_scores = len(TournamentScore.query.all())\n scores = len(ScoreDAO.query.all())\n\n self.enter_score_bad_values()\n\n compare(tournament_scores, len(TournamentScore.query.all()))\n compare(scores, len(ScoreDAO.query.all()))","def check_all(self):\n return self.check_rs() + self.check_hgvs()","def check_order(racers):\n racers_orderd = sorted(racers, key=lambda x: (x.group, x.time))\n a = 1\n b = 1\n for r in racers_orderd:\n if r.group == \"ALL\":\n r.rank = str(a)\n a += 1\n else:\n r.rank = str(b)\n b += 1\n return racers_orderd","def testProtractedNSESanityChecks(self):\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))","def user_story_4(self):\n for family in self.families.values():\n if family.married != 'NA':\n if family.wife_id != 'NA' and family.husb_id != 'NA' and family.divorced != 'NA':\n if family.divorced < family.married:\n print(\n f'US04 - {self.individuals[family.wife_id].name} and {self.individuals[family.husb_id].name} married after divorce on line {family._married_line}')","def _check_resident_matching(self):\n\n errors = []\n for resident in self.residents:\n if (\n resident.matching is not None\n and resident.matching not in resident.prefs\n ):\n errors.append(\n ValueError(\n f\"{resident} is matched to {resident.matching} but \"\n \"they do not appear in their preference list: \"\n f\"{resident.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def check_overall_energy(self):\n energy = 0\n for student in self.students:\n energy += int(student.energy_level)\n for mentor in self.mentors:\n energy += int(mentor.energy_level)\n print(\"Overall energy equals \", energy)","def testNumberIndividualsCorrect(self):\n for ref in self.coal.get_community_references():\n self.assertEqual(1000, self.coal.get_number_individuals(community_reference=ref))","def test_check_inputs_supervisor_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n supervisor.prefs.remove(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"supervisors\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert supervisor.name in message\n\n if clean:\n assert supervisor not in student.prefs\n assert all(\n project not in student.prefs for project in supervisor.projects\n )","def find_qualifying_loans(bank_data, credit_score, debt, income, loan, home_value):\n\n # Calculate the monthly debt ratio\n monthly_debt_ratio = calculate_monthly_debt_ratio(debt, income)\n print(\"\\n\", \"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\")\n print(\"\\n\", \"\\u001b[34;1m\")\n print(f\"------->>> Monthly debt-to-income ratio is: {monthly_debt_ratio:.02f}.\")\n print(\"\\u001b[0m\")\n\n # Calculate loan to value ratio\n loan_to_value_ratio = calculate_loan_to_value_ratio(loan, home_value)\n print(\"\\n\", \"\\u001b[34;1m\")\n print(f\"------->>> Loan-to-value ratio is {loan_to_value_ratio:.02f}.\")\n print(\"\\n\", \"\\u001b[0m\")\n print(\" * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\", \"\\n\")\n\n # Run qualification filters\n bank_data_filtered = filter_max_loan_size(loan, bank_data)\n bank_data_filtered = filter_credit_score(credit_score, bank_data_filtered)\n bank_data_filtered = filter_debt_to_income(monthly_debt_ratio, bank_data_filtered)\n bank_data_filtered = filter_loan_to_value(loan_to_value_ratio, bank_data_filtered)\n number_of_loans_available = len(bank_data_filtered)\n if number_of_loans_available <= 0:\n print(\"I'm sorry. We have no loans to offer at this time.\"), \"\\n\"\n exit()\n else: \n print(\"\\u001b[32m\", f\"Congratulations! You qualify for: {number_of_loans_available} loan(s).\", \"\\u001b[0m\", \"\\n\")\n\n return bank_data_filtered","def test_residues(parse_mmtf):\n parse_mmtf.assertEqual(len(parse_mmtf.mmcif_res), len(parse_mmtf.mmtf_res))\n for i, e in enumerate(parse_mmtf.mmcif_res):\n mmcif_r = parse_mmtf.mmcif_res[i]\n mmtf_r = parse_mmtf.mmtf_res[i]\n parse_mmtf.assertEqual(mmtf_r.level, mmcif_r.level)\n parse_mmtf.assertEqual(mmtf_r.disordered, mmcif_r.disordered)\n parse_mmtf.assertEqual(mmtf_r.resname, mmcif_r.resname)\n parse_mmtf.assertEqual(mmtf_r.segid, mmcif_r.segid)\n parse_mmtf.mmcif_atoms = [x for x in mmcif_r.get_atom()]\n parse_mmtf.mmtf_atoms = [x for x in mmtf_r.get_atom()]\n test_atoms(parse_mmtf=parse_mmtf)","def test_check_inputs_supervisor_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n projects = supervisor.projects\n for project in student.prefs:\n if project in projects:\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"supervisors\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(supervisor.name)\n assert student.name in message\n\n if clean:\n assert student not in supervisor.prefs\n assert all(\n student not in project.prefs for project in supervisor.projects\n )","def arsenalResults(dat):\n arsScore = int(dat[0])\n othScore = int(dat[2])\n if arsScore > othScore:\n res = 1\n elif arsScore == othScore:\n res = 2\n else:\n res = 0\n return res","def is_error(ranking, references):\n return 1 if average_precision(ranking, references) < 1 else 0","def get_ranks(d): \n raise NotImplementedError(\"Problem 3 Incomplete\")","def test_something(self):\n\n allure.dynamic.title(\"Testing compute_ranks\")\n allure.dynamic.severity(allure.severity_level.NORMAL)\n allure.dynamic.description_html('<h3>Codewars badge:</h3>'\n '<img src=\"https://www.codewars.com/users/myFirstCode'\n '/badges/large\">'\n '<h3>Test Description:</h3>'\n \"<p>Test the function taht organizes a sports league in a \"\n \"round-robin-system. Each team meets all other teams. \"\n \"In your league a win gives a team 2 points, a draw gives \"\n \"both teams 1 point. After some games you have to compute \"\n \"the order of the teams in your league. You use the following \"\n \"criteria to arrange the teams:</p>\"\n \"<ul><li>- Points</li>\"\n \"<li>- Scoring differential (the difference between goals \"\n \"scored and those conceded)</li>\"\n \"<li>- Goals scored</li></ul>\")\n\n test_data = [\n (6,\n [[0, 5, 2, 2],\n [1, 4, 0, 2],\n [2, 3, 1, 2],\n [1, 5, 2, 2],\n [2, 0, 1, 1],\n [3, 4, 1, 1],\n [2, 5, 0, 2],\n [3, 1, 1, 1],\n [4, 0, 2, 0]],\n [4, 4, 6, 3, 1, 2]),\n (6,\n [[0, 5, 2, 0],\n [1, 4, 2, 2],\n [2, 3, 1, 3],\n [1, 5, 0, 0],\n [2, 0, 2, 1],\n [3, 4, 3, 1]],\n [2, 3, 4, 1, 5, 6]),\n (4,\n [[0, 3, 1, 1],\n [1, 2, 2, 2],\n [1, 3, 2, 0],\n [2, 0, 2, 0]],\n [3, 1, 1, 3]),\n (10,\n [],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n (8,\n [[0, 7, 2, 0]],\n [1, 2, 2, 2, 2, 2, 2, 8])\n ]\n\n for data in test_data:\n number = data[0]\n games = data[1]\n expected = data[2]\n actual_result = compute_ranks(number, games)\n print_log(number=number,\n games=games,\n expected=expected,\n actual_result=actual_result)\n\n with allure.step(\"Enter a test data and verify the result:\"):\n self.assertEqual(expected, actual_result)","def __count_errors(node, testSet, res):\n training_results = __get_results(node) #Get a dictionary of labels and counts for the *training* data which made it to this node\n leaf_label = None #Initialize a label for this leaf\n majority_count = 0 #Initialize a variable to track the number of observations for the label with the most observations\n #Note that the steps below do not handle ties of the majority count in a nice way.\n for label, count in training_results.items(): #iterate through each pair of labels and counts from the training set\n if count > majority_count: #find the label with the highest count\n leaf_label = label #the label for the leaf is the label with the highest count\n majority_count = count #keep track of the count for the leaf_label\n \n wrong_labels = testSet[res].unique().tolist() #initialize wrong_labels to be all labels in the testSet\n if leaf_label in wrong_labels: #If the leaf label is in the list of labels for the part of the test set that got to this node\n wrong_labels.remove(leaf_label) #remove the leaf_label so that all which remains are incorrect labels\n \n wrong_count = 0 #Initialize a count of how many testSet observations will be classified incorrectly\n testCounts = testSet.groupby(res).size() #Get a series of the testSet labels and how many observations pertain to each label\n for label in wrong_labels: #Iterate over all the labels not equal to the leaf_label\n wrong_count += testCounts[label] #Sum up all of the observations with a label not equal to the leaf_label\n return wrong_count","def test_check_inputs_hospital_prefs_all_nonempty(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def enough_lords(self):\n real_numbers = {\n Title.count: len(self.get_lords_of_title(Title.count)),\n Title.baron: len(self.get_lords_of_title(Title.baron)),\n Title.baronet: len(self.get_lords_of_title(Title.baronet)),\n Title.chevalier: len(self.get_lords_of_title(Title.chevalier)),\n Title.client: len(self.get_lords_of_title(Title.client)),\n }\n\n counter = {Title.baron: 0, Title.baronet: 0, Title.chevalier: 0,\n Title.client: 0}\n\n for title, vassals in LORDS_VASSALS.items():\n for i in range(real_numbers[title]):\n for lower_title, value in vassals.items():\n counter[lower_title] += value\n\n for title, count in counter.items():\n if count > real_numbers[title]:\n print(f'Not enough lords of title: {title.value}, '\n f'required: {count}, actual: {real_numbers[title]}')\n return False\n return True","def _identify_anomalies(self, X, yhat, prob):\n\n y = DTA_LSTM._shift_traces_left(X)\n\n for i in range(len(X)):\n\n rp = self._compare_traces(y[i], yhat[i])\n self.rank_prob.append(rp)\n\n if self.explain:\n print('X[{}]: {} = {}'.format(i, X[i], rp))\n\n return self.rank_prob","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def test_get_state_comparison_stats_foreign_born(self):\n\n key = '% Foreign-born employees'\n\n us = CountryFactory(code2='US')\n germany = CountryFactory(name='Germany')\n\n # Mississippi: 0 employees born in Germany, 3 in US\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.mississippi)\n\n # Texas: 2 employees born in Germany, 2 in US\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 3 employees born in Germany, 1 employee in US, 3 unknown\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.kentucky)\n for _ in range(3):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n expected_output = [('Kentucky', 75), ('Texas', 50)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(top_states, expected_output,\n \"'{key}' should contain states with the top x % foreign-born employees\")","def test_calculates_risk_points_with_one_house(self):\n\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\n\n for house in self.risk_profile[\"home\"]:\n self.assertEquals(house[\"risk_points\"], 2)","def age_check(self):\r\n # check working status\r\n if 15 <= float(self.age) < 59:\r\n if self.work_status == 0:\r\n self.work_status = 1\r\n num_labor_list[self.hh_id] += 1\r\n labor_list.append(self.unique_id)\r\n if self.work_status == 1 and self.unique_id not in labor_list:\r\n labor_list.append(self.unique_id)\r\n else:\r\n self.work_status = 0\r\n\r\n # check education status; measured in years of education\r\n if 7 <= int(self.age) <= 19:\r\n if random.random() > 0.1:\r\n self.education += 1\r\n # most adults in the FNNR did not get a full 12-13 years of education\r\n elif 19 < float(self.age) < 23 and self.migration_status == 1:\r\n if random.random() < 0.5:\r\n self.education += 1 # went to college and got further education\r\n # this is rare; in the household list, a few received beyond 12 years of education\r\n\r\n # check age-based death rates\r\n if self.age > 65:\r\n self.death_rate = 0.001443 # 5-day death rate\r\n # The average death rate in China is 7.3 per 1,000 people/year, or 0.0073 (Google).\r\n # However, death rates should be higher for the elderly, or else the population structure will skew.\r\n # I set death rates for those over age 65 to be 10% per year--0.9 yearly survival rate.\r\n # The survival rate for each 5-day step is compounded 73 times, so x^73 = 0.85.\r\n # 0.998557 is the 5-day survival rate, and 1 - x is the 5-day death rate.\r\n else:\r\n self.death_rate = 0.00000425\r\n # I wanted people to have a 98% chance of reaching age 65 (death rate is lower if not elderly).\r\n # If a 'check' is every 5 days, 73 checks/year * 65 years = 4,745 checks.\r\n # x^4745 = 0.98; the 5-day survival rate is 0.99999575, and 1 - x is the 5-day death rate.\r\n\r\n # These rates are changeable later.\r","def test_check_yahtzee_bonus_false(self):\n\n not_yahtzee_bonus_fixtures = [[1, 1, 1, 1, 2],\n [1, 1, 1, 2, 1],\n [1, 1, 2, 1, 1],\n [1, 2, 1, 1, 1],\n [2, 1, 1, 1, 1],\n ]\n\n for fixture in not_yahtzee_bonus_fixtures:\n score = self.roll.check_yahtzee_bonus(fixture)\n\n self.assertNotEqual(score, 50)\n self.assertEqual(score, 0)\n self.assertEqual(len(fixture), 5)","def __statistics_best_situation(self):\n students_list = self.__grade_controller.get_list_of_students_with_best_situation()\n if len(students_list) == 0:\n print(\"There is no student with a grade!\")\n return\n\n for student in students_list:\n print(str(student) + \"\\n\")","def computeAssignments(solution, h, data, sumW, checkers, hini=None):\n minHours = data[\"minHours\"]\n hours = data[\"hours\"]\n z = solution[\"z\"]\n w = solution[\"w\"]\n\n mustWork = []\n canWork = []\n\n # sort nurses, first by those who work\n sorted_nurses = sorted(range(data[\"nNurses\"]), key=lambda n: solution[\"z\"][n], reverse=True)\n # print(solution[\"z\"])\n # print(sorted_nurses)\n # print(\"\")\n\n # for each nurse\n for n in sorted_nurses:\n\n # canWork Check-------------------------------\n canWork_check = checkIfCanWork_fast(solution, h, n, data, sumW, checkers=checkers, hini=hini)\n if canWork_check:\n\n\n mustWork_check = checkIfMustWork_fast(solution, h, n, data, sumW, True, checkers, hini)\n if mustWork_check:\n mustWork.append(n)\n else: \n canWork.append(n)\n\n # # mustWork Check-------------------------------\n # # avoid repeating canWork_check\n # mustWork_check = checkIfMustWork_fast(solution, h, n, data, sumW, canWork_check, checkers, hini)\n # if mustWork_check:\n # mustWork.append(n)\n # else:\n \n return mustWork, canWork","def check_evaluations(session, dates):\n user_ids = get_campus_students(session, 13)\n bad_evals = []\n\n print(f'found {len(user_ids)} students !')\n for user_id in user_ids:\n # Get all evaluations from student id in selected timeframe\n evaluations = get_all_pages(session, f'/users/{user_id}/scale_teams/as_corrected', 100, params=dates)\n if evaluations == '[]':\n continue\n # Check each returned evaluation if bad or not\n for evaluation in evaluations:\n # Object BadEvaluation is returned if rules come back True\n bad = detect_bad_eval(evaluation)\n if isinstance(bad, BadEvaluation):\n bad_evals.append(bad)\n\n return bad_evals","def test_get_state_comparison_stats_penmanship_contest(self):\n\n key = 'Left-hand penmanship contest entrants'\n\n # Mississippi: 0 employees who entered the contest, 3 who didn't\n for _ in range(3):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.mississippi)\n\n # Texas: 2 employees who entered the contest, 2 who didn't\n for _ in range(2):\n employee = EmployeeFactory(penmanship_contest=True)\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 3 employees who entered the contest, 1 who didn't\n for _ in range(3):\n employee = EmployeeFactory(penmanship_contest=True)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n expected_output = [('Kentucky', 3), ('Texas', 2)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x number of employees who entered contest\"\n )","def rank_err(data_mod, mod_dict):\n # checks that order of patients is identical for both models, and that correct data dictionary is passed\n np.testing.assert_array_equal(mod_dict['linear'].Y, mod_dict['rbf'].Y, err_msg='lin and rbf models not trained on same data')\n np.testing.assert_array_equal(mod_dict['linear'].X, mod_dict['rbf'].X, err_msg='lin and rbf models not trained on same data')\n np.testing.assert_array_equal(mod_dict['rbf'].X, data_mod['XA'], err_msg='mogp models and data dictionary not trained on same data') \n\n err_dict = {}\n for key in mod_dict.keys():\n err_dict[key] = calc_rmse_full_mod(data=data_mod, model_type=key, mod=mod_dict[key])\n\n return err_dict","def test_anvil_first_patient_everything(caplog, anvil_server, anvil_research_studies_with_observations):\n research_studies = {r.relativePath(): r for r in anvil_research_studies_with_observations if\n r.__class__.__name__ == 'ResearchStudy'}\n resource_counts = _validate_patient_everything(caplog, anvil_server,\n [research_studies['ResearchStudy/1000G-high-coverage-2019']])\n assert False, resource_counts","def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching.append(Player(name=\"foo\", pref_names=[]))\n\n with pytest.raises(Exception):\n match._check_reviewer_matching()","def check_results(player_list, dealer):\n dealer_score = dealer.get_score()\n dealer_hand = dealer.get_hand()\n blackjack_winners = []\n winners = []\n losers = []\n pushers = []\n dealer_plays = True\n if dealer_score > 21:\n dealer_plays = False\n for player in player_list:\n player_score = player.get_score()\n player_hand = player.get_hand()\n if dealer_plays and check_blackjack(dealer_score, dealer_hand):\n if check_blackjack(player_score, player_hand):\n pushers.append(player)\n else:\n losers.append(player)\n elif dealer_plays:\n if player_score > dealer_score and not(player.check_bust()):\n if check_blackjack(player_score, player_hand):\n blackjack_winners.append(player)\n else:\n winners.append(player)\n elif player_score == dealer_score:\n pushers.append(player)\n else:\n losers.append(player)\n else:\n if check_blackjack(player_score, player.get_hand()):\n blackjack_winners.append(player)\n break\n elif player_score <= 21:\n winners.append(player)\n else:\n losers.append(player)\n return winners, losers, pushers, blackjack_winners","def validate_step(self,\n x,\n y):\n\n statistics = dict()\n\n for i in range(self.bootstraps):\n fm = self.forward_models[i]\n\n # calculate the prediction error and accuracy of the model\n d = fm.get_distribution(x, training=False)\n nll = -d.log_prob(y)[:, 0]\n\n # evaluate how correct the rank fo the model predictions are\n rank_correlation = spearman(y[:, 0], d.mean()[:, 0])\n\n statistics[f'oracle_{i}/validate/nll'] = nll\n statistics[f'oracle_{i}/validate/rank_corr'] = rank_correlation\n\n return statistics","def test_check_full_house_false(self):\n not_full_house_fixtures = [[1, 1, 1, 1, 2],\n [2, 1, 1, 1, 1],\n [1, 1, 2, 2, 3],\n [1, 2, 3, 4, 5],\n ]\n\n for fixture in not_full_house_fixtures:\n score = self.roll.check_full_house(fixture)\n\n self.assertNotEqual(score, 25)\n self.assertEqual(score, 0)\n self.assertEqual(len(fixture), 5)","def test_rescoring_randomized_problem(self):\r\n # First define the custom response problem:\r\n problem_url_name = 'H1P1'\r\n self.define_randomized_custom_response_problem(problem_url_name)\r\n location = InstructorTaskModuleTestCase.problem_location(problem_url_name)\r\n descriptor = self.module_store.get_item(location)\r\n # run with more than one user\r\n userlist = ['u1', 'u2', 'u3', 'u4']\r\n for username in userlist:\r\n # first render the problem, so that a seed will be created for this user\r\n self.render_problem(username, problem_url_name)\r\n # submit a bogus answer, in order to get the problem to tell us its real answer\r\n dummy_answer = \"1000\"\r\n self.submit_student_answer(username, problem_url_name, [dummy_answer, dummy_answer])\r\n # we should have gotten the problem wrong, since we're way out of range:\r\n self.check_state(username, descriptor, 0, 1, 1)\r\n # dig the correct answer out of the problem's message\r\n module = self.get_student_module(username, descriptor)\r\n state = json.loads(module.state)\r\n correct_map = state['correct_map']\r\n log.info(\"Correct Map: %s\", correct_map)\r\n # only one response, so pull it out:\r\n answer = correct_map.values()[0]['msg']\r\n self.submit_student_answer(username, problem_url_name, [answer, answer])\r\n # we should now get the problem right, with a second attempt:\r\n self.check_state(username, descriptor, 1, 1, 2)\r\n\r\n # redefine the problem (as stored in Mongo) so that the definition of correct changes\r\n self.define_randomized_custom_response_problem(problem_url_name, redefine=True)\r\n # confirm that simply rendering the problem again does not result in a change\r\n # in the grade (or the attempts):\r\n self.render_problem('u1', problem_url_name)\r\n self.check_state('u1', descriptor, 1, 1, 2)\r\n\r\n # rescore the problem for only one student -- only that student's grade should change\r\n # (and none of the attempts):\r\n self.submit_rescore_one_student_answer('instructor', problem_url_name, User.objects.get(username='u1'))\r\n for username in userlist:\r\n self.check_state(username, descriptor, 0 if username == 'u1' else 1, 1, 2)\r\n\r\n # rescore the problem for all students\r\n self.submit_rescore_all_student_answers('instructor', problem_url_name)\r\n\r\n # all grades should change to being wrong (with no change in attempts)\r\n for username in userlist:\r\n self.check_state(username, descriptor, 0, 1, 2)","def test_dropping_all_correct(self):\r\n self.dropping_setup()\r\n\r\n self.dropping_homework_stage1()\r\n for name in self.hw3_names:\r\n self.submit_question_answer(name, {'2_1': 'Correct'})\r\n\r\n self.check_grade_percent(1.0)\r\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 2.0]) # Order matters\r\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 1.0])","def score_professor_conflicts(self):\n prof_conflict_score = 0\n multiplier = 4\n \n for day_num in range(self.num_days):\n \n current_day = self.days[ day_num ]\n num_conflicts = 0\n \n for prof_name in current_day.keys():\n if not self.get_prof_by_name[prof_name].available( day_num ):\n num_conflicts += 1\n \n prof_conflict_score += multiplier * ( num_conflicts ** 2 )\n \n self.prof_conflict_score = prof_conflict_score\n return self.prof_conflict_score","def is_legal_solution(self, solution):\r\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\r\n return self.fit_score(solution) == 0\r\n else:\r\n return self.fit_score(solution) == sum(x for x in range(1, 12))","def update(self):\r\n if self.games and all(game.result for game in self.games):\r\n self.rankings = self.compute_ranking()\r\n self.update_observers()\r\n\r\n if self.finals:\r\n for final in self.finals:\r\n final.update()","def test_affect_of_strategy(self):\n self.responses_test([C, C, C], [C, C, C], [C, C, C])\n # Make sure that the retaliations are increasing\n # Retaliate once and forgive\n self.responses_test([C], [D], [D])\n self.responses_test([C, D], [D, C], [C])\n self.responses_test([C, D, C], [D, C, C], [C])\n # Retaliate twice and forgive\n self.responses_test([C, D, C], [D, C, D], [D, D])\n self.responses_test([C, D, C, D, D], [D, C, D, C, C], [C])\n # Opponent defection during retaliation doesn't increase retaliation period\n self.responses_test([C, D, C, D, D], [D, C, D, D, C], [C])\n # Retaliate thrice and forgive\n self.responses_test([C, D, C, D, D, C], [D, C, D, C, C, D], [D, D, D])\n history_1 = [C, D, C, D, D, C, D, D, D]\n history_2 = [D, C, D, C, C, D, C, C, C]\n self.responses_test(history_1, history_2, [C])","def track_brokenness(log, parse_error_col, parsed_games):\n failures = 0\n wrongness = collections.defaultdict(int)\n overall = collections.defaultdict(int)\n for raw_game in parsed_games:\n accurately_parsed = check_game_sanity(game.Game(raw_game), log)\n if not accurately_parsed:\n log.warning('Failed to accurately parse game %s', raw_game['_id'])\n save_parse_error(parse_error_col, log, raw_game, 'check_game_sanity failed')\n failures += 1\n for card in raw_game[SUPPLY]:\n if not accurately_parsed:\n wrongness[card] += 1\n overall[card] += 1\n\n ratios = []\n for card in overall:\n ratios.append(((float(wrongness[card]) / overall[card]), index_to_card(card)))\n ratios.sort()\n if ratios and ratios[-1][0] > 0:\n log.warning(\"Ratios for problem cards %s, %d failures out of %d games\", ratios[-10:],\n failures, len(parsed_games))\n else:\n log.debug('Perfect parsing, %d games!', len(parsed_games))","def _check_family(self):\n for (s, (b, c)), (cond, ref) in families.items():\n if s != self.SYMBOL or len(b) != self._.d:\n continue\n vars = tuple(set(sum(map(variables, b + c), ())))\n sols = _solve([SR(l) == r for l, r\n in zip(self._.b[:-1] + self._.c[1:], b + c)],\n vars)\n if any(checkConditions(cond, sol) for sol in sols\n if is_integral(sol)):\n raise InfeasibleError(refs=ref)","def testAxiomReasoning(self):\n \n state = State.from_problem(self.prob)\n extstate, reasons, universalReasons = state.get_extended_state(getReasons=True)\n\n relevantVars = []\n relevantReplanVars = []\n load = self.dom.get_action(\"a_load\")\n with load.instantiate([\"agent\", \"obj11\", \"tru1\"], self.prob):\n self.assert_(extstate.is_satisfied(load.precondition, relevantVars))\n self.assert_(extstate.is_satisfied(load.replan, relevantReplanVars))\n\n all_reasons = set(relevantVars)\n for v in relevantVars:\n all_reasons |= reasons[v]\n\n all_replan_reasons = set(relevantReplanVars)\n for v in relevantReplanVars:\n all_replan_reasons |= reasons[v]\n \n s1 = StateVariable(self.prob.functions[\"location-of\"][0], [self.prob[\"tru1\"]])\n s2 = StateVariable(self.prob.functions[\"location-of\"][0], [self.prob[\"obj11\"]])\n\n self.assert_(s1 in all_reasons)\n self.assert_(s2 in all_reasons)\n self.assertFalse(s1 in all_replan_reasons)\n self.assert_(s2 in all_replan_reasons)","def test_teacher_check_homework_raises_homework_repeat_error_if_same_solution_was_already_submitted():\n with pytest.raises(HomeworkRepeatError):\n opp_teacher.check_homework(result_1)\n advanced_python_teacher.check_homework(result_1)\n Teacher.reset_results(oop_hw)","def assess_problematic_entries(n_problems: int, data: List[dict]) -> None:\n print(\n f\"Problematic entries: {n_problems}/{len(data)}\\\n ({100 * n_problems/len(data):3.1f}%)\"\n )","def tie_exists(self):\n return len(self.marks) == 9","def test_when_opponent_all_Ds(self):\n self.responses_test([D, D, D, D], [D, D, D, D], [D, D, D],\n random_seed=5)","def test_falsifications(self):\n implications = get_conditional_independencies(asia_example.graph)\n issues = get_falsifications(implications, asia_example.data)\n self.assertEqual(0, len(issues.failures))\n self.assertEqual(len(issues.evidence), len(implications))","def test_check_validity(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n assert match.check_validity()","def user_story_17(self):\n for f1 in self.families.values():\n for f2 in self.families.values():\n if f1.fid != f2.fid:\n if f1.husb_id == f2.husb_id and f2.wife_id in f1.children:\n print(f\"US17 - {self.individuals[f2.wife_id].name} and {self.individuals[f1.husb_id].name} are married on line {f1._married_line}\")\n if f1.wife_id == f2.wife_id and f2.husb_id in f1.children:\n print(f\"US17 - {self.individuals[f2.husb_id].name} and {self.individuals[f1.wife_id].name} are married on line {f1._married_line}\")","def test_mirror_ranking(self):\n updater = AptMirrorUpdater()\n # Make sure that multiple discovered mirrors are available.\n assert sum(m.is_available for m in updater.ranked_mirrors) > 10","def __check_partial_result(lines: List[Line], partial_result: PartialResult):\n\n x_wins = partial_result.x_wins\n o_wins = partial_result.o_wins\n has_empties = partial_result.has_empties\n\n for line in lines:\n stats = line.get_stats()\n if (not x_wins) and stats.x_count == SIZE:\n x_wins = True\n if (not o_wins) and stats.o_count == SIZE:\n o_wins = True\n if (not has_empties) and stats.has_empties:\n has_empties = True\n\n __merge_partial_result(partial_result, x_wins, o_wins, has_empties)","def test_residual_service_after_element_perturbation_isolating():\n F = FaultDiagnosis(\"tests/TOY_graph_nofaultresistant.csv\")\n F.simulate_element_perturbation([\"1\"])\n\n res_service_after_element_perturbation = {\n '2': 0.0,\n '3': 0.0,\n '4': 0.0,\n '5': 0.0,\n '6': 0.0,\n '7': 0.0,\n '8': 0.0,\n '9': 0.0,\n '10': 0.0,\n '11': 0.0,\n '12': 0.0,\n '13': 0.0,\n '14': 0.0,\n '15': 0.0,\n '16': 0.0,\n '17': 0.0,\n '18': 2.0,\n '19': 0.0\n }\n\n np.testing.assert_array_almost_equal(\n np.asarray(sorted(res_service_after_element_perturbation.values())),\n np.asarray(sorted(F.G.service.values())),\n err_msg=\"FINAL RESIDUAL SERVICE failure: perturbation of element 1\")","def test_get_state_comparison_stats_former_slaveholder(self):\n\n key = '% Former slaveholder employees'\n\n # Kentucky: 0 former slaveholder employees, and 2 who weren't\n for _ in range(2):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n # Mississippi: 1 former slaveholder employee who died during an assignment, and 1 who wasn't\n for _ in range(1):\n employee = EmployeeFactory(slaveholder=True)\n employee.bureau_states.add(self.mississippi)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.mississippi)\n\n # Texas: 1 former slaveholder employee, and 9 who weren't\n for _ in range(1):\n employee = EmployeeFactory(slaveholder=True)\n employee.bureau_states.add(self.texas)\n for _ in range(9):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.texas)\n\n expected_output = [('Mississippi', 50), ('Texas', 10)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(top_states, expected_output,\n f\"'{key}' should contain states with the top x % of former slaveholder employees\")","def fitness_function(individual: str, nurses_number: int = 10):\n\n genes = 21 * nurses_number\n violations_count = 0\n location_count = 0\n before = 0\n\n # Evaluate the second and third restrictions\n # If the value of location_count is other than 5, we have a restriction violation in the first rule\n # Here, for each 21 shifts we have a nurse. So we evaluate for each nurse if there is any restriction violation\n for i in range(0, genes, 21):\n for j in range(i, (i + 21)):\n if individual[j] == '1':\n location_count += 1\n\n # Here and in the following 'if' we are evaluating the third restriction, counting the number of\n # allocations and verifying if we have three consecutive allocations using the 'before' variable\n if before > 3:\n violations_count += 1\n\n if before <= 3:\n before += 1\n\n # When we are in a individual where the nurse isn't allocated, we set the value of before to zero\n else:\n before = 0\n\n # If location_count different of 5, we have a violation restriction\n if location_count != 5:\n violations_count += 1\n location_count = 0\n\n # Evaluate if the first restriction was affected\n shift_count = 0\n # For each shift is evaluated how many nurses are allocated in this period\n for i in range(0, 21):\n for j in range(i, 21 * nurses_number, 21):\n if individual[j] == '1':\n shift_count += 1\n\n # If the number of nurses allocated is out of this interval, we have a violation\n if (shift_count < 1) or (shift_count > 3):\n violations_count += 1\n shift_count = 0\n\n # Here we are evaluating the fourth restriction for each nurse\n # When an allocation is found it is checked if the following allocations are on the same shift\n line_count = 0\n for i in range(0, 21 * nurses_number, 21):\n j = i\n # First, we look for an allocation\n while j < 210 and individual[j] != '1' and (j <= (i + 21)):\n j += 1\n\n # When we meet, we check if the next ones are on the same shift\n for k in range(j, i + 21, 3):\n if individual[k] == '1':\n line_count += 1\n\n # If the number of allocations in the same shift is different of five, we have a violation\n if line_count != 5:\n violations_count += 1\n\n line_count = 0\n\n # After each verification, it is returned the number of restrictions violated\n return violations_count","def check(self):\r\n self.check_probabilities()\r\n self.check_sum()","def champion_check(self):\n better_champion = False\n\n for species in self.species:\n if species.leader.original_fitness > self.champion_fitness:\n self.age_since_improvement = 0\n self.champion_fitness = species.leader.original_fitness\n better_champion = True\n\n if not better_champion:\n self.age_since_improvement += 1"],"string":"[\n \"def _check_hospital_prefs(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n residents_that_ranked = [\\n res for res in self.residents if hospital in res.prefs\\n ]\\n if set(hospital.prefs) != set(residents_that_ranked):\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has not ranked all the residents that \\\"\\n f\\\"ranked it: {set(hospital.prefs)} != \\\"\\n f\\\"{set(residents_that_ranked)}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_stability():\\n\\n residents = [Player(\\\"A\\\", [1, 2]), Player(\\\"B\\\", [2]), Player(\\\"C\\\", [2, 1])]\\n hospitals = [Player(1, [\\\"C\\\", \\\"A\\\"], 3), Player(2, [\\\"A\\\", \\\"B\\\", \\\"C\\\"], 3)]\\n match = HospitalResident(residents, hospitals)\\n\\n matching = match.solve()\\n assert match.check_stability()\\n\\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\\n matching[P_1] = [P_C]\\n matching[P_2] = [P_A, P_B]\\n\\n assert not match.check_stability()\",\n \"def _check_ties(self,rank):\\n for k in self._run:\\n curr_dict = defaultdict(list)\\n qid = self._run[k][0].get_qid()\\n for t in self._run[k]:\\n # print t.get_str()\\n curr_dict[t.get_score()].append(t)\\n curr_dict = OrderedDict(sorted(curr_dict.iteritems(),reverse=True))\\n cnt = 0\\n for score in curr_dict:\\n cnt = cnt + 1\\n if len(curr_dict[score]) > 1 and cnt<rank:\\n self._ties[qid] = 1\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def _check_resident_prefs(self):\\n\\n errors = []\\n for resident in self.residents:\\n if not set(resident.prefs).issubset(set(self.hospitals)):\\n errors.append(\\n ValueError(\\n f\\\"{resident} has ranked a non-hospital: \\\"\\n f\\\"{set(resident.prefs)} != {set(self.hospitals)}\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_hospital_prefs_all_reciprocated(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n resident._forget(hospital)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert resident not in hospital.prefs\",\n \"def test_check_for_unacceptable_matches_residents(game):\\n\\n resident = game.residents[0]\\n hospital = Hospital(name=\\\"foo\\\", capacity=1)\\n resident.matching = hospital\\n\\n issues = game._check_for_unacceptable_matches(\\\"residents\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(resident.name)\\n assert issue.endswith(f\\\"{resident.prefs}.\\\")\\n assert hospital.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def _check_hospital_unhappy(resident, hospital):\\n\\n return len(hospital.matching) < hospital.capacity or any(\\n [hospital.prefers(resident, match) for match in hospital.matching]\\n )\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def test_check_inputs_hospital_prefs_all_residents(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert hospital.prefs == []\",\n \"def test_lsr_rankings():\\n for case in iter_testcases('rankings'):\\n n_items = case[\\\"n_items\\\"]\\n data = case[\\\"data\\\"]\\n assert np.allclose(\\n case[\\\"lsr_est\\\"], lsr_rankings(n_items, data),\\n atol=ATOL, rtol=RTOL)\",\n \"def _check_resident_unhappy(resident, hospital):\\n\\n return resident.matching is None or resident.prefers(\\n hospital, resident.matching\\n )\",\n \"def test_ilsr_rankings():\\n for case in iter_testcases('rankings'):\\n n_items = case[\\\"n_items\\\"]\\n data = case[\\\"data\\\"]\\n assert np.allclose(\\n case[\\\"ml_est\\\"], ilsr_rankings(n_items, data),\\n atol=ATOL, rtol=RTOL)\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_check_inputs_resident_prefs_all_hospitals(game):\\n\\n resident = game.residents[0]\\n resident.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert resident.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert resident.prefs == []\",\n \"def test_resident_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"resident\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n assert all(\\n [\\n r in set(residents)\\n for r in set(\\n [\\n r_match\\n for matches in matching.values()\\n for r_match in matches\\n ]\\n )\\n ]\\n )\\n\\n for resident in residents:\\n if resident.matching:\\n assert resident.pref_names.index(resident.matching.name) == 0\",\n \"def death_check(self):\\r\\n chance = random.random()\\r\\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\\r\\n if self.unique_id in head_of_household_list:\\r\\n try:\\r\\n head_of_household_list[self.hh_id] = 0\\r\\n except TypeError: # head of household migrated\\r\\n head_of_household_list[self.past_hh_id] = 0\\r\\n self.model.number_of_humans -= 1\\r\\n if self.unique_id in labor_list:\\r\\n labor_list.remove(self.unique_id)\\r\\n if self.work_status == 1:\\r\\n try:\\r\\n num_labor_list[self.hh_id] -= 1\\r\\n except TypeError:\\r\\n num_labor_list[self.past_hh_id] -= 1\\r\\n if self.unique_id in former_hoh_list:\\r\\n try:\\r\\n former_hoh_list[self.hh_id] = 0\\r\\n except:\\r\\n former_hoh_list[self.past_hh_id] = 0\\r\\n if [self.unique_id, self.hh_id] in single_male_list:\\r\\n single_male_list.remove([self.unique_id, self.hh_id])\\r\\n if self.unique_id in married_male_list:\\r\\n married_male_list.remove(self.unique_id)\\r\\n human_death_list.append(self.unique_id)\\r\\n try:\\r\\n hh_size_list[self.hh_id] -= 1\\r\\n except:\\r\\n hh_size_list[self.past_hh_id] -= 1\\r\\n human_demographic_structure_list[self.age_category] -= 1\\r\\n\\r\\n self.model.schedule.remove(self)\\r\\n if self in self.model.grid:\\r\\n self.model.grid.remove_agent(self)\",\n \"def test_check_for_oversubscribed_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n supervisor.matching = range(supervisor.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.capacity))\\n assert str(supervisor.matching) in e\",\n \"def fit_score(self, solution):\\r\\n illegal_neighbours = 0\\r\\n legal_neighbours = 0\\r\\n for polygon in solution.genetic_units.values():\\r\\n for neighbour_id in polygon.neighbours_ids:\\r\\n if polygon.color is solution.genetic_units[neighbour_id].color:\\r\\n illegal_neighbours += 1\\r\\n else:\\r\\n legal_neighbours += 1\\r\\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\\r\\n return illegal_neighbours\\r\\n else:\\r\\n return legal_neighbours\",\n \"def __statistics_students_failing(self):\\n students_list = self.__grade_controller.get_list_of_failing_students()\\n if len(students_list) == 0:\\n print(\\\"There is no student failing!\\\")\\n return\\n\\n for student in students_list:\\n print(str(student) + \\\"\\\\n\\\")\",\n \"def listErrors(individual): #TODO: add other errors implemented elsewhere\\n\\n results = [birthBeforeMarriage(individual), birthBeforeDeath(individual), marriageBeforeDeath(individual), \\n datesBeforeCurrentDate(individual), noBigamy(individual)]\\n results = [x for x in results if x is not None]\\n return not all(results)\",\n \"def check_stability(self):\\n\\n blocking_pairs = []\\n for resident in self.residents:\\n for hospital in self.hospitals:\\n if (\\n _check_mutual_preference(resident, hospital)\\n and _check_resident_unhappy(resident, hospital)\\n and _check_hospital_unhappy(resident, hospital)\\n ):\\n blocking_pairs.append((resident, hospital))\\n\\n self.blocking_pairs = blocking_pairs\\n return not any(blocking_pairs)\",\n \"def test_check_inputs_resident_prefs_all_nonempty(game):\\n\\n resident = game.residents[0]\\n resident.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n assert resident.name in str(record[0].message)\\n\\n if game.clean:\\n assert resident not in game.residents\",\n \"def test_check_inputs(game):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.residents == game._all_residents\\n assert game.hospitals == game._all_hospitals\",\n \"def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"hospital\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n\\n for hospital, matches in matching.items():\\n old_idx = -np.infty\\n for resident in matches:\\n idx = hospital.pref_names.index(resident.name)\\n assert idx >= old_idx\\n old_idx = idx\",\n \"def is_unhappy(self):\\n #checked!#\\n ###your code here###\\n same=0\\n for i in self.home.neighbors:\\n if i.occupant!=None:\\n if i.occupant.group==self.group:\\n same+=1\\n happniess=float(same)/len(self.home.neighbors)\\n if happniess<self.happiness_threshold:\\n return True\\n else:\\n return False\",\n \"def _check_hospital_matching(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n for resident in hospital.matching:\\n if resident not in hospital.prefs:\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has {resident} in their matching but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{hospital.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def assess_risk(black_marbles: list, white_marbles: list):\\n danger = Evaluator.danger_positions\\n black_risk = 0\\n white_risk = 0\\n for black_marble in black_marbles:\\n letter, num = Board.convert_to_nums(black_marble)\\n left = Board.convert_to_string(letter, num - 1)\\n right = Board.convert_to_string(letter, num + 1)\\n upL = Board.convert_to_string(letter + 1, num)\\n upR = Board.convert_to_string(letter + 1, num + 1)\\n downL = Board.convert_to_string(letter - 1, num - 1)\\n downR = Board.convert_to_string(letter - 1, num)\\n test_spots = [left, right, upL, upR, downL, downR]\\n if black_marble in danger:\\n black_risk += 2\\n for spot in test_spots:\\n if spot in white_marbles:\\n black_risk += 5\\n\\n for white_marble in white_marbles:\\n letter, num = Board.convert_to_nums(white_marble)\\n left = Board.convert_to_string(letter, num - 1)\\n right = Board.convert_to_string(letter, num + 1)\\n upL = Board.convert_to_string(letter + 1, num)\\n upR = Board.convert_to_string(letter + 1, num + 1)\\n downL = Board.convert_to_string(letter - 1, num - 1)\\n downR = Board.convert_to_string(letter - 1, num)\\n test_spots = [left, right, upL, upR, downL, downR]\\n if white_marble in danger:\\n white_risk += 2\\n for spot in test_spots:\\n if spot in black_marbles:\\n white_risk += 5\\n return black_risk, white_risk\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def test_solve(resident_names, hospital_names, capacities, seed):\\n\\n for optimal in [\\\"suitor\\\", \\\"resident\\\", \\\"reviewer\\\", \\\"hospital\\\"]:\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n matching = match.solve(optimal)\\n assert isinstance(matching, Matching)\\n assert set(matching.keys()) == set(hospitals)\\n matched_residents = [\\n res for match in matching.values() for res in match\\n ]\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(residents)\\n )\\n\\n for resident in set(residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def test_calculates_risk_points_with_mortgaged_house(self):\\n\\n self.user[\\\"houses\\\"].insert(0, {\\n \\\"id\\\": 123,\\n \\\"ownership_status\\\": \\\"mortgaged\\\"\\n })\\n\\n self.risk_profile[\\\"home\\\"].insert(0, {\\n \\\"id\\\": 123,\\n \\\"is_eligible\\\": True,\\n \\\"risk_points\\\": 1\\n })\\n\\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\\n\\n for house in self.risk_profile[\\\"home\\\"]:\\n if house[\\\"id\\\"] == 123:\\n self.assertEquals(house[\\\"risk_points\\\"], 2)\\n else: \\n self.assertEquals(house[\\\"risk_points\\\"], 1)\\n \\n self.assertEquals(self.risk_profile[\\\"disability\\\"][\\\"risk_points\\\"], 2)\",\n \"def test_hand_has_all_unique_ranks(hand, card_list, expected):\\n hand.add_cards(card_list)\\n assert hand.has_all_unique_ranks() == expected\",\n \"def test_solve(game, optimal):\\n\\n matching = game.solve(optimal)\\n assert isinstance(matching, MultipleMatching)\\n\\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\\n for game_hospital, hospital in zip(matching_keys, hospitals):\\n assert game_hospital.name == hospital.name\\n assert game_hospital._pref_names == hospital._pref_names\\n assert game_hospital.capacity == hospital.capacity\\n\\n matched_residents = [\\n resident for match in matching.values() for resident in match\\n ]\\n\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(game.residents)\\n )\\n\\n for resident in set(game.residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def _check_unstake_result(self) -> None:\\n balance_score = self.icx.get_balance(self.address) - self._daily_reward.get()\\n if balance_score > 0:\\n unstake_info_list = self.getUserUnstakeInfo()\\n for each_info in unstake_info_list:\\n value_to_transfer = each_info[0]\\n if value_to_transfer <= balance_score:\\n self._send_ICX(each_info[1], value_to_transfer)\\n self._linked_list_var.remove(self._linked_list_var._head_id.get())\\n break\",\n \"def test_check_stability():\\n\\n students = [Student(\\\"A\\\"), Student(\\\"B\\\"), Student(\\\"C\\\")]\\n projects = [Project(\\\"P\\\", 2), Project(\\\"Q\\\", 2)]\\n supervisors = [Supervisor(\\\"X\\\", 2), Supervisor(\\\"Y\\\", 2)]\\n\\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\\n\\n p.set_supervisor(x)\\n q.set_supervisor(y)\\n\\n a.set_prefs([p, q])\\n b.set_prefs([q])\\n c.set_prefs([q, p])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = StudentAllocation(students, projects, supervisors)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (p, q) = game.students, game.projects\\n\\n matching[p] = [c]\\n matching[q] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def test_get_state_comparison_stats_ailments(self):\\n\\n # AilmentType and Ailment breakdown per AilmentType should be in returned stats if present\\n ailment_type_headache = AilmentTypeFactory(name='Headache')\\n ailment_migraine_headache = AilmentFactory(name='Migraine Headache', type=ailment_type_headache)\\n ailment_tension_headache = AilmentFactory(name='Tension Headache', type=ailment_type_headache)\\n ailment_type_sprain_or_bruise = AilmentTypeFactory(name='Sprain or Bruise')\\n ailment_sprain = AilmentFactory(name='Sprain', type=ailment_type_sprain_or_bruise)\\n\\n # Texas: 2 with sprain and 2 with nothing\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_sprain)\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 1 with migraine headache, 1 with sprain, 1 with tension headache, and 1 with nothing\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_migraine_headache)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_sprain)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_tension_headache)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n # Mississippi: 2 with migraine headache, 2 with tension headache\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_migraine_headache)\\n employee.bureau_states.add(self.mississippi)\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_tension_headache)\\n employee.bureau_states.add(self.mississippi)\\n\\n stats = get_state_comparison_stats(number=2)\\n\\n # With ailment types, ailments should be grouped together\\n key = '% With Headache'\\n expected_output = [('Mississippi', 100), ('Kentucky', 50)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with headache\\\")\\n\\n key = '% With Sprain or Bruise'\\n expected_output = [('Texas', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with sprain or bruise\\\"\\n )\\n\\n # Ailments should be treated individually\\n key = '% With Migraine Headache'\\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with migraine headache\\\"\\n )\\n\\n key = '% With Tension Headache'\\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with tension headache\\\"\\n )\\n\\n # There should be no breakdown for sprain, because it's the only ailment of that type\\n key = '% With Sprain'\\n self.assertEqual(len([item for item in stats if key in item]), 0,\\n \\\"There should be no breakdown for an ailment if it's the only one of its type\\\")\",\n \"def rlWorkout(gameInfo):\\n \\n if gameInfo['myScore'] >= 0 and gameInfo['opponentScore'] >= 0:\\n if gameInfo['myScore'] > gameInfo['opponentScore']:\\n determineRocketLeagueWin(gameInfo['goals'], gameInfo['assist'], gameInfo['saves'])\\n else:\\n determineRocketLeagueLoss(gameInfo['goals'], gameInfo['assist'], gameInfo['saves'])\\n else:\\n print(\\\"Doesn't make sense dude\\\")\\n return workoutInfo\",\n \"def test_check_for_unacceptable_matches_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n student = Student(name=\\\"foo\\\")\\n supervisor.matching.append(student)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.prefs))\\n assert student.name in error\",\n \"def has_straight(self):\\n res = []\\n self.suit_hist()\\n for val in self.ranks.keys():\\n res.append(val)\\n res.sort()\\n self.is_sequence(res, 3)\",\n \"def testSpeciesRichnessCorrect(self):\\n self.assertEqual(136, self.coal.get_species_richness(1))\\n self.assertNotEqual(self.coal.get_species_richness(2), self.coal.get_species_richness(1))\\n self.assertNotEqual(self.coal.get_species_richness(3), self.coal.get_species_richness(2))\",\n \"def test_enter_score_cleanup(self):\\n tournament_scores = len(TournamentScore.query.all())\\n scores = len(ScoreDAO.query.all())\\n\\n self.enter_score_bad_values()\\n\\n compare(tournament_scores, len(TournamentScore.query.all()))\\n compare(scores, len(ScoreDAO.query.all()))\",\n \"def check_all(self):\\n return self.check_rs() + self.check_hgvs()\",\n \"def check_order(racers):\\n racers_orderd = sorted(racers, key=lambda x: (x.group, x.time))\\n a = 1\\n b = 1\\n for r in racers_orderd:\\n if r.group == \\\"ALL\\\":\\n r.rank = str(a)\\n a += 1\\n else:\\n r.rank = str(b)\\n b += 1\\n return racers_orderd\",\n \"def testProtractedNSESanityChecks(self):\\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))\",\n \"def user_story_4(self):\\n for family in self.families.values():\\n if family.married != 'NA':\\n if family.wife_id != 'NA' and family.husb_id != 'NA' and family.divorced != 'NA':\\n if family.divorced < family.married:\\n print(\\n f'US04 - {self.individuals[family.wife_id].name} and {self.individuals[family.husb_id].name} married after divorce on line {family._married_line}')\",\n \"def _check_resident_matching(self):\\n\\n errors = []\\n for resident in self.residents:\\n if (\\n resident.matching is not None\\n and resident.matching not in resident.prefs\\n ):\\n errors.append(\\n ValueError(\\n f\\\"{resident} is matched to {resident.matching} but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{resident.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def check_overall_energy(self):\\n energy = 0\\n for student in self.students:\\n energy += int(student.energy_level)\\n for mentor in self.mentors:\\n energy += int(mentor.energy_level)\\n print(\\\"Overall energy equals \\\", energy)\",\n \"def testNumberIndividualsCorrect(self):\\n for ref in self.coal.get_community_references():\\n self.assertEqual(1000, self.coal.get_number_individuals(community_reference=ref))\",\n \"def test_check_inputs_supervisor_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n supervisor.prefs.remove(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"supervisors\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert supervisor.name in message\\n\\n if clean:\\n assert supervisor not in student.prefs\\n assert all(\\n project not in student.prefs for project in supervisor.projects\\n )\",\n \"def find_qualifying_loans(bank_data, credit_score, debt, income, loan, home_value):\\n\\n # Calculate the monthly debt ratio\\n monthly_debt_ratio = calculate_monthly_debt_ratio(debt, income)\\n print(\\\"\\\\n\\\", \\\"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\\\")\\n print(\\\"\\\\n\\\", \\\"\\\\u001b[34;1m\\\")\\n print(f\\\"------->>> Monthly debt-to-income ratio is: {monthly_debt_ratio:.02f}.\\\")\\n print(\\\"\\\\u001b[0m\\\")\\n\\n # Calculate loan to value ratio\\n loan_to_value_ratio = calculate_loan_to_value_ratio(loan, home_value)\\n print(\\\"\\\\n\\\", \\\"\\\\u001b[34;1m\\\")\\n print(f\\\"------->>> Loan-to-value ratio is {loan_to_value_ratio:.02f}.\\\")\\n print(\\\"\\\\n\\\", \\\"\\\\u001b[0m\\\")\\n print(\\\" * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\\\", \\\"\\\\n\\\")\\n\\n # Run qualification filters\\n bank_data_filtered = filter_max_loan_size(loan, bank_data)\\n bank_data_filtered = filter_credit_score(credit_score, bank_data_filtered)\\n bank_data_filtered = filter_debt_to_income(monthly_debt_ratio, bank_data_filtered)\\n bank_data_filtered = filter_loan_to_value(loan_to_value_ratio, bank_data_filtered)\\n number_of_loans_available = len(bank_data_filtered)\\n if number_of_loans_available <= 0:\\n print(\\\"I'm sorry. We have no loans to offer at this time.\\\"), \\\"\\\\n\\\"\\n exit()\\n else: \\n print(\\\"\\\\u001b[32m\\\", f\\\"Congratulations! You qualify for: {number_of_loans_available} loan(s).\\\", \\\"\\\\u001b[0m\\\", \\\"\\\\n\\\")\\n\\n return bank_data_filtered\",\n \"def test_residues(parse_mmtf):\\n parse_mmtf.assertEqual(len(parse_mmtf.mmcif_res), len(parse_mmtf.mmtf_res))\\n for i, e in enumerate(parse_mmtf.mmcif_res):\\n mmcif_r = parse_mmtf.mmcif_res[i]\\n mmtf_r = parse_mmtf.mmtf_res[i]\\n parse_mmtf.assertEqual(mmtf_r.level, mmcif_r.level)\\n parse_mmtf.assertEqual(mmtf_r.disordered, mmcif_r.disordered)\\n parse_mmtf.assertEqual(mmtf_r.resname, mmcif_r.resname)\\n parse_mmtf.assertEqual(mmtf_r.segid, mmcif_r.segid)\\n parse_mmtf.mmcif_atoms = [x for x in mmcif_r.get_atom()]\\n parse_mmtf.mmtf_atoms = [x for x in mmtf_r.get_atom()]\\n test_atoms(parse_mmtf=parse_mmtf)\",\n \"def test_check_inputs_supervisor_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n projects = supervisor.projects\\n for project in student.prefs:\\n if project in projects:\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"supervisors\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(supervisor.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in supervisor.prefs\\n assert all(\\n student not in project.prefs for project in supervisor.projects\\n )\",\n \"def arsenalResults(dat):\\n arsScore = int(dat[0])\\n othScore = int(dat[2])\\n if arsScore > othScore:\\n res = 1\\n elif arsScore == othScore:\\n res = 2\\n else:\\n res = 0\\n return res\",\n \"def is_error(ranking, references):\\n return 1 if average_precision(ranking, references) < 1 else 0\",\n \"def get_ranks(d): \\n raise NotImplementedError(\\\"Problem 3 Incomplete\\\")\",\n \"def test_something(self):\\n\\n allure.dynamic.title(\\\"Testing compute_ranks\\\")\\n allure.dynamic.severity(allure.severity_level.NORMAL)\\n allure.dynamic.description_html('<h3>Codewars badge:</h3>'\\n '<img src=\\\"https://www.codewars.com/users/myFirstCode'\\n '/badges/large\\\">'\\n '<h3>Test Description:</h3>'\\n \\\"<p>Test the function taht organizes a sports league in a \\\"\\n \\\"round-robin-system. Each team meets all other teams. \\\"\\n \\\"In your league a win gives a team 2 points, a draw gives \\\"\\n \\\"both teams 1 point. After some games you have to compute \\\"\\n \\\"the order of the teams in your league. You use the following \\\"\\n \\\"criteria to arrange the teams:</p>\\\"\\n \\\"<ul><li>- Points</li>\\\"\\n \\\"<li>- Scoring differential (the difference between goals \\\"\\n \\\"scored and those conceded)</li>\\\"\\n \\\"<li>- Goals scored</li></ul>\\\")\\n\\n test_data = [\\n (6,\\n [[0, 5, 2, 2],\\n [1, 4, 0, 2],\\n [2, 3, 1, 2],\\n [1, 5, 2, 2],\\n [2, 0, 1, 1],\\n [3, 4, 1, 1],\\n [2, 5, 0, 2],\\n [3, 1, 1, 1],\\n [4, 0, 2, 0]],\\n [4, 4, 6, 3, 1, 2]),\\n (6,\\n [[0, 5, 2, 0],\\n [1, 4, 2, 2],\\n [2, 3, 1, 3],\\n [1, 5, 0, 0],\\n [2, 0, 2, 1],\\n [3, 4, 3, 1]],\\n [2, 3, 4, 1, 5, 6]),\\n (4,\\n [[0, 3, 1, 1],\\n [1, 2, 2, 2],\\n [1, 3, 2, 0],\\n [2, 0, 2, 0]],\\n [3, 1, 1, 3]),\\n (10,\\n [],\\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\\n (8,\\n [[0, 7, 2, 0]],\\n [1, 2, 2, 2, 2, 2, 2, 8])\\n ]\\n\\n for data in test_data:\\n number = data[0]\\n games = data[1]\\n expected = data[2]\\n actual_result = compute_ranks(number, games)\\n print_log(number=number,\\n games=games,\\n expected=expected,\\n actual_result=actual_result)\\n\\n with allure.step(\\\"Enter a test data and verify the result:\\\"):\\n self.assertEqual(expected, actual_result)\",\n \"def __count_errors(node, testSet, res):\\n training_results = __get_results(node) #Get a dictionary of labels and counts for the *training* data which made it to this node\\n leaf_label = None #Initialize a label for this leaf\\n majority_count = 0 #Initialize a variable to track the number of observations for the label with the most observations\\n #Note that the steps below do not handle ties of the majority count in a nice way.\\n for label, count in training_results.items(): #iterate through each pair of labels and counts from the training set\\n if count > majority_count: #find the label with the highest count\\n leaf_label = label #the label for the leaf is the label with the highest count\\n majority_count = count #keep track of the count for the leaf_label\\n \\n wrong_labels = testSet[res].unique().tolist() #initialize wrong_labels to be all labels in the testSet\\n if leaf_label in wrong_labels: #If the leaf label is in the list of labels for the part of the test set that got to this node\\n wrong_labels.remove(leaf_label) #remove the leaf_label so that all which remains are incorrect labels\\n \\n wrong_count = 0 #Initialize a count of how many testSet observations will be classified incorrectly\\n testCounts = testSet.groupby(res).size() #Get a series of the testSet labels and how many observations pertain to each label\\n for label in wrong_labels: #Iterate over all the labels not equal to the leaf_label\\n wrong_count += testCounts[label] #Sum up all of the observations with a label not equal to the leaf_label\\n return wrong_count\",\n \"def test_check_inputs_hospital_prefs_all_nonempty(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def enough_lords(self):\\n real_numbers = {\\n Title.count: len(self.get_lords_of_title(Title.count)),\\n Title.baron: len(self.get_lords_of_title(Title.baron)),\\n Title.baronet: len(self.get_lords_of_title(Title.baronet)),\\n Title.chevalier: len(self.get_lords_of_title(Title.chevalier)),\\n Title.client: len(self.get_lords_of_title(Title.client)),\\n }\\n\\n counter = {Title.baron: 0, Title.baronet: 0, Title.chevalier: 0,\\n Title.client: 0}\\n\\n for title, vassals in LORDS_VASSALS.items():\\n for i in range(real_numbers[title]):\\n for lower_title, value in vassals.items():\\n counter[lower_title] += value\\n\\n for title, count in counter.items():\\n if count > real_numbers[title]:\\n print(f'Not enough lords of title: {title.value}, '\\n f'required: {count}, actual: {real_numbers[title]}')\\n return False\\n return True\",\n \"def _identify_anomalies(self, X, yhat, prob):\\n\\n y = DTA_LSTM._shift_traces_left(X)\\n\\n for i in range(len(X)):\\n\\n rp = self._compare_traces(y[i], yhat[i])\\n self.rank_prob.append(rp)\\n\\n if self.explain:\\n print('X[{}]: {} = {}'.format(i, X[i], rp))\\n\\n return self.rank_prob\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def test_get_state_comparison_stats_foreign_born(self):\\n\\n key = '% Foreign-born employees'\\n\\n us = CountryFactory(code2='US')\\n germany = CountryFactory(name='Germany')\\n\\n # Mississippi: 0 employees born in Germany, 3 in US\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.mississippi)\\n\\n # Texas: 2 employees born in Germany, 2 in US\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 3 employees born in Germany, 1 employee in US, 3 unknown\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(3):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n expected_output = [('Kentucky', 75), ('Texas', 50)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(top_states, expected_output,\\n \\\"'{key}' should contain states with the top x % foreign-born employees\\\")\",\n \"def test_calculates_risk_points_with_one_house(self):\\n\\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\\n\\n for house in self.risk_profile[\\\"home\\\"]:\\n self.assertEquals(house[\\\"risk_points\\\"], 2)\",\n \"def age_check(self):\\r\\n # check working status\\r\\n if 15 <= float(self.age) < 59:\\r\\n if self.work_status == 0:\\r\\n self.work_status = 1\\r\\n num_labor_list[self.hh_id] += 1\\r\\n labor_list.append(self.unique_id)\\r\\n if self.work_status == 1 and self.unique_id not in labor_list:\\r\\n labor_list.append(self.unique_id)\\r\\n else:\\r\\n self.work_status = 0\\r\\n\\r\\n # check education status; measured in years of education\\r\\n if 7 <= int(self.age) <= 19:\\r\\n if random.random() > 0.1:\\r\\n self.education += 1\\r\\n # most adults in the FNNR did not get a full 12-13 years of education\\r\\n elif 19 < float(self.age) < 23 and self.migration_status == 1:\\r\\n if random.random() < 0.5:\\r\\n self.education += 1 # went to college and got further education\\r\\n # this is rare; in the household list, a few received beyond 12 years of education\\r\\n\\r\\n # check age-based death rates\\r\\n if self.age > 65:\\r\\n self.death_rate = 0.001443 # 5-day death rate\\r\\n # The average death rate in China is 7.3 per 1,000 people/year, or 0.0073 (Google).\\r\\n # However, death rates should be higher for the elderly, or else the population structure will skew.\\r\\n # I set death rates for those over age 65 to be 10% per year--0.9 yearly survival rate.\\r\\n # The survival rate for each 5-day step is compounded 73 times, so x^73 = 0.85.\\r\\n # 0.998557 is the 5-day survival rate, and 1 - x is the 5-day death rate.\\r\\n else:\\r\\n self.death_rate = 0.00000425\\r\\n # I wanted people to have a 98% chance of reaching age 65 (death rate is lower if not elderly).\\r\\n # If a 'check' is every 5 days, 73 checks/year * 65 years = 4,745 checks.\\r\\n # x^4745 = 0.98; the 5-day survival rate is 0.99999575, and 1 - x is the 5-day death rate.\\r\\n\\r\\n # These rates are changeable later.\\r\",\n \"def test_check_yahtzee_bonus_false(self):\\n\\n not_yahtzee_bonus_fixtures = [[1, 1, 1, 1, 2],\\n [1, 1, 1, 2, 1],\\n [1, 1, 2, 1, 1],\\n [1, 2, 1, 1, 1],\\n [2, 1, 1, 1, 1],\\n ]\\n\\n for fixture in not_yahtzee_bonus_fixtures:\\n score = self.roll.check_yahtzee_bonus(fixture)\\n\\n self.assertNotEqual(score, 50)\\n self.assertEqual(score, 0)\\n self.assertEqual(len(fixture), 5)\",\n \"def __statistics_best_situation(self):\\n students_list = self.__grade_controller.get_list_of_students_with_best_situation()\\n if len(students_list) == 0:\\n print(\\\"There is no student with a grade!\\\")\\n return\\n\\n for student in students_list:\\n print(str(student) + \\\"\\\\n\\\")\",\n \"def computeAssignments(solution, h, data, sumW, checkers, hini=None):\\n minHours = data[\\\"minHours\\\"]\\n hours = data[\\\"hours\\\"]\\n z = solution[\\\"z\\\"]\\n w = solution[\\\"w\\\"]\\n\\n mustWork = []\\n canWork = []\\n\\n # sort nurses, first by those who work\\n sorted_nurses = sorted(range(data[\\\"nNurses\\\"]), key=lambda n: solution[\\\"z\\\"][n], reverse=True)\\n # print(solution[\\\"z\\\"])\\n # print(sorted_nurses)\\n # print(\\\"\\\")\\n\\n # for each nurse\\n for n in sorted_nurses:\\n\\n # canWork Check-------------------------------\\n canWork_check = checkIfCanWork_fast(solution, h, n, data, sumW, checkers=checkers, hini=hini)\\n if canWork_check:\\n\\n\\n mustWork_check = checkIfMustWork_fast(solution, h, n, data, sumW, True, checkers, hini)\\n if mustWork_check:\\n mustWork.append(n)\\n else: \\n canWork.append(n)\\n\\n # # mustWork Check-------------------------------\\n # # avoid repeating canWork_check\\n # mustWork_check = checkIfMustWork_fast(solution, h, n, data, sumW, canWork_check, checkers, hini)\\n # if mustWork_check:\\n # mustWork.append(n)\\n # else:\\n \\n return mustWork, canWork\",\n \"def check_evaluations(session, dates):\\n user_ids = get_campus_students(session, 13)\\n bad_evals = []\\n\\n print(f'found {len(user_ids)} students !')\\n for user_id in user_ids:\\n # Get all evaluations from student id in selected timeframe\\n evaluations = get_all_pages(session, f'/users/{user_id}/scale_teams/as_corrected', 100, params=dates)\\n if evaluations == '[]':\\n continue\\n # Check each returned evaluation if bad or not\\n for evaluation in evaluations:\\n # Object BadEvaluation is returned if rules come back True\\n bad = detect_bad_eval(evaluation)\\n if isinstance(bad, BadEvaluation):\\n bad_evals.append(bad)\\n\\n return bad_evals\",\n \"def test_get_state_comparison_stats_penmanship_contest(self):\\n\\n key = 'Left-hand penmanship contest entrants'\\n\\n # Mississippi: 0 employees who entered the contest, 3 who didn't\\n for _ in range(3):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.mississippi)\\n\\n # Texas: 2 employees who entered the contest, 2 who didn't\\n for _ in range(2):\\n employee = EmployeeFactory(penmanship_contest=True)\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 3 employees who entered the contest, 1 who didn't\\n for _ in range(3):\\n employee = EmployeeFactory(penmanship_contest=True)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n expected_output = [('Kentucky', 3), ('Texas', 2)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x number of employees who entered contest\\\"\\n )\",\n \"def rank_err(data_mod, mod_dict):\\n # checks that order of patients is identical for both models, and that correct data dictionary is passed\\n np.testing.assert_array_equal(mod_dict['linear'].Y, mod_dict['rbf'].Y, err_msg='lin and rbf models not trained on same data')\\n np.testing.assert_array_equal(mod_dict['linear'].X, mod_dict['rbf'].X, err_msg='lin and rbf models not trained on same data')\\n np.testing.assert_array_equal(mod_dict['rbf'].X, data_mod['XA'], err_msg='mogp models and data dictionary not trained on same data') \\n\\n err_dict = {}\\n for key in mod_dict.keys():\\n err_dict[key] = calc_rmse_full_mod(data=data_mod, model_type=key, mod=mod_dict[key])\\n\\n return err_dict\",\n \"def test_anvil_first_patient_everything(caplog, anvil_server, anvil_research_studies_with_observations):\\n research_studies = {r.relativePath(): r for r in anvil_research_studies_with_observations if\\n r.__class__.__name__ == 'ResearchStudy'}\\n resource_counts = _validate_patient_everything(caplog, anvil_server,\\n [research_studies['ResearchStudy/1000G-high-coverage-2019']])\\n assert False, resource_counts\",\n \"def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.reviewers[0].matching.append(Player(name=\\\"foo\\\", pref_names=[]))\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_matching()\",\n \"def check_results(player_list, dealer):\\n dealer_score = dealer.get_score()\\n dealer_hand = dealer.get_hand()\\n blackjack_winners = []\\n winners = []\\n losers = []\\n pushers = []\\n dealer_plays = True\\n if dealer_score > 21:\\n dealer_plays = False\\n for player in player_list:\\n player_score = player.get_score()\\n player_hand = player.get_hand()\\n if dealer_plays and check_blackjack(dealer_score, dealer_hand):\\n if check_blackjack(player_score, player_hand):\\n pushers.append(player)\\n else:\\n losers.append(player)\\n elif dealer_plays:\\n if player_score > dealer_score and not(player.check_bust()):\\n if check_blackjack(player_score, player_hand):\\n blackjack_winners.append(player)\\n else:\\n winners.append(player)\\n elif player_score == dealer_score:\\n pushers.append(player)\\n else:\\n losers.append(player)\\n else:\\n if check_blackjack(player_score, player.get_hand()):\\n blackjack_winners.append(player)\\n break\\n elif player_score <= 21:\\n winners.append(player)\\n else:\\n losers.append(player)\\n return winners, losers, pushers, blackjack_winners\",\n \"def validate_step(self,\\n x,\\n y):\\n\\n statistics = dict()\\n\\n for i in range(self.bootstraps):\\n fm = self.forward_models[i]\\n\\n # calculate the prediction error and accuracy of the model\\n d = fm.get_distribution(x, training=False)\\n nll = -d.log_prob(y)[:, 0]\\n\\n # evaluate how correct the rank fo the model predictions are\\n rank_correlation = spearman(y[:, 0], d.mean()[:, 0])\\n\\n statistics[f'oracle_{i}/validate/nll'] = nll\\n statistics[f'oracle_{i}/validate/rank_corr'] = rank_correlation\\n\\n return statistics\",\n \"def test_check_full_house_false(self):\\n not_full_house_fixtures = [[1, 1, 1, 1, 2],\\n [2, 1, 1, 1, 1],\\n [1, 1, 2, 2, 3],\\n [1, 2, 3, 4, 5],\\n ]\\n\\n for fixture in not_full_house_fixtures:\\n score = self.roll.check_full_house(fixture)\\n\\n self.assertNotEqual(score, 25)\\n self.assertEqual(score, 0)\\n self.assertEqual(len(fixture), 5)\",\n \"def test_rescoring_randomized_problem(self):\\r\\n # First define the custom response problem:\\r\\n problem_url_name = 'H1P1'\\r\\n self.define_randomized_custom_response_problem(problem_url_name)\\r\\n location = InstructorTaskModuleTestCase.problem_location(problem_url_name)\\r\\n descriptor = self.module_store.get_item(location)\\r\\n # run with more than one user\\r\\n userlist = ['u1', 'u2', 'u3', 'u4']\\r\\n for username in userlist:\\r\\n # first render the problem, so that a seed will be created for this user\\r\\n self.render_problem(username, problem_url_name)\\r\\n # submit a bogus answer, in order to get the problem to tell us its real answer\\r\\n dummy_answer = \\\"1000\\\"\\r\\n self.submit_student_answer(username, problem_url_name, [dummy_answer, dummy_answer])\\r\\n # we should have gotten the problem wrong, since we're way out of range:\\r\\n self.check_state(username, descriptor, 0, 1, 1)\\r\\n # dig the correct answer out of the problem's message\\r\\n module = self.get_student_module(username, descriptor)\\r\\n state = json.loads(module.state)\\r\\n correct_map = state['correct_map']\\r\\n log.info(\\\"Correct Map: %s\\\", correct_map)\\r\\n # only one response, so pull it out:\\r\\n answer = correct_map.values()[0]['msg']\\r\\n self.submit_student_answer(username, problem_url_name, [answer, answer])\\r\\n # we should now get the problem right, with a second attempt:\\r\\n self.check_state(username, descriptor, 1, 1, 2)\\r\\n\\r\\n # redefine the problem (as stored in Mongo) so that the definition of correct changes\\r\\n self.define_randomized_custom_response_problem(problem_url_name, redefine=True)\\r\\n # confirm that simply rendering the problem again does not result in a change\\r\\n # in the grade (or the attempts):\\r\\n self.render_problem('u1', problem_url_name)\\r\\n self.check_state('u1', descriptor, 1, 1, 2)\\r\\n\\r\\n # rescore the problem for only one student -- only that student's grade should change\\r\\n # (and none of the attempts):\\r\\n self.submit_rescore_one_student_answer('instructor', problem_url_name, User.objects.get(username='u1'))\\r\\n for username in userlist:\\r\\n self.check_state(username, descriptor, 0 if username == 'u1' else 1, 1, 2)\\r\\n\\r\\n # rescore the problem for all students\\r\\n self.submit_rescore_all_student_answers('instructor', problem_url_name)\\r\\n\\r\\n # all grades should change to being wrong (with no change in attempts)\\r\\n for username in userlist:\\r\\n self.check_state(username, descriptor, 0, 1, 2)\",\n \"def test_dropping_all_correct(self):\\r\\n self.dropping_setup()\\r\\n\\r\\n self.dropping_homework_stage1()\\r\\n for name in self.hw3_names:\\r\\n self.submit_question_answer(name, {'2_1': 'Correct'})\\r\\n\\r\\n self.check_grade_percent(1.0)\\r\\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 2.0]) # Order matters\\r\\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 1.0])\",\n \"def score_professor_conflicts(self):\\n prof_conflict_score = 0\\n multiplier = 4\\n \\n for day_num in range(self.num_days):\\n \\n current_day = self.days[ day_num ]\\n num_conflicts = 0\\n \\n for prof_name in current_day.keys():\\n if not self.get_prof_by_name[prof_name].available( day_num ):\\n num_conflicts += 1\\n \\n prof_conflict_score += multiplier * ( num_conflicts ** 2 )\\n \\n self.prof_conflict_score = prof_conflict_score\\n return self.prof_conflict_score\",\n \"def is_legal_solution(self, solution):\\r\\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\\r\\n return self.fit_score(solution) == 0\\r\\n else:\\r\\n return self.fit_score(solution) == sum(x for x in range(1, 12))\",\n \"def update(self):\\r\\n if self.games and all(game.result for game in self.games):\\r\\n self.rankings = self.compute_ranking()\\r\\n self.update_observers()\\r\\n\\r\\n if self.finals:\\r\\n for final in self.finals:\\r\\n final.update()\",\n \"def test_affect_of_strategy(self):\\n self.responses_test([C, C, C], [C, C, C], [C, C, C])\\n # Make sure that the retaliations are increasing\\n # Retaliate once and forgive\\n self.responses_test([C], [D], [D])\\n self.responses_test([C, D], [D, C], [C])\\n self.responses_test([C, D, C], [D, C, C], [C])\\n # Retaliate twice and forgive\\n self.responses_test([C, D, C], [D, C, D], [D, D])\\n self.responses_test([C, D, C, D, D], [D, C, D, C, C], [C])\\n # Opponent defection during retaliation doesn't increase retaliation period\\n self.responses_test([C, D, C, D, D], [D, C, D, D, C], [C])\\n # Retaliate thrice and forgive\\n self.responses_test([C, D, C, D, D, C], [D, C, D, C, C, D], [D, D, D])\\n history_1 = [C, D, C, D, D, C, D, D, D]\\n history_2 = [D, C, D, C, C, D, C, C, C]\\n self.responses_test(history_1, history_2, [C])\",\n \"def track_brokenness(log, parse_error_col, parsed_games):\\n failures = 0\\n wrongness = collections.defaultdict(int)\\n overall = collections.defaultdict(int)\\n for raw_game in parsed_games:\\n accurately_parsed = check_game_sanity(game.Game(raw_game), log)\\n if not accurately_parsed:\\n log.warning('Failed to accurately parse game %s', raw_game['_id'])\\n save_parse_error(parse_error_col, log, raw_game, 'check_game_sanity failed')\\n failures += 1\\n for card in raw_game[SUPPLY]:\\n if not accurately_parsed:\\n wrongness[card] += 1\\n overall[card] += 1\\n\\n ratios = []\\n for card in overall:\\n ratios.append(((float(wrongness[card]) / overall[card]), index_to_card(card)))\\n ratios.sort()\\n if ratios and ratios[-1][0] > 0:\\n log.warning(\\\"Ratios for problem cards %s, %d failures out of %d games\\\", ratios[-10:],\\n failures, len(parsed_games))\\n else:\\n log.debug('Perfect parsing, %d games!', len(parsed_games))\",\n \"def _check_family(self):\\n for (s, (b, c)), (cond, ref) in families.items():\\n if s != self.SYMBOL or len(b) != self._.d:\\n continue\\n vars = tuple(set(sum(map(variables, b + c), ())))\\n sols = _solve([SR(l) == r for l, r\\n in zip(self._.b[:-1] + self._.c[1:], b + c)],\\n vars)\\n if any(checkConditions(cond, sol) for sol in sols\\n if is_integral(sol)):\\n raise InfeasibleError(refs=ref)\",\n \"def testAxiomReasoning(self):\\n \\n state = State.from_problem(self.prob)\\n extstate, reasons, universalReasons = state.get_extended_state(getReasons=True)\\n\\n relevantVars = []\\n relevantReplanVars = []\\n load = self.dom.get_action(\\\"a_load\\\")\\n with load.instantiate([\\\"agent\\\", \\\"obj11\\\", \\\"tru1\\\"], self.prob):\\n self.assert_(extstate.is_satisfied(load.precondition, relevantVars))\\n self.assert_(extstate.is_satisfied(load.replan, relevantReplanVars))\\n\\n all_reasons = set(relevantVars)\\n for v in relevantVars:\\n all_reasons |= reasons[v]\\n\\n all_replan_reasons = set(relevantReplanVars)\\n for v in relevantReplanVars:\\n all_replan_reasons |= reasons[v]\\n \\n s1 = StateVariable(self.prob.functions[\\\"location-of\\\"][0], [self.prob[\\\"tru1\\\"]])\\n s2 = StateVariable(self.prob.functions[\\\"location-of\\\"][0], [self.prob[\\\"obj11\\\"]])\\n\\n self.assert_(s1 in all_reasons)\\n self.assert_(s2 in all_reasons)\\n self.assertFalse(s1 in all_replan_reasons)\\n self.assert_(s2 in all_replan_reasons)\",\n \"def test_teacher_check_homework_raises_homework_repeat_error_if_same_solution_was_already_submitted():\\n with pytest.raises(HomeworkRepeatError):\\n opp_teacher.check_homework(result_1)\\n advanced_python_teacher.check_homework(result_1)\\n Teacher.reset_results(oop_hw)\",\n \"def assess_problematic_entries(n_problems: int, data: List[dict]) -> None:\\n print(\\n f\\\"Problematic entries: {n_problems}/{len(data)}\\\\\\n ({100 * n_problems/len(data):3.1f}%)\\\"\\n )\",\n \"def tie_exists(self):\\n return len(self.marks) == 9\",\n \"def test_when_opponent_all_Ds(self):\\n self.responses_test([D, D, D, D], [D, D, D, D], [D, D, D],\\n random_seed=5)\",\n \"def test_falsifications(self):\\n implications = get_conditional_independencies(asia_example.graph)\\n issues = get_falsifications(implications, asia_example.data)\\n self.assertEqual(0, len(issues.failures))\\n self.assertEqual(len(issues.evidence), len(implications))\",\n \"def test_check_validity(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n assert match.check_validity()\",\n \"def user_story_17(self):\\n for f1 in self.families.values():\\n for f2 in self.families.values():\\n if f1.fid != f2.fid:\\n if f1.husb_id == f2.husb_id and f2.wife_id in f1.children:\\n print(f\\\"US17 - {self.individuals[f2.wife_id].name} and {self.individuals[f1.husb_id].name} are married on line {f1._married_line}\\\")\\n if f1.wife_id == f2.wife_id and f2.husb_id in f1.children:\\n print(f\\\"US17 - {self.individuals[f2.husb_id].name} and {self.individuals[f1.wife_id].name} are married on line {f1._married_line}\\\")\",\n \"def test_mirror_ranking(self):\\n updater = AptMirrorUpdater()\\n # Make sure that multiple discovered mirrors are available.\\n assert sum(m.is_available for m in updater.ranked_mirrors) > 10\",\n \"def __check_partial_result(lines: List[Line], partial_result: PartialResult):\\n\\n x_wins = partial_result.x_wins\\n o_wins = partial_result.o_wins\\n has_empties = partial_result.has_empties\\n\\n for line in lines:\\n stats = line.get_stats()\\n if (not x_wins) and stats.x_count == SIZE:\\n x_wins = True\\n if (not o_wins) and stats.o_count == SIZE:\\n o_wins = True\\n if (not has_empties) and stats.has_empties:\\n has_empties = True\\n\\n __merge_partial_result(partial_result, x_wins, o_wins, has_empties)\",\n \"def test_residual_service_after_element_perturbation_isolating():\\n F = FaultDiagnosis(\\\"tests/TOY_graph_nofaultresistant.csv\\\")\\n F.simulate_element_perturbation([\\\"1\\\"])\\n\\n res_service_after_element_perturbation = {\\n '2': 0.0,\\n '3': 0.0,\\n '4': 0.0,\\n '5': 0.0,\\n '6': 0.0,\\n '7': 0.0,\\n '8': 0.0,\\n '9': 0.0,\\n '10': 0.0,\\n '11': 0.0,\\n '12': 0.0,\\n '13': 0.0,\\n '14': 0.0,\\n '15': 0.0,\\n '16': 0.0,\\n '17': 0.0,\\n '18': 2.0,\\n '19': 0.0\\n }\\n\\n np.testing.assert_array_almost_equal(\\n np.asarray(sorted(res_service_after_element_perturbation.values())),\\n np.asarray(sorted(F.G.service.values())),\\n err_msg=\\\"FINAL RESIDUAL SERVICE failure: perturbation of element 1\\\")\",\n \"def test_get_state_comparison_stats_former_slaveholder(self):\\n\\n key = '% Former slaveholder employees'\\n\\n # Kentucky: 0 former slaveholder employees, and 2 who weren't\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n # Mississippi: 1 former slaveholder employee who died during an assignment, and 1 who wasn't\\n for _ in range(1):\\n employee = EmployeeFactory(slaveholder=True)\\n employee.bureau_states.add(self.mississippi)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.mississippi)\\n\\n # Texas: 1 former slaveholder employee, and 9 who weren't\\n for _ in range(1):\\n employee = EmployeeFactory(slaveholder=True)\\n employee.bureau_states.add(self.texas)\\n for _ in range(9):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.texas)\\n\\n expected_output = [('Mississippi', 50), ('Texas', 10)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of former slaveholder employees\\\")\",\n \"def fitness_function(individual: str, nurses_number: int = 10):\\n\\n genes = 21 * nurses_number\\n violations_count = 0\\n location_count = 0\\n before = 0\\n\\n # Evaluate the second and third restrictions\\n # If the value of location_count is other than 5, we have a restriction violation in the first rule\\n # Here, for each 21 shifts we have a nurse. So we evaluate for each nurse if there is any restriction violation\\n for i in range(0, genes, 21):\\n for j in range(i, (i + 21)):\\n if individual[j] == '1':\\n location_count += 1\\n\\n # Here and in the following 'if' we are evaluating the third restriction, counting the number of\\n # allocations and verifying if we have three consecutive allocations using the 'before' variable\\n if before > 3:\\n violations_count += 1\\n\\n if before <= 3:\\n before += 1\\n\\n # When we are in a individual where the nurse isn't allocated, we set the value of before to zero\\n else:\\n before = 0\\n\\n # If location_count different of 5, we have a violation restriction\\n if location_count != 5:\\n violations_count += 1\\n location_count = 0\\n\\n # Evaluate if the first restriction was affected\\n shift_count = 0\\n # For each shift is evaluated how many nurses are allocated in this period\\n for i in range(0, 21):\\n for j in range(i, 21 * nurses_number, 21):\\n if individual[j] == '1':\\n shift_count += 1\\n\\n # If the number of nurses allocated is out of this interval, we have a violation\\n if (shift_count < 1) or (shift_count > 3):\\n violations_count += 1\\n shift_count = 0\\n\\n # Here we are evaluating the fourth restriction for each nurse\\n # When an allocation is found it is checked if the following allocations are on the same shift\\n line_count = 0\\n for i in range(0, 21 * nurses_number, 21):\\n j = i\\n # First, we look for an allocation\\n while j < 210 and individual[j] != '1' and (j <= (i + 21)):\\n j += 1\\n\\n # When we meet, we check if the next ones are on the same shift\\n for k in range(j, i + 21, 3):\\n if individual[k] == '1':\\n line_count += 1\\n\\n # If the number of allocations in the same shift is different of five, we have a violation\\n if line_count != 5:\\n violations_count += 1\\n\\n line_count = 0\\n\\n # After each verification, it is returned the number of restrictions violated\\n return violations_count\",\n \"def check(self):\\r\\n self.check_probabilities()\\r\\n self.check_sum()\",\n \"def champion_check(self):\\n better_champion = False\\n\\n for species in self.species:\\n if species.leader.original_fitness > self.champion_fitness:\\n self.age_since_improvement = 0\\n self.champion_fitness = species.leader.original_fitness\\n better_champion = True\\n\\n if not better_champion:\\n self.age_since_improvement += 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6707456","0.6244367","0.6030781","0.5817202","0.57967025","0.56785023","0.5596258","0.554864","0.5504701","0.5493291","0.54924923","0.5488279","0.5485713","0.54585725","0.5428704","0.5420342","0.5397366","0.5381196","0.53527856","0.53451216","0.53155655","0.5312574","0.53046435","0.52981126","0.5269889","0.5257607","0.525684","0.5236544","0.5207733","0.51997036","0.51762897","0.51725614","0.5156747","0.51481724","0.51443154","0.51431257","0.5137119","0.5115229","0.509958","0.5093797","0.50913644","0.5084557","0.5082125","0.5069243","0.50634557","0.50477386","0.50430346","0.5040076","0.50330496","0.50315267","0.50140023","0.50108474","0.4996969","0.49969238","0.49942666","0.49889874","0.4985289","0.49841097","0.49823424","0.49815652","0.49745297","0.49745297","0.49745297","0.49737322","0.4970837","0.4962393","0.4959951","0.4953588","0.4952133","0.49411383","0.49388552","0.49366638","0.4933898","0.49321377","0.49304435","0.492618","0.49198294","0.49196365","0.4916479","0.49075764","0.48932937","0.48877662","0.48698917","0.48680812","0.48664305","0.4862924","0.4861924","0.48599502","0.4853264","0.4851733","0.48375532","0.48347688","0.48232225","0.4822645","0.4817401","0.48163852","0.48132443","0.4808972","0.48028037","0.48018533"],"string":"[\n \"0.6707456\",\n \"0.6244367\",\n \"0.6030781\",\n \"0.5817202\",\n \"0.57967025\",\n \"0.56785023\",\n \"0.5596258\",\n \"0.554864\",\n \"0.5504701\",\n \"0.5493291\",\n \"0.54924923\",\n \"0.5488279\",\n \"0.5485713\",\n \"0.54585725\",\n \"0.5428704\",\n \"0.5420342\",\n \"0.5397366\",\n \"0.5381196\",\n \"0.53527856\",\n \"0.53451216\",\n \"0.53155655\",\n \"0.5312574\",\n \"0.53046435\",\n \"0.52981126\",\n \"0.5269889\",\n \"0.5257607\",\n \"0.525684\",\n \"0.5236544\",\n \"0.5207733\",\n \"0.51997036\",\n \"0.51762897\",\n \"0.51725614\",\n \"0.5156747\",\n \"0.51481724\",\n \"0.51443154\",\n \"0.51431257\",\n \"0.5137119\",\n \"0.5115229\",\n \"0.509958\",\n \"0.5093797\",\n \"0.50913644\",\n \"0.5084557\",\n \"0.5082125\",\n \"0.5069243\",\n \"0.50634557\",\n \"0.50477386\",\n \"0.50430346\",\n \"0.5040076\",\n \"0.50330496\",\n \"0.50315267\",\n \"0.50140023\",\n \"0.50108474\",\n \"0.4996969\",\n \"0.49969238\",\n \"0.49942666\",\n \"0.49889874\",\n \"0.4985289\",\n \"0.49841097\",\n \"0.49823424\",\n \"0.49815652\",\n \"0.49745297\",\n \"0.49745297\",\n \"0.49745297\",\n \"0.49737322\",\n \"0.4970837\",\n \"0.4962393\",\n \"0.4959951\",\n \"0.4953588\",\n \"0.4952133\",\n \"0.49411383\",\n \"0.49388552\",\n \"0.49366638\",\n \"0.4933898\",\n \"0.49321377\",\n \"0.49304435\",\n \"0.492618\",\n \"0.49198294\",\n \"0.49196365\",\n \"0.4916479\",\n \"0.49075764\",\n \"0.48932937\",\n \"0.48877662\",\n \"0.48698917\",\n \"0.48680812\",\n \"0.48664305\",\n \"0.4862924\",\n \"0.4861924\",\n \"0.48599502\",\n \"0.4853264\",\n \"0.4851733\",\n \"0.48375532\",\n \"0.48347688\",\n \"0.48232225\",\n \"0.4822645\",\n \"0.4817401\",\n \"0.48163852\",\n \"0.48132443\",\n \"0.4808972\",\n \"0.48028037\",\n \"0.48018533\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58555126"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":93792,"cells":{"query":{"kind":"string","value":"Test that each resident has a nonempty preference list. If not, check that a warning is caught and the resident has been removed from the game."},"document":{"kind":"string","value":"def test_check_inputs_resident_prefs_all_nonempty(game):\n\n resident = game.residents[0]\n resident.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n assert resident.name in str(record[0].message)\n\n if game.clean:\n assert resident not in game.residents"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert resident not in hospital.prefs","def test_check_inputs_resident_prefs_all_hospitals(game):\n\n resident = game.residents[0]\n resident.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert resident.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert resident.prefs == []","def test_check_inputs_hospital_reciprocated_all_prefs(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n hospital._forget(resident)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"hospitals\", \"residents\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert hospital not in resident.prefs","def test_check_inputs_hospital_prefs_all_nonempty(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def test_check_inputs_hospital_prefs_all_residents(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert hospital.prefs == []","def _check_resident_prefs(self):\n\n errors = []\n for resident in self.residents:\n if not set(resident.prefs).issubset(set(self.hospitals)):\n errors.append(\n ValueError(\n f\"{resident} has ranked a non-hospital: \"\n f\"{set(resident.prefs)} != {set(self.hospitals)}\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.append(\n ValueError(\n f\"{hospital} has not ranked all the residents that \"\n f\"ranked it: {set(hospital.prefs)} != \"\n f\"{set(residents_that_ranked)}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_supervisor_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n supervisor.prefs.remove(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"supervisors\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert supervisor.name in message\n\n if clean:\n assert supervisor not in student.prefs\n assert all(\n project not in student.prefs for project in supervisor.projects\n )","def test_check_inputs_supervisor_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n projects = supervisor.projects\n for project in student.prefs:\n if project in projects:\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"supervisors\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(supervisor.name)\n assert student.name in message\n\n if clean:\n assert student not in supervisor.prefs\n assert all(\n student not in project.prefs for project in supervisor.projects\n )","def test_check_inputs_project_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n student = project.prefs[0]\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"projects\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(project.name)\n assert student.name in message\n\n if clean:\n assert student not in project.prefs","def _check_resident_matching(self):\n\n errors = []\n for resident in self.residents:\n if (\n resident.matching is not None\n and resident.matching not in resident.prefs\n ):\n errors.append(\n ValueError(\n f\"{resident} is matched to {resident.matching} but \"\n \"they do not appear in their preference list: \"\n f\"{resident.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_project_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n student = project.prefs[0]\n project._forget(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"projects\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert project.name in message\n\n if clean:\n assert project not in student.prefs","def _check_mutual_preference(resident, hospital):\n\n return resident in hospital.prefs and hospital in resident.prefs","def _check_hospital_matching(self):\n\n errors = []\n for hospital in self.hospitals:\n for resident in hospital.matching:\n if resident not in hospital.prefs:\n errors.append(\n ValueError(\n f\"{hospital} has {resident} in their matching but \"\n \"they do not appear in their preference list: \"\n f\"{hospital.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_check_for_unacceptable_matches_residents(game):\n\n resident = game.residents[0]\n hospital = Hospital(name=\"foo\", capacity=1)\n resident.matching = hospital\n\n issues = game._check_for_unacceptable_matches(\"residents\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(resident.name)\n assert issue.endswith(f\"{resident.prefs}.\")\n assert hospital.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_inputs_reviewer_prefs(\n resident_names, hospital_names, capacities, seed\n):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_reviewer_prefs()\n\n match.reviewers[0].pref_names.pop()\n\n with pytest.raises(Exception):\n match._check_reviewer_prefs()","def testAssistantDoesNotGetUserPrefRole(self):\n self._testAssistantOwnershipAfter(task=\"create\")\n self.failIf('def456' in self.person.users_with_local_role('User Preferences Editor'), \"Assistant can edit user prefs after create\")\n self.simulateATGUIInteraction(task=\"edit\")\n self.failIf('def456' in self.person.users_with_local_role('User Preferences Editor'), \"Assistant can edit user prefs after edit\")","def check_puzzle_unicity(puzzle_list: list) -> None:\n if len(set(puzzle_list)) != len(puzzle_list):\n raise ParsingError(\"Puzzle numbers must be unique.\")","def _check_resident_unhappy(resident, hospital):\n\n return resident.matching is None or resident.prefers(\n hospital, resident.matching\n )","def __check_all_config_values_set(cfg: __Config):\n unset_values = [key for key, val in cfg.__dict__.items() if val is None]\n if len(unset_values) > 0:\n print(f\"WARNING: The following config variables have not been set: {unset_values}\")","def check_stability(self):\n\n blocking_pairs = []\n for resident in self.residents:\n for hospital in self.hospitals:\n if (\n _check_mutual_preference(resident, hospital)\n and _check_resident_unhappy(resident, hospital)\n and _check_hospital_unhappy(resident, hospital)\n ):\n blocking_pairs.append((resident, hospital))\n\n self.blocking_pairs = blocking_pairs\n return not any(blocking_pairs)","def test_delete_spawning_profile_not_in_list():\n tester = TestClass()\n spawning_profiles = tester.delete_spawning_profile()\n\n assert spawning_profiles\n\n assert len(spawning_profiles) == 1\n\n assert spawning_profiles[0].get_spawning_profile_name() == 'Default'","def assertNotPrefExists(self, user):\r\n self.assertFalse(\r\n UserPreference.objects.filter(user=user, key=NOTIFICATION_PREF_KEY).exists()\r\n )","def verifyReplicasDictionary(replicas_dict, guids):\n\n status = True\n pilotErrorDiag = \"\"\n\n # Loop over all GUIDs and see if they are all in the replicas dictionary\n for guid in guids:\n if not guid in replicas_dict.keys():\n status = False\n pilotErrorDiag = \"Replica with guid=%s missing in Rucio catalog\" % (guid)\n tolog(\"!!WARNING!!1122!! %s\" % (pilotErrorDiag))\n break\n\n return status, pilotErrorDiag","def avoids(w, forbidden):\n\treturn set(w).isdisjoint(set(forbidden))","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def notify_no_win(self):\n\n board_set = set()\n for row in self.board:\n board_set.update(row)\n if \"\" not in board_set:\n print \"Game is over. No one won.\"\n return True","def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_suitor_prefs()\n\n match.suitors[0].pref_names = [1, 2, 3]\n\n with pytest.raises(Exception):\n match._check_suitor_prefs()","def test_dropping_all_correct(self):\r\n self.dropping_setup()\r\n\r\n self.dropping_homework_stage1()\r\n for name in self.hw3_names:\r\n self.submit_question_answer(name, {'2_1': 'Correct'})\r\n\r\n self.check_grade_percent(1.0)\r\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 2.0]) # Order matters\r\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 1.0])","def test_ComputerPartition_error_CleanupResource_ConfirmedState(self):\n sequence_list = SequenceList()\n sequence_string = self.prepare_confirmed_cleanup_resource_packing_list + '\\\n SlapLoginCurrentComputer \\\n CheckSuccessComputerPartitionErrorCall \\\n Tic \\\n SlapLogout \\\n LoginDefaultUser \\\n CheckSalePackingListErrorText \\\n CheckConfirmedSalePackingList \\\n Logout \\\n LoginERP5TypeTestCase \\\n CheckSiteConsistency \\\n Logout \\\n '\n sequence_list.addSequenceString(sequence_string)\n sequence_list.play(self)","def _assert_problem_is_valid(self) -> None:\n # Will throw error if any of these are not set\n specified = self.ignore + self.silence + self.target\n if not all(\n [\n pr in specified\n for pr in self.observer.photoreceptors\n if pr is not None\n ]\n ):\n raise SilSubProblemError(\n \"At least one photoreceptor is not accounted for by the \"\n + \"*ignore*, *silence* and *target* properties.\"\n )","def check_conflicts(self, PlugLead):\n\n if len(self.plugleads) >= 10:\n raise ValueError('Plugboard full! Plugboard only supports up to 10 pairs')\n\n for plug in self.plugleads:\n if plug.pair[0] in PlugLead.pair or plug.pair[1] in PlugLead.pair:\n raise ValueError(\n 'Plug lead {} conflicts with existing pair, please ensure plug lead pairs are unique'.format(\n PlugLead.pair))","def check_for_list(check):","def no_oversuscribed_sessions(prefs, caps):\n out = []\n for room, cap in caps.items():\n # if room capacity > num students, go to next room \n # since this one is safely undersuscribed\n if cap >= len(prefs):\n continue\n # if a room contains N students, then in every group of N+1 students \n # there must be a student not in that session\n stud_combos = combos(prefs, cap+1)\n for group in stud_combos:\n out.append([('%s_%s' % (stud, room), False) for stud in group])\n return out","def check_auto_reject(self):\r\n for pr in self:\r\n if not pr.line_ids.filtered(lambda l: l.cancelled is False):\r\n pr.write({'state': 'rejected'})","def _check_hospital_unhappy(resident, hospital):\n\n return len(hospital.matching) < hospital.capacity or any(\n [hospital.prefers(resident, match) for match in hospital.matching]\n )","def check_loss(self):\n return POKEMON in self.get_game()","def test_remove_spawning_profile_from_intersection():\n tester = TestClass()\n intersections = tester.add_spawning_profile_to_intersection()\n\n for i in intersections:\n if len(i.get_spawning_profile_list()) != 0:\n assert True\n\n for spawn in i.get_spawning_profile_list():\n if spawn.get_spawning_profile_name() == 'Default':\n assert True\n break\n\n tester.delete_spawning_profile_from_intersection()\n\n for i in intersections:\n if len(i.get_spawning_profile_list()) == 0:\n assert True","def test_tally_no_candidates(self):\n self.init_elect_types()\n\n userA = models.User(\n name = \"UserA\",\n email = \"userA@eLect.com\",\n password = \"asdf\")\n\n session.add(userA)\n session.commit()\n\n electionA = models.Election(\n title = \"Election A\",\n admin_id = userA.id)\n\n session.add(electionA)\n session.commit()\n\n raceA = models.Race(\n title = \"Race A\",\n election_id = electionA.id\n )\n\n session.add(raceA)\n session.commit()\n\n with self.assertRaises(NoCandidates):\n self.wta.check_race(raceA.id)\n\n with self.assertRaises(NoCandidates):\n self.proportional.check_race(raceA.id)\n\n with self.assertRaises(NoCandidates):\n self.schulze.check_race(raceA.id)","def all_votesp(self, game_key):\n participants = models.Participant.query(\n models.Participant.playing == True,\n models.Participant.vote == None,\n ancestor=game_key).fetch()\n logging.info(\n \"participants who have not voted: %s\", \n [p.plus_id for p in participants])\n if participants:\n return False\n else:\n return True","def check_primers(self):\n primer_keys=['sequence','description']\n if self.data.has_key('primer_dict'):\n for primer in self.data['primer_dict'].keys():\n for key in primer_keys:\n if not self.data['primer_dict'][primer].has_key(key):\n self.data['primer_dict'][primer][key]='None'","def death_check(self):\r\n chance = random.random()\r\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\r\n if self.unique_id in head_of_household_list:\r\n try:\r\n head_of_household_list[self.hh_id] = 0\r\n except TypeError: # head of household migrated\r\n head_of_household_list[self.past_hh_id] = 0\r\n self.model.number_of_humans -= 1\r\n if self.unique_id in labor_list:\r\n labor_list.remove(self.unique_id)\r\n if self.work_status == 1:\r\n try:\r\n num_labor_list[self.hh_id] -= 1\r\n except TypeError:\r\n num_labor_list[self.past_hh_id] -= 1\r\n if self.unique_id in former_hoh_list:\r\n try:\r\n former_hoh_list[self.hh_id] = 0\r\n except:\r\n former_hoh_list[self.past_hh_id] = 0\r\n if [self.unique_id, self.hh_id] in single_male_list:\r\n single_male_list.remove([self.unique_id, self.hh_id])\r\n if self.unique_id in married_male_list:\r\n married_male_list.remove(self.unique_id)\r\n human_death_list.append(self.unique_id)\r\n try:\r\n hh_size_list[self.hh_id] -= 1\r\n except:\r\n hh_size_list[self.past_hh_id] -= 1\r\n human_demographic_structure_list[self.age_category] -= 1\r\n\r\n self.model.schedule.remove(self)\r\n if self in self.model.grid:\r\n self.model.grid.remove_agent(self)","def validate(candidates: List[str], choices: List[str]) -> List[str]:\n for candidate in candidates:\n assert candidate in choices, f\"Specified {candidate}, but not in available list: {choices}.\"\n return candidates","def testNoSpecialties(self):\n self.failUnlessEqual(self.person.getSpecialties(), [])","def check_prerequisites(self):\n self.courses_not_completed = self.prerequisite_set - set(self.user_courses.keys())","def check_win(self):\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)","def verify_no_cable_errors(self):\n i = 0\n for dpid in self.dpids:\n i += 1\n labels = {'dp_id': '0x%x' % int(dpid), 'dp_name': 'faucet-%u' % i}\n self.assertEqual(\n 0, self.scrape_prometheus_var(\n var='stack_cabling_errors_total', labels=labels, default=None))\n self.assertGreater(\n self.scrape_prometheus_var(\n var='stack_probes_received_total', labels=labels), 0)","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def test_effective_metrics_not_in_PM():\n assert \"effective_sample_size\" not in PM_METRICS\n assert \"pearson_r_eff_p_value\" not in PM_METRICS\n assert \"spearman_r_eff_p_value\" not in PM_METRICS","def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals","def test_tally_no_votes(self):\n self.populate_database()\n self.electionA.elect_open = False\n with self.assertRaises(NoVotes):\n self.wta.check_race(self.raceA.id)\n\n with self.assertRaises(NoVotes):\n self.proportional.check_race(self.raceA.id)\n\n with self.assertRaises(NoVotes):\n self.schulze.check_race(self.raceA.id)","def test_can_detect_when_player_has_no_pieces(self):\n board = Board()\n board.pieces = [Piece(0, 0, 0, 0), Piece(2, 2, 2, 2)]\n\n player_1_has_piece = board.does_player_have_pieces(1)\n\n assert not player_1_has_piece","def test_check_for_oversubscribed_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n supervisor.matching = range(supervisor.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.capacity))\n assert str(supervisor.matching) in e","def verify_valid_game(self, lotto_game:List[int]):\n is_valid = True;\n lotto_game_temp = []\n for number in lotto_game:\n if number not in lotto_game_temp:\n lotto_game_temp.append(number)\n else:\n print(number, lotto_game, lotto_game_temp)\n is_valid = False\n raise InvalidLottoGame\n \n lotto_game_temp_length = len(lotto_game_temp)\n if lotto_game_temp_length != LOTTO_GAME_LENGTH and lotto_game_temp_length != LOTTO_GAME_WITH_BONUS_LENGTH:\n print(lotto_game_temp)\n is_valid = False\n raise InvalidLottoGame\n\n return is_valid","def test_dropping_nochange(self):\r\n self.dropping_setup()\r\n self.dropping_homework_stage1()\r\n self.submit_question_answer(self.hw3_names[0], {'2_1': 'Correct'})\r\n\r\n self.assertEqual(self.score_for_hw('homework1'), [1.0, 0.0])\r\n self.assertEqual(self.score_for_hw('homework2'), [1.0, 1.0])\r\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 0.0])\r\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 1.0]) # Order matters\r\n self.check_grade_percent(0.75)","def reject_fairness(experiment: List[bool]) -> bool:\n num_heads = len([flip for flip in experiment if flip])\n return num_heads < 469 or num_heads > 531","def testProtractedNSESanityChecks(self):\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))","def _list_validity_check(l, valid_l):\n\n if not Settings._is_in_list(l, valid_l):\n raise InvalidSettingError()","def test_prometheus_rule_failures():\n prometheus = ocs_ci.utility.prometheus.PrometheusAPI()\n alerts_response = prometheus.get(\n \"alerts\", payload={\"silenced\": False, \"inhibited\": False}\n )\n assert alerts_response.ok is True\n alerts = alerts_response.json()[\"data\"][\"alerts\"]\n log.info(f\"Prometheus Alerts: {alerts}\")\n assert constants.ALERT_PROMETHEUSRULEFAILURES not in [\n alert[\"labels\"][\"alertname\"] for alert in alerts\n ]","def test_preferences_properties(self):\n with pytest.raises(AssertionError):\n self.preferences.exchange_params_by_currency_id\n with pytest.raises(AssertionError):\n self.preferences.utility_params_by_good_id","def test_check_for_unacceptable_matches_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n student = Student(name=\"foo\")\n supervisor.matching.append(student)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.prefs))\n assert student.name in error","def checkConflicts(self):\n\t\treturn","def delete_error():\r\n item = core.get_all_items()\r\n for i in item:\r\n if \"Error\" in i or \"Warning\" in i:\r\n if core.does_item_exist(i):\r\n reset_error(i)","def check_collisions(self):","def test_delete_vehicle_profile_not_in_list():\n tester = TestClass()\n vehicle_profiles = tester.delete_vehicle_profile()\n\n assert vehicle_profiles\n assert len(vehicle_profiles) == 1\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'","def test_check_full_house_false(self):\n not_full_house_fixtures = [[1, 1, 1, 1, 2],\n [2, 1, 1, 1, 1],\n [1, 1, 2, 2, 3],\n [1, 2, 3, 4, 5],\n ]\n\n for fixture in not_full_house_fixtures:\n score = self.roll.check_full_house(fixture)\n\n self.assertNotEqual(score, 25)\n self.assertEqual(score, 0)\n self.assertEqual(len(fixture), 5)","def test_race_voteval_check(self):\n self.populate_database()\n self.assertEqual(len(self.raceB.candidates), 4)\n self.assertEqual(self.raceB.min_vote_val, 0)\n self.assertEqual(self.raceB.max_vote_val, 1)\n\n self.raceB.election_type = \"Schulze\"\n self.assertEqual(len(self.raceB.candidates), 4)\n self.assertEqual(self.raceB.min_vote_val, 0)\n self.assertEqual(self.raceB.max_vote_val, 4)\n\n ### WHY DO THESE WORK:\n self.raceB.candidates.append(self.candidateAB)\n self.assertEqual(len(self.raceB.candidates), 5)\n self.assertEqual(self.raceB.min_vote_val, 0)\n self.assertEqual(self.raceB.max_vote_val, 5)\n\n self.raceB.candidates.remove(self.candidateBD)\n # Throw a couple of wrenches in the works\n self.raceB.max_vote_val = 1\n self.raceB.min_vote_val = 7\n\n self.assertEqual(len(self.raceB.candidates), 4)\n self.assertEqual(self.raceB.min_vote_val, 0)\n self.assertEqual(self.raceB.max_vote_val, 4)\n\n ### BUT THESE DO NOT ?!?!? ( obviously indirect changes to the \n ### db/collection aren't handled by the validator event)\n # session.delete(self.candidateBD)\n # self.candidateAB.race_id = self.raceB.id","def negative_test_for_check_local_storage(profile_obj):\n status = True\n for profile in profile_obj:\n elements_test_list = [profile.emptyChar, profile.invalidChars]\n\n \"\"\"\n Setting invalid values for: \"Name\" and \"Number of physical drivers\"\n \"\"\"\n for element in elements_test_list:\n # Fill Logical Drive form\n if __fill_logical_drive_form(element):\n # Click Logical Drive Create button\n if ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_BTN_CREATE_LOGICAL_DRIVE_FORM):\n if element == '':\n # Check warning messages\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_NAME_FORM) and __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\n logger.warn(\"At least one of the warning messages was NOT found \")\n status = False\n\n else:\n # Check warning messages\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\n logger.warn(\"Warning message NOT found\")\n status = False\n else:\n logger.warn(\"Logical Drive Form 'Button' returns an error. Please, check the test report for more details\")\n status = False\n else:\n logger.warn(\"Logical Drive Form returns an error. Please, check the test report for more details\")\n status = False\n\n # Click 'Cancel' form button\n if not ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_CANCEL_LOCAL_STORAGE_FORM):\n logger.warn(\"Cancel button element NOT found\")\n status = False\n\n return status","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def __updateLureTimeouts(self):\n # check for lured suits and determine which should no longer be\n # lured\n if self.notify.getDebug():\n self.notify.debug(\"__updateLureTimeouts()\")\n self.notify.debug(\"Lured suits: \" + str(self.currentlyLuredSuits))\n noLongerLured = []\n\n # check each lured suit, add to the lured suit's count of how many\n # rounds it has been lured, then check to see if the suit has now been\n # lured for the maximum rounds allowed for the particular lure used\n # on the suit, if not, randomly decide if the suit should 'magically\n # wake up' from the lure based on the fail chance of the particular\n # lure originally used on the suit\n for currLuredSuit in self.currentlyLuredSuits.keys():\n self.__incLuredCurrRound(currLuredSuit)\n if self.__luredMaxRoundsReached(currLuredSuit) or \\\n self.__luredWakeupTime(currLuredSuit):\n noLongerLured.append(currLuredSuit)\n\n # now we can safely clear out the no-longer-lured suits\n for currLuredSuit in noLongerLured:\n# self.suitsUnluredThisRound.append(currLuredSuit)\n self.__removeLured(currLuredSuit)\n if self.notify.getDebug():\n self.notify.debug(\"Lured suits: \" + str(self.currentlyLuredSuits))","async def test_skipped_already_unsilenced(self):\n self.cog.scheduler.__contains__.return_value = False\n self.cog.previous_overwrites.get.return_value = None\n\n for channel in (MockVoiceChannel(), MockTextChannel()):\n with self.subTest(channel=channel):\n self.assertFalse(await self.cog._unsilence(channel))\n channel.set_permissions.assert_not_called()","def test_blocked_shot_type():\n with session_scope() as session:\n all_shot_types = session.query(distinct(Block.shot_type)).all()\n all_shot_types = [shot_type for (shot_type,) in all_shot_types]\n for shot_type in all_shot_types:\n # null is an acceptable shot type, too\n if shot_type is None:\n continue\n assert shot_type in VALID_SHOT_TYPES","def test_vote_badpklist(self):\r\n mock_module = CHModuleFactory.create()\r\n json_in = {'answer': '24.0', 'hint': '0', 'pk_list': json.dumps([['24.0', 0], ['24.0', 12]])}\r\n hint_and_votes = mock_module.tally_vote(json_in)['hint_and_votes']\r\n self.assertTrue(['Best hint', 41] in hint_and_votes)\r\n self.assertTrue(len(hint_and_votes) == 1)","def violated(self) -> bool:\n ...","def test_apply_subscription_rules__non_match(self):\n changes = [{'prop_name': 'shipped_android_milestone'}]\n\n # No milestones of any kind set.\n actual = notifier.apply_subscription_rules(self.feature_1, changes)\n self.assertEqual({}, actual)\n\n # Webview is also set\n self.feature_1.shipped_android_milestone = 88\n self.feature_1.shipped_webview_milestone = 89\n actual = notifier.apply_subscription_rules(self.feature_1, changes)\n self.assertEqual({}, actual)","def test_for_empty_list(self):\n emptylist = []\n self.assertEqual(self.place.amenity_ids, emptylist)","def test_ComputerPartition_error_CleanupResource_DeliveredState(self):\n sequence_list = SequenceList()\n sequence_string = self.prepare_confirmed_cleanup_resource_packing_list + '\\\n LoginDefaultUser \\\n StartSalePackingList \\\n CleanTic \\\n StopSalePackingList \\\n CleanTic \\\n DeliverSalePackingList \\\n Tic \\\n Logout \\\n SlapLoginCurrentComputer \\\n CheckSuccessComputerPartitionErrorCall \\\n CleanTic \\\n SlapLogout \\\n LoginDefaultUser \\\n CheckSalePackingListErrorText \\\n CheckDeliveredSalePackingList \\\n Logout \\\n LoginERP5TypeTestCase \\\n CheckSiteConsistency \\\n Logout \\\n '\n sequence_list.addSequenceString(sequence_string)\n sequence_list.play(self)","def done(poles):\n if len(poles[0]) == 0 and len(poles[1]) == 0:\n return True\n else:\n return False","def isStable(couples,mPref,wPref) -> bool:\n couples_dict = {woman:man for man,woman in couples}\n \n for m,w in couples:\n\n # w's (m's partner) rank in m's preference list\n w_rank = mPref[m].index(w)\n \n # list of all women that m prefers over w\n preferred_over_w = mPref[m][:w_rank]\n \n\n for hotter in preferred_over_w:\n\n # get each woman's current partner (in preferred list)\n hotters_man = couples_dict[hotter]\n\n # if she prefers m over her current partner then marriage is not stable\n if wPref[hotter].index(m) < wPref[hotter].index(hotters_man): \n msg = \"{}'s marriage to {} is unstable:\\n\\t\" + \\\n \"{} prefers {} > {} (his current wife)\\n\\t\" +\\\n \"{} prefers {} > {} (her current husband)\"\n print(msg.format(m, w, m, hotter, w, hotter, m, hotters_man) )\n return False\n return True","def test_check_for_oversubscribed_projects(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n project = game.projects[0]\n project.matching = range(project.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(project.name)\n assert error.endswith(str(project.capacity))\n assert str(project.matching) in e","def check_correct_numbers(puzzle_size: int, puzzle_list: list) -> None:\n for number in range(puzzle_size * puzzle_size):\n if number not in puzzle_list:\n raise ParsingError(\"Puzzle does not contain expected numbers.\")","def validate_loans(loans):\n def validate_loan(loan):\n return (type(loan)==list or type(loan)==tuple) and len(loan)==3 \\\n and type(loan[0])==str and type(loan[1])==str and loan[1] in database.LOANABLE_RESOURCES and type(loan[2])==int and loan[2]>=0\n return type(loans)==list and False not in [validate_loan(load) for loan in loans]","def test_bonus_disc_no_bonus(self):\r\n gm_discs = set([1, 2])\r\n sp_discs = set([1, 2])\r\n self.assertFalse(gmspotify._bonus_disc_added(gm_discs, sp_discs))","def check_items(self, items):\n for item in items:\n if item['user'] in self.required_list and item['user'] not in self.approved_required_list:\n if item['result'] is True or item['result'] == 'approved':\n logger.info(\"%s approved\", item['user'])\n self.approved_required_list.append(item['user'])\n else:\n logger.info(\"%s declined\", item['user'])\n error_msg = \"Since {} is a required member for approval, this approval step fails. \".format(item['user'])\n logger.error(error_msg)\n self.exit(rc=2, detail=error_msg)\n if item['user'] in self.optional_list and item['user'] not in self.approved_optional_list:\n if item['result'] is True or item['result'] == 'approved':\n logger.info(\"%s approved\", item['user'])\n if item['user'] in self.declined_optional_list:\n self.declined_optional_list.remove(item['user'])\n self.approved_optional_list.append(item['user'])\n else:\n logger.info(\"%s declined\", item['user'])\n self.declined_optional_list.append(item['user'])\n if len(self.declined_optional_list) >= (len(self.optional_list) - self.number_optional):\n error_msg = \"Not be able to fulfill requirement that {} optional approvals, since {} declined request.\".format(\n self.number_optional, self.declined_optional_list)\n logger.error(error_msg)\n self.exit(rc=2, detail=error_msg)\n\n if len(self.approved_required_list) >= len(self.required_list) \\\n and len(self.approved_optional_list) >= self.number_optional:\n logger.info(\"Approval requirements are fully met. Exit gracefully.\")\n self.exit(0)","def check_win(game, pokemon_locations):\n #traverse game\n for i in range(len(game)):\n if game[i] == FLAG:#flagged\n if i not in pokemon_locations:\n return False\n elif game[i] == '~':\n return False\n return True","def test_missing_tracks(self):\n\n # Taylor Swift doesn't have her albums on Spotify\n expected_missing = [track_id for track_id, track in self.ilibrary.tracks.iteritems()\n if track.artists[0] == 'Taylor Swift']\n\n for missing_id in expected_missing:\n self.assertNotIn(missing_id, [track.i_id for track in self.tracks])","def checkIdentifiersPresent(self, checkDict):\n assert self._registeredIdentifiers.issubset(set(checkDict.keys())), f'missing identifiers: {self._registeredIdentifiers - set(checkDict.keys())}'","def check_for_unguarded_rooms(museum):\r\n\tempty_rooms = []\r\n\r\n\tfor row_idx in range(len(museum)):\t\r\n\t\t\r\n\t\tfor item_idx in range(len(museum[row_idx])): #Go back and fix this to be enumerate instead\r\n\t\t\t\r\n\t\t\tif museum[row_idx][item_idx] == \"0\":\r\n\t\t\t\tempty_rooms.append([row_idx, item_idx])\r\n\r\n\t# for row_idx, row_value in enumerate(museum):\r\n\t# \tfor item_idx, item_value in enumerate(row):\r\n\t# \t\tif item_value == \" \":\r\n\t# \t\t\tprint(item)\r\n\t# \t\t\tempty_rooms.append([row_idx, item_idx]) # need index\r\n\r\n\tif not empty_rooms:\r\n\t\tprint(\"true\")\r\n\t\r\n\telse:\r\n\t\tprint(\"false\")\r\n\r\n\t\tfor room in empty_rooms:\r\n\t\t\tprint(str(room[0]) + \" \" + str(room[1]))","def gameOver():\n if len(p1)==0 and len(p1winnings)==0:\n return True\n elif len(p2)==0 and len(p2winnings)==0:\n return True\n return False","def test_fav_6(self):\n\t\tplayer_list = [Player(\"Blake Base\", 1, 300000, 10), Player(\"Corey Catcher\", 2, 500000, 20), Player(\"Dexter Dugout\", 3, 200000, 50)]\n\t\tself.assertEqual( free_agent_vorp(player_list, 100000, 4), (0, 0, []) )","def inconsistent(p, guesses):\n for guess in guesses:\n res = check(guess[0], p)\n (rightly_positioned, permutated) = guess[1]\n if res != [rightly_positioned, permutated]:\n return True # inconsistent\n return False # i.e. consistent","def missing(name):\n return name not in get_all()","def check_restrictions(restrictions, element, keys, verbose):\n params = OrderedDict(zip(keys, element))\n for restrict in restrictions:\n if not eval(replace_param_occurrences(restrict, params)):\n if verbose:\n print(\"skipping config\", get_instance_string(params), \"reason: config fails restriction\")\n return False\n return True","def list_should_not_contain_duplicates(self,list_,msg=None):\r\n if not isinstance(list_,list):\r\n list_= list(list_)\r\n dupes = []\r\n for item in list_:\r\n if item not in dupes:\r\n count = list_.count(item)\r\n if count >1:\r\n logger.info(\" '%s' found %d times\" %(item,count))\r\n dupes.append(item)\r\n if dupes:\r\n raise AssertionError(msg or '%s found multiple times' %seq2str(dupes))","def check_proportion_list(proportions):\r\n \r\n if str(type(proportions[0])) == \"<class 'float'>\":\r\n prop_type = 'list'\r\n count = 0.00\r\n for element in proportions:\r\n count += float(element)\r\n \r\n if count != float(1):\r\n diff = 1 - count\r\n bad_prop = proportions[-1]\r\n proportions[-1] = round(float(proportions[-1]) + diff,6)\r\n print('Proportion Set 0:\\n----------------\\n' +\r\n 'Entered proportions not equivalent to 1,\\n' \r\n + str(bad_prop) + ' changed to ' + str(proportions[-1])\r\n + '\\n')\r\n \r\n \r\n \r\n \r\n elif str(type(proportions[0])) == \"<class 'list'>\":\r\n for i in range(len(proportions)):\r\n prop_type = 'list/list'\r\n count = 0.00\r\n for element in proportions[i]:\r\n count += float(element)\r\n \r\n if count != float(1):\r\n diff = 1 - count\r\n bad_prop = proportions[i][-1]\r\n proportions[i][-1] = round(float(proportions[i][-1]) + diff,6)\r\n print('Proportion Set ' + str(i) + ':\\n----------------\\n' +\r\n 'Entered proportions not equivalent to 1,\\n' \r\n + str(bad_prop) + ' changed to ' + str(proportions[i][-1])\r\n + '\\n')\r\n \r\n \r\n\r\n return proportions, prop_type","def test_half_life_unstable_isotopes():\n for isotope in data_about_isotopes:\n if (\n \"half_life\" not in data_about_isotopes[isotope]\n and not data_about_isotopes[isotope]\n ):\n with pytest.raises(MissingParticleDataError):\n half_life(isotope)"],"string":"[\n \"def test_check_inputs_hospital_prefs_all_reciprocated(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n resident._forget(hospital)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert resident not in hospital.prefs\",\n \"def test_check_inputs_resident_prefs_all_hospitals(game):\\n\\n resident = game.residents[0]\\n resident.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert resident.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert resident.prefs == []\",\n \"def test_check_inputs_hospital_reciprocated_all_prefs(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n hospital._forget(resident)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"hospitals\\\", \\\"residents\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert hospital not in resident.prefs\",\n \"def test_check_inputs_hospital_prefs_all_nonempty(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def test_check_inputs_hospital_prefs_all_residents(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert hospital.prefs == []\",\n \"def _check_resident_prefs(self):\\n\\n errors = []\\n for resident in self.residents:\\n if not set(resident.prefs).issubset(set(self.hospitals)):\\n errors.append(\\n ValueError(\\n f\\\"{resident} has ranked a non-hospital: \\\"\\n f\\\"{set(resident.prefs)} != {set(self.hospitals)}\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def _check_hospital_prefs(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n residents_that_ranked = [\\n res for res in self.residents if hospital in res.prefs\\n ]\\n if set(hospital.prefs) != set(residents_that_ranked):\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has not ranked all the residents that \\\"\\n f\\\"ranked it: {set(hospital.prefs)} != \\\"\\n f\\\"{set(residents_that_ranked)}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_supervisor_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n supervisor.prefs.remove(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"supervisors\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert supervisor.name in message\\n\\n if clean:\\n assert supervisor not in student.prefs\\n assert all(\\n project not in student.prefs for project in supervisor.projects\\n )\",\n \"def test_check_inputs_supervisor_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n projects = supervisor.projects\\n for project in student.prefs:\\n if project in projects:\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"supervisors\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(supervisor.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in supervisor.prefs\\n assert all(\\n student not in project.prefs for project in supervisor.projects\\n )\",\n \"def test_check_inputs_project_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n student = project.prefs[0]\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"projects\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(project.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in project.prefs\",\n \"def _check_resident_matching(self):\\n\\n errors = []\\n for resident in self.residents:\\n if (\\n resident.matching is not None\\n and resident.matching not in resident.prefs\\n ):\\n errors.append(\\n ValueError(\\n f\\\"{resident} is matched to {resident.matching} but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{resident.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_project_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n student = project.prefs[0]\\n project._forget(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"projects\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert project.name in message\\n\\n if clean:\\n assert project not in student.prefs\",\n \"def _check_mutual_preference(resident, hospital):\\n\\n return resident in hospital.prefs and hospital in resident.prefs\",\n \"def _check_hospital_matching(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n for resident in hospital.matching:\\n if resident not in hospital.prefs:\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has {resident} in their matching but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{hospital.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_check_for_unacceptable_matches_residents(game):\\n\\n resident = game.residents[0]\\n hospital = Hospital(name=\\\"foo\\\", capacity=1)\\n resident.matching = hospital\\n\\n issues = game._check_for_unacceptable_matches(\\\"residents\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(resident.name)\\n assert issue.endswith(f\\\"{resident.prefs}.\\\")\\n assert hospital.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_inputs_reviewer_prefs(\\n resident_names, hospital_names, capacities, seed\\n):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_reviewer_prefs()\\n\\n match.reviewers[0].pref_names.pop()\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_prefs()\",\n \"def testAssistantDoesNotGetUserPrefRole(self):\\n self._testAssistantOwnershipAfter(task=\\\"create\\\")\\n self.failIf('def456' in self.person.users_with_local_role('User Preferences Editor'), \\\"Assistant can edit user prefs after create\\\")\\n self.simulateATGUIInteraction(task=\\\"edit\\\")\\n self.failIf('def456' in self.person.users_with_local_role('User Preferences Editor'), \\\"Assistant can edit user prefs after edit\\\")\",\n \"def check_puzzle_unicity(puzzle_list: list) -> None:\\n if len(set(puzzle_list)) != len(puzzle_list):\\n raise ParsingError(\\\"Puzzle numbers must be unique.\\\")\",\n \"def _check_resident_unhappy(resident, hospital):\\n\\n return resident.matching is None or resident.prefers(\\n hospital, resident.matching\\n )\",\n \"def __check_all_config_values_set(cfg: __Config):\\n unset_values = [key for key, val in cfg.__dict__.items() if val is None]\\n if len(unset_values) > 0:\\n print(f\\\"WARNING: The following config variables have not been set: {unset_values}\\\")\",\n \"def check_stability(self):\\n\\n blocking_pairs = []\\n for resident in self.residents:\\n for hospital in self.hospitals:\\n if (\\n _check_mutual_preference(resident, hospital)\\n and _check_resident_unhappy(resident, hospital)\\n and _check_hospital_unhappy(resident, hospital)\\n ):\\n blocking_pairs.append((resident, hospital))\\n\\n self.blocking_pairs = blocking_pairs\\n return not any(blocking_pairs)\",\n \"def test_delete_spawning_profile_not_in_list():\\n tester = TestClass()\\n spawning_profiles = tester.delete_spawning_profile()\\n\\n assert spawning_profiles\\n\\n assert len(spawning_profiles) == 1\\n\\n assert spawning_profiles[0].get_spawning_profile_name() == 'Default'\",\n \"def assertNotPrefExists(self, user):\\r\\n self.assertFalse(\\r\\n UserPreference.objects.filter(user=user, key=NOTIFICATION_PREF_KEY).exists()\\r\\n )\",\n \"def verifyReplicasDictionary(replicas_dict, guids):\\n\\n status = True\\n pilotErrorDiag = \\\"\\\"\\n\\n # Loop over all GUIDs and see if they are all in the replicas dictionary\\n for guid in guids:\\n if not guid in replicas_dict.keys():\\n status = False\\n pilotErrorDiag = \\\"Replica with guid=%s missing in Rucio catalog\\\" % (guid)\\n tolog(\\\"!!WARNING!!1122!! %s\\\" % (pilotErrorDiag))\\n break\\n\\n return status, pilotErrorDiag\",\n \"def avoids(w, forbidden):\\n\\treturn set(w).isdisjoint(set(forbidden))\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def notify_no_win(self):\\n\\n board_set = set()\\n for row in self.board:\\n board_set.update(row)\\n if \\\"\\\" not in board_set:\\n print \\\"Game is over. No one won.\\\"\\n return True\",\n \"def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_suitor_prefs()\\n\\n match.suitors[0].pref_names = [1, 2, 3]\\n\\n with pytest.raises(Exception):\\n match._check_suitor_prefs()\",\n \"def test_dropping_all_correct(self):\\r\\n self.dropping_setup()\\r\\n\\r\\n self.dropping_homework_stage1()\\r\\n for name in self.hw3_names:\\r\\n self.submit_question_answer(name, {'2_1': 'Correct'})\\r\\n\\r\\n self.check_grade_percent(1.0)\\r\\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 2.0]) # Order matters\\r\\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 1.0])\",\n \"def test_ComputerPartition_error_CleanupResource_ConfirmedState(self):\\n sequence_list = SequenceList()\\n sequence_string = self.prepare_confirmed_cleanup_resource_packing_list + '\\\\\\n SlapLoginCurrentComputer \\\\\\n CheckSuccessComputerPartitionErrorCall \\\\\\n Tic \\\\\\n SlapLogout \\\\\\n LoginDefaultUser \\\\\\n CheckSalePackingListErrorText \\\\\\n CheckConfirmedSalePackingList \\\\\\n Logout \\\\\\n LoginERP5TypeTestCase \\\\\\n CheckSiteConsistency \\\\\\n Logout \\\\\\n '\\n sequence_list.addSequenceString(sequence_string)\\n sequence_list.play(self)\",\n \"def _assert_problem_is_valid(self) -> None:\\n # Will throw error if any of these are not set\\n specified = self.ignore + self.silence + self.target\\n if not all(\\n [\\n pr in specified\\n for pr in self.observer.photoreceptors\\n if pr is not None\\n ]\\n ):\\n raise SilSubProblemError(\\n \\\"At least one photoreceptor is not accounted for by the \\\"\\n + \\\"*ignore*, *silence* and *target* properties.\\\"\\n )\",\n \"def check_conflicts(self, PlugLead):\\n\\n if len(self.plugleads) >= 10:\\n raise ValueError('Plugboard full! Plugboard only supports up to 10 pairs')\\n\\n for plug in self.plugleads:\\n if plug.pair[0] in PlugLead.pair or plug.pair[1] in PlugLead.pair:\\n raise ValueError(\\n 'Plug lead {} conflicts with existing pair, please ensure plug lead pairs are unique'.format(\\n PlugLead.pair))\",\n \"def check_for_list(check):\",\n \"def no_oversuscribed_sessions(prefs, caps):\\n out = []\\n for room, cap in caps.items():\\n # if room capacity > num students, go to next room \\n # since this one is safely undersuscribed\\n if cap >= len(prefs):\\n continue\\n # if a room contains N students, then in every group of N+1 students \\n # there must be a student not in that session\\n stud_combos = combos(prefs, cap+1)\\n for group in stud_combos:\\n out.append([('%s_%s' % (stud, room), False) for stud in group])\\n return out\",\n \"def check_auto_reject(self):\\r\\n for pr in self:\\r\\n if not pr.line_ids.filtered(lambda l: l.cancelled is False):\\r\\n pr.write({'state': 'rejected'})\",\n \"def _check_hospital_unhappy(resident, hospital):\\n\\n return len(hospital.matching) < hospital.capacity or any(\\n [hospital.prefers(resident, match) for match in hospital.matching]\\n )\",\n \"def check_loss(self):\\n return POKEMON in self.get_game()\",\n \"def test_remove_spawning_profile_from_intersection():\\n tester = TestClass()\\n intersections = tester.add_spawning_profile_to_intersection()\\n\\n for i in intersections:\\n if len(i.get_spawning_profile_list()) != 0:\\n assert True\\n\\n for spawn in i.get_spawning_profile_list():\\n if spawn.get_spawning_profile_name() == 'Default':\\n assert True\\n break\\n\\n tester.delete_spawning_profile_from_intersection()\\n\\n for i in intersections:\\n if len(i.get_spawning_profile_list()) == 0:\\n assert True\",\n \"def test_tally_no_candidates(self):\\n self.init_elect_types()\\n\\n userA = models.User(\\n name = \\\"UserA\\\",\\n email = \\\"userA@eLect.com\\\",\\n password = \\\"asdf\\\")\\n\\n session.add(userA)\\n session.commit()\\n\\n electionA = models.Election(\\n title = \\\"Election A\\\",\\n admin_id = userA.id)\\n\\n session.add(electionA)\\n session.commit()\\n\\n raceA = models.Race(\\n title = \\\"Race A\\\",\\n election_id = electionA.id\\n )\\n\\n session.add(raceA)\\n session.commit()\\n\\n with self.assertRaises(NoCandidates):\\n self.wta.check_race(raceA.id)\\n\\n with self.assertRaises(NoCandidates):\\n self.proportional.check_race(raceA.id)\\n\\n with self.assertRaises(NoCandidates):\\n self.schulze.check_race(raceA.id)\",\n \"def all_votesp(self, game_key):\\n participants = models.Participant.query(\\n models.Participant.playing == True,\\n models.Participant.vote == None,\\n ancestor=game_key).fetch()\\n logging.info(\\n \\\"participants who have not voted: %s\\\", \\n [p.plus_id for p in participants])\\n if participants:\\n return False\\n else:\\n return True\",\n \"def check_primers(self):\\n primer_keys=['sequence','description']\\n if self.data.has_key('primer_dict'):\\n for primer in self.data['primer_dict'].keys():\\n for key in primer_keys:\\n if not self.data['primer_dict'][primer].has_key(key):\\n self.data['primer_dict'][primer][key]='None'\",\n \"def death_check(self):\\r\\n chance = random.random()\\r\\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\\r\\n if self.unique_id in head_of_household_list:\\r\\n try:\\r\\n head_of_household_list[self.hh_id] = 0\\r\\n except TypeError: # head of household migrated\\r\\n head_of_household_list[self.past_hh_id] = 0\\r\\n self.model.number_of_humans -= 1\\r\\n if self.unique_id in labor_list:\\r\\n labor_list.remove(self.unique_id)\\r\\n if self.work_status == 1:\\r\\n try:\\r\\n num_labor_list[self.hh_id] -= 1\\r\\n except TypeError:\\r\\n num_labor_list[self.past_hh_id] -= 1\\r\\n if self.unique_id in former_hoh_list:\\r\\n try:\\r\\n former_hoh_list[self.hh_id] = 0\\r\\n except:\\r\\n former_hoh_list[self.past_hh_id] = 0\\r\\n if [self.unique_id, self.hh_id] in single_male_list:\\r\\n single_male_list.remove([self.unique_id, self.hh_id])\\r\\n if self.unique_id in married_male_list:\\r\\n married_male_list.remove(self.unique_id)\\r\\n human_death_list.append(self.unique_id)\\r\\n try:\\r\\n hh_size_list[self.hh_id] -= 1\\r\\n except:\\r\\n hh_size_list[self.past_hh_id] -= 1\\r\\n human_demographic_structure_list[self.age_category] -= 1\\r\\n\\r\\n self.model.schedule.remove(self)\\r\\n if self in self.model.grid:\\r\\n self.model.grid.remove_agent(self)\",\n \"def validate(candidates: List[str], choices: List[str]) -> List[str]:\\n for candidate in candidates:\\n assert candidate in choices, f\\\"Specified {candidate}, but not in available list: {choices}.\\\"\\n return candidates\",\n \"def testNoSpecialties(self):\\n self.failUnlessEqual(self.person.getSpecialties(), [])\",\n \"def check_prerequisites(self):\\n self.courses_not_completed = self.prerequisite_set - set(self.user_courses.keys())\",\n \"def check_win(self):\\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)\",\n \"def verify_no_cable_errors(self):\\n i = 0\\n for dpid in self.dpids:\\n i += 1\\n labels = {'dp_id': '0x%x' % int(dpid), 'dp_name': 'faucet-%u' % i}\\n self.assertEqual(\\n 0, self.scrape_prometheus_var(\\n var='stack_cabling_errors_total', labels=labels, default=None))\\n self.assertGreater(\\n self.scrape_prometheus_var(\\n var='stack_probes_received_total', labels=labels), 0)\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def test_effective_metrics_not_in_PM():\\n assert \\\"effective_sample_size\\\" not in PM_METRICS\\n assert \\\"pearson_r_eff_p_value\\\" not in PM_METRICS\\n assert \\\"spearman_r_eff_p_value\\\" not in PM_METRICS\",\n \"def test_check_inputs(game):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.residents == game._all_residents\\n assert game.hospitals == game._all_hospitals\",\n \"def test_tally_no_votes(self):\\n self.populate_database()\\n self.electionA.elect_open = False\\n with self.assertRaises(NoVotes):\\n self.wta.check_race(self.raceA.id)\\n\\n with self.assertRaises(NoVotes):\\n self.proportional.check_race(self.raceA.id)\\n\\n with self.assertRaises(NoVotes):\\n self.schulze.check_race(self.raceA.id)\",\n \"def test_can_detect_when_player_has_no_pieces(self):\\n board = Board()\\n board.pieces = [Piece(0, 0, 0, 0), Piece(2, 2, 2, 2)]\\n\\n player_1_has_piece = board.does_player_have_pieces(1)\\n\\n assert not player_1_has_piece\",\n \"def test_check_for_oversubscribed_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n supervisor.matching = range(supervisor.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.capacity))\\n assert str(supervisor.matching) in e\",\n \"def verify_valid_game(self, lotto_game:List[int]):\\n is_valid = True;\\n lotto_game_temp = []\\n for number in lotto_game:\\n if number not in lotto_game_temp:\\n lotto_game_temp.append(number)\\n else:\\n print(number, lotto_game, lotto_game_temp)\\n is_valid = False\\n raise InvalidLottoGame\\n \\n lotto_game_temp_length = len(lotto_game_temp)\\n if lotto_game_temp_length != LOTTO_GAME_LENGTH and lotto_game_temp_length != LOTTO_GAME_WITH_BONUS_LENGTH:\\n print(lotto_game_temp)\\n is_valid = False\\n raise InvalidLottoGame\\n\\n return is_valid\",\n \"def test_dropping_nochange(self):\\r\\n self.dropping_setup()\\r\\n self.dropping_homework_stage1()\\r\\n self.submit_question_answer(self.hw3_names[0], {'2_1': 'Correct'})\\r\\n\\r\\n self.assertEqual(self.score_for_hw('homework1'), [1.0, 0.0])\\r\\n self.assertEqual(self.score_for_hw('homework2'), [1.0, 1.0])\\r\\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 0.0])\\r\\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 1.0]) # Order matters\\r\\n self.check_grade_percent(0.75)\",\n \"def reject_fairness(experiment: List[bool]) -> bool:\\n num_heads = len([flip for flip in experiment if flip])\\n return num_heads < 469 or num_heads > 531\",\n \"def testProtractedNSESanityChecks(self):\\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))\",\n \"def _list_validity_check(l, valid_l):\\n\\n if not Settings._is_in_list(l, valid_l):\\n raise InvalidSettingError()\",\n \"def test_prometheus_rule_failures():\\n prometheus = ocs_ci.utility.prometheus.PrometheusAPI()\\n alerts_response = prometheus.get(\\n \\\"alerts\\\", payload={\\\"silenced\\\": False, \\\"inhibited\\\": False}\\n )\\n assert alerts_response.ok is True\\n alerts = alerts_response.json()[\\\"data\\\"][\\\"alerts\\\"]\\n log.info(f\\\"Prometheus Alerts: {alerts}\\\")\\n assert constants.ALERT_PROMETHEUSRULEFAILURES not in [\\n alert[\\\"labels\\\"][\\\"alertname\\\"] for alert in alerts\\n ]\",\n \"def test_preferences_properties(self):\\n with pytest.raises(AssertionError):\\n self.preferences.exchange_params_by_currency_id\\n with pytest.raises(AssertionError):\\n self.preferences.utility_params_by_good_id\",\n \"def test_check_for_unacceptable_matches_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n student = Student(name=\\\"foo\\\")\\n supervisor.matching.append(student)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.prefs))\\n assert student.name in error\",\n \"def checkConflicts(self):\\n\\t\\treturn\",\n \"def delete_error():\\r\\n item = core.get_all_items()\\r\\n for i in item:\\r\\n if \\\"Error\\\" in i or \\\"Warning\\\" in i:\\r\\n if core.does_item_exist(i):\\r\\n reset_error(i)\",\n \"def check_collisions(self):\",\n \"def test_delete_vehicle_profile_not_in_list():\\n tester = TestClass()\\n vehicle_profiles = tester.delete_vehicle_profile()\\n\\n assert vehicle_profiles\\n assert len(vehicle_profiles) == 1\\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'\",\n \"def test_check_full_house_false(self):\\n not_full_house_fixtures = [[1, 1, 1, 1, 2],\\n [2, 1, 1, 1, 1],\\n [1, 1, 2, 2, 3],\\n [1, 2, 3, 4, 5],\\n ]\\n\\n for fixture in not_full_house_fixtures:\\n score = self.roll.check_full_house(fixture)\\n\\n self.assertNotEqual(score, 25)\\n self.assertEqual(score, 0)\\n self.assertEqual(len(fixture), 5)\",\n \"def test_race_voteval_check(self):\\n self.populate_database()\\n self.assertEqual(len(self.raceB.candidates), 4)\\n self.assertEqual(self.raceB.min_vote_val, 0)\\n self.assertEqual(self.raceB.max_vote_val, 1)\\n\\n self.raceB.election_type = \\\"Schulze\\\"\\n self.assertEqual(len(self.raceB.candidates), 4)\\n self.assertEqual(self.raceB.min_vote_val, 0)\\n self.assertEqual(self.raceB.max_vote_val, 4)\\n\\n ### WHY DO THESE WORK:\\n self.raceB.candidates.append(self.candidateAB)\\n self.assertEqual(len(self.raceB.candidates), 5)\\n self.assertEqual(self.raceB.min_vote_val, 0)\\n self.assertEqual(self.raceB.max_vote_val, 5)\\n\\n self.raceB.candidates.remove(self.candidateBD)\\n # Throw a couple of wrenches in the works\\n self.raceB.max_vote_val = 1\\n self.raceB.min_vote_val = 7\\n\\n self.assertEqual(len(self.raceB.candidates), 4)\\n self.assertEqual(self.raceB.min_vote_val, 0)\\n self.assertEqual(self.raceB.max_vote_val, 4)\\n\\n ### BUT THESE DO NOT ?!?!? ( obviously indirect changes to the \\n ### db/collection aren't handled by the validator event)\\n # session.delete(self.candidateBD)\\n # self.candidateAB.race_id = self.raceB.id\",\n \"def negative_test_for_check_local_storage(profile_obj):\\n status = True\\n for profile in profile_obj:\\n elements_test_list = [profile.emptyChar, profile.invalidChars]\\n\\n \\\"\\\"\\\"\\n Setting invalid values for: \\\"Name\\\" and \\\"Number of physical drivers\\\"\\n \\\"\\\"\\\"\\n for element in elements_test_list:\\n # Fill Logical Drive form\\n if __fill_logical_drive_form(element):\\n # Click Logical Drive Create button\\n if ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_BTN_CREATE_LOGICAL_DRIVE_FORM):\\n if element == '':\\n # Check warning messages\\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_NAME_FORM) and __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\\n logger.warn(\\\"At least one of the warning messages was NOT found \\\")\\n status = False\\n\\n else:\\n # Check warning messages\\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\\n logger.warn(\\\"Warning message NOT found\\\")\\n status = False\\n else:\\n logger.warn(\\\"Logical Drive Form 'Button' returns an error. Please, check the test report for more details\\\")\\n status = False\\n else:\\n logger.warn(\\\"Logical Drive Form returns an error. Please, check the test report for more details\\\")\\n status = False\\n\\n # Click 'Cancel' form button\\n if not ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_CANCEL_LOCAL_STORAGE_FORM):\\n logger.warn(\\\"Cancel button element NOT found\\\")\\n status = False\\n\\n return status\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def __updateLureTimeouts(self):\\n # check for lured suits and determine which should no longer be\\n # lured\\n if self.notify.getDebug():\\n self.notify.debug(\\\"__updateLureTimeouts()\\\")\\n self.notify.debug(\\\"Lured suits: \\\" + str(self.currentlyLuredSuits))\\n noLongerLured = []\\n\\n # check each lured suit, add to the lured suit's count of how many\\n # rounds it has been lured, then check to see if the suit has now been\\n # lured for the maximum rounds allowed for the particular lure used\\n # on the suit, if not, randomly decide if the suit should 'magically\\n # wake up' from the lure based on the fail chance of the particular\\n # lure originally used on the suit\\n for currLuredSuit in self.currentlyLuredSuits.keys():\\n self.__incLuredCurrRound(currLuredSuit)\\n if self.__luredMaxRoundsReached(currLuredSuit) or \\\\\\n self.__luredWakeupTime(currLuredSuit):\\n noLongerLured.append(currLuredSuit)\\n\\n # now we can safely clear out the no-longer-lured suits\\n for currLuredSuit in noLongerLured:\\n# self.suitsUnluredThisRound.append(currLuredSuit)\\n self.__removeLured(currLuredSuit)\\n if self.notify.getDebug():\\n self.notify.debug(\\\"Lured suits: \\\" + str(self.currentlyLuredSuits))\",\n \"async def test_skipped_already_unsilenced(self):\\n self.cog.scheduler.__contains__.return_value = False\\n self.cog.previous_overwrites.get.return_value = None\\n\\n for channel in (MockVoiceChannel(), MockTextChannel()):\\n with self.subTest(channel=channel):\\n self.assertFalse(await self.cog._unsilence(channel))\\n channel.set_permissions.assert_not_called()\",\n \"def test_blocked_shot_type():\\n with session_scope() as session:\\n all_shot_types = session.query(distinct(Block.shot_type)).all()\\n all_shot_types = [shot_type for (shot_type,) in all_shot_types]\\n for shot_type in all_shot_types:\\n # null is an acceptable shot type, too\\n if shot_type is None:\\n continue\\n assert shot_type in VALID_SHOT_TYPES\",\n \"def test_vote_badpklist(self):\\r\\n mock_module = CHModuleFactory.create()\\r\\n json_in = {'answer': '24.0', 'hint': '0', 'pk_list': json.dumps([['24.0', 0], ['24.0', 12]])}\\r\\n hint_and_votes = mock_module.tally_vote(json_in)['hint_and_votes']\\r\\n self.assertTrue(['Best hint', 41] in hint_and_votes)\\r\\n self.assertTrue(len(hint_and_votes) == 1)\",\n \"def violated(self) -> bool:\\n ...\",\n \"def test_apply_subscription_rules__non_match(self):\\n changes = [{'prop_name': 'shipped_android_milestone'}]\\n\\n # No milestones of any kind set.\\n actual = notifier.apply_subscription_rules(self.feature_1, changes)\\n self.assertEqual({}, actual)\\n\\n # Webview is also set\\n self.feature_1.shipped_android_milestone = 88\\n self.feature_1.shipped_webview_milestone = 89\\n actual = notifier.apply_subscription_rules(self.feature_1, changes)\\n self.assertEqual({}, actual)\",\n \"def test_for_empty_list(self):\\n emptylist = []\\n self.assertEqual(self.place.amenity_ids, emptylist)\",\n \"def test_ComputerPartition_error_CleanupResource_DeliveredState(self):\\n sequence_list = SequenceList()\\n sequence_string = self.prepare_confirmed_cleanup_resource_packing_list + '\\\\\\n LoginDefaultUser \\\\\\n StartSalePackingList \\\\\\n CleanTic \\\\\\n StopSalePackingList \\\\\\n CleanTic \\\\\\n DeliverSalePackingList \\\\\\n Tic \\\\\\n Logout \\\\\\n SlapLoginCurrentComputer \\\\\\n CheckSuccessComputerPartitionErrorCall \\\\\\n CleanTic \\\\\\n SlapLogout \\\\\\n LoginDefaultUser \\\\\\n CheckSalePackingListErrorText \\\\\\n CheckDeliveredSalePackingList \\\\\\n Logout \\\\\\n LoginERP5TypeTestCase \\\\\\n CheckSiteConsistency \\\\\\n Logout \\\\\\n '\\n sequence_list.addSequenceString(sequence_string)\\n sequence_list.play(self)\",\n \"def done(poles):\\n if len(poles[0]) == 0 and len(poles[1]) == 0:\\n return True\\n else:\\n return False\",\n \"def isStable(couples,mPref,wPref) -> bool:\\n couples_dict = {woman:man for man,woman in couples}\\n \\n for m,w in couples:\\n\\n # w's (m's partner) rank in m's preference list\\n w_rank = mPref[m].index(w)\\n \\n # list of all women that m prefers over w\\n preferred_over_w = mPref[m][:w_rank]\\n \\n\\n for hotter in preferred_over_w:\\n\\n # get each woman's current partner (in preferred list)\\n hotters_man = couples_dict[hotter]\\n\\n # if she prefers m over her current partner then marriage is not stable\\n if wPref[hotter].index(m) < wPref[hotter].index(hotters_man): \\n msg = \\\"{}'s marriage to {} is unstable:\\\\n\\\\t\\\" + \\\\\\n \\\"{} prefers {} > {} (his current wife)\\\\n\\\\t\\\" +\\\\\\n \\\"{} prefers {} > {} (her current husband)\\\"\\n print(msg.format(m, w, m, hotter, w, hotter, m, hotters_man) )\\n return False\\n return True\",\n \"def test_check_for_oversubscribed_projects(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n project = game.projects[0]\\n project.matching = range(project.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(project.name)\\n assert error.endswith(str(project.capacity))\\n assert str(project.matching) in e\",\n \"def check_correct_numbers(puzzle_size: int, puzzle_list: list) -> None:\\n for number in range(puzzle_size * puzzle_size):\\n if number not in puzzle_list:\\n raise ParsingError(\\\"Puzzle does not contain expected numbers.\\\")\",\n \"def validate_loans(loans):\\n def validate_loan(loan):\\n return (type(loan)==list or type(loan)==tuple) and len(loan)==3 \\\\\\n and type(loan[0])==str and type(loan[1])==str and loan[1] in database.LOANABLE_RESOURCES and type(loan[2])==int and loan[2]>=0\\n return type(loans)==list and False not in [validate_loan(load) for loan in loans]\",\n \"def test_bonus_disc_no_bonus(self):\\r\\n gm_discs = set([1, 2])\\r\\n sp_discs = set([1, 2])\\r\\n self.assertFalse(gmspotify._bonus_disc_added(gm_discs, sp_discs))\",\n \"def check_items(self, items):\\n for item in items:\\n if item['user'] in self.required_list and item['user'] not in self.approved_required_list:\\n if item['result'] is True or item['result'] == 'approved':\\n logger.info(\\\"%s approved\\\", item['user'])\\n self.approved_required_list.append(item['user'])\\n else:\\n logger.info(\\\"%s declined\\\", item['user'])\\n error_msg = \\\"Since {} is a required member for approval, this approval step fails. \\\".format(item['user'])\\n logger.error(error_msg)\\n self.exit(rc=2, detail=error_msg)\\n if item['user'] in self.optional_list and item['user'] not in self.approved_optional_list:\\n if item['result'] is True or item['result'] == 'approved':\\n logger.info(\\\"%s approved\\\", item['user'])\\n if item['user'] in self.declined_optional_list:\\n self.declined_optional_list.remove(item['user'])\\n self.approved_optional_list.append(item['user'])\\n else:\\n logger.info(\\\"%s declined\\\", item['user'])\\n self.declined_optional_list.append(item['user'])\\n if len(self.declined_optional_list) >= (len(self.optional_list) - self.number_optional):\\n error_msg = \\\"Not be able to fulfill requirement that {} optional approvals, since {} declined request.\\\".format(\\n self.number_optional, self.declined_optional_list)\\n logger.error(error_msg)\\n self.exit(rc=2, detail=error_msg)\\n\\n if len(self.approved_required_list) >= len(self.required_list) \\\\\\n and len(self.approved_optional_list) >= self.number_optional:\\n logger.info(\\\"Approval requirements are fully met. Exit gracefully.\\\")\\n self.exit(0)\",\n \"def check_win(game, pokemon_locations):\\n #traverse game\\n for i in range(len(game)):\\n if game[i] == FLAG:#flagged\\n if i not in pokemon_locations:\\n return False\\n elif game[i] == '~':\\n return False\\n return True\",\n \"def test_missing_tracks(self):\\n\\n # Taylor Swift doesn't have her albums on Spotify\\n expected_missing = [track_id for track_id, track in self.ilibrary.tracks.iteritems()\\n if track.artists[0] == 'Taylor Swift']\\n\\n for missing_id in expected_missing:\\n self.assertNotIn(missing_id, [track.i_id for track in self.tracks])\",\n \"def checkIdentifiersPresent(self, checkDict):\\n assert self._registeredIdentifiers.issubset(set(checkDict.keys())), f'missing identifiers: {self._registeredIdentifiers - set(checkDict.keys())}'\",\n \"def check_for_unguarded_rooms(museum):\\r\\n\\tempty_rooms = []\\r\\n\\r\\n\\tfor row_idx in range(len(museum)):\\t\\r\\n\\t\\t\\r\\n\\t\\tfor item_idx in range(len(museum[row_idx])): #Go back and fix this to be enumerate instead\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif museum[row_idx][item_idx] == \\\"0\\\":\\r\\n\\t\\t\\t\\tempty_rooms.append([row_idx, item_idx])\\r\\n\\r\\n\\t# for row_idx, row_value in enumerate(museum):\\r\\n\\t# \\tfor item_idx, item_value in enumerate(row):\\r\\n\\t# \\t\\tif item_value == \\\" \\\":\\r\\n\\t# \\t\\t\\tprint(item)\\r\\n\\t# \\t\\t\\tempty_rooms.append([row_idx, item_idx]) # need index\\r\\n\\r\\n\\tif not empty_rooms:\\r\\n\\t\\tprint(\\\"true\\\")\\r\\n\\t\\r\\n\\telse:\\r\\n\\t\\tprint(\\\"false\\\")\\r\\n\\r\\n\\t\\tfor room in empty_rooms:\\r\\n\\t\\t\\tprint(str(room[0]) + \\\" \\\" + str(room[1]))\",\n \"def gameOver():\\n if len(p1)==0 and len(p1winnings)==0:\\n return True\\n elif len(p2)==0 and len(p2winnings)==0:\\n return True\\n return False\",\n \"def test_fav_6(self):\\n\\t\\tplayer_list = [Player(\\\"Blake Base\\\", 1, 300000, 10), Player(\\\"Corey Catcher\\\", 2, 500000, 20), Player(\\\"Dexter Dugout\\\", 3, 200000, 50)]\\n\\t\\tself.assertEqual( free_agent_vorp(player_list, 100000, 4), (0, 0, []) )\",\n \"def inconsistent(p, guesses):\\n for guess in guesses:\\n res = check(guess[0], p)\\n (rightly_positioned, permutated) = guess[1]\\n if res != [rightly_positioned, permutated]:\\n return True # inconsistent\\n return False # i.e. consistent\",\n \"def missing(name):\\n return name not in get_all()\",\n \"def check_restrictions(restrictions, element, keys, verbose):\\n params = OrderedDict(zip(keys, element))\\n for restrict in restrictions:\\n if not eval(replace_param_occurrences(restrict, params)):\\n if verbose:\\n print(\\\"skipping config\\\", get_instance_string(params), \\\"reason: config fails restriction\\\")\\n return False\\n return True\",\n \"def list_should_not_contain_duplicates(self,list_,msg=None):\\r\\n if not isinstance(list_,list):\\r\\n list_= list(list_)\\r\\n dupes = []\\r\\n for item in list_:\\r\\n if item not in dupes:\\r\\n count = list_.count(item)\\r\\n if count >1:\\r\\n logger.info(\\\" '%s' found %d times\\\" %(item,count))\\r\\n dupes.append(item)\\r\\n if dupes:\\r\\n raise AssertionError(msg or '%s found multiple times' %seq2str(dupes))\",\n \"def check_proportion_list(proportions):\\r\\n \\r\\n if str(type(proportions[0])) == \\\"<class 'float'>\\\":\\r\\n prop_type = 'list'\\r\\n count = 0.00\\r\\n for element in proportions:\\r\\n count += float(element)\\r\\n \\r\\n if count != float(1):\\r\\n diff = 1 - count\\r\\n bad_prop = proportions[-1]\\r\\n proportions[-1] = round(float(proportions[-1]) + diff,6)\\r\\n print('Proportion Set 0:\\\\n----------------\\\\n' +\\r\\n 'Entered proportions not equivalent to 1,\\\\n' \\r\\n + str(bad_prop) + ' changed to ' + str(proportions[-1])\\r\\n + '\\\\n')\\r\\n \\r\\n \\r\\n \\r\\n \\r\\n elif str(type(proportions[0])) == \\\"<class 'list'>\\\":\\r\\n for i in range(len(proportions)):\\r\\n prop_type = 'list/list'\\r\\n count = 0.00\\r\\n for element in proportions[i]:\\r\\n count += float(element)\\r\\n \\r\\n if count != float(1):\\r\\n diff = 1 - count\\r\\n bad_prop = proportions[i][-1]\\r\\n proportions[i][-1] = round(float(proportions[i][-1]) + diff,6)\\r\\n print('Proportion Set ' + str(i) + ':\\\\n----------------\\\\n' +\\r\\n 'Entered proportions not equivalent to 1,\\\\n' \\r\\n + str(bad_prop) + ' changed to ' + str(proportions[i][-1])\\r\\n + '\\\\n')\\r\\n \\r\\n \\r\\n\\r\\n return proportions, prop_type\",\n \"def test_half_life_unstable_isotopes():\\n for isotope in data_about_isotopes:\\n if (\\n \\\"half_life\\\" not in data_about_isotopes[isotope]\\n and not data_about_isotopes[isotope]\\n ):\\n with pytest.raises(MissingParticleDataError):\\n half_life(isotope)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7198795","0.71749365","0.71210027","0.6954522","0.6899493","0.6747819","0.6641792","0.6345878","0.61837107","0.6050496","0.5979725","0.59595793","0.5754744","0.57290685","0.5725816","0.56024337","0.5517876","0.5488233","0.54230016","0.5410084","0.5394656","0.5382351","0.53728974","0.53533256","0.5343611","0.53373414","0.5323798","0.5319668","0.5283251","0.52639294","0.5235524","0.5224042","0.5208418","0.51704293","0.516559","0.5164694","0.5159379","0.5157128","0.51497406","0.51422703","0.51395935","0.5121154","0.5118195","0.5114684","0.511132","0.5110956","0.5100315","0.50970894","0.50964844","0.5088846","0.5081221","0.5077844","0.5071163","0.5065341","0.50614643","0.506138","0.5057477","0.5055052","0.5053549","0.5051112","0.50504434","0.5022645","0.50189745","0.5011345","0.50102955","0.500863","0.5007605","0.500195","0.5001131","0.50005877","0.49987164","0.49987164","0.49987164","0.4996643","0.49923286","0.4991005","0.49885175","0.49834818","0.4977527","0.4963099","0.49571845","0.4955267","0.493974","0.49380645","0.49336985","0.49168113","0.49161708","0.49068445","0.49053234","0.49016574","0.48948404","0.48935863","0.48915425","0.48892337","0.4882034","0.48817974","0.487641","0.4874697","0.48742744","0.48724824"],"string":"[\n \"0.7198795\",\n \"0.71749365\",\n \"0.71210027\",\n \"0.6954522\",\n \"0.6899493\",\n \"0.6747819\",\n \"0.6641792\",\n \"0.6345878\",\n \"0.61837107\",\n \"0.6050496\",\n \"0.5979725\",\n \"0.59595793\",\n \"0.5754744\",\n \"0.57290685\",\n \"0.5725816\",\n \"0.56024337\",\n \"0.5517876\",\n \"0.5488233\",\n \"0.54230016\",\n \"0.5410084\",\n \"0.5394656\",\n \"0.5382351\",\n \"0.53728974\",\n \"0.53533256\",\n \"0.5343611\",\n \"0.53373414\",\n \"0.5323798\",\n \"0.5319668\",\n \"0.5283251\",\n \"0.52639294\",\n \"0.5235524\",\n \"0.5224042\",\n \"0.5208418\",\n \"0.51704293\",\n \"0.516559\",\n \"0.5164694\",\n \"0.5159379\",\n \"0.5157128\",\n \"0.51497406\",\n \"0.51422703\",\n \"0.51395935\",\n \"0.5121154\",\n \"0.5118195\",\n \"0.5114684\",\n \"0.511132\",\n \"0.5110956\",\n \"0.5100315\",\n \"0.50970894\",\n \"0.50964844\",\n \"0.5088846\",\n \"0.5081221\",\n \"0.5077844\",\n \"0.5071163\",\n \"0.5065341\",\n \"0.50614643\",\n \"0.506138\",\n \"0.5057477\",\n \"0.5055052\",\n \"0.5053549\",\n \"0.5051112\",\n \"0.50504434\",\n \"0.5022645\",\n \"0.50189745\",\n \"0.5011345\",\n \"0.50102955\",\n \"0.500863\",\n \"0.5007605\",\n \"0.500195\",\n \"0.5001131\",\n \"0.50005877\",\n \"0.49987164\",\n \"0.49987164\",\n \"0.49987164\",\n \"0.4996643\",\n \"0.49923286\",\n \"0.4991005\",\n \"0.49885175\",\n \"0.49834818\",\n \"0.4977527\",\n \"0.4963099\",\n \"0.49571845\",\n \"0.4955267\",\n \"0.493974\",\n \"0.49380645\",\n \"0.49336985\",\n \"0.49168113\",\n \"0.49161708\",\n \"0.49068445\",\n \"0.49053234\",\n \"0.49016574\",\n \"0.48948404\",\n \"0.48935863\",\n \"0.48915425\",\n \"0.48892337\",\n \"0.4882034\",\n \"0.48817974\",\n \"0.487641\",\n \"0.4874697\",\n \"0.48742744\",\n \"0.48724824\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7836128"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93793,"cells":{"query":{"kind":"string","value":"Test that each hospital has a nonempty preference list. If not, check that a warning is caught and the player has been removed from the game."},"document":{"kind":"string","value":"def test_check_inputs_hospital_prefs_all_nonempty(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert resident not in hospital.prefs","def test_check_inputs_hospital_prefs_all_residents(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert hospital.prefs == []","def test_check_inputs_resident_prefs_all_nonempty(game):\n\n resident = game.residents[0]\n resident.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n assert resident.name in str(record[0].message)\n\n if game.clean:\n assert resident not in game.residents","def test_check_inputs_hospital_reciprocated_all_prefs(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n hospital._forget(resident)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"hospitals\", \"residents\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert hospital not in resident.prefs","def test_check_inputs_resident_prefs_all_hospitals(game):\n\n resident = game.residents[0]\n resident.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert resident.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert resident.prefs == []","def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.append(\n ValueError(\n f\"{hospital} has not ranked all the residents that \"\n f\"ranked it: {set(hospital.prefs)} != \"\n f\"{set(residents_that_ranked)}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def _check_hospital_matching(self):\n\n errors = []\n for hospital in self.hospitals:\n for resident in hospital.matching:\n if resident not in hospital.prefs:\n errors.append(\n ValueError(\n f\"{hospital} has {resident} in their matching but \"\n \"they do not appear in their preference list: \"\n f\"{hospital.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def test_check_inputs_hospital_capacity(game):\n\n hospital = game.hospitals[0]\n capacity = hospital.capacity\n hospital.capacity = 0\n\n assert hospital._original_capacity == capacity\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_capacity(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def test_check_inputs_supervisor_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n supervisor.prefs.remove(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"supervisors\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert supervisor.name in message\n\n if clean:\n assert supervisor not in student.prefs\n assert all(\n project not in student.prefs for project in supervisor.projects\n )","def _check_resident_prefs(self):\n\n errors = []\n for resident in self.residents:\n if not set(resident.prefs).issubset(set(self.hospitals)):\n errors.append(\n ValueError(\n f\"{resident} has ranked a non-hospital: \"\n f\"{set(resident.prefs)} != {set(self.hospitals)}\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_check_inputs_project_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n student = project.prefs[0]\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"projects\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(project.name)\n assert student.name in message\n\n if clean:\n assert student not in project.prefs","def test_check_inputs_project_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n student = project.prefs[0]\n project._forget(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"projects\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert project.name in message\n\n if clean:\n assert project not in student.prefs","def test_check_inputs_supervisor_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n projects = supervisor.projects\n for project in student.prefs:\n if project in projects:\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"supervisors\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(supervisor.name)\n assert student.name in message\n\n if clean:\n assert student not in supervisor.prefs\n assert all(\n student not in project.prefs for project in supervisor.projects\n )","def _check_hospital_unhappy(resident, hospital):\n\n return len(hospital.matching) < hospital.capacity or any(\n [hospital.prefers(resident, match) for match in hospital.matching]\n )","def test_can_detect_when_player_has_no_pieces(self):\n board = Board()\n board.pieces = [Piece(0, 0, 0, 0), Piece(2, 2, 2, 2)]\n\n player_1_has_piece = board.does_player_have_pieces(1)\n\n assert not player_1_has_piece","def loop_until_thalom(list_of_players):\n player_that_said_thalom = 0\n someone_said_thalom = False\n check = True\n while check: #makes the loop re-read list_of_players\n for player in list_of_players:\n if player == player_that_said_thalom:\n check = False\n break\n player.launch_turn()\n player.power(list_of_players)\n if not someone_said_thalom:\n player.say_thalom()\n throwing_proposition(list_of_players)\n Card.shuffle_new_deck() #happens only if the deck's empty\n if player.thalom:\n player_that_said_thalom = player\n someone_said_thalom = True","def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_suitor_prefs()\n\n match.suitors[0].pref_names = [1, 2, 3]\n\n with pytest.raises(Exception):\n match._check_suitor_prefs()","def _check_mutual_preference(resident, hospital):\n\n return resident in hospital.prefs and hospital in resident.prefs","def test_dropping_all_correct(self):\r\n self.dropping_setup()\r\n\r\n self.dropping_homework_stage1()\r\n for name in self.hw3_names:\r\n self.submit_question_answer(name, {'2_1': 'Correct'})\r\n\r\n self.check_grade_percent(1.0)\r\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 2.0]) # Order matters\r\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 1.0])","def negative_test_for_check_local_storage(profile_obj):\n status = True\n for profile in profile_obj:\n elements_test_list = [profile.emptyChar, profile.invalidChars]\n\n \"\"\"\n Setting invalid values for: \"Name\" and \"Number of physical drivers\"\n \"\"\"\n for element in elements_test_list:\n # Fill Logical Drive form\n if __fill_logical_drive_form(element):\n # Click Logical Drive Create button\n if ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_BTN_CREATE_LOGICAL_DRIVE_FORM):\n if element == '':\n # Check warning messages\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_NAME_FORM) and __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\n logger.warn(\"At least one of the warning messages was NOT found \")\n status = False\n\n else:\n # Check warning messages\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\n logger.warn(\"Warning message NOT found\")\n status = False\n else:\n logger.warn(\"Logical Drive Form 'Button' returns an error. Please, check the test report for more details\")\n status = False\n else:\n logger.warn(\"Logical Drive Form returns an error. Please, check the test report for more details\")\n status = False\n\n # Click 'Cancel' form button\n if not ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_CANCEL_LOCAL_STORAGE_FORM):\n logger.warn(\"Cancel button element NOT found\")\n status = False\n\n return status","def test_habits_without_trackings():\n untracked_habits = analytics.habits_without_trackings(analytics.habits_table,\n [])\n assert untracked_habits == []","def test_delete_spawning_profile_not_in_list():\n tester = TestClass()\n spawning_profiles = tester.delete_spawning_profile()\n\n assert spawning_profiles\n\n assert len(spawning_profiles) == 1\n\n assert spawning_profiles[0].get_spawning_profile_name() == 'Default'","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def test_fav_6(self):\n\t\tplayer_list = [Player(\"Blake Base\", 1, 300000, 10), Player(\"Corey Catcher\", 2, 500000, 20), Player(\"Dexter Dugout\", 3, 200000, 50)]\n\t\tself.assertEqual( free_agent_vorp(player_list, 100000, 4), (0, 0, []) )","def _check_resident_unhappy(resident, hospital):\n\n return resident.matching is None or resident.prefers(\n hospital, resident.matching\n )","def death_check(self):\r\n chance = random.random()\r\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\r\n if self.unique_id in head_of_household_list:\r\n try:\r\n head_of_household_list[self.hh_id] = 0\r\n except TypeError: # head of household migrated\r\n head_of_household_list[self.past_hh_id] = 0\r\n self.model.number_of_humans -= 1\r\n if self.unique_id in labor_list:\r\n labor_list.remove(self.unique_id)\r\n if self.work_status == 1:\r\n try:\r\n num_labor_list[self.hh_id] -= 1\r\n except TypeError:\r\n num_labor_list[self.past_hh_id] -= 1\r\n if self.unique_id in former_hoh_list:\r\n try:\r\n former_hoh_list[self.hh_id] = 0\r\n except:\r\n former_hoh_list[self.past_hh_id] = 0\r\n if [self.unique_id, self.hh_id] in single_male_list:\r\n single_male_list.remove([self.unique_id, self.hh_id])\r\n if self.unique_id in married_male_list:\r\n married_male_list.remove(self.unique_id)\r\n human_death_list.append(self.unique_id)\r\n try:\r\n hh_size_list[self.hh_id] -= 1\r\n except:\r\n hh_size_list[self.past_hh_id] -= 1\r\n human_demographic_structure_list[self.age_category] -= 1\r\n\r\n self.model.schedule.remove(self)\r\n if self in self.model.grid:\r\n self.model.grid.remove_agent(self)","def test_privatize_fountain_card(self):\n g = Game()\n g.add_player(uuid4(), 'p0')\n g.add_player(uuid4(), 'p1')\n\n gs = g\n p0, p1 = gs.players\n\n latrine, insula, statue, road = cm.get_cards(['Latrine', 'Insula', 'Statue', 'Road'])\n p0.fountain_card = latrine\n\n gs_private = g.privatized_game_state_copy('p1')\n p0, p1 = gs_private.players\n\n self.assertEqual(p0.fountain_card, Card(-1))","def test_dropping_nochange(self):\r\n self.dropping_setup()\r\n self.dropping_homework_stage1()\r\n self.submit_question_answer(self.hw3_names[0], {'2_1': 'Correct'})\r\n\r\n self.assertEqual(self.score_for_hw('homework1'), [1.0, 0.0])\r\n self.assertEqual(self.score_for_hw('homework2'), [1.0, 1.0])\r\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 0.0])\r\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 1.0]) # Order matters\r\n self.check_grade_percent(0.75)","def check_stability(self):\n\n blocking_pairs = []\n for resident in self.residents:\n for hospital in self.hospitals:\n if (\n _check_mutual_preference(resident, hospital)\n and _check_resident_unhappy(resident, hospital)\n and _check_hospital_unhappy(resident, hospital)\n ):\n blocking_pairs.append((resident, hospital))\n\n self.blocking_pairs = blocking_pairs\n return not any(blocking_pairs)","def check_puzzle_unicity(puzzle_list: list) -> None:\n if len(set(puzzle_list)) != len(puzzle_list):\n raise ParsingError(\"Puzzle numbers must be unique.\")","def test_fav_4(self):\n\t\tplayer_list = [Player(\"Sam Slinger\", 1, 100000, 10), Player(\"Terry Toss\", 6, 100000, 20), Player(\"Urvine Umpire\", 2, 200000, 50), Player(\"Victor Vorp\", 10, 200000, 60), Player(\"Wesley Walk\", 9, 100000, 99)]\n\t\tself.assertEqual( free_agent_vorp(player_list, 300000, 5), (60, 300000, [\"Urvine Umpire\", \"Sam Slinger\"]) )","def test_when_opponent_all_Ds(self):\n self.responses_test([C, C, C, C], [D, D, D, D], [D, D, D], random_seed=5)","def testDefaultSettingOfOnePlayack(self):\n\t\tpolicy = FixedCountPolicy()\n\t\tself.failIf(policy.hasBeenPlayedBack)\n\t\tself.failIf(policy.isReadyForRemoval)\n\t\tpolicy.playback()\n\t\tself.failUnless(policy.hasBeenPlayedBack)\n\t\tself.failUnless(policy.isReadyForRemoval)","def game_over(self, leaderboard: list, cheating_players: list, failing_players: list) -> None:\n # Validate params\n if not isinstance(leaderboard, list):\n raise TypeError('Expected list for leaderboard!')\n\n if not isinstance(cheating_players, list):\n raise TypeError('Expected list for cheating_players!')\n\n if not isinstance(failing_players, list):\n raise TypeError('Expected list for failing_players!')\n\n return None","def verify_no_cable_errors(self):\n i = 0\n for dpid in self.dpids:\n i += 1\n labels = {'dp_id': '0x%x' % int(dpid), 'dp_name': 'faucet-%u' % i}\n self.assertEqual(\n 0, self.scrape_prometheus_var(\n var='stack_cabling_errors_total', labels=labels, default=None))\n self.assertGreater(\n self.scrape_prometheus_var(\n var='stack_probes_received_total', labels=labels), 0)","def test_when_opponent_all_Ds(self):\n self.responses_test([D, D, D, D], [D, D, D, D], [D, D, D],\n random_seed=5)","def check_pp_goals(game, key, opp_key, gsl):\n # loading events data\n game_type = get_game_type_from_season_type(game)\n game_events_src_path = os.path.join(\n CONFIG['base_data_dir'], 'game_events', str(game['season']), str(game_type), \"%d.json\" % game['game_id'])\n events_data = json.loads(open(game_events_src_path).read())\n\n pp_goals_from_events = {'home': 0, 'visitor': 0}\n\n for period in events_data:\n for event in events_data[period]:\n if event['type'] != 'goal':\n continue\n # fixing penalty shots erroneously identified as power play goals\n if event['data']['balance'] == 'PP0':\n event['data']['balance'] = 'PS'\n if event['data']['balance'].startswith('PP'):\n pp_goals_from_events[event['data']['team']] += 1\n\n pp_goals_discrepancy = False\n\n pp_goals_from_events['road'] = pp_goals_from_events['visitor']\n\n if pp_goals_from_events[key] != gsl['pp_goals']:\n pp_goals_discrepancy = True\n gsl['pp_goals'] = pp_goals_from_events[key]\n if pp_goals_from_events[opp_key] != gsl['opp_pp_goals']:\n pp_goals_discrepancy = True\n gsl['opp_pp_goals'] = pp_goals_from_events[opp_key]\n\n if pp_goals_discrepancy:\n print(\"\\t+ Found and corrected different number of pp goals retrieved from team stats and game event data\")\n\n return gsl","def __check_all_config_values_set(cfg: __Config):\n unset_values = [key for key, val in cfg.__dict__.items() if val is None]\n if len(unset_values) > 0:\n print(f\"WARNING: The following config variables have not been set: {unset_values}\")","def test_preferences_properties(self):\n with pytest.raises(AssertionError):\n self.preferences.exchange_params_by_currency_id\n with pytest.raises(AssertionError):\n self.preferences.utility_params_by_good_id","def test_fav_3(self):\n\t\tplayer_list = [Player(\"Kevin Knuckler\", 1, 100000, 10), Player(\"Larry Left\", 2, 100000, 20), Player(\"Morgan Mariner\", 2, 300000, 50), Player(\"Nick National\", 4, 200000, 70), Player(\"Oscar Outfield\", 1, 700000, 99), Player(\"Peter Pitcher\", 3, 100000, 5), Player(\"Quinn Quality\", 6, 400000, 44), Player(\"Ricky Right\", 7, 200000, 69)]\n\t\tself.assertEqual( free_agent_vorp(player_list, 1000000, 7), (213, 1000000, [\"Nick National\", \"Ricky Right\", \"Larry Left\", \"Quinn Quality\", \"Kevin Knuckler\" ]) )","def isStable(couples,mPref,wPref) -> bool:\n couples_dict = {woman:man for man,woman in couples}\n \n for m,w in couples:\n\n # w's (m's partner) rank in m's preference list\n w_rank = mPref[m].index(w)\n \n # list of all women that m prefers over w\n preferred_over_w = mPref[m][:w_rank]\n \n\n for hotter in preferred_over_w:\n\n # get each woman's current partner (in preferred list)\n hotters_man = couples_dict[hotter]\n\n # if she prefers m over her current partner then marriage is not stable\n if wPref[hotter].index(m) < wPref[hotter].index(hotters_man): \n msg = \"{}'s marriage to {} is unstable:\\n\\t\" + \\\n \"{} prefers {} > {} (his current wife)\\n\\t\" +\\\n \"{} prefers {} > {} (her current husband)\"\n print(msg.format(m, w, m, hotter, w, hotter, m, hotters_man) )\n return False\n return True","def check_death(player_list):\n\n count = 0\n remaining_players = []\n\n for cycle in player_list:\n if not cycle.is_dead():\n count += 1\n remaining_players.append(cycle.get_name())\n\n if count == 0:\n pygame.time.wait(1000)\n return True, \"None\"\n elif count == 1:\n pygame.time.wait(1000)\n return True, remaining_players[0]\n\n return False, remaining_players","def test_inputs_reviewer_prefs(\n resident_names, hospital_names, capacities, seed\n):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_reviewer_prefs()\n\n match.reviewers[0].pref_names.pop()\n\n with pytest.raises(Exception):\n match._check_reviewer_prefs()","def test_check_for_unacceptable_matches_residents(game):\n\n resident = game.residents[0]\n hospital = Hospital(name=\"foo\", capacity=1)\n resident.matching = hospital\n\n issues = game._check_for_unacceptable_matches(\"residents\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(resident.name)\n assert issue.endswith(f\"{resident.prefs}.\")\n assert hospital.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test002_league_commish_settings_no_membership(self):\n\t\tresponse = self.client.get(self.test_url, follow=True)\n\t\tself.assertFalse(is_on_page(response, 'League: league_name1'))\n\t\tself.assertTrue(is_on_page(response, 'Fantasy Web - Home'))","def test_set_player_names(self):\n\n # Setup new games and attempt to set their players' names\n valid_players = [\n [\"Bob\", \"Sam\", \"Cal\", \"Kris\"],\n [\"Player 1\", \"Player 2\", \"Player 3\", \"Player 4\", \"Player 5\"],\n [\"Bot\"],\n [\"P1\", \"P2\", \"P3\"],\n ]\n for players in valid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(len(players))\n game.set_player_names(players)\n self.assertEqual(game.player_names, players, msg=\"The game's player names were not correctly set with: \" + str(players))\n\n # Make sure that the new game state is corectly set\n self.assertEqual(game.state.name, \"start_game\", msg=\"The game's state was not correctly set after setting the player names.\")\n\n # Try to set invalid players\n invalid_players = [\n None,\n [None, None],\n [123, 456, 789],\n [\"Bob\", \"Sam\", 123],\n [\"John\", \"\"],\n ]\n for players in invalid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(len(players or \"1\"))\n success = False\n try:\n game.set_player_names(players)\n except InvalidGamePlayerNames:\n success = True\n self.assertTrue(success, msg=\"The following invalid series of player names was able to be set: \" + str(players))\n\n # Test the case where the number of players given is not the same as the number of names given\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(2)\n success = False\n try:\n game.set_player_names([\"P1\", \"P2\", \"P3\"])\n except InvalidGamePlayerNames:\n success = True\n self.assertTrue(success, msg=\"A number of player names unequal to the number to the number of players in the game was able to be set.\")\n\n # Try to reset the names of the players to throw an error\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(3)\n game.set_player_names([\"P1\", \"P2\", \"P3\"])\n success = False\n try:\n game.set_player_names([\"P01\", \"P02\", \"P03\"])\n except InvalidGameMethodOrder:\n success = True\n self.assertTrue(success, msg=\"The names of the players was incorrectly able to be reset.\")","def check_win(players: List[Player]) -> Tuple[bool, Optional[Player]]:\n total_players = len(players)\n for player in players:\n if player.influence == 0:\n total_players -= 1\n if total_players == 1:\n for player in players:\n if player.influence >0:\n return True, player\n return False, None","def testOddPlayersWithBye():\n deleteMatches()\n deletePlayers()\n registerPlayer(\"Alien 1\")\n registerPlayer(\"Alien 2\")\n registerPlayer(\"Alien 3\")\n standings = playerStandings()\n [id1, id2, id3] = [row[0] for row in standings]\n reportMatch(id1, id2)\n reportMatch(id3, id3, False, True)\n reportMatch(id2, id3)\n reportMatch(id1, id1, False, True)\n\n # id1 and id3 have one bye each. In this round, bye should be given to id2\n pairings = swissPairings()\n for pairing in pairings:\n if pairing[0]!=id2 and pairing[0]==pairing[2]:\n raise ValueError(\n \"swissPairings() should not award bye to a player who already\"\n \"has a bye.\"\n )\n if pairing[0]==id2 and pairing[2]!=id2:\n raise ValueError(\n \"swissPairings() has to award a bye when there is an odd number\"\n \"of players.\"\n )\n print \"2. Bye is not given to a player who already has a bye.\"","def test_play_no_gain(self):\n self.card = self.g[\"Festival\"].remove()\n self.plr.piles[Piles.HAND].set(\"Duchy\")\n self.plr.add_card(self.card, Piles.HAND)\n self.plr.favors.set(2)\n self.plr.test_input = [\"No\"]\n self.plr.play_card(self.card)\n self.assertEqual(self.plr.favors.get(), 2)\n self.assertEqual(self.plr.piles[Piles.HAND].size(), 1)","def test_vote_badpklist(self):\r\n mock_module = CHModuleFactory.create()\r\n json_in = {'answer': '24.0', 'hint': '0', 'pk_list': json.dumps([['24.0', 0], ['24.0', 12]])}\r\n hint_and_votes = mock_module.tally_vote(json_in)['hint_and_votes']\r\n self.assertTrue(['Best hint', 41] in hint_and_votes)\r\n self.assertTrue(len(hint_and_votes) == 1)","def test_compatible_data_presence(allparams):\n compatible_data_keys_set = set(['puf', 'cps'])\n\n # Nested function used only in test_compatible_data_presence\n def valid_compatible_data(compatible_data):\n \"\"\"\n Return True if compatible_data is a valid dictionary;\n otherwise return False\n \"\"\"\n if not isinstance(compatible_data, dict):\n return False\n if set(compatible_data.keys()) != compatible_data_keys_set:\n return False\n for key in compatible_data:\n boolean = (compatible_data[key] is True or\n compatible_data[key] is False)\n if not boolean:\n return False\n return True\n\n # Main logic of test_compatible_data_presence function\n problem_pnames = list()\n for pname in allparams:\n if 'compatible_data' in allparams[pname]:\n compatible_data = allparams[pname]['compatible_data']\n else:\n compatible_data = None\n if not valid_compatible_data(compatible_data):\n problem_pnames.append(pname)\n if problem_pnames:\n msg = '{} has no or invalid compatible_data field'\n for pname in problem_pnames:\n print(msg.format(pname))\n assert 'list of problem_pnames' == 'empty list'","def test_delete_vehicle_profile_not_in_list():\n tester = TestClass()\n vehicle_profiles = tester.delete_vehicle_profile()\n\n assert vehicle_profiles\n assert len(vehicle_profiles) == 1\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'","def check_for_list(check):","def second_phase(players):\n\n player = next(p for p in players if len(p.prefs) > 1)\n while True:\n cycle = locate_all_or_nothing_cycle(player)\n pairs = get_pairs_to_delete(cycle)\n for player, other in pairs:\n _delete_pair(player, other)\n\n if any(p.prefs == [] for p in players):\n warnings.warn(\n NoStableMatchingWarning(\n \"The following players have emptied their preferences: \"\n f\"{[p for p in players if not p.prefs]}\"\n )\n )\n break\n\n try:\n player = next(p for p in players if len(p.prefs) > 1)\n except StopIteration:\n break\n\n for player in players:\n player._unmatch()\n if player.prefs:\n player._match(player.get_favourite())\n\n return players","def test_check_for_oversubscribed_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n supervisor.matching = range(supervisor.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.capacity))\n assert str(supervisor.matching) in e","def test_check_for_oversubscribed_projects(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n project = game.projects[0]\n project.matching = range(project.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(project.name)\n assert error.endswith(str(project.capacity))\n assert str(project.matching) in e","def _find_homeless_mps(self):\n mps = Person.objects.filter(\n active=True,\n house__name=HOUSE_OF_COMMONS,\n constituency=None,\n )\n\n self.stdout('MPs with missing constituency:')\n for mp in mps:\n self.stdout(f' [{mp.parliamentdotuk}] {mp.name} has no constituency')","def gameOver():\n if len(p1)==0 and len(p1winnings)==0:\n return True\n elif len(p2)==0 and len(p2winnings)==0:\n return True\n return False","def test_check_stability():\n\n residents = [Player(\"A\", [1, 2]), Player(\"B\", [2]), Player(\"C\", [2, 1])]\n hospitals = [Player(1, [\"C\", \"A\"], 3), Player(2, [\"A\", \"B\", \"C\"], 3)]\n match = HospitalResident(residents, hospitals)\n\n matching = match.solve()\n assert match.check_stability()\n\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\n matching[P_1] = [P_C]\n matching[P_2] = [P_A, P_B]\n\n assert not match.check_stability()","def stable_roommates(players):\n\n players = first_phase(players)\n\n if any(p.prefs == [] for p in players):\n warnings.warn(\n NoStableMatchingWarning(\n \"The following players have been rejected by all others, \"\n \"emptying their preference list: \"\n f\"{[p for p in players if not p.prefs]}\"\n )\n )\n\n if any(len(p.prefs) > 1 for p in players):\n players = second_phase(players)\n\n return {player: player.matching for player in players}","def test_filter_not_available_plugins(plugin_dialog_constructor):\n item = plugin_dialog_constructor.available_list.item(0)\n widget = plugin_dialog_constructor.available_list.itemWidget(item)\n if widget:\n assert not widget.action_button.isEnabled()\n assert widget.warning_tooltip.isVisible()\n\n item = plugin_dialog_constructor.available_list.item(1)\n widget = plugin_dialog_constructor.available_list.itemWidget(item)\n assert widget.action_button.isEnabled()\n assert not widget.warning_tooltip.isVisible()","def game_over(players):\n active_players = players_with_decks(players)\n if not active_players or len(active_players) == 1:\n return True\n return False","def test_for_empty_list(self):\n emptylist = []\n self.assertEqual(self.place.amenity_ids, emptylist)","def test_half_life_unstable_isotopes():\n for isotope in data_about_isotopes:\n if (\n \"half_life\" not in data_about_isotopes[isotope]\n and not data_about_isotopes[isotope]\n ):\n with pytest.raises(MissingParticleDataError):\n half_life(isotope)","def test_lpdaac_good(self):\n self.assertIsNone(api.inventory.check(self.lpdaac_order_good))","def test_given_none(self):\n llists = None\n result = solution(llists)\n self.assertEqual(None, result)","def test_not_found_pair(self):\n self.assertTrue(\"No matches found\", show_players_sumheigh_is_input(100))","def test_can_detect_when_player_has_pieces(self):\n board = Board()\n board.pieces = [Piece(0, 0, 0, 0), Piece(2, 2, 2, 2)]\n\n player_2_has_piece = board.does_player_have_pieces(2)\n\n assert player_2_has_piece","def test_check_ess_settings(self):\n ess_settings1 = {'gaussian': [self.servers[0]], 'molpro': [self.servers[1], self.servers[0]],\n 'qchem': [self.servers[0]]}\n ess_settings2 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\n ess_settings3 = {'gaussian': self.servers[0], 'molpro': [self.servers[1], self.servers[0]],\n 'qchem': self.servers[0]}\n ess_settings4 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\n ess_settings5 = {'gaussian': 'local', 'molpro': self.servers[1], 'qchem': self.servers[0]}\n\n ess_settings1 = check_ess_settings(ess_settings1)\n ess_settings2 = check_ess_settings(ess_settings2)\n ess_settings3 = check_ess_settings(ess_settings3)\n ess_settings4 = check_ess_settings(ess_settings4)\n ess_settings5 = check_ess_settings(ess_settings5)\n\n ess_list = [ess_settings1, ess_settings2, ess_settings3, ess_settings4, ess_settings5]\n\n for ess in ess_list:\n for soft, server_list in ess.items():\n self.assertTrue(soft in ['gaussian', 'molpro', 'qchem'])\n self.assertIsInstance(server_list, list)\n\n with self.assertRaises(SettingsError):\n ess_settings6 = {'nosoft': ['server1']}\n check_ess_settings(ess_settings6)\n with self.assertRaises(SettingsError):\n ess_settings7 = {'gaussian': ['noserver']}\n check_ess_settings(ess_settings7)","def test_fav_7(self):\n\t\tplayer_list = [Player(\"Evan Even\", 1, 100000, 10), Player(\"Favian Fly\", 2, 100000, 20), Player(\"Guy Ground\", 2, 300000, 50), Player(\"Hans Hand\", 4, 200000, 70), Player(\"Igor Infield\", 1, 700000, 99), Player(\"Jerry Jump\", 3, 100000, 5), Player(\"Kenny K\", 6, 400000, 44), Player(\"Louis Lefty\", 7, 200000, 69), Player(\"Manny Mound\", 9, 500000, 107), Player(\"Negan Nubber\", 12, 300000, 54), Player(\"Omar Oppo\", 6, 100000, 44), Player(\"Perry Park\", 5, 300000, 63), Player(\"Quaid Quit\", 11, 200000, 44)]\n\t\tself.assertEqual( free_agent_vorp(player_list, 2500000, 13), (486, 2100000, [\"Omar Oppo\", \"Hans Hand\", \"Louis Lefty\", \"Quaid Quit\", \"Manny Mound\", \"Perry Park\", \"Favian Fly\", \"Negan Nubber\", \"Evan Even\", \"Jerry Jump\"]) )","def test_fav_2(self):\n\t\tplayer_list = [Player(\"Eric Eephus\", 1, 200000, 10), Player(\"Fred First\", 2, 100000, 20), Player(\"Gary Glove\", 2, 200000, 50), Player(\"Henry Hitter\", 4, 300000, 70), Player(\"Ian Inning\", 1, 100000, 30), Player(\"Jack Junk\", 3, 200000, 5)]\n\t\tself.assertEqual( free_agent_vorp(player_list, 800000, 4), (155, 800000, [\"Ian Inning\", \"Gary Glove\", \"Henry Hitter\", \"Jack Junk\"]) )","def test_check_full_house_false(self):\n not_full_house_fixtures = [[1, 1, 1, 1, 2],\n [2, 1, 1, 1, 1],\n [1, 1, 2, 2, 3],\n [1, 2, 3, 4, 5],\n ]\n\n for fixture in not_full_house_fixtures:\n score = self.roll.check_full_house(fixture)\n\n self.assertNotEqual(score, 25)\n self.assertEqual(score, 0)\n self.assertEqual(len(fixture), 5)","def testNoSpecialties(self):\n self.failUnlessEqual(self.person.getSpecialties(), [])","def verify_valid_game(self, lotto_game:List[int]):\n is_valid = True;\n lotto_game_temp = []\n for number in lotto_game:\n if number not in lotto_game_temp:\n lotto_game_temp.append(number)\n else:\n print(number, lotto_game, lotto_game_temp)\n is_valid = False\n raise InvalidLottoGame\n \n lotto_game_temp_length = len(lotto_game_temp)\n if lotto_game_temp_length != LOTTO_GAME_LENGTH and lotto_game_temp_length != LOTTO_GAME_WITH_BONUS_LENGTH:\n print(lotto_game_temp)\n is_valid = False\n raise InvalidLottoGame\n\n return is_valid","def test_decision():\n player = sequence_threshold.Player(threshold=10)\n\n player.cards = SortedSet()\n assert player.play(20, 11) == True\n assert player.play(20, 10) == True\n assert player.play(20, 9) == False\n\n player.cards = SortedSet([30])\n assert player.play(20, 11) == True\n assert player.play(20, 10) == True\n assert player.play(20, 9) == False\n assert player.play(31, 1) == True\n assert player.play(29, 1) == True\n assert player.play(32, 1) == False\n with pytest.raises(Exception):\n player.play(30, 1)\n\n player.threshold = 5\n\n player.cards = SortedSet()\n assert player.play(20, 16) == True\n assert player.play(20, 15) == True\n assert player.play(20, 14) == False\n\n player.cards = SortedSet([30])\n assert player.play(20, 16) == True\n assert player.play(20, 15) == True\n assert player.play(20, 14) == False\n assert player.play(31, 1) == True\n assert player.play(29, 1) == True\n assert player.play(32, 1) == False\n with pytest.raises(Exception):\n player.play(30, 1)","def throwing_proposition(list_of_players):\n list_of_players_name = [player.name.lower() for player in list_of_players]\n while True:\n print(\"\\nIf you want to throw a card over {},\".format(Card.rejected_cards[-1].name))\n print(\"Write your name and press enter\")\n print(\"If everyone's done enter 'END' to continue the round\")\n user_entry = input().lower()\n if user_entry == 'end':\n break\n if user_entry in list_of_players_name:\n get_player_index = list_of_players_name.index(user_entry)\n player = list_of_players[get_player_index]\n player.throw()\n print(\"Anyone else?\")\n else:\n print(\"Incorrect entry\")","def test_statuses_exist(self):\n\n assert hasattr(Partner, 'AVAILABLE')\n assert hasattr(Partner, 'NOT_AVAILABLE')\n assert hasattr(Partner, 'WAITLIST')\n\n assert hasattr(Partner, 'STATUS_CHOICES')\n\n assert len(Partner.STATUS_CHOICES) == 3\n\n database_statuses = [x[0] for x in Partner.STATUS_CHOICES]\n\n assert Partner.AVAILABLE in database_statuses\n assert Partner.NOT_AVAILABLE in database_statuses\n assert Partner.WAITLIST in database_statuses","def test_get_empty_vehicle_profile_list():\n tester = TestClass()\n vehicle_profiles = tester.get_empty_vehicle_profile_list()\n assert len(vehicle_profiles) == 1\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'","def enough_players():\n return True","def test_check_inputs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n with warnings.catch_warnings(record=True):\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.students == game._all_students\n assert game.projects == game._all_projects\n assert game.supervisors == game._all_supervisors","def check_cards_eligibility(self):\n for c in self.hand:\n c.check_actions(self)\n for c in self.phand:\n c.check_actions(self)\n for c in self.discard:\n c.check_actions(self)\n for c in self.active_player.phand:\n c.check_actions(self)\n for c in self.active_player.hand:\n c.check_actions(self)\n for c in self.active_player.discard:\n c.check_actions(self)\n for c in self.played_user_cards:\n c.check_actions(self)\n if ACTION_KEEP in self.actions:\n for p in self.players:\n for c in p.phand:\n c.check_actions(self)\n for c in p.hand:\n c.check_actions(self)\n for c in p.discard:\n c.check_actions(self)","def forwardcheck(var, val, assignment, user_dict):\n\t\tif curr_domains:\n\t\t\tfor (meal, restaurant) in curr_deleted[var]:\n\t\t\t\tcurr_domains[meal].append(restaurant)\n\t\t\tcurr_deleted[var] = []\n\n\t\t\tfor meal in neighbors[var]:\n\t\t\t\tif meal not in assignment:\n\t\t\t\t\tfor restaurant in curr_domains[meal][:]:\n\t\t\t\t\t\tnum_cats = count_categories(assignment.values())\n\t\t\t\t\t\tif not constraints_match(num_cats, user_dict):\n\t\t\t\t\t\t#if not user_solution_checker(user_dict, meal, restaurant, assignment):\n\t\t\t\t\t\t\tcurr_domains[meal].remove(restaurant)\n\t\t\t\t\t\t\tcurr_deleted[var].append((meal, restaurant))","def test_correct_p_values_empty(self):\r\n exp = []\r\n obs = self.mc._correct_p_values([])\r\n assert_almost_equal(obs, exp)","def test_listEmpty(self):\n store = Store()\n self.assertSuccessStatus(self._makeConfig(store), [\"list\"])\n self.assertIn(\"There are no ports configured.\", sys.stdout.getvalue())","def test_missing_tracks(self):\n\n # Taylor Swift doesn't have her albums on Spotify\n expected_missing = [track_id for track_id, track in self.ilibrary.tracks.iteritems()\n if track.artists[0] == 'Taylor Swift']\n\n for missing_id in expected_missing:\n self.assertNotIn(missing_id, [track.i_id for track in self.tracks])","def notify_no_win(self):\n\n board_set = set()\n for row in self.board:\n board_set.update(row)\n if \"\" not in board_set:\n print \"Game is over. No one won.\"\n return True","def check_items(self, items):\n for item in items:\n if item['user'] in self.required_list and item['user'] not in self.approved_required_list:\n if item['result'] is True or item['result'] == 'approved':\n logger.info(\"%s approved\", item['user'])\n self.approved_required_list.append(item['user'])\n else:\n logger.info(\"%s declined\", item['user'])\n error_msg = \"Since {} is a required member for approval, this approval step fails. \".format(item['user'])\n logger.error(error_msg)\n self.exit(rc=2, detail=error_msg)\n if item['user'] in self.optional_list and item['user'] not in self.approved_optional_list:\n if item['result'] is True or item['result'] == 'approved':\n logger.info(\"%s approved\", item['user'])\n if item['user'] in self.declined_optional_list:\n self.declined_optional_list.remove(item['user'])\n self.approved_optional_list.append(item['user'])\n else:\n logger.info(\"%s declined\", item['user'])\n self.declined_optional_list.append(item['user'])\n if len(self.declined_optional_list) >= (len(self.optional_list) - self.number_optional):\n error_msg = \"Not be able to fulfill requirement that {} optional approvals, since {} declined request.\".format(\n self.number_optional, self.declined_optional_list)\n logger.error(error_msg)\n self.exit(rc=2, detail=error_msg)\n\n if len(self.approved_required_list) >= len(self.required_list) \\\n and len(self.approved_optional_list) >= self.number_optional:\n logger.info(\"Approval requirements are fully met. Exit gracefully.\")\n self.exit(0)","def test_remove_spawning_profile_from_intersection():\n tester = TestClass()\n intersections = tester.add_spawning_profile_to_intersection()\n\n for i in intersections:\n if len(i.get_spawning_profile_list()) != 0:\n assert True\n\n for spawn in i.get_spawning_profile_list():\n if spawn.get_spawning_profile_name() == 'Default':\n assert True\n break\n\n tester.delete_spawning_profile_from_intersection()\n\n for i in intersections:\n if len(i.get_spawning_profile_list()) == 0:\n assert True","def test003_league_commish_settings_membership_no_commish(self):\n\t\tadd_league_member(self.user, self.league, \"team1\")\n\t\tresponse = self.client.get(self.test_url, follow=True)\n\n\t\tself.assertFalse(is_on_page(response, 'Commish Settings'))\n\t\tself.assertTrue(is_on_page(response, 'League: league_name1'))\n\t\tself.assertTrue(is_on_page(response, 'Standings'))","def test_tally_no_candidates(self):\n self.init_elect_types()\n\n userA = models.User(\n name = \"UserA\",\n email = \"userA@eLect.com\",\n password = \"asdf\")\n\n session.add(userA)\n session.commit()\n\n electionA = models.Election(\n title = \"Election A\",\n admin_id = userA.id)\n\n session.add(electionA)\n session.commit()\n\n raceA = models.Race(\n title = \"Race A\",\n election_id = electionA.id\n )\n\n session.add(raceA)\n session.commit()\n\n with self.assertRaises(NoCandidates):\n self.wta.check_race(raceA.id)\n\n with self.assertRaises(NoCandidates):\n self.proportional.check_race(raceA.id)\n\n with self.assertRaises(NoCandidates):\n self.schulze.check_race(raceA.id)","def test_not_notified(scraper):\n\n assert scraper.scrape_profile('https://www.punters.com.au/') is None","def test_problem_list(self):\r\n\r\n # Initialize peer grading module.\r\n peer_grading = self.get_module_from_location(self.problem_location)\r\n\r\n # Ensure that it cannot find any peer grading.\r\n html = peer_grading.peer_grading()\r\n self.assertNotIn(\"Peer-Graded\", html)\r\n\r\n # Swap for our mock class, which will find peer grading.\r\n peer_grading.peer_gs = MockPeerGradingServiceProblemList()\r\n html = peer_grading.peer_grading()\r\n self.assertIn(\"Peer-Graded\", html)","def confirm_choices(self):\n stack = self.stack_choice.get()\n # valid list below created January 2016\n valid_list = ['BMDHL', 'GPHP', 'GPHL', 'IDBLH', 'IDD']\n if stack not in valid_list:\n showerror('Under Development',\n 'Those stages are not yet available, program will exit')\n close_quit()\n else:\n self.config_window.destroy()","def test_delete_driver_profile_not_in_list():\n tester = TestClass()\n driver_profiles = tester.delete_driver_profile()\n\n assert driver_profiles\n assert len(driver_profiles) == 1\n assert driver_profiles[0].get_driver_profile_name() == 'Default'","def test_teacher_reset_result_with_arg_positive():\n expected = []\n Teacher.reset_results(oop_hw)\n assert Teacher.homework_done[oop_hw] == expected","def test_fav_5(self):\n\t\tplayer_list = [Player(\"Xavier X\", 1, 100000, 10), Player(\"Zane Zero\", 2, 100000, 20), Player(\"Alex Athlete\", 3, 200000, 50)]\n\t\tself.assertEqual( free_agent_vorp(player_list, 900000, 3), (80, 400000, [\"Alex Athlete\", \"Zane Zero\", \"Xavier X\"]) )"],"string":"[\n \"def test_check_inputs_hospital_prefs_all_reciprocated(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n resident._forget(hospital)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert resident not in hospital.prefs\",\n \"def test_check_inputs_hospital_prefs_all_residents(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert hospital.prefs == []\",\n \"def test_check_inputs_resident_prefs_all_nonempty(game):\\n\\n resident = game.residents[0]\\n resident.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n assert resident.name in str(record[0].message)\\n\\n if game.clean:\\n assert resident not in game.residents\",\n \"def test_check_inputs_hospital_reciprocated_all_prefs(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n hospital._forget(resident)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"hospitals\\\", \\\"residents\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert hospital not in resident.prefs\",\n \"def test_check_inputs_resident_prefs_all_hospitals(game):\\n\\n resident = game.residents[0]\\n resident.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert resident.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert resident.prefs == []\",\n \"def _check_hospital_prefs(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n residents_that_ranked = [\\n res for res in self.residents if hospital in res.prefs\\n ]\\n if set(hospital.prefs) != set(residents_that_ranked):\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has not ranked all the residents that \\\"\\n f\\\"ranked it: {set(hospital.prefs)} != \\\"\\n f\\\"{set(residents_that_ranked)}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def _check_hospital_matching(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n for resident in hospital.matching:\\n if resident not in hospital.prefs:\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has {resident} in their matching but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{hospital.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def test_check_inputs_hospital_capacity(game):\\n\\n hospital = game.hospitals[0]\\n capacity = hospital.capacity\\n hospital.capacity = 0\\n\\n assert hospital._original_capacity == capacity\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_capacity(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def test_check_inputs_supervisor_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n supervisor.prefs.remove(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"supervisors\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert supervisor.name in message\\n\\n if clean:\\n assert supervisor not in student.prefs\\n assert all(\\n project not in student.prefs for project in supervisor.projects\\n )\",\n \"def _check_resident_prefs(self):\\n\\n errors = []\\n for resident in self.residents:\\n if not set(resident.prefs).issubset(set(self.hospitals)):\\n errors.append(\\n ValueError(\\n f\\\"{resident} has ranked a non-hospital: \\\"\\n f\\\"{set(resident.prefs)} != {set(self.hospitals)}\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_check_inputs_project_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n student = project.prefs[0]\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"projects\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(project.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in project.prefs\",\n \"def test_check_inputs_project_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n student = project.prefs[0]\\n project._forget(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"projects\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert project.name in message\\n\\n if clean:\\n assert project not in student.prefs\",\n \"def test_check_inputs_supervisor_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n projects = supervisor.projects\\n for project in student.prefs:\\n if project in projects:\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"supervisors\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(supervisor.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in supervisor.prefs\\n assert all(\\n student not in project.prefs for project in supervisor.projects\\n )\",\n \"def _check_hospital_unhappy(resident, hospital):\\n\\n return len(hospital.matching) < hospital.capacity or any(\\n [hospital.prefers(resident, match) for match in hospital.matching]\\n )\",\n \"def test_can_detect_when_player_has_no_pieces(self):\\n board = Board()\\n board.pieces = [Piece(0, 0, 0, 0), Piece(2, 2, 2, 2)]\\n\\n player_1_has_piece = board.does_player_have_pieces(1)\\n\\n assert not player_1_has_piece\",\n \"def loop_until_thalom(list_of_players):\\n player_that_said_thalom = 0\\n someone_said_thalom = False\\n check = True\\n while check: #makes the loop re-read list_of_players\\n for player in list_of_players:\\n if player == player_that_said_thalom:\\n check = False\\n break\\n player.launch_turn()\\n player.power(list_of_players)\\n if not someone_said_thalom:\\n player.say_thalom()\\n throwing_proposition(list_of_players)\\n Card.shuffle_new_deck() #happens only if the deck's empty\\n if player.thalom:\\n player_that_said_thalom = player\\n someone_said_thalom = True\",\n \"def test_check_inputs(game):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.residents == game._all_residents\\n assert game.hospitals == game._all_hospitals\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_suitor_prefs()\\n\\n match.suitors[0].pref_names = [1, 2, 3]\\n\\n with pytest.raises(Exception):\\n match._check_suitor_prefs()\",\n \"def _check_mutual_preference(resident, hospital):\\n\\n return resident in hospital.prefs and hospital in resident.prefs\",\n \"def test_dropping_all_correct(self):\\r\\n self.dropping_setup()\\r\\n\\r\\n self.dropping_homework_stage1()\\r\\n for name in self.hw3_names:\\r\\n self.submit_question_answer(name, {'2_1': 'Correct'})\\r\\n\\r\\n self.check_grade_percent(1.0)\\r\\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 2.0]) # Order matters\\r\\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 1.0])\",\n \"def negative_test_for_check_local_storage(profile_obj):\\n status = True\\n for profile in profile_obj:\\n elements_test_list = [profile.emptyChar, profile.invalidChars]\\n\\n \\\"\\\"\\\"\\n Setting invalid values for: \\\"Name\\\" and \\\"Number of physical drivers\\\"\\n \\\"\\\"\\\"\\n for element in elements_test_list:\\n # Fill Logical Drive form\\n if __fill_logical_drive_form(element):\\n # Click Logical Drive Create button\\n if ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_BTN_CREATE_LOGICAL_DRIVE_FORM):\\n if element == '':\\n # Check warning messages\\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_NAME_FORM) and __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\\n logger.warn(\\\"At least one of the warning messages was NOT found \\\")\\n status = False\\n\\n else:\\n # Check warning messages\\n if not __check_warning_message_form(FusionServerProfilesPage.ID_WARNING_MESSAGE_PHYSICAL_DRIVES_FORM):\\n logger.warn(\\\"Warning message NOT found\\\")\\n status = False\\n else:\\n logger.warn(\\\"Logical Drive Form 'Button' returns an error. Please, check the test report for more details\\\")\\n status = False\\n else:\\n logger.warn(\\\"Logical Drive Form returns an error. Please, check the test report for more details\\\")\\n status = False\\n\\n # Click 'Cancel' form button\\n if not ui_lib.wait_for_element_and_click(FusionServerProfilesPage.ID_CANCEL_LOCAL_STORAGE_FORM):\\n logger.warn(\\\"Cancel button element NOT found\\\")\\n status = False\\n\\n return status\",\n \"def test_habits_without_trackings():\\n untracked_habits = analytics.habits_without_trackings(analytics.habits_table,\\n [])\\n assert untracked_habits == []\",\n \"def test_delete_spawning_profile_not_in_list():\\n tester = TestClass()\\n spawning_profiles = tester.delete_spawning_profile()\\n\\n assert spawning_profiles\\n\\n assert len(spawning_profiles) == 1\\n\\n assert spawning_profiles[0].get_spawning_profile_name() == 'Default'\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def test_fav_6(self):\\n\\t\\tplayer_list = [Player(\\\"Blake Base\\\", 1, 300000, 10), Player(\\\"Corey Catcher\\\", 2, 500000, 20), Player(\\\"Dexter Dugout\\\", 3, 200000, 50)]\\n\\t\\tself.assertEqual( free_agent_vorp(player_list, 100000, 4), (0, 0, []) )\",\n \"def _check_resident_unhappy(resident, hospital):\\n\\n return resident.matching is None or resident.prefers(\\n hospital, resident.matching\\n )\",\n \"def death_check(self):\\r\\n chance = random.random()\\r\\n if decimal.Decimal(chance) < decimal.Decimal(self.death_rate):\\r\\n if self.unique_id in head_of_household_list:\\r\\n try:\\r\\n head_of_household_list[self.hh_id] = 0\\r\\n except TypeError: # head of household migrated\\r\\n head_of_household_list[self.past_hh_id] = 0\\r\\n self.model.number_of_humans -= 1\\r\\n if self.unique_id in labor_list:\\r\\n labor_list.remove(self.unique_id)\\r\\n if self.work_status == 1:\\r\\n try:\\r\\n num_labor_list[self.hh_id] -= 1\\r\\n except TypeError:\\r\\n num_labor_list[self.past_hh_id] -= 1\\r\\n if self.unique_id in former_hoh_list:\\r\\n try:\\r\\n former_hoh_list[self.hh_id] = 0\\r\\n except:\\r\\n former_hoh_list[self.past_hh_id] = 0\\r\\n if [self.unique_id, self.hh_id] in single_male_list:\\r\\n single_male_list.remove([self.unique_id, self.hh_id])\\r\\n if self.unique_id in married_male_list:\\r\\n married_male_list.remove(self.unique_id)\\r\\n human_death_list.append(self.unique_id)\\r\\n try:\\r\\n hh_size_list[self.hh_id] -= 1\\r\\n except:\\r\\n hh_size_list[self.past_hh_id] -= 1\\r\\n human_demographic_structure_list[self.age_category] -= 1\\r\\n\\r\\n self.model.schedule.remove(self)\\r\\n if self in self.model.grid:\\r\\n self.model.grid.remove_agent(self)\",\n \"def test_privatize_fountain_card(self):\\n g = Game()\\n g.add_player(uuid4(), 'p0')\\n g.add_player(uuid4(), 'p1')\\n\\n gs = g\\n p0, p1 = gs.players\\n\\n latrine, insula, statue, road = cm.get_cards(['Latrine', 'Insula', 'Statue', 'Road'])\\n p0.fountain_card = latrine\\n\\n gs_private = g.privatized_game_state_copy('p1')\\n p0, p1 = gs_private.players\\n\\n self.assertEqual(p0.fountain_card, Card(-1))\",\n \"def test_dropping_nochange(self):\\r\\n self.dropping_setup()\\r\\n self.dropping_homework_stage1()\\r\\n self.submit_question_answer(self.hw3_names[0], {'2_1': 'Correct'})\\r\\n\\r\\n self.assertEqual(self.score_for_hw('homework1'), [1.0, 0.0])\\r\\n self.assertEqual(self.score_for_hw('homework2'), [1.0, 1.0])\\r\\n self.assertEqual(self.score_for_hw('homework3'), [1.0, 0.0])\\r\\n self.assertEqual(self.earned_hw_scores(), [1.0, 2.0, 1.0]) # Order matters\\r\\n self.check_grade_percent(0.75)\",\n \"def check_stability(self):\\n\\n blocking_pairs = []\\n for resident in self.residents:\\n for hospital in self.hospitals:\\n if (\\n _check_mutual_preference(resident, hospital)\\n and _check_resident_unhappy(resident, hospital)\\n and _check_hospital_unhappy(resident, hospital)\\n ):\\n blocking_pairs.append((resident, hospital))\\n\\n self.blocking_pairs = blocking_pairs\\n return not any(blocking_pairs)\",\n \"def check_puzzle_unicity(puzzle_list: list) -> None:\\n if len(set(puzzle_list)) != len(puzzle_list):\\n raise ParsingError(\\\"Puzzle numbers must be unique.\\\")\",\n \"def test_fav_4(self):\\n\\t\\tplayer_list = [Player(\\\"Sam Slinger\\\", 1, 100000, 10), Player(\\\"Terry Toss\\\", 6, 100000, 20), Player(\\\"Urvine Umpire\\\", 2, 200000, 50), Player(\\\"Victor Vorp\\\", 10, 200000, 60), Player(\\\"Wesley Walk\\\", 9, 100000, 99)]\\n\\t\\tself.assertEqual( free_agent_vorp(player_list, 300000, 5), (60, 300000, [\\\"Urvine Umpire\\\", \\\"Sam Slinger\\\"]) )\",\n \"def test_when_opponent_all_Ds(self):\\n self.responses_test([C, C, C, C], [D, D, D, D], [D, D, D], random_seed=5)\",\n \"def testDefaultSettingOfOnePlayack(self):\\n\\t\\tpolicy = FixedCountPolicy()\\n\\t\\tself.failIf(policy.hasBeenPlayedBack)\\n\\t\\tself.failIf(policy.isReadyForRemoval)\\n\\t\\tpolicy.playback()\\n\\t\\tself.failUnless(policy.hasBeenPlayedBack)\\n\\t\\tself.failUnless(policy.isReadyForRemoval)\",\n \"def game_over(self, leaderboard: list, cheating_players: list, failing_players: list) -> None:\\n # Validate params\\n if not isinstance(leaderboard, list):\\n raise TypeError('Expected list for leaderboard!')\\n\\n if not isinstance(cheating_players, list):\\n raise TypeError('Expected list for cheating_players!')\\n\\n if not isinstance(failing_players, list):\\n raise TypeError('Expected list for failing_players!')\\n\\n return None\",\n \"def verify_no_cable_errors(self):\\n i = 0\\n for dpid in self.dpids:\\n i += 1\\n labels = {'dp_id': '0x%x' % int(dpid), 'dp_name': 'faucet-%u' % i}\\n self.assertEqual(\\n 0, self.scrape_prometheus_var(\\n var='stack_cabling_errors_total', labels=labels, default=None))\\n self.assertGreater(\\n self.scrape_prometheus_var(\\n var='stack_probes_received_total', labels=labels), 0)\",\n \"def test_when_opponent_all_Ds(self):\\n self.responses_test([D, D, D, D], [D, D, D, D], [D, D, D],\\n random_seed=5)\",\n \"def check_pp_goals(game, key, opp_key, gsl):\\n # loading events data\\n game_type = get_game_type_from_season_type(game)\\n game_events_src_path = os.path.join(\\n CONFIG['base_data_dir'], 'game_events', str(game['season']), str(game_type), \\\"%d.json\\\" % game['game_id'])\\n events_data = json.loads(open(game_events_src_path).read())\\n\\n pp_goals_from_events = {'home': 0, 'visitor': 0}\\n\\n for period in events_data:\\n for event in events_data[period]:\\n if event['type'] != 'goal':\\n continue\\n # fixing penalty shots erroneously identified as power play goals\\n if event['data']['balance'] == 'PP0':\\n event['data']['balance'] = 'PS'\\n if event['data']['balance'].startswith('PP'):\\n pp_goals_from_events[event['data']['team']] += 1\\n\\n pp_goals_discrepancy = False\\n\\n pp_goals_from_events['road'] = pp_goals_from_events['visitor']\\n\\n if pp_goals_from_events[key] != gsl['pp_goals']:\\n pp_goals_discrepancy = True\\n gsl['pp_goals'] = pp_goals_from_events[key]\\n if pp_goals_from_events[opp_key] != gsl['opp_pp_goals']:\\n pp_goals_discrepancy = True\\n gsl['opp_pp_goals'] = pp_goals_from_events[opp_key]\\n\\n if pp_goals_discrepancy:\\n print(\\\"\\\\t+ Found and corrected different number of pp goals retrieved from team stats and game event data\\\")\\n\\n return gsl\",\n \"def __check_all_config_values_set(cfg: __Config):\\n unset_values = [key for key, val in cfg.__dict__.items() if val is None]\\n if len(unset_values) > 0:\\n print(f\\\"WARNING: The following config variables have not been set: {unset_values}\\\")\",\n \"def test_preferences_properties(self):\\n with pytest.raises(AssertionError):\\n self.preferences.exchange_params_by_currency_id\\n with pytest.raises(AssertionError):\\n self.preferences.utility_params_by_good_id\",\n \"def test_fav_3(self):\\n\\t\\tplayer_list = [Player(\\\"Kevin Knuckler\\\", 1, 100000, 10), Player(\\\"Larry Left\\\", 2, 100000, 20), Player(\\\"Morgan Mariner\\\", 2, 300000, 50), Player(\\\"Nick National\\\", 4, 200000, 70), Player(\\\"Oscar Outfield\\\", 1, 700000, 99), Player(\\\"Peter Pitcher\\\", 3, 100000, 5), Player(\\\"Quinn Quality\\\", 6, 400000, 44), Player(\\\"Ricky Right\\\", 7, 200000, 69)]\\n\\t\\tself.assertEqual( free_agent_vorp(player_list, 1000000, 7), (213, 1000000, [\\\"Nick National\\\", \\\"Ricky Right\\\", \\\"Larry Left\\\", \\\"Quinn Quality\\\", \\\"Kevin Knuckler\\\" ]) )\",\n \"def isStable(couples,mPref,wPref) -> bool:\\n couples_dict = {woman:man for man,woman in couples}\\n \\n for m,w in couples:\\n\\n # w's (m's partner) rank in m's preference list\\n w_rank = mPref[m].index(w)\\n \\n # list of all women that m prefers over w\\n preferred_over_w = mPref[m][:w_rank]\\n \\n\\n for hotter in preferred_over_w:\\n\\n # get each woman's current partner (in preferred list)\\n hotters_man = couples_dict[hotter]\\n\\n # if she prefers m over her current partner then marriage is not stable\\n if wPref[hotter].index(m) < wPref[hotter].index(hotters_man): \\n msg = \\\"{}'s marriage to {} is unstable:\\\\n\\\\t\\\" + \\\\\\n \\\"{} prefers {} > {} (his current wife)\\\\n\\\\t\\\" +\\\\\\n \\\"{} prefers {} > {} (her current husband)\\\"\\n print(msg.format(m, w, m, hotter, w, hotter, m, hotters_man) )\\n return False\\n return True\",\n \"def check_death(player_list):\\n\\n count = 0\\n remaining_players = []\\n\\n for cycle in player_list:\\n if not cycle.is_dead():\\n count += 1\\n remaining_players.append(cycle.get_name())\\n\\n if count == 0:\\n pygame.time.wait(1000)\\n return True, \\\"None\\\"\\n elif count == 1:\\n pygame.time.wait(1000)\\n return True, remaining_players[0]\\n\\n return False, remaining_players\",\n \"def test_inputs_reviewer_prefs(\\n resident_names, hospital_names, capacities, seed\\n):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_reviewer_prefs()\\n\\n match.reviewers[0].pref_names.pop()\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_prefs()\",\n \"def test_check_for_unacceptable_matches_residents(game):\\n\\n resident = game.residents[0]\\n hospital = Hospital(name=\\\"foo\\\", capacity=1)\\n resident.matching = hospital\\n\\n issues = game._check_for_unacceptable_matches(\\\"residents\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(resident.name)\\n assert issue.endswith(f\\\"{resident.prefs}.\\\")\\n assert hospital.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test002_league_commish_settings_no_membership(self):\\n\\t\\tresponse = self.client.get(self.test_url, follow=True)\\n\\t\\tself.assertFalse(is_on_page(response, 'League: league_name1'))\\n\\t\\tself.assertTrue(is_on_page(response, 'Fantasy Web - Home'))\",\n \"def test_set_player_names(self):\\n\\n # Setup new games and attempt to set their players' names\\n valid_players = [\\n [\\\"Bob\\\", \\\"Sam\\\", \\\"Cal\\\", \\\"Kris\\\"],\\n [\\\"Player 1\\\", \\\"Player 2\\\", \\\"Player 3\\\", \\\"Player 4\\\", \\\"Player 5\\\"],\\n [\\\"Bot\\\"],\\n [\\\"P1\\\", \\\"P2\\\", \\\"P3\\\"],\\n ]\\n for players in valid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(len(players))\\n game.set_player_names(players)\\n self.assertEqual(game.player_names, players, msg=\\\"The game's player names were not correctly set with: \\\" + str(players))\\n\\n # Make sure that the new game state is corectly set\\n self.assertEqual(game.state.name, \\\"start_game\\\", msg=\\\"The game's state was not correctly set after setting the player names.\\\")\\n\\n # Try to set invalid players\\n invalid_players = [\\n None,\\n [None, None],\\n [123, 456, 789],\\n [\\\"Bob\\\", \\\"Sam\\\", 123],\\n [\\\"John\\\", \\\"\\\"],\\n ]\\n for players in invalid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(len(players or \\\"1\\\"))\\n success = False\\n try:\\n game.set_player_names(players)\\n except InvalidGamePlayerNames:\\n success = True\\n self.assertTrue(success, msg=\\\"The following invalid series of player names was able to be set: \\\" + str(players))\\n\\n # Test the case where the number of players given is not the same as the number of names given\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(2)\\n success = False\\n try:\\n game.set_player_names([\\\"P1\\\", \\\"P2\\\", \\\"P3\\\"])\\n except InvalidGamePlayerNames:\\n success = True\\n self.assertTrue(success, msg=\\\"A number of player names unequal to the number to the number of players in the game was able to be set.\\\")\\n\\n # Try to reset the names of the players to throw an error\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(3)\\n game.set_player_names([\\\"P1\\\", \\\"P2\\\", \\\"P3\\\"])\\n success = False\\n try:\\n game.set_player_names([\\\"P01\\\", \\\"P02\\\", \\\"P03\\\"])\\n except InvalidGameMethodOrder:\\n success = True\\n self.assertTrue(success, msg=\\\"The names of the players was incorrectly able to be reset.\\\")\",\n \"def check_win(players: List[Player]) -> Tuple[bool, Optional[Player]]:\\n total_players = len(players)\\n for player in players:\\n if player.influence == 0:\\n total_players -= 1\\n if total_players == 1:\\n for player in players:\\n if player.influence >0:\\n return True, player\\n return False, None\",\n \"def testOddPlayersWithBye():\\n deleteMatches()\\n deletePlayers()\\n registerPlayer(\\\"Alien 1\\\")\\n registerPlayer(\\\"Alien 2\\\")\\n registerPlayer(\\\"Alien 3\\\")\\n standings = playerStandings()\\n [id1, id2, id3] = [row[0] for row in standings]\\n reportMatch(id1, id2)\\n reportMatch(id3, id3, False, True)\\n reportMatch(id2, id3)\\n reportMatch(id1, id1, False, True)\\n\\n # id1 and id3 have one bye each. In this round, bye should be given to id2\\n pairings = swissPairings()\\n for pairing in pairings:\\n if pairing[0]!=id2 and pairing[0]==pairing[2]:\\n raise ValueError(\\n \\\"swissPairings() should not award bye to a player who already\\\"\\n \\\"has a bye.\\\"\\n )\\n if pairing[0]==id2 and pairing[2]!=id2:\\n raise ValueError(\\n \\\"swissPairings() has to award a bye when there is an odd number\\\"\\n \\\"of players.\\\"\\n )\\n print \\\"2. Bye is not given to a player who already has a bye.\\\"\",\n \"def test_play_no_gain(self):\\n self.card = self.g[\\\"Festival\\\"].remove()\\n self.plr.piles[Piles.HAND].set(\\\"Duchy\\\")\\n self.plr.add_card(self.card, Piles.HAND)\\n self.plr.favors.set(2)\\n self.plr.test_input = [\\\"No\\\"]\\n self.plr.play_card(self.card)\\n self.assertEqual(self.plr.favors.get(), 2)\\n self.assertEqual(self.plr.piles[Piles.HAND].size(), 1)\",\n \"def test_vote_badpklist(self):\\r\\n mock_module = CHModuleFactory.create()\\r\\n json_in = {'answer': '24.0', 'hint': '0', 'pk_list': json.dumps([['24.0', 0], ['24.0', 12]])}\\r\\n hint_and_votes = mock_module.tally_vote(json_in)['hint_and_votes']\\r\\n self.assertTrue(['Best hint', 41] in hint_and_votes)\\r\\n self.assertTrue(len(hint_and_votes) == 1)\",\n \"def test_compatible_data_presence(allparams):\\n compatible_data_keys_set = set(['puf', 'cps'])\\n\\n # Nested function used only in test_compatible_data_presence\\n def valid_compatible_data(compatible_data):\\n \\\"\\\"\\\"\\n Return True if compatible_data is a valid dictionary;\\n otherwise return False\\n \\\"\\\"\\\"\\n if not isinstance(compatible_data, dict):\\n return False\\n if set(compatible_data.keys()) != compatible_data_keys_set:\\n return False\\n for key in compatible_data:\\n boolean = (compatible_data[key] is True or\\n compatible_data[key] is False)\\n if not boolean:\\n return False\\n return True\\n\\n # Main logic of test_compatible_data_presence function\\n problem_pnames = list()\\n for pname in allparams:\\n if 'compatible_data' in allparams[pname]:\\n compatible_data = allparams[pname]['compatible_data']\\n else:\\n compatible_data = None\\n if not valid_compatible_data(compatible_data):\\n problem_pnames.append(pname)\\n if problem_pnames:\\n msg = '{} has no or invalid compatible_data field'\\n for pname in problem_pnames:\\n print(msg.format(pname))\\n assert 'list of problem_pnames' == 'empty list'\",\n \"def test_delete_vehicle_profile_not_in_list():\\n tester = TestClass()\\n vehicle_profiles = tester.delete_vehicle_profile()\\n\\n assert vehicle_profiles\\n assert len(vehicle_profiles) == 1\\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'\",\n \"def check_for_list(check):\",\n \"def second_phase(players):\\n\\n player = next(p for p in players if len(p.prefs) > 1)\\n while True:\\n cycle = locate_all_or_nothing_cycle(player)\\n pairs = get_pairs_to_delete(cycle)\\n for player, other in pairs:\\n _delete_pair(player, other)\\n\\n if any(p.prefs == [] for p in players):\\n warnings.warn(\\n NoStableMatchingWarning(\\n \\\"The following players have emptied their preferences: \\\"\\n f\\\"{[p for p in players if not p.prefs]}\\\"\\n )\\n )\\n break\\n\\n try:\\n player = next(p for p in players if len(p.prefs) > 1)\\n except StopIteration:\\n break\\n\\n for player in players:\\n player._unmatch()\\n if player.prefs:\\n player._match(player.get_favourite())\\n\\n return players\",\n \"def test_check_for_oversubscribed_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n supervisor.matching = range(supervisor.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.capacity))\\n assert str(supervisor.matching) in e\",\n \"def test_check_for_oversubscribed_projects(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n project = game.projects[0]\\n project.matching = range(project.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(project.name)\\n assert error.endswith(str(project.capacity))\\n assert str(project.matching) in e\",\n \"def _find_homeless_mps(self):\\n mps = Person.objects.filter(\\n active=True,\\n house__name=HOUSE_OF_COMMONS,\\n constituency=None,\\n )\\n\\n self.stdout('MPs with missing constituency:')\\n for mp in mps:\\n self.stdout(f' [{mp.parliamentdotuk}] {mp.name} has no constituency')\",\n \"def gameOver():\\n if len(p1)==0 and len(p1winnings)==0:\\n return True\\n elif len(p2)==0 and len(p2winnings)==0:\\n return True\\n return False\",\n \"def test_check_stability():\\n\\n residents = [Player(\\\"A\\\", [1, 2]), Player(\\\"B\\\", [2]), Player(\\\"C\\\", [2, 1])]\\n hospitals = [Player(1, [\\\"C\\\", \\\"A\\\"], 3), Player(2, [\\\"A\\\", \\\"B\\\", \\\"C\\\"], 3)]\\n match = HospitalResident(residents, hospitals)\\n\\n matching = match.solve()\\n assert match.check_stability()\\n\\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\\n matching[P_1] = [P_C]\\n matching[P_2] = [P_A, P_B]\\n\\n assert not match.check_stability()\",\n \"def stable_roommates(players):\\n\\n players = first_phase(players)\\n\\n if any(p.prefs == [] for p in players):\\n warnings.warn(\\n NoStableMatchingWarning(\\n \\\"The following players have been rejected by all others, \\\"\\n \\\"emptying their preference list: \\\"\\n f\\\"{[p for p in players if not p.prefs]}\\\"\\n )\\n )\\n\\n if any(len(p.prefs) > 1 for p in players):\\n players = second_phase(players)\\n\\n return {player: player.matching for player in players}\",\n \"def test_filter_not_available_plugins(plugin_dialog_constructor):\\n item = plugin_dialog_constructor.available_list.item(0)\\n widget = plugin_dialog_constructor.available_list.itemWidget(item)\\n if widget:\\n assert not widget.action_button.isEnabled()\\n assert widget.warning_tooltip.isVisible()\\n\\n item = plugin_dialog_constructor.available_list.item(1)\\n widget = plugin_dialog_constructor.available_list.itemWidget(item)\\n assert widget.action_button.isEnabled()\\n assert not widget.warning_tooltip.isVisible()\",\n \"def game_over(players):\\n active_players = players_with_decks(players)\\n if not active_players or len(active_players) == 1:\\n return True\\n return False\",\n \"def test_for_empty_list(self):\\n emptylist = []\\n self.assertEqual(self.place.amenity_ids, emptylist)\",\n \"def test_half_life_unstable_isotopes():\\n for isotope in data_about_isotopes:\\n if (\\n \\\"half_life\\\" not in data_about_isotopes[isotope]\\n and not data_about_isotopes[isotope]\\n ):\\n with pytest.raises(MissingParticleDataError):\\n half_life(isotope)\",\n \"def test_lpdaac_good(self):\\n self.assertIsNone(api.inventory.check(self.lpdaac_order_good))\",\n \"def test_given_none(self):\\n llists = None\\n result = solution(llists)\\n self.assertEqual(None, result)\",\n \"def test_not_found_pair(self):\\n self.assertTrue(\\\"No matches found\\\", show_players_sumheigh_is_input(100))\",\n \"def test_can_detect_when_player_has_pieces(self):\\n board = Board()\\n board.pieces = [Piece(0, 0, 0, 0), Piece(2, 2, 2, 2)]\\n\\n player_2_has_piece = board.does_player_have_pieces(2)\\n\\n assert player_2_has_piece\",\n \"def test_check_ess_settings(self):\\n ess_settings1 = {'gaussian': [self.servers[0]], 'molpro': [self.servers[1], self.servers[0]],\\n 'qchem': [self.servers[0]]}\\n ess_settings2 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n ess_settings3 = {'gaussian': self.servers[0], 'molpro': [self.servers[1], self.servers[0]],\\n 'qchem': self.servers[0]}\\n ess_settings4 = {'gaussian': self.servers[0], 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n ess_settings5 = {'gaussian': 'local', 'molpro': self.servers[1], 'qchem': self.servers[0]}\\n\\n ess_settings1 = check_ess_settings(ess_settings1)\\n ess_settings2 = check_ess_settings(ess_settings2)\\n ess_settings3 = check_ess_settings(ess_settings3)\\n ess_settings4 = check_ess_settings(ess_settings4)\\n ess_settings5 = check_ess_settings(ess_settings5)\\n\\n ess_list = [ess_settings1, ess_settings2, ess_settings3, ess_settings4, ess_settings5]\\n\\n for ess in ess_list:\\n for soft, server_list in ess.items():\\n self.assertTrue(soft in ['gaussian', 'molpro', 'qchem'])\\n self.assertIsInstance(server_list, list)\\n\\n with self.assertRaises(SettingsError):\\n ess_settings6 = {'nosoft': ['server1']}\\n check_ess_settings(ess_settings6)\\n with self.assertRaises(SettingsError):\\n ess_settings7 = {'gaussian': ['noserver']}\\n check_ess_settings(ess_settings7)\",\n \"def test_fav_7(self):\\n\\t\\tplayer_list = [Player(\\\"Evan Even\\\", 1, 100000, 10), Player(\\\"Favian Fly\\\", 2, 100000, 20), Player(\\\"Guy Ground\\\", 2, 300000, 50), Player(\\\"Hans Hand\\\", 4, 200000, 70), Player(\\\"Igor Infield\\\", 1, 700000, 99), Player(\\\"Jerry Jump\\\", 3, 100000, 5), Player(\\\"Kenny K\\\", 6, 400000, 44), Player(\\\"Louis Lefty\\\", 7, 200000, 69), Player(\\\"Manny Mound\\\", 9, 500000, 107), Player(\\\"Negan Nubber\\\", 12, 300000, 54), Player(\\\"Omar Oppo\\\", 6, 100000, 44), Player(\\\"Perry Park\\\", 5, 300000, 63), Player(\\\"Quaid Quit\\\", 11, 200000, 44)]\\n\\t\\tself.assertEqual( free_agent_vorp(player_list, 2500000, 13), (486, 2100000, [\\\"Omar Oppo\\\", \\\"Hans Hand\\\", \\\"Louis Lefty\\\", \\\"Quaid Quit\\\", \\\"Manny Mound\\\", \\\"Perry Park\\\", \\\"Favian Fly\\\", \\\"Negan Nubber\\\", \\\"Evan Even\\\", \\\"Jerry Jump\\\"]) )\",\n \"def test_fav_2(self):\\n\\t\\tplayer_list = [Player(\\\"Eric Eephus\\\", 1, 200000, 10), Player(\\\"Fred First\\\", 2, 100000, 20), Player(\\\"Gary Glove\\\", 2, 200000, 50), Player(\\\"Henry Hitter\\\", 4, 300000, 70), Player(\\\"Ian Inning\\\", 1, 100000, 30), Player(\\\"Jack Junk\\\", 3, 200000, 5)]\\n\\t\\tself.assertEqual( free_agent_vorp(player_list, 800000, 4), (155, 800000, [\\\"Ian Inning\\\", \\\"Gary Glove\\\", \\\"Henry Hitter\\\", \\\"Jack Junk\\\"]) )\",\n \"def test_check_full_house_false(self):\\n not_full_house_fixtures = [[1, 1, 1, 1, 2],\\n [2, 1, 1, 1, 1],\\n [1, 1, 2, 2, 3],\\n [1, 2, 3, 4, 5],\\n ]\\n\\n for fixture in not_full_house_fixtures:\\n score = self.roll.check_full_house(fixture)\\n\\n self.assertNotEqual(score, 25)\\n self.assertEqual(score, 0)\\n self.assertEqual(len(fixture), 5)\",\n \"def testNoSpecialties(self):\\n self.failUnlessEqual(self.person.getSpecialties(), [])\",\n \"def verify_valid_game(self, lotto_game:List[int]):\\n is_valid = True;\\n lotto_game_temp = []\\n for number in lotto_game:\\n if number not in lotto_game_temp:\\n lotto_game_temp.append(number)\\n else:\\n print(number, lotto_game, lotto_game_temp)\\n is_valid = False\\n raise InvalidLottoGame\\n \\n lotto_game_temp_length = len(lotto_game_temp)\\n if lotto_game_temp_length != LOTTO_GAME_LENGTH and lotto_game_temp_length != LOTTO_GAME_WITH_BONUS_LENGTH:\\n print(lotto_game_temp)\\n is_valid = False\\n raise InvalidLottoGame\\n\\n return is_valid\",\n \"def test_decision():\\n player = sequence_threshold.Player(threshold=10)\\n\\n player.cards = SortedSet()\\n assert player.play(20, 11) == True\\n assert player.play(20, 10) == True\\n assert player.play(20, 9) == False\\n\\n player.cards = SortedSet([30])\\n assert player.play(20, 11) == True\\n assert player.play(20, 10) == True\\n assert player.play(20, 9) == False\\n assert player.play(31, 1) == True\\n assert player.play(29, 1) == True\\n assert player.play(32, 1) == False\\n with pytest.raises(Exception):\\n player.play(30, 1)\\n\\n player.threshold = 5\\n\\n player.cards = SortedSet()\\n assert player.play(20, 16) == True\\n assert player.play(20, 15) == True\\n assert player.play(20, 14) == False\\n\\n player.cards = SortedSet([30])\\n assert player.play(20, 16) == True\\n assert player.play(20, 15) == True\\n assert player.play(20, 14) == False\\n assert player.play(31, 1) == True\\n assert player.play(29, 1) == True\\n assert player.play(32, 1) == False\\n with pytest.raises(Exception):\\n player.play(30, 1)\",\n \"def throwing_proposition(list_of_players):\\n list_of_players_name = [player.name.lower() for player in list_of_players]\\n while True:\\n print(\\\"\\\\nIf you want to throw a card over {},\\\".format(Card.rejected_cards[-1].name))\\n print(\\\"Write your name and press enter\\\")\\n print(\\\"If everyone's done enter 'END' to continue the round\\\")\\n user_entry = input().lower()\\n if user_entry == 'end':\\n break\\n if user_entry in list_of_players_name:\\n get_player_index = list_of_players_name.index(user_entry)\\n player = list_of_players[get_player_index]\\n player.throw()\\n print(\\\"Anyone else?\\\")\\n else:\\n print(\\\"Incorrect entry\\\")\",\n \"def test_statuses_exist(self):\\n\\n assert hasattr(Partner, 'AVAILABLE')\\n assert hasattr(Partner, 'NOT_AVAILABLE')\\n assert hasattr(Partner, 'WAITLIST')\\n\\n assert hasattr(Partner, 'STATUS_CHOICES')\\n\\n assert len(Partner.STATUS_CHOICES) == 3\\n\\n database_statuses = [x[0] for x in Partner.STATUS_CHOICES]\\n\\n assert Partner.AVAILABLE in database_statuses\\n assert Partner.NOT_AVAILABLE in database_statuses\\n assert Partner.WAITLIST in database_statuses\",\n \"def test_get_empty_vehicle_profile_list():\\n tester = TestClass()\\n vehicle_profiles = tester.get_empty_vehicle_profile_list()\\n assert len(vehicle_profiles) == 1\\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'\",\n \"def enough_players():\\n return True\",\n \"def test_check_inputs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n with warnings.catch_warnings(record=True):\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.students == game._all_students\\n assert game.projects == game._all_projects\\n assert game.supervisors == game._all_supervisors\",\n \"def check_cards_eligibility(self):\\n for c in self.hand:\\n c.check_actions(self)\\n for c in self.phand:\\n c.check_actions(self)\\n for c in self.discard:\\n c.check_actions(self)\\n for c in self.active_player.phand:\\n c.check_actions(self)\\n for c in self.active_player.hand:\\n c.check_actions(self)\\n for c in self.active_player.discard:\\n c.check_actions(self)\\n for c in self.played_user_cards:\\n c.check_actions(self)\\n if ACTION_KEEP in self.actions:\\n for p in self.players:\\n for c in p.phand:\\n c.check_actions(self)\\n for c in p.hand:\\n c.check_actions(self)\\n for c in p.discard:\\n c.check_actions(self)\",\n \"def forwardcheck(var, val, assignment, user_dict):\\n\\t\\tif curr_domains:\\n\\t\\t\\tfor (meal, restaurant) in curr_deleted[var]:\\n\\t\\t\\t\\tcurr_domains[meal].append(restaurant)\\n\\t\\t\\tcurr_deleted[var] = []\\n\\n\\t\\t\\tfor meal in neighbors[var]:\\n\\t\\t\\t\\tif meal not in assignment:\\n\\t\\t\\t\\t\\tfor restaurant in curr_domains[meal][:]:\\n\\t\\t\\t\\t\\t\\tnum_cats = count_categories(assignment.values())\\n\\t\\t\\t\\t\\t\\tif not constraints_match(num_cats, user_dict):\\n\\t\\t\\t\\t\\t\\t#if not user_solution_checker(user_dict, meal, restaurant, assignment):\\n\\t\\t\\t\\t\\t\\t\\tcurr_domains[meal].remove(restaurant)\\n\\t\\t\\t\\t\\t\\t\\tcurr_deleted[var].append((meal, restaurant))\",\n \"def test_correct_p_values_empty(self):\\r\\n exp = []\\r\\n obs = self.mc._correct_p_values([])\\r\\n assert_almost_equal(obs, exp)\",\n \"def test_listEmpty(self):\\n store = Store()\\n self.assertSuccessStatus(self._makeConfig(store), [\\\"list\\\"])\\n self.assertIn(\\\"There are no ports configured.\\\", sys.stdout.getvalue())\",\n \"def test_missing_tracks(self):\\n\\n # Taylor Swift doesn't have her albums on Spotify\\n expected_missing = [track_id for track_id, track in self.ilibrary.tracks.iteritems()\\n if track.artists[0] == 'Taylor Swift']\\n\\n for missing_id in expected_missing:\\n self.assertNotIn(missing_id, [track.i_id for track in self.tracks])\",\n \"def notify_no_win(self):\\n\\n board_set = set()\\n for row in self.board:\\n board_set.update(row)\\n if \\\"\\\" not in board_set:\\n print \\\"Game is over. No one won.\\\"\\n return True\",\n \"def check_items(self, items):\\n for item in items:\\n if item['user'] in self.required_list and item['user'] not in self.approved_required_list:\\n if item['result'] is True or item['result'] == 'approved':\\n logger.info(\\\"%s approved\\\", item['user'])\\n self.approved_required_list.append(item['user'])\\n else:\\n logger.info(\\\"%s declined\\\", item['user'])\\n error_msg = \\\"Since {} is a required member for approval, this approval step fails. \\\".format(item['user'])\\n logger.error(error_msg)\\n self.exit(rc=2, detail=error_msg)\\n if item['user'] in self.optional_list and item['user'] not in self.approved_optional_list:\\n if item['result'] is True or item['result'] == 'approved':\\n logger.info(\\\"%s approved\\\", item['user'])\\n if item['user'] in self.declined_optional_list:\\n self.declined_optional_list.remove(item['user'])\\n self.approved_optional_list.append(item['user'])\\n else:\\n logger.info(\\\"%s declined\\\", item['user'])\\n self.declined_optional_list.append(item['user'])\\n if len(self.declined_optional_list) >= (len(self.optional_list) - self.number_optional):\\n error_msg = \\\"Not be able to fulfill requirement that {} optional approvals, since {} declined request.\\\".format(\\n self.number_optional, self.declined_optional_list)\\n logger.error(error_msg)\\n self.exit(rc=2, detail=error_msg)\\n\\n if len(self.approved_required_list) >= len(self.required_list) \\\\\\n and len(self.approved_optional_list) >= self.number_optional:\\n logger.info(\\\"Approval requirements are fully met. Exit gracefully.\\\")\\n self.exit(0)\",\n \"def test_remove_spawning_profile_from_intersection():\\n tester = TestClass()\\n intersections = tester.add_spawning_profile_to_intersection()\\n\\n for i in intersections:\\n if len(i.get_spawning_profile_list()) != 0:\\n assert True\\n\\n for spawn in i.get_spawning_profile_list():\\n if spawn.get_spawning_profile_name() == 'Default':\\n assert True\\n break\\n\\n tester.delete_spawning_profile_from_intersection()\\n\\n for i in intersections:\\n if len(i.get_spawning_profile_list()) == 0:\\n assert True\",\n \"def test003_league_commish_settings_membership_no_commish(self):\\n\\t\\tadd_league_member(self.user, self.league, \\\"team1\\\")\\n\\t\\tresponse = self.client.get(self.test_url, follow=True)\\n\\n\\t\\tself.assertFalse(is_on_page(response, 'Commish Settings'))\\n\\t\\tself.assertTrue(is_on_page(response, 'League: league_name1'))\\n\\t\\tself.assertTrue(is_on_page(response, 'Standings'))\",\n \"def test_tally_no_candidates(self):\\n self.init_elect_types()\\n\\n userA = models.User(\\n name = \\\"UserA\\\",\\n email = \\\"userA@eLect.com\\\",\\n password = \\\"asdf\\\")\\n\\n session.add(userA)\\n session.commit()\\n\\n electionA = models.Election(\\n title = \\\"Election A\\\",\\n admin_id = userA.id)\\n\\n session.add(electionA)\\n session.commit()\\n\\n raceA = models.Race(\\n title = \\\"Race A\\\",\\n election_id = electionA.id\\n )\\n\\n session.add(raceA)\\n session.commit()\\n\\n with self.assertRaises(NoCandidates):\\n self.wta.check_race(raceA.id)\\n\\n with self.assertRaises(NoCandidates):\\n self.proportional.check_race(raceA.id)\\n\\n with self.assertRaises(NoCandidates):\\n self.schulze.check_race(raceA.id)\",\n \"def test_not_notified(scraper):\\n\\n assert scraper.scrape_profile('https://www.punters.com.au/') is None\",\n \"def test_problem_list(self):\\r\\n\\r\\n # Initialize peer grading module.\\r\\n peer_grading = self.get_module_from_location(self.problem_location)\\r\\n\\r\\n # Ensure that it cannot find any peer grading.\\r\\n html = peer_grading.peer_grading()\\r\\n self.assertNotIn(\\\"Peer-Graded\\\", html)\\r\\n\\r\\n # Swap for our mock class, which will find peer grading.\\r\\n peer_grading.peer_gs = MockPeerGradingServiceProblemList()\\r\\n html = peer_grading.peer_grading()\\r\\n self.assertIn(\\\"Peer-Graded\\\", html)\",\n \"def confirm_choices(self):\\n stack = self.stack_choice.get()\\n # valid list below created January 2016\\n valid_list = ['BMDHL', 'GPHP', 'GPHL', 'IDBLH', 'IDD']\\n if stack not in valid_list:\\n showerror('Under Development',\\n 'Those stages are not yet available, program will exit')\\n close_quit()\\n else:\\n self.config_window.destroy()\",\n \"def test_delete_driver_profile_not_in_list():\\n tester = TestClass()\\n driver_profiles = tester.delete_driver_profile()\\n\\n assert driver_profiles\\n assert len(driver_profiles) == 1\\n assert driver_profiles[0].get_driver_profile_name() == 'Default'\",\n \"def test_teacher_reset_result_with_arg_positive():\\n expected = []\\n Teacher.reset_results(oop_hw)\\n assert Teacher.homework_done[oop_hw] == expected\",\n \"def test_fav_5(self):\\n\\t\\tplayer_list = [Player(\\\"Xavier X\\\", 1, 100000, 10), Player(\\\"Zane Zero\\\", 2, 100000, 20), Player(\\\"Alex Athlete\\\", 3, 200000, 50)]\\n\\t\\tself.assertEqual( free_agent_vorp(player_list, 900000, 3), (80, 400000, [\\\"Alex Athlete\\\", \\\"Zane Zero\\\", \\\"Xavier X\\\"]) )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74706024","0.7364109","0.7355341","0.7349609","0.7254875","0.6616572","0.62527543","0.6152463","0.61230946","0.60876256","0.6085861","0.6065636","0.5856437","0.58475614","0.58438146","0.58048016","0.5630108","0.56236094","0.5622575","0.5621679","0.5602183","0.559239","0.55538726","0.54811746","0.54785067","0.5456971","0.5428878","0.54125917","0.5403857","0.5386629","0.5361497","0.5340337","0.5339929","0.5312637","0.53046274","0.52978617","0.52933484","0.5255479","0.525231","0.5247166","0.52464116","0.5245117","0.52447665","0.524101","0.52208555","0.52176243","0.5210167","0.5203461","0.51956785","0.51850003","0.51758194","0.5164025","0.51627433","0.51581025","0.5154591","0.51436687","0.51429105","0.5140683","0.51311326","0.5122633","0.5121411","0.51204884","0.5117565","0.50938916","0.50922894","0.50895715","0.5078584","0.50782615","0.5077163","0.5059474","0.5046803","0.5043083","0.50335366","0.5033268","0.50327414","0.5029596","0.50275415","0.5022152","0.5016589","0.50128233","0.5012202","0.5006065","0.5004299","0.500394","0.5000795","0.4995182","0.4990101","0.49830344","0.4982136","0.49795973","0.4974924","0.49743116","0.49722037","0.49700806","0.496879","0.4965136","0.4957408","0.4956153","0.49555707","0.49522904"],"string":"[\n \"0.74706024\",\n \"0.7364109\",\n \"0.7355341\",\n \"0.7349609\",\n \"0.7254875\",\n \"0.6616572\",\n \"0.62527543\",\n \"0.6152463\",\n \"0.61230946\",\n \"0.60876256\",\n \"0.6085861\",\n \"0.6065636\",\n \"0.5856437\",\n \"0.58475614\",\n \"0.58438146\",\n \"0.58048016\",\n \"0.5630108\",\n \"0.56236094\",\n \"0.5622575\",\n \"0.5621679\",\n \"0.5602183\",\n \"0.559239\",\n \"0.55538726\",\n \"0.54811746\",\n \"0.54785067\",\n \"0.5456971\",\n \"0.5428878\",\n \"0.54125917\",\n \"0.5403857\",\n \"0.5386629\",\n \"0.5361497\",\n \"0.5340337\",\n \"0.5339929\",\n \"0.5312637\",\n \"0.53046274\",\n \"0.52978617\",\n \"0.52933484\",\n \"0.5255479\",\n \"0.525231\",\n \"0.5247166\",\n \"0.52464116\",\n \"0.5245117\",\n \"0.52447665\",\n \"0.524101\",\n \"0.52208555\",\n \"0.52176243\",\n \"0.5210167\",\n \"0.5203461\",\n \"0.51956785\",\n \"0.51850003\",\n \"0.51758194\",\n \"0.5164025\",\n \"0.51627433\",\n \"0.51581025\",\n \"0.5154591\",\n \"0.51436687\",\n \"0.51429105\",\n \"0.5140683\",\n \"0.51311326\",\n \"0.5122633\",\n \"0.5121411\",\n \"0.51204884\",\n \"0.5117565\",\n \"0.50938916\",\n \"0.50922894\",\n \"0.50895715\",\n \"0.5078584\",\n \"0.50782615\",\n \"0.5077163\",\n \"0.5059474\",\n \"0.5046803\",\n \"0.5043083\",\n \"0.50335366\",\n \"0.5033268\",\n \"0.50327414\",\n \"0.5029596\",\n \"0.50275415\",\n \"0.5022152\",\n \"0.5016589\",\n \"0.50128233\",\n \"0.5012202\",\n \"0.5006065\",\n \"0.5004299\",\n \"0.500394\",\n \"0.5000795\",\n \"0.4995182\",\n \"0.4990101\",\n \"0.49830344\",\n \"0.4982136\",\n \"0.49795973\",\n \"0.4974924\",\n \"0.49743116\",\n \"0.49722037\",\n \"0.49700806\",\n \"0.496879\",\n \"0.4965136\",\n \"0.4957408\",\n \"0.4956153\",\n \"0.49555707\",\n \"0.49522904\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81164616"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93794,"cells":{"query":{"kind":"string","value":"Test each hospital has a positive integer capacity. If not, raise an Exception detailing the hospital."},"document":{"kind":"string","value":"def test_check_inputs_hospital_capacity(game):\n\n hospital = game.hospitals[0]\n capacity = hospital.capacity\n hospital.capacity = 0\n\n assert hospital._original_capacity == capacity\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_capacity(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _check_hospital_capacity(self):\n\n errors = []\n for hospital in self.hospitals:\n if len(hospital.matching) > hospital.capacity:\n errors.append(\n ValueError(\n f\"{hospital} is matched to {hospital.matching} which \"\n f\"is over their capacity of {hospital.capacity}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_capacity_cannot_be_empty(self):\n with self.assertRaises(Exception) as context:\n self.client.post(\n url_for('teams'),\n data={\n 'name': 'team',\n 'capacity': 'hello',\n 'number_players': '1',\n 'pitch_postcode': 'E1 6LT',\n 'time': '2019-01-01 13:00'\n }\n )\n self.assertTrue('Capacity must be a number' in context.exception)\n self.assertEqual(db.session.query(Team).count(), 0)","def test_check_inputs_supervisor_capacities_sufficient(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n project = game.projects[0]\n supervisor_capacity = project.supervisor.capacity\n project.capacity = supervisor_capacity + 1\n\n with pytest.warns(CapacityChangedWarning) as record:\n game._check_inputs_supervisor_capacities_sufficient()\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(project.name)\n assert str(project.capacity) in message\n assert str(supervisor_capacity) in message\n\n if clean:\n assert project.capacity == supervisor_capacity","def test_set_glass_capacity__with_valid_numbers__returns_expected():\n glass = moet.create_glass(\"A\")\n numbers = [0, 1, 250, 0.0, 100.5]\n for number in numbers:\n glass.capacity = number\n assert glass.capacity == number","def test_set_glass_capacity__with_invalid_numbers__returns_expected():\n glass = moet.create_glass(\"A\")\n with pytest.raises(ValueError):\n glass.capacity = -100","def _check_hospital_unhappy(resident, hospital):\n\n return len(hospital.matching) < hospital.capacity or any(\n [hospital.prefers(resident, match) for match in hospital.matching]\n )","def test_check_inputs_supervisor_capacities_necessary(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n total_project_capacity = sum(p.capacity for p in supervisor.projects)\n supervisor.capacity = total_project_capacity + 1\n\n with pytest.warns(CapacityChangedWarning) as record:\n game._check_inputs_supervisor_capacities_necessary()\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(supervisor.name)\n assert str(supervisor.capacity) in message\n assert str(total_project_capacity) in message\n\n if clean:\n assert supervisor.capacity == total_project_capacity","def check_hydro_capacity(plant_name, value, logger):\n try:\n max_value = HYDRO_CAPACITIES[plant_name]\n except KeyError:\n if value != 0.0:\n logger.warning('New hydro plant seen - {} - {}MW'.format(plant_name, value), extra={'key': 'KR'})\n return True\n\n if value > max_value:\n logger.warning('{} reports {}MW generation with capacity of {}MW - discarding'.format(plant_name, value, max_value), extra={'key': 'KR'})\n raise ValueError\n else:\n return True","def checksufficientresources(ingredients):\n for item in ingredients:\n if ingredients[item] >= resources[item]:\n print(f\"Sorry there is not enough {item}\")\n return False\n return True","def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t, level):\n # note \"amt\" has units of AMOUNT not RATE (resource, not resource per second)\n sign = np.sign(amt)\n # are we storing or providing?\n #print('DEBUGG supposed current level:', level)\n if sign < 0:\n # we are being asked to consume some\n cap, meta = self.get_capacity(meta, raven_vars, dispatch, t)\n available_amount = cap[res] - level\n #print('Supposed Capacity, Only calculated ins sign<0 (being asked to consumer)',cap)\n else:\n # we are being asked to produce some\n available_amount = level\n # the amount we can consume is the minimum of the requested or what's available\n delta = sign * min(available_amount, abs(amt))\n return {res: delta}, meta","def Capacity(self) -> int:","def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\n pass","def has_vacancy(self):\n return len(self.occupants) < self.capacity","def test_no_loc_techs_energy_capacity_storage_constraint(self, caplog):\n with caplog.at_level(logging.INFO):\n m = build_model(model_file=\"energy_cap_per_storage_cap.yaml\")\n\n m.run(build_only=True)\n assert not any(\n [\n hasattr(\n m._backend_model, \"energy_capacity_storage_{}_constraint\".format(i)\n )\n for i in [\"max\", \"equals\", \"min\"]\n ]\n )","def is_resource_sufficient(self, drink):\n can_make = True\n for item in drink.ingredients:\n if drink.ingredients[item] > self.resources[item]:\n print(f\"Sorry there is not enough {item}.\")\n can_make = False\n return can_make","def test_reviewer_capacity(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching = range(match.reviewers[0].capacity + 1)\n\n with pytest.raises(Exception):\n match._check_reviewer_capacity()","def test_excess_quantity(self):\n excess = self._uncertain_demand.excess_stock\n avg_order = sum([int(item) for item in self._data_set.values()]) //len(self._data_set)\n variance = [(item - avg_order) for item in self._data_set.values()]\n stdev = pow(sum([pow(j, 2) for j in variance]) / len(self._data_set), 0.5)\n cal_safety = lambda x, y, z: x * y * (z ** 0.5)\n safety_stock = cal_safety(float(self._z_value), float(stdev), float(self._lead_time))\n cal_reorder_level = lambda x, y, z: ((x ** 0.5) * y) + z\n reorder = cal_reorder_level(float(self._lead_time), avg_order, float(safety_stock))\n cal_excess = lambda x, y, z: round(x - (y + (y - z)), 0) if x > y + (y - z) else 0\n test_excess = cal_excess(self._quantity_on_hand, reorder, safety_stock)\n self.assertEqual(int(excess), int(test_excess))","def is_resource_sufficient(self, drink):\n for ingredient in self.resources:\n if drink.ingredients[ingredient] > self.resources[ingredient]:\n print(f\"Sorry there is not enough {ingredient}\")\n return False\n return True","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def test_environmental_impact_compliance():\n emissions = 12000\n legal_limit = 300\n assert emissions < legal_limit","def test_get_damage_out_of_limit(self):\n self.veh.health = 0.24\n for op in self.veh.operators:\n op.health = 0.1\n self.veh.get_damage(0.5)\n self.assertEqual(self.veh.health, 0)\n self.assertEqual(self.veh.operators[0].health, 0.05)\n self.assertEqual(self.veh.operators[1].health, 0.05)","def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t):\n cap = self.get_capacity(meta, raven_vars, dispatch, t)[0][self._capacity_var]\n try:\n if abs(balance[self._capacity_var]) > abs(cap):\n #ttttt\n # do the inverse problem: how much can we make?\n balance, meta = self.produce_max(meta, raven_vars, dispatch, t)\n print('The full requested amount ({res}: {req}) was not possible, so accessing maximum available instead ({res}: {blc}).'.format(res=res, req=amt, blc=balance[res]))\n except KeyError:\n raise SyntaxError('Resource \"{}\" is listed as capacity limiter, but not an output of the component! Got: {}'.format(self._capacity_var, balance))\n return balance, meta","def sufficient_resources(drink_type):\r\n\r\n is_sufficient = True\r\n for resource in MENU[drink_type][\"ingredients\"]:\r\n if not(MENU[drink_type][\"ingredients\"][resource] <= resources[resource]):\r\n print(f\"Sorry there is not enough {resource}.\")\r\n is_sufficient = False\r\n return is_sufficient","def test_capacity_factor(pudl_out_eia):\n print(\"\\nCalculating generator capacity factors...\")\n cf = pudl_out_eia.capacity_factor()\n print(f\" capacity_factor: {len(cf)} records\")","def is_resource_sufficient(order_ingredients):\r\n for item in order_ingredients:\r\n if order_ingredients[item] > resources[item]:\r\n print(f\"Sorry there is not enough {item}.\")\r\n return False\r\n return True","def is_resource_sufficient(self, drink):\n can_make = True\n for item in drink.ingredients:\n if drink.ingredients[item] > self.resources[item]:\n print(f\"Sorry there is not enough {item}.\")\n can_make = False\n return can_make","def testsize(self):\n for size in range(1, 5):\n a = WeaponAbility('Defender', size=size)\n self.assert_(str(size) in str(a))","def capacity(self):\n raise NotImplementedError()","def test_capacity(self, space_each_type):\n tspace = build_required_space(space_each_type, type_requirement=\"real\")\n assert tspace.cardinality == numpy.inf\n\n space = Space()\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", 2, 3, 4)\n dim = Categorical(\"yolo\", OrderedDict(zip(categories, probs)), shape=2)\n space.register(dim)\n dim = Integer(\"yolo2\", \"uniform\", -3, 6)\n space.register(dim)\n tspace = build_required_space(space, type_requirement=\"integer\")\n assert tspace.cardinality == (4**2) * (6 + 1)\n\n dim = Integer(\"yolo3\", \"uniform\", -3, 6, shape=(2, 1))\n space.register(dim)\n tspace = build_required_space(space, type_requirement=\"integer\")\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\n\n tspace = build_required_space(\n space, type_requirement=\"integer\", shape_requirement=\"flattened\"\n )\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\n\n tspace = build_required_space(\n space, type_requirement=\"integer\", dist_requirement=\"linear\"\n )\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))","def _check_cost(self, cr, uid, ids, context=None):\n for enrich in self.browse(cr, uid, ids, context=context):\n if enrich.amount <= 0:\n raise osv.except_osv(_('ValidateError'), _('The Cost Must Be Greater Than Zero!'))\n return True","def validate_task_capacity(value):\n if value < 0:\n raise ValidationError(\n _('Task capacity cannot be negative.'),\n params={'value': value},\n )","def test_deal_insufficient_cards(self):\n cards = self.deck._deal(100)\n self.assertEqual(len(cards), 52)\n self.assertEqual(self.deck.count(), 0)","def capacity_used(self):\n raise NotImplementedError()","def wh_capacity_check(agent_full_name, agent_directory):\r\n agent_data_df = pd.read_csv(f'{agent_full_name}.csv', header=0, delimiter=\",\", engine='python')\r\n if agent_data_df.loc[0, 'capacity'] == agent_data_df.loc[0, 'load']:\r\n msg_body = \"negative\"\r\n elif agent_data_df.loc[0, 'capacity'] > agent_data_df.loc[0, 'load']:\r\n msg_body = \"positive\"\r\n else:\r\n msg_body = \"negative\"\r\n return msg_body","def __len__(self):\n return self.capacity","def exceeds_shelf_capacity(shelf, fabric):\n shelf_total = Decimal(shelf.fabrics.all().aggregate(Sum('quantity_th'))['quantity_th__sum'] or 0)\n return True if (shelf_total) + fabric.quantity > max_shelf_qty else False","def enough_resources(order_ingredients):\n is_enough = True\n for item in order_ingredients:\n if order_ingredients[item] >= resources[item]:\n print(f\"Sorry there is not enough {item}.\")\n is_enough = False\n return True","def test_glass_capacity__has_expected_default_value():\n glass = moet.create_glass(\"A\")\n assert glass.capacity == 250","def are_ingredients_sufficient(order_ingredients):\n for item in order_ingredients:\n if order_ingredients[item] > resources[item]:\n print(f\"Sorry there is not enough {item}.\")\n return False\n return True","def illegal(self):\r\n ## First checks for integer value\r\n if type(self.blockList) is int: return 1\r\n ## Then checks for 6 rows\r\n if len(self.blockList) > 6: return 1\r\n for row in self.blockList:\r\n ## Then checks that each row has 6 columns\r\n if len(self.blockList[row]) > 6: return 1\r\n for column in self.blockList[row]:\r\n ## 18 blocks is the maximum number of blocks that can be on the board\r\n if block < 0 or block > 18: return 1\r\n return 0","def test_creation_incorrect_hardbounds_count():\n with pytest.raises(ValueError) as __:\n value = 1\n __ = param.Integer(value=value, hardbounds=[0, 10, 20])","def sufficient_resources(menu, drink, resources):\r\n menu['espresso']['ingredients']['milk'] = 0 # because espresso doesn't need any milk\r\n if resources['Water'] < menu[drink]['ingredients']['water']:\r\n print(\"Sorry there isn't enough water to make the drink.\")\r\n return False\r\n elif resources['Milk'] < menu[drink]['ingredients']['milk']:\r\n print(\"Sorry there isn't enough milk to make the drink.\")\r\n return False\r\n elif resources['Coffee'] < menu[drink]['ingredients']['coffee']:\r\n print(\"Sorry there isn't enough coffee to make the drink.\")\r\n return False\r\n else:\r\n return True","def capacitygroup_group():","def test_deal_sufficient_cards(self):\n cards = self.deck._deal(10)\n self.assertEqual(len(cards), 10)\n self.assertEqual(self.deck.count(), 42)","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[int]:\n return pulumi.get(self, \"capacity\")","def testsize(self):\n for size in range(5):\n a = AmuletAbility('Skepticism', size=size+1)\n self.assert_(str(size+1) in str(a))\n self.assertEqual(a.size, size+1)\n self.assertTrue(isinstance(a.AC, int))\n self.assertTrue(isinstance(a.description(), str))","def _check_multiplicity(self, k, i):\n for j in range(self._.d + 1):\n if self._.a[i] >= k[i]:\n raise InfeasibleError(\"%s of %s %d too large\" %\n (self.SIZE, self.PART, i))","def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"hospital\")\n\n assert set(hospitals) == set(matching.keys())\n\n for hospital, matches in matching.items():\n old_idx = -np.infty\n for resident in matches:\n idx = hospital.pref_names.index(resident.name)\n assert idx >= old_idx\n old_idx = idx","def getSusceptible(self):\n\n # use a mask and sum it to see the number of healthy people, designated as having a value equal to zero\n self.susceptible = np.sum((self.getSpace()) == 0)\n\n return self.susceptible","def _capacity_rule(self, prod_name, r, cap, m, t):\n prod = getattr(m, prod_name)\n # note that a negative capacity means a CONSUMPTION capacity instead of PRODUCTION\n if cap > 0:\n return prod[r, t] <= cap\n else:\n return prod[r, t] >= cap","def test_get_damage_out_of_limit(self):\n self.sold.health = 0.2\n self.sold.get_damage(0.32)\n self.assertEqual(self.sold.health, 0)","def isFull(self):\n return self.count == self.capacity","def isFull(self):\n return self.count == self.capacity","def test_check_for_oversubscribed_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n supervisor.matching = range(supervisor.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.capacity))\n assert str(supervisor.matching) in e","def test_creation_incorrect_softbounds_count():\n with pytest.raises(ValueError) as __:\n value = 1\n __ = param.Integer(value=value, softbounds=[0, 10, 20])","def test_compute_inventory():\n T = [1000]\n c_max = [1e20]\n time = 1e3\n inv, sig = divHretention.compute_inventory(T, c_max, time)\n assert len(inv) == len(sig)\n assert len(inv) == len(T)","def accommodates_must_be_positive(cls, value):\n assert value > 0, f'accommodates == {value}, must be > 0'\n return value","def verify_no_cable_errors(self):\n i = 0\n for dpid in self.dpids:\n i += 1\n labels = {'dp_id': '0x%x' % int(dpid), 'dp_name': 'faucet-%u' % i}\n self.assertEqual(\n 0, self.scrape_prometheus_var(\n var='stack_cabling_errors_total', labels=labels, default=None))\n self.assertGreater(\n self.scrape_prometheus_var(\n var='stack_probes_received_total', labels=labels), 0)","def is_feasible(self):\n keys = Counter(self.keys)\n for chest in self.chests:\n keys.update(chest['keys'])\n keys.subtract([chest['lock']])\n for value in keys.values():\n if value < 0:\n if DEBUG:\n print \"Not enough keys: %r\" % keys\n return False\n # TBD - find cycles\n return True","def isFull(self) -> bool:\n return self.count == self.capacity","def isFull(self) -> bool:\n return self.count == self.capacity","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def capacity(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"capacity\")","def invalidsize(self):\n self.assertRaises(AbilityError, AmuletAbility, 'Skepticism', size=0)\n self.assertRaises(AbilityError, AmuletAbility, 'Skepticism', size=6)\n self.assertRaises(AbilityError, AmuletAbility, 'Control NPC', size=3)","def _check_shrink(self):\n # As an example, if length is 1/4 of capacity and growth factor is 2,\n # then the capacity should shrink in half to keep length proportional\n # to capacity\n if self._length < int(self._capacity / (self._growth_factor ** 2)):\n self._shrink_arr()","def test_sufficient_statistics(self):\n assert (\n len(self.data),\n self.data.var(),\n self.data.mean(),\n ) == sufficient_statistics(self.data)","def check_overall_energy(self):\n energy = 0\n for student in self.students:\n energy += int(student.energy_level)\n for mentor in self.mentors:\n energy += int(mentor.energy_level)\n print(\"Overall energy equals \", energy)","def isLegal(self):\n counter = 0\n for t in self.types:\n if t > 0:\n counter = counter + 1\n if counter < 4:\n return True\n else:\n return False","def test_loc_techs_storage_capacity_constraint(self):\n m = build_model({}, \"simple_storage,two_hours,investment_costs\")\n m.run(build_only=True)\n assert hasattr(m._backend_model, \"storage_max_constraint\")\n\n m = build_model({}, \"simple_supply_and_supply_plus,two_hours,investment_costs\")\n m.run(build_only=True)\n assert hasattr(m._backend_model, \"storage_max_constraint\")\n\n m = build_model(\n {\"techs.test_storage.constraints.storage_cap_equals\": 20},\n \"simple_storage,two_hours,investment_costs\",\n )\n m.run(build_only=True)\n assert m._backend_model.storage_cap[\"a\", \"test_storage\"].ub == 20\n assert m._backend_model.storage_cap[\"a\", \"test_storage\"].lb == 20","def _check_if_ingredients_sufficient(self, drink_type: str, drink_details: Beverages) -> None:\n drink_composition = drink_details.get_receipe(drink_type=drink_type)\n for ingredient in drink_composition:\n if not self._inventory.check_if_ingredient_sufficient(\n ingredient=ingredient, quantity=drink_composition.get(\n ingredient, 0)\n ):\n raise InventoryInSufficient(\n inventory_type=ingredient, drink_type=drink_type\n )","def testSchdulerCheckIngestReady(self):\n\t\tpipelines = {\n\t\t\t\"continuum\": {\n\t\t\t\t\"demand\": 5\n\t\t\t}\n\t\t}\n\t\tobservation = Observation(\n\t\t\t'planner_observation',\n\t\t\tOBS_START_TME,\n\t\t\tOBS_DURATION,\n\t\t\tOBS_DEMAND,\n\t\t\tOBS_WORKFLOW,\n\t\t\ttype=\"continuum\",\n\t\t\tdata_rate=2\n\t\t)\n\t\t# There should be capacity\n\t\tself.assertEqual(0.0, self.env.now)\n\t\tret = self.scheduler.check_ingest_capacity(observation, pipelines)\n\t\tself.assertTrue(ret)\n\n\t\t# Let's remove capacity to check it returns false\n\t\ttmp = self.cluster.available_resources\n\t\tself.cluster.available_resources = self.cluster.available_resources[:3]\n\t\tret = self.scheduler.check_ingest_capacity(observation, pipelines)\n\t\tself.assertFalse(ret)\n\t\tself.cluster.available_resources = tmp\n\t\tself.assertEqual(10, len(self.cluster.available_resources))","def ensure_valid_data():\n cursor = connection.cursor()\n cursor.execute(\"SELECT id, name, st_area(geom) FROM firestation_firedepartment where st_area(geom)>6.99\")\n messages = []\n\n for id, name, area in cursor.fetchall():\n messages.append('{0} ({1}) has an area of {2}.'.format(name, id, area))\n\n if messages:\n mail_admins('Invalid Geometries Detected', message='\\n'.join(messages))\n\n cursor.execute(\"SELECT COUNT(*) FROM genericm2m_relatedobject;\")\n generic_count = cursor.fetchone()\n\n if generic_count[0] < 2940:\n generic_count_message = \"Related government units has dropped below 2,940.\"\n mail_admins('Low number of government units alert.', message=generic_count_message)","def test_techs_energy_capacity_systemwide_constraint(self, bound):\n\n def check_bounds(constraint):\n assert po.value(constraint.upper) == 20\n if bound == \"equals\":\n assert po.value(constraint.lower) == 20\n if bound == \"max\":\n assert po.value(constraint.lower) is None\n\n m = build_model(\n {f\"techs.test_supply_elec.constraints.energy_cap_{bound}_systemwide\": 20},\n \"simple_supply,two_hours,investment_costs\",\n )\n m.run(build_only=True)\n assert hasattr(m._backend_model, \"energy_capacity_systemwide_constraint\")\n assert (\n \"test_supply_elec\"\n in m._backend_model.energy_capacity_systemwide_constraint.keys()\n )\n check_bounds(\n m._backend_model.energy_capacity_systemwide_constraint[\"test_supply_elec\"]\n )\n\n # Check that a model without transmission techs doesn't cause an error\n m = build_model(\n {f\"techs.test_supply_elec.constraints.energy_cap_{bound}_systemwide\": 20},\n \"simple_supply,two_hours,investment_costs\",\n model_file=\"model_minimal.yaml\",\n )\n m.run(build_only=True)\n assert hasattr(m._backend_model, \"energy_capacity_systemwide_constraint\")\n check_bounds(\n m._backend_model.energy_capacity_systemwide_constraint[\"test_supply_elec\"]\n )","def test_health_facilities_endpoint_gets_health_facilities(self):\n response = self.client.get(\"search/health-facilities?q=eldoret\")\n self.assertIn(b\"OK\", response.data)","def validate_capacity(capacity):\n\n VALID_MYSQL_SCALING_CONFIGURATION_CAPACITIES = (1, 2, 4, 8, 16, 32, 64, 128, 256)\n VALID_POSTGRESL_SCALING_CONFIGURATION_CAPACITIES = (2, 4, 8, 16, 32, 64, 192, 384)\n if (\n capacity not in VALID_POSTGRESL_SCALING_CONFIGURATION_CAPACITIES\n and capacity not in VALID_MYSQL_SCALING_CONFIGURATION_CAPACITIES\n ):\n raise ValueError(\n \"ScalingConfiguration capacity must be one of: {}\".format(\n \", \".join(\n map(\n str,\n VALID_MYSQL_SCALING_CONFIGURATION_CAPACITIES\n + VALID_POSTGRESL_SCALING_CONFIGURATION_CAPACITIES,\n )\n )\n )\n )\n return capacity","def _check(self):\n assert self._leaves, (\n 'Need to validate AssetAllocation before using it.')","def check_upgrades(self):\n if \"HMI-25\" in self.player.get_item_names():\n self.player.set_capacity(DEFAULT_CAPACITY + 25)\n if \"HMI-50\" in self.player.get_item_names():\n self.player.set_capacity(DEFAULT_CAPACITY + 50)","def available_capacity(self):\r\n return self.capacity - len(self.passengers)","def test_age_is_positive(self):\n nt.assert_greater_equal(self.herb.age, 0)","def knapsack(items, capacity):\r\n pass","def check_empty(self):\n if self.size():\n raise AttributeError","def test_topo_current_occupants_positive():\n instance = topo.Topography()\n assert instance.current_occupants()[\"Herbivores\"] >= 0\\\n and instance.current_occupants()[\"Carnivores\"] >= 0","def test_constructed_is_small(self):\n self.assertTrue(all(elt<10 for elt in goodwinsheaf.checkradii()))#check all entries have small radii","def get_num_slots(self):\n # Your code here\n return self.capacity","def test_order_constraint(self):\n orders_placed = [25, 25, 25]\n with self.assertRaises(Exception):\n analyse_uncertain_demand.UncertainDemand(\n orders=orders_placed,\n sku='Rx493-90',\n lead_time=Decimal(4),\n unit_cost=Decimal(40),\n reorder_cost=Decimal(400),\n retail_price=Decimal(600),\n currency='USD'\n )","def testinvalidsize(self):\n self.assertRaises(AbilityError, AttributeAbility, size=0)\n self.assertRaises(AbilityError, AttributeAbility, size=6)","def _check_amount_with_priority(self):\n\t\tfor slc in self:\n\t\t\tif slc.max_amount and self.search([('priority', '<', slc.priority), ('max_amount', '>=', slc.max_amount)]):\n\t\t\t\traise Warning(_(\"There are below slides [Priority less than %s] with bigger amount from [%s]\"\n\t\t\t\t \" which against the logic!!!\\n You can increase amount or handel priority\")\n\t\t\t\t % (slc.priority, slc.max_amount))","def _get_new_capacity(self):\n for prime in primes:\n if prime > 2 * self.size:\n return prime\n raise ValueError(\"Error: Table size overflow!\")","def initialize_supply(self):\n unit_count = 0\n for i in range(self.start_allocation[0 ] -1, self.start_allocation[1]):\n for j in range(len(self.capacity_list[i][1])):\n self.capacity_list[i][1][j] = 1\n unit_count += 1\n self.total_supply -= unit_count","def check_miss_count(self):\n first = self.attk_arry[-1]\n second = self.attk_arry[-2]\n third = self.attk_arry[-3]\n fourth = self.attk_arry[-4]\n sum_of_attk = first + second + third + fourth\n if sum_of_attk == 8:\n self.column_arry.append(10)\n self.row_arry.append(10)\n else:\n pass","def not_assignable(self):\n return self.unavailable + self.occupied","def lackingAtomsForMolecule(self):\n createErrors = [(atom.name, count) for atom, count in self.available.items() if self.available[atom] < atom.value]\n if len(createErrors) > 0:\n print(\"too few atoms produced to create a new molecule\")\n print(\"Too low atom counts:\", createErrors)\n return False\n return True"],"string":"[\n \"def _check_hospital_capacity(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n if len(hospital.matching) > hospital.capacity:\\n errors.append(\\n ValueError(\\n f\\\"{hospital} is matched to {hospital.matching} which \\\"\\n f\\\"is over their capacity of {hospital.capacity}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_capacity_cannot_be_empty(self):\\n with self.assertRaises(Exception) as context:\\n self.client.post(\\n url_for('teams'),\\n data={\\n 'name': 'team',\\n 'capacity': 'hello',\\n 'number_players': '1',\\n 'pitch_postcode': 'E1 6LT',\\n 'time': '2019-01-01 13:00'\\n }\\n )\\n self.assertTrue('Capacity must be a number' in context.exception)\\n self.assertEqual(db.session.query(Team).count(), 0)\",\n \"def test_check_inputs_supervisor_capacities_sufficient(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n project = game.projects[0]\\n supervisor_capacity = project.supervisor.capacity\\n project.capacity = supervisor_capacity + 1\\n\\n with pytest.warns(CapacityChangedWarning) as record:\\n game._check_inputs_supervisor_capacities_sufficient()\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(project.name)\\n assert str(project.capacity) in message\\n assert str(supervisor_capacity) in message\\n\\n if clean:\\n assert project.capacity == supervisor_capacity\",\n \"def test_set_glass_capacity__with_valid_numbers__returns_expected():\\n glass = moet.create_glass(\\\"A\\\")\\n numbers = [0, 1, 250, 0.0, 100.5]\\n for number in numbers:\\n glass.capacity = number\\n assert glass.capacity == number\",\n \"def test_set_glass_capacity__with_invalid_numbers__returns_expected():\\n glass = moet.create_glass(\\\"A\\\")\\n with pytest.raises(ValueError):\\n glass.capacity = -100\",\n \"def _check_hospital_unhappy(resident, hospital):\\n\\n return len(hospital.matching) < hospital.capacity or any(\\n [hospital.prefers(resident, match) for match in hospital.matching]\\n )\",\n \"def test_check_inputs_supervisor_capacities_necessary(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n total_project_capacity = sum(p.capacity for p in supervisor.projects)\\n supervisor.capacity = total_project_capacity + 1\\n\\n with pytest.warns(CapacityChangedWarning) as record:\\n game._check_inputs_supervisor_capacities_necessary()\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(supervisor.name)\\n assert str(supervisor.capacity) in message\\n assert str(total_project_capacity) in message\\n\\n if clean:\\n assert supervisor.capacity == total_project_capacity\",\n \"def check_hydro_capacity(plant_name, value, logger):\\n try:\\n max_value = HYDRO_CAPACITIES[plant_name]\\n except KeyError:\\n if value != 0.0:\\n logger.warning('New hydro plant seen - {} - {}MW'.format(plant_name, value), extra={'key': 'KR'})\\n return True\\n\\n if value > max_value:\\n logger.warning('{} reports {}MW generation with capacity of {}MW - discarding'.format(plant_name, value, max_value), extra={'key': 'KR'})\\n raise ValueError\\n else:\\n return True\",\n \"def checksufficientresources(ingredients):\\n for item in ingredients:\\n if ingredients[item] >= resources[item]:\\n print(f\\\"Sorry there is not enough {item}\\\")\\n return False\\n return True\",\n \"def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t, level):\\n # note \\\"amt\\\" has units of AMOUNT not RATE (resource, not resource per second)\\n sign = np.sign(amt)\\n # are we storing or providing?\\n #print('DEBUGG supposed current level:', level)\\n if sign < 0:\\n # we are being asked to consume some\\n cap, meta = self.get_capacity(meta, raven_vars, dispatch, t)\\n available_amount = cap[res] - level\\n #print('Supposed Capacity, Only calculated ins sign<0 (being asked to consumer)',cap)\\n else:\\n # we are being asked to produce some\\n available_amount = level\\n # the amount we can consume is the minimum of the requested or what's available\\n delta = sign * min(available_amount, abs(amt))\\n return {res: delta}, meta\",\n \"def Capacity(self) -> int:\",\n \"def test_rest_v20_dd_systems_systemid_stats_capacity_get(self):\\n pass\",\n \"def has_vacancy(self):\\n return len(self.occupants) < self.capacity\",\n \"def test_no_loc_techs_energy_capacity_storage_constraint(self, caplog):\\n with caplog.at_level(logging.INFO):\\n m = build_model(model_file=\\\"energy_cap_per_storage_cap.yaml\\\")\\n\\n m.run(build_only=True)\\n assert not any(\\n [\\n hasattr(\\n m._backend_model, \\\"energy_capacity_storage_{}_constraint\\\".format(i)\\n )\\n for i in [\\\"max\\\", \\\"equals\\\", \\\"min\\\"]\\n ]\\n )\",\n \"def is_resource_sufficient(self, drink):\\n can_make = True\\n for item in drink.ingredients:\\n if drink.ingredients[item] > self.resources[item]:\\n print(f\\\"Sorry there is not enough {item}.\\\")\\n can_make = False\\n return can_make\",\n \"def test_reviewer_capacity(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.reviewers[0].matching = range(match.reviewers[0].capacity + 1)\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_capacity()\",\n \"def test_excess_quantity(self):\\n excess = self._uncertain_demand.excess_stock\\n avg_order = sum([int(item) for item in self._data_set.values()]) //len(self._data_set)\\n variance = [(item - avg_order) for item in self._data_set.values()]\\n stdev = pow(sum([pow(j, 2) for j in variance]) / len(self._data_set), 0.5)\\n cal_safety = lambda x, y, z: x * y * (z ** 0.5)\\n safety_stock = cal_safety(float(self._z_value), float(stdev), float(self._lead_time))\\n cal_reorder_level = lambda x, y, z: ((x ** 0.5) * y) + z\\n reorder = cal_reorder_level(float(self._lead_time), avg_order, float(safety_stock))\\n cal_excess = lambda x, y, z: round(x - (y + (y - z)), 0) if x > y + (y - z) else 0\\n test_excess = cal_excess(self._quantity_on_hand, reorder, safety_stock)\\n self.assertEqual(int(excess), int(test_excess))\",\n \"def is_resource_sufficient(self, drink):\\n for ingredient in self.resources:\\n if drink.ingredients[ingredient] > self.resources[ingredient]:\\n print(f\\\"Sorry there is not enough {ingredient}\\\")\\n return False\\n return True\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def test_environmental_impact_compliance():\\n emissions = 12000\\n legal_limit = 300\\n assert emissions < legal_limit\",\n \"def test_get_damage_out_of_limit(self):\\n self.veh.health = 0.24\\n for op in self.veh.operators:\\n op.health = 0.1\\n self.veh.get_damage(0.5)\\n self.assertEqual(self.veh.health, 0)\\n self.assertEqual(self.veh.operators[0].health, 0.05)\\n self.assertEqual(self.veh.operators[1].health, 0.05)\",\n \"def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t):\\n cap = self.get_capacity(meta, raven_vars, dispatch, t)[0][self._capacity_var]\\n try:\\n if abs(balance[self._capacity_var]) > abs(cap):\\n #ttttt\\n # do the inverse problem: how much can we make?\\n balance, meta = self.produce_max(meta, raven_vars, dispatch, t)\\n print('The full requested amount ({res}: {req}) was not possible, so accessing maximum available instead ({res}: {blc}).'.format(res=res, req=amt, blc=balance[res]))\\n except KeyError:\\n raise SyntaxError('Resource \\\"{}\\\" is listed as capacity limiter, but not an output of the component! Got: {}'.format(self._capacity_var, balance))\\n return balance, meta\",\n \"def sufficient_resources(drink_type):\\r\\n\\r\\n is_sufficient = True\\r\\n for resource in MENU[drink_type][\\\"ingredients\\\"]:\\r\\n if not(MENU[drink_type][\\\"ingredients\\\"][resource] <= resources[resource]):\\r\\n print(f\\\"Sorry there is not enough {resource}.\\\")\\r\\n is_sufficient = False\\r\\n return is_sufficient\",\n \"def test_capacity_factor(pudl_out_eia):\\n print(\\\"\\\\nCalculating generator capacity factors...\\\")\\n cf = pudl_out_eia.capacity_factor()\\n print(f\\\" capacity_factor: {len(cf)} records\\\")\",\n \"def is_resource_sufficient(order_ingredients):\\r\\n for item in order_ingredients:\\r\\n if order_ingredients[item] > resources[item]:\\r\\n print(f\\\"Sorry there is not enough {item}.\\\")\\r\\n return False\\r\\n return True\",\n \"def is_resource_sufficient(self, drink):\\n can_make = True\\n for item in drink.ingredients:\\n if drink.ingredients[item] > self.resources[item]:\\n print(f\\\"Sorry there is not enough {item}.\\\")\\n can_make = False\\n return can_make\",\n \"def testsize(self):\\n for size in range(1, 5):\\n a = WeaponAbility('Defender', size=size)\\n self.assert_(str(size) in str(a))\",\n \"def capacity(self):\\n raise NotImplementedError()\",\n \"def test_capacity(self, space_each_type):\\n tspace = build_required_space(space_each_type, type_requirement=\\\"real\\\")\\n assert tspace.cardinality == numpy.inf\\n\\n space = Space()\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", 2, 3, 4)\\n dim = Categorical(\\\"yolo\\\", OrderedDict(zip(categories, probs)), shape=2)\\n space.register(dim)\\n dim = Integer(\\\"yolo2\\\", \\\"uniform\\\", -3, 6)\\n space.register(dim)\\n tspace = build_required_space(space, type_requirement=\\\"integer\\\")\\n assert tspace.cardinality == (4**2) * (6 + 1)\\n\\n dim = Integer(\\\"yolo3\\\", \\\"uniform\\\", -3, 6, shape=(2, 1))\\n space.register(dim)\\n tspace = build_required_space(space, type_requirement=\\\"integer\\\")\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\\n\\n tspace = build_required_space(\\n space, type_requirement=\\\"integer\\\", shape_requirement=\\\"flattened\\\"\\n )\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\\n\\n tspace = build_required_space(\\n space, type_requirement=\\\"integer\\\", dist_requirement=\\\"linear\\\"\\n )\\n assert tspace.cardinality == (4**2) * (6 + 1) * ((6 + 1) ** (2 * 1))\",\n \"def _check_cost(self, cr, uid, ids, context=None):\\n for enrich in self.browse(cr, uid, ids, context=context):\\n if enrich.amount <= 0:\\n raise osv.except_osv(_('ValidateError'), _('The Cost Must Be Greater Than Zero!'))\\n return True\",\n \"def validate_task_capacity(value):\\n if value < 0:\\n raise ValidationError(\\n _('Task capacity cannot be negative.'),\\n params={'value': value},\\n )\",\n \"def test_deal_insufficient_cards(self):\\n cards = self.deck._deal(100)\\n self.assertEqual(len(cards), 52)\\n self.assertEqual(self.deck.count(), 0)\",\n \"def capacity_used(self):\\n raise NotImplementedError()\",\n \"def wh_capacity_check(agent_full_name, agent_directory):\\r\\n agent_data_df = pd.read_csv(f'{agent_full_name}.csv', header=0, delimiter=\\\",\\\", engine='python')\\r\\n if agent_data_df.loc[0, 'capacity'] == agent_data_df.loc[0, 'load']:\\r\\n msg_body = \\\"negative\\\"\\r\\n elif agent_data_df.loc[0, 'capacity'] > agent_data_df.loc[0, 'load']:\\r\\n msg_body = \\\"positive\\\"\\r\\n else:\\r\\n msg_body = \\\"negative\\\"\\r\\n return msg_body\",\n \"def __len__(self):\\n return self.capacity\",\n \"def exceeds_shelf_capacity(shelf, fabric):\\n shelf_total = Decimal(shelf.fabrics.all().aggregate(Sum('quantity_th'))['quantity_th__sum'] or 0)\\n return True if (shelf_total) + fabric.quantity > max_shelf_qty else False\",\n \"def enough_resources(order_ingredients):\\n is_enough = True\\n for item in order_ingredients:\\n if order_ingredients[item] >= resources[item]:\\n print(f\\\"Sorry there is not enough {item}.\\\")\\n is_enough = False\\n return True\",\n \"def test_glass_capacity__has_expected_default_value():\\n glass = moet.create_glass(\\\"A\\\")\\n assert glass.capacity == 250\",\n \"def are_ingredients_sufficient(order_ingredients):\\n for item in order_ingredients:\\n if order_ingredients[item] > resources[item]:\\n print(f\\\"Sorry there is not enough {item}.\\\")\\n return False\\n return True\",\n \"def illegal(self):\\r\\n ## First checks for integer value\\r\\n if type(self.blockList) is int: return 1\\r\\n ## Then checks for 6 rows\\r\\n if len(self.blockList) > 6: return 1\\r\\n for row in self.blockList:\\r\\n ## Then checks that each row has 6 columns\\r\\n if len(self.blockList[row]) > 6: return 1\\r\\n for column in self.blockList[row]:\\r\\n ## 18 blocks is the maximum number of blocks that can be on the board\\r\\n if block < 0 or block > 18: return 1\\r\\n return 0\",\n \"def test_creation_incorrect_hardbounds_count():\\n with pytest.raises(ValueError) as __:\\n value = 1\\n __ = param.Integer(value=value, hardbounds=[0, 10, 20])\",\n \"def sufficient_resources(menu, drink, resources):\\r\\n menu['espresso']['ingredients']['milk'] = 0 # because espresso doesn't need any milk\\r\\n if resources['Water'] < menu[drink]['ingredients']['water']:\\r\\n print(\\\"Sorry there isn't enough water to make the drink.\\\")\\r\\n return False\\r\\n elif resources['Milk'] < menu[drink]['ingredients']['milk']:\\r\\n print(\\\"Sorry there isn't enough milk to make the drink.\\\")\\r\\n return False\\r\\n elif resources['Coffee'] < menu[drink]['ingredients']['coffee']:\\r\\n print(\\\"Sorry there isn't enough coffee to make the drink.\\\")\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def capacitygroup_group():\",\n \"def test_deal_sufficient_cards(self):\\n cards = self.deck._deal(10)\\n self.assertEqual(len(cards), 10)\\n self.assertEqual(self.deck.count(), 42)\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[int]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def testsize(self):\\n for size in range(5):\\n a = AmuletAbility('Skepticism', size=size+1)\\n self.assert_(str(size+1) in str(a))\\n self.assertEqual(a.size, size+1)\\n self.assertTrue(isinstance(a.AC, int))\\n self.assertTrue(isinstance(a.description(), str))\",\n \"def _check_multiplicity(self, k, i):\\n for j in range(self._.d + 1):\\n if self._.a[i] >= k[i]:\\n raise InfeasibleError(\\\"%s of %s %d too large\\\" %\\n (self.SIZE, self.PART, i))\",\n \"def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"hospital\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n\\n for hospital, matches in matching.items():\\n old_idx = -np.infty\\n for resident in matches:\\n idx = hospital.pref_names.index(resident.name)\\n assert idx >= old_idx\\n old_idx = idx\",\n \"def getSusceptible(self):\\n\\n # use a mask and sum it to see the number of healthy people, designated as having a value equal to zero\\n self.susceptible = np.sum((self.getSpace()) == 0)\\n\\n return self.susceptible\",\n \"def _capacity_rule(self, prod_name, r, cap, m, t):\\n prod = getattr(m, prod_name)\\n # note that a negative capacity means a CONSUMPTION capacity instead of PRODUCTION\\n if cap > 0:\\n return prod[r, t] <= cap\\n else:\\n return prod[r, t] >= cap\",\n \"def test_get_damage_out_of_limit(self):\\n self.sold.health = 0.2\\n self.sold.get_damage(0.32)\\n self.assertEqual(self.sold.health, 0)\",\n \"def isFull(self):\\n return self.count == self.capacity\",\n \"def isFull(self):\\n return self.count == self.capacity\",\n \"def test_check_for_oversubscribed_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n supervisor.matching = range(supervisor.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.capacity))\\n assert str(supervisor.matching) in e\",\n \"def test_creation_incorrect_softbounds_count():\\n with pytest.raises(ValueError) as __:\\n value = 1\\n __ = param.Integer(value=value, softbounds=[0, 10, 20])\",\n \"def test_compute_inventory():\\n T = [1000]\\n c_max = [1e20]\\n time = 1e3\\n inv, sig = divHretention.compute_inventory(T, c_max, time)\\n assert len(inv) == len(sig)\\n assert len(inv) == len(T)\",\n \"def accommodates_must_be_positive(cls, value):\\n assert value > 0, f'accommodates == {value}, must be > 0'\\n return value\",\n \"def verify_no_cable_errors(self):\\n i = 0\\n for dpid in self.dpids:\\n i += 1\\n labels = {'dp_id': '0x%x' % int(dpid), 'dp_name': 'faucet-%u' % i}\\n self.assertEqual(\\n 0, self.scrape_prometheus_var(\\n var='stack_cabling_errors_total', labels=labels, default=None))\\n self.assertGreater(\\n self.scrape_prometheus_var(\\n var='stack_probes_received_total', labels=labels), 0)\",\n \"def is_feasible(self):\\n keys = Counter(self.keys)\\n for chest in self.chests:\\n keys.update(chest['keys'])\\n keys.subtract([chest['lock']])\\n for value in keys.values():\\n if value < 0:\\n if DEBUG:\\n print \\\"Not enough keys: %r\\\" % keys\\n return False\\n # TBD - find cycles\\n return True\",\n \"def isFull(self) -> bool:\\n return self.count == self.capacity\",\n \"def isFull(self) -> bool:\\n return self.count == self.capacity\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def capacity(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"capacity\\\")\",\n \"def invalidsize(self):\\n self.assertRaises(AbilityError, AmuletAbility, 'Skepticism', size=0)\\n self.assertRaises(AbilityError, AmuletAbility, 'Skepticism', size=6)\\n self.assertRaises(AbilityError, AmuletAbility, 'Control NPC', size=3)\",\n \"def _check_shrink(self):\\n # As an example, if length is 1/4 of capacity and growth factor is 2,\\n # then the capacity should shrink in half to keep length proportional\\n # to capacity\\n if self._length < int(self._capacity / (self._growth_factor ** 2)):\\n self._shrink_arr()\",\n \"def test_sufficient_statistics(self):\\n assert (\\n len(self.data),\\n self.data.var(),\\n self.data.mean(),\\n ) == sufficient_statistics(self.data)\",\n \"def check_overall_energy(self):\\n energy = 0\\n for student in self.students:\\n energy += int(student.energy_level)\\n for mentor in self.mentors:\\n energy += int(mentor.energy_level)\\n print(\\\"Overall energy equals \\\", energy)\",\n \"def isLegal(self):\\n counter = 0\\n for t in self.types:\\n if t > 0:\\n counter = counter + 1\\n if counter < 4:\\n return True\\n else:\\n return False\",\n \"def test_loc_techs_storage_capacity_constraint(self):\\n m = build_model({}, \\\"simple_storage,two_hours,investment_costs\\\")\\n m.run(build_only=True)\\n assert hasattr(m._backend_model, \\\"storage_max_constraint\\\")\\n\\n m = build_model({}, \\\"simple_supply_and_supply_plus,two_hours,investment_costs\\\")\\n m.run(build_only=True)\\n assert hasattr(m._backend_model, \\\"storage_max_constraint\\\")\\n\\n m = build_model(\\n {\\\"techs.test_storage.constraints.storage_cap_equals\\\": 20},\\n \\\"simple_storage,two_hours,investment_costs\\\",\\n )\\n m.run(build_only=True)\\n assert m._backend_model.storage_cap[\\\"a\\\", \\\"test_storage\\\"].ub == 20\\n assert m._backend_model.storage_cap[\\\"a\\\", \\\"test_storage\\\"].lb == 20\",\n \"def _check_if_ingredients_sufficient(self, drink_type: str, drink_details: Beverages) -> None:\\n drink_composition = drink_details.get_receipe(drink_type=drink_type)\\n for ingredient in drink_composition:\\n if not self._inventory.check_if_ingredient_sufficient(\\n ingredient=ingredient, quantity=drink_composition.get(\\n ingredient, 0)\\n ):\\n raise InventoryInSufficient(\\n inventory_type=ingredient, drink_type=drink_type\\n )\",\n \"def testSchdulerCheckIngestReady(self):\\n\\t\\tpipelines = {\\n\\t\\t\\t\\\"continuum\\\": {\\n\\t\\t\\t\\t\\\"demand\\\": 5\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tobservation = Observation(\\n\\t\\t\\t'planner_observation',\\n\\t\\t\\tOBS_START_TME,\\n\\t\\t\\tOBS_DURATION,\\n\\t\\t\\tOBS_DEMAND,\\n\\t\\t\\tOBS_WORKFLOW,\\n\\t\\t\\ttype=\\\"continuum\\\",\\n\\t\\t\\tdata_rate=2\\n\\t\\t)\\n\\t\\t# There should be capacity\\n\\t\\tself.assertEqual(0.0, self.env.now)\\n\\t\\tret = self.scheduler.check_ingest_capacity(observation, pipelines)\\n\\t\\tself.assertTrue(ret)\\n\\n\\t\\t# Let's remove capacity to check it returns false\\n\\t\\ttmp = self.cluster.available_resources\\n\\t\\tself.cluster.available_resources = self.cluster.available_resources[:3]\\n\\t\\tret = self.scheduler.check_ingest_capacity(observation, pipelines)\\n\\t\\tself.assertFalse(ret)\\n\\t\\tself.cluster.available_resources = tmp\\n\\t\\tself.assertEqual(10, len(self.cluster.available_resources))\",\n \"def ensure_valid_data():\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT id, name, st_area(geom) FROM firestation_firedepartment where st_area(geom)>6.99\\\")\\n messages = []\\n\\n for id, name, area in cursor.fetchall():\\n messages.append('{0} ({1}) has an area of {2}.'.format(name, id, area))\\n\\n if messages:\\n mail_admins('Invalid Geometries Detected', message='\\\\n'.join(messages))\\n\\n cursor.execute(\\\"SELECT COUNT(*) FROM genericm2m_relatedobject;\\\")\\n generic_count = cursor.fetchone()\\n\\n if generic_count[0] < 2940:\\n generic_count_message = \\\"Related government units has dropped below 2,940.\\\"\\n mail_admins('Low number of government units alert.', message=generic_count_message)\",\n \"def test_techs_energy_capacity_systemwide_constraint(self, bound):\\n\\n def check_bounds(constraint):\\n assert po.value(constraint.upper) == 20\\n if bound == \\\"equals\\\":\\n assert po.value(constraint.lower) == 20\\n if bound == \\\"max\\\":\\n assert po.value(constraint.lower) is None\\n\\n m = build_model(\\n {f\\\"techs.test_supply_elec.constraints.energy_cap_{bound}_systemwide\\\": 20},\\n \\\"simple_supply,two_hours,investment_costs\\\",\\n )\\n m.run(build_only=True)\\n assert hasattr(m._backend_model, \\\"energy_capacity_systemwide_constraint\\\")\\n assert (\\n \\\"test_supply_elec\\\"\\n in m._backend_model.energy_capacity_systemwide_constraint.keys()\\n )\\n check_bounds(\\n m._backend_model.energy_capacity_systemwide_constraint[\\\"test_supply_elec\\\"]\\n )\\n\\n # Check that a model without transmission techs doesn't cause an error\\n m = build_model(\\n {f\\\"techs.test_supply_elec.constraints.energy_cap_{bound}_systemwide\\\": 20},\\n \\\"simple_supply,two_hours,investment_costs\\\",\\n model_file=\\\"model_minimal.yaml\\\",\\n )\\n m.run(build_only=True)\\n assert hasattr(m._backend_model, \\\"energy_capacity_systemwide_constraint\\\")\\n check_bounds(\\n m._backend_model.energy_capacity_systemwide_constraint[\\\"test_supply_elec\\\"]\\n )\",\n \"def test_health_facilities_endpoint_gets_health_facilities(self):\\n response = self.client.get(\\\"search/health-facilities?q=eldoret\\\")\\n self.assertIn(b\\\"OK\\\", response.data)\",\n \"def validate_capacity(capacity):\\n\\n VALID_MYSQL_SCALING_CONFIGURATION_CAPACITIES = (1, 2, 4, 8, 16, 32, 64, 128, 256)\\n VALID_POSTGRESL_SCALING_CONFIGURATION_CAPACITIES = (2, 4, 8, 16, 32, 64, 192, 384)\\n if (\\n capacity not in VALID_POSTGRESL_SCALING_CONFIGURATION_CAPACITIES\\n and capacity not in VALID_MYSQL_SCALING_CONFIGURATION_CAPACITIES\\n ):\\n raise ValueError(\\n \\\"ScalingConfiguration capacity must be one of: {}\\\".format(\\n \\\", \\\".join(\\n map(\\n str,\\n VALID_MYSQL_SCALING_CONFIGURATION_CAPACITIES\\n + VALID_POSTGRESL_SCALING_CONFIGURATION_CAPACITIES,\\n )\\n )\\n )\\n )\\n return capacity\",\n \"def _check(self):\\n assert self._leaves, (\\n 'Need to validate AssetAllocation before using it.')\",\n \"def check_upgrades(self):\\n if \\\"HMI-25\\\" in self.player.get_item_names():\\n self.player.set_capacity(DEFAULT_CAPACITY + 25)\\n if \\\"HMI-50\\\" in self.player.get_item_names():\\n self.player.set_capacity(DEFAULT_CAPACITY + 50)\",\n \"def available_capacity(self):\\r\\n return self.capacity - len(self.passengers)\",\n \"def test_age_is_positive(self):\\n nt.assert_greater_equal(self.herb.age, 0)\",\n \"def knapsack(items, capacity):\\r\\n pass\",\n \"def check_empty(self):\\n if self.size():\\n raise AttributeError\",\n \"def test_topo_current_occupants_positive():\\n instance = topo.Topography()\\n assert instance.current_occupants()[\\\"Herbivores\\\"] >= 0\\\\\\n and instance.current_occupants()[\\\"Carnivores\\\"] >= 0\",\n \"def test_constructed_is_small(self):\\n self.assertTrue(all(elt<10 for elt in goodwinsheaf.checkradii()))#check all entries have small radii\",\n \"def get_num_slots(self):\\n # Your code here\\n return self.capacity\",\n \"def test_order_constraint(self):\\n orders_placed = [25, 25, 25]\\n with self.assertRaises(Exception):\\n analyse_uncertain_demand.UncertainDemand(\\n orders=orders_placed,\\n sku='Rx493-90',\\n lead_time=Decimal(4),\\n unit_cost=Decimal(40),\\n reorder_cost=Decimal(400),\\n retail_price=Decimal(600),\\n currency='USD'\\n )\",\n \"def testinvalidsize(self):\\n self.assertRaises(AbilityError, AttributeAbility, size=0)\\n self.assertRaises(AbilityError, AttributeAbility, size=6)\",\n \"def _check_amount_with_priority(self):\\n\\t\\tfor slc in self:\\n\\t\\t\\tif slc.max_amount and self.search([('priority', '<', slc.priority), ('max_amount', '>=', slc.max_amount)]):\\n\\t\\t\\t\\traise Warning(_(\\\"There are below slides [Priority less than %s] with bigger amount from [%s]\\\"\\n\\t\\t\\t\\t \\\" which against the logic!!!\\\\n You can increase amount or handel priority\\\")\\n\\t\\t\\t\\t % (slc.priority, slc.max_amount))\",\n \"def _get_new_capacity(self):\\n for prime in primes:\\n if prime > 2 * self.size:\\n return prime\\n raise ValueError(\\\"Error: Table size overflow!\\\")\",\n \"def initialize_supply(self):\\n unit_count = 0\\n for i in range(self.start_allocation[0 ] -1, self.start_allocation[1]):\\n for j in range(len(self.capacity_list[i][1])):\\n self.capacity_list[i][1][j] = 1\\n unit_count += 1\\n self.total_supply -= unit_count\",\n \"def check_miss_count(self):\\n first = self.attk_arry[-1]\\n second = self.attk_arry[-2]\\n third = self.attk_arry[-3]\\n fourth = self.attk_arry[-4]\\n sum_of_attk = first + second + third + fourth\\n if sum_of_attk == 8:\\n self.column_arry.append(10)\\n self.row_arry.append(10)\\n else:\\n pass\",\n \"def not_assignable(self):\\n return self.unavailable + self.occupied\",\n \"def lackingAtomsForMolecule(self):\\n createErrors = [(atom.name, count) for atom, count in self.available.items() if self.available[atom] < atom.value]\\n if len(createErrors) > 0:\\n print(\\\"too few atoms produced to create a new molecule\\\")\\n print(\\\"Too low atom counts:\\\", createErrors)\\n return False\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7667984","0.6397596","0.62737614","0.6177663","0.60829747","0.6006602","0.59802","0.5944431","0.5858093","0.58271384","0.58146626","0.5800991","0.57918227","0.5761161","0.5714757","0.5673754","0.56648004","0.5643236","0.56302327","0.56134015","0.55817956","0.5578489","0.5564471","0.55361754","0.5533132","0.5530944","0.55183434","0.5494716","0.54810596","0.54784954","0.5469564","0.54106045","0.54091537","0.5404461","0.53867507","0.5370868","0.5366705","0.53550214","0.5348046","0.5341837","0.53297","0.5321682","0.53149813","0.53078884","0.52992314","0.52992314","0.52992314","0.52992314","0.5283028","0.52704227","0.5259644","0.52487886","0.5239766","0.52206445","0.5203968","0.5203968","0.5202309","0.5191861","0.5191595","0.51846856","0.51835227","0.51619166","0.5154475","0.5154475","0.51462525","0.51462525","0.51462525","0.51462525","0.51462525","0.51462525","0.51462525","0.51453406","0.51429296","0.5133403","0.51316315","0.51187515","0.5105877","0.5089403","0.50880253","0.508723","0.5083382","0.5078947","0.50709903","0.5052515","0.5043382","0.50405085","0.5039634","0.5038467","0.503693","0.5036234","0.5033738","0.5024913","0.5024686","0.50233036","0.5018824","0.5017297","0.5007318","0.50027686","0.49971712","0.49970144"],"string":"[\n \"0.7667984\",\n \"0.6397596\",\n \"0.62737614\",\n \"0.6177663\",\n \"0.60829747\",\n \"0.6006602\",\n \"0.59802\",\n \"0.5944431\",\n \"0.5858093\",\n \"0.58271384\",\n \"0.58146626\",\n \"0.5800991\",\n \"0.57918227\",\n \"0.5761161\",\n \"0.5714757\",\n \"0.5673754\",\n \"0.56648004\",\n \"0.5643236\",\n \"0.56302327\",\n \"0.56134015\",\n \"0.55817956\",\n \"0.5578489\",\n \"0.5564471\",\n \"0.55361754\",\n \"0.5533132\",\n \"0.5530944\",\n \"0.55183434\",\n \"0.5494716\",\n \"0.54810596\",\n \"0.54784954\",\n \"0.5469564\",\n \"0.54106045\",\n \"0.54091537\",\n \"0.5404461\",\n \"0.53867507\",\n \"0.5370868\",\n \"0.5366705\",\n \"0.53550214\",\n \"0.5348046\",\n \"0.5341837\",\n \"0.53297\",\n \"0.5321682\",\n \"0.53149813\",\n \"0.53078884\",\n \"0.52992314\",\n \"0.52992314\",\n \"0.52992314\",\n \"0.52992314\",\n \"0.5283028\",\n \"0.52704227\",\n \"0.5259644\",\n \"0.52487886\",\n \"0.5239766\",\n \"0.52206445\",\n \"0.5203968\",\n \"0.5203968\",\n \"0.5202309\",\n \"0.5191861\",\n \"0.5191595\",\n \"0.51846856\",\n \"0.51835227\",\n \"0.51619166\",\n \"0.5154475\",\n \"0.5154475\",\n \"0.51462525\",\n \"0.51462525\",\n \"0.51462525\",\n \"0.51462525\",\n \"0.51462525\",\n \"0.51462525\",\n \"0.51462525\",\n \"0.51453406\",\n \"0.51429296\",\n \"0.5133403\",\n \"0.51316315\",\n \"0.51187515\",\n \"0.5105877\",\n \"0.5089403\",\n \"0.50880253\",\n \"0.508723\",\n \"0.5083382\",\n \"0.5078947\",\n \"0.50709903\",\n \"0.5052515\",\n \"0.5043382\",\n \"0.50405085\",\n \"0.5039634\",\n \"0.5038467\",\n \"0.503693\",\n \"0.5036234\",\n \"0.5033738\",\n \"0.5024913\",\n \"0.5024686\",\n \"0.50233036\",\n \"0.5018824\",\n \"0.5017297\",\n \"0.5007318\",\n \"0.50027686\",\n \"0.49971712\",\n \"0.49970144\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73591745"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":93795,"cells":{"query":{"kind":"string","value":"Test for the correct solving of games."},"document":{"kind":"string","value":"def test_solve(game, optimal):\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_hospital, hospital in zip(matching_keys, hospitals):\n assert game_hospital.name == hospital.name\n assert game_hospital._pref_names == hospital._pref_names\n assert game_hospital.capacity == hospital.capacity\n\n matched_residents = [\n resident for match in matching.values() for resident in match\n ]\n\n assert matched_residents != [] and set(matched_residents).issubset(\n set(game.residents)\n )\n\n for resident in set(game.residents) - set(matched_residents):\n assert resident.matching is None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_check_validity(game):\n\n game.solve()\n assert game.check_validity()","def test_check_solved():\n game = Game()\n game.word = 'word'\n game.pattern = 'word'\n game.check_solved()\n assert game.solved is True","def test_is_solved_when_puzzle_is_solved(self):\n self.assertTrue(self.sudoku.is_solved())","def test_check_validity(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n game.solve()\n assert game.check_validity()","def test_is_solved(self):\n p = hw.TilePuzzle([[1, 2], [3, 0]])\n self.assertTrue(p.is_solved())\n p = hw.TilePuzzle([[0, 1], [3, 2]])\n self.assertFalse(p.is_solved())","def check_game_sanity(game_val, log):\n\n global __problem_deck_index__\n\n supply = game_val.get_supply()\n if game_val.any_resigned():\n return True # Resign leaves deck in arbitrary state, don't catch that\n\n # ignore known bugs.\n #if set(supply).intersection([get_card('Masquerade'), get_card('Black Market'), get_card('Trader')]):\n #return True\n\n # TODO: add score sanity checking here\n last_state = None\n game_state_iterator = game_val.game_state_iterator()\n for game_state in game_state_iterator:\n last_state = game_state\n for player_deck in game_val.get_player_decks():\n parsed_deck_comp = player_deck.Deck()\n computed_deck_comp = last_state.get_deck_composition(\n player_deck.name())\n\n parse_common.delete_keys_with_empty_vals(parsed_deck_comp)\n computed_dict_comp = dict(computed_deck_comp)\n parse_common.delete_keys_with_empty_vals(computed_dict_comp)\n\n if parsed_deck_comp != computed_deck_comp:\n found_something_wrong = False\n for card in set(parsed_deck_comp.keys() +\n computed_deck_comp.keys()):\n if parsed_deck_comp.get(card, 0) != \\\n computed_deck_comp.get(card, 0):\n if not found_something_wrong:\n __problem_deck_index__ += 1\n log.debug('[%d] %18s %9s %9s', __problem_deck_index__, 'card', 'from-data', 'from-sim')\n log.debug('[%d] %-18s %9d %9d', __problem_deck_index__, card, parsed_deck_comp.get(card, 0),\n computed_deck_comp.get(card, 0))\n found_something_wrong = True\n if found_something_wrong:\n try:\n log.debug('[%d] insane game for %s %s: %s', __problem_deck_index__, player_deck.name(), game_val.get_id(),\n ' '.join(map(str, game_val.get_supply())))\n except UnicodeEncodeError, e:\n None\n return False\n return True","def test_init_with_existing_game(self):\n pass\n # Ensure judge is the same","def test_solve_one_player_2(self):\n self.rush_hour_data = rush_hour_data_2\n self.state_data = state_data_2\n self.execute_minimax_single_player()","def test_play_game(self):\r\n\r\n \r\n a_players = [RandomPlayer(1), RandomPlayer(2)]\r\n a_x_dist = 3\r\n a_y_dist = 3\r\n a_num_to_win = 1\r\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n #Game is played to competion\r\n a_game.play_game()\r\n\r\n a_history = a_game.get_history()\r\n\r\n #Go through each move and check to be sure it's valid\r\n for i in range(1,len(a_history)):\r\n #Get copy of the board\r\n prev_board = a_history[i-1]\r\n cur_board = a_history[i]\r\n\r\n #Check if the board chosen is in valid states\r\n self.assertTrue(cur_board in prev_board.get_states(a_players[0].get_id()) or cur_board in prev_board.get_states(a_players[1].get_id()),\\\r\n \"An invalid board state was added to the history\")\r\n\r\n if i == len(a_history) - 1:\r\n self.assertTrue(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\r\n else: \r\n self.assertFalse(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())","def test_is_solved_when_puzzle_is_not_solved(self):\n sudoku = sudolver.Sudoku()\n self.assertFalse(sudoku.is_solved())","def test_solve_one_player_3(self):\n self.rush_hour_data = rush_hour_data_3\n self.state_data = state_data_3\n self.execute_minimax_single_player()","def isValid(self, game):\n return True","def test_complete_game1(self):\n m = Game()\n\n plays = [\n ['a:-', 'a:-', 'a:b', 'b:b', 'b:-', 'b:b'],\n ['c:3i', 'c:f', 'c:f', 'c:-', 'd:f', 'd:-', 'd:-'],\n ['a:3i', 'a:2f', 'a:f', 'a:-', 'b:-', 'b:-', 'b:-'],\n ['c:3i', 'c:2f', 'c:-', 'c:f', 'd:-', 'd:b', 'd:b'],\n ['a:5i2rp', 'a:4f', 'a:f', 'a:b-', 'b:-', 'b:b', 'b:k']\n ]\n m.run(plays)\n m.print_stats()\n\n # Did the right team win?\n assert m.teams['a'].win == True\n\n # Did we get 5 turns?\n assert m.turn_count == 5\n\n # There should have been 30 throws in the game\n assert m.get_throw_count() == 30","def test_solve_one_player_1(self):\n self.rush_hour_data = rush_hour_data_1\n self.state_data = state_data_1\n self.execute_minimax_single_player()","def check_game(self):\n gameOver = None\n if self.turn > 4:\n gameOver = self.check_x_won()\n if gameOver is True:\n self.game_x_won()\n return\n\n gameOver = None\n if self.turn > 5:\n gameOver = self.check_o_won()\n if gameOver is True:\n self.game_o_won()\n return\n\n if self.turn >= 9:\n self.game_tie()\n return","def test_get_game(self):\n pass","def do_the_things():\n returned_no_game = False\n returned_game_time = False\n returned_game_final = False\n returned_rival_final = False\n returned_game_soon = False\n returned_rival_soon = False\n returned_game_start = False\n compare_scores = ['0', '0']\n timeout = time.time() + 60 * 60 * 23\n\n while not (returned_no_game or returned_game_final):\n\n try:\n message = False\n rival_message = False\n\n \"\"\" Infinite loops are cool \"\"\"\n if time.time() > timeout:\n break\n\n game_data = get_fresh_data(team)\n\n if game_data:\n \"\"\" The default TZ for mlb is US/Eastern, we'll do some things and make it local TZ \"\"\"\n tz = pytz.timezone('US/Eastern')\n eastern_time = datetime.datetime.strptime(\"%s %s\" % (game_data['time_date'], game_data['ampm']), '%Y/%m/%d %I:%M %p')\n eastern_time = tz.localize(eastern_time)\n pacific_time = eastern_time.astimezone(pytz.timezone(timezone))\n\n opponent, our_score, their_score, venue = set_vars(game_data)\n scores = sorted([our_score, their_score], reverse=True)\n\n if not game_data and not returned_no_game:\n returned_no_game = True\n message = (\"The %s don't have a game scheduled today. Rest well guys!\" % team_hashtag)\n\n if game_data and not returned_game_time:\n returned_game_time = True\n message = ((\"The %s are playing against the %s today, first pitch is at \" + pacific_time.strftime(\"%-I:%M%p %Z\") + \" at %s. %s\") % (team_hashtag, opponent, venue, team_hash2))\n\n if \"Warmup\" in game_data[\"status\"] and not returned_game_soon:\n returned_game_soon = True\n message = ((\"The %s are playing against the %s in a moment, first pitch is at \" + pacific_time.strftime(\"%-I:%M%p %Z\") + \" at %s. %s\") % (team_hashtag, opponent, venue, team_hash2))\n\n if \"In Progress\" in game_data[\"status\"] and compare_scores == ['0', '0'] and not returned_game_start:\n returned_game_start = True\n message = (\"It's gametime! Go %s!!! %s\" % (team_hashtag, team_hash2))\n\n if \"In Progress\" in game_data[\"status\"] and not compare_scores == [our_score, their_score]:\n compare_scores = [our_score, their_score]\n if int(our_score) > int(their_score):\n message = (\"The %s are leading the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n elif int(our_score) < int(their_score):\n message = (\"The %s are trailing the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n elif int(our_score) == int(their_score):\n message = (\"The %s are tied with the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n\n if (\"Game Over\" or \"Final\") in game_data[\"status\"]:\n returned_game_final = True\n if our_score > their_score:\n message = (\"The %s beat the %s today at %s with a score of %s-%s. %s\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\n else:\n message = (\"The %s lost against the %s today at %s with a score of %s-%s. Get 'em tomorrow! %s\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\n\n if not returned_rival_final:\n rival_game_data = get_fresh_data(rival_team)\n rival_opponent, rival_our_score, rival_their_score, rival_venue = set_vars(rival_game_data)\n rival_scores = sorted([rival_our_score, rival_their_score], reverse=True)\n\n if \"Warmup\" in rival_game_data[\"status\"] and not returned_rival_soon:\n returned_rival_soon = True\n rival_message = (\"The %s game is about to start, go %s!!! %s\") % (rival_team_hashtag, rival_opponent, rival_team_hash2)\n\n if (\"Game Over\" or \"Final\") in rival_game_data[\"status\"]:\n returned_rival_final = True\n if rival_our_score > rival_their_score:\n rival_message = (\"The %s beat the %s today at %s with a score of %s-%s, boooo!\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\n else:\n rival_message = (\"The %s lost against the %s today at %s with a score of %s-%s. Hell yeah!\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\n\n if message:\n if testmode:\n print(message)\n else:\n twitter.tweet(message)\n\n if rival_message:\n if testmode:\n print(rival_message)\n else:\n twitter.tweet(rival_message)\n\n except tweepy.TweepError as e:\n print e.message[0]['code']\n print e.args[0][0]['code']\n\n time.sleep(5)","def win_check(self):\n\t\t# Create a temp var to capture the number of correct matches\n\t\tright = 0\n\t\t# retrieve peg_guess_color_list for current round\n\t\tguess = self.model.guesses[self.model.status]\n\t\t# retreive solution list\n\t\tsolution = self.model.guesses[\"solution\"]\n\t\t# compare values in each index for both lists against eachother\n\t\tfor i in range(len(solution.pegs)):\n\t\t\t# \n\t\t\tif solution.pegs[i].peg_color == guess.pegs[i].peg_color:\n\t\t\t\tright += 1\n\n\t\t\t\tprint(\"Yay, it works!\")\n\n\t\t# If all indexes of the peg_colors in the solution and guess are True:\n\t\tif right == 4:\n\t\t\treturn True\n\n\t\telse:\n\t\t\treturn False","def is_legal_solution(self, solution):\r\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\r\n return self.fit_score(solution) == 0\r\n else:\r\n return self.fit_score(solution) == sum(x for x in range(1, 12))","def run(game):\n name = cli.welcome_user(game.DESCRIPTION)\n\n for _try_num in range(0, 3):\n question, right_answer = game.run_round()\n print('Question: {question}'.format(question=question))\n user_answer = prompt.string('Your answer: ')\n\n if user_answer != right_answer:\n print(WRONG_ANSWER_TEMPLATE.format(user_answer, right_answer, name))\n break\n print('Correct!')\n else:\n print('Congratulations, {name}!'.format(name=name))","def test_is_winner(self):\n TestGame = TTT_Game(\"John\", \"Jane\")\n\n # Check verticals\n TestGame.board = [1, 2, \"X\", 4, 5, \"X\", 7, 8, \"X\"]\n self.assertTrue(TestGame.is_winner(\"X\"))\n TestGame.board = [\"O\", 2, 3, \"O\", 5, \"X\", \"O\", 8, \"X\"]\n self.assertTrue(TestGame.is_winner(\"O\"))\n TestGame.board = [1, \"X\", \"O\", \"O\", \"X\", 6, 7, \"X\", \"X\"]\n self.assertTrue(TestGame.is_winner(\"X\"))\n\n # Check horizontals\n TestGame.board = [\"O\", \"O\", \"O\", \"O\", 5, \"X\", 7, 8, 9]\n self.assertTrue(TestGame.is_winner(\"O\"))\n TestGame.board = [1, 2, 3, \"X\", \"X\", \"X\", 7, 8, 9]\n self.assertTrue(TestGame.is_winner(\"X\"))\n TestGame.board = [1, 2, 3, \"O\", 5, 6, \"O\", \"O\", \"O\"]\n self.assertTrue(TestGame.is_winner(\"O\"))\n\n # Check diagonals\n TestGame.board = [\"O\", \"X\", 3, 4, \"O\", \"X\", \"X\", \"O\", \"O\"]\n self.assertTrue(TestGame.is_winner(\"O\"))\n TestGame.board = [1, 2, \"X\", 4, \"X\", 6, \"X\", 8, 9]\n self.assertTrue(TestGame.is_winner(\"X\"))","def test_is_advancing_to_next_stage_no(self):\n\n # test_input_cases =\n # [(die_a_value, die_b_value, stage, ok_output),]\n test_input_cases = [\n (\"1\", \"2\", 2, False),\n (\"2\", \"1\", 3, False),\n (\"1\", \"1\", 1, False),\n (\"1\", \"1\", 2, False),\n (\"1\", \"1\", 3, False),\n (\"ANGRY\", \"1\", 1, False),\n (\"ANGRY\", \"1\", 2, False),\n ]\n\n for test_io in test_input_cases:\n self._test_is_game_over(*test_io)","def test(self):\n\t\tx = Team.objects.get(short_name='SF')\n\t\tself.assertEqual(x.nick_name, '49ers')\n\n\t\tx = Game.objects.get(week_number=1,game_number=1)\n\t\tx.fav_score = 21\n\t\tx.udog_score = 14\n\t\tx.spread = 5\n\t\tself.assertEqual(x.favWins(), True)\n\t\tx.spread = 10\n\t\tself.assertEqual(x.favWins(), False)\n\n\t\t# Sunday game at 4pm should be open if time before 1 else closed\n\t\tx.game_date = datetime.datetime(2020,3,1,16,0,0)\n\t\tcurrent_time = datetime.datetime(2020,3,1,11,0,0)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), False)\n\t\tcurrent_time = datetime.datetime(2020,3,1,14,0,0)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\n\t\t# Saturday game. Should be closed only if after kick off\n\t\tx.game_date = datetime.datetime(2020,2,29,16,0,0)\n\t\tcurrent_time = datetime.datetime(2020,2,29,15,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), False)\n\t\tcurrent_time = datetime.datetime(2020,2,29,16,0,1)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\n\t\t# Monday game, should be closed as long as it's after 1pm sunday\n\t\tx.game_date = datetime.datetime(2020,3,2,21,0,0)\n\t\tcurrent_time = datetime.datetime(2020,3,1,15,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\t\tcurrent_time = datetime.datetime(2020,3,1,11,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), False)\n\t\tself.assertEqual(x.isOpen(current_time=current_time), True)\n\t\tcurrent_time = datetime.datetime(2020,3,2,11,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\t\tcurrent_time = datetime.datetime(2020,3,3,11,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\n\t\t# Sunday 9a game should be open until kick off\n\t\tx.game_date = datetime.datetime(2020,3,1,9,0,0)\n\t\tcurrent_time = datetime.datetime(2020,3,1,8,0,0)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), False)\n\t\tcurrent_time = datetime.datetime(2020,3,1,10,0,0)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\t\tself.assertEqual(x.isOpen(current_time=current_time), False)\n\n\t\t# Now one check with not setting current_time\n\t\tx.game_date = datetime.datetime(1971,3,1,9,0,0)\n\t\tself.assertEqual(x.isClosed(), True)\n\n\n\t\tuser = User.objects.get(username='Tammer')\n\t\tx = Pick.objects.get(player=user,week_number=1,game_number=1)\n\t\tg = Game.objects.get(week_number=1,game_number=1)\n\t\tself.assertEqual(x.picked_fav, True)\n\t\tself.assertEqual(x.isCorrect(), g.fav_score - g.udog_score > g.spread)","def start_game(table_index):\n initiate_game(table_index)\n\n uId = uIds[table_index]\n current_map = maps[table_index]\n current_alg = applied_algs[table_index].func\n game_over = False\n previousmoves = []\n\n while True:\n try:\n if not game_over:\n # print('Move scores')\n # print(evaluate.evaluate(current_map, 0))\n # print(evaluate.evaluate(current_map, 1))\n # print(evaluate.evaluate(current_map, 2))\n # print(evaluate.evaluate(current_map, 3))\n\n move = current_alg(current_map)\n\n print(previousmoves)\n # check for bug\n # first case\n if len(previousmoves) == 0:\n previousmoves.append(move)\n # same movement\n elif previousmoves[-1] != move:\n previousmoves = []\n elif previousmoves[-1] == move:\n previousmoves.append(move)\n # 10 same movement\n\n\n if len(previousmoves) >= 4 and move == previousmoves[-1]:\n wrongdirection = previousmoves[-1]\n previousmoves = []\n moves = [\"w\", \"a\", \"s\", \"d\"]\n moves.remove(wrongdirection)\n\n move = random.choice(moves)\n\n print(move)\n\n request = requests.post(url=base_URL + \"/api/play_the_game\",\n json={'direction': move,\n 'uId': uId})\n current_map = request.json()['board']\n\n print(request.json())\n\n # TODO: Type checking, error handling (HTTP response?)\n game_over = request.json()[\"game_over\"]\n\n else:\n c_score = request.json()[\"c_score\"]\n SESSION_NAME = TEAM_NAME + \"_\" + applied_algs[table_index].label\n\n with open('score_data.txt', 'a') as f:\n f.write(datetime.datetime.now().strftime('%H:%M:%S') + \" \" + SESSION_NAME + \" \" + '%d' % c_score + \"\\n\")\n\n print()\n print(f\"Game Over. Your score is: {c_score}\")\n print()\n\n initiate_game(table_index)\n game_over = False\n uId = uIds[table_index]\n current_map = maps[table_index]\n except:\n print(\"Error\")","async def check(self):\n\n while not self.solved:\n # Get list of possible numbers this square can have\n possibles = self.get_possible_numbers()\n # If there's only once possibility, then use this number...this square is now solved\n if len(possibles) == 1:\n self.num = possibles.pop()\n # If there are no possible squares well...something's wrong, that shouldn't be possible\n # This check is done because we want to be able to guess and check, and figure out if a guess is invalid\n elif len(possibles) == 0:\n raise ValueError(\"Impossible square; no possible numbers based on restrictions\")\n # Otherwise wait a small amount and continue\n else:\n await asyncio.sleep(0.05)","def test_game_logic(self, client: FlaskClient) -> None:\n test_user = \"janedoe\"\n n_wins_first = 3\n n_drops_next = 5\n test_tricks = [] # List[models.Trick]\n with server.app.app_context() as server_context:\n # Just some trick id's to use\n test_tricks = models.Trick.query.limit(n_wins_first + n_drops_next +\n 1)\n\n # Declare game object\n game = models.Game(attempts=[], user=test_user)\n models.db.session.add(game)\n models.db.session.commit()\n\n # Use lands first 3, opp misses all, should be opp: SKA\n for trick in test_tricks[0:n_wins_first]:\n # Note these call model functions not client turns, because client attempt\n # would also automatically choose oppoenent response, we want to test that\n _game_turn(trick.id, True, test_user, game.id, client,\n server_context)\n _game_turn(trick.id, False, \"past_\" + test_user, game.id,\n client, server_context)\n\n models.db.session.add(game)\n models.db.session.commit() # re-sync attempts\n game_state = models.get_game_state(game.attempts, test_user)\n assert game_state.user_score == 0\n assert game_state.opponent_score == n_wins_first\n\n # Now if both miss one, expect no score change. Use last trick ID\n _game_turn(test_tricks[-1].id, False, test_user, game.id, client,\n server_context)\n _game_turn(test_tricks[-1].id, False, \"past_\" + test_user, game.id,\n client, server_context)\n\n # Now if both land one, expect no score change. Use last trick ID\n _game_turn(test_tricks[-1].id, True, test_user, game.id, client,\n server_context)\n _game_turn(test_tricks[-1].id, True, \"past_\" + test_user, game.id,\n client, server_context)\n\n models.db.session.add(game)\n models.db.session.commit() # re-sync attempts\n game_state = models.get_game_state(game.attempts, test_user)\n assert game_state.user_score == 0\n assert game_state.opponent_score == n_wins_first\n\n # A one-off repeat should count as fail, turn over priority to opponent\n _game_turn(test_tricks[0].id, False, test_user, game.id, client,\n server_context)\n\n # Now opponent lands the next n_drops_next (5, enough to win)\n for trick in test_tricks[n_wins_first:n_wins_first + n_drops_next]:\n _game_turn(trick.id, True, \"past_\" + test_user, game.id, client,\n server_context)\n _game_turn(trick.id, False, test_user, game.id, client,\n server_context)\n\n models.db.session.add(game)\n models.db.session.commit() # re-sync attempts\n game_state = models.get_game_state(game.attempts, test_user)\n assert game_state.user_score == n_drops_next\n assert game_state.opponent_score == n_wins_first\n assert game_state.status_feed[\n -1].msg_text == \"[Turn 20]: Past you wins!\"","def check_if_game_over():\n check_for_winner()\n check_for_tie()","def test_strategy(self):\n\n axelrod.seed(8)\n opponent = axelrod.Cooperator()\n player = axelrod.WorseAndWorse()\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'D'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C')])\n\n # Test that behaviour does not depend on opponent\n opponent = axelrod.Defector()\n player = axelrod.WorseAndWorse()\n axelrod.seed(8)\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'D'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C')])","def check_winner(self):\n pass","def checkPuzzle(self):\n print('Got to checkPuzzle')","def test_get_solution(self):\n pass","def test_scores_eat_all(self):\n game = self.ending(['bwwwwww.'], 8, 1)\n game.man_move(0, 7)\n self.assertEqual(game.finish_state,\n (1600, game.second_player, 'No opponent chips'))","def test_check_win(self):\n # Horizontal wins\n # First row\n self.game.move(0, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 2, PLAYERX)\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(0, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\n\n # Second row\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(1, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 2, PLAYERX)\n self.assertEqual(self.game.check_win(1, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 2, PLAYERX), PLAYERX)\n\n # Third row\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(2, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 2, PLAYERX)\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\n\n # Vertical wins\n # First column\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 0, PLAYERX)\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\n\n # Second column\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 1, PLAYERX)\n self.assertEqual(self.game.check_win(0, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 1, PLAYERX), PLAYERX)\n\n # Third column\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 2, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 2, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 2, PLAYERX)\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 2, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\n\n # Diagonal wins\n # Upper left to bottom right\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 2, PLAYERX)\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\n\n # Bottom left to upper right\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(2, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 2, PLAYERX)\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\n\n # Draw\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 2, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 0, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 2, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 1, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 2, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), DRAW)","def test_not_found_pair(self):\n self.assertTrue(\"No matches found\", show_players_sumheigh_is_input(100))","def game_over(self) -> bool:\n for row in range(9):\n for col in range(9):\n if self._grid_sol[row][col] != self.get_cell(row, col):\n return False\n return True","def test_get_game_diff(self):\n pass","def play_strategic_game():\n board, winner = create_board(), 0\n board[1,1] = 1\n while winner == 0:\n for player in [2,1]:\n board = random_place(board, player)\n winner = evaluate(board)\n if winner != 0:\n break\n return winner","def test_equal(self):\r\n\r\n a_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n a_x_dist = 3\r\n a_y_dist = 3\r\n a_num_to_win = 1\r\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n b_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n b_x_dist = 3\r\n b_y_dist = 3\r\n b_num_to_win = 1\r\n b_game = Game(b_players, b_x_dist, b_y_dist, b_num_to_win)\r\n\r\n c_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n c_x_dist = 3\r\n c_y_dist = 3\r\n c_num_to_win = 1\r\n c_game = Game(c_players, c_x_dist, c_y_dist, c_num_to_win)\r\n\r\n self.assertTrue(b_game == a_game == c_game)\r\n\r\n a_game.play_game()\r\n b_game.play_game()\r\n\r\n self.assertTrue(a_game == b_game)\r\n self.assertFalse(c_game == a_game)\r\n\r\n c_game.play_game()\r\n\r\n self.assertTrue(b_game == a_game == c_game)","def test_win(self):\n game = self.ending(['bw.wwwww'], 8, 1)\n game.man_move(0, 2)\n self.assertEqual(game.finish_state, (250, game.first_player, 'Win'))","def solve(gameBoard):\n \n if gameBoard.isFull(): return gameBoard\n \n chosenSquare = mrv.ordering(gameBoard)\n for someValue in lcv.ordering(gameBoard, chosenSquare):\n \n # Place the provisional value on a test board.\n testBoard = gameBoard.copy()\n testBoard.placeValueAt(someValue, chosenSquare)\n \n # Winnow domains by forward checking:\n fwdcheck.checkBoard(testBoard, chosenSquare)\n if not testBoard.areAllDomainsOpen(): continue\n if runStats: bookkeeping.boardsPassedFwdcheck += 1\n \n # Winnow domains further by AC3:\n ac3.checkBoard(testBoard)\n if not testBoard.areAllDomainsOpen(): continue\n if runStats: bookkeeping.boardsPassedAc3 += 1\n \n # Provisional value OK,\n # punt down and try next square.\n if doProg: bookkeeping.reportPunt()\n puntBoard = solve(testBoard) # <--------- Recursive Call\n if doProg: bookkeeping.reportShoonk()\n if puntBoard is not None: return puntBoard\n \n \n if runStats: bookkeeping.impossibleBoards += 1\n return None # Implicitly no solution","def is_solved(self):\n # Iterate through each square of the puzzle\n for row in range(self.sl):\n for col in range(self.sl):\n val = self.puzzle[row][col]\n\n # If any square value is blank (0), not solved, return False\n if val == 0:\n return False\n\n # Trick to keep DRY code: replace each value temporarily with a\n # 0, and use valid_square method with original value to determine\n # if every square is valid\n self.puzzle[row][col] = 0\n valid = self.valid_square(row, col, val)\n self.puzzle[row][col] = val\n \n # If not a valid value for square, return False\n if not valid:\n return False\n return True","def test_single_game_works(self):\n sim = ss.Simulation(seed=154)\n game1 = sim.single_game()\n sim = ss.Simulation(seed=79)\n game2 = sim.single_game()\n assert game1 != game2, 'Your method single_game is not working.'","def test_teacher_check_homework_negative_if_solution_is_not_ok():\n assert not opp_teacher.check_homework(result_3)","def test_runGame(self):\n # this is tested by playing the game. No good way to unit test this.\n pass","def test_newsmall6dgame(self):\n with open(\"test_outputs/test_newsmall6dgame.json\") as f:\n expected = json.load(f)\n with open(\"test_inputs/test_newsmall6dgame.json\") as f:\n inputs = json.load(f)\n result = lab.HyperMinesGame(inputs[\"dimensions\"], inputs[\"bombs\"])\n for i in ('dimensions', 'board', 'mask', 'state'):\n self.assertEqual(getattr(result, i), expected[i])","def test_hard_bot_map1(self):\n game = self.bot_test_map1(Difficulty.hard)\n self.assertEqual(game.first_player.ask_for_move(), (0, 11))","def test_get_game_boxscore(self):\n pass","def test_create_satifiable_problems(self):\n # runs 10 attempts to generate satisfiable problems\n for _ in range(10):\n clauses = create_sat_problem(True, 'kcolor', 3, 'gnp', 20, 0.2)\n with Solver(name='Glucose3', bootstrap_with=clauses) as solver:\n self.assertTrue(solver.solve())","def solve(self):\n if not self.running or self.state == \"stopping\":\n return False\n\n # Find first empty tile\n target = ()\n for i in range(9**2):\n if self.board[i // 9, i % 9] == 0:\n target = (i // 9, i % 9)\n break\n\n # If there are no empty tiles, the puzzle is solved\n if not target:\n return True\n\n # Tests all possible values\n for value in range(1, 10):\n if not self.isPossibleAssign(target, value):\n continue\n\n self.update_board(target, value)\n\n if self.solve():\n return True\n\n # In case of failure, reset and return False\n self.update_board(target, 0)\n\n return False","def test_play_game_hard(self):\r\n wins = [0,0,0]\r\n\r\n for i in range(1,10):\r\n a_player_1_id = 1\r\n a_player_2_id = 2\r\n a_players = [RandomPlayer(a_player_1_id), RandomPlayer(a_player_2_id)]\r\n a_x_dist = i\r\n a_y_dist = i\r\n a_num_to_win = 3\r\n a_game = Game(a_players,a_x_dist,a_y_dist,a_num_to_win)\r\n a_game.play_game()\r\n\r\n wins[a_game.winner] += 1\r\n\r\n print(wins)","def play_game():\n board = create_board()\n while True:\n for player in [1, 2]:\n random_place(board, player)\n result = evaluate(board)\n if result != 0:\n return result","def _check_end_of_game(self, force=False):\n if not force:\n self.is_end_of_game = not bool(\n sum(sum(x is 0 for x in sl) for sl in self.board))\n else:\n self.is_end_of_game = True\n flatten = sum(self.board, [])\n self.score = 32 - sum(1 for item in flatten if item is 1)","def check_if_game_over():\n # Calling check for winners.\n check_for_winner()\n # Calling check it's tie or not.\n check_if_tie()","def did_solve(self) -> bool:\n pass","def test_solve(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n for optimal in [\"student\", \"supervisor\"]:\n students, projects, _, game = make_game(\n student_names,\n project_names,\n supervisor_names,\n capacities,\n seed,\n clean,\n )\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n projects = sorted(projects, key=lambda p: p.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_project, project in zip(matching_keys, projects):\n assert game_project.name == project.name\n assert game_project._pref_names == project._pref_names\n assert game_project.capacity == project.capacity\n assert game_project.supervisor.name == project.supervisor.name\n\n matched_students = [\n stud for match in matching.values() for stud in match\n ]\n assert matched_students != [] and set(matched_students).issubset(\n set(game.students)\n )\n\n for student in matched_students:\n supervisor = student.matching.supervisor\n assert student in supervisor.matching\n\n for student in set(game.students) - set(matched_students):\n assert student.matching is None","def has_solution(self) -> bool:\n pass","def test_newlarge4dgame(self):\n with open(\"test_outputs/test_newlarge4dgame.json\") as f:\n expected = json.load(f)\n with open(\"test_inputs/test_newlarge4dgame.json\") as f:\n inputs = json.load(f)\n result = lab.HyperMinesGame(inputs[\"dimensions\"], inputs[\"bombs\"])\n for i in ('dimensions', 'board', 'mask', 'state'):\n self.assertEqual(getattr(result, i), expected[i])","def is_solvable(board: list) -> bool:\n inv_count = invserion_count(board)\n return inv_count%2 == 0","def test_is_advancing_to_next_stage_yes(self):\n\n # test_input_cases =\n # [(die_a_value, die_b_value, stage, ok_output),]\n test_input_cases = [\n (\"1\", \"2\", 1, True),\n (\"2\", \"1\", 1, True),\n (\"ANGRY\", \"4\", 2, True),\n (\"4\", \"ANGRY\", 2, True),\n ]\n\n for test_io in test_input_cases:\n self._test_is_game_over(*test_io)","def solveOneStep(self):\n ### Student code goes here\n\n if (self.currentState.state == self.victoryCondition):\n self.visited[self.currentState] = True\n win_or_not = (self.currentState.state == self.victoryCondition)\n return win_or_not\n\n game_depth = self.currentState.depth\n continue_game = True\n test_its = 0\n while continue_game:\n test_its += 1\n # too long \n # time test\n if test_its == \"too long\":\n return \"too long\"\n result = self.solveOneStep_helper(game_depth)\n if result:\n victory_satisfied = (self.currentState.state == self.victoryCondition)\n if victory_satisfied:\n result_bool = True\n return result_bool\n else:\n game_depth = game_depth + 1\n else:\n return False","def test_simple():\n game = Game(3, [0, 0], -1, 5, -5, 10, 1, [[0, 1]], [0.0])\n\n print(f\"Check the baby exists\\n{game.baby}\")\n\n print(\"\\nCheck the berry exists\")\n for berry in game.get_berries():\n print(berry)\n\n print(f\"\\nHere is the board\\n{game.get_board()}\")\n\n print(\"First let's perform an illegal move Northwards\")\n board, reward, done = game.step(\"N\")\n print(f\"Here is the board\\n{game.get_board()}\")\n print(f\"And the reward experienced: {reward}\")\n print(f\"And whether the game is over: {done}\")\n\n print(\"\\nNow let's perform a legal move which does NOT eat the berry\")\n board, reward, done = game.step(\"E\")\n print(f\"Here is the board\\n{game.get_board()}\")\n print(f\"And the reward experienced: {reward}\")\n print(f\"And whether the game is over: {done}\")\n\n print(\"\\nNow we will move back to the original place and then eat the berry\")\n board, reward, done = game.step(\"W\")\n print(f\"Here is the board\\n{game.get_board()}\")\n print(f\"And the reward experienced: {reward}\")\n print(f\"And whether the game is over: {done}\")\n\n print(\"\\nNow let's perform a legal move which does NOT eat the berry\")\n board, reward, done = game.step(\"S\")\n print(f\"Here is the board\\n{game.get_board()}\")\n print(f\"And the reward experienced: {reward}\")\n print(f\"And whether the game is over: {done}\")","def checkSolution(self):\n movesToEndblock = self.gridSize - self.changeable[0] - 2\n if self.checkMove(0,movesToEndblock) == 0:\n return 0\n return 1","def AI(current_board, AI_symbol, opponent_symbol, difficulty): #Written by Cody West\n victory_conditions = [[0,4,8],[2,4,6],[0,1,2],[3,4,5],[6,7,8],[0,3,6],[1,4,7],[2,5,8]] #Establishes victory conditions to be checked\n if difficulty >= 2: #If difficulty is at least 2\n ## Cody -- you could just write:\n ## for slots in victory_conditions\n for n in range(len(victory_conditions)): #For each victory condition in victory_conditions ## Oops\n slots = victory_conditions[n] #Take the victory conditions and put them in a new list ## Oops \n check = [] #Creates empty folder called check\n for i in range(len(slots)): #For each spot in slots\n check.append(current_board[slots[i]]) #Add the corresponding spot from the current board to check\n ## This you can do even more efficiently using a beautiful syntax called\n ## \"list comprehension\" which entered python some years ago -- watch\n ## me do it in one line:\n ## check = [current_board[s] for s in slots]\n if check.count(AI_symbol)==2 and check.count(\" \")==1: #If there are any rows where the AI has two symbols and there's one empty spot\n return(slots[check.index(\" \")]) #Return the empty spot from that row\n ## Oops -- you repeat the code again here for no reason\n for n in range(len(victory_conditions)): #For each victory condition in victory_conditions\n slots = victory_conditions[n] #Take the victory conditions and put them in a new list\n check = [] #Creates empty folder called check\n for i in range(len(slots)): #For each spot in slots\n check.append(current_board[slots[i]]) #Add the corresponding spot from the current board to check\n if check.count(opponent_symbol)==2 and check.count(\" \")==1: #If there are any rows where the opponent has two symbols and there's one empty spot\n return(slots[check.index(\" \")]) #Return the empty spot from that row\n if difficulty >= 3: #If difficulty is at least 3\n ## It looks like you're doing an identical loop here -- I\n ## wonder why you don't move the if statement inside the loop\n ## -- I believe that would significantly shorten your code\n for n in range(len(victory_conditions)): #For each victory condition in victory_conditions\n slots = victory_conditions[n] #Take the victory conditions and put them in a new list\n check = [] #Creates empty folder called check\n for i in range(len(slots)): #For each spot in slots\n check.append(current_board[slots[i]]) #Add the corresponding spot from the current board to check\n if check.count(AI_symbol)==1 and check.count(\" \")==2: #If there are any rows where the AI has one symbol and there's two empty spots\n if check[0] == \" \": #If the first slot from check is empty\n return(slots[0]) #Return the first slot\n else: \n return(slots[2]) #Return the third slot\n if difficulty == 4: #If difficulty is 4\n if current_board[4] == \" \": #If the center is empty\n return(4) #Take the center\n elif current_board[0] or current_board[2] or current_board[6] or current_board[8] == \" \": #Else, if a corner is open\n corners = 2*random.randint(0,4) #Selects a random corner (or center, which will reject)\n while current_board[corners] != \" \": #Until the corner selected is empty\n corners = 2*random.randint(0,4) #Select a new corner or center\n return(corners) #Return empty corner\n else:\n sides = 2*random.randint(0,3)+1 #Selects a side\n while current_board[sides] != \" \": #Until the side is empty\n sides = 2*random.randint(0,3)+1 #Selects a new side\n return(sides) #Returns empty side\n if difficulty < 4: #If difficulty is less than 4\n ran = random.randint(0,8) #Picks random spot on board\n while current_board[ran] != \" \": #Until the spot is empty\n ran = random.randint(0,8) #Picks a new spot\n return(ran) #Returns empty spot","def check_pp_goals(game, key, opp_key, gsl):\n # loading events data\n game_type = get_game_type_from_season_type(game)\n game_events_src_path = os.path.join(\n CONFIG['base_data_dir'], 'game_events', str(game['season']), str(game_type), \"%d.json\" % game['game_id'])\n events_data = json.loads(open(game_events_src_path).read())\n\n pp_goals_from_events = {'home': 0, 'visitor': 0}\n\n for period in events_data:\n for event in events_data[period]:\n if event['type'] != 'goal':\n continue\n # fixing penalty shots erroneously identified as power play goals\n if event['data']['balance'] == 'PP0':\n event['data']['balance'] = 'PS'\n if event['data']['balance'].startswith('PP'):\n pp_goals_from_events[event['data']['team']] += 1\n\n pp_goals_discrepancy = False\n\n pp_goals_from_events['road'] = pp_goals_from_events['visitor']\n\n if pp_goals_from_events[key] != gsl['pp_goals']:\n pp_goals_discrepancy = True\n gsl['pp_goals'] = pp_goals_from_events[key]\n if pp_goals_from_events[opp_key] != gsl['opp_pp_goals']:\n pp_goals_discrepancy = True\n gsl['opp_pp_goals'] = pp_goals_from_events[opp_key]\n\n if pp_goals_discrepancy:\n print(\"\\t+ Found and corrected different number of pp goals retrieved from team stats and game event data\")\n\n return gsl","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def solve(task: str) -> int:\n players_number, marbles = parse_task(task)\n game = Game(players_number, marbles)\n\n while not game.finished:\n game.make_turn()\n\n return game.winner.score","def test_1_do_valid_sim(self) :\n self.banner(\"Testing if simulation gives the right percentages.\")\n filename = self.find_file('project9.py')\n self.assertIsNotNone(filename, \"I can't find your project file (project9.py)\")\n\n with open('logs/test_1_valid_sim.out', 'a') as log :\n test = pexpect.spawnu('python \"' + filename.as_posix() + '\"', logfile=log, encoding='utf-8')\n turns = random.randrange(100, 100000)\n test.sendline(str(turns))\n try : \n got = test.expect(['(\\d+\\.\\d+)\\s*%'], timeout=5)\n switch_percent = float(test.match.group(1))\n got = test.expect(['(\\d+\\.\\d+)\\s*%'], timeout=5)\n stay_percent = float(test.match.group(1))\n except :\n self.fail(\"The grader program failed to parse the output of your project.\")\n\n if not (64 < switch_percent < 68) :\n self.fail('Your switch percentage ({}) is out of range.'.format(switch_percent))\n if not (31 < stay_percent < 35) :\n self.fail('Your stay percentage ({}) is out of range.'.format(stay_percent))\n test.close()","def test_game_creation(self):\n self.assertTrue(len(models.Game.objects.all()) == 1)\n game = models.Game.objects.all()[0]\n\n # Hydrocarbon piles initialization test\n number_of_piles = len(settings.HYDROCARBON_STOCKS_PER_PLAYER)\n for i_pile in range(number_of_piles):\n self.assertEqual(0, game.hydrocarbon_piles.get(index=i_pile).stock_amount)\n\n # Events initialization test [TO DO]\n\n # Source buildings loading test\n version_source_buildings = models.SourceBuilding.objects.filter(version=game.version)\n self.assertEqual(version_source_buildings.count(), game.source_buildings.count())\n for version_source_building in version_source_buildings:\n self.assertEqual(version_source_building, game.source_buildings.get(slug=version_source_building.slug))\n\n # Source events loading test [TO DO]\n\n # Source technologies loading test\n version_source_technologies = models.SourceTechnology.objects.filter(version=game.version)\n self.assertEqual(version_source_technologies.count(), game.source_technologies.count())\n for version_source_technology in version_source_technologies:\n self.assertEqual(version_source_technology,\n game.source_technologies.get(slug=version_source_technology.slug))","def check_win(self):\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)","def checkWin(self):\n winstates = [(0, 1, 2),\n (3, 4, 5),\n (6, 7, 8),\n (0, 3, 6),\n (1, 4, 7),\n (2, 5, 8),\n (0, 4, 8),\n (2, 4, 6)]\n win = False\n for state in winstates:\n if (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == 3:\n self.handleWin(1)\n win = True\n elif (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == -3:\n self.handleWin(-1)\n win = True\n\n if len([i for i in range(9) if self.gameState[i] == 0]) == 0 and not win:\n print(\"Draw yo\")\n self.handleDraw()\n return None","def test_movement(self):\n running = True\n\n self.refresh_tile_maps()\n\n self.data = self.board.data\n self.board_objects = self.board.board_objects\n self.weapons = self.board.weapons\n self.rooms = self.board.rooms\n self.players = self.board.players\n self.player_cards = self.board.player_cards\n self.combined_tiles = self.board.combined_tiles\n self.weapon_tokens = self.board.weapon_tokens\n self.player_tokens = self.board.player_tokens\n self.dice = self.board.dice\n\n cont = True\n movements = {'W': [0, -1], 'S': [0, 1], 'A': [-1, 0], 'D': [1, 0]}\n misc_options_one = ['E', 'D']\n misc_options_two = ['E']\n correct = False\n key = ''\n\n # self.move_players_testing()\n # self.remove_players_testing()\n\n while cont:\n for player_char in self.players: \n player_token = self.player_tokens[player_char]\n player_object = self.players[player_char]\n\n out_count = 0\n for p in self.players:\n if self.players[p].out:\n out_count += 1\n\n if out_count == len(self.players):\n cards = self.board.solution.get_solution()\n print('You all lost!')\n print('The solution was:')\n for card in cards:\n sym, a_card = list(card.items())[0]\n print('%s : %s' % (sym, a_card.name))\n input('Press enter to quit')\n cont = False\n break\n\n if not player_object.out:\n if key == 'P':\n cont = False\n break\n \n roll_one, roll_two = self.dice.roll()\n steps = roll_one + roll_two\n player_not_stopped = True\n \n while player_not_stopped and steps > 0 and not correct:\n if key == 'P':\n cont = False\n player_not_stopped = False\n break\n\n\n key_incorrect = True\n while key_incorrect:\n key, option = self.menu_refresh(player_token, player_char, steps)\n\n if key == 'P':\n cont = False\n player_not_stopped = False\n key_incorrect = False\n break\n \n if key =='/':\n cards = self.board.solution.get_solution()\n for card in cards:\n sym, a_card = list(card.items())[0]\n print('%s : %s' % (sym, a_card.name))\n input()\n\n elif key == 'H':\n for s, pl in player_object.hand.deck.items():\n print('%s : %s' % (s, pl.name))\n input('Continue?')\n\n elif key == '!':\n player_not_stopped = False\n key_incorrect = False\n player_token.reset_has_entered()\n \n elif key == '£' or (key == '\"' and (option == 1 or option == 3)):\n player_not_stopped = False\n # options order: player_cards, rooms, weapons\n if key == '£':\n options = self.board.get_card_options(False)\n if key == '\"' and (option == 1 or option == 3):\n options = self.board.get_card_options(True)\n \n \"\"\" temp for debugging \"\"\"\n # player_token_list = list(self.player_tokens.items())\n\n # for i, p in enumerate(player_token_list):\n # if p[0] == player_char:\n # p_pos = i\n\n # print(player_token_list[p_pos - 1 % len(player_token_list)][1].player.hand.deck)\n \"\"\" \"\"\"\n\n \"\"\" Also temp for debugging \"\"\"\n # cards = self.board.solution.get_solution()\n # print('cards: ', cards)\n \"\"\" \"\"\"\n\n selection = []\n for option_type in options:\n for i, card_details in enumerate(option_type):\n print('%s : %s - %s' % (i, card_details[1], card_details[3]))\n \n cont_three = True\n while cont_three:\n inp = input('Select from above: ')\n\n try: \n inp = int(inp)\n is_int = True\n except ValueError:\n is_int = False\n\n if is_int and inp < len(option_type) and inp >= 0:\n selection.append(option_type[inp])\n cont_three = False\n else:\n print('incorrect number')\n \n if key == '\"' and (option == 1 or option == 3):\n # Room for suggest doesn't need to be selected, is derrived from current room of the current player\n steps = 0\n key_incorrect = False\n player_not_stopped = False\n player_token_list = list(self.player_tokens.items())\n player_token.reset_has_entered()\n\n for i, p in enumerate(player_token_list):\n if p[0] == player_char:\n p_pos = i\n \n left_player = player_token_list[p_pos - 1 % len(player_token_list)]\n result = player_object.suggest(selection[0], {selection[0][1]: self.player_cards[selection[0][1]]}, {player_token.current_room: self.rooms[player_token.current_room]}, selection[1], left_player, self.board)\n if result != False:\n input('Card: %s, Enter to continue.' % result.name)\n else:\n input('Incorrect, Enter to continue.')\n\n elif key == '£':\n correct = player_object.accuse(selection[0][2], selection[1][2], selection[2][2], self.board.solution)\n steps = 0\n if correct:\n print('Yay you won :3')\n cont = False\n\n key = 'P'\n else:\n # print(player_object.out)\n player_object.make_out()\n # print(player_object.out)\n # input()\n print('Oof, you are out')\n \n key_incorrect = False\n player_not_stopped = False\n input()\n else:\n print('wut')\n input()\n\n elif key in movements or key in misc_options_one or key in misc_options_two:\n if option == 0 and key in movements:\n cont_two = True\n switch = True\n while cont_two:\n if switch:\n switch = False\n else:\n key, temp_option = self.menu_refresh(player_token, player_char, steps)\n\n if key == 'P':\n cont_two = False\n cont = False\n steps -= 1\n key_incorrect = False\n elif key in movements:\n off_x, off_y = movements[key] \n cont_two, has_entered = player_token.move_by_direction(off_x, off_y)\n cont_two = not cont_two\n\n if has_entered:\n steps = 1\n else:\n if not cont_two:\n steps -= 1\n key_incorrect = False\n elif option in [1, 2] and key in misc_options_one:\n if key == 'D':\n player_token.reset_has_entered()\n player_token.enter_secret_door()\n steps = 1\n else:\n player_token.exit_door()\n player_token.reset_has_entered()\n steps -= 1\n \n key_incorrect = False\n elif option in [3, 4] and key in misc_options_two:\n player_token.exit_door()\n player_token.reset_has_entered()\n steps -= 1\n key_incorrect = False\n player_token.reset_has_entered()","def play_game():\n difficulty = select_game_difficulty()\n mad_lib, answers = get_mad_lib_and_answers(difficulty)\n max_guesses = find_max_guesses()\n current_blank = 1\n while current_blank <= len(answers):\n mad_lib, current_blank = ask_question(\n mad_lib, current_blank, answers[current_blank - 1], max_guesses\n )\n if mad_lib is None:\n return False\n print(mad_lib + \"\\nYou won!\\n\")\n return True","def check_evolve(self):\n if self.team == 'white':\n if self.position[0] == 0:\n self.evolve()\n \n else:\n if self.position[0] == 7:\n self.evolve()","def test_update_game(self): \n for i in range(3):\n for j in range(3):\n self.game.board[i][j] = self.game.patterns[\"Blinker\"][i][j]\n self.game.update_game()\n self.assertEqual(self.game.board[0][1], 1)\n self.assertEqual(self.game.board[1][1], 1)\n self.assertEqual(self.game.board[2][1], 1)","def test_legit_player(self):\n board = Board()\n player1 = LegitPlayer()\n player2 = LegitPlayer()\n player_guard1 = PlayerGuard(player1)\n player_guard2 = PlayerGuard(player2)\n\n # set ids\n p1id = uuid.uuid4()\n p2id = uuid.uuid4()\n player_guard1.set_id(p1id)\n player_guard2.set_id(p2id)\n\n # test methods don't error out\n player_guard1.start_of_game()\n player_guard2.start_of_game()\n board.place_worker(*player_guard1.place_worker(board))\n board.place_worker(*player_guard2.place_worker(board))\n board.place_worker(*player_guard2.place_worker(board))\n board.place_worker(*player_guard1.place_worker(board))\n player_guard1.play_turn(board)\n player_guard2.play_turn(board)\n player_guard1.end_of_game(\"legit player\")\n player_guard2.end_of_game(\"legit player\")","def determineEndGame(self):\n\n print(\"noWinners: \" + str(self.noWinners) + \", noTotKids: \" + str(self.noTotKids))\n\n # TODO scegliere come determinare la fine del gioco\n # if self.noWinners == self.noTotKids - 1: # end-game test\n if self.noWinners == self.noTotKids:\n print(\"ho determinato la fine del gioco\")\n return True\n else:\n print(\"colore toccato ma la partita non e' finita\")\n return False","def find_opponent(standings,odds):\n\n # simulate all games\n for i in range(len(odds)):\n play_game(odds.loc[i],standings)\n\n # update the points and GD tally\n standings['P']=standings['W']*3 + standings['D']\n standings['GD']=standings['F']-standings['A']\n\n # see if teams have equal amount of points, and award h2h_points for\n # h2h results against those teams.\n for group in \"ABCDEF\":\n gelijk = standings.loc[standings['Group']==group][standings.loc[standings['Group']==group].duplicated(subset='P',keep=False)]\n gelijk[\"h2h_points\"]=np.zeros(len(gelijk))\n\n for i in gelijk.index:\n for team1 in gelijk.loc[i][\"h2h\"]:\n for team2 in gelijk[\"TEAMS\"]:\n if team1==team2:\n standings.loc[i,\"h2h_points\"]+=1\n\n # sort the final standings\n standings = standings.sort_values(by=['Group','P',\"h2h_points\",'GD','F','W'],ascending=[True,False,False,False,False,False])\n\n # determine third placed teams\n standings = standings.reset_index()\n third = standings.loc[[2,6,10,14,18,22]]\n\n # determine best number threes\n third = third.sort_values(by=['P','GD','F','W'],ascending=False)\n\n groups_of_best_no_3 = \"\"\n for i in third.head(4).Group:\n groups_of_best_no_3+=i\n groups_of_best_no_3 = ''.join(sorted(groups_of_best_no_3))\n\n # look up the opponent of the dutch team\n a = third.loc[third.Group == opponent_matrix[groups_of_best_no_3]]['TEAMS']\n\n return a.reset_index().TEAMS[0]","def check_game_end(self):\r\n\r\n if np.all(self.remaining == -1): # end of game\r\n self.show_results() # show the final results\r\n sys.exit() # exit the program\r","def solve(self):\n\n tracks_copy = self.tracks.copy()\n vehicles_sorted = sorted(self.vehicles, key=lambda x: x.departure_time)\n\n vehicles_added = 0\n while len(vehicles_sorted) != 0:\n best_ratio = - sys.maxsize - 1\n best_track = None\n best_vehicle = None\n shuffle(tracks_copy)\n\n for vehicle in vehicles_sorted:\n for track in tracks_copy:\n if track.add_vehicle(vehicle, self.tracks):\n self.grader.reinitialize_grader()\n goal1 = self.grader.calculate_first_global_goal()\n goal2 = self.grader.calculate_second_global_goal()\n ratio = goal2 / goal1\n if ratio > best_ratio:\n best_ratio = ratio\n best_track = track\n best_vehicle = vehicle\n\n track.remove_last()\n\n if best_vehicle is not None and best_track is not None:\n vehicles_added += 1\n best_track.add_vehicle(best_vehicle, self.tracks)\n vehicles_sorted.remove(best_vehicle)\n else:\n self.grader.reinitialize_grader()\n goal1 = self.grader.calculate_first_global_goal()\n goal2 = self.grader.calculate_second_global_goal()\n if goal1 < self.optimal_gg1 and goal2 > self.optimal_gg2:\n self.optimal_gg1 = goal1\n self.optimal_gg2 = goal2\n self.optimal_tracks = self.tracks\n return False\n\n self.grader.reinitialize_grader()\n goal1 = self.grader.calculate_first_global_goal()\n goal2 = self.grader.calculate_second_global_goal()\n print(\"Success:\", goal1, goal2)\n if goal1 < self.best_gg1 and goal2 > self.best_gg2:\n self.best_gg1 = goal1\n self.best_gg2 = goal2\n self.best_tracks = self.tracks\n\n return True","def solve(self):\n boss_stats = self.as_dict()\n for key, val in boss_stats.items():\n boss_stats[key] = int(val)\n\n player = PlayerState(50, 500)\n boss = BossState(boss_stats['Hit Points'], boss_stats['Damage'])\n return self.does_player_win_fight(player, boss)","def win_game(self):\n\n def horizontal_win():\n \"\"\"Return whether there is horizontal win\"\"\"\n\n for i in range(0, board_size):\n if set(self.board[i]) == set([o_symbol]) or set(self.board[i]) == set([x_symbol]):\n print \"horizontal win\"\n return True\n\n def vertical_win():\n \"\"\"Return whether there is vertical win\"\"\"\n\n vert_set = set()\n for i in range(0, board_size):\n for j in range(0, board_size):\n vert_set.add(self.board[j][i])\n if vert_set == set([o_symbol]) or vert_set == set([x_symbol]):\n print \"vertical win\"\n return True \n vert_set = set()\n\n def diagonal_win():\n \"\"\"Return whether there is diagonal win\"\"\"\n\n diagonal_set = set()\n for i in range(0, board_size):\n diagonal_set.add(self.board[i][i]) \n\n if diagonal_set == set([o_symbol]) or diagonal_set == set([x_symbol]):\n print \"diagonal win 1\"\n return True\n \n diagonal_set = set()\n for i in range(0, board_size):\n diagonal_set.add(self.board[i][board_size - 1 - i])\n\n if diagonal_set == set([o_symbol]) or diagonal_set == set([x_symbol]):\n print \"diagonal win 2\"\n return True\n\n if horizontal_win() or vertical_win() or diagonal_win():\n print \"You have won.\"\n return True","def gameOver():\n if len(p1)==0 and len(p1winnings)==0:\n return True\n elif len(p2)==0 and len(p2winnings)==0:\n return True\n return False","def _solve_puzzle(self, test_puzzle) -> bool:\n global counter\n row = 0\n col = 0\n for i in range(81):\n # current cell\n row = i // 9\n col = i % 9\n\n # if cell is empty we check to see possible placements\n if test_puzzle[row][col] == 0:\n # trying to place number in current cell\n for n in range(1, 10):\n\n # checking if we can place n in current cell\n if not SudokuGrid.check_valid_placement(n, row, col,\n test_puzzle):\n # placing n in cell\n test_puzzle[row][col] = n\n\n # check if grid is full increment number of solutions\n # and break loop to go to previous recursions to try\n # other combinations\n if SudokuGrid.check_grid(test_puzzle):\n counter += 1\n break\n\n # otherwise recurse to place next cell\n elif self._solve_puzzle(test_puzzle):\n return True\n\n # break loop if no valid placement in cell\n break\n\n # will set current square to 0 and go back to previous recursion\n # to find another valid placement\n test_puzzle[row][col] = 0\n return False","def judge(self):\n self.bounds = 0.0\n self.best = self.lives[0]\n for life in self.lives:\n life.score = self.matchFun(life)\n self.bounds += life.score\n if self.best.score < life.score:\n self.best = life","def judge(self):\n self.bounds = 0.0\n self.best = self.lives[0]\n for life in self.lives:\n life.score = self.matchFun(life)\n self.bounds += life.score\n if self.best.score < life.score:\n self.best = life","def check_if_solved(self):\n for cell in self.board.values():\n if not cell.value:\n return False\n return True","def test_valid_new_game(self):\n self._game.new_game()\n self.assertIsRUNNING(self._game)\n self.assertIsNotNone(self._game.table)\n self.assertEqual(self._game.table.player_turn, 1,\n \"Game.table unsuccessful init.\")","def update_status(self):\n if len(self.invalid) != 0:\n return False\n for row in self.grid:\n for num in row:\n if num == 0:\n return False\n self.solved = True\n print(\"solved\")\n return True","def solve(self):\n dim = self.puzzle.dimension\n\n # initial loop\n for value, (row, col) in self.puzzle:\n if value:\n self.clear_row(row, value)\n self.clear_col(col, value)\n self.clear_subgrid(row, col, value)\n self.updates.add((value, (row, col)))\n for ps in self.possibilities:\n ps.discard((row, col))\n\n while self.updates:\n while self.updates:\n # while self.updates:\n value, (row, col) = self.updates.pop()\n for i in range(1, dim + 1):\n self.check_row(i, value)\n self.check_col(i, value)\n for i in range(2, 8, 3):\n self.check_subgrid(row, i, value)\n self.check_subgrid(i, col, value)\n\n for value, (row, col) in self.puzzle:\n if not value:\n self.check_cell(row, col)\n\n # for value in range(1, dim + 1):\n # for row in [2, 5, 8]:\n # for col in [2, 5, 8]:\n # self.check_subgrid(row, col, value)","def test_set_players_number(self):\n\n # Setup new games and attempt to set thier number of players\n valid_players = [\n 1,\n 2,\n 10,\n 999,\n ]\n for players in valid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(players)\n self.assertEqual(game.players_number, players, msg=\"The number of players was not correctly set with \" + str(players) + \" players.\")\n\n # Make sure that the new game state is correctly set\n self.assertEqual(game.state.name, \"get_player_names\", msg=\"The game state was not correctly set after setting the number of players in the game.\")\n\n # Try to set invalid player numbers\n invalid_players = [\n 0,\n -1,\n -100,\n 1.5,\n ]\n for players in invalid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n success = False\n try:\n game.set_players_number(players)\n except InvalidGamePlayersNumber:\n success = True\n self.assertTrue(success, msg=\"An invalid number of players \" + str(players) + \" was able to be set.\")\n\n # Try to reset the number of players to throw an error\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(3)\n success = False\n try:\n game.set_players_number(2)\n except InvalidGameMethodOrder:\n success = True\n self.assertTrue(success, msg=\"The number of players was incorrectly able to be reset.\")","def verify_valid_game(self, lotto_game:List[int]):\n is_valid = True;\n lotto_game_temp = []\n for number in lotto_game:\n if number not in lotto_game_temp:\n lotto_game_temp.append(number)\n else:\n print(number, lotto_game, lotto_game_temp)\n is_valid = False\n raise InvalidLottoGame\n \n lotto_game_temp_length = len(lotto_game_temp)\n if lotto_game_temp_length != LOTTO_GAME_LENGTH and lotto_game_temp_length != LOTTO_GAME_WITH_BONUS_LENGTH:\n print(lotto_game_temp)\n is_valid = False\n raise InvalidLottoGame\n\n return is_valid","def solve_board(bd):\n if is_solved(bd):\n print_board(bd)\n return\n elif len(next_valid_boards(bd)) == 0:\n return False\n else:\n for board in next_valid_boards(bd):\n solve_board(board)","def evaluationFunction(self, currentGameState, action):\n # Useful information you can extract from a GameState (pacman.py)\n successorGameState = currentGameState.generatePacmanSuccessor(action)\n newPos = successorGameState.getPacmanPosition()\n newFood = successorGameState.getFood()\n newGhostStates = successorGameState.getGhostStates()\n newScaredTimes = [\n ghostState.scaredTimer for ghostState in newGhostStates]\n\n \"*** YOUR CODE HERE ***\"\n # print \"newScaredTimes\", newScaredTimes\n # print successorGameState.getCapsules()\n\n newGhostPos = newGhostStates[0].getPosition()\n ghost_dist = ghost_distance(newPos, newGhostPos)\n capsules = successorGameState.getCapsules()\n # food_dist = food_distance(newPos, newFood)\n\n # approach 1: 2/4 win = 10, average < 500\n # if ghost_dist <= 1:\n # return -999999\n # return -food_num(newFood)\n\n # approach 2: 2/4 win = 10, average < 500 but close to 500\n # if newScaredTimes[0] == 0:\n # if ghost_dist <= 1:\n # return -999999\n # return -food_num(newFood) -capsule_distance(newPos, capsules)\n\n # final approach: 4/4 win = 10, average = 1310.5\n if newScaredTimes[0] == 0:\n if ghost_dist <= 1:\n return -999999\n return -food_distance(newPos, newFood) * .01 - food_num(newFood) - capsule_distance(newPos, capsules)","def _test_is_game_over(self, die_a_value, die_b_value, stage, ok_output):\n\n self.new_angry_dice.die_a.current_value = die_a_value\n self.new_angry_dice.die_b.current_value = die_b_value\n self.new_angry_dice.game_stage = stage\n\n self.assertEqual(self.new_angry_dice.is_advancing_to_next_stage(),\n ok_output,\n \"Incorrect output for die_a:{}, \"\n \"die_b:{} in stage:{}.\"\n .format(die_a_value, die_b_value, stage))","def testPointSystem():\n deleteMatches()\n deletePlayers()\n registerPlayer(\"Pikachu\")\n registerPlayer(\"Charmander\")\n registerPlayer(\"Bulbasaur\")\n registerPlayer(\"Squirtle\")\n registerPlayer(\"MewTwo\")\n standings = playerStandings()\n [id1, id2, id3, id4, id5] = [row[0] for row in standings]\n reportMatch(id1, id2)\n reportMatch(id3, id4, True)\n reportMatch(id5, id5, False, True)\n reportMatch(id1, id5)\n reportMatch(id3, id4)\n reportMatch(id2, id2, False, True)\n reportMatch(id1, id3)\n reportMatch(id5, id2)\n reportMatch(id4, id4, False, True)\n standings = playerStandings()\n if not (standings[0][0]==id2 and standings[0][2]==2 and\n standings[1][0]==id4 and standings[0][2]==2 and\n standings[2][0]==id3 and standings[0][2]==2 and\n standings[3][0]==id5 and standings[0][2]==2 and\n standings[4][0]==id1 and standings[0][2]==2):\n raise ValueError(\n \"Points are not tallied correctly.\"\n )\n\n print \"4. Points are tallied correctly.\"","def game_check(self, mode, row=None, col=None, is_set=True,\r\n show_fail=True):\r\n self.add_play_move(PlayMove.GAME_CHECK, mode=mode, row=row, is_set=is_set, show_fail=show_fail)\r\n if mode == \"h\" or mode == \"v\":\r\n part = self.get_part(type=\"edge\", sub_type=mode, row=row, col=col)\r\n if part is None:\r\n raise SelectError(f\"game_check: no edge({mode}) found at row={row} col={col}\") \r\n is_on = part.is_turned_on()\r\n if is_on != is_set:\r\n result = False\r\n msg = (f\"Unexpected test result: {result}\"\r\n f\" for line({mode}) at row={row} col={col}\")\r\n SlTrace.lg(f\"game_check: {msg}\")\r\n if show_fail:\r\n raise SelectFail(msg)\r\n return False\r\n elif mode == \"sq\":\r\n part = self.get_part(type=\"region\", row=row, col=col)\r\n is_on = part.is_turned_on()\r\n if is_on != is_set:\r\n result = False\r\n msg = (f\"Unexpected test result: {result}\"\r\n f\" for square at row={row} col={col}\")\r\n SlTrace.lg(f\"game_check: {msg}\")\r\n if show_fail:\r\n raise SelectFail(msg)\r\n return False\r\n else:\r\n raise SelectFail(f\"Unrecognized game_check mode({mode}\")\r\n \r\n return True","def solve_step(self,puzzle_grid,x,y):\n self.puzzleGrid = puzzle_grid\n if(self.foundStep == False):\n self.targetCell = self.puzzleGrid.grid[x][y]\n if(self.targetCell.isSolved == False):\n self.calculate_possibilities()\n if len(self.targetCell.possibilities) == 1: #README method 1\n self.targetCell.solve()\n return True\n else:\n return self.check_neighbours() #README method 2","def solve_puzzle(self):\r\n \r\n counter = 0\r\n rows = self._height-1\r\n cols = self._width-1\r\n # print rows, cols\r\n # print 'The greed has %s rows and %s coloumn indexes' %(rows, cols) \r\n solution_move = ''\r\n if self.get_number(0,0) == 0 and \\\r\n self.get_number(0,1) == 1:\r\n # print 'Congrads Puxxle is Aolved at start!!!!!'\r\n return ''\r\n #appropriate_number = (self._height * self._width) - 1\r\n appropriate_number = (rows+1) * (cols+1) -1\r\n # print 'First appropriate_number=',appropriate_number\r\n # print \"Grid first tile that we will solwing has value =\", self._grid[rows][cols]\r\n \r\n while counter < 300:\r\n counter +=1\r\n # print self\r\n #appropriate_number = (rows+1) * (cols+1) -1\r\n # print 'Appropriate number in loop=',appropriate_number\r\n # print 'We are solving %s index_row and %s index_col' %(rows, cols) \r\n ####Case when we use solve_interior_tile\r\n if rows > 1 and cols > 0:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n cols -= 1\r\n appropriate_number -=1\r\n else:\r\n # print 'We are solving interior tile', (rows, cols)\r\n solution_move += self.solve_interior_tile(rows, cols)\r\n # print 'Solution move=', solution_move\r\n cols -= 1\r\n #### Case when we use solve_col0_tile\r\n elif rows > 1 and cols == 0:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows -= 1\r\n cols = self._width-1\r\n appropriate_number -=1\r\n else:\r\n # print 'We are solwing tile 0 in row', rows\r\n # print 'Appropriate number here ='\r\n solution_move += self.solve_col0_tile(rows)\r\n # print 'Solution move=', solution_move\r\n rows -=1\r\n cols = self._width-1\r\n\r\n\r\n #### Cases when we use solve_row0_tile\r\n elif rows == 1 and cols > 1:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows -= 1\r\n #cols = self._width-1\r\n appropriate_number -= self._width\r\n\r\n else:\r\n # print 'Solving upper 2 rows right side'\r\n solution_move += self.solve_row1_tile(cols)\r\n rows -=1\r\n appropriate_number -= self._width\r\n #### Cases when we use solve_row1_tile \r\n if rows < 1 and cols > 1:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows += 1\r\n cols -= 1\r\n appropriate_number +=self._width-1\r\n else:\r\n # print '(1,J) tile solved, lets solwe tile (0,j) in tile',(rows,cols)\r\n # print 'Greed after move solve_row1_tile'\r\n # print self\r\n solution_move += self.solve_row0_tile(cols)\r\n rows +=1\r\n cols -=1\r\n appropriate_number +=self._width-1\r\n\r\n\r\n #### Case when we use solve_2x2\r\n elif rows <= 1 and cols <= 1:\r\n # print 'We are solving 2x2 puzzle'\r\n solution_move += self.solve_2x2()\r\n if self._grid[0][0] == 0 and \\\r\n self._grid[0][1] == 1:\r\n # print 'Congrads Puxxle is SOLVED!!!!!'\r\n break\r\n\r\n\r\n\r\n\r\n if counter > 100:\r\n # print 'COUNTER BREAK'\r\n break\r\n # print solution_move, len(solution_move)\r\n return solution_move\r\n\r\n\r\n\r\n\r\n\r\n\r\n # for row in solution_greed._grid[::-1]:\r\n # print solution_greed._grid\r\n # print 'Row =',row\r\n \r\n # if solution_greed._grid.index(row) > 1:\r\n # print \"Case when we solwing Interior and Tile0 part\"\r\n \r\n\r\n # for col in solution_greed._grid[solution_greed._grid.index(row)][::-1]:\r\n # print 'Coloumn value=', col\r\n #print row[0]\r\n # if col !=row[0]:\r\n # print 'Case when we use just Interior tile solution'\r\n # print solution_greed._grid.index(row)\r\n # print row.index(col)\r\n \r\n # solution += solution_greed.solve_interior_tile(solution_greed._grid.index(row) , row.index(col))\r\n # print 'Solution =', solution\r\n # print self \r\n # print solution_greed._grid\r\n # elif col ==row[0]:\r\n # print 'Case when we use just Col0 solution'\r\n\r\n # else:\r\n # print 'Case when we solwing first two rows'\r\n\r\n #return \"\"\r","def check_win(self, player):\n def check_row_win(player):\n for row in self.game_state:\n if player == row[0] == row[1] == row[2]:\n return True\n return False\n\n def check_column_win(player):\n # For doing a column check, transpose the grid and do a row check\n trans_game_state = numpy.transpose(self.game_state)\n for row in trans_game_state:\n if player == row[0] == row[1] == row[2]:\n return True\n return False\n\n def check_diag_win(player):\n # Left to right diagonal\n if player == self.game_state[0][0] == self.game_state[1][1] == self.game_state[2][2]:\n return True\n # Right to left diagonal\n if player == self.game_state[0][2] == self.game_state[1][1] == self.game_state[2][0]:\n return True\n return False\n\n if check_column_win(player) or check_diag_win(player) or check_row_win(player):\n return True\n return False","def play_best_guess(self, game):\n\n\n # create a list of cells\n cells = [game.board[i][j]\n for i in xrange(game.rows)\n for j in xrange(game.cols)]\n\n first_cell = cells[0]\n game.reveal_cell(first_cell.row, first_cell.col)\n\n # draw updated board and pause for a second\n game.draw_board()\n if PAUSE == True:\n time.sleep(1)\n\n\n total_flagged = 0\n while not game.lost_game and not game.won_game:\n\n # remember if we've made a move in the while loop\n # so we know whether to make a random move later on\n made_move = False\n\n # look through all revealed cells for any with a number of neighboring mines.\n # if the cell has the same number of unrevealed neighbors as the cell's\n # number of neighboring mines, all the unrevealed neighbors must be mines.\n revealed_numbered_cells = [c for c in cells if c.revealed and (not c.flagged) and (c.neighbors > 0)]\n while revealed_numbered_cells:\n cell = revealed_numbered_cells.pop()\n # cell may have been marked flagged after revealed_numbered_cells was compiled\n if not cell.flagged:\n neighbor_cells = ms.Minesweeper.get_neighbors(cell.row, cell.col, game.board)\n flagged_neighbors = [n for n in neighbor_cells if n.flagged]\n number_remaining_mines = cell.neighbors - len(flagged_neighbors)\n unknown_neighbors = [n for n in neighbor_cells if not n.flagged and not n.revealed]\n if number_remaining_mines > 0 and len(unknown_neighbors) == number_remaining_mines:\n # flag every neighbor\n for c in unknown_neighbors:\n if total_flagged < game.mines:\n total_flagged += 1\n game.flag_cell(c.row, c.col)\n if (game.test_did_win()):\n game.game_over()\n game.draw_board()\n if PAUSE == True:\n time.sleep(1)\n made_move = True\n\n # we may have won with the flag above so test whether we're still playing\n # before further calculations\n if not game.lost_game and not game.won_game:\n # loop through all unrevealed, unflagged cells and see if we know it's safe to reveal\n for c in cells:\n if not c.revealed and not c.flagged and self.is_cell_safe(c, game.board):\n game.reveal_cell(c.row, c.col)\n if (game.test_did_win()):\n game.game_over()\n game.draw_board()\n if PAUSE == True:\n time.sleep(1)\n made_move = True\n\n # assume we've made our best guesses and now have to guess randomly\n # this will prevent us from looping forever if no obvious moves are available\n if not made_move:\n unrevealed = [c for c in cells if not c.revealed and not c.flagged]\n if len(unrevealed) > 0:\n cell = random.choice(unrevealed)\n game.reveal_cell(cell.row, cell.col)\n if (game.test_did_win()):\n game.game_over()\n game.draw_board()\n if PAUSE == True:\n time.sleep(3)"],"string":"[\n \"def test_check_validity(game):\\n\\n game.solve()\\n assert game.check_validity()\",\n \"def test_check_solved():\\n game = Game()\\n game.word = 'word'\\n game.pattern = 'word'\\n game.check_solved()\\n assert game.solved is True\",\n \"def test_is_solved_when_puzzle_is_solved(self):\\n self.assertTrue(self.sudoku.is_solved())\",\n \"def test_check_validity(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n game.solve()\\n assert game.check_validity()\",\n \"def test_is_solved(self):\\n p = hw.TilePuzzle([[1, 2], [3, 0]])\\n self.assertTrue(p.is_solved())\\n p = hw.TilePuzzle([[0, 1], [3, 2]])\\n self.assertFalse(p.is_solved())\",\n \"def check_game_sanity(game_val, log):\\n\\n global __problem_deck_index__\\n\\n supply = game_val.get_supply()\\n if game_val.any_resigned():\\n return True # Resign leaves deck in arbitrary state, don't catch that\\n\\n # ignore known bugs.\\n #if set(supply).intersection([get_card('Masquerade'), get_card('Black Market'), get_card('Trader')]):\\n #return True\\n\\n # TODO: add score sanity checking here\\n last_state = None\\n game_state_iterator = game_val.game_state_iterator()\\n for game_state in game_state_iterator:\\n last_state = game_state\\n for player_deck in game_val.get_player_decks():\\n parsed_deck_comp = player_deck.Deck()\\n computed_deck_comp = last_state.get_deck_composition(\\n player_deck.name())\\n\\n parse_common.delete_keys_with_empty_vals(parsed_deck_comp)\\n computed_dict_comp = dict(computed_deck_comp)\\n parse_common.delete_keys_with_empty_vals(computed_dict_comp)\\n\\n if parsed_deck_comp != computed_deck_comp:\\n found_something_wrong = False\\n for card in set(parsed_deck_comp.keys() +\\n computed_deck_comp.keys()):\\n if parsed_deck_comp.get(card, 0) != \\\\\\n computed_deck_comp.get(card, 0):\\n if not found_something_wrong:\\n __problem_deck_index__ += 1\\n log.debug('[%d] %18s %9s %9s', __problem_deck_index__, 'card', 'from-data', 'from-sim')\\n log.debug('[%d] %-18s %9d %9d', __problem_deck_index__, card, parsed_deck_comp.get(card, 0),\\n computed_deck_comp.get(card, 0))\\n found_something_wrong = True\\n if found_something_wrong:\\n try:\\n log.debug('[%d] insane game for %s %s: %s', __problem_deck_index__, player_deck.name(), game_val.get_id(),\\n ' '.join(map(str, game_val.get_supply())))\\n except UnicodeEncodeError, e:\\n None\\n return False\\n return True\",\n \"def test_init_with_existing_game(self):\\n pass\\n # Ensure judge is the same\",\n \"def test_solve_one_player_2(self):\\n self.rush_hour_data = rush_hour_data_2\\n self.state_data = state_data_2\\n self.execute_minimax_single_player()\",\n \"def test_play_game(self):\\r\\n\\r\\n \\r\\n a_players = [RandomPlayer(1), RandomPlayer(2)]\\r\\n a_x_dist = 3\\r\\n a_y_dist = 3\\r\\n a_num_to_win = 1\\r\\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\\r\\n\\r\\n #Game is played to competion\\r\\n a_game.play_game()\\r\\n\\r\\n a_history = a_game.get_history()\\r\\n\\r\\n #Go through each move and check to be sure it's valid\\r\\n for i in range(1,len(a_history)):\\r\\n #Get copy of the board\\r\\n prev_board = a_history[i-1]\\r\\n cur_board = a_history[i]\\r\\n\\r\\n #Check if the board chosen is in valid states\\r\\n self.assertTrue(cur_board in prev_board.get_states(a_players[0].get_id()) or cur_board in prev_board.get_states(a_players[1].get_id()),\\\\\\r\\n \\\"An invalid board state was added to the history\\\")\\r\\n\\r\\n if i == len(a_history) - 1:\\r\\n self.assertTrue(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\\r\\n else: \\r\\n self.assertFalse(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\",\n \"def test_is_solved_when_puzzle_is_not_solved(self):\\n sudoku = sudolver.Sudoku()\\n self.assertFalse(sudoku.is_solved())\",\n \"def test_solve_one_player_3(self):\\n self.rush_hour_data = rush_hour_data_3\\n self.state_data = state_data_3\\n self.execute_minimax_single_player()\",\n \"def isValid(self, game):\\n return True\",\n \"def test_complete_game1(self):\\n m = Game()\\n\\n plays = [\\n ['a:-', 'a:-', 'a:b', 'b:b', 'b:-', 'b:b'],\\n ['c:3i', 'c:f', 'c:f', 'c:-', 'd:f', 'd:-', 'd:-'],\\n ['a:3i', 'a:2f', 'a:f', 'a:-', 'b:-', 'b:-', 'b:-'],\\n ['c:3i', 'c:2f', 'c:-', 'c:f', 'd:-', 'd:b', 'd:b'],\\n ['a:5i2rp', 'a:4f', 'a:f', 'a:b-', 'b:-', 'b:b', 'b:k']\\n ]\\n m.run(plays)\\n m.print_stats()\\n\\n # Did the right team win?\\n assert m.teams['a'].win == True\\n\\n # Did we get 5 turns?\\n assert m.turn_count == 5\\n\\n # There should have been 30 throws in the game\\n assert m.get_throw_count() == 30\",\n \"def test_solve_one_player_1(self):\\n self.rush_hour_data = rush_hour_data_1\\n self.state_data = state_data_1\\n self.execute_minimax_single_player()\",\n \"def check_game(self):\\n gameOver = None\\n if self.turn > 4:\\n gameOver = self.check_x_won()\\n if gameOver is True:\\n self.game_x_won()\\n return\\n\\n gameOver = None\\n if self.turn > 5:\\n gameOver = self.check_o_won()\\n if gameOver is True:\\n self.game_o_won()\\n return\\n\\n if self.turn >= 9:\\n self.game_tie()\\n return\",\n \"def test_get_game(self):\\n pass\",\n \"def do_the_things():\\n returned_no_game = False\\n returned_game_time = False\\n returned_game_final = False\\n returned_rival_final = False\\n returned_game_soon = False\\n returned_rival_soon = False\\n returned_game_start = False\\n compare_scores = ['0', '0']\\n timeout = time.time() + 60 * 60 * 23\\n\\n while not (returned_no_game or returned_game_final):\\n\\n try:\\n message = False\\n rival_message = False\\n\\n \\\"\\\"\\\" Infinite loops are cool \\\"\\\"\\\"\\n if time.time() > timeout:\\n break\\n\\n game_data = get_fresh_data(team)\\n\\n if game_data:\\n \\\"\\\"\\\" The default TZ for mlb is US/Eastern, we'll do some things and make it local TZ \\\"\\\"\\\"\\n tz = pytz.timezone('US/Eastern')\\n eastern_time = datetime.datetime.strptime(\\\"%s %s\\\" % (game_data['time_date'], game_data['ampm']), '%Y/%m/%d %I:%M %p')\\n eastern_time = tz.localize(eastern_time)\\n pacific_time = eastern_time.astimezone(pytz.timezone(timezone))\\n\\n opponent, our_score, their_score, venue = set_vars(game_data)\\n scores = sorted([our_score, their_score], reverse=True)\\n\\n if not game_data and not returned_no_game:\\n returned_no_game = True\\n message = (\\\"The %s don't have a game scheduled today. Rest well guys!\\\" % team_hashtag)\\n\\n if game_data and not returned_game_time:\\n returned_game_time = True\\n message = ((\\\"The %s are playing against the %s today, first pitch is at \\\" + pacific_time.strftime(\\\"%-I:%M%p %Z\\\") + \\\" at %s. %s\\\") % (team_hashtag, opponent, venue, team_hash2))\\n\\n if \\\"Warmup\\\" in game_data[\\\"status\\\"] and not returned_game_soon:\\n returned_game_soon = True\\n message = ((\\\"The %s are playing against the %s in a moment, first pitch is at \\\" + pacific_time.strftime(\\\"%-I:%M%p %Z\\\") + \\\" at %s. %s\\\") % (team_hashtag, opponent, venue, team_hash2))\\n\\n if \\\"In Progress\\\" in game_data[\\\"status\\\"] and compare_scores == ['0', '0'] and not returned_game_start:\\n returned_game_start = True\\n message = (\\\"It's gametime! Go %s!!! %s\\\" % (team_hashtag, team_hash2))\\n\\n if \\\"In Progress\\\" in game_data[\\\"status\\\"] and not compare_scores == [our_score, their_score]:\\n compare_scores = [our_score, their_score]\\n if int(our_score) > int(their_score):\\n message = (\\\"The %s are leading the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n elif int(our_score) < int(their_score):\\n message = (\\\"The %s are trailing the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n elif int(our_score) == int(their_score):\\n message = (\\\"The %s are tied with the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n\\n if (\\\"Game Over\\\" or \\\"Final\\\") in game_data[\\\"status\\\"]:\\n returned_game_final = True\\n if our_score > their_score:\\n message = (\\\"The %s beat the %s today at %s with a score of %s-%s. %s\\\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\\n else:\\n message = (\\\"The %s lost against the %s today at %s with a score of %s-%s. Get 'em tomorrow! %s\\\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\\n\\n if not returned_rival_final:\\n rival_game_data = get_fresh_data(rival_team)\\n rival_opponent, rival_our_score, rival_their_score, rival_venue = set_vars(rival_game_data)\\n rival_scores = sorted([rival_our_score, rival_their_score], reverse=True)\\n\\n if \\\"Warmup\\\" in rival_game_data[\\\"status\\\"] and not returned_rival_soon:\\n returned_rival_soon = True\\n rival_message = (\\\"The %s game is about to start, go %s!!! %s\\\") % (rival_team_hashtag, rival_opponent, rival_team_hash2)\\n\\n if (\\\"Game Over\\\" or \\\"Final\\\") in rival_game_data[\\\"status\\\"]:\\n returned_rival_final = True\\n if rival_our_score > rival_their_score:\\n rival_message = (\\\"The %s beat the %s today at %s with a score of %s-%s, boooo!\\\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\\n else:\\n rival_message = (\\\"The %s lost against the %s today at %s with a score of %s-%s. Hell yeah!\\\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\\n\\n if message:\\n if testmode:\\n print(message)\\n else:\\n twitter.tweet(message)\\n\\n if rival_message:\\n if testmode:\\n print(rival_message)\\n else:\\n twitter.tweet(rival_message)\\n\\n except tweepy.TweepError as e:\\n print e.message[0]['code']\\n print e.args[0][0]['code']\\n\\n time.sleep(5)\",\n \"def win_check(self):\\n\\t\\t# Create a temp var to capture the number of correct matches\\n\\t\\tright = 0\\n\\t\\t# retrieve peg_guess_color_list for current round\\n\\t\\tguess = self.model.guesses[self.model.status]\\n\\t\\t# retreive solution list\\n\\t\\tsolution = self.model.guesses[\\\"solution\\\"]\\n\\t\\t# compare values in each index for both lists against eachother\\n\\t\\tfor i in range(len(solution.pegs)):\\n\\t\\t\\t# \\n\\t\\t\\tif solution.pegs[i].peg_color == guess.pegs[i].peg_color:\\n\\t\\t\\t\\tright += 1\\n\\n\\t\\t\\t\\tprint(\\\"Yay, it works!\\\")\\n\\n\\t\\t# If all indexes of the peg_colors in the solution and guess are True:\\n\\t\\tif right == 4:\\n\\t\\t\\treturn True\\n\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def is_legal_solution(self, solution):\\r\\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\\r\\n return self.fit_score(solution) == 0\\r\\n else:\\r\\n return self.fit_score(solution) == sum(x for x in range(1, 12))\",\n \"def run(game):\\n name = cli.welcome_user(game.DESCRIPTION)\\n\\n for _try_num in range(0, 3):\\n question, right_answer = game.run_round()\\n print('Question: {question}'.format(question=question))\\n user_answer = prompt.string('Your answer: ')\\n\\n if user_answer != right_answer:\\n print(WRONG_ANSWER_TEMPLATE.format(user_answer, right_answer, name))\\n break\\n print('Correct!')\\n else:\\n print('Congratulations, {name}!'.format(name=name))\",\n \"def test_is_winner(self):\\n TestGame = TTT_Game(\\\"John\\\", \\\"Jane\\\")\\n\\n # Check verticals\\n TestGame.board = [1, 2, \\\"X\\\", 4, 5, \\\"X\\\", 7, 8, \\\"X\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"X\\\"))\\n TestGame.board = [\\\"O\\\", 2, 3, \\\"O\\\", 5, \\\"X\\\", \\\"O\\\", 8, \\\"X\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"O\\\"))\\n TestGame.board = [1, \\\"X\\\", \\\"O\\\", \\\"O\\\", \\\"X\\\", 6, 7, \\\"X\\\", \\\"X\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"X\\\"))\\n\\n # Check horizontals\\n TestGame.board = [\\\"O\\\", \\\"O\\\", \\\"O\\\", \\\"O\\\", 5, \\\"X\\\", 7, 8, 9]\\n self.assertTrue(TestGame.is_winner(\\\"O\\\"))\\n TestGame.board = [1, 2, 3, \\\"X\\\", \\\"X\\\", \\\"X\\\", 7, 8, 9]\\n self.assertTrue(TestGame.is_winner(\\\"X\\\"))\\n TestGame.board = [1, 2, 3, \\\"O\\\", 5, 6, \\\"O\\\", \\\"O\\\", \\\"O\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"O\\\"))\\n\\n # Check diagonals\\n TestGame.board = [\\\"O\\\", \\\"X\\\", 3, 4, \\\"O\\\", \\\"X\\\", \\\"X\\\", \\\"O\\\", \\\"O\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"O\\\"))\\n TestGame.board = [1, 2, \\\"X\\\", 4, \\\"X\\\", 6, \\\"X\\\", 8, 9]\\n self.assertTrue(TestGame.is_winner(\\\"X\\\"))\",\n \"def test_is_advancing_to_next_stage_no(self):\\n\\n # test_input_cases =\\n # [(die_a_value, die_b_value, stage, ok_output),]\\n test_input_cases = [\\n (\\\"1\\\", \\\"2\\\", 2, False),\\n (\\\"2\\\", \\\"1\\\", 3, False),\\n (\\\"1\\\", \\\"1\\\", 1, False),\\n (\\\"1\\\", \\\"1\\\", 2, False),\\n (\\\"1\\\", \\\"1\\\", 3, False),\\n (\\\"ANGRY\\\", \\\"1\\\", 1, False),\\n (\\\"ANGRY\\\", \\\"1\\\", 2, False),\\n ]\\n\\n for test_io in test_input_cases:\\n self._test_is_game_over(*test_io)\",\n \"def test(self):\\n\\t\\tx = Team.objects.get(short_name='SF')\\n\\t\\tself.assertEqual(x.nick_name, '49ers')\\n\\n\\t\\tx = Game.objects.get(week_number=1,game_number=1)\\n\\t\\tx.fav_score = 21\\n\\t\\tx.udog_score = 14\\n\\t\\tx.spread = 5\\n\\t\\tself.assertEqual(x.favWins(), True)\\n\\t\\tx.spread = 10\\n\\t\\tself.assertEqual(x.favWins(), False)\\n\\n\\t\\t# Sunday game at 4pm should be open if time before 1 else closed\\n\\t\\tx.game_date = datetime.datetime(2020,3,1,16,0,0)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,11,0,0)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), False)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,14,0,0)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\n\\t\\t# Saturday game. Should be closed only if after kick off\\n\\t\\tx.game_date = datetime.datetime(2020,2,29,16,0,0)\\n\\t\\tcurrent_time = datetime.datetime(2020,2,29,15,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), False)\\n\\t\\tcurrent_time = datetime.datetime(2020,2,29,16,0,1)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\n\\t\\t# Monday game, should be closed as long as it's after 1pm sunday\\n\\t\\tx.game_date = datetime.datetime(2020,3,2,21,0,0)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,15,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,11,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), False)\\n\\t\\tself.assertEqual(x.isOpen(current_time=current_time), True)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,2,11,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,3,11,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\n\\t\\t# Sunday 9a game should be open until kick off\\n\\t\\tx.game_date = datetime.datetime(2020,3,1,9,0,0)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,8,0,0)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), False)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,10,0,0)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\t\\tself.assertEqual(x.isOpen(current_time=current_time), False)\\n\\n\\t\\t# Now one check with not setting current_time\\n\\t\\tx.game_date = datetime.datetime(1971,3,1,9,0,0)\\n\\t\\tself.assertEqual(x.isClosed(), True)\\n\\n\\n\\t\\tuser = User.objects.get(username='Tammer')\\n\\t\\tx = Pick.objects.get(player=user,week_number=1,game_number=1)\\n\\t\\tg = Game.objects.get(week_number=1,game_number=1)\\n\\t\\tself.assertEqual(x.picked_fav, True)\\n\\t\\tself.assertEqual(x.isCorrect(), g.fav_score - g.udog_score > g.spread)\",\n \"def start_game(table_index):\\n initiate_game(table_index)\\n\\n uId = uIds[table_index]\\n current_map = maps[table_index]\\n current_alg = applied_algs[table_index].func\\n game_over = False\\n previousmoves = []\\n\\n while True:\\n try:\\n if not game_over:\\n # print('Move scores')\\n # print(evaluate.evaluate(current_map, 0))\\n # print(evaluate.evaluate(current_map, 1))\\n # print(evaluate.evaluate(current_map, 2))\\n # print(evaluate.evaluate(current_map, 3))\\n\\n move = current_alg(current_map)\\n\\n print(previousmoves)\\n # check for bug\\n # first case\\n if len(previousmoves) == 0:\\n previousmoves.append(move)\\n # same movement\\n elif previousmoves[-1] != move:\\n previousmoves = []\\n elif previousmoves[-1] == move:\\n previousmoves.append(move)\\n # 10 same movement\\n\\n\\n if len(previousmoves) >= 4 and move == previousmoves[-1]:\\n wrongdirection = previousmoves[-1]\\n previousmoves = []\\n moves = [\\\"w\\\", \\\"a\\\", \\\"s\\\", \\\"d\\\"]\\n moves.remove(wrongdirection)\\n\\n move = random.choice(moves)\\n\\n print(move)\\n\\n request = requests.post(url=base_URL + \\\"/api/play_the_game\\\",\\n json={'direction': move,\\n 'uId': uId})\\n current_map = request.json()['board']\\n\\n print(request.json())\\n\\n # TODO: Type checking, error handling (HTTP response?)\\n game_over = request.json()[\\\"game_over\\\"]\\n\\n else:\\n c_score = request.json()[\\\"c_score\\\"]\\n SESSION_NAME = TEAM_NAME + \\\"_\\\" + applied_algs[table_index].label\\n\\n with open('score_data.txt', 'a') as f:\\n f.write(datetime.datetime.now().strftime('%H:%M:%S') + \\\" \\\" + SESSION_NAME + \\\" \\\" + '%d' % c_score + \\\"\\\\n\\\")\\n\\n print()\\n print(f\\\"Game Over. Your score is: {c_score}\\\")\\n print()\\n\\n initiate_game(table_index)\\n game_over = False\\n uId = uIds[table_index]\\n current_map = maps[table_index]\\n except:\\n print(\\\"Error\\\")\",\n \"async def check(self):\\n\\n while not self.solved:\\n # Get list of possible numbers this square can have\\n possibles = self.get_possible_numbers()\\n # If there's only once possibility, then use this number...this square is now solved\\n if len(possibles) == 1:\\n self.num = possibles.pop()\\n # If there are no possible squares well...something's wrong, that shouldn't be possible\\n # This check is done because we want to be able to guess and check, and figure out if a guess is invalid\\n elif len(possibles) == 0:\\n raise ValueError(\\\"Impossible square; no possible numbers based on restrictions\\\")\\n # Otherwise wait a small amount and continue\\n else:\\n await asyncio.sleep(0.05)\",\n \"def test_game_logic(self, client: FlaskClient) -> None:\\n test_user = \\\"janedoe\\\"\\n n_wins_first = 3\\n n_drops_next = 5\\n test_tricks = [] # List[models.Trick]\\n with server.app.app_context() as server_context:\\n # Just some trick id's to use\\n test_tricks = models.Trick.query.limit(n_wins_first + n_drops_next +\\n 1)\\n\\n # Declare game object\\n game = models.Game(attempts=[], user=test_user)\\n models.db.session.add(game)\\n models.db.session.commit()\\n\\n # Use lands first 3, opp misses all, should be opp: SKA\\n for trick in test_tricks[0:n_wins_first]:\\n # Note these call model functions not client turns, because client attempt\\n # would also automatically choose oppoenent response, we want to test that\\n _game_turn(trick.id, True, test_user, game.id, client,\\n server_context)\\n _game_turn(trick.id, False, \\\"past_\\\" + test_user, game.id,\\n client, server_context)\\n\\n models.db.session.add(game)\\n models.db.session.commit() # re-sync attempts\\n game_state = models.get_game_state(game.attempts, test_user)\\n assert game_state.user_score == 0\\n assert game_state.opponent_score == n_wins_first\\n\\n # Now if both miss one, expect no score change. Use last trick ID\\n _game_turn(test_tricks[-1].id, False, test_user, game.id, client,\\n server_context)\\n _game_turn(test_tricks[-1].id, False, \\\"past_\\\" + test_user, game.id,\\n client, server_context)\\n\\n # Now if both land one, expect no score change. Use last trick ID\\n _game_turn(test_tricks[-1].id, True, test_user, game.id, client,\\n server_context)\\n _game_turn(test_tricks[-1].id, True, \\\"past_\\\" + test_user, game.id,\\n client, server_context)\\n\\n models.db.session.add(game)\\n models.db.session.commit() # re-sync attempts\\n game_state = models.get_game_state(game.attempts, test_user)\\n assert game_state.user_score == 0\\n assert game_state.opponent_score == n_wins_first\\n\\n # A one-off repeat should count as fail, turn over priority to opponent\\n _game_turn(test_tricks[0].id, False, test_user, game.id, client,\\n server_context)\\n\\n # Now opponent lands the next n_drops_next (5, enough to win)\\n for trick in test_tricks[n_wins_first:n_wins_first + n_drops_next]:\\n _game_turn(trick.id, True, \\\"past_\\\" + test_user, game.id, client,\\n server_context)\\n _game_turn(trick.id, False, test_user, game.id, client,\\n server_context)\\n\\n models.db.session.add(game)\\n models.db.session.commit() # re-sync attempts\\n game_state = models.get_game_state(game.attempts, test_user)\\n assert game_state.user_score == n_drops_next\\n assert game_state.opponent_score == n_wins_first\\n assert game_state.status_feed[\\n -1].msg_text == \\\"[Turn 20]: Past you wins!\\\"\",\n \"def check_if_game_over():\\n check_for_winner()\\n check_for_tie()\",\n \"def test_strategy(self):\\n\\n axelrod.seed(8)\\n opponent = axelrod.Cooperator()\\n player = axelrod.WorseAndWorse()\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'D'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C')])\\n\\n # Test that behaviour does not depend on opponent\\n opponent = axelrod.Defector()\\n player = axelrod.WorseAndWorse()\\n axelrod.seed(8)\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'D'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C')])\",\n \"def check_winner(self):\\n pass\",\n \"def checkPuzzle(self):\\n print('Got to checkPuzzle')\",\n \"def test_get_solution(self):\\n pass\",\n \"def test_scores_eat_all(self):\\n game = self.ending(['bwwwwww.'], 8, 1)\\n game.man_move(0, 7)\\n self.assertEqual(game.finish_state,\\n (1600, game.second_player, 'No opponent chips'))\",\n \"def test_check_win(self):\\n # Horizontal wins\\n # First row\\n self.game.move(0, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(0, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\\n\\n # Second row\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(1, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(1, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 2, PLAYERX), PLAYERX)\\n\\n # Third row\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(2, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\\n\\n # Vertical wins\\n # First column\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 0, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\\n\\n # Second column\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 1, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 1, PLAYERX), PLAYERX)\\n\\n # Third column\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 2, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 2, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 2, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\\n\\n # Diagonal wins\\n # Upper left to bottom right\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\\n\\n # Bottom left to upper right\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(2, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\\n\\n # Draw\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 2, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 0, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 2, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 1, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 2, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), DRAW)\",\n \"def test_not_found_pair(self):\\n self.assertTrue(\\\"No matches found\\\", show_players_sumheigh_is_input(100))\",\n \"def game_over(self) -> bool:\\n for row in range(9):\\n for col in range(9):\\n if self._grid_sol[row][col] != self.get_cell(row, col):\\n return False\\n return True\",\n \"def test_get_game_diff(self):\\n pass\",\n \"def play_strategic_game():\\n board, winner = create_board(), 0\\n board[1,1] = 1\\n while winner == 0:\\n for player in [2,1]:\\n board = random_place(board, player)\\n winner = evaluate(board)\\n if winner != 0:\\n break\\n return winner\",\n \"def test_equal(self):\\r\\n\\r\\n a_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n a_x_dist = 3\\r\\n a_y_dist = 3\\r\\n a_num_to_win = 1\\r\\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\\r\\n\\r\\n b_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n b_x_dist = 3\\r\\n b_y_dist = 3\\r\\n b_num_to_win = 1\\r\\n b_game = Game(b_players, b_x_dist, b_y_dist, b_num_to_win)\\r\\n\\r\\n c_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n c_x_dist = 3\\r\\n c_y_dist = 3\\r\\n c_num_to_win = 1\\r\\n c_game = Game(c_players, c_x_dist, c_y_dist, c_num_to_win)\\r\\n\\r\\n self.assertTrue(b_game == a_game == c_game)\\r\\n\\r\\n a_game.play_game()\\r\\n b_game.play_game()\\r\\n\\r\\n self.assertTrue(a_game == b_game)\\r\\n self.assertFalse(c_game == a_game)\\r\\n\\r\\n c_game.play_game()\\r\\n\\r\\n self.assertTrue(b_game == a_game == c_game)\",\n \"def test_win(self):\\n game = self.ending(['bw.wwwww'], 8, 1)\\n game.man_move(0, 2)\\n self.assertEqual(game.finish_state, (250, game.first_player, 'Win'))\",\n \"def solve(gameBoard):\\n \\n if gameBoard.isFull(): return gameBoard\\n \\n chosenSquare = mrv.ordering(gameBoard)\\n for someValue in lcv.ordering(gameBoard, chosenSquare):\\n \\n # Place the provisional value on a test board.\\n testBoard = gameBoard.copy()\\n testBoard.placeValueAt(someValue, chosenSquare)\\n \\n # Winnow domains by forward checking:\\n fwdcheck.checkBoard(testBoard, chosenSquare)\\n if not testBoard.areAllDomainsOpen(): continue\\n if runStats: bookkeeping.boardsPassedFwdcheck += 1\\n \\n # Winnow domains further by AC3:\\n ac3.checkBoard(testBoard)\\n if not testBoard.areAllDomainsOpen(): continue\\n if runStats: bookkeeping.boardsPassedAc3 += 1\\n \\n # Provisional value OK,\\n # punt down and try next square.\\n if doProg: bookkeeping.reportPunt()\\n puntBoard = solve(testBoard) # <--------- Recursive Call\\n if doProg: bookkeeping.reportShoonk()\\n if puntBoard is not None: return puntBoard\\n \\n \\n if runStats: bookkeeping.impossibleBoards += 1\\n return None # Implicitly no solution\",\n \"def is_solved(self):\\n # Iterate through each square of the puzzle\\n for row in range(self.sl):\\n for col in range(self.sl):\\n val = self.puzzle[row][col]\\n\\n # If any square value is blank (0), not solved, return False\\n if val == 0:\\n return False\\n\\n # Trick to keep DRY code: replace each value temporarily with a\\n # 0, and use valid_square method with original value to determine\\n # if every square is valid\\n self.puzzle[row][col] = 0\\n valid = self.valid_square(row, col, val)\\n self.puzzle[row][col] = val\\n \\n # If not a valid value for square, return False\\n if not valid:\\n return False\\n return True\",\n \"def test_single_game_works(self):\\n sim = ss.Simulation(seed=154)\\n game1 = sim.single_game()\\n sim = ss.Simulation(seed=79)\\n game2 = sim.single_game()\\n assert game1 != game2, 'Your method single_game is not working.'\",\n \"def test_teacher_check_homework_negative_if_solution_is_not_ok():\\n assert not opp_teacher.check_homework(result_3)\",\n \"def test_runGame(self):\\n # this is tested by playing the game. No good way to unit test this.\\n pass\",\n \"def test_newsmall6dgame(self):\\n with open(\\\"test_outputs/test_newsmall6dgame.json\\\") as f:\\n expected = json.load(f)\\n with open(\\\"test_inputs/test_newsmall6dgame.json\\\") as f:\\n inputs = json.load(f)\\n result = lab.HyperMinesGame(inputs[\\\"dimensions\\\"], inputs[\\\"bombs\\\"])\\n for i in ('dimensions', 'board', 'mask', 'state'):\\n self.assertEqual(getattr(result, i), expected[i])\",\n \"def test_hard_bot_map1(self):\\n game = self.bot_test_map1(Difficulty.hard)\\n self.assertEqual(game.first_player.ask_for_move(), (0, 11))\",\n \"def test_get_game_boxscore(self):\\n pass\",\n \"def test_create_satifiable_problems(self):\\n # runs 10 attempts to generate satisfiable problems\\n for _ in range(10):\\n clauses = create_sat_problem(True, 'kcolor', 3, 'gnp', 20, 0.2)\\n with Solver(name='Glucose3', bootstrap_with=clauses) as solver:\\n self.assertTrue(solver.solve())\",\n \"def solve(self):\\n if not self.running or self.state == \\\"stopping\\\":\\n return False\\n\\n # Find first empty tile\\n target = ()\\n for i in range(9**2):\\n if self.board[i // 9, i % 9] == 0:\\n target = (i // 9, i % 9)\\n break\\n\\n # If there are no empty tiles, the puzzle is solved\\n if not target:\\n return True\\n\\n # Tests all possible values\\n for value in range(1, 10):\\n if not self.isPossibleAssign(target, value):\\n continue\\n\\n self.update_board(target, value)\\n\\n if self.solve():\\n return True\\n\\n # In case of failure, reset and return False\\n self.update_board(target, 0)\\n\\n return False\",\n \"def test_play_game_hard(self):\\r\\n wins = [0,0,0]\\r\\n\\r\\n for i in range(1,10):\\r\\n a_player_1_id = 1\\r\\n a_player_2_id = 2\\r\\n a_players = [RandomPlayer(a_player_1_id), RandomPlayer(a_player_2_id)]\\r\\n a_x_dist = i\\r\\n a_y_dist = i\\r\\n a_num_to_win = 3\\r\\n a_game = Game(a_players,a_x_dist,a_y_dist,a_num_to_win)\\r\\n a_game.play_game()\\r\\n\\r\\n wins[a_game.winner] += 1\\r\\n\\r\\n print(wins)\",\n \"def play_game():\\n board = create_board()\\n while True:\\n for player in [1, 2]:\\n random_place(board, player)\\n result = evaluate(board)\\n if result != 0:\\n return result\",\n \"def _check_end_of_game(self, force=False):\\n if not force:\\n self.is_end_of_game = not bool(\\n sum(sum(x is 0 for x in sl) for sl in self.board))\\n else:\\n self.is_end_of_game = True\\n flatten = sum(self.board, [])\\n self.score = 32 - sum(1 for item in flatten if item is 1)\",\n \"def check_if_game_over():\\n # Calling check for winners.\\n check_for_winner()\\n # Calling check it's tie or not.\\n check_if_tie()\",\n \"def did_solve(self) -> bool:\\n pass\",\n \"def test_solve(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n for optimal in [\\\"student\\\", \\\"supervisor\\\"]:\\n students, projects, _, game = make_game(\\n student_names,\\n project_names,\\n supervisor_names,\\n capacities,\\n seed,\\n clean,\\n )\\n\\n matching = game.solve(optimal)\\n assert isinstance(matching, MultipleMatching)\\n\\n projects = sorted(projects, key=lambda p: p.name)\\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\\n for game_project, project in zip(matching_keys, projects):\\n assert game_project.name == project.name\\n assert game_project._pref_names == project._pref_names\\n assert game_project.capacity == project.capacity\\n assert game_project.supervisor.name == project.supervisor.name\\n\\n matched_students = [\\n stud for match in matching.values() for stud in match\\n ]\\n assert matched_students != [] and set(matched_students).issubset(\\n set(game.students)\\n )\\n\\n for student in matched_students:\\n supervisor = student.matching.supervisor\\n assert student in supervisor.matching\\n\\n for student in set(game.students) - set(matched_students):\\n assert student.matching is None\",\n \"def has_solution(self) -> bool:\\n pass\",\n \"def test_newlarge4dgame(self):\\n with open(\\\"test_outputs/test_newlarge4dgame.json\\\") as f:\\n expected = json.load(f)\\n with open(\\\"test_inputs/test_newlarge4dgame.json\\\") as f:\\n inputs = json.load(f)\\n result = lab.HyperMinesGame(inputs[\\\"dimensions\\\"], inputs[\\\"bombs\\\"])\\n for i in ('dimensions', 'board', 'mask', 'state'):\\n self.assertEqual(getattr(result, i), expected[i])\",\n \"def is_solvable(board: list) -> bool:\\n inv_count = invserion_count(board)\\n return inv_count%2 == 0\",\n \"def test_is_advancing_to_next_stage_yes(self):\\n\\n # test_input_cases =\\n # [(die_a_value, die_b_value, stage, ok_output),]\\n test_input_cases = [\\n (\\\"1\\\", \\\"2\\\", 1, True),\\n (\\\"2\\\", \\\"1\\\", 1, True),\\n (\\\"ANGRY\\\", \\\"4\\\", 2, True),\\n (\\\"4\\\", \\\"ANGRY\\\", 2, True),\\n ]\\n\\n for test_io in test_input_cases:\\n self._test_is_game_over(*test_io)\",\n \"def solveOneStep(self):\\n ### Student code goes here\\n\\n if (self.currentState.state == self.victoryCondition):\\n self.visited[self.currentState] = True\\n win_or_not = (self.currentState.state == self.victoryCondition)\\n return win_or_not\\n\\n game_depth = self.currentState.depth\\n continue_game = True\\n test_its = 0\\n while continue_game:\\n test_its += 1\\n # too long \\n # time test\\n if test_its == \\\"too long\\\":\\n return \\\"too long\\\"\\n result = self.solveOneStep_helper(game_depth)\\n if result:\\n victory_satisfied = (self.currentState.state == self.victoryCondition)\\n if victory_satisfied:\\n result_bool = True\\n return result_bool\\n else:\\n game_depth = game_depth + 1\\n else:\\n return False\",\n \"def test_simple():\\n game = Game(3, [0, 0], -1, 5, -5, 10, 1, [[0, 1]], [0.0])\\n\\n print(f\\\"Check the baby exists\\\\n{game.baby}\\\")\\n\\n print(\\\"\\\\nCheck the berry exists\\\")\\n for berry in game.get_berries():\\n print(berry)\\n\\n print(f\\\"\\\\nHere is the board\\\\n{game.get_board()}\\\")\\n\\n print(\\\"First let's perform an illegal move Northwards\\\")\\n board, reward, done = game.step(\\\"N\\\")\\n print(f\\\"Here is the board\\\\n{game.get_board()}\\\")\\n print(f\\\"And the reward experienced: {reward}\\\")\\n print(f\\\"And whether the game is over: {done}\\\")\\n\\n print(\\\"\\\\nNow let's perform a legal move which does NOT eat the berry\\\")\\n board, reward, done = game.step(\\\"E\\\")\\n print(f\\\"Here is the board\\\\n{game.get_board()}\\\")\\n print(f\\\"And the reward experienced: {reward}\\\")\\n print(f\\\"And whether the game is over: {done}\\\")\\n\\n print(\\\"\\\\nNow we will move back to the original place and then eat the berry\\\")\\n board, reward, done = game.step(\\\"W\\\")\\n print(f\\\"Here is the board\\\\n{game.get_board()}\\\")\\n print(f\\\"And the reward experienced: {reward}\\\")\\n print(f\\\"And whether the game is over: {done}\\\")\\n\\n print(\\\"\\\\nNow let's perform a legal move which does NOT eat the berry\\\")\\n board, reward, done = game.step(\\\"S\\\")\\n print(f\\\"Here is the board\\\\n{game.get_board()}\\\")\\n print(f\\\"And the reward experienced: {reward}\\\")\\n print(f\\\"And whether the game is over: {done}\\\")\",\n \"def checkSolution(self):\\n movesToEndblock = self.gridSize - self.changeable[0] - 2\\n if self.checkMove(0,movesToEndblock) == 0:\\n return 0\\n return 1\",\n \"def AI(current_board, AI_symbol, opponent_symbol, difficulty): #Written by Cody West\\n victory_conditions = [[0,4,8],[2,4,6],[0,1,2],[3,4,5],[6,7,8],[0,3,6],[1,4,7],[2,5,8]] #Establishes victory conditions to be checked\\n if difficulty >= 2: #If difficulty is at least 2\\n ## Cody -- you could just write:\\n ## for slots in victory_conditions\\n for n in range(len(victory_conditions)): #For each victory condition in victory_conditions ## Oops\\n slots = victory_conditions[n] #Take the victory conditions and put them in a new list ## Oops \\n check = [] #Creates empty folder called check\\n for i in range(len(slots)): #For each spot in slots\\n check.append(current_board[slots[i]]) #Add the corresponding spot from the current board to check\\n ## This you can do even more efficiently using a beautiful syntax called\\n ## \\\"list comprehension\\\" which entered python some years ago -- watch\\n ## me do it in one line:\\n ## check = [current_board[s] for s in slots]\\n if check.count(AI_symbol)==2 and check.count(\\\" \\\")==1: #If there are any rows where the AI has two symbols and there's one empty spot\\n return(slots[check.index(\\\" \\\")]) #Return the empty spot from that row\\n ## Oops -- you repeat the code again here for no reason\\n for n in range(len(victory_conditions)): #For each victory condition in victory_conditions\\n slots = victory_conditions[n] #Take the victory conditions and put them in a new list\\n check = [] #Creates empty folder called check\\n for i in range(len(slots)): #For each spot in slots\\n check.append(current_board[slots[i]]) #Add the corresponding spot from the current board to check\\n if check.count(opponent_symbol)==2 and check.count(\\\" \\\")==1: #If there are any rows where the opponent has two symbols and there's one empty spot\\n return(slots[check.index(\\\" \\\")]) #Return the empty spot from that row\\n if difficulty >= 3: #If difficulty is at least 3\\n ## It looks like you're doing an identical loop here -- I\\n ## wonder why you don't move the if statement inside the loop\\n ## -- I believe that would significantly shorten your code\\n for n in range(len(victory_conditions)): #For each victory condition in victory_conditions\\n slots = victory_conditions[n] #Take the victory conditions and put them in a new list\\n check = [] #Creates empty folder called check\\n for i in range(len(slots)): #For each spot in slots\\n check.append(current_board[slots[i]]) #Add the corresponding spot from the current board to check\\n if check.count(AI_symbol)==1 and check.count(\\\" \\\")==2: #If there are any rows where the AI has one symbol and there's two empty spots\\n if check[0] == \\\" \\\": #If the first slot from check is empty\\n return(slots[0]) #Return the first slot\\n else: \\n return(slots[2]) #Return the third slot\\n if difficulty == 4: #If difficulty is 4\\n if current_board[4] == \\\" \\\": #If the center is empty\\n return(4) #Take the center\\n elif current_board[0] or current_board[2] or current_board[6] or current_board[8] == \\\" \\\": #Else, if a corner is open\\n corners = 2*random.randint(0,4) #Selects a random corner (or center, which will reject)\\n while current_board[corners] != \\\" \\\": #Until the corner selected is empty\\n corners = 2*random.randint(0,4) #Select a new corner or center\\n return(corners) #Return empty corner\\n else:\\n sides = 2*random.randint(0,3)+1 #Selects a side\\n while current_board[sides] != \\\" \\\": #Until the side is empty\\n sides = 2*random.randint(0,3)+1 #Selects a new side\\n return(sides) #Returns empty side\\n if difficulty < 4: #If difficulty is less than 4\\n ran = random.randint(0,8) #Picks random spot on board\\n while current_board[ran] != \\\" \\\": #Until the spot is empty\\n ran = random.randint(0,8) #Picks a new spot\\n return(ran) #Returns empty spot\",\n \"def check_pp_goals(game, key, opp_key, gsl):\\n # loading events data\\n game_type = get_game_type_from_season_type(game)\\n game_events_src_path = os.path.join(\\n CONFIG['base_data_dir'], 'game_events', str(game['season']), str(game_type), \\\"%d.json\\\" % game['game_id'])\\n events_data = json.loads(open(game_events_src_path).read())\\n\\n pp_goals_from_events = {'home': 0, 'visitor': 0}\\n\\n for period in events_data:\\n for event in events_data[period]:\\n if event['type'] != 'goal':\\n continue\\n # fixing penalty shots erroneously identified as power play goals\\n if event['data']['balance'] == 'PP0':\\n event['data']['balance'] = 'PS'\\n if event['data']['balance'].startswith('PP'):\\n pp_goals_from_events[event['data']['team']] += 1\\n\\n pp_goals_discrepancy = False\\n\\n pp_goals_from_events['road'] = pp_goals_from_events['visitor']\\n\\n if pp_goals_from_events[key] != gsl['pp_goals']:\\n pp_goals_discrepancy = True\\n gsl['pp_goals'] = pp_goals_from_events[key]\\n if pp_goals_from_events[opp_key] != gsl['opp_pp_goals']:\\n pp_goals_discrepancy = True\\n gsl['opp_pp_goals'] = pp_goals_from_events[opp_key]\\n\\n if pp_goals_discrepancy:\\n print(\\\"\\\\t+ Found and corrected different number of pp goals retrieved from team stats and game event data\\\")\\n\\n return gsl\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def solve(task: str) -> int:\\n players_number, marbles = parse_task(task)\\n game = Game(players_number, marbles)\\n\\n while not game.finished:\\n game.make_turn()\\n\\n return game.winner.score\",\n \"def test_1_do_valid_sim(self) :\\n self.banner(\\\"Testing if simulation gives the right percentages.\\\")\\n filename = self.find_file('project9.py')\\n self.assertIsNotNone(filename, \\\"I can't find your project file (project9.py)\\\")\\n\\n with open('logs/test_1_valid_sim.out', 'a') as log :\\n test = pexpect.spawnu('python \\\"' + filename.as_posix() + '\\\"', logfile=log, encoding='utf-8')\\n turns = random.randrange(100, 100000)\\n test.sendline(str(turns))\\n try : \\n got = test.expect(['(\\\\d+\\\\.\\\\d+)\\\\s*%'], timeout=5)\\n switch_percent = float(test.match.group(1))\\n got = test.expect(['(\\\\d+\\\\.\\\\d+)\\\\s*%'], timeout=5)\\n stay_percent = float(test.match.group(1))\\n except :\\n self.fail(\\\"The grader program failed to parse the output of your project.\\\")\\n\\n if not (64 < switch_percent < 68) :\\n self.fail('Your switch percentage ({}) is out of range.'.format(switch_percent))\\n if not (31 < stay_percent < 35) :\\n self.fail('Your stay percentage ({}) is out of range.'.format(stay_percent))\\n test.close()\",\n \"def test_game_creation(self):\\n self.assertTrue(len(models.Game.objects.all()) == 1)\\n game = models.Game.objects.all()[0]\\n\\n # Hydrocarbon piles initialization test\\n number_of_piles = len(settings.HYDROCARBON_STOCKS_PER_PLAYER)\\n for i_pile in range(number_of_piles):\\n self.assertEqual(0, game.hydrocarbon_piles.get(index=i_pile).stock_amount)\\n\\n # Events initialization test [TO DO]\\n\\n # Source buildings loading test\\n version_source_buildings = models.SourceBuilding.objects.filter(version=game.version)\\n self.assertEqual(version_source_buildings.count(), game.source_buildings.count())\\n for version_source_building in version_source_buildings:\\n self.assertEqual(version_source_building, game.source_buildings.get(slug=version_source_building.slug))\\n\\n # Source events loading test [TO DO]\\n\\n # Source technologies loading test\\n version_source_technologies = models.SourceTechnology.objects.filter(version=game.version)\\n self.assertEqual(version_source_technologies.count(), game.source_technologies.count())\\n for version_source_technology in version_source_technologies:\\n self.assertEqual(version_source_technology,\\n game.source_technologies.get(slug=version_source_technology.slug))\",\n \"def check_win(self):\\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)\",\n \"def checkWin(self):\\n winstates = [(0, 1, 2),\\n (3, 4, 5),\\n (6, 7, 8),\\n (0, 3, 6),\\n (1, 4, 7),\\n (2, 5, 8),\\n (0, 4, 8),\\n (2, 4, 6)]\\n win = False\\n for state in winstates:\\n if (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == 3:\\n self.handleWin(1)\\n win = True\\n elif (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == -3:\\n self.handleWin(-1)\\n win = True\\n\\n if len([i for i in range(9) if self.gameState[i] == 0]) == 0 and not win:\\n print(\\\"Draw yo\\\")\\n self.handleDraw()\\n return None\",\n \"def test_movement(self):\\n running = True\\n\\n self.refresh_tile_maps()\\n\\n self.data = self.board.data\\n self.board_objects = self.board.board_objects\\n self.weapons = self.board.weapons\\n self.rooms = self.board.rooms\\n self.players = self.board.players\\n self.player_cards = self.board.player_cards\\n self.combined_tiles = self.board.combined_tiles\\n self.weapon_tokens = self.board.weapon_tokens\\n self.player_tokens = self.board.player_tokens\\n self.dice = self.board.dice\\n\\n cont = True\\n movements = {'W': [0, -1], 'S': [0, 1], 'A': [-1, 0], 'D': [1, 0]}\\n misc_options_one = ['E', 'D']\\n misc_options_two = ['E']\\n correct = False\\n key = ''\\n\\n # self.move_players_testing()\\n # self.remove_players_testing()\\n\\n while cont:\\n for player_char in self.players: \\n player_token = self.player_tokens[player_char]\\n player_object = self.players[player_char]\\n\\n out_count = 0\\n for p in self.players:\\n if self.players[p].out:\\n out_count += 1\\n\\n if out_count == len(self.players):\\n cards = self.board.solution.get_solution()\\n print('You all lost!')\\n print('The solution was:')\\n for card in cards:\\n sym, a_card = list(card.items())[0]\\n print('%s : %s' % (sym, a_card.name))\\n input('Press enter to quit')\\n cont = False\\n break\\n\\n if not player_object.out:\\n if key == 'P':\\n cont = False\\n break\\n \\n roll_one, roll_two = self.dice.roll()\\n steps = roll_one + roll_two\\n player_not_stopped = True\\n \\n while player_not_stopped and steps > 0 and not correct:\\n if key == 'P':\\n cont = False\\n player_not_stopped = False\\n break\\n\\n\\n key_incorrect = True\\n while key_incorrect:\\n key, option = self.menu_refresh(player_token, player_char, steps)\\n\\n if key == 'P':\\n cont = False\\n player_not_stopped = False\\n key_incorrect = False\\n break\\n \\n if key =='/':\\n cards = self.board.solution.get_solution()\\n for card in cards:\\n sym, a_card = list(card.items())[0]\\n print('%s : %s' % (sym, a_card.name))\\n input()\\n\\n elif key == 'H':\\n for s, pl in player_object.hand.deck.items():\\n print('%s : %s' % (s, pl.name))\\n input('Continue?')\\n\\n elif key == '!':\\n player_not_stopped = False\\n key_incorrect = False\\n player_token.reset_has_entered()\\n \\n elif key == '£' or (key == '\\\"' and (option == 1 or option == 3)):\\n player_not_stopped = False\\n # options order: player_cards, rooms, weapons\\n if key == '£':\\n options = self.board.get_card_options(False)\\n if key == '\\\"' and (option == 1 or option == 3):\\n options = self.board.get_card_options(True)\\n \\n \\\"\\\"\\\" temp for debugging \\\"\\\"\\\"\\n # player_token_list = list(self.player_tokens.items())\\n\\n # for i, p in enumerate(player_token_list):\\n # if p[0] == player_char:\\n # p_pos = i\\n\\n # print(player_token_list[p_pos - 1 % len(player_token_list)][1].player.hand.deck)\\n \\\"\\\"\\\" \\\"\\\"\\\"\\n\\n \\\"\\\"\\\" Also temp for debugging \\\"\\\"\\\"\\n # cards = self.board.solution.get_solution()\\n # print('cards: ', cards)\\n \\\"\\\"\\\" \\\"\\\"\\\"\\n\\n selection = []\\n for option_type in options:\\n for i, card_details in enumerate(option_type):\\n print('%s : %s - %s' % (i, card_details[1], card_details[3]))\\n \\n cont_three = True\\n while cont_three:\\n inp = input('Select from above: ')\\n\\n try: \\n inp = int(inp)\\n is_int = True\\n except ValueError:\\n is_int = False\\n\\n if is_int and inp < len(option_type) and inp >= 0:\\n selection.append(option_type[inp])\\n cont_three = False\\n else:\\n print('incorrect number')\\n \\n if key == '\\\"' and (option == 1 or option == 3):\\n # Room for suggest doesn't need to be selected, is derrived from current room of the current player\\n steps = 0\\n key_incorrect = False\\n player_not_stopped = False\\n player_token_list = list(self.player_tokens.items())\\n player_token.reset_has_entered()\\n\\n for i, p in enumerate(player_token_list):\\n if p[0] == player_char:\\n p_pos = i\\n \\n left_player = player_token_list[p_pos - 1 % len(player_token_list)]\\n result = player_object.suggest(selection[0], {selection[0][1]: self.player_cards[selection[0][1]]}, {player_token.current_room: self.rooms[player_token.current_room]}, selection[1], left_player, self.board)\\n if result != False:\\n input('Card: %s, Enter to continue.' % result.name)\\n else:\\n input('Incorrect, Enter to continue.')\\n\\n elif key == '£':\\n correct = player_object.accuse(selection[0][2], selection[1][2], selection[2][2], self.board.solution)\\n steps = 0\\n if correct:\\n print('Yay you won :3')\\n cont = False\\n\\n key = 'P'\\n else:\\n # print(player_object.out)\\n player_object.make_out()\\n # print(player_object.out)\\n # input()\\n print('Oof, you are out')\\n \\n key_incorrect = False\\n player_not_stopped = False\\n input()\\n else:\\n print('wut')\\n input()\\n\\n elif key in movements or key in misc_options_one or key in misc_options_two:\\n if option == 0 and key in movements:\\n cont_two = True\\n switch = True\\n while cont_two:\\n if switch:\\n switch = False\\n else:\\n key, temp_option = self.menu_refresh(player_token, player_char, steps)\\n\\n if key == 'P':\\n cont_two = False\\n cont = False\\n steps -= 1\\n key_incorrect = False\\n elif key in movements:\\n off_x, off_y = movements[key] \\n cont_two, has_entered = player_token.move_by_direction(off_x, off_y)\\n cont_two = not cont_two\\n\\n if has_entered:\\n steps = 1\\n else:\\n if not cont_two:\\n steps -= 1\\n key_incorrect = False\\n elif option in [1, 2] and key in misc_options_one:\\n if key == 'D':\\n player_token.reset_has_entered()\\n player_token.enter_secret_door()\\n steps = 1\\n else:\\n player_token.exit_door()\\n player_token.reset_has_entered()\\n steps -= 1\\n \\n key_incorrect = False\\n elif option in [3, 4] and key in misc_options_two:\\n player_token.exit_door()\\n player_token.reset_has_entered()\\n steps -= 1\\n key_incorrect = False\\n player_token.reset_has_entered()\",\n \"def play_game():\\n difficulty = select_game_difficulty()\\n mad_lib, answers = get_mad_lib_and_answers(difficulty)\\n max_guesses = find_max_guesses()\\n current_blank = 1\\n while current_blank <= len(answers):\\n mad_lib, current_blank = ask_question(\\n mad_lib, current_blank, answers[current_blank - 1], max_guesses\\n )\\n if mad_lib is None:\\n return False\\n print(mad_lib + \\\"\\\\nYou won!\\\\n\\\")\\n return True\",\n \"def check_evolve(self):\\n if self.team == 'white':\\n if self.position[0] == 0:\\n self.evolve()\\n \\n else:\\n if self.position[0] == 7:\\n self.evolve()\",\n \"def test_update_game(self): \\n for i in range(3):\\n for j in range(3):\\n self.game.board[i][j] = self.game.patterns[\\\"Blinker\\\"][i][j]\\n self.game.update_game()\\n self.assertEqual(self.game.board[0][1], 1)\\n self.assertEqual(self.game.board[1][1], 1)\\n self.assertEqual(self.game.board[2][1], 1)\",\n \"def test_legit_player(self):\\n board = Board()\\n player1 = LegitPlayer()\\n player2 = LegitPlayer()\\n player_guard1 = PlayerGuard(player1)\\n player_guard2 = PlayerGuard(player2)\\n\\n # set ids\\n p1id = uuid.uuid4()\\n p2id = uuid.uuid4()\\n player_guard1.set_id(p1id)\\n player_guard2.set_id(p2id)\\n\\n # test methods don't error out\\n player_guard1.start_of_game()\\n player_guard2.start_of_game()\\n board.place_worker(*player_guard1.place_worker(board))\\n board.place_worker(*player_guard2.place_worker(board))\\n board.place_worker(*player_guard2.place_worker(board))\\n board.place_worker(*player_guard1.place_worker(board))\\n player_guard1.play_turn(board)\\n player_guard2.play_turn(board)\\n player_guard1.end_of_game(\\\"legit player\\\")\\n player_guard2.end_of_game(\\\"legit player\\\")\",\n \"def determineEndGame(self):\\n\\n print(\\\"noWinners: \\\" + str(self.noWinners) + \\\", noTotKids: \\\" + str(self.noTotKids))\\n\\n # TODO scegliere come determinare la fine del gioco\\n # if self.noWinners == self.noTotKids - 1: # end-game test\\n if self.noWinners == self.noTotKids:\\n print(\\\"ho determinato la fine del gioco\\\")\\n return True\\n else:\\n print(\\\"colore toccato ma la partita non e' finita\\\")\\n return False\",\n \"def find_opponent(standings,odds):\\n\\n # simulate all games\\n for i in range(len(odds)):\\n play_game(odds.loc[i],standings)\\n\\n # update the points and GD tally\\n standings['P']=standings['W']*3 + standings['D']\\n standings['GD']=standings['F']-standings['A']\\n\\n # see if teams have equal amount of points, and award h2h_points for\\n # h2h results against those teams.\\n for group in \\\"ABCDEF\\\":\\n gelijk = standings.loc[standings['Group']==group][standings.loc[standings['Group']==group].duplicated(subset='P',keep=False)]\\n gelijk[\\\"h2h_points\\\"]=np.zeros(len(gelijk))\\n\\n for i in gelijk.index:\\n for team1 in gelijk.loc[i][\\\"h2h\\\"]:\\n for team2 in gelijk[\\\"TEAMS\\\"]:\\n if team1==team2:\\n standings.loc[i,\\\"h2h_points\\\"]+=1\\n\\n # sort the final standings\\n standings = standings.sort_values(by=['Group','P',\\\"h2h_points\\\",'GD','F','W'],ascending=[True,False,False,False,False,False])\\n\\n # determine third placed teams\\n standings = standings.reset_index()\\n third = standings.loc[[2,6,10,14,18,22]]\\n\\n # determine best number threes\\n third = third.sort_values(by=['P','GD','F','W'],ascending=False)\\n\\n groups_of_best_no_3 = \\\"\\\"\\n for i in third.head(4).Group:\\n groups_of_best_no_3+=i\\n groups_of_best_no_3 = ''.join(sorted(groups_of_best_no_3))\\n\\n # look up the opponent of the dutch team\\n a = third.loc[third.Group == opponent_matrix[groups_of_best_no_3]]['TEAMS']\\n\\n return a.reset_index().TEAMS[0]\",\n \"def check_game_end(self):\\r\\n\\r\\n if np.all(self.remaining == -1): # end of game\\r\\n self.show_results() # show the final results\\r\\n sys.exit() # exit the program\\r\",\n \"def solve(self):\\n\\n tracks_copy = self.tracks.copy()\\n vehicles_sorted = sorted(self.vehicles, key=lambda x: x.departure_time)\\n\\n vehicles_added = 0\\n while len(vehicles_sorted) != 0:\\n best_ratio = - sys.maxsize - 1\\n best_track = None\\n best_vehicle = None\\n shuffle(tracks_copy)\\n\\n for vehicle in vehicles_sorted:\\n for track in tracks_copy:\\n if track.add_vehicle(vehicle, self.tracks):\\n self.grader.reinitialize_grader()\\n goal1 = self.grader.calculate_first_global_goal()\\n goal2 = self.grader.calculate_second_global_goal()\\n ratio = goal2 / goal1\\n if ratio > best_ratio:\\n best_ratio = ratio\\n best_track = track\\n best_vehicle = vehicle\\n\\n track.remove_last()\\n\\n if best_vehicle is not None and best_track is not None:\\n vehicles_added += 1\\n best_track.add_vehicle(best_vehicle, self.tracks)\\n vehicles_sorted.remove(best_vehicle)\\n else:\\n self.grader.reinitialize_grader()\\n goal1 = self.grader.calculate_first_global_goal()\\n goal2 = self.grader.calculate_second_global_goal()\\n if goal1 < self.optimal_gg1 and goal2 > self.optimal_gg2:\\n self.optimal_gg1 = goal1\\n self.optimal_gg2 = goal2\\n self.optimal_tracks = self.tracks\\n return False\\n\\n self.grader.reinitialize_grader()\\n goal1 = self.grader.calculate_first_global_goal()\\n goal2 = self.grader.calculate_second_global_goal()\\n print(\\\"Success:\\\", goal1, goal2)\\n if goal1 < self.best_gg1 and goal2 > self.best_gg2:\\n self.best_gg1 = goal1\\n self.best_gg2 = goal2\\n self.best_tracks = self.tracks\\n\\n return True\",\n \"def solve(self):\\n boss_stats = self.as_dict()\\n for key, val in boss_stats.items():\\n boss_stats[key] = int(val)\\n\\n player = PlayerState(50, 500)\\n boss = BossState(boss_stats['Hit Points'], boss_stats['Damage'])\\n return self.does_player_win_fight(player, boss)\",\n \"def win_game(self):\\n\\n def horizontal_win():\\n \\\"\\\"\\\"Return whether there is horizontal win\\\"\\\"\\\"\\n\\n for i in range(0, board_size):\\n if set(self.board[i]) == set([o_symbol]) or set(self.board[i]) == set([x_symbol]):\\n print \\\"horizontal win\\\"\\n return True\\n\\n def vertical_win():\\n \\\"\\\"\\\"Return whether there is vertical win\\\"\\\"\\\"\\n\\n vert_set = set()\\n for i in range(0, board_size):\\n for j in range(0, board_size):\\n vert_set.add(self.board[j][i])\\n if vert_set == set([o_symbol]) or vert_set == set([x_symbol]):\\n print \\\"vertical win\\\"\\n return True \\n vert_set = set()\\n\\n def diagonal_win():\\n \\\"\\\"\\\"Return whether there is diagonal win\\\"\\\"\\\"\\n\\n diagonal_set = set()\\n for i in range(0, board_size):\\n diagonal_set.add(self.board[i][i]) \\n\\n if diagonal_set == set([o_symbol]) or diagonal_set == set([x_symbol]):\\n print \\\"diagonal win 1\\\"\\n return True\\n \\n diagonal_set = set()\\n for i in range(0, board_size):\\n diagonal_set.add(self.board[i][board_size - 1 - i])\\n\\n if diagonal_set == set([o_symbol]) or diagonal_set == set([x_symbol]):\\n print \\\"diagonal win 2\\\"\\n return True\\n\\n if horizontal_win() or vertical_win() or diagonal_win():\\n print \\\"You have won.\\\"\\n return True\",\n \"def gameOver():\\n if len(p1)==0 and len(p1winnings)==0:\\n return True\\n elif len(p2)==0 and len(p2winnings)==0:\\n return True\\n return False\",\n \"def _solve_puzzle(self, test_puzzle) -> bool:\\n global counter\\n row = 0\\n col = 0\\n for i in range(81):\\n # current cell\\n row = i // 9\\n col = i % 9\\n\\n # if cell is empty we check to see possible placements\\n if test_puzzle[row][col] == 0:\\n # trying to place number in current cell\\n for n in range(1, 10):\\n\\n # checking if we can place n in current cell\\n if not SudokuGrid.check_valid_placement(n, row, col,\\n test_puzzle):\\n # placing n in cell\\n test_puzzle[row][col] = n\\n\\n # check if grid is full increment number of solutions\\n # and break loop to go to previous recursions to try\\n # other combinations\\n if SudokuGrid.check_grid(test_puzzle):\\n counter += 1\\n break\\n\\n # otherwise recurse to place next cell\\n elif self._solve_puzzle(test_puzzle):\\n return True\\n\\n # break loop if no valid placement in cell\\n break\\n\\n # will set current square to 0 and go back to previous recursion\\n # to find another valid placement\\n test_puzzle[row][col] = 0\\n return False\",\n \"def judge(self):\\n self.bounds = 0.0\\n self.best = self.lives[0]\\n for life in self.lives:\\n life.score = self.matchFun(life)\\n self.bounds += life.score\\n if self.best.score < life.score:\\n self.best = life\",\n \"def judge(self):\\n self.bounds = 0.0\\n self.best = self.lives[0]\\n for life in self.lives:\\n life.score = self.matchFun(life)\\n self.bounds += life.score\\n if self.best.score < life.score:\\n self.best = life\",\n \"def check_if_solved(self):\\n for cell in self.board.values():\\n if not cell.value:\\n return False\\n return True\",\n \"def test_valid_new_game(self):\\n self._game.new_game()\\n self.assertIsRUNNING(self._game)\\n self.assertIsNotNone(self._game.table)\\n self.assertEqual(self._game.table.player_turn, 1,\\n \\\"Game.table unsuccessful init.\\\")\",\n \"def update_status(self):\\n if len(self.invalid) != 0:\\n return False\\n for row in self.grid:\\n for num in row:\\n if num == 0:\\n return False\\n self.solved = True\\n print(\\\"solved\\\")\\n return True\",\n \"def solve(self):\\n dim = self.puzzle.dimension\\n\\n # initial loop\\n for value, (row, col) in self.puzzle:\\n if value:\\n self.clear_row(row, value)\\n self.clear_col(col, value)\\n self.clear_subgrid(row, col, value)\\n self.updates.add((value, (row, col)))\\n for ps in self.possibilities:\\n ps.discard((row, col))\\n\\n while self.updates:\\n while self.updates:\\n # while self.updates:\\n value, (row, col) = self.updates.pop()\\n for i in range(1, dim + 1):\\n self.check_row(i, value)\\n self.check_col(i, value)\\n for i in range(2, 8, 3):\\n self.check_subgrid(row, i, value)\\n self.check_subgrid(i, col, value)\\n\\n for value, (row, col) in self.puzzle:\\n if not value:\\n self.check_cell(row, col)\\n\\n # for value in range(1, dim + 1):\\n # for row in [2, 5, 8]:\\n # for col in [2, 5, 8]:\\n # self.check_subgrid(row, col, value)\",\n \"def test_set_players_number(self):\\n\\n # Setup new games and attempt to set thier number of players\\n valid_players = [\\n 1,\\n 2,\\n 10,\\n 999,\\n ]\\n for players in valid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(players)\\n self.assertEqual(game.players_number, players, msg=\\\"The number of players was not correctly set with \\\" + str(players) + \\\" players.\\\")\\n\\n # Make sure that the new game state is correctly set\\n self.assertEqual(game.state.name, \\\"get_player_names\\\", msg=\\\"The game state was not correctly set after setting the number of players in the game.\\\")\\n\\n # Try to set invalid player numbers\\n invalid_players = [\\n 0,\\n -1,\\n -100,\\n 1.5,\\n ]\\n for players in invalid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n success = False\\n try:\\n game.set_players_number(players)\\n except InvalidGamePlayersNumber:\\n success = True\\n self.assertTrue(success, msg=\\\"An invalid number of players \\\" + str(players) + \\\" was able to be set.\\\")\\n\\n # Try to reset the number of players to throw an error\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(3)\\n success = False\\n try:\\n game.set_players_number(2)\\n except InvalidGameMethodOrder:\\n success = True\\n self.assertTrue(success, msg=\\\"The number of players was incorrectly able to be reset.\\\")\",\n \"def verify_valid_game(self, lotto_game:List[int]):\\n is_valid = True;\\n lotto_game_temp = []\\n for number in lotto_game:\\n if number not in lotto_game_temp:\\n lotto_game_temp.append(number)\\n else:\\n print(number, lotto_game, lotto_game_temp)\\n is_valid = False\\n raise InvalidLottoGame\\n \\n lotto_game_temp_length = len(lotto_game_temp)\\n if lotto_game_temp_length != LOTTO_GAME_LENGTH and lotto_game_temp_length != LOTTO_GAME_WITH_BONUS_LENGTH:\\n print(lotto_game_temp)\\n is_valid = False\\n raise InvalidLottoGame\\n\\n return is_valid\",\n \"def solve_board(bd):\\n if is_solved(bd):\\n print_board(bd)\\n return\\n elif len(next_valid_boards(bd)) == 0:\\n return False\\n else:\\n for board in next_valid_boards(bd):\\n solve_board(board)\",\n \"def evaluationFunction(self, currentGameState, action):\\n # Useful information you can extract from a GameState (pacman.py)\\n successorGameState = currentGameState.generatePacmanSuccessor(action)\\n newPos = successorGameState.getPacmanPosition()\\n newFood = successorGameState.getFood()\\n newGhostStates = successorGameState.getGhostStates()\\n newScaredTimes = [\\n ghostState.scaredTimer for ghostState in newGhostStates]\\n\\n \\\"*** YOUR CODE HERE ***\\\"\\n # print \\\"newScaredTimes\\\", newScaredTimes\\n # print successorGameState.getCapsules()\\n\\n newGhostPos = newGhostStates[0].getPosition()\\n ghost_dist = ghost_distance(newPos, newGhostPos)\\n capsules = successorGameState.getCapsules()\\n # food_dist = food_distance(newPos, newFood)\\n\\n # approach 1: 2/4 win = 10, average < 500\\n # if ghost_dist <= 1:\\n # return -999999\\n # return -food_num(newFood)\\n\\n # approach 2: 2/4 win = 10, average < 500 but close to 500\\n # if newScaredTimes[0] == 0:\\n # if ghost_dist <= 1:\\n # return -999999\\n # return -food_num(newFood) -capsule_distance(newPos, capsules)\\n\\n # final approach: 4/4 win = 10, average = 1310.5\\n if newScaredTimes[0] == 0:\\n if ghost_dist <= 1:\\n return -999999\\n return -food_distance(newPos, newFood) * .01 - food_num(newFood) - capsule_distance(newPos, capsules)\",\n \"def _test_is_game_over(self, die_a_value, die_b_value, stage, ok_output):\\n\\n self.new_angry_dice.die_a.current_value = die_a_value\\n self.new_angry_dice.die_b.current_value = die_b_value\\n self.new_angry_dice.game_stage = stage\\n\\n self.assertEqual(self.new_angry_dice.is_advancing_to_next_stage(),\\n ok_output,\\n \\\"Incorrect output for die_a:{}, \\\"\\n \\\"die_b:{} in stage:{}.\\\"\\n .format(die_a_value, die_b_value, stage))\",\n \"def testPointSystem():\\n deleteMatches()\\n deletePlayers()\\n registerPlayer(\\\"Pikachu\\\")\\n registerPlayer(\\\"Charmander\\\")\\n registerPlayer(\\\"Bulbasaur\\\")\\n registerPlayer(\\\"Squirtle\\\")\\n registerPlayer(\\\"MewTwo\\\")\\n standings = playerStandings()\\n [id1, id2, id3, id4, id5] = [row[0] for row in standings]\\n reportMatch(id1, id2)\\n reportMatch(id3, id4, True)\\n reportMatch(id5, id5, False, True)\\n reportMatch(id1, id5)\\n reportMatch(id3, id4)\\n reportMatch(id2, id2, False, True)\\n reportMatch(id1, id3)\\n reportMatch(id5, id2)\\n reportMatch(id4, id4, False, True)\\n standings = playerStandings()\\n if not (standings[0][0]==id2 and standings[0][2]==2 and\\n standings[1][0]==id4 and standings[0][2]==2 and\\n standings[2][0]==id3 and standings[0][2]==2 and\\n standings[3][0]==id5 and standings[0][2]==2 and\\n standings[4][0]==id1 and standings[0][2]==2):\\n raise ValueError(\\n \\\"Points are not tallied correctly.\\\"\\n )\\n\\n print \\\"4. Points are tallied correctly.\\\"\",\n \"def game_check(self, mode, row=None, col=None, is_set=True,\\r\\n show_fail=True):\\r\\n self.add_play_move(PlayMove.GAME_CHECK, mode=mode, row=row, is_set=is_set, show_fail=show_fail)\\r\\n if mode == \\\"h\\\" or mode == \\\"v\\\":\\r\\n part = self.get_part(type=\\\"edge\\\", sub_type=mode, row=row, col=col)\\r\\n if part is None:\\r\\n raise SelectError(f\\\"game_check: no edge({mode}) found at row={row} col={col}\\\") \\r\\n is_on = part.is_turned_on()\\r\\n if is_on != is_set:\\r\\n result = False\\r\\n msg = (f\\\"Unexpected test result: {result}\\\"\\r\\n f\\\" for line({mode}) at row={row} col={col}\\\")\\r\\n SlTrace.lg(f\\\"game_check: {msg}\\\")\\r\\n if show_fail:\\r\\n raise SelectFail(msg)\\r\\n return False\\r\\n elif mode == \\\"sq\\\":\\r\\n part = self.get_part(type=\\\"region\\\", row=row, col=col)\\r\\n is_on = part.is_turned_on()\\r\\n if is_on != is_set:\\r\\n result = False\\r\\n msg = (f\\\"Unexpected test result: {result}\\\"\\r\\n f\\\" for square at row={row} col={col}\\\")\\r\\n SlTrace.lg(f\\\"game_check: {msg}\\\")\\r\\n if show_fail:\\r\\n raise SelectFail(msg)\\r\\n return False\\r\\n else:\\r\\n raise SelectFail(f\\\"Unrecognized game_check mode({mode}\\\")\\r\\n \\r\\n return True\",\n \"def solve_step(self,puzzle_grid,x,y):\\n self.puzzleGrid = puzzle_grid\\n if(self.foundStep == False):\\n self.targetCell = self.puzzleGrid.grid[x][y]\\n if(self.targetCell.isSolved == False):\\n self.calculate_possibilities()\\n if len(self.targetCell.possibilities) == 1: #README method 1\\n self.targetCell.solve()\\n return True\\n else:\\n return self.check_neighbours() #README method 2\",\n \"def solve_puzzle(self):\\r\\n \\r\\n counter = 0\\r\\n rows = self._height-1\\r\\n cols = self._width-1\\r\\n # print rows, cols\\r\\n # print 'The greed has %s rows and %s coloumn indexes' %(rows, cols) \\r\\n solution_move = ''\\r\\n if self.get_number(0,0) == 0 and \\\\\\r\\n self.get_number(0,1) == 1:\\r\\n # print 'Congrads Puxxle is Aolved at start!!!!!'\\r\\n return ''\\r\\n #appropriate_number = (self._height * self._width) - 1\\r\\n appropriate_number = (rows+1) * (cols+1) -1\\r\\n # print 'First appropriate_number=',appropriate_number\\r\\n # print \\\"Grid first tile that we will solwing has value =\\\", self._grid[rows][cols]\\r\\n \\r\\n while counter < 300:\\r\\n counter +=1\\r\\n # print self\\r\\n #appropriate_number = (rows+1) * (cols+1) -1\\r\\n # print 'Appropriate number in loop=',appropriate_number\\r\\n # print 'We are solving %s index_row and %s index_col' %(rows, cols) \\r\\n ####Case when we use solve_interior_tile\\r\\n if rows > 1 and cols > 0:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n cols -= 1\\r\\n appropriate_number -=1\\r\\n else:\\r\\n # print 'We are solving interior tile', (rows, cols)\\r\\n solution_move += self.solve_interior_tile(rows, cols)\\r\\n # print 'Solution move=', solution_move\\r\\n cols -= 1\\r\\n #### Case when we use solve_col0_tile\\r\\n elif rows > 1 and cols == 0:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows -= 1\\r\\n cols = self._width-1\\r\\n appropriate_number -=1\\r\\n else:\\r\\n # print 'We are solwing tile 0 in row', rows\\r\\n # print 'Appropriate number here ='\\r\\n solution_move += self.solve_col0_tile(rows)\\r\\n # print 'Solution move=', solution_move\\r\\n rows -=1\\r\\n cols = self._width-1\\r\\n\\r\\n\\r\\n #### Cases when we use solve_row0_tile\\r\\n elif rows == 1 and cols > 1:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows -= 1\\r\\n #cols = self._width-1\\r\\n appropriate_number -= self._width\\r\\n\\r\\n else:\\r\\n # print 'Solving upper 2 rows right side'\\r\\n solution_move += self.solve_row1_tile(cols)\\r\\n rows -=1\\r\\n appropriate_number -= self._width\\r\\n #### Cases when we use solve_row1_tile \\r\\n if rows < 1 and cols > 1:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows += 1\\r\\n cols -= 1\\r\\n appropriate_number +=self._width-1\\r\\n else:\\r\\n # print '(1,J) tile solved, lets solwe tile (0,j) in tile',(rows,cols)\\r\\n # print 'Greed after move solve_row1_tile'\\r\\n # print self\\r\\n solution_move += self.solve_row0_tile(cols)\\r\\n rows +=1\\r\\n cols -=1\\r\\n appropriate_number +=self._width-1\\r\\n\\r\\n\\r\\n #### Case when we use solve_2x2\\r\\n elif rows <= 1 and cols <= 1:\\r\\n # print 'We are solving 2x2 puzzle'\\r\\n solution_move += self.solve_2x2()\\r\\n if self._grid[0][0] == 0 and \\\\\\r\\n self._grid[0][1] == 1:\\r\\n # print 'Congrads Puxxle is SOLVED!!!!!'\\r\\n break\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n if counter > 100:\\r\\n # print 'COUNTER BREAK'\\r\\n break\\r\\n # print solution_move, len(solution_move)\\r\\n return solution_move\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n # for row in solution_greed._grid[::-1]:\\r\\n # print solution_greed._grid\\r\\n # print 'Row =',row\\r\\n \\r\\n # if solution_greed._grid.index(row) > 1:\\r\\n # print \\\"Case when we solwing Interior and Tile0 part\\\"\\r\\n \\r\\n\\r\\n # for col in solution_greed._grid[solution_greed._grid.index(row)][::-1]:\\r\\n # print 'Coloumn value=', col\\r\\n #print row[0]\\r\\n # if col !=row[0]:\\r\\n # print 'Case when we use just Interior tile solution'\\r\\n # print solution_greed._grid.index(row)\\r\\n # print row.index(col)\\r\\n \\r\\n # solution += solution_greed.solve_interior_tile(solution_greed._grid.index(row) , row.index(col))\\r\\n # print 'Solution =', solution\\r\\n # print self \\r\\n # print solution_greed._grid\\r\\n # elif col ==row[0]:\\r\\n # print 'Case when we use just Col0 solution'\\r\\n\\r\\n # else:\\r\\n # print 'Case when we solwing first two rows'\\r\\n\\r\\n #return \\\"\\\"\\r\",\n \"def check_win(self, player):\\n def check_row_win(player):\\n for row in self.game_state:\\n if player == row[0] == row[1] == row[2]:\\n return True\\n return False\\n\\n def check_column_win(player):\\n # For doing a column check, transpose the grid and do a row check\\n trans_game_state = numpy.transpose(self.game_state)\\n for row in trans_game_state:\\n if player == row[0] == row[1] == row[2]:\\n return True\\n return False\\n\\n def check_diag_win(player):\\n # Left to right diagonal\\n if player == self.game_state[0][0] == self.game_state[1][1] == self.game_state[2][2]:\\n return True\\n # Right to left diagonal\\n if player == self.game_state[0][2] == self.game_state[1][1] == self.game_state[2][0]:\\n return True\\n return False\\n\\n if check_column_win(player) or check_diag_win(player) or check_row_win(player):\\n return True\\n return False\",\n \"def play_best_guess(self, game):\\n\\n\\n # create a list of cells\\n cells = [game.board[i][j]\\n for i in xrange(game.rows)\\n for j in xrange(game.cols)]\\n\\n first_cell = cells[0]\\n game.reveal_cell(first_cell.row, first_cell.col)\\n\\n # draw updated board and pause for a second\\n game.draw_board()\\n if PAUSE == True:\\n time.sleep(1)\\n\\n\\n total_flagged = 0\\n while not game.lost_game and not game.won_game:\\n\\n # remember if we've made a move in the while loop\\n # so we know whether to make a random move later on\\n made_move = False\\n\\n # look through all revealed cells for any with a number of neighboring mines.\\n # if the cell has the same number of unrevealed neighbors as the cell's\\n # number of neighboring mines, all the unrevealed neighbors must be mines.\\n revealed_numbered_cells = [c for c in cells if c.revealed and (not c.flagged) and (c.neighbors > 0)]\\n while revealed_numbered_cells:\\n cell = revealed_numbered_cells.pop()\\n # cell may have been marked flagged after revealed_numbered_cells was compiled\\n if not cell.flagged:\\n neighbor_cells = ms.Minesweeper.get_neighbors(cell.row, cell.col, game.board)\\n flagged_neighbors = [n for n in neighbor_cells if n.flagged]\\n number_remaining_mines = cell.neighbors - len(flagged_neighbors)\\n unknown_neighbors = [n for n in neighbor_cells if not n.flagged and not n.revealed]\\n if number_remaining_mines > 0 and len(unknown_neighbors) == number_remaining_mines:\\n # flag every neighbor\\n for c in unknown_neighbors:\\n if total_flagged < game.mines:\\n total_flagged += 1\\n game.flag_cell(c.row, c.col)\\n if (game.test_did_win()):\\n game.game_over()\\n game.draw_board()\\n if PAUSE == True:\\n time.sleep(1)\\n made_move = True\\n\\n # we may have won with the flag above so test whether we're still playing\\n # before further calculations\\n if not game.lost_game and not game.won_game:\\n # loop through all unrevealed, unflagged cells and see if we know it's safe to reveal\\n for c in cells:\\n if not c.revealed and not c.flagged and self.is_cell_safe(c, game.board):\\n game.reveal_cell(c.row, c.col)\\n if (game.test_did_win()):\\n game.game_over()\\n game.draw_board()\\n if PAUSE == True:\\n time.sleep(1)\\n made_move = True\\n\\n # assume we've made our best guesses and now have to guess randomly\\n # this will prevent us from looping forever if no obvious moves are available\\n if not made_move:\\n unrevealed = [c for c in cells if not c.revealed and not c.flagged]\\n if len(unrevealed) > 0:\\n cell = random.choice(unrevealed)\\n game.reveal_cell(cell.row, cell.col)\\n if (game.test_did_win()):\\n game.game_over()\\n game.draw_board()\\n if PAUSE == True:\\n time.sleep(3)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7135749","0.711226","0.70119655","0.6942161","0.69317514","0.68992364","0.68689203","0.68571925","0.68241847","0.6820517","0.6752076","0.6728036","0.6698164","0.6654949","0.6626608","0.6581106","0.65573555","0.65466815","0.6539139","0.6468664","0.6427255","0.6378252","0.63097453","0.6303555","0.6291169","0.62847507","0.62825245","0.6248755","0.6235335","0.6218387","0.62181556","0.6215881","0.6208214","0.6194488","0.61876297","0.6179093","0.6177204","0.61743104","0.6166365","0.6153871","0.61522377","0.6146874","0.6140935","0.6139525","0.61326414","0.61253536","0.61219203","0.6115174","0.61131614","0.6112495","0.6103892","0.609945","0.6096874","0.60951877","0.6094113","0.60935867","0.6090312","0.60861677","0.6084662","0.6084015","0.6083828","0.60825855","0.60680115","0.6064548","0.6054649","0.60501075","0.60366535","0.60259914","0.6022729","0.6021249","0.6014635","0.6014161","0.601289","0.6007801","0.60000676","0.59956735","0.5991995","0.5989057","0.59824437","0.59809506","0.59804153","0.5978668","0.5973067","0.5965027","0.5965027","0.5961401","0.59547377","0.59531677","0.59521115","0.59495085","0.5937744","0.5936367","0.59350467","0.5934847","0.5932226","0.5931332","0.59227496","0.5919974","0.591707","0.5906768"],"string":"[\n \"0.7135749\",\n \"0.711226\",\n \"0.70119655\",\n \"0.6942161\",\n \"0.69317514\",\n \"0.68992364\",\n \"0.68689203\",\n \"0.68571925\",\n \"0.68241847\",\n \"0.6820517\",\n \"0.6752076\",\n \"0.6728036\",\n \"0.6698164\",\n \"0.6654949\",\n \"0.6626608\",\n \"0.6581106\",\n \"0.65573555\",\n \"0.65466815\",\n \"0.6539139\",\n \"0.6468664\",\n \"0.6427255\",\n \"0.6378252\",\n \"0.63097453\",\n \"0.6303555\",\n \"0.6291169\",\n \"0.62847507\",\n \"0.62825245\",\n \"0.6248755\",\n \"0.6235335\",\n \"0.6218387\",\n \"0.62181556\",\n \"0.6215881\",\n \"0.6208214\",\n \"0.6194488\",\n \"0.61876297\",\n \"0.6179093\",\n \"0.6177204\",\n \"0.61743104\",\n \"0.6166365\",\n \"0.6153871\",\n \"0.61522377\",\n \"0.6146874\",\n \"0.6140935\",\n \"0.6139525\",\n \"0.61326414\",\n \"0.61253536\",\n \"0.61219203\",\n \"0.6115174\",\n \"0.61131614\",\n \"0.6112495\",\n \"0.6103892\",\n \"0.609945\",\n \"0.6096874\",\n \"0.60951877\",\n \"0.6094113\",\n \"0.60935867\",\n \"0.6090312\",\n \"0.60861677\",\n \"0.6084662\",\n \"0.6084015\",\n \"0.6083828\",\n \"0.60825855\",\n \"0.60680115\",\n \"0.6064548\",\n \"0.6054649\",\n \"0.60501075\",\n \"0.60366535\",\n \"0.60259914\",\n \"0.6022729\",\n \"0.6021249\",\n \"0.6014635\",\n \"0.6014161\",\n \"0.601289\",\n \"0.6007801\",\n \"0.60000676\",\n \"0.59956735\",\n \"0.5991995\",\n \"0.5989057\",\n \"0.59824437\",\n \"0.59809506\",\n \"0.59804153\",\n \"0.5978668\",\n \"0.5973067\",\n \"0.5965027\",\n \"0.5965027\",\n \"0.5961401\",\n \"0.59547377\",\n \"0.59531677\",\n \"0.59521115\",\n \"0.59495085\",\n \"0.5937744\",\n \"0.5936367\",\n \"0.59350467\",\n \"0.5934847\",\n \"0.5932226\",\n \"0.5931332\",\n \"0.59227496\",\n \"0.5919974\",\n \"0.591707\",\n \"0.5906768\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68758893"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":93796,"cells":{"query":{"kind":"string","value":"Test for a valid matching when the game is solved."},"document":{"kind":"string","value":"def test_check_validity(game):\n\n game.solve()\n assert game.check_validity()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_match(self):\n self.assertEqual(solution( \"\"\"p=<-6,0,0>, v=< 3,0,0>, a=< 0,0,0>\np=<-4,0,0>, v=< 2,0,0>, a=< 0,0,0>\np=<-2,0,0>, v=< 1,0,0>, a=< 0,0,0>\np=< 3,0,0>, v=<-1,0,0>, a=< 0,0,0>\"\"\"), 1)","def test_check_validity(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n assert match.check_validity()","def test_not_found_pair(self):\n self.assertTrue(\"No matches found\", show_players_sumheigh_is_input(100))","def match(self) -> bool:","def test_check_solved():\n game = Game()\n game.word = 'word'\n game.pattern = 'word'\n game.check_solved()\n assert game.solved is True","def test_game_has_ended_match(self):\n game = Game.objects.create(\n allowed_attempts=10,\n end_date=date.today() + timedelta(1),\n min_value=1,\n max_value=10\n )\n self.assertTrue(game.is_active)\n self.assertIsNone(game.compleation_date)\n\n game.attempt_set.create(\n our_number=10,\n user_number=10,\n ip_addr='127.0.0.1'\n )\n self.assertEqual(len(self.recieved), 1)\n self.assertListEqual(self.recieved[0], ['match'])\n self.assertFalse(game.is_active)\n self.assertIsNotNone(game.compleation_date)","def test_solve(game, optimal):\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_hospital, hospital in zip(matching_keys, hospitals):\n assert game_hospital.name == hospital.name\n assert game_hospital._pref_names == hospital._pref_names\n assert game_hospital.capacity == hospital.capacity\n\n matched_residents = [\n resident for match in matching.values() for resident in match\n ]\n\n assert matched_residents != [] and set(matched_residents).issubset(\n set(game.residents)\n )\n\n for resident in set(game.residents) - set(matched_residents):\n assert resident.matching is None","def matches(self):\n return False","def test_suitor_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.suitors[0].matching = Player(name=\"foo\", pref_names=[])\n\n with pytest.raises(Exception):\n match._check_suitor_matching()","def test_match_incorrect(self, event: Event, pr_algorithm: ProgressiveResizing, minimal_state: State):\n assert not pr_algorithm.match(event, minimal_state)","def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching.append(Player(name=\"foo\", pref_names=[]))\n\n with pytest.raises(Exception):\n match._check_reviewer_matching()","def setup_match():\n\n # Teams are identified by team name.\n teamname = input(\"Team name: \")\n team_data_filename = \"teams/team\" + teamname + \".json\"\n # Check if the team file exists\n try:\n # Open the team file\n with open(team_data_filename, 'r') as team_data_file:\n # Read from the json file\n team_data_json = json.load(team_data_file)\n team_key = input(\"Enter your team key: \")\n # Check if the key matches.\n if hashfunc.verify_password(team_key, team_data_json[\"team_key\"]):\n # The team will play against an opponent.\n print(\"Team\", teamname, \"is now ready to play a match!\")\n # Determine if the opponent is a human or a computer.\n print('Do you want to play against a human team or computer team?')\n print(\"To play against a human team, type 'human' and hit 'Enter'\")\n print(\"To play against the usual computer team, just hit 'Enter'.\")\n opponent_choice = input()\n # Get game ID.\n gameidfile = savegamedata.getUniqueSavefile()\n gameid = gameidfile[12:-5]\n thisGameID = True\n # If opponent is computer, load the computer team as opponent.\n if(opponent_choice != 'human'):\n pairing = {\n \"team1\": teamname,\n \"team1_human\": True,\n \"team1_confirm\": True,\n \"team2\": \"Computer\",\n \"team2_human\": False,\n \"team2_confirm\": True,\n \"gameid\": hashfunc.hash_password(gameid),\n \"timestamp\": datetime.datetime.now().timestamp()\n }\n insertGameLog(pairing)\n # If opponent is human, pick an opponent or create a fresh pairing.\n if(opponent_choice == \"human\"):\n print(\"Do you want to choose your opponent (Y/N)\")\n chooseopponentflag = input()\n if chooseopponentflag == 'Y':\n # Create a match pairing.\n opponent_teamname = findHumanOpponentExists(teamname)\n if not checkPairingChosen(teamname, opponent_teamname):\n pairing = {\n \"team1\": teamname,\n \"team1_human\": True,\n \"team1_confirm\": True,\n \"team2\": opponent_teamname,\n \"team2_human\": True,\n \"team2_confirm\": False,\n \"gameid\": hashfunc.hash_password(gameid),\n \"timestamp\": datetime.datetime.now().timestamp()\n }\n insertGameLog(pairing)\n else:\n thisGameID = False\n if chooseopponentflag != 'Y':\n pairing = {\n \"team1\": teamname,\n \"team1_human\": True,\n \"team1_confirm\": True,\n \"team2\": None,\n \"team2_human\": True,\n \"team2_confirm\": False,\n \"gameid\": hashfunc.hash_password(gameid),\n \"timestamp\": datetime.datetime.now().timestamp()\n }\n insertGameLog(pairing)\n print(\"Your match is registered.\")\n if thisGameID:\n print(\"Your match ID is: \")\n print(gameid)\n print(\"Share this match ID only with your opponent.\")\n else:\n print(\"Please ask your opponent for the match ID.\")\n confirm_read = input(\"Press 'Enter' key to complete this process.\")\n else:\n print(\"The keys did not match.\")\n print(\"Forgot your key? Contact support for key reset.\")\n assume_forgotten = input()\n\n # If team file doesn't exist, we can't setup the match.\n except:\n print(\"Team\", teamname, \"is not available.\")\n print(\"Please set up your team before proceeding.\")\n confirm_unavailable = input()","def win_check(self):\n\t\t# Create a temp var to capture the number of correct matches\n\t\tright = 0\n\t\t# retrieve peg_guess_color_list for current round\n\t\tguess = self.model.guesses[self.model.status]\n\t\t# retreive solution list\n\t\tsolution = self.model.guesses[\"solution\"]\n\t\t# compare values in each index for both lists against eachother\n\t\tfor i in range(len(solution.pegs)):\n\t\t\t# \n\t\t\tif solution.pegs[i].peg_color == guess.pegs[i].peg_color:\n\t\t\t\tright += 1\n\n\t\t\t\tprint(\"Yay, it works!\")\n\n\t\t# If all indexes of the peg_colors in the solution and guess are True:\n\t\tif right == 4:\n\t\t\treturn True\n\n\t\telse:\n\t\t\treturn False","def run_match(player_move, comp_move, state):\n #Player/computer is vulnerable if they chose to reload\n player_vuln = False\n comp_vuln = False\n state['player_prev'] = player_move\n state['comp_prev'] = comp_move\n\n #Update game variables\n if player_move == 'd':\n state['player_ammo'] += 1\n player_vuln = True\n if comp_move == 'd':\n state['comp_ammo'] += 1\n comp_vuln = True\n if player_move == 'a':\n if comp_vuln:\n return 'N'\n state['player_ammo'] -= 1\n if comp_move == 'a':\n if player_vuln:\n return 'Y'\n state['comp_ammo'] -= 1\n\n return game_states[get_values(state)]","def match(self, target, guess):\r\n return guess == target","def test_check_for_unacceptable_matches_residents(game):\n\n resident = game.residents[0]\n hospital = Hospital(name=\"foo\", capacity=1)\n resident.matching = hospital\n\n issues = game._check_for_unacceptable_matches(\"residents\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(resident.name)\n assert issue.endswith(f\"{resident.prefs}.\")\n assert hospital.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def isValid(self, game):\n return True","def test_match_or_none():\r\n runmatch(lcode)","def test_check_validity(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n game.solve()\n assert game.check_validity()","def check_winner(self):\n pass","def valid_match(self, home_stats, away_stats):\n # If either of the statistics is None then you cannot evaluate the\n # match\n if home_stats is None or away_stats is None:\n return False\n # Otherwise if we have not specified a number of matches to ignore then\n # this match can be evaluated.\n if self.ignore_matches is None:\n return True\n # If we have specified a number of matches to ignore, check both teams\n # have played at least that many:\n if (len(home_stats.games) < self.ignore_matches or\n len(away_stats.games) < self.ignore_matches):\n return False\n return True","def matches(self):\n pass","def test_play_game(self):\r\n\r\n \r\n a_players = [RandomPlayer(1), RandomPlayer(2)]\r\n a_x_dist = 3\r\n a_y_dist = 3\r\n a_num_to_win = 1\r\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n #Game is played to competion\r\n a_game.play_game()\r\n\r\n a_history = a_game.get_history()\r\n\r\n #Go through each move and check to be sure it's valid\r\n for i in range(1,len(a_history)):\r\n #Get copy of the board\r\n prev_board = a_history[i-1]\r\n cur_board = a_history[i]\r\n\r\n #Check if the board chosen is in valid states\r\n self.assertTrue(cur_board in prev_board.get_states(a_players[0].get_id()) or cur_board in prev_board.get_states(a_players[1].get_id()),\\\r\n \"An invalid board state was added to the history\")\r\n\r\n if i == len(a_history) - 1:\r\n self.assertTrue(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\r\n else: \r\n self.assertFalse(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())","def main():\n num_matches = 1000\n matchlist = [\n [ RandomAI, RandomAI ],\n #[ VectorAI, RandomAI ],\n #[ VectorAI, VectorAI ],\n #[ LeftmostAI, RandomAI ],\n #[ LeftmostAI, VectorAI ],\n #[ LeftmostAI, LeftmostAI ],\n\t\t#[ RightmostAI, RandomAI ],\n #[ RightmostAI, VectorAI ],\n #[ RightmostAI, LeftmostAI ],\n #[ RightmostAI, RightmostAI ]\n ]\n\n x=0\n while x < len(matchlist):\n \n print (\"Running \" + str(num_matches) + \" matches: \" + matchlist[x][0].__name__ + \" vs \" + matchlist[x][1].__name__)\n counter = 0 \n \n while counter < num_matches:\n counter = counter + 1\n match = Match(player1_type=matchlist[x][0], player2_type=matchlist[x][1], param_print_game_status=False, param_matchgroup=x)\n match.handle_next_move()\n\n if matchlist[x][0] != matchlist[x][1]:\n print (\"Running \" + str(num_matches) + \" matches: \" + matchlist[x][1].__name__ + \" vs \" + matchlist[x][0].__name__)\n counter = 0\n\n while counter < num_matches:\n counter = counter + 1\n match = Match(player1_type=matchlist[x][1], player2_type=matchlist[x][0], param_print_game_status=False, param_matchgroup=x)\n match.handle_next_move()\n x = x+1\n \n print (\"Matches Finished\")","def test_check_for_unacceptable_matches_projects(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n project = game.projects[0]\n student = Student(name=\"foo\")\n project.matching.append(student)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert error.startswith(project.name)\n assert error.endswith(str(project.prefs))\n assert str(student.name) in error","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_match_or_none2():\r\n runmatch(lcode)","def check_matches(known, match): \n #array containing tuples of corrected spellings\n out_vals = []\n\n #clear screen\n os.system('clear')\n \n for k,v in known.iteritems():\n matched_vals = [ik for ik, iv in match.iteritems() if iv == v]\n cur_matches = process.extract(k, matched_vals, limit = MATCH_LIMIT)\n \n #append a none option and option for actual val\n cur_matches.insert(0,(\"NONE\",0))\n cur_matches.append((k,100))\n\n\n #if there is a match of 100, skip\n if cur_matches[1][1] == 86 or cur_matches[1][1] < 50:\n #special case where probably means there really isn't a match\n choice = 'NONE'\n print 'No match!'\n\n elif cur_matches[1][1] < 95:\n #print out options for each word\n print_options(k, cur_matches)\n\n #cycle through prompt for input if valid (numeric, within MATCH_LIMIT)\n choice = None\n while choice == None:\n try:\n choice = cur_matches[get_choice()][0]\n except:\n print '!Invalid match! Try again'\n print_options(k, cur_matches)\n \n print choice\n\n else:\n #exact match\n choice = cur_matches[1][0]\n print '%s - Match!'%choice\n\n print \n print\n #create a new tuple with correct and incorrect spelling\n out_vals.append((k, choice))\n output(out_vals)\n\n #we're done, return vals array\n return out_vals","def test_match_correct(self, event: Event, pr_algorithm, minimal_state: State):\n assert pr_algorithm.match(event, minimal_state)","def test_is_solved(self):\n p = hw.TilePuzzle([[1, 2], [3, 0]])\n self.assertTrue(p.is_solved())\n p = hw.TilePuzzle([[0, 1], [3, 2]])\n self.assertFalse(p.is_solved())","def test_strategy(self):\n axelrod.seed(1)\n opponent = axelrod.Cooperator()\n player = axelrod.KnowledgeableWorseAndWorse()\n match = axelrod.Match((opponent, player), turns=5)\n self.assertEqual(match.play(), [('C', 'C'),\n ('C', 'D'),\n ('C', 'D'),\n ('C', 'D'),\n ('C', 'D')])\n\n # Test that behaviour does not depend on opponent\n opponent = axelrod.Defector()\n player = axelrod.KnowledgeableWorseAndWorse()\n axelrod.seed(1)\n match = axelrod.Match((opponent, player), turns=5)\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'D'),\n ('D', 'D'),\n ('D', 'D'),\n ('D', 'D')])\n\n # Test that behaviour changes when does not know length.\n axelrod.seed(1)\n match = axelrod.Match((opponent, player), turns=5,\n match_attributes={'length': float('inf')})\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C')])","def test_strategy(self):\n\n axelrod.seed(8)\n opponent = axelrod.Cooperator()\n player = axelrod.WorseAndWorse()\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'D'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C')])\n\n # Test that behaviour does not depend on opponent\n opponent = axelrod.Defector()\n player = axelrod.WorseAndWorse()\n axelrod.seed(8)\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'D'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C')])","def test_match_right_none():\r\n runmatch(lcode_right_none)","def test_check_for_unacceptable_matches_students(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n student = game.students[0]\n project = Project(name=\"foo\", capacity=0)\n student.matching = project\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert error.startswith(student.name)\n assert error.endswith(str(student.prefs))\n assert project.name in error","def test_solve(resident_names, hospital_names, capacities, seed):\n\n for optimal in [\"suitor\", \"resident\", \"reviewer\", \"hospital\"]:\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n matching = match.solve(optimal)\n assert isinstance(matching, Matching)\n assert set(matching.keys()) == set(hospitals)\n matched_residents = [\n res for match in matching.values() for res in match\n ]\n assert matched_residents != [] and set(matched_residents).issubset(\n set(residents)\n )\n\n for resident in set(residents) - set(matched_residents):\n assert resident.matching is None","def test_is_solved_when_puzzle_is_solved(self):\n self.assertTrue(self.sudoku.is_solved())","def verify_winner(self):\r\n return self.count_pegs() == 1","def doMatching(self):\n\n self.alltrans = []\n curdir = os.getcwd()\n os.chdir(self.astromdir)\n\n for cat in self.projcats:\n self.logfile.write(\"Beginning match attempt for %s\" %cat)\n nobj = max(80,self.Nrefobjs)\n base = 'match %s 1 2 3 %s 1 2 3 identity recalc nobj=%d medtf medsigclip=2.7 '\\\n %(cat,self.GSCmatchin,nobj)\n converged=0\n mrad=4.5\n xsh=0.\n ysh=0.\n Retry=0\n while not converged and mrad<10 and Retry<11:\n ferr,mdict=self._runmatch(base,mrad,xsh,ysh)\n if ferr:\n # match not found, make radius bigger and retry\n mrad += 0.5\n Retry += 1\n self.logfile.write(\"Retry: %d ... set mrad: %s\" %(Retry,mrad))\n continue\n\n dx = mdict['dx']\n dy = mdict['dy']\n sigx = mdict['sigx']\n sigy = mdict['sigy']\n\n # if the extra shift is too big compared to the\n # match radius, update input shift and retry\n if max(abs(dx-xsh),abs(dy-ysh)) > min(0.4, mrad/10.):\n xsh=dx\n ysh=dy\n Retry += 1\n continue\n\n # if sigma seems too big, shrink mrad\n if (max(sigx,sigy) > 0.7 and mrad>2.9):\n mrad -= 0.5\n Retry += 1\n continue\n\n # otherwise, it looks like we have a good fit\n # but we want to tune it up with smaller matchrad\n # Don't increment retry in this final case.\n if mrad > 3.9 or (mrad>2.9 and max(sigx,sigy)>0.4):\n mrad = max(mrad-1,2.5)\n continue\n\n self.alltrans.append(mdict)\n converged=1\n self.logfile.write(\"\"\"%s trans:\n mdx,mdy = %.4f %.4f\n edx,edy = %.4f %.4f\n sigx,sigy = %.4f %.4f\n \"\"\"%(cat, mdict['dx'],mdict['dy'],mdict['dx_err'],mdict['dy_err'],\\\n mdict['sigx'],mdict['sigy']))\n if len(self.alltrans)<1:\n errtxt = \"WARNING: No Image successfully matched against extended GSC!\"\n self.logfile.write(errtxt)\n self.errorList.append((self.modName,errtxt))\n return -1\n\n os.chdir(curdir)\n # finally, choose best match transform and calculate\n # the new CRVAL's\n self._pickBestrans()\n\n return 0","def checkForRematch(player_id, opponent_id):\n conn, cur = connect()\n query = \"SELECT check_for_rematch(%s, %s);\"\n params = (player_id, opponent_id,)\n try:\n cur.execute(query, params)\n except:\n print(\"Error encountered when checking if playerId:\" + str(player_id) +\n \" has already played opponentId:\" + str(opponent_id))\n isRematch = cur.fetchone()\n conn.close()\n return int(isRematch[0])","def is_legal_solution(self, solution):\r\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\r\n return self.fit_score(solution) == 0\r\n else:\r\n return self.fit_score(solution) == sum(x for x in range(1, 12))","def test_no_match(self):\n\n conn, cursor = get_db_cursor()\n build = \"toy_build\"\n database = \"scratch/toy.db\"\n run_info = talon.init_run_info(database, build)\n talon.get_counters(database)\n\n edge_dict = init_refs.make_edge_dict(cursor)\n location_dict = init_refs.make_location_dict(build, cursor)\n transcript_dict = init_refs.make_transcript_dict(cursor, build)\n gene_starts = init_refs.make_gene_start_or_end_dict(cursor, build, \"start\")\n gene_ends = init_refs.make_gene_start_or_end_dict(cursor, build, \"end\")\n\n chrom = \"chr1\"\n strand = \"+\"\n positions = [ 1, 100, 900, 1000 ]\n edge_IDs = [200]\n vertex_IDs = [2, 5]\n v_novelty = [0, 0]\n\n all_matches = talon.search_for_ISM(edge_IDs, transcript_dict)\n assert all_matches == None\n conn.close()","def test_match(self):\n form_data = self.form_data('r*A9x=^2hg&v7u?u9tg?u',\n 'A=r7-%=K?K@B^!9Q8=C+')\n form = self.form(data=form_data, user=self.u)\n self.assertFalse(form.is_valid())","def test_bad_match(self):\n log.info('===== START TEST BAD MATCH =====')\n\n # Telemetered\n file_path = os.path.join(RESOURCE_PATH, '11129553_SNA_SNA.txt')\n\n stream_handle = open(file_path, MODE_ASCII_READ)\n\n self.create_parser(stream_handle)\n\n particles = self.parser.get_records(57)\n\n log.debug(\"*** test_bad_match Num particles %s\", len(particles))\n\n # 2 bad samples\n self.assertEqual(len(self.exception_callback_value), 2)\n stream_handle.close()\n\n log.info('===== END TEST BAD MATCH =====')","def solve(self):\n if not self.running or self.state == \"stopping\":\n return False\n\n # Find first empty tile\n target = ()\n for i in range(9**2):\n if self.board[i // 9, i % 9] == 0:\n target = (i // 9, i % 9)\n break\n\n # If there are no empty tiles, the puzzle is solved\n if not target:\n return True\n\n # Tests all possible values\n for value in range(1, 10):\n if not self.isPossibleAssign(target, value):\n continue\n\n self.update_board(target, value)\n\n if self.solve():\n return True\n\n # In case of failure, reset and return False\n self.update_board(target, 0)\n\n return False","def testRegistration():\n deleteMatches()\n deletePlayers()\n registerPlayer(\"Red Ranger\")\n registerPlayer(\"Blue Ranger\")\n registerPlayer(\"Yellow Ranger\")\n registerPlayer(\"Pink Ranger\")\n registerPlayer(\"Green Ranger\")\n standings = playerStandings()\n [id1, id2, id3, id4, id5] = [row[0] for row in standings]\n reportMatch(id1, id2)\n reportMatch(id3, id4)\n completeTournament('Battle Royale')\n currentPlayers = countPlayers()\n oldPlayers = countPlayers('Battle Royale')\n if not (currentPlayers==0 and oldPlayers==5):\n raise ValueError(\n \"Players' status were not updated correctly when tournament\"\n \" completes\"\n )\n\n print \"6. Keeping of completed tournaments in database is checked.\"","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def test_check_stability():\n\n residents = [Player(\"A\", [1, 2]), Player(\"B\", [2]), Player(\"C\", [2, 1])]\n hospitals = [Player(1, [\"C\", \"A\"], 3), Player(2, [\"A\", \"B\", \"C\"], 3)]\n match = HospitalResident(residents, hospitals)\n\n matching = match.solve()\n assert match.check_stability()\n\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\n matching[P_1] = [P_C]\n matching[P_2] = [P_A, P_B]\n\n assert not match.check_stability()","def play_match(self):\r\n time.sleep(1)\r\n print(\"Let's play Rock, Paper or Scissors!\\n\")\r\n while True:\r\n self.plyer_op()\r\n while self.you.score != 3 or self.opposite.score != 3:\r\n\r\n self.play_round()\r\n print('your score :' + str(self.you.score) + ' vs ' +\r\n \"the opposite player :\" +\r\n str(self.opposite.score) + '\\n')\r\n if self.you.score == 3:\r\n print(\"you win!! the match\\n\")\r\n break\r\n elif self.opposite.score == 3:\r\n print(\"opposite player win!! the match\\n\")\r\n break\r\n self.you.score = 0\r\n self.opposite.score = 0","def test_match_left_none():\r\n runmatch(lcode_left_none)","def test_complete_game1(self):\n m = Game()\n\n plays = [\n ['a:-', 'a:-', 'a:b', 'b:b', 'b:-', 'b:b'],\n ['c:3i', 'c:f', 'c:f', 'c:-', 'd:f', 'd:-', 'd:-'],\n ['a:3i', 'a:2f', 'a:f', 'a:-', 'b:-', 'b:-', 'b:-'],\n ['c:3i', 'c:2f', 'c:-', 'c:f', 'd:-', 'd:b', 'd:b'],\n ['a:5i2rp', 'a:4f', 'a:f', 'a:b-', 'b:-', 'b:b', 'b:k']\n ]\n m.run(plays)\n m.print_stats()\n\n # Did the right team win?\n assert m.teams['a'].win == True\n\n # Did we get 5 turns?\n assert m.turn_count == 5\n\n # There should have been 30 throws in the game\n assert m.get_throw_count() == 30","def does_match(self):\n if self.stopped:\n return False\n return self.current_state == self.q3","def _play_match(self, team, opponent, point, mode, match_id):","def simulate_match(match_id, bo=3.0) -> None:\n\n match = Match.query.filter_by(id=match_id).first()\n\n team1_players = get_players(match.team1_id)\n team2_players = get_players(match.team2_id)\n\n team1_stats = get_team_stats(team1_players)\n team2_stats = get_team_stats(team2_players)\n\n print(f\"{team1_stats=}\")\n print(f\"{team2_stats=}\")\n\n win_count_team1 = 0\n win_count_team2 = 0\n\n while win_count_team1 < (bo / 2.0) and win_count_team2 < (bo / 2.0):\n eff_stats_team1 = get_effective_team_stats(team1_stats)\n eff_stats_team2 = get_effective_team_stats(team2_stats)\n\n print(f\"{eff_stats_team1=}\")\n print(f\"{eff_stats_team2=}\")\n\n if eff_stats_team1 > eff_stats_team2:\n win_count_team1 += 1\n else:\n win_count_team2 += 1\n\n print(f\"{win_count_team1=}\")\n print(f\"{win_count_team2=}\")\n\n if win_count_team1 > win_count_team2:\n match.winner = match.team1_id\n print(\"win_count_team1 > win_count_team2\")\n elif win_count_team1 < win_count_team2:\n match.winner = match.team2_id\n print(\"win_count_team1 < win_count_team2\")\n else:\n match.winner = 0\n\n match.status = \"finished\"\n\n logging.info(f\"Match {match.id} ({match.team1_name} vs. {match.team2_name} finished.\"\n f\"Winnerteam: {match.winner}\")\n\n db.session.commit()\n\n logging.info(f\"DB updated after finishing match {match.id}\")","def matchbetween(self):\n team1_toss_factor, team2_toss_factor = self.toss_factor()\n\n avgScoredByTeam1 = self.team1.attack / self.team2.defense * team1_toss_factor\n avgScoredByTeam2 = self.team2.attack / self.team1.defense * team2_toss_factor\n\n\n while True:\n self.team1score = np.random.poisson(avgScoredByTeam1)\n self.team2score = np.random.poisson(avgScoredByTeam2)\n if self.team1score > self.team2score:\n self.team1.points += 3\n self.team1.won += 1\n self.team2.lost += 1\n self.winner = self.team1\n break\n elif self.team1score < self.team2score:\n self.team2.points += 3\n self.team2.won += 1\n self.team1.lost += 1\n self.winner = self.team2\n break\n else:\n if self.groupcheck is True:\n self.team1.points += 1\n self.team2.points += 1\n self.team1.tie += 1\n self.team2.tie += 1\n break\n self.team1.scored += self.team1score\n self.team2.scored += self.team2score\n self.team1.conceded += self.team2score\n self.team2.conceded += self.team1score\n self.team1.goaldifference += self.team1score-self.team2score\n self.team2.goaldifference += self.team2score-self.team1score","def check_results(bet_tup, res_tup):\n colors = [\"red\", \"green\", \"black\"]\n if colors.count(bet_tup[1]) > 0:\n if bet_tup[1] == res_tup[1]:\n print(\"You've guessed the right color!\\n\")\n return(1)\n else:\n print(\"Sorry, but you didn't win this round.\\n\")\n return(-1)\n\n elif bet_tup[1]:\n if bet_tup[1] == res_tup[0]:\n print(\"I don't know if it's luck or not but you've guessed the right number!\\n\")\n return(2)\n else:\n print(\"Sorry, but you didn't win this round.\\n\")\n return(-2)","async def check(self):\n\n while not self.solved:\n # Get list of possible numbers this square can have\n possibles = self.get_possible_numbers()\n # If there's only once possibility, then use this number...this square is now solved\n if len(possibles) == 1:\n self.num = possibles.pop()\n # If there are no possible squares well...something's wrong, that shouldn't be possible\n # This check is done because we want to be able to guess and check, and figure out if a guess is invalid\n elif len(possibles) == 0:\n raise ValueError(\"Impossible square; no possible numbers based on restrictions\")\n # Otherwise wait a small amount and continue\n else:\n await asyncio.sleep(0.05)","def match(self, inp):\n return 0","def testDraw():\n deleteMatches()\n deletePlayers()\n registerPlayer(\"Pikachu\")\n registerPlayer(\"Charmander\")\n registerPlayer(\"Bulbasaur\")\n registerPlayer(\"Squirtle\")\n standings = playerStandings()\n [id1, id2, id3, id4] = [row[0] for row in standings]\n reportMatch(id1, id2, True)\n reportMatch(id3, id4, True)\n standings = playerStandings()\n if not (standings[0][2]==standings[1][2]==standings[2][2]==standings[3][2]):\n raise ValueError(\n \"Players should have the same number of points after drawing\"\n )\n\n print \"3. Draw is recorded properly.\"","def test_can_match(self):\n assert self.RNA(\"\").can_match(\"\")\n assert self.RNA(\"UCAG\").can_match(\"UCAG\")\n assert not self.RNA(\"UCAG\").can_match(\"ucag\")\n assert self.RNA(\"UCAG\").can_match(\"NNNN\")\n assert self.RNA(\"NNNN\").can_match(\"UCAG\")\n assert self.RNA(\"NNNN\").can_match(\"NNNN\")\n assert not self.RNA(\"N\").can_match(\"x\")\n assert not self.RNA(\"N\").can_match(\"-\")\n assert self.RNA(\"UCAG\").can_match(\"YYRR\")\n assert self.RNA(\"UCAG\").can_match(\"KMWS\")","def test_solve(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n for optimal in [\"student\", \"supervisor\"]:\n students, projects, _, game = make_game(\n student_names,\n project_names,\n supervisor_names,\n capacities,\n seed,\n clean,\n )\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n projects = sorted(projects, key=lambda p: p.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_project, project in zip(matching_keys, projects):\n assert game_project.name == project.name\n assert game_project._pref_names == project._pref_names\n assert game_project.capacity == project.capacity\n assert game_project.supervisor.name == project.supervisor.name\n\n matched_students = [\n stud for match in matching.values() for stud in match\n ]\n assert matched_students != [] and set(matched_students).issubset(\n set(game.students)\n )\n\n for student in matched_students:\n supervisor = student.matching.supervisor\n assert student in supervisor.matching\n\n for student in set(game.students) - set(matched_students):\n assert student.matching is None","def check_match(possible_match):\n top = \"\"\n bottom = \"\"\n for tup in possible_match:\n top += tup[0]\n bottom += tup[1]\n return top == bottom","def has_explicit_match(self) -> bool:\n if self.matching is not None:\n return True\n else:\n return False","def run_one_match(match, chance):\n home = match[0]\n away = match[1]\n home_win = chance[0]\n draw = chance[1]\n random_number = random.get_random_number_0_1() * 100\n if 0 <= random_number < home_win:\n return home\n elif home_win <= random_number < (home_win + draw):\n return \"Draw\"\n elif (home_win + draw) <= random_number < 100:\n return away","def test_get_match_loser(self):\n p_a = Participant()\n p_b = Participant()\n matchup = TournamentMatchup(\n participant_a=p_a,\n participant_b=p_b,\n winner=p_b,\n )\n self.assertEqual(matchup.not_winner(), matchup.participant_a)","def test_is_solved_when_puzzle_is_not_solved(self):\n sudoku = sudolver.Sudoku()\n self.assertFalse(sudoku.is_solved())","def is_winner(self, player: str) -> bool:\n total_result = self.current_state.hori_result + self.current_state.left_result + self.current_state.right_result\n total_line = len(total_result)\n p1_taken = 0\n p2_taken = 0\n for item in total_result:\n if item == '1':\n p1_taken+=1\n elif item == '2':\n p2_taken += 1\n if player == \"p1\":\n return float(p1_taken) >= total_line/2\n return float(p2_taken) >= total_line/2","def handleMatch(self, m):\r\n pass","def do_the_things():\n returned_no_game = False\n returned_game_time = False\n returned_game_final = False\n returned_rival_final = False\n returned_game_soon = False\n returned_rival_soon = False\n returned_game_start = False\n compare_scores = ['0', '0']\n timeout = time.time() + 60 * 60 * 23\n\n while not (returned_no_game or returned_game_final):\n\n try:\n message = False\n rival_message = False\n\n \"\"\" Infinite loops are cool \"\"\"\n if time.time() > timeout:\n break\n\n game_data = get_fresh_data(team)\n\n if game_data:\n \"\"\" The default TZ for mlb is US/Eastern, we'll do some things and make it local TZ \"\"\"\n tz = pytz.timezone('US/Eastern')\n eastern_time = datetime.datetime.strptime(\"%s %s\" % (game_data['time_date'], game_data['ampm']), '%Y/%m/%d %I:%M %p')\n eastern_time = tz.localize(eastern_time)\n pacific_time = eastern_time.astimezone(pytz.timezone(timezone))\n\n opponent, our_score, their_score, venue = set_vars(game_data)\n scores = sorted([our_score, their_score], reverse=True)\n\n if not game_data and not returned_no_game:\n returned_no_game = True\n message = (\"The %s don't have a game scheduled today. Rest well guys!\" % team_hashtag)\n\n if game_data and not returned_game_time:\n returned_game_time = True\n message = ((\"The %s are playing against the %s today, first pitch is at \" + pacific_time.strftime(\"%-I:%M%p %Z\") + \" at %s. %s\") % (team_hashtag, opponent, venue, team_hash2))\n\n if \"Warmup\" in game_data[\"status\"] and not returned_game_soon:\n returned_game_soon = True\n message = ((\"The %s are playing against the %s in a moment, first pitch is at \" + pacific_time.strftime(\"%-I:%M%p %Z\") + \" at %s. %s\") % (team_hashtag, opponent, venue, team_hash2))\n\n if \"In Progress\" in game_data[\"status\"] and compare_scores == ['0', '0'] and not returned_game_start:\n returned_game_start = True\n message = (\"It's gametime! Go %s!!! %s\" % (team_hashtag, team_hash2))\n\n if \"In Progress\" in game_data[\"status\"] and not compare_scores == [our_score, their_score]:\n compare_scores = [our_score, their_score]\n if int(our_score) > int(their_score):\n message = (\"The %s are leading the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n elif int(our_score) < int(their_score):\n message = (\"The %s are trailing the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n elif int(our_score) == int(their_score):\n message = (\"The %s are tied with the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n\n if (\"Game Over\" or \"Final\") in game_data[\"status\"]:\n returned_game_final = True\n if our_score > their_score:\n message = (\"The %s beat the %s today at %s with a score of %s-%s. %s\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\n else:\n message = (\"The %s lost against the %s today at %s with a score of %s-%s. Get 'em tomorrow! %s\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\n\n if not returned_rival_final:\n rival_game_data = get_fresh_data(rival_team)\n rival_opponent, rival_our_score, rival_their_score, rival_venue = set_vars(rival_game_data)\n rival_scores = sorted([rival_our_score, rival_their_score], reverse=True)\n\n if \"Warmup\" in rival_game_data[\"status\"] and not returned_rival_soon:\n returned_rival_soon = True\n rival_message = (\"The %s game is about to start, go %s!!! %s\") % (rival_team_hashtag, rival_opponent, rival_team_hash2)\n\n if (\"Game Over\" or \"Final\") in rival_game_data[\"status\"]:\n returned_rival_final = True\n if rival_our_score > rival_their_score:\n rival_message = (\"The %s beat the %s today at %s with a score of %s-%s, boooo!\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\n else:\n rival_message = (\"The %s lost against the %s today at %s with a score of %s-%s. Hell yeah!\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\n\n if message:\n if testmode:\n print(message)\n else:\n twitter.tweet(message)\n\n if rival_message:\n if testmode:\n print(rival_message)\n else:\n twitter.tweet(rival_message)\n\n except tweepy.TweepError as e:\n print e.message[0]['code']\n print e.args[0][0]['code']\n\n time.sleep(5)","def match_pair(cls, game, pair_1, pair_2):\n game.attempts += 1\n game.put()\n card_1 = Card.query(Card.game == game.key).filter(Card.index == pair_1).get()\n card_2 = Card.query(Card.game == game.key).filter(Card.index == pair_2).get()\n\n if card_1.matched or card_2.matched:\n raise RuntimeError('Could not rematch a matched card')\n\n form = MatchResultForm()\n if card_1.value == card_2.value:\n card_1.matched = True\n card_1.put()\n card_2.matched = True\n card_2.put()\n game.matched += 2\n game.put()\n form.message = 'Success'\n else:\n form.message = 'Fail'\n\n # Construct return info form\n form.card_1 = card_1.to_form()\n form.card_2 = card_2.to_form()\n form.matched_count = game.matched\n\n if game.matched == 52:\n game.game_over = True\n game.put()\n Card.delete_cards_for_game(game)\n\n # Update average attempts of user\n user = game.user.get()\n games = Game.get_user_finished_games(user)\n count = len(games)\n if user.average_attempts == float('inf'):\n user.average_attempts = 0\n user.average_attempts = ((count - 1) * user.average_attempts + game.attempts) / count\n user.put()\n\n score = Score(user=game.user, date=date.today(), attempts=game.attempts)\n score.put()\n form.message = 'Win'\n\n # Create history log\n History.create_history(game=game,\n card_1=card_1,\n card_2=card_2,\n message=form.message)\n return form","def check_match_over(self) -> bool:\n # check if programs filled\n self.program_is_full = [any(~np.isnan(program[:self.program_slots[i]]))\n for i, program in enumerate(self.program_matches)]\n if np.all(self.program_is_full):\n return True\n\n # check if candidates matched\n # I think this is inaccurate -- a candidate can tentatively match but not\n # fully match?\n self.applicant_is_matched = self.applicant_matches != -1\n if np.all(self.applicant_is_matched):\n return True\n\n # check for overlap between unfilled programs and unmatched applicants\n for i, program_matched in enumerate(self.program_is_full):\n if not program_matched:\n for j, applicant_matched in enumerate(self.applicant_is_matched):\n if not applicant_matched:\n if i in self.applicant_ranks[j] and j in self.program_ranks[i]:\n return True\n\n # otherwise, match isn't over\n return False","def testPointSystem():\n deleteMatches()\n deletePlayers()\n registerPlayer(\"Pikachu\")\n registerPlayer(\"Charmander\")\n registerPlayer(\"Bulbasaur\")\n registerPlayer(\"Squirtle\")\n registerPlayer(\"MewTwo\")\n standings = playerStandings()\n [id1, id2, id3, id4, id5] = [row[0] for row in standings]\n reportMatch(id1, id2)\n reportMatch(id3, id4, True)\n reportMatch(id5, id5, False, True)\n reportMatch(id1, id5)\n reportMatch(id3, id4)\n reportMatch(id2, id2, False, True)\n reportMatch(id1, id3)\n reportMatch(id5, id2)\n reportMatch(id4, id4, False, True)\n standings = playerStandings()\n if not (standings[0][0]==id2 and standings[0][2]==2 and\n standings[1][0]==id4 and standings[0][2]==2 and\n standings[2][0]==id3 and standings[0][2]==2 and\n standings[3][0]==id5 and standings[0][2]==2 and\n standings[4][0]==id1 and standings[0][2]==2):\n raise ValueError(\n \"Points are not tallied correctly.\"\n )\n\n print \"4. Points are tallied correctly.\"","def test_match_nones():\r\n runmatch(lcode_nones)","def test_assigned_match(self):\n user1 = get_user_model().objects.get(username='test1@example.com')\n self.client.login(username='test1@example.com', password='1')\n\n office = OfficeLocation.objects.all()[0]\n org = OrgGroup.objects.filter(parent__isnull=True)[0]\n\n submission1 = Interest()\n submission1.owner = user1\n submission1.for_coffee = True\n submission1.save()\n submission1.locations.add(office)\n submission1.departments.add(org)\n\n resp = self.client.get(reverse('mystery:mystery'))\n self.assertContains(resp, \"Cancel this\", status_code=200)\n\n user2 = random_user()\n submission2 = Interest()\n submission2.owner = user2\n submission2.is_active = False\n submission2.save()\n submission2.for_coffee = True\n submission2.locations.add(office)\n submission2.departments.add(org)\n submission2.is_active = True\n submission2.save()\n\n resp = self.client.get(reverse('mystery:mystery'))\n self.assertContains(resp, reverse(\"mystery:close_incomplete\", args=(submission1.id,)))\n self.assertContains(resp, reverse(\"mystery:close_complete\", args=(submission1.id,)))\n\n # verify assigned match page requires login\n self.client.logout()\n resp = self.client.get(reverse('mystery:mystery'))\n self.assertEqual(resp.status_code, 302)\n self.assertIn('login', resp['Location'])","def check_win(self, player):\n def check_row_win(player):\n for row in self.game_state:\n if player == row[0] == row[1] == row[2]:\n return True\n return False\n\n def check_column_win(player):\n # For doing a column check, transpose the grid and do a row check\n trans_game_state = numpy.transpose(self.game_state)\n for row in trans_game_state:\n if player == row[0] == row[1] == row[2]:\n return True\n return False\n\n def check_diag_win(player):\n # Left to right diagonal\n if player == self.game_state[0][0] == self.game_state[1][1] == self.game_state[2][2]:\n return True\n # Right to left diagonal\n if player == self.game_state[0][2] == self.game_state[1][1] == self.game_state[2][0]:\n return True\n return False\n\n if check_column_win(player) or check_diag_win(player) or check_row_win(player):\n return True\n return False","def test_equal(self):\r\n\r\n a_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n a_x_dist = 3\r\n a_y_dist = 3\r\n a_num_to_win = 1\r\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n b_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n b_x_dist = 3\r\n b_y_dist = 3\r\n b_num_to_win = 1\r\n b_game = Game(b_players, b_x_dist, b_y_dist, b_num_to_win)\r\n\r\n c_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n c_x_dist = 3\r\n c_y_dist = 3\r\n c_num_to_win = 1\r\n c_game = Game(c_players, c_x_dist, c_y_dist, c_num_to_win)\r\n\r\n self.assertTrue(b_game == a_game == c_game)\r\n\r\n a_game.play_game()\r\n b_game.play_game()\r\n\r\n self.assertTrue(a_game == b_game)\r\n self.assertFalse(c_game == a_game)\r\n\r\n c_game.play_game()\r\n\r\n self.assertTrue(b_game == a_game == c_game)","def match(self) -> \"MatchResult\":\n raise NotImplementedError","def is_solved(self):\n # Iterate through each square of the puzzle\n for row in range(self.sl):\n for col in range(self.sl):\n val = self.puzzle[row][col]\n\n # If any square value is blank (0), not solved, return False\n if val == 0:\n return False\n\n # Trick to keep DRY code: replace each value temporarily with a\n # 0, and use valid_square method with original value to determine\n # if every square is valid\n self.puzzle[row][col] = 0\n valid = self.valid_square(row, col, val)\n self.puzzle[row][col] = val\n \n # If not a valid value for square, return False\n if not valid:\n return False\n return True","def did_solve(self) -> bool:\n pass","def reportMatch(winner, loser):\n\n tournID = main.getTournamentIDFromName(\"Tournament for legacy tests\")\n\n main.playMatch(tournID, winner, loser, \"p1 wins\")","def is_valid_board(self):\n total = sum(range(1, self.n+1))\n d = {x : [set([]), set([])] for x in range(1, self.n+1)}\n for row_index in range(self.n):\n for col_index in range(self.n):\n num = self.board[row_index][col_index]\n try:\n if row_index in d[num][0] or col_index in d[num][1]:\n print(\"Invalid solution.\")\n return\n except KeyError:\n print(\"Unsolved solution.\") # d[0]\n return\n\n d[num][0].add(row_index)\n d[num][1].add(col_index)\n print(\"Valid solution!\")","def test_is_winner(self):\n TestGame = TTT_Game(\"John\", \"Jane\")\n\n # Check verticals\n TestGame.board = [1, 2, \"X\", 4, 5, \"X\", 7, 8, \"X\"]\n self.assertTrue(TestGame.is_winner(\"X\"))\n TestGame.board = [\"O\", 2, 3, \"O\", 5, \"X\", \"O\", 8, \"X\"]\n self.assertTrue(TestGame.is_winner(\"O\"))\n TestGame.board = [1, \"X\", \"O\", \"O\", \"X\", 6, 7, \"X\", \"X\"]\n self.assertTrue(TestGame.is_winner(\"X\"))\n\n # Check horizontals\n TestGame.board = [\"O\", \"O\", \"O\", \"O\", 5, \"X\", 7, 8, 9]\n self.assertTrue(TestGame.is_winner(\"O\"))\n TestGame.board = [1, 2, 3, \"X\", \"X\", \"X\", 7, 8, 9]\n self.assertTrue(TestGame.is_winner(\"X\"))\n TestGame.board = [1, 2, 3, \"O\", 5, 6, \"O\", \"O\", \"O\"]\n self.assertTrue(TestGame.is_winner(\"O\"))\n\n # Check diagonals\n TestGame.board = [\"O\", \"X\", 3, 4, \"O\", \"X\", \"X\", \"O\", \"O\"]\n self.assertTrue(TestGame.is_winner(\"O\"))\n TestGame.board = [1, 2, \"X\", 4, \"X\", 6, \"X\", 8, 9]\n self.assertTrue(TestGame.is_winner(\"X\"))","def test_solve_one_player_2(self):\n self.rush_hour_data = rush_hour_data_2\n self.state_data = state_data_2\n self.execute_minimax_single_player()","def is_matching(self, plays):\n match = self.plays_to_edges(plays)\n return nx.is_maximal_matching(self.graph, match) # TODO","def judge(self):\n self.bounds = 0.0\n self.best = self.lives[0]\n for life in self.lives:\n life.score = self.matchFun(life)\n self.bounds += life.score\n if self.best.score < life.score:\n self.best = life","def judge(self):\n self.bounds = 0.0\n self.best = self.lives[0]\n for life in self.lives:\n life.score = self.matchFun(life)\n self.bounds += life.score\n if self.best.score < life.score:\n self.best = life","def check_if_solved(self):\n for cell in self.board.values():\n if not cell.value:\n return False\n return True","def check_grue(self, tile):\n if tile[2] == 'grue':\n if self.lab.inventory > 0:\n self.lab.fire()\n print 'Lighted match'","def match(self, segment):\n\n match_len = self._win_length\n\n subsegments = [[segment[j+i] for i in range(match_len)] for j in range(len(segment)-match_len+1)]\n\n for subsegment in subsegments:\n if set(subsegment).pop() == self.current_player and len(list(set(subsegment))) == 1:\n return True\n\n return False","def test_init_with_existing_game(self):\n pass\n # Ensure judge is the same","def _check_resident_matching(self):\n\n errors = []\n for resident in self.residents:\n if (\n resident.matching is not None\n and resident.matching not in resident.prefs\n ):\n errors.append(\n ValueError(\n f\"{resident} is matched to {resident.matching} but \"\n \"they do not appear in their preference list: \"\n f\"{resident.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def checkSolution(self):\n movesToEndblock = self.gridSize - self.changeable[0] - 2\n if self.checkMove(0,movesToEndblock) == 0:\n return 0\n return 1","def check_game_sanity(game_val, log):\n\n global __problem_deck_index__\n\n supply = game_val.get_supply()\n if game_val.any_resigned():\n return True # Resign leaves deck in arbitrary state, don't catch that\n\n # ignore known bugs.\n #if set(supply).intersection([get_card('Masquerade'), get_card('Black Market'), get_card('Trader')]):\n #return True\n\n # TODO: add score sanity checking here\n last_state = None\n game_state_iterator = game_val.game_state_iterator()\n for game_state in game_state_iterator:\n last_state = game_state\n for player_deck in game_val.get_player_decks():\n parsed_deck_comp = player_deck.Deck()\n computed_deck_comp = last_state.get_deck_composition(\n player_deck.name())\n\n parse_common.delete_keys_with_empty_vals(parsed_deck_comp)\n computed_dict_comp = dict(computed_deck_comp)\n parse_common.delete_keys_with_empty_vals(computed_dict_comp)\n\n if parsed_deck_comp != computed_deck_comp:\n found_something_wrong = False\n for card in set(parsed_deck_comp.keys() +\n computed_deck_comp.keys()):\n if parsed_deck_comp.get(card, 0) != \\\n computed_deck_comp.get(card, 0):\n if not found_something_wrong:\n __problem_deck_index__ += 1\n log.debug('[%d] %18s %9s %9s', __problem_deck_index__, 'card', 'from-data', 'from-sim')\n log.debug('[%d] %-18s %9d %9d', __problem_deck_index__, card, parsed_deck_comp.get(card, 0),\n computed_deck_comp.get(card, 0))\n found_something_wrong = True\n if found_something_wrong:\n try:\n log.debug('[%d] insane game for %s %s: %s', __problem_deck_index__, player_deck.name(), game_val.get_id(),\n ' '.join(map(str, game_val.get_supply())))\n except UnicodeEncodeError, e:\n None\n return False\n return True","def _checkRoundOver(self):\n\n if not any(player.isAlive() for player in self.teams[0].players):\n self.endGame()","def reportMatch(player1, player2, winner = None):\n #Check for Bye matchup (player1=player2)\n bye_match = False\n if player1 == player2:\n winner = player1\n bye_match = True\n\n #Generate random winner if no winner param is passed in\n if winner is None:\n rand = random.random()\n if rand < 0.5:\n winner = player1\n else:\n winner = player2\n\n conn, cur = connect()\n #Insert record of match\n query = \"\"\"INSERT INTO matches (player1_id, player2_id, winner)\n VALUES (%s, %s, %s)\"\"\"\n params = (player1, player2, winner,)\n try:\n cur.execute(query, params)\n except:\n print(\"Error encountered when inserting match record into the database\")\n\n #Save record of player having a bye in Players table\n if bye_match:\n query = \"UPDATE players SET had_bye = TRUE WHERE player_id = %s;\"\n param = (player1,)\n try:\n cur.execute(query, param)\n except:\n print(\"Error encountered when updating player's Bye round status.\")\n conn.commit()\n conn.close()","def __checkForPattern(self):\n if self._keyCode in self._patterns:\n assert(self.notify.debug(\"Pattern Match: \" + self._keyCode))\n messenger.send(KeyCodes.PATTERN_MATCH_EVENT, [self._keyCode])\n self.reset()\n \n # If the key code is longer than the longest pattern possible,\n # Then reset! \n elif self._keyCodeCount == self._patternLimit or len(self.getPossibleMatchesList()) == 0:\n assert(self.notify.debug(\"No pattern match!\"))\n messenger.send(KeyCodes.PATTERN_NO_MATCH_EVENT)\n self.reset()","def gameOver():\n if len(p1)==0 and len(p1winnings)==0:\n return True\n elif len(p2)==0 and len(p2winnings)==0:\n return True\n return False","def _runmatch(self,base,mrad,xsh,ysh):\n cmd = base+'matchrad='+str(mrad)+' xsh=%.2f ysh=%.2f' %(xsh,ysh)\n sproc = popen2.Popen3(cmd,1)\n output = sproc.fromchild.readlines()\n errs = sproc.childerr.readlines()\n sproc.fromchild.close()\n sproc.childerr.close()\n \n if errs and not (len(errs) == 1 and errs[0][0:27] == 'atFindMedtf: RMS <= 0.0, so'):\n self.logfile.write('match produced the following message(s): ')\n for f in errs:\n self.logfile.write(string.strip(f))\n self.errorList.append(('match',string.strip(f)))\n matchline = ''\n medianline = ''\n for line in output:\n if line[0:6] == 'TRANS:':\n matchline = line\n elif line[0:6] == 'MEDTF:':\n medianline = line\n else:\n self.logfile.write(\"ERROR:. Unexpected output from match program:\\n\\t\\t\\t\"+line)\n return (-1,None)\n if (not matchline) or (not medianline):\n self.logfile.write(\"Match program failed.\")\n return (-1,None)\n \n mdict = self._process_median(medianline,mrad,xsh,ysh)\n if not mdict:\n return (-1,None)\n return (0,mdict)","def matches(\n draw,\n min_turns=1,\n max_turns=200,\n min_noise=0,\n max_noise=1,\n):\n strategies = draw(strategy_lists(min_size=2, max_size=2))\n players = [s() for s in strategies]\n turns = draw(integers(min_value=min_turns, max_value=max_turns))\n noise = draw(floats(min_value=min_noise, max_value=max_noise))\n match = axl.Match(players, turns=turns, noise=noise)\n return match","def do_matchmaking(self):\n # Remove seeks, offers, etc for lagged-out players\n now = time.time()\n\n to_remove = {}\n for pname in self.last_ping:\n elapsed = now - self.last_ping[pname]\n logging.debug('Time since last ping: %f' % elapsed)\n if elapsed > TIMEOUT:\n msg = 'Lost contact with %s' % pname\n to_remove[pname] = msg\n \n for pname in to_remove:\n msg = to_remove[pname]\n self._rem_player(pname, msg)\n\n # Remove outstanding (and now expired) match offers\n #for match in self.offers.values():\n # msg = 'Match offer %s expired' % match.matchid\n # self._rem_offer(match, msg)\n\n # Generate new match offers\n for m in self.matchmaker.generate_matches(self.seeks.values()):\n self.offers[m.matchid] = m\n for s in m.seeks:\n msg = 'Seek %s matched Match %s' % (s.seekid, m.matchid)\n self.seeks.pop(s.seekid, None)\n self.comm.offer_match(m)","def is_solved(self):\n if not self._find_empty():\n return True\n else:\n return False"],"string":"[\n \"def test_match(self):\\n self.assertEqual(solution( \\\"\\\"\\\"p=<-6,0,0>, v=< 3,0,0>, a=< 0,0,0>\\np=<-4,0,0>, v=< 2,0,0>, a=< 0,0,0>\\np=<-2,0,0>, v=< 1,0,0>, a=< 0,0,0>\\np=< 3,0,0>, v=<-1,0,0>, a=< 0,0,0>\\\"\\\"\\\"), 1)\",\n \"def test_check_validity(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n assert match.check_validity()\",\n \"def test_not_found_pair(self):\\n self.assertTrue(\\\"No matches found\\\", show_players_sumheigh_is_input(100))\",\n \"def match(self) -> bool:\",\n \"def test_check_solved():\\n game = Game()\\n game.word = 'word'\\n game.pattern = 'word'\\n game.check_solved()\\n assert game.solved is True\",\n \"def test_game_has_ended_match(self):\\n game = Game.objects.create(\\n allowed_attempts=10,\\n end_date=date.today() + timedelta(1),\\n min_value=1,\\n max_value=10\\n )\\n self.assertTrue(game.is_active)\\n self.assertIsNone(game.compleation_date)\\n\\n game.attempt_set.create(\\n our_number=10,\\n user_number=10,\\n ip_addr='127.0.0.1'\\n )\\n self.assertEqual(len(self.recieved), 1)\\n self.assertListEqual(self.recieved[0], ['match'])\\n self.assertFalse(game.is_active)\\n self.assertIsNotNone(game.compleation_date)\",\n \"def test_solve(game, optimal):\\n\\n matching = game.solve(optimal)\\n assert isinstance(matching, MultipleMatching)\\n\\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\\n for game_hospital, hospital in zip(matching_keys, hospitals):\\n assert game_hospital.name == hospital.name\\n assert game_hospital._pref_names == hospital._pref_names\\n assert game_hospital.capacity == hospital.capacity\\n\\n matched_residents = [\\n resident for match in matching.values() for resident in match\\n ]\\n\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(game.residents)\\n )\\n\\n for resident in set(game.residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def matches(self):\\n return False\",\n \"def test_suitor_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.suitors[0].matching = Player(name=\\\"foo\\\", pref_names=[])\\n\\n with pytest.raises(Exception):\\n match._check_suitor_matching()\",\n \"def test_match_incorrect(self, event: Event, pr_algorithm: ProgressiveResizing, minimal_state: State):\\n assert not pr_algorithm.match(event, minimal_state)\",\n \"def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.reviewers[0].matching.append(Player(name=\\\"foo\\\", pref_names=[]))\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_matching()\",\n \"def setup_match():\\n\\n # Teams are identified by team name.\\n teamname = input(\\\"Team name: \\\")\\n team_data_filename = \\\"teams/team\\\" + teamname + \\\".json\\\"\\n # Check if the team file exists\\n try:\\n # Open the team file\\n with open(team_data_filename, 'r') as team_data_file:\\n # Read from the json file\\n team_data_json = json.load(team_data_file)\\n team_key = input(\\\"Enter your team key: \\\")\\n # Check if the key matches.\\n if hashfunc.verify_password(team_key, team_data_json[\\\"team_key\\\"]):\\n # The team will play against an opponent.\\n print(\\\"Team\\\", teamname, \\\"is now ready to play a match!\\\")\\n # Determine if the opponent is a human or a computer.\\n print('Do you want to play against a human team or computer team?')\\n print(\\\"To play against a human team, type 'human' and hit 'Enter'\\\")\\n print(\\\"To play against the usual computer team, just hit 'Enter'.\\\")\\n opponent_choice = input()\\n # Get game ID.\\n gameidfile = savegamedata.getUniqueSavefile()\\n gameid = gameidfile[12:-5]\\n thisGameID = True\\n # If opponent is computer, load the computer team as opponent.\\n if(opponent_choice != 'human'):\\n pairing = {\\n \\\"team1\\\": teamname,\\n \\\"team1_human\\\": True,\\n \\\"team1_confirm\\\": True,\\n \\\"team2\\\": \\\"Computer\\\",\\n \\\"team2_human\\\": False,\\n \\\"team2_confirm\\\": True,\\n \\\"gameid\\\": hashfunc.hash_password(gameid),\\n \\\"timestamp\\\": datetime.datetime.now().timestamp()\\n }\\n insertGameLog(pairing)\\n # If opponent is human, pick an opponent or create a fresh pairing.\\n if(opponent_choice == \\\"human\\\"):\\n print(\\\"Do you want to choose your opponent (Y/N)\\\")\\n chooseopponentflag = input()\\n if chooseopponentflag == 'Y':\\n # Create a match pairing.\\n opponent_teamname = findHumanOpponentExists(teamname)\\n if not checkPairingChosen(teamname, opponent_teamname):\\n pairing = {\\n \\\"team1\\\": teamname,\\n \\\"team1_human\\\": True,\\n \\\"team1_confirm\\\": True,\\n \\\"team2\\\": opponent_teamname,\\n \\\"team2_human\\\": True,\\n \\\"team2_confirm\\\": False,\\n \\\"gameid\\\": hashfunc.hash_password(gameid),\\n \\\"timestamp\\\": datetime.datetime.now().timestamp()\\n }\\n insertGameLog(pairing)\\n else:\\n thisGameID = False\\n if chooseopponentflag != 'Y':\\n pairing = {\\n \\\"team1\\\": teamname,\\n \\\"team1_human\\\": True,\\n \\\"team1_confirm\\\": True,\\n \\\"team2\\\": None,\\n \\\"team2_human\\\": True,\\n \\\"team2_confirm\\\": False,\\n \\\"gameid\\\": hashfunc.hash_password(gameid),\\n \\\"timestamp\\\": datetime.datetime.now().timestamp()\\n }\\n insertGameLog(pairing)\\n print(\\\"Your match is registered.\\\")\\n if thisGameID:\\n print(\\\"Your match ID is: \\\")\\n print(gameid)\\n print(\\\"Share this match ID only with your opponent.\\\")\\n else:\\n print(\\\"Please ask your opponent for the match ID.\\\")\\n confirm_read = input(\\\"Press 'Enter' key to complete this process.\\\")\\n else:\\n print(\\\"The keys did not match.\\\")\\n print(\\\"Forgot your key? Contact support for key reset.\\\")\\n assume_forgotten = input()\\n\\n # If team file doesn't exist, we can't setup the match.\\n except:\\n print(\\\"Team\\\", teamname, \\\"is not available.\\\")\\n print(\\\"Please set up your team before proceeding.\\\")\\n confirm_unavailable = input()\",\n \"def win_check(self):\\n\\t\\t# Create a temp var to capture the number of correct matches\\n\\t\\tright = 0\\n\\t\\t# retrieve peg_guess_color_list for current round\\n\\t\\tguess = self.model.guesses[self.model.status]\\n\\t\\t# retreive solution list\\n\\t\\tsolution = self.model.guesses[\\\"solution\\\"]\\n\\t\\t# compare values in each index for both lists against eachother\\n\\t\\tfor i in range(len(solution.pegs)):\\n\\t\\t\\t# \\n\\t\\t\\tif solution.pegs[i].peg_color == guess.pegs[i].peg_color:\\n\\t\\t\\t\\tright += 1\\n\\n\\t\\t\\t\\tprint(\\\"Yay, it works!\\\")\\n\\n\\t\\t# If all indexes of the peg_colors in the solution and guess are True:\\n\\t\\tif right == 4:\\n\\t\\t\\treturn True\\n\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def run_match(player_move, comp_move, state):\\n #Player/computer is vulnerable if they chose to reload\\n player_vuln = False\\n comp_vuln = False\\n state['player_prev'] = player_move\\n state['comp_prev'] = comp_move\\n\\n #Update game variables\\n if player_move == 'd':\\n state['player_ammo'] += 1\\n player_vuln = True\\n if comp_move == 'd':\\n state['comp_ammo'] += 1\\n comp_vuln = True\\n if player_move == 'a':\\n if comp_vuln:\\n return 'N'\\n state['player_ammo'] -= 1\\n if comp_move == 'a':\\n if player_vuln:\\n return 'Y'\\n state['comp_ammo'] -= 1\\n\\n return game_states[get_values(state)]\",\n \"def match(self, target, guess):\\r\\n return guess == target\",\n \"def test_check_for_unacceptable_matches_residents(game):\\n\\n resident = game.residents[0]\\n hospital = Hospital(name=\\\"foo\\\", capacity=1)\\n resident.matching = hospital\\n\\n issues = game._check_for_unacceptable_matches(\\\"residents\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(resident.name)\\n assert issue.endswith(f\\\"{resident.prefs}.\\\")\\n assert hospital.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def isValid(self, game):\\n return True\",\n \"def test_match_or_none():\\r\\n runmatch(lcode)\",\n \"def test_check_validity(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n game.solve()\\n assert game.check_validity()\",\n \"def check_winner(self):\\n pass\",\n \"def valid_match(self, home_stats, away_stats):\\n # If either of the statistics is None then you cannot evaluate the\\n # match\\n if home_stats is None or away_stats is None:\\n return False\\n # Otherwise if we have not specified a number of matches to ignore then\\n # this match can be evaluated.\\n if self.ignore_matches is None:\\n return True\\n # If we have specified a number of matches to ignore, check both teams\\n # have played at least that many:\\n if (len(home_stats.games) < self.ignore_matches or\\n len(away_stats.games) < self.ignore_matches):\\n return False\\n return True\",\n \"def matches(self):\\n pass\",\n \"def test_play_game(self):\\r\\n\\r\\n \\r\\n a_players = [RandomPlayer(1), RandomPlayer(2)]\\r\\n a_x_dist = 3\\r\\n a_y_dist = 3\\r\\n a_num_to_win = 1\\r\\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\\r\\n\\r\\n #Game is played to competion\\r\\n a_game.play_game()\\r\\n\\r\\n a_history = a_game.get_history()\\r\\n\\r\\n #Go through each move and check to be sure it's valid\\r\\n for i in range(1,len(a_history)):\\r\\n #Get copy of the board\\r\\n prev_board = a_history[i-1]\\r\\n cur_board = a_history[i]\\r\\n\\r\\n #Check if the board chosen is in valid states\\r\\n self.assertTrue(cur_board in prev_board.get_states(a_players[0].get_id()) or cur_board in prev_board.get_states(a_players[1].get_id()),\\\\\\r\\n \\\"An invalid board state was added to the history\\\")\\r\\n\\r\\n if i == len(a_history) - 1:\\r\\n self.assertTrue(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\\r\\n else: \\r\\n self.assertFalse(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\",\n \"def main():\\n num_matches = 1000\\n matchlist = [\\n [ RandomAI, RandomAI ],\\n #[ VectorAI, RandomAI ],\\n #[ VectorAI, VectorAI ],\\n #[ LeftmostAI, RandomAI ],\\n #[ LeftmostAI, VectorAI ],\\n #[ LeftmostAI, LeftmostAI ],\\n\\t\\t#[ RightmostAI, RandomAI ],\\n #[ RightmostAI, VectorAI ],\\n #[ RightmostAI, LeftmostAI ],\\n #[ RightmostAI, RightmostAI ]\\n ]\\n\\n x=0\\n while x < len(matchlist):\\n \\n print (\\\"Running \\\" + str(num_matches) + \\\" matches: \\\" + matchlist[x][0].__name__ + \\\" vs \\\" + matchlist[x][1].__name__)\\n counter = 0 \\n \\n while counter < num_matches:\\n counter = counter + 1\\n match = Match(player1_type=matchlist[x][0], player2_type=matchlist[x][1], param_print_game_status=False, param_matchgroup=x)\\n match.handle_next_move()\\n\\n if matchlist[x][0] != matchlist[x][1]:\\n print (\\\"Running \\\" + str(num_matches) + \\\" matches: \\\" + matchlist[x][1].__name__ + \\\" vs \\\" + matchlist[x][0].__name__)\\n counter = 0\\n\\n while counter < num_matches:\\n counter = counter + 1\\n match = Match(player1_type=matchlist[x][1], player2_type=matchlist[x][0], param_print_game_status=False, param_matchgroup=x)\\n match.handle_next_move()\\n x = x+1\\n \\n print (\\\"Matches Finished\\\")\",\n \"def test_check_for_unacceptable_matches_projects(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n project = game.projects[0]\\n student = Student(name=\\\"foo\\\")\\n project.matching.append(student)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert error.startswith(project.name)\\n assert error.endswith(str(project.prefs))\\n assert str(student.name) in error\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_match_or_none2():\\r\\n runmatch(lcode)\",\n \"def check_matches(known, match): \\n #array containing tuples of corrected spellings\\n out_vals = []\\n\\n #clear screen\\n os.system('clear')\\n \\n for k,v in known.iteritems():\\n matched_vals = [ik for ik, iv in match.iteritems() if iv == v]\\n cur_matches = process.extract(k, matched_vals, limit = MATCH_LIMIT)\\n \\n #append a none option and option for actual val\\n cur_matches.insert(0,(\\\"NONE\\\",0))\\n cur_matches.append((k,100))\\n\\n\\n #if there is a match of 100, skip\\n if cur_matches[1][1] == 86 or cur_matches[1][1] < 50:\\n #special case where probably means there really isn't a match\\n choice = 'NONE'\\n print 'No match!'\\n\\n elif cur_matches[1][1] < 95:\\n #print out options for each word\\n print_options(k, cur_matches)\\n\\n #cycle through prompt for input if valid (numeric, within MATCH_LIMIT)\\n choice = None\\n while choice == None:\\n try:\\n choice = cur_matches[get_choice()][0]\\n except:\\n print '!Invalid match! Try again'\\n print_options(k, cur_matches)\\n \\n print choice\\n\\n else:\\n #exact match\\n choice = cur_matches[1][0]\\n print '%s - Match!'%choice\\n\\n print \\n print\\n #create a new tuple with correct and incorrect spelling\\n out_vals.append((k, choice))\\n output(out_vals)\\n\\n #we're done, return vals array\\n return out_vals\",\n \"def test_match_correct(self, event: Event, pr_algorithm, minimal_state: State):\\n assert pr_algorithm.match(event, minimal_state)\",\n \"def test_is_solved(self):\\n p = hw.TilePuzzle([[1, 2], [3, 0]])\\n self.assertTrue(p.is_solved())\\n p = hw.TilePuzzle([[0, 1], [3, 2]])\\n self.assertFalse(p.is_solved())\",\n \"def test_strategy(self):\\n axelrod.seed(1)\\n opponent = axelrod.Cooperator()\\n player = axelrod.KnowledgeableWorseAndWorse()\\n match = axelrod.Match((opponent, player), turns=5)\\n self.assertEqual(match.play(), [('C', 'C'),\\n ('C', 'D'),\\n ('C', 'D'),\\n ('C', 'D'),\\n ('C', 'D')])\\n\\n # Test that behaviour does not depend on opponent\\n opponent = axelrod.Defector()\\n player = axelrod.KnowledgeableWorseAndWorse()\\n axelrod.seed(1)\\n match = axelrod.Match((opponent, player), turns=5)\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'D'),\\n ('D', 'D'),\\n ('D', 'D'),\\n ('D', 'D')])\\n\\n # Test that behaviour changes when does not know length.\\n axelrod.seed(1)\\n match = axelrod.Match((opponent, player), turns=5,\\n match_attributes={'length': float('inf')})\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C')])\",\n \"def test_strategy(self):\\n\\n axelrod.seed(8)\\n opponent = axelrod.Cooperator()\\n player = axelrod.WorseAndWorse()\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'D'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C')])\\n\\n # Test that behaviour does not depend on opponent\\n opponent = axelrod.Defector()\\n player = axelrod.WorseAndWorse()\\n axelrod.seed(8)\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'D'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C')])\",\n \"def test_match_right_none():\\r\\n runmatch(lcode_right_none)\",\n \"def test_check_for_unacceptable_matches_students(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n student = game.students[0]\\n project = Project(name=\\\"foo\\\", capacity=0)\\n student.matching = project\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert error.startswith(student.name)\\n assert error.endswith(str(student.prefs))\\n assert project.name in error\",\n \"def test_solve(resident_names, hospital_names, capacities, seed):\\n\\n for optimal in [\\\"suitor\\\", \\\"resident\\\", \\\"reviewer\\\", \\\"hospital\\\"]:\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n matching = match.solve(optimal)\\n assert isinstance(matching, Matching)\\n assert set(matching.keys()) == set(hospitals)\\n matched_residents = [\\n res for match in matching.values() for res in match\\n ]\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(residents)\\n )\\n\\n for resident in set(residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def test_is_solved_when_puzzle_is_solved(self):\\n self.assertTrue(self.sudoku.is_solved())\",\n \"def verify_winner(self):\\r\\n return self.count_pegs() == 1\",\n \"def doMatching(self):\\n\\n self.alltrans = []\\n curdir = os.getcwd()\\n os.chdir(self.astromdir)\\n\\n for cat in self.projcats:\\n self.logfile.write(\\\"Beginning match attempt for %s\\\" %cat)\\n nobj = max(80,self.Nrefobjs)\\n base = 'match %s 1 2 3 %s 1 2 3 identity recalc nobj=%d medtf medsigclip=2.7 '\\\\\\n %(cat,self.GSCmatchin,nobj)\\n converged=0\\n mrad=4.5\\n xsh=0.\\n ysh=0.\\n Retry=0\\n while not converged and mrad<10 and Retry<11:\\n ferr,mdict=self._runmatch(base,mrad,xsh,ysh)\\n if ferr:\\n # match not found, make radius bigger and retry\\n mrad += 0.5\\n Retry += 1\\n self.logfile.write(\\\"Retry: %d ... set mrad: %s\\\" %(Retry,mrad))\\n continue\\n\\n dx = mdict['dx']\\n dy = mdict['dy']\\n sigx = mdict['sigx']\\n sigy = mdict['sigy']\\n\\n # if the extra shift is too big compared to the\\n # match radius, update input shift and retry\\n if max(abs(dx-xsh),abs(dy-ysh)) > min(0.4, mrad/10.):\\n xsh=dx\\n ysh=dy\\n Retry += 1\\n continue\\n\\n # if sigma seems too big, shrink mrad\\n if (max(sigx,sigy) > 0.7 and mrad>2.9):\\n mrad -= 0.5\\n Retry += 1\\n continue\\n\\n # otherwise, it looks like we have a good fit\\n # but we want to tune it up with smaller matchrad\\n # Don't increment retry in this final case.\\n if mrad > 3.9 or (mrad>2.9 and max(sigx,sigy)>0.4):\\n mrad = max(mrad-1,2.5)\\n continue\\n\\n self.alltrans.append(mdict)\\n converged=1\\n self.logfile.write(\\\"\\\"\\\"%s trans:\\n mdx,mdy = %.4f %.4f\\n edx,edy = %.4f %.4f\\n sigx,sigy = %.4f %.4f\\n \\\"\\\"\\\"%(cat, mdict['dx'],mdict['dy'],mdict['dx_err'],mdict['dy_err'],\\\\\\n mdict['sigx'],mdict['sigy']))\\n if len(self.alltrans)<1:\\n errtxt = \\\"WARNING: No Image successfully matched against extended GSC!\\\"\\n self.logfile.write(errtxt)\\n self.errorList.append((self.modName,errtxt))\\n return -1\\n\\n os.chdir(curdir)\\n # finally, choose best match transform and calculate\\n # the new CRVAL's\\n self._pickBestrans()\\n\\n return 0\",\n \"def checkForRematch(player_id, opponent_id):\\n conn, cur = connect()\\n query = \\\"SELECT check_for_rematch(%s, %s);\\\"\\n params = (player_id, opponent_id,)\\n try:\\n cur.execute(query, params)\\n except:\\n print(\\\"Error encountered when checking if playerId:\\\" + str(player_id) +\\n \\\" has already played opponentId:\\\" + str(opponent_id))\\n isRematch = cur.fetchone()\\n conn.close()\\n return int(isRematch[0])\",\n \"def is_legal_solution(self, solution):\\r\\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\\r\\n return self.fit_score(solution) == 0\\r\\n else:\\r\\n return self.fit_score(solution) == sum(x for x in range(1, 12))\",\n \"def test_no_match(self):\\n\\n conn, cursor = get_db_cursor()\\n build = \\\"toy_build\\\"\\n database = \\\"scratch/toy.db\\\"\\n run_info = talon.init_run_info(database, build)\\n talon.get_counters(database)\\n\\n edge_dict = init_refs.make_edge_dict(cursor)\\n location_dict = init_refs.make_location_dict(build, cursor)\\n transcript_dict = init_refs.make_transcript_dict(cursor, build)\\n gene_starts = init_refs.make_gene_start_or_end_dict(cursor, build, \\\"start\\\")\\n gene_ends = init_refs.make_gene_start_or_end_dict(cursor, build, \\\"end\\\")\\n\\n chrom = \\\"chr1\\\"\\n strand = \\\"+\\\"\\n positions = [ 1, 100, 900, 1000 ]\\n edge_IDs = [200]\\n vertex_IDs = [2, 5]\\n v_novelty = [0, 0]\\n\\n all_matches = talon.search_for_ISM(edge_IDs, transcript_dict)\\n assert all_matches == None\\n conn.close()\",\n \"def test_match(self):\\n form_data = self.form_data('r*A9x=^2hg&v7u?u9tg?u',\\n 'A=r7-%=K?K@B^!9Q8=C+')\\n form = self.form(data=form_data, user=self.u)\\n self.assertFalse(form.is_valid())\",\n \"def test_bad_match(self):\\n log.info('===== START TEST BAD MATCH =====')\\n\\n # Telemetered\\n file_path = os.path.join(RESOURCE_PATH, '11129553_SNA_SNA.txt')\\n\\n stream_handle = open(file_path, MODE_ASCII_READ)\\n\\n self.create_parser(stream_handle)\\n\\n particles = self.parser.get_records(57)\\n\\n log.debug(\\\"*** test_bad_match Num particles %s\\\", len(particles))\\n\\n # 2 bad samples\\n self.assertEqual(len(self.exception_callback_value), 2)\\n stream_handle.close()\\n\\n log.info('===== END TEST BAD MATCH =====')\",\n \"def solve(self):\\n if not self.running or self.state == \\\"stopping\\\":\\n return False\\n\\n # Find first empty tile\\n target = ()\\n for i in range(9**2):\\n if self.board[i // 9, i % 9] == 0:\\n target = (i // 9, i % 9)\\n break\\n\\n # If there are no empty tiles, the puzzle is solved\\n if not target:\\n return True\\n\\n # Tests all possible values\\n for value in range(1, 10):\\n if not self.isPossibleAssign(target, value):\\n continue\\n\\n self.update_board(target, value)\\n\\n if self.solve():\\n return True\\n\\n # In case of failure, reset and return False\\n self.update_board(target, 0)\\n\\n return False\",\n \"def testRegistration():\\n deleteMatches()\\n deletePlayers()\\n registerPlayer(\\\"Red Ranger\\\")\\n registerPlayer(\\\"Blue Ranger\\\")\\n registerPlayer(\\\"Yellow Ranger\\\")\\n registerPlayer(\\\"Pink Ranger\\\")\\n registerPlayer(\\\"Green Ranger\\\")\\n standings = playerStandings()\\n [id1, id2, id3, id4, id5] = [row[0] for row in standings]\\n reportMatch(id1, id2)\\n reportMatch(id3, id4)\\n completeTournament('Battle Royale')\\n currentPlayers = countPlayers()\\n oldPlayers = countPlayers('Battle Royale')\\n if not (currentPlayers==0 and oldPlayers==5):\\n raise ValueError(\\n \\\"Players' status were not updated correctly when tournament\\\"\\n \\\" completes\\\"\\n )\\n\\n print \\\"6. Keeping of completed tournaments in database is checked.\\\"\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def test_check_stability():\\n\\n residents = [Player(\\\"A\\\", [1, 2]), Player(\\\"B\\\", [2]), Player(\\\"C\\\", [2, 1])]\\n hospitals = [Player(1, [\\\"C\\\", \\\"A\\\"], 3), Player(2, [\\\"A\\\", \\\"B\\\", \\\"C\\\"], 3)]\\n match = HospitalResident(residents, hospitals)\\n\\n matching = match.solve()\\n assert match.check_stability()\\n\\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\\n matching[P_1] = [P_C]\\n matching[P_2] = [P_A, P_B]\\n\\n assert not match.check_stability()\",\n \"def play_match(self):\\r\\n time.sleep(1)\\r\\n print(\\\"Let's play Rock, Paper or Scissors!\\\\n\\\")\\r\\n while True:\\r\\n self.plyer_op()\\r\\n while self.you.score != 3 or self.opposite.score != 3:\\r\\n\\r\\n self.play_round()\\r\\n print('your score :' + str(self.you.score) + ' vs ' +\\r\\n \\\"the opposite player :\\\" +\\r\\n str(self.opposite.score) + '\\\\n')\\r\\n if self.you.score == 3:\\r\\n print(\\\"you win!! the match\\\\n\\\")\\r\\n break\\r\\n elif self.opposite.score == 3:\\r\\n print(\\\"opposite player win!! the match\\\\n\\\")\\r\\n break\\r\\n self.you.score = 0\\r\\n self.opposite.score = 0\",\n \"def test_match_left_none():\\r\\n runmatch(lcode_left_none)\",\n \"def test_complete_game1(self):\\n m = Game()\\n\\n plays = [\\n ['a:-', 'a:-', 'a:b', 'b:b', 'b:-', 'b:b'],\\n ['c:3i', 'c:f', 'c:f', 'c:-', 'd:f', 'd:-', 'd:-'],\\n ['a:3i', 'a:2f', 'a:f', 'a:-', 'b:-', 'b:-', 'b:-'],\\n ['c:3i', 'c:2f', 'c:-', 'c:f', 'd:-', 'd:b', 'd:b'],\\n ['a:5i2rp', 'a:4f', 'a:f', 'a:b-', 'b:-', 'b:b', 'b:k']\\n ]\\n m.run(plays)\\n m.print_stats()\\n\\n # Did the right team win?\\n assert m.teams['a'].win == True\\n\\n # Did we get 5 turns?\\n assert m.turn_count == 5\\n\\n # There should have been 30 throws in the game\\n assert m.get_throw_count() == 30\",\n \"def does_match(self):\\n if self.stopped:\\n return False\\n return self.current_state == self.q3\",\n \"def _play_match(self, team, opponent, point, mode, match_id):\",\n \"def simulate_match(match_id, bo=3.0) -> None:\\n\\n match = Match.query.filter_by(id=match_id).first()\\n\\n team1_players = get_players(match.team1_id)\\n team2_players = get_players(match.team2_id)\\n\\n team1_stats = get_team_stats(team1_players)\\n team2_stats = get_team_stats(team2_players)\\n\\n print(f\\\"{team1_stats=}\\\")\\n print(f\\\"{team2_stats=}\\\")\\n\\n win_count_team1 = 0\\n win_count_team2 = 0\\n\\n while win_count_team1 < (bo / 2.0) and win_count_team2 < (bo / 2.0):\\n eff_stats_team1 = get_effective_team_stats(team1_stats)\\n eff_stats_team2 = get_effective_team_stats(team2_stats)\\n\\n print(f\\\"{eff_stats_team1=}\\\")\\n print(f\\\"{eff_stats_team2=}\\\")\\n\\n if eff_stats_team1 > eff_stats_team2:\\n win_count_team1 += 1\\n else:\\n win_count_team2 += 1\\n\\n print(f\\\"{win_count_team1=}\\\")\\n print(f\\\"{win_count_team2=}\\\")\\n\\n if win_count_team1 > win_count_team2:\\n match.winner = match.team1_id\\n print(\\\"win_count_team1 > win_count_team2\\\")\\n elif win_count_team1 < win_count_team2:\\n match.winner = match.team2_id\\n print(\\\"win_count_team1 < win_count_team2\\\")\\n else:\\n match.winner = 0\\n\\n match.status = \\\"finished\\\"\\n\\n logging.info(f\\\"Match {match.id} ({match.team1_name} vs. {match.team2_name} finished.\\\"\\n f\\\"Winnerteam: {match.winner}\\\")\\n\\n db.session.commit()\\n\\n logging.info(f\\\"DB updated after finishing match {match.id}\\\")\",\n \"def matchbetween(self):\\n team1_toss_factor, team2_toss_factor = self.toss_factor()\\n\\n avgScoredByTeam1 = self.team1.attack / self.team2.defense * team1_toss_factor\\n avgScoredByTeam2 = self.team2.attack / self.team1.defense * team2_toss_factor\\n\\n\\n while True:\\n self.team1score = np.random.poisson(avgScoredByTeam1)\\n self.team2score = np.random.poisson(avgScoredByTeam2)\\n if self.team1score > self.team2score:\\n self.team1.points += 3\\n self.team1.won += 1\\n self.team2.lost += 1\\n self.winner = self.team1\\n break\\n elif self.team1score < self.team2score:\\n self.team2.points += 3\\n self.team2.won += 1\\n self.team1.lost += 1\\n self.winner = self.team2\\n break\\n else:\\n if self.groupcheck is True:\\n self.team1.points += 1\\n self.team2.points += 1\\n self.team1.tie += 1\\n self.team2.tie += 1\\n break\\n self.team1.scored += self.team1score\\n self.team2.scored += self.team2score\\n self.team1.conceded += self.team2score\\n self.team2.conceded += self.team1score\\n self.team1.goaldifference += self.team1score-self.team2score\\n self.team2.goaldifference += self.team2score-self.team1score\",\n \"def check_results(bet_tup, res_tup):\\n colors = [\\\"red\\\", \\\"green\\\", \\\"black\\\"]\\n if colors.count(bet_tup[1]) > 0:\\n if bet_tup[1] == res_tup[1]:\\n print(\\\"You've guessed the right color!\\\\n\\\")\\n return(1)\\n else:\\n print(\\\"Sorry, but you didn't win this round.\\\\n\\\")\\n return(-1)\\n\\n elif bet_tup[1]:\\n if bet_tup[1] == res_tup[0]:\\n print(\\\"I don't know if it's luck or not but you've guessed the right number!\\\\n\\\")\\n return(2)\\n else:\\n print(\\\"Sorry, but you didn't win this round.\\\\n\\\")\\n return(-2)\",\n \"async def check(self):\\n\\n while not self.solved:\\n # Get list of possible numbers this square can have\\n possibles = self.get_possible_numbers()\\n # If there's only once possibility, then use this number...this square is now solved\\n if len(possibles) == 1:\\n self.num = possibles.pop()\\n # If there are no possible squares well...something's wrong, that shouldn't be possible\\n # This check is done because we want to be able to guess and check, and figure out if a guess is invalid\\n elif len(possibles) == 0:\\n raise ValueError(\\\"Impossible square; no possible numbers based on restrictions\\\")\\n # Otherwise wait a small amount and continue\\n else:\\n await asyncio.sleep(0.05)\",\n \"def match(self, inp):\\n return 0\",\n \"def testDraw():\\n deleteMatches()\\n deletePlayers()\\n registerPlayer(\\\"Pikachu\\\")\\n registerPlayer(\\\"Charmander\\\")\\n registerPlayer(\\\"Bulbasaur\\\")\\n registerPlayer(\\\"Squirtle\\\")\\n standings = playerStandings()\\n [id1, id2, id3, id4] = [row[0] for row in standings]\\n reportMatch(id1, id2, True)\\n reportMatch(id3, id4, True)\\n standings = playerStandings()\\n if not (standings[0][2]==standings[1][2]==standings[2][2]==standings[3][2]):\\n raise ValueError(\\n \\\"Players should have the same number of points after drawing\\\"\\n )\\n\\n print \\\"3. Draw is recorded properly.\\\"\",\n \"def test_can_match(self):\\n assert self.RNA(\\\"\\\").can_match(\\\"\\\")\\n assert self.RNA(\\\"UCAG\\\").can_match(\\\"UCAG\\\")\\n assert not self.RNA(\\\"UCAG\\\").can_match(\\\"ucag\\\")\\n assert self.RNA(\\\"UCAG\\\").can_match(\\\"NNNN\\\")\\n assert self.RNA(\\\"NNNN\\\").can_match(\\\"UCAG\\\")\\n assert self.RNA(\\\"NNNN\\\").can_match(\\\"NNNN\\\")\\n assert not self.RNA(\\\"N\\\").can_match(\\\"x\\\")\\n assert not self.RNA(\\\"N\\\").can_match(\\\"-\\\")\\n assert self.RNA(\\\"UCAG\\\").can_match(\\\"YYRR\\\")\\n assert self.RNA(\\\"UCAG\\\").can_match(\\\"KMWS\\\")\",\n \"def test_solve(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n for optimal in [\\\"student\\\", \\\"supervisor\\\"]:\\n students, projects, _, game = make_game(\\n student_names,\\n project_names,\\n supervisor_names,\\n capacities,\\n seed,\\n clean,\\n )\\n\\n matching = game.solve(optimal)\\n assert isinstance(matching, MultipleMatching)\\n\\n projects = sorted(projects, key=lambda p: p.name)\\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\\n for game_project, project in zip(matching_keys, projects):\\n assert game_project.name == project.name\\n assert game_project._pref_names == project._pref_names\\n assert game_project.capacity == project.capacity\\n assert game_project.supervisor.name == project.supervisor.name\\n\\n matched_students = [\\n stud for match in matching.values() for stud in match\\n ]\\n assert matched_students != [] and set(matched_students).issubset(\\n set(game.students)\\n )\\n\\n for student in matched_students:\\n supervisor = student.matching.supervisor\\n assert student in supervisor.matching\\n\\n for student in set(game.students) - set(matched_students):\\n assert student.matching is None\",\n \"def check_match(possible_match):\\n top = \\\"\\\"\\n bottom = \\\"\\\"\\n for tup in possible_match:\\n top += tup[0]\\n bottom += tup[1]\\n return top == bottom\",\n \"def has_explicit_match(self) -> bool:\\n if self.matching is not None:\\n return True\\n else:\\n return False\",\n \"def run_one_match(match, chance):\\n home = match[0]\\n away = match[1]\\n home_win = chance[0]\\n draw = chance[1]\\n random_number = random.get_random_number_0_1() * 100\\n if 0 <= random_number < home_win:\\n return home\\n elif home_win <= random_number < (home_win + draw):\\n return \\\"Draw\\\"\\n elif (home_win + draw) <= random_number < 100:\\n return away\",\n \"def test_get_match_loser(self):\\n p_a = Participant()\\n p_b = Participant()\\n matchup = TournamentMatchup(\\n participant_a=p_a,\\n participant_b=p_b,\\n winner=p_b,\\n )\\n self.assertEqual(matchup.not_winner(), matchup.participant_a)\",\n \"def test_is_solved_when_puzzle_is_not_solved(self):\\n sudoku = sudolver.Sudoku()\\n self.assertFalse(sudoku.is_solved())\",\n \"def is_winner(self, player: str) -> bool:\\n total_result = self.current_state.hori_result + self.current_state.left_result + self.current_state.right_result\\n total_line = len(total_result)\\n p1_taken = 0\\n p2_taken = 0\\n for item in total_result:\\n if item == '1':\\n p1_taken+=1\\n elif item == '2':\\n p2_taken += 1\\n if player == \\\"p1\\\":\\n return float(p1_taken) >= total_line/2\\n return float(p2_taken) >= total_line/2\",\n \"def handleMatch(self, m):\\r\\n pass\",\n \"def do_the_things():\\n returned_no_game = False\\n returned_game_time = False\\n returned_game_final = False\\n returned_rival_final = False\\n returned_game_soon = False\\n returned_rival_soon = False\\n returned_game_start = False\\n compare_scores = ['0', '0']\\n timeout = time.time() + 60 * 60 * 23\\n\\n while not (returned_no_game or returned_game_final):\\n\\n try:\\n message = False\\n rival_message = False\\n\\n \\\"\\\"\\\" Infinite loops are cool \\\"\\\"\\\"\\n if time.time() > timeout:\\n break\\n\\n game_data = get_fresh_data(team)\\n\\n if game_data:\\n \\\"\\\"\\\" The default TZ for mlb is US/Eastern, we'll do some things and make it local TZ \\\"\\\"\\\"\\n tz = pytz.timezone('US/Eastern')\\n eastern_time = datetime.datetime.strptime(\\\"%s %s\\\" % (game_data['time_date'], game_data['ampm']), '%Y/%m/%d %I:%M %p')\\n eastern_time = tz.localize(eastern_time)\\n pacific_time = eastern_time.astimezone(pytz.timezone(timezone))\\n\\n opponent, our_score, their_score, venue = set_vars(game_data)\\n scores = sorted([our_score, their_score], reverse=True)\\n\\n if not game_data and not returned_no_game:\\n returned_no_game = True\\n message = (\\\"The %s don't have a game scheduled today. Rest well guys!\\\" % team_hashtag)\\n\\n if game_data and not returned_game_time:\\n returned_game_time = True\\n message = ((\\\"The %s are playing against the %s today, first pitch is at \\\" + pacific_time.strftime(\\\"%-I:%M%p %Z\\\") + \\\" at %s. %s\\\") % (team_hashtag, opponent, venue, team_hash2))\\n\\n if \\\"Warmup\\\" in game_data[\\\"status\\\"] and not returned_game_soon:\\n returned_game_soon = True\\n message = ((\\\"The %s are playing against the %s in a moment, first pitch is at \\\" + pacific_time.strftime(\\\"%-I:%M%p %Z\\\") + \\\" at %s. %s\\\") % (team_hashtag, opponent, venue, team_hash2))\\n\\n if \\\"In Progress\\\" in game_data[\\\"status\\\"] and compare_scores == ['0', '0'] and not returned_game_start:\\n returned_game_start = True\\n message = (\\\"It's gametime! Go %s!!! %s\\\" % (team_hashtag, team_hash2))\\n\\n if \\\"In Progress\\\" in game_data[\\\"status\\\"] and not compare_scores == [our_score, their_score]:\\n compare_scores = [our_score, their_score]\\n if int(our_score) > int(their_score):\\n message = (\\\"The %s are leading the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n elif int(our_score) < int(their_score):\\n message = (\\\"The %s are trailing the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n elif int(our_score) == int(their_score):\\n message = (\\\"The %s are tied with the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n\\n if (\\\"Game Over\\\" or \\\"Final\\\") in game_data[\\\"status\\\"]:\\n returned_game_final = True\\n if our_score > their_score:\\n message = (\\\"The %s beat the %s today at %s with a score of %s-%s. %s\\\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\\n else:\\n message = (\\\"The %s lost against the %s today at %s with a score of %s-%s. Get 'em tomorrow! %s\\\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\\n\\n if not returned_rival_final:\\n rival_game_data = get_fresh_data(rival_team)\\n rival_opponent, rival_our_score, rival_their_score, rival_venue = set_vars(rival_game_data)\\n rival_scores = sorted([rival_our_score, rival_their_score], reverse=True)\\n\\n if \\\"Warmup\\\" in rival_game_data[\\\"status\\\"] and not returned_rival_soon:\\n returned_rival_soon = True\\n rival_message = (\\\"The %s game is about to start, go %s!!! %s\\\") % (rival_team_hashtag, rival_opponent, rival_team_hash2)\\n\\n if (\\\"Game Over\\\" or \\\"Final\\\") in rival_game_data[\\\"status\\\"]:\\n returned_rival_final = True\\n if rival_our_score > rival_their_score:\\n rival_message = (\\\"The %s beat the %s today at %s with a score of %s-%s, boooo!\\\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\\n else:\\n rival_message = (\\\"The %s lost against the %s today at %s with a score of %s-%s. Hell yeah!\\\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\\n\\n if message:\\n if testmode:\\n print(message)\\n else:\\n twitter.tweet(message)\\n\\n if rival_message:\\n if testmode:\\n print(rival_message)\\n else:\\n twitter.tweet(rival_message)\\n\\n except tweepy.TweepError as e:\\n print e.message[0]['code']\\n print e.args[0][0]['code']\\n\\n time.sleep(5)\",\n \"def match_pair(cls, game, pair_1, pair_2):\\n game.attempts += 1\\n game.put()\\n card_1 = Card.query(Card.game == game.key).filter(Card.index == pair_1).get()\\n card_2 = Card.query(Card.game == game.key).filter(Card.index == pair_2).get()\\n\\n if card_1.matched or card_2.matched:\\n raise RuntimeError('Could not rematch a matched card')\\n\\n form = MatchResultForm()\\n if card_1.value == card_2.value:\\n card_1.matched = True\\n card_1.put()\\n card_2.matched = True\\n card_2.put()\\n game.matched += 2\\n game.put()\\n form.message = 'Success'\\n else:\\n form.message = 'Fail'\\n\\n # Construct return info form\\n form.card_1 = card_1.to_form()\\n form.card_2 = card_2.to_form()\\n form.matched_count = game.matched\\n\\n if game.matched == 52:\\n game.game_over = True\\n game.put()\\n Card.delete_cards_for_game(game)\\n\\n # Update average attempts of user\\n user = game.user.get()\\n games = Game.get_user_finished_games(user)\\n count = len(games)\\n if user.average_attempts == float('inf'):\\n user.average_attempts = 0\\n user.average_attempts = ((count - 1) * user.average_attempts + game.attempts) / count\\n user.put()\\n\\n score = Score(user=game.user, date=date.today(), attempts=game.attempts)\\n score.put()\\n form.message = 'Win'\\n\\n # Create history log\\n History.create_history(game=game,\\n card_1=card_1,\\n card_2=card_2,\\n message=form.message)\\n return form\",\n \"def check_match_over(self) -> bool:\\n # check if programs filled\\n self.program_is_full = [any(~np.isnan(program[:self.program_slots[i]]))\\n for i, program in enumerate(self.program_matches)]\\n if np.all(self.program_is_full):\\n return True\\n\\n # check if candidates matched\\n # I think this is inaccurate -- a candidate can tentatively match but not\\n # fully match?\\n self.applicant_is_matched = self.applicant_matches != -1\\n if np.all(self.applicant_is_matched):\\n return True\\n\\n # check for overlap between unfilled programs and unmatched applicants\\n for i, program_matched in enumerate(self.program_is_full):\\n if not program_matched:\\n for j, applicant_matched in enumerate(self.applicant_is_matched):\\n if not applicant_matched:\\n if i in self.applicant_ranks[j] and j in self.program_ranks[i]:\\n return True\\n\\n # otherwise, match isn't over\\n return False\",\n \"def testPointSystem():\\n deleteMatches()\\n deletePlayers()\\n registerPlayer(\\\"Pikachu\\\")\\n registerPlayer(\\\"Charmander\\\")\\n registerPlayer(\\\"Bulbasaur\\\")\\n registerPlayer(\\\"Squirtle\\\")\\n registerPlayer(\\\"MewTwo\\\")\\n standings = playerStandings()\\n [id1, id2, id3, id4, id5] = [row[0] for row in standings]\\n reportMatch(id1, id2)\\n reportMatch(id3, id4, True)\\n reportMatch(id5, id5, False, True)\\n reportMatch(id1, id5)\\n reportMatch(id3, id4)\\n reportMatch(id2, id2, False, True)\\n reportMatch(id1, id3)\\n reportMatch(id5, id2)\\n reportMatch(id4, id4, False, True)\\n standings = playerStandings()\\n if not (standings[0][0]==id2 and standings[0][2]==2 and\\n standings[1][0]==id4 and standings[0][2]==2 and\\n standings[2][0]==id3 and standings[0][2]==2 and\\n standings[3][0]==id5 and standings[0][2]==2 and\\n standings[4][0]==id1 and standings[0][2]==2):\\n raise ValueError(\\n \\\"Points are not tallied correctly.\\\"\\n )\\n\\n print \\\"4. Points are tallied correctly.\\\"\",\n \"def test_match_nones():\\r\\n runmatch(lcode_nones)\",\n \"def test_assigned_match(self):\\n user1 = get_user_model().objects.get(username='test1@example.com')\\n self.client.login(username='test1@example.com', password='1')\\n\\n office = OfficeLocation.objects.all()[0]\\n org = OrgGroup.objects.filter(parent__isnull=True)[0]\\n\\n submission1 = Interest()\\n submission1.owner = user1\\n submission1.for_coffee = True\\n submission1.save()\\n submission1.locations.add(office)\\n submission1.departments.add(org)\\n\\n resp = self.client.get(reverse('mystery:mystery'))\\n self.assertContains(resp, \\\"Cancel this\\\", status_code=200)\\n\\n user2 = random_user()\\n submission2 = Interest()\\n submission2.owner = user2\\n submission2.is_active = False\\n submission2.save()\\n submission2.for_coffee = True\\n submission2.locations.add(office)\\n submission2.departments.add(org)\\n submission2.is_active = True\\n submission2.save()\\n\\n resp = self.client.get(reverse('mystery:mystery'))\\n self.assertContains(resp, reverse(\\\"mystery:close_incomplete\\\", args=(submission1.id,)))\\n self.assertContains(resp, reverse(\\\"mystery:close_complete\\\", args=(submission1.id,)))\\n\\n # verify assigned match page requires login\\n self.client.logout()\\n resp = self.client.get(reverse('mystery:mystery'))\\n self.assertEqual(resp.status_code, 302)\\n self.assertIn('login', resp['Location'])\",\n \"def check_win(self, player):\\n def check_row_win(player):\\n for row in self.game_state:\\n if player == row[0] == row[1] == row[2]:\\n return True\\n return False\\n\\n def check_column_win(player):\\n # For doing a column check, transpose the grid and do a row check\\n trans_game_state = numpy.transpose(self.game_state)\\n for row in trans_game_state:\\n if player == row[0] == row[1] == row[2]:\\n return True\\n return False\\n\\n def check_diag_win(player):\\n # Left to right diagonal\\n if player == self.game_state[0][0] == self.game_state[1][1] == self.game_state[2][2]:\\n return True\\n # Right to left diagonal\\n if player == self.game_state[0][2] == self.game_state[1][1] == self.game_state[2][0]:\\n return True\\n return False\\n\\n if check_column_win(player) or check_diag_win(player) or check_row_win(player):\\n return True\\n return False\",\n \"def test_equal(self):\\r\\n\\r\\n a_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n a_x_dist = 3\\r\\n a_y_dist = 3\\r\\n a_num_to_win = 1\\r\\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\\r\\n\\r\\n b_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n b_x_dist = 3\\r\\n b_y_dist = 3\\r\\n b_num_to_win = 1\\r\\n b_game = Game(b_players, b_x_dist, b_y_dist, b_num_to_win)\\r\\n\\r\\n c_players = [ZeroPlayer(1), ZeroPlayer(2)]\\r\\n c_x_dist = 3\\r\\n c_y_dist = 3\\r\\n c_num_to_win = 1\\r\\n c_game = Game(c_players, c_x_dist, c_y_dist, c_num_to_win)\\r\\n\\r\\n self.assertTrue(b_game == a_game == c_game)\\r\\n\\r\\n a_game.play_game()\\r\\n b_game.play_game()\\r\\n\\r\\n self.assertTrue(a_game == b_game)\\r\\n self.assertFalse(c_game == a_game)\\r\\n\\r\\n c_game.play_game()\\r\\n\\r\\n self.assertTrue(b_game == a_game == c_game)\",\n \"def match(self) -> \\\"MatchResult\\\":\\n raise NotImplementedError\",\n \"def is_solved(self):\\n # Iterate through each square of the puzzle\\n for row in range(self.sl):\\n for col in range(self.sl):\\n val = self.puzzle[row][col]\\n\\n # If any square value is blank (0), not solved, return False\\n if val == 0:\\n return False\\n\\n # Trick to keep DRY code: replace each value temporarily with a\\n # 0, and use valid_square method with original value to determine\\n # if every square is valid\\n self.puzzle[row][col] = 0\\n valid = self.valid_square(row, col, val)\\n self.puzzle[row][col] = val\\n \\n # If not a valid value for square, return False\\n if not valid:\\n return False\\n return True\",\n \"def did_solve(self) -> bool:\\n pass\",\n \"def reportMatch(winner, loser):\\n\\n tournID = main.getTournamentIDFromName(\\\"Tournament for legacy tests\\\")\\n\\n main.playMatch(tournID, winner, loser, \\\"p1 wins\\\")\",\n \"def is_valid_board(self):\\n total = sum(range(1, self.n+1))\\n d = {x : [set([]), set([])] for x in range(1, self.n+1)}\\n for row_index in range(self.n):\\n for col_index in range(self.n):\\n num = self.board[row_index][col_index]\\n try:\\n if row_index in d[num][0] or col_index in d[num][1]:\\n print(\\\"Invalid solution.\\\")\\n return\\n except KeyError:\\n print(\\\"Unsolved solution.\\\") # d[0]\\n return\\n\\n d[num][0].add(row_index)\\n d[num][1].add(col_index)\\n print(\\\"Valid solution!\\\")\",\n \"def test_is_winner(self):\\n TestGame = TTT_Game(\\\"John\\\", \\\"Jane\\\")\\n\\n # Check verticals\\n TestGame.board = [1, 2, \\\"X\\\", 4, 5, \\\"X\\\", 7, 8, \\\"X\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"X\\\"))\\n TestGame.board = [\\\"O\\\", 2, 3, \\\"O\\\", 5, \\\"X\\\", \\\"O\\\", 8, \\\"X\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"O\\\"))\\n TestGame.board = [1, \\\"X\\\", \\\"O\\\", \\\"O\\\", \\\"X\\\", 6, 7, \\\"X\\\", \\\"X\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"X\\\"))\\n\\n # Check horizontals\\n TestGame.board = [\\\"O\\\", \\\"O\\\", \\\"O\\\", \\\"O\\\", 5, \\\"X\\\", 7, 8, 9]\\n self.assertTrue(TestGame.is_winner(\\\"O\\\"))\\n TestGame.board = [1, 2, 3, \\\"X\\\", \\\"X\\\", \\\"X\\\", 7, 8, 9]\\n self.assertTrue(TestGame.is_winner(\\\"X\\\"))\\n TestGame.board = [1, 2, 3, \\\"O\\\", 5, 6, \\\"O\\\", \\\"O\\\", \\\"O\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"O\\\"))\\n\\n # Check diagonals\\n TestGame.board = [\\\"O\\\", \\\"X\\\", 3, 4, \\\"O\\\", \\\"X\\\", \\\"X\\\", \\\"O\\\", \\\"O\\\"]\\n self.assertTrue(TestGame.is_winner(\\\"O\\\"))\\n TestGame.board = [1, 2, \\\"X\\\", 4, \\\"X\\\", 6, \\\"X\\\", 8, 9]\\n self.assertTrue(TestGame.is_winner(\\\"X\\\"))\",\n \"def test_solve_one_player_2(self):\\n self.rush_hour_data = rush_hour_data_2\\n self.state_data = state_data_2\\n self.execute_minimax_single_player()\",\n \"def is_matching(self, plays):\\n match = self.plays_to_edges(plays)\\n return nx.is_maximal_matching(self.graph, match) # TODO\",\n \"def judge(self):\\n self.bounds = 0.0\\n self.best = self.lives[0]\\n for life in self.lives:\\n life.score = self.matchFun(life)\\n self.bounds += life.score\\n if self.best.score < life.score:\\n self.best = life\",\n \"def judge(self):\\n self.bounds = 0.0\\n self.best = self.lives[0]\\n for life in self.lives:\\n life.score = self.matchFun(life)\\n self.bounds += life.score\\n if self.best.score < life.score:\\n self.best = life\",\n \"def check_if_solved(self):\\n for cell in self.board.values():\\n if not cell.value:\\n return False\\n return True\",\n \"def check_grue(self, tile):\\n if tile[2] == 'grue':\\n if self.lab.inventory > 0:\\n self.lab.fire()\\n print 'Lighted match'\",\n \"def match(self, segment):\\n\\n match_len = self._win_length\\n\\n subsegments = [[segment[j+i] for i in range(match_len)] for j in range(len(segment)-match_len+1)]\\n\\n for subsegment in subsegments:\\n if set(subsegment).pop() == self.current_player and len(list(set(subsegment))) == 1:\\n return True\\n\\n return False\",\n \"def test_init_with_existing_game(self):\\n pass\\n # Ensure judge is the same\",\n \"def _check_resident_matching(self):\\n\\n errors = []\\n for resident in self.residents:\\n if (\\n resident.matching is not None\\n and resident.matching not in resident.prefs\\n ):\\n errors.append(\\n ValueError(\\n f\\\"{resident} is matched to {resident.matching} but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{resident.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def checkSolution(self):\\n movesToEndblock = self.gridSize - self.changeable[0] - 2\\n if self.checkMove(0,movesToEndblock) == 0:\\n return 0\\n return 1\",\n \"def check_game_sanity(game_val, log):\\n\\n global __problem_deck_index__\\n\\n supply = game_val.get_supply()\\n if game_val.any_resigned():\\n return True # Resign leaves deck in arbitrary state, don't catch that\\n\\n # ignore known bugs.\\n #if set(supply).intersection([get_card('Masquerade'), get_card('Black Market'), get_card('Trader')]):\\n #return True\\n\\n # TODO: add score sanity checking here\\n last_state = None\\n game_state_iterator = game_val.game_state_iterator()\\n for game_state in game_state_iterator:\\n last_state = game_state\\n for player_deck in game_val.get_player_decks():\\n parsed_deck_comp = player_deck.Deck()\\n computed_deck_comp = last_state.get_deck_composition(\\n player_deck.name())\\n\\n parse_common.delete_keys_with_empty_vals(parsed_deck_comp)\\n computed_dict_comp = dict(computed_deck_comp)\\n parse_common.delete_keys_with_empty_vals(computed_dict_comp)\\n\\n if parsed_deck_comp != computed_deck_comp:\\n found_something_wrong = False\\n for card in set(parsed_deck_comp.keys() +\\n computed_deck_comp.keys()):\\n if parsed_deck_comp.get(card, 0) != \\\\\\n computed_deck_comp.get(card, 0):\\n if not found_something_wrong:\\n __problem_deck_index__ += 1\\n log.debug('[%d] %18s %9s %9s', __problem_deck_index__, 'card', 'from-data', 'from-sim')\\n log.debug('[%d] %-18s %9d %9d', __problem_deck_index__, card, parsed_deck_comp.get(card, 0),\\n computed_deck_comp.get(card, 0))\\n found_something_wrong = True\\n if found_something_wrong:\\n try:\\n log.debug('[%d] insane game for %s %s: %s', __problem_deck_index__, player_deck.name(), game_val.get_id(),\\n ' '.join(map(str, game_val.get_supply())))\\n except UnicodeEncodeError, e:\\n None\\n return False\\n return True\",\n \"def _checkRoundOver(self):\\n\\n if not any(player.isAlive() for player in self.teams[0].players):\\n self.endGame()\",\n \"def reportMatch(player1, player2, winner = None):\\n #Check for Bye matchup (player1=player2)\\n bye_match = False\\n if player1 == player2:\\n winner = player1\\n bye_match = True\\n\\n #Generate random winner if no winner param is passed in\\n if winner is None:\\n rand = random.random()\\n if rand < 0.5:\\n winner = player1\\n else:\\n winner = player2\\n\\n conn, cur = connect()\\n #Insert record of match\\n query = \\\"\\\"\\\"INSERT INTO matches (player1_id, player2_id, winner)\\n VALUES (%s, %s, %s)\\\"\\\"\\\"\\n params = (player1, player2, winner,)\\n try:\\n cur.execute(query, params)\\n except:\\n print(\\\"Error encountered when inserting match record into the database\\\")\\n\\n #Save record of player having a bye in Players table\\n if bye_match:\\n query = \\\"UPDATE players SET had_bye = TRUE WHERE player_id = %s;\\\"\\n param = (player1,)\\n try:\\n cur.execute(query, param)\\n except:\\n print(\\\"Error encountered when updating player's Bye round status.\\\")\\n conn.commit()\\n conn.close()\",\n \"def __checkForPattern(self):\\n if self._keyCode in self._patterns:\\n assert(self.notify.debug(\\\"Pattern Match: \\\" + self._keyCode))\\n messenger.send(KeyCodes.PATTERN_MATCH_EVENT, [self._keyCode])\\n self.reset()\\n \\n # If the key code is longer than the longest pattern possible,\\n # Then reset! \\n elif self._keyCodeCount == self._patternLimit or len(self.getPossibleMatchesList()) == 0:\\n assert(self.notify.debug(\\\"No pattern match!\\\"))\\n messenger.send(KeyCodes.PATTERN_NO_MATCH_EVENT)\\n self.reset()\",\n \"def gameOver():\\n if len(p1)==0 and len(p1winnings)==0:\\n return True\\n elif len(p2)==0 and len(p2winnings)==0:\\n return True\\n return False\",\n \"def _runmatch(self,base,mrad,xsh,ysh):\\n cmd = base+'matchrad='+str(mrad)+' xsh=%.2f ysh=%.2f' %(xsh,ysh)\\n sproc = popen2.Popen3(cmd,1)\\n output = sproc.fromchild.readlines()\\n errs = sproc.childerr.readlines()\\n sproc.fromchild.close()\\n sproc.childerr.close()\\n \\n if errs and not (len(errs) == 1 and errs[0][0:27] == 'atFindMedtf: RMS <= 0.0, so'):\\n self.logfile.write('match produced the following message(s): ')\\n for f in errs:\\n self.logfile.write(string.strip(f))\\n self.errorList.append(('match',string.strip(f)))\\n matchline = ''\\n medianline = ''\\n for line in output:\\n if line[0:6] == 'TRANS:':\\n matchline = line\\n elif line[0:6] == 'MEDTF:':\\n medianline = line\\n else:\\n self.logfile.write(\\\"ERROR:. Unexpected output from match program:\\\\n\\\\t\\\\t\\\\t\\\"+line)\\n return (-1,None)\\n if (not matchline) or (not medianline):\\n self.logfile.write(\\\"Match program failed.\\\")\\n return (-1,None)\\n \\n mdict = self._process_median(medianline,mrad,xsh,ysh)\\n if not mdict:\\n return (-1,None)\\n return (0,mdict)\",\n \"def matches(\\n draw,\\n min_turns=1,\\n max_turns=200,\\n min_noise=0,\\n max_noise=1,\\n):\\n strategies = draw(strategy_lists(min_size=2, max_size=2))\\n players = [s() for s in strategies]\\n turns = draw(integers(min_value=min_turns, max_value=max_turns))\\n noise = draw(floats(min_value=min_noise, max_value=max_noise))\\n match = axl.Match(players, turns=turns, noise=noise)\\n return match\",\n \"def do_matchmaking(self):\\n # Remove seeks, offers, etc for lagged-out players\\n now = time.time()\\n\\n to_remove = {}\\n for pname in self.last_ping:\\n elapsed = now - self.last_ping[pname]\\n logging.debug('Time since last ping: %f' % elapsed)\\n if elapsed > TIMEOUT:\\n msg = 'Lost contact with %s' % pname\\n to_remove[pname] = msg\\n \\n for pname in to_remove:\\n msg = to_remove[pname]\\n self._rem_player(pname, msg)\\n\\n # Remove outstanding (and now expired) match offers\\n #for match in self.offers.values():\\n # msg = 'Match offer %s expired' % match.matchid\\n # self._rem_offer(match, msg)\\n\\n # Generate new match offers\\n for m in self.matchmaker.generate_matches(self.seeks.values()):\\n self.offers[m.matchid] = m\\n for s in m.seeks:\\n msg = 'Seek %s matched Match %s' % (s.seekid, m.matchid)\\n self.seeks.pop(s.seekid, None)\\n self.comm.offer_match(m)\",\n \"def is_solved(self):\\n if not self._find_empty():\\n return True\\n else:\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6855574","0.6748413","0.67361814","0.66506636","0.65528977","0.6500697","0.64891684","0.64536655","0.6449834","0.6446524","0.64406365","0.6418722","0.6375467","0.6332083","0.6327593","0.6303921","0.6292576","0.6269725","0.62239105","0.62134755","0.6204718","0.6200739","0.61998755","0.6190295","0.61751175","0.6148276","0.6141993","0.614195","0.6092036","0.60789317","0.60458016","0.60436964","0.60326433","0.6008848","0.5980838","0.5977832","0.59748447","0.59691024","0.5965985","0.59465283","0.59415674","0.5931503","0.59305364","0.59234035","0.5921066","0.5915618","0.5907034","0.59061474","0.58973426","0.5893553","0.5891274","0.58910143","0.5887389","0.58673376","0.5855163","0.5851978","0.5845804","0.5829573","0.58286035","0.5827446","0.58219683","0.5808202","0.58065695","0.5797514","0.57829237","0.5781702","0.5770972","0.57676065","0.5747087","0.57331795","0.5712594","0.5701577","0.5699518","0.56852525","0.5685251","0.56803","0.5674648","0.5671684","0.56643885","0.56561196","0.56524706","0.5650561","0.5634443","0.56331044","0.56331044","0.5632701","0.5629039","0.5623101","0.5623002","0.56210023","0.5619995","0.5601261","0.55929923","0.55906594","0.55905914","0.55902064","0.55842286","0.55828995","0.5580261","0.5579883"],"string":"[\n \"0.6855574\",\n \"0.6748413\",\n \"0.67361814\",\n \"0.66506636\",\n \"0.65528977\",\n \"0.6500697\",\n \"0.64891684\",\n \"0.64536655\",\n \"0.6449834\",\n \"0.6446524\",\n \"0.64406365\",\n \"0.6418722\",\n \"0.6375467\",\n \"0.6332083\",\n \"0.6327593\",\n \"0.6303921\",\n \"0.6292576\",\n \"0.6269725\",\n \"0.62239105\",\n \"0.62134755\",\n \"0.6204718\",\n \"0.6200739\",\n \"0.61998755\",\n \"0.6190295\",\n \"0.61751175\",\n \"0.6148276\",\n \"0.6141993\",\n \"0.614195\",\n \"0.6092036\",\n \"0.60789317\",\n \"0.60458016\",\n \"0.60436964\",\n \"0.60326433\",\n \"0.6008848\",\n \"0.5980838\",\n \"0.5977832\",\n \"0.59748447\",\n \"0.59691024\",\n \"0.5965985\",\n \"0.59465283\",\n \"0.59415674\",\n \"0.5931503\",\n \"0.59305364\",\n \"0.59234035\",\n \"0.5921066\",\n \"0.5915618\",\n \"0.5907034\",\n \"0.59061474\",\n \"0.58973426\",\n \"0.5893553\",\n \"0.5891274\",\n \"0.58910143\",\n \"0.5887389\",\n \"0.58673376\",\n \"0.5855163\",\n \"0.5851978\",\n \"0.5845804\",\n \"0.5829573\",\n \"0.58286035\",\n \"0.5827446\",\n \"0.58219683\",\n \"0.5808202\",\n \"0.58065695\",\n \"0.5797514\",\n \"0.57829237\",\n \"0.5781702\",\n \"0.5770972\",\n \"0.57676065\",\n \"0.5747087\",\n \"0.57331795\",\n \"0.5712594\",\n \"0.5701577\",\n \"0.5699518\",\n \"0.56852525\",\n \"0.5685251\",\n \"0.56803\",\n \"0.5674648\",\n \"0.5671684\",\n \"0.56643885\",\n \"0.56561196\",\n \"0.56524706\",\n \"0.5650561\",\n \"0.5634443\",\n \"0.56331044\",\n \"0.56331044\",\n \"0.5632701\",\n \"0.5629039\",\n \"0.5623101\",\n \"0.5623002\",\n \"0.56210023\",\n \"0.5619995\",\n \"0.5601261\",\n \"0.55929923\",\n \"0.55906594\",\n \"0.55905914\",\n \"0.55902064\",\n \"0.55842286\",\n \"0.55828995\",\n \"0.5580261\",\n \"0.5579883\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5917138"},"document_rank":{"kind":"string","value":"45"}}},{"rowIdx":93797,"cells":{"query":{"kind":"string","value":"Test that matched residents have a preference of their match."},"document":{"kind":"string","value":"def test_check_for_unacceptable_matches_residents(game):\n\n resident = game.residents[0]\n hospital = Hospital(name=\"foo\", capacity=1)\n resident.matching = hospital\n\n issues = game._check_for_unacceptable_matches(\"residents\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(resident.name)\n assert issue.endswith(f\"{resident.prefs}.\")\n assert hospital.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching.append(Player(name=\"foo\", pref_names=[]))\n\n with pytest.raises(Exception):\n match._check_reviewer_matching()","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def test_resident_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"resident\")\n\n assert set(hospitals) == set(matching.keys())\n assert all(\n [\n r in set(residents)\n for r in set(\n [\n r_match\n for matches in matching.values()\n for r_match in matches\n ]\n )\n ]\n )\n\n for resident in residents:\n if resident.matching:\n assert resident.pref_names.index(resident.matching.name) == 0","def test_check_stability():\n\n residents = [Player(\"A\", [1, 2]), Player(\"B\", [2]), Player(\"C\", [2, 1])]\n hospitals = [Player(1, [\"C\", \"A\"], 3), Player(2, [\"A\", \"B\", \"C\"], 3)]\n match = HospitalResident(residents, hospitals)\n\n matching = match.solve()\n assert match.check_stability()\n\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\n matching[P_1] = [P_C]\n matching[P_2] = [P_A, P_B]\n\n assert not match.check_stability()","def _check_resident_matching(self):\n\n errors = []\n for resident in self.residents:\n if (\n resident.matching is not None\n and resident.matching not in resident.prefs\n ):\n errors.append(\n ValueError(\n f\"{resident} is matched to {resident.matching} but \"\n \"they do not appear in their preference list: \"\n f\"{resident.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_inputs_reviewer_prefs(\n resident_names, hospital_names, capacities, seed\n):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_reviewer_prefs()\n\n match.reviewers[0].pref_names.pop()\n\n with pytest.raises(Exception):\n match._check_reviewer_prefs()","def test_prefilter_exact_matches(self):\r\n seqs = [('s1 comment1', 'ACCTTGTTACTTT'), # three copies\r\n ('s2 comment2', 'ACCTTGTTACTTTC'), # one copy\r\n ('s3 comment3', 'ACCTTGTTACTTTCC'), # two copies\r\n ('s4 comment4', 'ACCTTGTTACTTT'),\r\n ('s5 comment5', 'ACCTTGTTACTTTCC'),\r\n ('s6 comment6', 'ACCTTGTTACTTT')]\r\n expected0 = [('QiimeExactMatch.s1', 'ACCTTGTTACTTT'),\r\n ('QiimeExactMatch.s2', 'ACCTTGTTACTTTC'),\r\n ('QiimeExactMatch.s3', 'ACCTTGTTACTTTCC')]\r\n expected1 = {'QiimeExactMatch.s1': ['s1', 's4', 's6'],\r\n 'QiimeExactMatch.s2': ['s2'],\r\n 'QiimeExactMatch.s3': ['s3', 's5']}\r\n expected = (expected0, expected1)\r\n p = OtuPicker({})\r\n actual = p._prefilter_exact_matches(seqs)\r\n self.assertEqual(actual, expected)","def test_solve(resident_names, hospital_names, capacities, seed):\n\n for optimal in [\"suitor\", \"resident\", \"reviewer\", \"hospital\"]:\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n matching = match.solve(optimal)\n assert isinstance(matching, Matching)\n assert set(matching.keys()) == set(hospitals)\n matched_residents = [\n res for match in matching.values() for res in match\n ]\n assert matched_residents != [] and set(matched_residents).issubset(\n set(residents)\n )\n\n for resident in set(residents) - set(matched_residents):\n assert resident.matching is None","def test_match(self):\n self.assertEqual(solution( \"\"\"p=<-6,0,0>, v=< 3,0,0>, a=< 0,0,0>\np=<-4,0,0>, v=< 2,0,0>, a=< 0,0,0>\np=<-2,0,0>, v=< 1,0,0>, a=< 0,0,0>\np=< 3,0,0>, v=<-1,0,0>, a=< 0,0,0>\"\"\"), 1)","def exact_match_score(prediction, ground_truth):\n return normalize_answer(prediction) == normalize_answer(ground_truth)","def matchGenres(toPredictGenresString, toCompareGenresString):\n\n #Get the sets of genres\n toPredictGenres = str(toPredictGenresString).split(\"|\")\n toCompareGenres = str(toCompareGenresString).split(\"|\")\n\n toCompareGenresSet = set(toCompareGenres)\n\n commonCount = 0\n\n #Count how many are common to the two sets\n for genre in toPredictGenres:\n if genre in toCompareGenresSet:\n commonCount += 1\n\n #Return 100 times the proportion in both\n return 100 * commonCount/len(toPredictGenres)","def testSameRMSEWhenPairsAreSpecifiedOrNot(self):\n test_class1 = TrimmedMatchGeoXDesign(\n GeoXType.HEAVY_UP,\n pretest_data=self.nontrivial_data,\n time_window_for_design=self.design_window,\n time_window_for_eval=self.evaluation_window,\n matching_metrics={'response': 1.0},\n pairs=None)\n\n for geox_type in GeoXType:\n if geox_type == GeoXType.CONTROL:\n continue\n\n test_class1._geox_type = geox_type\n _, expected_detailed_results = test_class1.report_candidate_designs(\n budget_list=[30, 40],\n iroas_list=[0, 2],\n use_cross_validation=True,\n num_simulations=10)\n\n test_class2 = TrimmedMatchGeoXDesign(\n geox_type,\n pretest_data=self.nontrivial_data,\n time_window_for_design=self.design_window,\n time_window_for_eval=self.evaluation_window,\n matching_metrics={'response': 1.0},\n pairs=test_class1.pairs)\n _, detailed_results = test_class2.report_candidate_designs(\n budget_list=[30, 40],\n iroas_list=[0, 2],\n use_cross_validation=True,\n num_simulations=10)\n\n for key in detailed_results.keys():\n self.assertTrue(\n np.array_equal(detailed_results[key]['estimate'],\n expected_detailed_results[key]['estimate']))","def _exact_match_score_(prediction, ground_truth):\n return _normalize_answer(prediction) == _normalize_answer(ground_truth)","def test_fuzzy_matching_multi_match(\n nlp: Language, countries: List[Dict[str, Any]]\n) -> None:\n ruler = SpaczzRuler(nlp, spaczz_fuzzy_defaults={\"min_r2\": 85})\n ruler.add_patterns(countries)\n doc = nlp(\"This is a test that should find Northern Ireland and Ireland\")\n doc = ruler(doc)\n matches = [(ent.ent_id_, ent.text) for ent in doc.ents if ent.label_ == \"COUNTRY\"]\n assert matches == [(\"Northern Ireland\", \"Northern Ireland\"), (\"Ireland\", \"Ireland\")]","def test_strategy(self):\n axelrod.seed(1)\n opponent = axelrod.Cooperator()\n player = axelrod.KnowledgeableWorseAndWorse()\n match = axelrod.Match((opponent, player), turns=5)\n self.assertEqual(match.play(), [('C', 'C'),\n ('C', 'D'),\n ('C', 'D'),\n ('C', 'D'),\n ('C', 'D')])\n\n # Test that behaviour does not depend on opponent\n opponent = axelrod.Defector()\n player = axelrod.KnowledgeableWorseAndWorse()\n axelrod.seed(1)\n match = axelrod.Match((opponent, player), turns=5)\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'D'),\n ('D', 'D'),\n ('D', 'D'),\n ('D', 'D')])\n\n # Test that behaviour changes when does not know length.\n axelrod.seed(1)\n match = axelrod.Match((opponent, player), turns=5,\n match_attributes={'length': float('inf')})\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C')])","def is_equivalence(self) -> bool:","def test_solve(game, optimal):\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_hospital, hospital in zip(matching_keys, hospitals):\n assert game_hospital.name == hospital.name\n assert game_hospital._pref_names == hospital._pref_names\n assert game_hospital.capacity == hospital.capacity\n\n matched_residents = [\n resident for match in matching.values() for resident in match\n ]\n\n assert matched_residents != [] and set(matched_residents).issubset(\n set(game.residents)\n )\n\n for resident in set(game.residents) - set(matched_residents):\n assert resident.matching is None","def test_strategy(self):\n\n axelrod.seed(8)\n opponent = axelrod.Cooperator()\n player = axelrod.WorseAndWorse()\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'D'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C')])\n\n # Test that behaviour does not depend on opponent\n opponent = axelrod.Defector()\n player = axelrod.WorseAndWorse()\n axelrod.seed(8)\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'D'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C')])","def matches_p1(info: Mapping[str, int],\n test_result: Mapping[str, int]) -> bool:\n for key, value in info.items():\n if test_result[key] != value:\n return False\n return True","def areResonancesProchiral(resonanceA, resonanceB):\n\n if resonanceA.resonanceSet:\n if resonanceB.resonanceSet:\n atomA = resonanceA.resonanceSet.findFirstAtomSet().findFirstAtom()\n atomB = resonanceB.resonanceSet.findFirstAtomSet().findFirstAtom()\n \n residueA = atomA.residue\n residueB = atomB.residue\n \n if residueA is residueB:\n chemAtomSetA = atomA.chemAtom.chemAtomSet\n chemAtomSetB = atomB.chemAtom.chemAtomSet\n\n if chemAtomSetA and (chemAtomSetA is chemAtomSetB):\n return True\n \n return False","def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_suitor_prefs()\n\n match.suitors[0].pref_names = [1, 2, 3]\n\n with pytest.raises(Exception):\n match._check_suitor_prefs()","def testProtractedNSESanityChecks(self):\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))","def testSameRMSEWhenPairsAreSpecifiedOrNotDifferentGeoOrder(self):\n test_class1 = TrimmedMatchGeoXDesign(\n GeoXType.HEAVY_UP,\n pretest_data=self.nontrivial_data,\n time_window_for_design=self.design_window,\n time_window_for_eval=self.evaluation_window,\n matching_metrics={'response': 1.0},\n pairs=None)\n\n for geox_type in GeoXType:\n if geox_type == GeoXType.CONTROL:\n continue\n\n test_class1._geox_type = geox_type\n _, expected_detailed_results = test_class1.report_candidate_designs(\n budget_list=[30, 40],\n iroas_list=[0, 2],\n use_cross_validation=True,\n num_simulations=10)\n\n # change the order of geo1 and geo2 in some of the pairs\n pairs = [x.copy() for x in test_class1.pairs]\n pairs[0].loc[0:3, 'geo1'] = test_class1.pairs[0].loc[0:3, 'geo2']\n pairs[0].loc[0:3, 'geo2'] = test_class1.pairs[0].loc[0:3, 'geo1']\n\n test_class2 = TrimmedMatchGeoXDesign(\n geox_type,\n pretest_data=self.nontrivial_data,\n time_window_for_design=self.design_window,\n time_window_for_eval=self.evaluation_window,\n matching_metrics={'response': 1.0},\n pairs=pairs)\n _, detailed_results = test_class2.report_candidate_designs(\n budget_list=[30, 40],\n iroas_list=[0, 2],\n use_cross_validation=True,\n num_simulations=10)\n\n for key in detailed_results.keys():\n self.assertTrue(\n np.array_equal(detailed_results[key]['estimate'],\n expected_detailed_results[key]['estimate']))","def test_suitor_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.suitors[0].matching = Player(name=\"foo\", pref_names=[])\n\n with pytest.raises(Exception):\n match._check_suitor_matching()","def calculate_exact_match(pred_dict, ref_dict):\n num, em = 0, 0\n for key in pred_dict.keys():\n num += 1\n if ' '.join(pred_dict[key]).strip() == ' '.join(ref_dict[key]).strip():\n em += 1\n return em / num * 100","def test_results_prefit() -> None:\n X_train_val, X_test, y_train_val, y_test = train_test_split(\n X, y, test_size=1/10, random_state=1\n )\n X_train, X_val, y_train, y_val = train_test_split(\n X_train_val, y_train_val, test_size=1/9, random_state=1\n )\n estimator = LinearRegression().fit(X_train, y_train)\n mapie = MapieRegressor(estimator=estimator, cv=\"prefit\")\n mapie.fit(X_val, y_val)\n _, y_pis = mapie.predict(X_test, alpha=0.05)\n width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()\n coverage = regression_coverage_score(\n y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]\n )\n np.testing.assert_allclose(width_mean, WIDTHS[\"prefit\"], rtol=1e-2)\n np.testing.assert_allclose(coverage, COVERAGES[\"prefit\"], rtol=1e-2)","def compare_results(self):\n return self.guess_number == self.secret_number","def perform_re_qualify(responder, options):\n match = options['<match-id>']\n tla = options['<tla>']\n scores.re_qualify(match, tla)\n responder('Re-qualified {0} in match {1}'.format(tla, match))","def test_compare_genomes_2(self):\n self.pmr_gnm.annotation_status = \"final\"\n self.pmr_gnm.name = \"Trixie\"\n import_genome.compare_genomes(self.genome_pair, self.eval_flags)\n count = count_status(self.genome_pair, \"error\", \"warning\")\n with self.subTest():\n self.assertEqual(len(self.genome_pair.evaluations), 13)\n with self.subTest():\n self.assertEqual(count, 0)","def test_equal(self):\n\n candidate1 = pyrankvote.Candidate(\"Per\")\n candidate2 = pyrankvote.Candidate(\"Per\")\n candidate3 = pyrankvote.Candidate(\"Aase\")\n\n self.assertEqual(candidate1, candidate2, \"These candidates should be equal/the same candidate.\")\n self.assertNotEqual(candidate1, candidate3, \"These candidates should NOT be equal/the same candidate.\")","def test_student_optimal(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n np.random.seed(seed)\n students, projects, _ = make_players(\n student_names, project_names, supervisor_names, capacities\n )\n matching = student_optimal(students, projects)\n\n assert set(projects) == set(matching.keys())\n assert all(\n [\n s in set(students)\n for s in {\n match for matches in matching.values() for match in matches\n }\n ]\n )\n\n for student in students:\n if student.matching:\n assert student.prefs.index(student.matching) == 0","def test_init(resident_names, hospital_names, capacities, seed):\n\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n assert match.suitors == residents\n assert match.reviewers == hospitals\n assert all([suitor.matching is None for suitor in match.suitors])\n assert all([reviewer.matching == [] for reviewer in match.reviewers])\n assert match.matching is None","def testRichnessMethodsMatch(self):\n self.assertEqual(\n self.tree.get_species_richness(reference=1),\n self.tree.get_species_richness(reference=1),\n msg=\"Landscape richness is not as expected.\",\n )\n self.assertEqual(\n self.tree.get_species_richness(reference=3),\n self.tree.get_species_richness(reference=3),\n msg=\"Landscape richness is not as expected.\",\n )\n self.assertEqual(\n self.tree.get_species_richness(reference=100),\n self.tree.get_species_richness(reference=100),\n msg=\"Landscape richness is not as expected.\",\n )\n self.assertEqual(\n self.tree.get_species_richness(reference=101),\n self.tree.get_species_richness(reference=101),\n msg=\"Landscape richness is not as expected.\",\n )","def analyze_similarities():\r\n print('Total number of candidate pairs:', len(pairs))\r\n print(f'\\nNumber of actual item pairs in the train set: {pairs[\"true_match\"].sum()}\\n')\r\n\r\n for feature in ['text_score', 'image_score', 'txt_img_score', 'words_ratio', 'txt_img_words']:\r\n\r\n # Check distribution of True and False predictions for various similarity scores\r\n print('-' * 50)\r\n print(f'\\nDistribution of True/False predictions for {feature}')\r\n for thr in (0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95):\r\n print('-' * 50)\r\n print(f'Similarity score over {thr}')\r\n pairs_sample = pairs[pairs[feature] >= thr]\r\n print(f'Number of similar item pairs: {len(pairs_sample)}')\r\n print(pairs_sample['true_match'].value_counts(normalize=True))\r\n\r\n # Check if identical phash can be used to improve the accuracy\r\n same_phash = pairs[pairs['phash_match'] == 1]\r\n different_phash = pairs[pairs['phash_match'] == 0]\r\n\r\n print('\\nFor item pairs with the same phash:')\r\n print(same_phash['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(same_phash))\r\n\r\n print('\\nFor item pairs with different phash:')\r\n print(different_phash['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(different_phash))\r\n\r\n # Check if numbers in titles can be used to improve the accuracy\r\n same_numbers = pairs[pairs['nums_match'] == 1]\r\n different_numbers = pairs[pairs['nums_match'] == 0]\r\n\r\n print('\\nFor item pairs with the same numbers:')\r\n print(same_numbers['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(same_numbers))\r\n\r\n print('\\nFor item pairs with different numbers:')\r\n print(different_numbers['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(different_numbers))","def testFindCorrespondence(self):\n # Create some dummy keypoints and descriptors to match. Make the descriptors really far apart to be sure.\n keypoints1 = []\n descriptors1 = numpy.zeros(shape=(3, 1))\n keypoint = cv2.KeyPoint()\n for i in range(3):\n keypoint.pt = (float(i), 0.0)\n keypoints1.append(keypoint)\n descriptors1[i] = i*100.0\n keypoints2 = []\n descriptors2 = numpy.zeros(shape=(5, 1))\n for i in range(5):\n keypoint.pt = (0.0, float(i))\n keypoints2.append(keypoint)\n descriptors2[i] = i*105.0\n (first_points, second_points) = self.evaluator._findCorrespondence(\n keypoints1, descriptors1, keypoints2, descriptors2)\n expected_first = numpy.array([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])\n expected_second = numpy.array([[0.0, 0.0], [0.0, 1.0], [0.0, 2.0]])\n self.assertTrue(numpy.array_equal(first_points, expected_first))\n self.assertTrue(numpy.array_equal(second_points, expected_second))","def matches(self, feature):\n pass","def match(self, target, guess):\r\n return guess == target","def match(self, proof: dict) -> bool:\n return proof.get(\"proofPurpose\") == self.term","def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"hospital\")\n\n assert set(hospitals) == set(matching.keys())\n\n for hospital, matches in matching.items():\n old_idx = -np.infty\n for resident in matches:\n idx = hospital.pref_names.index(resident.name)\n assert idx >= old_idx\n old_idx = idx","def test_affect_of_strategy(self):\n self.responses_test([C, C, C], [C, C, C], [C, C, C])\n # Make sure that the retaliations are increasing\n # Retaliate once and forgive\n self.responses_test([C], [D], [D])\n self.responses_test([C, D], [D, C], [C])\n self.responses_test([C, D, C], [D, C, C], [C])\n # Retaliate twice and forgive\n self.responses_test([C, D, C], [D, C, D], [D, D])\n self.responses_test([C, D, C, D, D], [D, C, D, C, C], [C])\n # Opponent defection during retaliation doesn't increase retaliation period\n self.responses_test([C, D, C, D, D], [D, C, D, D, C], [C])\n # Retaliate thrice and forgive\n self.responses_test([C, D, C, D, D, C], [D, C, D, C, C, D], [D, D, D])\n history_1 = [C, D, C, D, D, C, D, D, D]\n history_2 = [D, C, D, C, C, D, C, C, C]\n self.responses_test(history_1, history_2, [C])","def test_perceive_residues_sorting(self, strict_chirality):\n molecule = Molecule.from_file(get_data_file_path(\"proteins/ace-a10-nme.sdf\"))\n\n molecule.perceive_residues(strict_chirality=strict_chirality)\n\n for index, residue in enumerate(molecule.residues):\n found = residue.residue_number\n expected = str(index + 1)\n\n assert isinstance(found, str)\n assert found == expected","def matches(self):\n pass","def test_result_n(self):\r\n saber = self.saber\r\n movies = self.movies\r\n res = nearest_n(movies, saber, PRED).drop(\"genres\", axis=1)\r\n intersection = res.merge(self.training, how=\"left\", on=\"movieId\").dropna()\r\n self.assertTrue(not intersection.empty)","def ruleMatch (ruleSet, node, parents):\n def getProb (node):\n for rule in ruleSet:\n #print rule\n if (rule.child.eq(node) and\n len(rule.parentList)==len(parents) and\n all([n[0].eq(n[1]) for n in zip(rule.parentList,parents)])):\n #print \"winning eq\", [n for n in zip(rule.parentList,parents)]\n return rule.prob\n else:\n return -1\n \n prob = getProb (node)\n if prob == -1 and functorRangeSize(node.functor) == 2:\n tn = copy.copy(node)\n tn.val = functorOtherValue(tn.functor, tn.val)\n prob = getProb (tn)\n if prob != -1:\n return 1 - prob\n else:\n return prob\n return prob","def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):\n overlaps = jaccard(truths, point_form(priors))\n best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)\n valid_gt_idx = best_prior_overlap[:, 0] >= 0.2\n best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]\n if best_prior_idx_filter.shape[0] <= 0:\n loc_t[idx] = 0\n conf_t[idx] = 0\n return\n best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)\n best_truth_idx.squeeze_(0)\n best_truth_overlap.squeeze_(0)\n best_prior_idx.squeeze_(1)\n best_prior_idx_filter.squeeze_(1)\n best_prior_overlap.squeeze_(1)\n best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)\n for j in range(best_prior_idx.size(0)):\n best_truth_idx[best_prior_idx[j]] = j\n matches = truths[best_truth_idx]\n conf = labels[best_truth_idx]\n conf[best_truth_overlap < threshold] = 0\n loc = encode(matches, priors, variances)\n matches_landm = landms[best_truth_idx]\n landm = encode_landm(matches_landm, priors, variances)\n loc_t[idx] = loc\n conf_t[idx] = conf\n landm_t[idx] = landm","def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):\n overlaps = jaccard(truths, point_form(priors))\n best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)\n valid_gt_idx = best_prior_overlap[:, 0] >= 0.2\n best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]\n if best_prior_idx_filter.shape[0] <= 0:\n loc_t[idx] = 0\n conf_t[idx] = 0\n return\n best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)\n best_truth_idx.squeeze_(0)\n best_truth_overlap.squeeze_(0)\n best_prior_idx.squeeze_(1)\n best_prior_idx_filter.squeeze_(1)\n best_prior_overlap.squeeze_(1)\n best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)\n for j in range(best_prior_idx.size(0)):\n best_truth_idx[best_prior_idx[j]] = j\n matches = truths[best_truth_idx]\n conf = labels[best_truth_idx]\n conf[best_truth_overlap < threshold] = 0\n loc = encode(matches, priors, variances)\n matches_landm = landms[best_truth_idx]\n landm = encode_landm(matches_landm, priors, variances)\n loc_t[idx] = loc\n conf_t[idx] = conf\n landm_t[idx] = landm","def test_check_stability():\n\n students = [Student(\"A\"), Student(\"B\"), Student(\"C\")]\n projects = [Project(\"P\", 2), Project(\"Q\", 2)]\n supervisors = [Supervisor(\"X\", 2), Supervisor(\"Y\", 2)]\n\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\n\n p.set_supervisor(x)\n q.set_supervisor(y)\n\n a.set_prefs([p, q])\n b.set_prefs([q])\n c.set_prefs([q, p])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = StudentAllocation(students, projects, supervisors)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (p, q) = game.students, game.projects\n\n matching[p] = [c]\n matching[q] = [a, b]\n\n assert not game.check_stability()","def evaluate_candidate(gene):\n def get_score(output):\n if output == None:\n output = 0\n s = 0;\n if output % 3 == 0:\n s += 1\n if output % 7 == 0:\n s += 1\n if output == 21:\n s += 1 \n return s\n \n out1,out2 = gene.run(3,7)\n \n s1 = get_score(out1)\n s2 = get_score(out2)\n \n if out1 == out2 == None: # or out1 == out2 == 0:\n s1 = 0\n s2 = 0\n \n gene.score = s1 if s1 <= s2 else s2\n \n return gene.score","def test_get_matches(self):\n\n results = GenomePropertiesResultsWithMatches(*self.test_genome_property_results, properties_tree=self.test_tree)\n\n self.assertEqual(len(\n results.step_matches.reset_index()[['Property_Identifier', 'Step_Number']].drop_duplicates()), 1)\n self.assertEqual(len(results.get_sample_matches('C_chlorochromatii_CaD3')), 5)\n self.assertEqual(len(results.get_sample_matches('C_luteolum_DSM_273')), 4)\n\n self.assertEqual(results.get_sample_matches('Your moms house'), None)","def test(self, songs, genres):\n logging.info('Starting testing.')\n num_matches = 0\n confusion_matrix = ConfusionMatrix(genres)\n for song, actual_genre in zip(songs, genres):\n predicted_genre = self.classify(song)\n logging.info('Actual genre: {}, predicted genre: {}'.format(actual_genre, predicted_genre))\n confusion_matrix.add_genres(actual_genre, predicted_genre)\n if actual_genre == predicted_genre:\n num_matches += 1\n return num_matches, confusion_matrix","def test_boolean_and_selection(self):\n\n # The selection loop:\n sel = list(mol_res_spin.residue_loop(\"#Ap4Aase:4 & :Pro\"))\n\n # Test:\n self.assertEqual(len(sel), 1)\n for res in sel:\n self.assert_(res.name == \"Pro\" and res.num == 4)","def test_relevance_commutative_single_query_row():\n view = gen_view_cgpm(get_data_separated)\n rp_view_0 = view.relevance_probability(3, [8], 1)\n rp_view_1 = view.relevance_probability(8, [3], 1)\n assert np.allclose(rp_view_0, rp_view_1)\n\n state = gen_state_cgpm(get_data_separated)\n rp_state_0 = state.relevance_probability(3, [8], 1)\n rp_state_1 = state.relevance_probability(8, [3], 1)\n assert np.allclose(rp_state_0, rp_state_1)","def dupable_matches_required(self):\n return 2","def exact_match(hypothesis, reference):\n hypothesis_list, reference_list = _generate_enums(hypothesis, reference)\n return _match_enums(hypothesis_list, reference_list)","def test_reciprocal_operations(ph_zr3n4: Phonopy):\n ptg = ph_zr3n4.symmetry.pointgroup_operations\n rops = ph_zr3n4.symmetry.reciprocal_operations\n matches = []\n for r in ptg:\n for i, rec_r in enumerate(rops):\n if (r == rec_r).all():\n matches.append(i)\n break\n assert len(np.unique(matches)) == len(ptg)\n found_inv = False\n for rec_r in rops:\n if (rec_r == -np.eye(3, dtype=int)).all():\n found_inv = True\n break\n assert found_inv","def test_match_correct(self, event: Event, pr_algorithm, minimal_state: State):\n assert pr_algorithm.match(event, minimal_state)","def test_lr(self):\n\n probas = self.lr_classifier.classify_doc(self.feat)\n rprobas = {\n 'category': 'sport',\n 'probas': {\n 'sport': 0.95130333681813339,\n 'non-sport': 0.048696663181866606\n }\n }\n\n self.assertEqual(rprobas['category'], probas['category'])\n\n for field, value in rprobas['probas'].items():\n self.assertIn(field, probas['probas'])\n self.assertAlmostEqual(value, probas['probas'][field], places=7)","def test_parsimony(get_test_cases):\n for case in get_test_cases:\n trivial_splits = list(all_splits(case['tree'].get_num_taxa(), True, True))\n scores = [case['tree'].parsimony_score(s) for s in trivial_splits]\n corr_triv_scores = all([s == 1 for s in scores])\n pars_tests = case['pars_tests']\n corr_other_scores = all(case['tree'].parsimony_score(test[0]) == test[1] for test in pars_tests.items())\n assert corr_other_scores and corr_triv_scores","def get_correctness_for_response(self, response):\n for answer in self.my_osid_object.get_answers():\n if self._is_match(response, answer):\n try:\n return answer.get_score()\n except AttributeError:\n return 100\n for answer in self.my_osid_object.get_wrong_answers():\n if self._is_match(response, answer):\n try:\n return answer.get_score()\n except AttributeError:\n return 0","def test_compare_genomes_3(self):\n self.eval_flags = {\"check_replace\": False}\n import_genome.compare_genomes(self.genome_pair, self.eval_flags)\n count = count_status(self.genome_pair, \"error\", \"warning\")\n with self.subTest():\n self.assertEqual(len(self.genome_pair.evaluations), 9)\n with self.subTest():\n self.assertEqual(count, 0)","def match(self) -> bool:","def test_possibilites(self):\n self.assertEqual(self.RNA(\"\").possibilities(), 1)\n self.assertEqual(self.RNA(\"ACGUgcaucagUCGuGCAU\").possibilities(), 1)\n self.assertEqual(self.RNA(\"N\").possibilities(), 4)\n self.assertEqual(self.RNA(\"R\").possibilities(), 2)\n self.assertEqual(self.RNA(\"H\").possibilities(), 3)\n self.assertEqual(self.RNA(\"nRh\").possibilities(), 24)\n self.assertEqual(\n self.RNA(\"AUGCnGUCAg-aurGauc--gauhcgauacgws\").possibilities(), 96\n )","def test_relevance_with_itself():\n state = gen_state_cgpm(get_data_separated)\n assert np.allclose(state.relevance_probability(2, [2], 1), 1.0)","def testReportCandidateDesign(self):\n\n np.random.seed(0)\n new_data = pd.concat([self.test_data, self.add_pair], sort=False)\n test_class = TrimmedMatchGeoXDesign(\n GeoXType.HEAVY_UP,\n pretest_data=new_data,\n time_window_for_design=self.design_window,\n time_window_for_eval=self.evaluation_window,\n matching_metrics={'response': 1.0},\n )\n for geox_type in GeoXType:\n if geox_type == GeoXType.CONTROL:\n continue\n test_class._geox_type = geox_type\n (results, detailed_results) = test_class.report_candidate_designs(\n budget_list=[30, 40],\n iroas_list=[0, 0.002],\n use_cross_validation=True,\n num_simulations=10)\n\n expected_results = pd.DataFrame({\n 'num_pairs': [11, 10] * 4,\n 'experiment_response': [373, 364] * 4,\n 'experiment_spend': [191.5, 183] * 4,\n 'spend_response_ratio': [191.5 / 373, 183 / 364] * 4,\n 'budget': [30, 30, 40, 40, 30, 30, 40, 40],\n 'iroas': [0, 0, 0, 0, 0.002, 0.002, 0.002, 0.002],\n 'proportion_cost_in_experiment': [1, 183 / 191.5] * 4\n })\n\n self.assertTrue(results[[\n 'num_pairs', 'experiment_response', 'experiment_spend',\n 'spend_response_ratio', 'budget', 'iroas',\n 'proportion_cost_in_experiment'\n ]].equals(expected_results))\n for (_, iroas, _), value in detailed_results.items():\n self.assertAlmostEqual(\n value['estimate'].mean(), iroas, delta=0.05 + 0.1 * iroas)","def test_results_prefit_naive() -> None:\n estimator = LinearRegression().fit(X, y)\n mapie = MapieRegressor(estimator=estimator, cv=\"prefit\")\n mapie.fit(X, y)\n _, y_pis = mapie.predict(X, alpha=0.05)\n width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()\n coverage = regression_coverage_score(y, y_pis[:, 0, 0], y_pis[:, 1, 0])\n np.testing.assert_allclose(width_mean, WIDTHS[\"naive\"], rtol=1e-2)\n np.testing.assert_allclose(coverage, COVERAGES[\"naive\"], rtol=1e-2)","def test_compare_taxa_summaries_paired_pearson(self):\r\n exp = ('Taxon\\tS1\\tS2\\nArchaea\\t0.4\\t0.4\\nBacteria\\t0.5\\t'\r\n '0.7\\nEukarya\\t0.4\\t0.5\\n', 'Taxon\\tS1\\tS2\\nArchaea\\t0.5\\t'\r\n '0.6\\nBacteria\\t0.7\\t0.8\\nEukarya\\t0.5\\t0.6\\n',\r\n '# Correlation coefficient: pearson.\\n# The parametric p-value(s) '\r\n 'were calculated using a two-sided test of significance using a '\r\n 't-distribution.\\n# The nonparametric p-value(s) were calculated '\r\n 'using a two-sided permutation test with permutations.\\n# The '\r\n 'confidence interval(s) were constructed at a confidence level of '\r\n '.% using Fisher\\'s z-transformation (see Sokal and Rohlf rd '\r\n 'edition pg. ). The confidence interval(s) are two-sided.\\n# '\r\n 'Number of samples that matched between the taxa summary files: '\r\n '\\nCorrelation coefficient\\tParametric p-value\\tNonparametric '\r\n 'p-value\\tCI (lower)\\tCI (upper)\\n.\\t.\\t.\\t.\\t.\\n', None)\r\n\r\n obs = compare_taxa_summaries(self.taxa_summary_paired1,\r\n self.taxa_summary_paired2, 'paired', 'pearson')\r\n obs = (obs[0], obs[1], self.remove_nums(obs[2]),\r\n obs[3])\r\n self.assertEqual(obs, exp)","def match(self, other):","def test_can_match(self):\n assert self.RNA(\"\").can_match(\"\")\n assert self.RNA(\"UCAG\").can_match(\"UCAG\")\n assert not self.RNA(\"UCAG\").can_match(\"ucag\")\n assert self.RNA(\"UCAG\").can_match(\"NNNN\")\n assert self.RNA(\"NNNN\").can_match(\"UCAG\")\n assert self.RNA(\"NNNN\").can_match(\"NNNN\")\n assert not self.RNA(\"N\").can_match(\"x\")\n assert not self.RNA(\"N\").can_match(\"-\")\n assert self.RNA(\"UCAG\").can_match(\"YYRR\")\n assert self.RNA(\"UCAG\").can_match(\"KMWS\")","def computeCorrespondence(self):\n assert self.result and self.dst\n assert self.result.num == self.dst.num","def test_compare_answer(self):\r\n problem = self.build_problem(answer=\"42\")\r\n responder = problem.responders.values()[0]\r\n self.assertTrue(responder.compare_answer('48', '8*6'))\r\n self.assertFalse(responder.compare_answer('48', '9*5'))","def test_compare_confs(self):\n ch4_1 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\n 'isotopes': (12, 1, 1, 1, 1),\n 'coords': ((0.0, 0.0, 0.0),\n (0.6300326, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))}\n self.assertTrue(converter.compare_confs(ch4_1, ch4_1))\n self.assertEqual(converter.compare_confs(ch4_1, ch4_1, rmsd_score=True), 0.0)\n\n ch4_2 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\n 'isotopes': (12, 1, 1, 1, 1),\n 'coords': ((0.0, 0.0, 0.0),\n (0.630032999999999999, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))}\n self.assertTrue(converter.compare_confs(ch4_1, ch4_2))\n self.assertAlmostEqual(converter.compare_confs(ch4_1, ch4_2, rmsd_score=True), 0.0, places=4)\n\n ch4_3 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\n 'isotopes': (12, 1, 1, 1, 1),\n 'coords': ((0.0, 0.0, 0.0),\n (0.81, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))}\n self.assertFalse(converter.compare_confs(ch4_1, ch4_3))\n self.assertAlmostEqual(converter.compare_confs(ch4_1, ch4_3, rmsd_score=True), 0.0973755, 5)\n\n occco_1 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\n 'coords': ((-1.645138856907744, 0.4855258453193825, -1.269098295435587),\n (-1.826155038047953, 0.3950495294463964, 0.14042133077839267),\n (-1.809422000751755, 1.7890058616007063, 0.7598491740499427),\n (-0.5144047765537177, 2.5412406931938802, 0.46679697338621373),\n (-0.4166415293412751, 2.87702090326361, -0.9112962716273996),\n (-1.7523716337780308, -0.4153106947877299, -1.6252584016046343),\n (-2.785951955397608, -0.09251469392615361, 0.33828359656775064),\n (-1.0240452528998532, -0.22437445960667116, 0.5540970106165476),\n (-2.6481280678220354, 2.3642983699082096, 0.34958017984053996),\n (-1.948589699885299, 1.7026595551213293, 1.842711580226941),\n (0.3611954633790452, 1.9455154222553817, 0.7436960027091665),\n (-0.4846097814890448, 3.4746190180148613, 1.0370826136306412),\n (-0.7517118479102434, 2.0995465744609016, -1.4084474547843668))}\n self.assertTrue(converter.compare_confs(occco_1, occco_1))\n self.assertEqual(converter.compare_confs(occco_1, occco_1, rmsd_score=True), 0.0)\n\n occco_2 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\n 'coords': ((-1.7947360038254172, -0.2800502342395655, -0.94718691702442),\n (-1.3484923712374388, 0.3115024061252556, 0.26578718691263836),\n (0.048477626636679344, -0.18145884392561823, 0.6324014558703148),\n (0.06768533157823732, -1.6675011458530478, 0.9732242593946415),\n (-0.3530408338556874, -2.4414941005488964, -0.14539995931042984),\n (-1.6993365552401258, -1.24904809118412, -0.8336640895923935),\n (-1.3364382254722125, 1.3960688766201377, 0.12095890535651209),\n (-2.0720954664081472, 0.08255875951942339, 1.0547463625584224),\n (0.7166058894071794, -0.006871191098481536, -0.21939247767770087),\n (0.42416153066953804, 0.38997256785153595, 1.4878132135000073),\n (-0.6073710844027356, -1.8828156181698352, 1.8073934203380306),\n (1.0743805139106757, -1.9882575918786236, 1.2595102280098387),\n (0.35195568839394714, -2.3791987519096245, -0.81652943836054))}\n self.assertFalse(converter.compare_confs(occco_1, occco_2))\n self.assertAlmostEqual(converter.compare_confs(occco_1, occco_2, rmsd_score=True), 1.00940798, 5)\n\n occco_3 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\n 'coords': ((-2.1449415573976087, 0.22095421320805855, -1.1761667487081628),\n (-1.8458576972054956, 0.34850267283416353, 0.21018774322610537),\n (-1.7946333596155588, 1.8224048429109774, 0.6001541290065803),\n (-0.7643941488427433, 2.603963636650936, -0.21014179435545444),\n (-1.160550573561837, 2.7172772321690157, -1.5709018700345496),\n (-2.2541573481252226, -0.7309807530987936, -1.3543442072197793),\n (-2.624019853257233, -0.1650323593077789, 0.7836186270202112),\n (-0.885183881101036, -0.13905038811734782, 0.4040751763466029),\n (-2.7832838991835382, 2.2645645737400506, 0.427600829327477),\n (-1.562954529947271, 1.9038127448704014, 1.6674459390753227),\n (0.21794991835079414, 2.1225495495404854, -0.17169963700580776),\n (-0.6621011473071822, 3.6174144955483376, 0.18956005497753062),\n (-1.544021016988015, 1.848253867499642, -1.8191893347265315))}\n self.assertTrue(converter.compare_confs(occco_1, occco_3))\n self.assertAlmostEqual(converter.compare_confs(occco_1, occco_3, rmsd_score=True), 0.0, places=4)\n\n occco_4 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\n 'coords': ((-2.149273800177333, 0.3878561319363274, -1.067994536622897),\n (-1.8905197406541119, 0.16731522843849514, 0.3147948930907352),\n (-2.2871289950009728, 1.398560959638718, 1.1233863494505283),\n (-1.5514369216953363, 2.6559008371480006, 0.6687883285931627),\n (-1.97578532983754, 3.0584350426221696, -0.6271056229665328),\n (-1.9649535743321451, -0.45216201833697967, -1.5265252922322194),\n (-2.469954748031858, -0.7010050003658949, 0.6439649209501082),\n (-0.8252433635746496, -0.052453567250684806, 0.43760424203717113),\n (-3.36502756891988, 1.5624906595612045, 1.0051865026931144),\n (-2.0814509723064316, 1.2139856682154606, 2.18312358675434),\n (-0.4688204416342118, 2.495542838358611, 0.6472342384223745),\n (-1.759053255113454, 3.4819317812208626, 1.3557210162758644),\n (-2.0785703072969466, 2.2346795710060765, -1.151280970188824))}\n self.assertTrue(converter.compare_confs(occco_1, occco_4))\n self.assertAlmostEqual(converter.compare_confs(occco_1, occco_4, rmsd_score=True), 0.0, places=4)\n\n occco_5 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\n 'coords': (\n (-2.4583263702786398, 0.0718285271168676, -1.073208642860504),\n (-1.937015981064579, 0.0890930305098165, 0.25179580507902494),\n (-2.348398726927575, 1.376978495961958, 0.9583384372437844),\n (-1.8770165015484177, 2.6245423907540975, 0.21659228399276115),\n (-2.569162634647042, 2.781228761671768, -1.015409941988472),\n (-2.2457141392131477, -0.8022675472463131, -1.4481857725877),\n (-2.3295554159352525, -0.7779156570908263, 0.792431073403323),\n (-0.8469088168553344, 0.006439213644634347, 0.19675913068823528),\n (-3.4425715658912557, 1.4040720104173179, 1.0273387203822888),\n (-1.939605978646692, 1.375711847707625, 1.9743606178959265),\n (-0.8029097001610058, 2.5806396078360736, 0.010183430117069694),\n (-2.07095189274604, 3.516416462694289, 0.8201309204066712),\n (-2.650256378769789, 1.8823222470150054, -1.4017891959903757))}\n self.assertTrue(converter.compare_confs(occco_1, occco_5))\n self.assertAlmostEqual(converter.compare_confs(occco_1, occco_5, rmsd_score=True), 0.0, places=4)","def test_compare_genomes_1(self):\n import_genome.compare_genomes(self.genome_pair, self.eval_flags)\n count = count_status(self.genome_pair, \"error\", \"warning\")\n with self.subTest():\n self.assertEqual(len(self.genome_pair.evaluations), 12)\n with self.subTest():\n self.assertEqual(count, 0)","def test_matching_tracks(self):\n\n # 5037: Pop 101 (feat. Anami Vice) by Marianas Trench\n # 8755 : Satisfied (feat. Miguel & Queen Latifah) by Sia\n # 6699 : Un Besito Mas (feat. Juan Luis Guerra) by Jesse & Joy\n targets = {5037: '2fGFaTDbE8aS4f31fM0XE4',\n 8755: '1ybJ2itxCxPCPkcA9sOgTO',\n 6699: '1182pxG4uNxr3QqIH8b8k0',\n }\n\n matches = {track.i_id: track.id\n for track in self.tracks\n if track.i_id in targets}\n\n for i_id, s_id in targets.iteritems():\n self.assertEqual(s_id, matches[i_id])","def test_residential_to_secondary():\n assert query_row(db_conf, 'osm_roads', 40001)['type'] == 'residential'\n assert not query_row(db_conf, 'osm_roads_gen0', 40001)\n assert not query_row(db_conf, 'osm_roads_gen1', 40001)","def testGetsPresentRichness(self):\n self.assertEqual(37, self.coal.get_species_richness(1))\n self.assertEqual(53, self.coal.get_species_richness(3))","def test_eq(self):\n r1 = Resources(4, 2, {\"Hadamard\": 1, \"CNOT\": 1}, {1: 1, 2: 1}, 2, Shots(100))\n r2 = Resources(4, 2, {\"Hadamard\": 1, \"CNOT\": 1}, {1: 1, 2: 1}, 2, Shots(100))\n r3 = Resources(4, 2, {\"CNOT\": 1, \"Hadamard\": 1}, {2: 1, 1: 1}, 2, Shots(100)) # all equal\n\n r4 = Resources(1, 2, {\"Hadamard\": 1, \"CNOT\": 1}, {1: 1, 2: 1}, 2, Shots(100)) # diff wires\n r5 = Resources(\n 4, 1, {\"Hadamard\": 1, \"CNOT\": 1}, {1: 1, 2: 1}, 2, Shots(100)\n ) # diff num_gates\n r6 = Resources(4, 2, {\"CNOT\": 1}, {1: 1, 2: 1}, 2, Shots(100)) # diff gate_types\n r7 = Resources(\n 4, 2, {\"Hadamard\": 1, \"CNOT\": 1}, {1: 3, 2: 2}, 2, Shots(100)\n ) # diff gate_sizes\n r8 = Resources(4, 2, {\"Hadamard\": 1, \"CNOT\": 1}, {1: 1, 2: 1}, 1, Shots(100)) # diff depth\n r9 = Resources(\n 4, 2, {\"Hadamard\": 1, \"CNOT\": 1}, {1: 1, 2: 1}, 2, Shots((10, 10))\n ) # diff shots\n\n assert r1.__eq__(r1)\n assert r1.__eq__(r2)\n assert r1.__eq__(r3)\n\n assert not r1.__eq__(r4)\n assert not r1.__eq__(r5)\n assert not r1.__eq__(r6)\n assert not r1.__eq__(r7)\n assert not r1.__eq__(r8)\n assert not r1.__eq__(r9)","def test_reviewer_capacity(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching = range(match.reviewers[0].capacity + 1)\n\n with pytest.raises(Exception):\n match._check_reviewer_capacity()","def matches(self, tgt_residence_dir: str) -> bool:","def findMatches3(personDict,matches,skepticalMatches,additionalMatches,personDict2):\n dictConsidered = personDict['ALIAS']\n for alias in dictConsidered:\n if alias == \"\":\n continue\n pairs = itertools.combinations(dictConsidered[alias],2)\n for p in pairs:\n k = tuple(sorted(p))\n if (k not in matches) and (k not in skepticalMatches) and (k not in additionalMatches):\n info1 = personDict['EnterpriseID'][p[0]]\n info2 = personDict['EnterpriseID'][p[1]]\n \n info1b = personDict2['EnterpriseID'][p[0]]\n info2b = personDict2['EnterpriseID'][p[1]]\n score = getScorePair(info1b,info2b)\n if score>=7:\n additionalMatches[k] = score\n\n return additionalMatches","def has_matches(self):\n for rank, rank_count in self._rank_counts.items():\n if rank_count > 1:\n return True\n return False","def test_fuzzy_matching_basic(nlp: Language, countries: List[Dict[str, Any]]) -> None:\n ruler = SpaczzRuler(nlp)\n ruler.add_patterns(countries)\n doc = nlp(\"This is a test that should find Egypt and Argentina\")\n doc = ruler(doc)\n matches = [(ent.ent_id_, ent.text) for ent in doc.ents if ent.label_ == \"COUNTRY\"]\n assert matches == [(\"Egypt\", \"Egypt\"), (\"Argentina\", \"Argentina\")]","def partial_match_score(\n truth: List[Rationale], pred: List[Rationale], thresholds: List[float]\n) -> List[PartialMatchScore]:\n\n ann_to_rat = _keyed_rationale_from_list(truth)\n pred_to_rat = _keyed_rationale_from_list(pred)\n\n num_classifications = {k: len(v) for k, v in pred_to_rat.items()}\n num_truth = {k: len(v) for k, v in ann_to_rat.items()}\n ious: Dict[str, Dict[str, float]] = defaultdict(dict)\n for k in set(ann_to_rat.keys()) | set(pred_to_rat.keys()):\n for p in pred_to_rat.get(k, []):\n best_iou = 0.0\n for t in ann_to_rat.get(k, []):\n num = len(\n set(range(p.start_token, p.end_token))\n & set(range(t.start_token, t.end_token))\n )\n denom = len(\n set(range(p.start_token, p.end_token))\n | set(range(t.start_token, t.end_token))\n )\n iou = 0 if denom == 0 else num / denom\n if iou > best_iou:\n best_iou = iou\n ious[k][p] = best_iou\n\n scores: List[PartialMatchScore] = []\n for threshold in thresholds:\n threshold_tps: Dict[str, float] = {}\n for k, vs in ious.items():\n threshold_tps[k] = sum(int(x >= threshold) for x in vs.values())\n micro_r = (\n sum(threshold_tps.values()) / sum(num_truth.values())\n if sum(num_truth.values()) > 0\n else 0\n )\n micro_p = (\n sum(threshold_tps.values()) / sum(num_classifications.values())\n if sum(num_classifications.values()) > 0\n else 0\n )\n micro_f1 = _f1(micro_r, micro_p)\n macro_rs = list(\n threshold_tps.get(k, 0.0) / n if n > 0 else 0 for k, n in num_truth.items()\n )\n macro_ps = list(\n threshold_tps.get(k, 0.0) / n if n > 0 else 0\n for k, n in num_classifications.items()\n )\n macro_r = sum(macro_rs) / len(macro_rs) if len(macro_rs) > 0 else 0\n macro_p = sum(macro_ps) / len(macro_ps) if len(macro_ps) > 0 else 0\n macro_f1 = _f1(macro_r, macro_p)\n\n scores.append(\n PartialMatchScore(\n threshold=threshold,\n micro=InstanceScore(p=micro_p, r=micro_r, f1=micro_f1),\n macro=InstanceScore(p=macro_p, r=macro_r, f1=macro_f1),\n )\n )\n\n return scores","def precision_score_candidate_match(standard, selected_types, candidate_docs):\n\tscore = 0\n\tfor cdoc in candidate_docs:\n\t\tcounts, all_events = standard[cdoc]\n\n\t\tevent_match = True\n\t\tfor event_type in selected_types:\n\t\t\tif counts[EVENT_TYPES[event_type]] == 0:\n\t\t\t\tevent_match = False\n\n\t\tif event_match:\n\t\t\tscore += 1\n\n\tassert(score <= TOP_K), str(score)\n\treturn score","def matches_p2(info: Mapping[str, int],\n result: Mapping[str, int]) -> bool:\n for key, value in info.items():\n if key in ['cats', 'trees']:\n if value <= result[key]:\n return False\n elif key in ['pomeranians', 'goldfish']:\n if value >= result[key]:\n return False\n else:\n if result[key] != value:\n return False\n return True","def test_boolean_complex_selection(self):\n\n # The residue selection loop.\n sel = list(mol_res_spin.residue_loop(\"#Ap4Aase:4 & :Pro | #RNA\"))\n\n # Residue names and numbers.\n names = ['Pro', None, None]\n numbers = [4, -5, -4]\n\n # The residues.\n self.assertEqual(len(sel), 3)\n for i in range(3):\n self.assertEqual(sel[i].name, names[i])\n self.assertEqual(sel[i].num, numbers[i])","def test_compare_taxa_summaries_paired_sample_id_map(self):\r\n exp = ('Taxon\\tS1\\tS2\\nArchaea\\t0.4\\t0.4\\nBacteria\\t0.5\\t'\r\n '0.7\\nEukarya\\t0.4\\t0.5\\n', 'Taxon\\tE1\\tE2\\nArchaea\\t0.5\\t'\r\n '0.6\\nBacteria\\t0.7\\t0.8\\nEukarya\\t0.5\\t0.6\\n',\r\n '# Correlation coefficient: pearson.\\n# The parametric p-value(s) '\r\n 'were calculated using a two-sided test of significance using a '\r\n 't-distribution.\\n# The nonparametric p-value(s) were calculated '\r\n 'using a two-sided permutation test with permutations.\\n# The '\r\n 'confidence interval(s) were constructed at a confidence level of '\r\n '.% using Fisher\\'s z-transformation (see Sokal and Rohlf rd '\r\n 'edition pg. ). The confidence interval(s) are two-sided.\\n# '\r\n 'Number of samples that matched between the taxa summary files: '\r\n '\\nCorrelation coefficient\\tParametric p-value\\tNonparametric '\r\n 'p-value\\tCI (lower)\\tCI (upper)\\n.\\t.\\t.\\t.\\t.\\n', None)\r\n obs = compare_taxa_summaries(self.taxa_summary_paired1,\r\n self.taxa_summary_paired4, 'paired', 'pearson',\r\n perform_detailed_comparisons=False,\r\n sample_id_map=self.taxa_summary_paired_samp_id_map1)\r\n self.assertEqual(obs[0], exp[0])\r\n self.assertEqual(obs[1], exp[1])\r\n self.assertEqual(self.remove_nums(obs[2]), exp[2])","def evaluate_rouge_match(ref_data, pred_data, align=\"rouge\"):\n\n scorer = RougeScorer([\"rougeL\"])\n agg = scoring.BootstrapAggregator(confidence_interval=0.95)\n sample_evaluated = 0\n\n for tid in tqdm(pred_data):\n cur_sys = pred_data[tid][\"pred\"]\n if not tid in ref_data: continue\n cur_ref_lst = [x[\"tgt_words\"] for x in ref_data[tid]]\n if len(cur_ref_lst) > 0:\n if align == \"rouge\":\n best_rouge_scrs = __rouge_pairs(cur_sys, cur_ref_lst, scorer)\n agg.add_scores(best_rouge_scrs)\n else:\n best_paired_ref, _ = utils.pair_ref_sys_by_coverage(cur_sys, cur_ref_lst)\n agg.add_scores(scorer.score(best_paired_ref, cur_sys))\n sample_evaluated += 1\n\n print(sample_evaluated)\n result = agg.aggregate()\n for rouge_type in result:\n print(rouge_type)\n print(result[rouge_type].mid)\n\n print(agg.aggregate())\n return","def test_counts(contrib_data):\n ## Find the probands - they should always contribute their own alleles\n P = ped.Pedigree(args.pedfile)\n\n ## TODO: Test individuals with multiple offspring +p2 id:112\n ## Case when no regions are specified - only all probands\n regions = contrib_data.keys()\n if len(regions) == 1:\n all_contribs = contrib_data[regions[0]]\n for prob in P.probands:\n ## Probands should always contribute 1 allele\n assert all_contribs[prob, 1] == args.iterations\n\n ## Test specific individuals\n counts = Counter(all_contribs[11])\n binomial_sd = np.sqrt(args.iterations * 0.25)\n mean = np.mean(counts.values())\n conf95 = (mean - 2 * binomial_sd, mean + 2 * binomial_sd)\n\n for count in counts.values():\n assert conf95[0] < count < conf95[1]","def test_recommendation_evaluation_7(model, interactions_ds):\n assert recommendation_evaluation(model, interactions_ds[1], cn_test_users=None, k=2, n_pos_interactions=None,\n novelty=False, interaction_threshold=2) == \\\n {'HitRatio@2': 0.0069, 'NDCG@2': 0.0116, 'Precision@2': 0.0104, 'Recall@2': 0.0069}","def PredictMatch(yourName, tgtName, teamModels, db):\n\n teamTgtPipe, teamYourPipe = QueryTeamData(tgtName, yourName, db)\n featureCoefTgt, probTgt = PredictOp(teamTgtPipe, teamYourPipe, tgtName, teamModels)\n featureCoefYour, probYour = PredictOp(teamYourPipe, teamTgtPipe, yourName, teamModels)\n odds = round(probYour / (probYour + probTgt), 2)\n\n # In featureCoefYour, you want INCREASE those with POSTIVE COEF, DECREASE those with NEGATIVE COEF\n # In featureCoefTgt, you want to do the opposite\n\n # reverse both the sign of the coef, and '_op' in features so as to be the same with featureCoefYour\n featureCoefTgt['coef'] = - featureCoefTgt['coef']\n featureCoefTgt.features = [ii[:-3] if \"_op\" in ii else ii + '_op' for ii in featureCoefTgt.features]\n\n # Combine only the most important 10 features\n # featureBoth = featureCoefTgt[11:].append(featureCoefYour[11:])\n\n # Combine only all the most important features\n featureBoth = featureCoefTgt.append(featureCoefYour)\n\n # get action recommendations\n # Somehow the pandas here uses a deprecated para cols, instaed of the new one subset\n #featureBoth.drop_duplicates(subset = 'features', take_last = True, inplace = True)\n featureBoth.drop_duplicates(cols = 'features', take_last = True, inplace = True)\n actions, featureImprove = GetActions(featureBoth)\n Imp = 0.1\n oddsNew = ImprovedScore(tgtName, yourName, teamModels, featureImprove, teamTgtPipe, teamYourPipe, Imp)\n\n return odds, oddsNew, actions","def reportMatch(winner, loser):\n conn = connect()\n cursor = conn.cursor()\n cursor.execute(\"INSERT INTO results VALUES (%s, %s, true)\",\n (winner, loser))\n cursor.execute(\"INSERT INTO results VALUES (%s, %s, false)\",\n (loser, winner))\n conn.commit()\n conn.close()","def check_matches(known, match): \n #array containing tuples of corrected spellings\n out_vals = []\n\n #clear screen\n os.system('clear')\n \n for k,v in known.iteritems():\n matched_vals = [ik for ik, iv in match.iteritems() if iv == v]\n cur_matches = process.extract(k, matched_vals, limit = MATCH_LIMIT)\n \n #append a none option and option for actual val\n cur_matches.insert(0,(\"NONE\",0))\n cur_matches.append((k,100))\n\n\n #if there is a match of 100, skip\n if cur_matches[1][1] == 86 or cur_matches[1][1] < 50:\n #special case where probably means there really isn't a match\n choice = 'NONE'\n print 'No match!'\n\n elif cur_matches[1][1] < 95:\n #print out options for each word\n print_options(k, cur_matches)\n\n #cycle through prompt for input if valid (numeric, within MATCH_LIMIT)\n choice = None\n while choice == None:\n try:\n choice = cur_matches[get_choice()][0]\n except:\n print '!Invalid match! Try again'\n print_options(k, cur_matches)\n \n print choice\n\n else:\n #exact match\n choice = cur_matches[1][0]\n print '%s - Match!'%choice\n\n print \n print\n #create a new tuple with correct and incorrect spelling\n out_vals.append((k, choice))\n output(out_vals)\n\n #we're done, return vals array\n return out_vals","def compute_matches(self):\n\t\tself.local_database[\"figure_number\"] = [0] * len(self.local_database[\"feature_vectors\"])\n\t\tForensics = wbForensicsHOG(Database=self.local_database)\n\t\tForensics.KDTree_pairs(leaf_size = len(self.local_database)+1)\n\t\tForensics.d_rank(pairs=Forensics.pairs, distances=Forensics.dists, ratios=Forensics.ratios)\n\n\t\tself.local_matches = Forensics.Dist_Rank","def test4(self):\n tgts = [ ('CCC(O)C(=O)O',\n ('CCC','OCC','OCC=O','OCCO','CCCC','OC=O','CC(O)C')),\n ]\n for smi,matches in tgts:\n m = Chem.MolFromSmiles(smi)\n fp1 = Chem.RDKFingerprint(m,2,7,9192,4,0)\n obs = fp1.GetOnBits()\n for match in matches:\n m2 = Chem.MolFromSmiles(match)\n fp2 = Chem.RDKFingerprint(m2,2,7,9192,4,0)\n v1,v2 = DataStructs.OnBitProjSimilarity(fp2,fp1)\n assert feq(v1,1.0000),'substruct %s not properly contained in %s'%(match,smi)","def _prediction_match(self, thermo, ref_values, eps=0.05):\n singlet_array = self._get_singlet_array(thermo)\n for cur_array, ref_array in zip(singlet_array, ref_values):\n for cur_val, ref_val in zip(cur_array, ref_array):\n if abs(cur_val - ref_val) > eps:\n return False\n return True","def test_check_validity(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n assert match.check_validity()","def test_check_inputs_resident_prefs_all_hospitals(game):\n\n resident = game.residents[0]\n resident.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert resident.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert resident.prefs == []","def test_perceive_residues_cyclic_peptide_chirality(self, strict_chirality):\n smiles = (\n \"[H]c1c(c(c(c(c1[H])[H])C([H])([H])[C@]2(C(=O)N([C@](C(=O)N([C@@](C(=O)N3[C@@](C(=O)N2[H])\"\n \"(C(C(C3([H])[H])([H])[H])([H])[H])[H])([H])C([H])([H])[H])[H])([H])C([H])([H])C4=C(N(c5c4c(c(c(c5[H])\"\n \"[H])[H])[H])[H])[H])[H])[H])[H])[H]\"\n )\n offmol = Molecule.from_smiles(smiles)\n # perceive residues\n offmol.perceive_residues(strict_chirality=strict_chirality)\n counter = 0 # matched atom counter\n for atom in offmol.atoms:\n if atom.metadata:\n counter += 1\n if strict_chirality:\n # Make sure it fails if strict_chirality=True\n with pytest.raises(AssertionError):\n assert counter == offmol.n_atoms\n else:\n assert counter == offmol.n_atoms","def refuse_matches(self):\n return self._refuse_matches","def testSpeciesRichnessCorrect(self):\n self.assertEqual(136, self.coal.get_species_richness(1))\n self.assertNotEqual(self.coal.get_species_richness(2), self.coal.get_species_richness(1))\n self.assertNotEqual(self.coal.get_species_richness(3), self.coal.get_species_richness(2))"],"string":"[\n \"def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.reviewers[0].matching.append(Player(name=\\\"foo\\\", pref_names=[]))\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_matching()\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def test_resident_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"resident\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n assert all(\\n [\\n r in set(residents)\\n for r in set(\\n [\\n r_match\\n for matches in matching.values()\\n for r_match in matches\\n ]\\n )\\n ]\\n )\\n\\n for resident in residents:\\n if resident.matching:\\n assert resident.pref_names.index(resident.matching.name) == 0\",\n \"def test_check_stability():\\n\\n residents = [Player(\\\"A\\\", [1, 2]), Player(\\\"B\\\", [2]), Player(\\\"C\\\", [2, 1])]\\n hospitals = [Player(1, [\\\"C\\\", \\\"A\\\"], 3), Player(2, [\\\"A\\\", \\\"B\\\", \\\"C\\\"], 3)]\\n match = HospitalResident(residents, hospitals)\\n\\n matching = match.solve()\\n assert match.check_stability()\\n\\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\\n matching[P_1] = [P_C]\\n matching[P_2] = [P_A, P_B]\\n\\n assert not match.check_stability()\",\n \"def _check_resident_matching(self):\\n\\n errors = []\\n for resident in self.residents:\\n if (\\n resident.matching is not None\\n and resident.matching not in resident.prefs\\n ):\\n errors.append(\\n ValueError(\\n f\\\"{resident} is matched to {resident.matching} but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{resident.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_inputs_reviewer_prefs(\\n resident_names, hospital_names, capacities, seed\\n):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_reviewer_prefs()\\n\\n match.reviewers[0].pref_names.pop()\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_prefs()\",\n \"def test_prefilter_exact_matches(self):\\r\\n seqs = [('s1 comment1', 'ACCTTGTTACTTT'), # three copies\\r\\n ('s2 comment2', 'ACCTTGTTACTTTC'), # one copy\\r\\n ('s3 comment3', 'ACCTTGTTACTTTCC'), # two copies\\r\\n ('s4 comment4', 'ACCTTGTTACTTT'),\\r\\n ('s5 comment5', 'ACCTTGTTACTTTCC'),\\r\\n ('s6 comment6', 'ACCTTGTTACTTT')]\\r\\n expected0 = [('QiimeExactMatch.s1', 'ACCTTGTTACTTT'),\\r\\n ('QiimeExactMatch.s2', 'ACCTTGTTACTTTC'),\\r\\n ('QiimeExactMatch.s3', 'ACCTTGTTACTTTCC')]\\r\\n expected1 = {'QiimeExactMatch.s1': ['s1', 's4', 's6'],\\r\\n 'QiimeExactMatch.s2': ['s2'],\\r\\n 'QiimeExactMatch.s3': ['s3', 's5']}\\r\\n expected = (expected0, expected1)\\r\\n p = OtuPicker({})\\r\\n actual = p._prefilter_exact_matches(seqs)\\r\\n self.assertEqual(actual, expected)\",\n \"def test_solve(resident_names, hospital_names, capacities, seed):\\n\\n for optimal in [\\\"suitor\\\", \\\"resident\\\", \\\"reviewer\\\", \\\"hospital\\\"]:\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n matching = match.solve(optimal)\\n assert isinstance(matching, Matching)\\n assert set(matching.keys()) == set(hospitals)\\n matched_residents = [\\n res for match in matching.values() for res in match\\n ]\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(residents)\\n )\\n\\n for resident in set(residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def test_match(self):\\n self.assertEqual(solution( \\\"\\\"\\\"p=<-6,0,0>, v=< 3,0,0>, a=< 0,0,0>\\np=<-4,0,0>, v=< 2,0,0>, a=< 0,0,0>\\np=<-2,0,0>, v=< 1,0,0>, a=< 0,0,0>\\np=< 3,0,0>, v=<-1,0,0>, a=< 0,0,0>\\\"\\\"\\\"), 1)\",\n \"def exact_match_score(prediction, ground_truth):\\n return normalize_answer(prediction) == normalize_answer(ground_truth)\",\n \"def matchGenres(toPredictGenresString, toCompareGenresString):\\n\\n #Get the sets of genres\\n toPredictGenres = str(toPredictGenresString).split(\\\"|\\\")\\n toCompareGenres = str(toCompareGenresString).split(\\\"|\\\")\\n\\n toCompareGenresSet = set(toCompareGenres)\\n\\n commonCount = 0\\n\\n #Count how many are common to the two sets\\n for genre in toPredictGenres:\\n if genre in toCompareGenresSet:\\n commonCount += 1\\n\\n #Return 100 times the proportion in both\\n return 100 * commonCount/len(toPredictGenres)\",\n \"def testSameRMSEWhenPairsAreSpecifiedOrNot(self):\\n test_class1 = TrimmedMatchGeoXDesign(\\n GeoXType.HEAVY_UP,\\n pretest_data=self.nontrivial_data,\\n time_window_for_design=self.design_window,\\n time_window_for_eval=self.evaluation_window,\\n matching_metrics={'response': 1.0},\\n pairs=None)\\n\\n for geox_type in GeoXType:\\n if geox_type == GeoXType.CONTROL:\\n continue\\n\\n test_class1._geox_type = geox_type\\n _, expected_detailed_results = test_class1.report_candidate_designs(\\n budget_list=[30, 40],\\n iroas_list=[0, 2],\\n use_cross_validation=True,\\n num_simulations=10)\\n\\n test_class2 = TrimmedMatchGeoXDesign(\\n geox_type,\\n pretest_data=self.nontrivial_data,\\n time_window_for_design=self.design_window,\\n time_window_for_eval=self.evaluation_window,\\n matching_metrics={'response': 1.0},\\n pairs=test_class1.pairs)\\n _, detailed_results = test_class2.report_candidate_designs(\\n budget_list=[30, 40],\\n iroas_list=[0, 2],\\n use_cross_validation=True,\\n num_simulations=10)\\n\\n for key in detailed_results.keys():\\n self.assertTrue(\\n np.array_equal(detailed_results[key]['estimate'],\\n expected_detailed_results[key]['estimate']))\",\n \"def _exact_match_score_(prediction, ground_truth):\\n return _normalize_answer(prediction) == _normalize_answer(ground_truth)\",\n \"def test_fuzzy_matching_multi_match(\\n nlp: Language, countries: List[Dict[str, Any]]\\n) -> None:\\n ruler = SpaczzRuler(nlp, spaczz_fuzzy_defaults={\\\"min_r2\\\": 85})\\n ruler.add_patterns(countries)\\n doc = nlp(\\\"This is a test that should find Northern Ireland and Ireland\\\")\\n doc = ruler(doc)\\n matches = [(ent.ent_id_, ent.text) for ent in doc.ents if ent.label_ == \\\"COUNTRY\\\"]\\n assert matches == [(\\\"Northern Ireland\\\", \\\"Northern Ireland\\\"), (\\\"Ireland\\\", \\\"Ireland\\\")]\",\n \"def test_strategy(self):\\n axelrod.seed(1)\\n opponent = axelrod.Cooperator()\\n player = axelrod.KnowledgeableWorseAndWorse()\\n match = axelrod.Match((opponent, player), turns=5)\\n self.assertEqual(match.play(), [('C', 'C'),\\n ('C', 'D'),\\n ('C', 'D'),\\n ('C', 'D'),\\n ('C', 'D')])\\n\\n # Test that behaviour does not depend on opponent\\n opponent = axelrod.Defector()\\n player = axelrod.KnowledgeableWorseAndWorse()\\n axelrod.seed(1)\\n match = axelrod.Match((opponent, player), turns=5)\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'D'),\\n ('D', 'D'),\\n ('D', 'D'),\\n ('D', 'D')])\\n\\n # Test that behaviour changes when does not know length.\\n axelrod.seed(1)\\n match = axelrod.Match((opponent, player), turns=5,\\n match_attributes={'length': float('inf')})\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C')])\",\n \"def is_equivalence(self) -> bool:\",\n \"def test_solve(game, optimal):\\n\\n matching = game.solve(optimal)\\n assert isinstance(matching, MultipleMatching)\\n\\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\\n for game_hospital, hospital in zip(matching_keys, hospitals):\\n assert game_hospital.name == hospital.name\\n assert game_hospital._pref_names == hospital._pref_names\\n assert game_hospital.capacity == hospital.capacity\\n\\n matched_residents = [\\n resident for match in matching.values() for resident in match\\n ]\\n\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(game.residents)\\n )\\n\\n for resident in set(game.residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def test_strategy(self):\\n\\n axelrod.seed(8)\\n opponent = axelrod.Cooperator()\\n player = axelrod.WorseAndWorse()\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'D'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C')])\\n\\n # Test that behaviour does not depend on opponent\\n opponent = axelrod.Defector()\\n player = axelrod.WorseAndWorse()\\n axelrod.seed(8)\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'D'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C')])\",\n \"def matches_p1(info: Mapping[str, int],\\n test_result: Mapping[str, int]) -> bool:\\n for key, value in info.items():\\n if test_result[key] != value:\\n return False\\n return True\",\n \"def areResonancesProchiral(resonanceA, resonanceB):\\n\\n if resonanceA.resonanceSet:\\n if resonanceB.resonanceSet:\\n atomA = resonanceA.resonanceSet.findFirstAtomSet().findFirstAtom()\\n atomB = resonanceB.resonanceSet.findFirstAtomSet().findFirstAtom()\\n \\n residueA = atomA.residue\\n residueB = atomB.residue\\n \\n if residueA is residueB:\\n chemAtomSetA = atomA.chemAtom.chemAtomSet\\n chemAtomSetB = atomB.chemAtom.chemAtomSet\\n\\n if chemAtomSetA and (chemAtomSetA is chemAtomSetB):\\n return True\\n \\n return False\",\n \"def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_suitor_prefs()\\n\\n match.suitors[0].pref_names = [1, 2, 3]\\n\\n with pytest.raises(Exception):\\n match._check_suitor_prefs()\",\n \"def testProtractedNSESanityChecks(self):\\n self.assertGreater(self.c3.get_species_richness(1), self.c2.get_species_richness(1))\\n self.assertLess(self.c4.get_species_richness(1), self.c3.get_species_richness(1))\",\n \"def testSameRMSEWhenPairsAreSpecifiedOrNotDifferentGeoOrder(self):\\n test_class1 = TrimmedMatchGeoXDesign(\\n GeoXType.HEAVY_UP,\\n pretest_data=self.nontrivial_data,\\n time_window_for_design=self.design_window,\\n time_window_for_eval=self.evaluation_window,\\n matching_metrics={'response': 1.0},\\n pairs=None)\\n\\n for geox_type in GeoXType:\\n if geox_type == GeoXType.CONTROL:\\n continue\\n\\n test_class1._geox_type = geox_type\\n _, expected_detailed_results = test_class1.report_candidate_designs(\\n budget_list=[30, 40],\\n iroas_list=[0, 2],\\n use_cross_validation=True,\\n num_simulations=10)\\n\\n # change the order of geo1 and geo2 in some of the pairs\\n pairs = [x.copy() for x in test_class1.pairs]\\n pairs[0].loc[0:3, 'geo1'] = test_class1.pairs[0].loc[0:3, 'geo2']\\n pairs[0].loc[0:3, 'geo2'] = test_class1.pairs[0].loc[0:3, 'geo1']\\n\\n test_class2 = TrimmedMatchGeoXDesign(\\n geox_type,\\n pretest_data=self.nontrivial_data,\\n time_window_for_design=self.design_window,\\n time_window_for_eval=self.evaluation_window,\\n matching_metrics={'response': 1.0},\\n pairs=pairs)\\n _, detailed_results = test_class2.report_candidate_designs(\\n budget_list=[30, 40],\\n iroas_list=[0, 2],\\n use_cross_validation=True,\\n num_simulations=10)\\n\\n for key in detailed_results.keys():\\n self.assertTrue(\\n np.array_equal(detailed_results[key]['estimate'],\\n expected_detailed_results[key]['estimate']))\",\n \"def test_suitor_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.suitors[0].matching = Player(name=\\\"foo\\\", pref_names=[])\\n\\n with pytest.raises(Exception):\\n match._check_suitor_matching()\",\n \"def calculate_exact_match(pred_dict, ref_dict):\\n num, em = 0, 0\\n for key in pred_dict.keys():\\n num += 1\\n if ' '.join(pred_dict[key]).strip() == ' '.join(ref_dict[key]).strip():\\n em += 1\\n return em / num * 100\",\n \"def test_results_prefit() -> None:\\n X_train_val, X_test, y_train_val, y_test = train_test_split(\\n X, y, test_size=1/10, random_state=1\\n )\\n X_train, X_val, y_train, y_val = train_test_split(\\n X_train_val, y_train_val, test_size=1/9, random_state=1\\n )\\n estimator = LinearRegression().fit(X_train, y_train)\\n mapie = MapieRegressor(estimator=estimator, cv=\\\"prefit\\\")\\n mapie.fit(X_val, y_val)\\n _, y_pis = mapie.predict(X_test, alpha=0.05)\\n width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()\\n coverage = regression_coverage_score(\\n y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]\\n )\\n np.testing.assert_allclose(width_mean, WIDTHS[\\\"prefit\\\"], rtol=1e-2)\\n np.testing.assert_allclose(coverage, COVERAGES[\\\"prefit\\\"], rtol=1e-2)\",\n \"def compare_results(self):\\n return self.guess_number == self.secret_number\",\n \"def perform_re_qualify(responder, options):\\n match = options['<match-id>']\\n tla = options['<tla>']\\n scores.re_qualify(match, tla)\\n responder('Re-qualified {0} in match {1}'.format(tla, match))\",\n \"def test_compare_genomes_2(self):\\n self.pmr_gnm.annotation_status = \\\"final\\\"\\n self.pmr_gnm.name = \\\"Trixie\\\"\\n import_genome.compare_genomes(self.genome_pair, self.eval_flags)\\n count = count_status(self.genome_pair, \\\"error\\\", \\\"warning\\\")\\n with self.subTest():\\n self.assertEqual(len(self.genome_pair.evaluations), 13)\\n with self.subTest():\\n self.assertEqual(count, 0)\",\n \"def test_equal(self):\\n\\n candidate1 = pyrankvote.Candidate(\\\"Per\\\")\\n candidate2 = pyrankvote.Candidate(\\\"Per\\\")\\n candidate3 = pyrankvote.Candidate(\\\"Aase\\\")\\n\\n self.assertEqual(candidate1, candidate2, \\\"These candidates should be equal/the same candidate.\\\")\\n self.assertNotEqual(candidate1, candidate3, \\\"These candidates should NOT be equal/the same candidate.\\\")\",\n \"def test_student_optimal(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n np.random.seed(seed)\\n students, projects, _ = make_players(\\n student_names, project_names, supervisor_names, capacities\\n )\\n matching = student_optimal(students, projects)\\n\\n assert set(projects) == set(matching.keys())\\n assert all(\\n [\\n s in set(students)\\n for s in {\\n match for matches in matching.values() for match in matches\\n }\\n ]\\n )\\n\\n for student in students:\\n if student.matching:\\n assert student.prefs.index(student.matching) == 0\",\n \"def test_init(resident_names, hospital_names, capacities, seed):\\n\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n assert match.suitors == residents\\n assert match.reviewers == hospitals\\n assert all([suitor.matching is None for suitor in match.suitors])\\n assert all([reviewer.matching == [] for reviewer in match.reviewers])\\n assert match.matching is None\",\n \"def testRichnessMethodsMatch(self):\\n self.assertEqual(\\n self.tree.get_species_richness(reference=1),\\n self.tree.get_species_richness(reference=1),\\n msg=\\\"Landscape richness is not as expected.\\\",\\n )\\n self.assertEqual(\\n self.tree.get_species_richness(reference=3),\\n self.tree.get_species_richness(reference=3),\\n msg=\\\"Landscape richness is not as expected.\\\",\\n )\\n self.assertEqual(\\n self.tree.get_species_richness(reference=100),\\n self.tree.get_species_richness(reference=100),\\n msg=\\\"Landscape richness is not as expected.\\\",\\n )\\n self.assertEqual(\\n self.tree.get_species_richness(reference=101),\\n self.tree.get_species_richness(reference=101),\\n msg=\\\"Landscape richness is not as expected.\\\",\\n )\",\n \"def analyze_similarities():\\r\\n print('Total number of candidate pairs:', len(pairs))\\r\\n print(f'\\\\nNumber of actual item pairs in the train set: {pairs[\\\"true_match\\\"].sum()}\\\\n')\\r\\n\\r\\n for feature in ['text_score', 'image_score', 'txt_img_score', 'words_ratio', 'txt_img_words']:\\r\\n\\r\\n # Check distribution of True and False predictions for various similarity scores\\r\\n print('-' * 50)\\r\\n print(f'\\\\nDistribution of True/False predictions for {feature}')\\r\\n for thr in (0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95):\\r\\n print('-' * 50)\\r\\n print(f'Similarity score over {thr}')\\r\\n pairs_sample = pairs[pairs[feature] >= thr]\\r\\n print(f'Number of similar item pairs: {len(pairs_sample)}')\\r\\n print(pairs_sample['true_match'].value_counts(normalize=True))\\r\\n\\r\\n # Check if identical phash can be used to improve the accuracy\\r\\n same_phash = pairs[pairs['phash_match'] == 1]\\r\\n different_phash = pairs[pairs['phash_match'] == 0]\\r\\n\\r\\n print('\\\\nFor item pairs with the same phash:')\\r\\n print(same_phash['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(same_phash))\\r\\n\\r\\n print('\\\\nFor item pairs with different phash:')\\r\\n print(different_phash['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(different_phash))\\r\\n\\r\\n # Check if numbers in titles can be used to improve the accuracy\\r\\n same_numbers = pairs[pairs['nums_match'] == 1]\\r\\n different_numbers = pairs[pairs['nums_match'] == 0]\\r\\n\\r\\n print('\\\\nFor item pairs with the same numbers:')\\r\\n print(same_numbers['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(same_numbers))\\r\\n\\r\\n print('\\\\nFor item pairs with different numbers:')\\r\\n print(different_numbers['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(different_numbers))\",\n \"def testFindCorrespondence(self):\\n # Create some dummy keypoints and descriptors to match. Make the descriptors really far apart to be sure.\\n keypoints1 = []\\n descriptors1 = numpy.zeros(shape=(3, 1))\\n keypoint = cv2.KeyPoint()\\n for i in range(3):\\n keypoint.pt = (float(i), 0.0)\\n keypoints1.append(keypoint)\\n descriptors1[i] = i*100.0\\n keypoints2 = []\\n descriptors2 = numpy.zeros(shape=(5, 1))\\n for i in range(5):\\n keypoint.pt = (0.0, float(i))\\n keypoints2.append(keypoint)\\n descriptors2[i] = i*105.0\\n (first_points, second_points) = self.evaluator._findCorrespondence(\\n keypoints1, descriptors1, keypoints2, descriptors2)\\n expected_first = numpy.array([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])\\n expected_second = numpy.array([[0.0, 0.0], [0.0, 1.0], [0.0, 2.0]])\\n self.assertTrue(numpy.array_equal(first_points, expected_first))\\n self.assertTrue(numpy.array_equal(second_points, expected_second))\",\n \"def matches(self, feature):\\n pass\",\n \"def match(self, target, guess):\\r\\n return guess == target\",\n \"def match(self, proof: dict) -> bool:\\n return proof.get(\\\"proofPurpose\\\") == self.term\",\n \"def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"hospital\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n\\n for hospital, matches in matching.items():\\n old_idx = -np.infty\\n for resident in matches:\\n idx = hospital.pref_names.index(resident.name)\\n assert idx >= old_idx\\n old_idx = idx\",\n \"def test_affect_of_strategy(self):\\n self.responses_test([C, C, C], [C, C, C], [C, C, C])\\n # Make sure that the retaliations are increasing\\n # Retaliate once and forgive\\n self.responses_test([C], [D], [D])\\n self.responses_test([C, D], [D, C], [C])\\n self.responses_test([C, D, C], [D, C, C], [C])\\n # Retaliate twice and forgive\\n self.responses_test([C, D, C], [D, C, D], [D, D])\\n self.responses_test([C, D, C, D, D], [D, C, D, C, C], [C])\\n # Opponent defection during retaliation doesn't increase retaliation period\\n self.responses_test([C, D, C, D, D], [D, C, D, D, C], [C])\\n # Retaliate thrice and forgive\\n self.responses_test([C, D, C, D, D, C], [D, C, D, C, C, D], [D, D, D])\\n history_1 = [C, D, C, D, D, C, D, D, D]\\n history_2 = [D, C, D, C, C, D, C, C, C]\\n self.responses_test(history_1, history_2, [C])\",\n \"def test_perceive_residues_sorting(self, strict_chirality):\\n molecule = Molecule.from_file(get_data_file_path(\\\"proteins/ace-a10-nme.sdf\\\"))\\n\\n molecule.perceive_residues(strict_chirality=strict_chirality)\\n\\n for index, residue in enumerate(molecule.residues):\\n found = residue.residue_number\\n expected = str(index + 1)\\n\\n assert isinstance(found, str)\\n assert found == expected\",\n \"def matches(self):\\n pass\",\n \"def test_result_n(self):\\r\\n saber = self.saber\\r\\n movies = self.movies\\r\\n res = nearest_n(movies, saber, PRED).drop(\\\"genres\\\", axis=1)\\r\\n intersection = res.merge(self.training, how=\\\"left\\\", on=\\\"movieId\\\").dropna()\\r\\n self.assertTrue(not intersection.empty)\",\n \"def ruleMatch (ruleSet, node, parents):\\n def getProb (node):\\n for rule in ruleSet:\\n #print rule\\n if (rule.child.eq(node) and\\n len(rule.parentList)==len(parents) and\\n all([n[0].eq(n[1]) for n in zip(rule.parentList,parents)])):\\n #print \\\"winning eq\\\", [n for n in zip(rule.parentList,parents)]\\n return rule.prob\\n else:\\n return -1\\n \\n prob = getProb (node)\\n if prob == -1 and functorRangeSize(node.functor) == 2:\\n tn = copy.copy(node)\\n tn.val = functorOtherValue(tn.functor, tn.val)\\n prob = getProb (tn)\\n if prob != -1:\\n return 1 - prob\\n else:\\n return prob\\n return prob\",\n \"def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):\\n overlaps = jaccard(truths, point_form(priors))\\n best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)\\n valid_gt_idx = best_prior_overlap[:, 0] >= 0.2\\n best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]\\n if best_prior_idx_filter.shape[0] <= 0:\\n loc_t[idx] = 0\\n conf_t[idx] = 0\\n return\\n best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)\\n best_truth_idx.squeeze_(0)\\n best_truth_overlap.squeeze_(0)\\n best_prior_idx.squeeze_(1)\\n best_prior_idx_filter.squeeze_(1)\\n best_prior_overlap.squeeze_(1)\\n best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)\\n for j in range(best_prior_idx.size(0)):\\n best_truth_idx[best_prior_idx[j]] = j\\n matches = truths[best_truth_idx]\\n conf = labels[best_truth_idx]\\n conf[best_truth_overlap < threshold] = 0\\n loc = encode(matches, priors, variances)\\n matches_landm = landms[best_truth_idx]\\n landm = encode_landm(matches_landm, priors, variances)\\n loc_t[idx] = loc\\n conf_t[idx] = conf\\n landm_t[idx] = landm\",\n \"def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):\\n overlaps = jaccard(truths, point_form(priors))\\n best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)\\n valid_gt_idx = best_prior_overlap[:, 0] >= 0.2\\n best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]\\n if best_prior_idx_filter.shape[0] <= 0:\\n loc_t[idx] = 0\\n conf_t[idx] = 0\\n return\\n best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)\\n best_truth_idx.squeeze_(0)\\n best_truth_overlap.squeeze_(0)\\n best_prior_idx.squeeze_(1)\\n best_prior_idx_filter.squeeze_(1)\\n best_prior_overlap.squeeze_(1)\\n best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)\\n for j in range(best_prior_idx.size(0)):\\n best_truth_idx[best_prior_idx[j]] = j\\n matches = truths[best_truth_idx]\\n conf = labels[best_truth_idx]\\n conf[best_truth_overlap < threshold] = 0\\n loc = encode(matches, priors, variances)\\n matches_landm = landms[best_truth_idx]\\n landm = encode_landm(matches_landm, priors, variances)\\n loc_t[idx] = loc\\n conf_t[idx] = conf\\n landm_t[idx] = landm\",\n \"def test_check_stability():\\n\\n students = [Student(\\\"A\\\"), Student(\\\"B\\\"), Student(\\\"C\\\")]\\n projects = [Project(\\\"P\\\", 2), Project(\\\"Q\\\", 2)]\\n supervisors = [Supervisor(\\\"X\\\", 2), Supervisor(\\\"Y\\\", 2)]\\n\\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\\n\\n p.set_supervisor(x)\\n q.set_supervisor(y)\\n\\n a.set_prefs([p, q])\\n b.set_prefs([q])\\n c.set_prefs([q, p])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = StudentAllocation(students, projects, supervisors)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (p, q) = game.students, game.projects\\n\\n matching[p] = [c]\\n matching[q] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def evaluate_candidate(gene):\\n def get_score(output):\\n if output == None:\\n output = 0\\n s = 0;\\n if output % 3 == 0:\\n s += 1\\n if output % 7 == 0:\\n s += 1\\n if output == 21:\\n s += 1 \\n return s\\n \\n out1,out2 = gene.run(3,7)\\n \\n s1 = get_score(out1)\\n s2 = get_score(out2)\\n \\n if out1 == out2 == None: # or out1 == out2 == 0:\\n s1 = 0\\n s2 = 0\\n \\n gene.score = s1 if s1 <= s2 else s2\\n \\n return gene.score\",\n \"def test_get_matches(self):\\n\\n results = GenomePropertiesResultsWithMatches(*self.test_genome_property_results, properties_tree=self.test_tree)\\n\\n self.assertEqual(len(\\n results.step_matches.reset_index()[['Property_Identifier', 'Step_Number']].drop_duplicates()), 1)\\n self.assertEqual(len(results.get_sample_matches('C_chlorochromatii_CaD3')), 5)\\n self.assertEqual(len(results.get_sample_matches('C_luteolum_DSM_273')), 4)\\n\\n self.assertEqual(results.get_sample_matches('Your moms house'), None)\",\n \"def test(self, songs, genres):\\n logging.info('Starting testing.')\\n num_matches = 0\\n confusion_matrix = ConfusionMatrix(genres)\\n for song, actual_genre in zip(songs, genres):\\n predicted_genre = self.classify(song)\\n logging.info('Actual genre: {}, predicted genre: {}'.format(actual_genre, predicted_genre))\\n confusion_matrix.add_genres(actual_genre, predicted_genre)\\n if actual_genre == predicted_genre:\\n num_matches += 1\\n return num_matches, confusion_matrix\",\n \"def test_boolean_and_selection(self):\\n\\n # The selection loop:\\n sel = list(mol_res_spin.residue_loop(\\\"#Ap4Aase:4 & :Pro\\\"))\\n\\n # Test:\\n self.assertEqual(len(sel), 1)\\n for res in sel:\\n self.assert_(res.name == \\\"Pro\\\" and res.num == 4)\",\n \"def test_relevance_commutative_single_query_row():\\n view = gen_view_cgpm(get_data_separated)\\n rp_view_0 = view.relevance_probability(3, [8], 1)\\n rp_view_1 = view.relevance_probability(8, [3], 1)\\n assert np.allclose(rp_view_0, rp_view_1)\\n\\n state = gen_state_cgpm(get_data_separated)\\n rp_state_0 = state.relevance_probability(3, [8], 1)\\n rp_state_1 = state.relevance_probability(8, [3], 1)\\n assert np.allclose(rp_state_0, rp_state_1)\",\n \"def dupable_matches_required(self):\\n return 2\",\n \"def exact_match(hypothesis, reference):\\n hypothesis_list, reference_list = _generate_enums(hypothesis, reference)\\n return _match_enums(hypothesis_list, reference_list)\",\n \"def test_reciprocal_operations(ph_zr3n4: Phonopy):\\n ptg = ph_zr3n4.symmetry.pointgroup_operations\\n rops = ph_zr3n4.symmetry.reciprocal_operations\\n matches = []\\n for r in ptg:\\n for i, rec_r in enumerate(rops):\\n if (r == rec_r).all():\\n matches.append(i)\\n break\\n assert len(np.unique(matches)) == len(ptg)\\n found_inv = False\\n for rec_r in rops:\\n if (rec_r == -np.eye(3, dtype=int)).all():\\n found_inv = True\\n break\\n assert found_inv\",\n \"def test_match_correct(self, event: Event, pr_algorithm, minimal_state: State):\\n assert pr_algorithm.match(event, minimal_state)\",\n \"def test_lr(self):\\n\\n probas = self.lr_classifier.classify_doc(self.feat)\\n rprobas = {\\n 'category': 'sport',\\n 'probas': {\\n 'sport': 0.95130333681813339,\\n 'non-sport': 0.048696663181866606\\n }\\n }\\n\\n self.assertEqual(rprobas['category'], probas['category'])\\n\\n for field, value in rprobas['probas'].items():\\n self.assertIn(field, probas['probas'])\\n self.assertAlmostEqual(value, probas['probas'][field], places=7)\",\n \"def test_parsimony(get_test_cases):\\n for case in get_test_cases:\\n trivial_splits = list(all_splits(case['tree'].get_num_taxa(), True, True))\\n scores = [case['tree'].parsimony_score(s) for s in trivial_splits]\\n corr_triv_scores = all([s == 1 for s in scores])\\n pars_tests = case['pars_tests']\\n corr_other_scores = all(case['tree'].parsimony_score(test[0]) == test[1] for test in pars_tests.items())\\n assert corr_other_scores and corr_triv_scores\",\n \"def get_correctness_for_response(self, response):\\n for answer in self.my_osid_object.get_answers():\\n if self._is_match(response, answer):\\n try:\\n return answer.get_score()\\n except AttributeError:\\n return 100\\n for answer in self.my_osid_object.get_wrong_answers():\\n if self._is_match(response, answer):\\n try:\\n return answer.get_score()\\n except AttributeError:\\n return 0\",\n \"def test_compare_genomes_3(self):\\n self.eval_flags = {\\\"check_replace\\\": False}\\n import_genome.compare_genomes(self.genome_pair, self.eval_flags)\\n count = count_status(self.genome_pair, \\\"error\\\", \\\"warning\\\")\\n with self.subTest():\\n self.assertEqual(len(self.genome_pair.evaluations), 9)\\n with self.subTest():\\n self.assertEqual(count, 0)\",\n \"def match(self) -> bool:\",\n \"def test_possibilites(self):\\n self.assertEqual(self.RNA(\\\"\\\").possibilities(), 1)\\n self.assertEqual(self.RNA(\\\"ACGUgcaucagUCGuGCAU\\\").possibilities(), 1)\\n self.assertEqual(self.RNA(\\\"N\\\").possibilities(), 4)\\n self.assertEqual(self.RNA(\\\"R\\\").possibilities(), 2)\\n self.assertEqual(self.RNA(\\\"H\\\").possibilities(), 3)\\n self.assertEqual(self.RNA(\\\"nRh\\\").possibilities(), 24)\\n self.assertEqual(\\n self.RNA(\\\"AUGCnGUCAg-aurGauc--gauhcgauacgws\\\").possibilities(), 96\\n )\",\n \"def test_relevance_with_itself():\\n state = gen_state_cgpm(get_data_separated)\\n assert np.allclose(state.relevance_probability(2, [2], 1), 1.0)\",\n \"def testReportCandidateDesign(self):\\n\\n np.random.seed(0)\\n new_data = pd.concat([self.test_data, self.add_pair], sort=False)\\n test_class = TrimmedMatchGeoXDesign(\\n GeoXType.HEAVY_UP,\\n pretest_data=new_data,\\n time_window_for_design=self.design_window,\\n time_window_for_eval=self.evaluation_window,\\n matching_metrics={'response': 1.0},\\n )\\n for geox_type in GeoXType:\\n if geox_type == GeoXType.CONTROL:\\n continue\\n test_class._geox_type = geox_type\\n (results, detailed_results) = test_class.report_candidate_designs(\\n budget_list=[30, 40],\\n iroas_list=[0, 0.002],\\n use_cross_validation=True,\\n num_simulations=10)\\n\\n expected_results = pd.DataFrame({\\n 'num_pairs': [11, 10] * 4,\\n 'experiment_response': [373, 364] * 4,\\n 'experiment_spend': [191.5, 183] * 4,\\n 'spend_response_ratio': [191.5 / 373, 183 / 364] * 4,\\n 'budget': [30, 30, 40, 40, 30, 30, 40, 40],\\n 'iroas': [0, 0, 0, 0, 0.002, 0.002, 0.002, 0.002],\\n 'proportion_cost_in_experiment': [1, 183 / 191.5] * 4\\n })\\n\\n self.assertTrue(results[[\\n 'num_pairs', 'experiment_response', 'experiment_spend',\\n 'spend_response_ratio', 'budget', 'iroas',\\n 'proportion_cost_in_experiment'\\n ]].equals(expected_results))\\n for (_, iroas, _), value in detailed_results.items():\\n self.assertAlmostEqual(\\n value['estimate'].mean(), iroas, delta=0.05 + 0.1 * iroas)\",\n \"def test_results_prefit_naive() -> None:\\n estimator = LinearRegression().fit(X, y)\\n mapie = MapieRegressor(estimator=estimator, cv=\\\"prefit\\\")\\n mapie.fit(X, y)\\n _, y_pis = mapie.predict(X, alpha=0.05)\\n width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()\\n coverage = regression_coverage_score(y, y_pis[:, 0, 0], y_pis[:, 1, 0])\\n np.testing.assert_allclose(width_mean, WIDTHS[\\\"naive\\\"], rtol=1e-2)\\n np.testing.assert_allclose(coverage, COVERAGES[\\\"naive\\\"], rtol=1e-2)\",\n \"def test_compare_taxa_summaries_paired_pearson(self):\\r\\n exp = ('Taxon\\\\tS1\\\\tS2\\\\nArchaea\\\\t0.4\\\\t0.4\\\\nBacteria\\\\t0.5\\\\t'\\r\\n '0.7\\\\nEukarya\\\\t0.4\\\\t0.5\\\\n', 'Taxon\\\\tS1\\\\tS2\\\\nArchaea\\\\t0.5\\\\t'\\r\\n '0.6\\\\nBacteria\\\\t0.7\\\\t0.8\\\\nEukarya\\\\t0.5\\\\t0.6\\\\n',\\r\\n '# Correlation coefficient: pearson.\\\\n# The parametric p-value(s) '\\r\\n 'were calculated using a two-sided test of significance using a '\\r\\n 't-distribution.\\\\n# The nonparametric p-value(s) were calculated '\\r\\n 'using a two-sided permutation test with permutations.\\\\n# The '\\r\\n 'confidence interval(s) were constructed at a confidence level of '\\r\\n '.% using Fisher\\\\'s z-transformation (see Sokal and Rohlf rd '\\r\\n 'edition pg. ). The confidence interval(s) are two-sided.\\\\n# '\\r\\n 'Number of samples that matched between the taxa summary files: '\\r\\n '\\\\nCorrelation coefficient\\\\tParametric p-value\\\\tNonparametric '\\r\\n 'p-value\\\\tCI (lower)\\\\tCI (upper)\\\\n.\\\\t.\\\\t.\\\\t.\\\\t.\\\\n', None)\\r\\n\\r\\n obs = compare_taxa_summaries(self.taxa_summary_paired1,\\r\\n self.taxa_summary_paired2, 'paired', 'pearson')\\r\\n obs = (obs[0], obs[1], self.remove_nums(obs[2]),\\r\\n obs[3])\\r\\n self.assertEqual(obs, exp)\",\n \"def match(self, other):\",\n \"def test_can_match(self):\\n assert self.RNA(\\\"\\\").can_match(\\\"\\\")\\n assert self.RNA(\\\"UCAG\\\").can_match(\\\"UCAG\\\")\\n assert not self.RNA(\\\"UCAG\\\").can_match(\\\"ucag\\\")\\n assert self.RNA(\\\"UCAG\\\").can_match(\\\"NNNN\\\")\\n assert self.RNA(\\\"NNNN\\\").can_match(\\\"UCAG\\\")\\n assert self.RNA(\\\"NNNN\\\").can_match(\\\"NNNN\\\")\\n assert not self.RNA(\\\"N\\\").can_match(\\\"x\\\")\\n assert not self.RNA(\\\"N\\\").can_match(\\\"-\\\")\\n assert self.RNA(\\\"UCAG\\\").can_match(\\\"YYRR\\\")\\n assert self.RNA(\\\"UCAG\\\").can_match(\\\"KMWS\\\")\",\n \"def computeCorrespondence(self):\\n assert self.result and self.dst\\n assert self.result.num == self.dst.num\",\n \"def test_compare_answer(self):\\r\\n problem = self.build_problem(answer=\\\"42\\\")\\r\\n responder = problem.responders.values()[0]\\r\\n self.assertTrue(responder.compare_answer('48', '8*6'))\\r\\n self.assertFalse(responder.compare_answer('48', '9*5'))\",\n \"def test_compare_confs(self):\\n ch4_1 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\\n 'isotopes': (12, 1, 1, 1, 1),\\n 'coords': ((0.0, 0.0, 0.0),\\n (0.6300326, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))}\\n self.assertTrue(converter.compare_confs(ch4_1, ch4_1))\\n self.assertEqual(converter.compare_confs(ch4_1, ch4_1, rmsd_score=True), 0.0)\\n\\n ch4_2 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\\n 'isotopes': (12, 1, 1, 1, 1),\\n 'coords': ((0.0, 0.0, 0.0),\\n (0.630032999999999999, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))}\\n self.assertTrue(converter.compare_confs(ch4_1, ch4_2))\\n self.assertAlmostEqual(converter.compare_confs(ch4_1, ch4_2, rmsd_score=True), 0.0, places=4)\\n\\n ch4_3 = {'symbols': ('C', 'H', 'H', 'H', 'H'),\\n 'isotopes': (12, 1, 1, 1, 1),\\n 'coords': ((0.0, 0.0, 0.0),\\n (0.81, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))}\\n self.assertFalse(converter.compare_confs(ch4_1, ch4_3))\\n self.assertAlmostEqual(converter.compare_confs(ch4_1, ch4_3, rmsd_score=True), 0.0973755, 5)\\n\\n occco_1 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\\n 'coords': ((-1.645138856907744, 0.4855258453193825, -1.269098295435587),\\n (-1.826155038047953, 0.3950495294463964, 0.14042133077839267),\\n (-1.809422000751755, 1.7890058616007063, 0.7598491740499427),\\n (-0.5144047765537177, 2.5412406931938802, 0.46679697338621373),\\n (-0.4166415293412751, 2.87702090326361, -0.9112962716273996),\\n (-1.7523716337780308, -0.4153106947877299, -1.6252584016046343),\\n (-2.785951955397608, -0.09251469392615361, 0.33828359656775064),\\n (-1.0240452528998532, -0.22437445960667116, 0.5540970106165476),\\n (-2.6481280678220354, 2.3642983699082096, 0.34958017984053996),\\n (-1.948589699885299, 1.7026595551213293, 1.842711580226941),\\n (0.3611954633790452, 1.9455154222553817, 0.7436960027091665),\\n (-0.4846097814890448, 3.4746190180148613, 1.0370826136306412),\\n (-0.7517118479102434, 2.0995465744609016, -1.4084474547843668))}\\n self.assertTrue(converter.compare_confs(occco_1, occco_1))\\n self.assertEqual(converter.compare_confs(occco_1, occco_1, rmsd_score=True), 0.0)\\n\\n occco_2 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\\n 'coords': ((-1.7947360038254172, -0.2800502342395655, -0.94718691702442),\\n (-1.3484923712374388, 0.3115024061252556, 0.26578718691263836),\\n (0.048477626636679344, -0.18145884392561823, 0.6324014558703148),\\n (0.06768533157823732, -1.6675011458530478, 0.9732242593946415),\\n (-0.3530408338556874, -2.4414941005488964, -0.14539995931042984),\\n (-1.6993365552401258, -1.24904809118412, -0.8336640895923935),\\n (-1.3364382254722125, 1.3960688766201377, 0.12095890535651209),\\n (-2.0720954664081472, 0.08255875951942339, 1.0547463625584224),\\n (0.7166058894071794, -0.006871191098481536, -0.21939247767770087),\\n (0.42416153066953804, 0.38997256785153595, 1.4878132135000073),\\n (-0.6073710844027356, -1.8828156181698352, 1.8073934203380306),\\n (1.0743805139106757, -1.9882575918786236, 1.2595102280098387),\\n (0.35195568839394714, -2.3791987519096245, -0.81652943836054))}\\n self.assertFalse(converter.compare_confs(occco_1, occco_2))\\n self.assertAlmostEqual(converter.compare_confs(occco_1, occco_2, rmsd_score=True), 1.00940798, 5)\\n\\n occco_3 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\\n 'coords': ((-2.1449415573976087, 0.22095421320805855, -1.1761667487081628),\\n (-1.8458576972054956, 0.34850267283416353, 0.21018774322610537),\\n (-1.7946333596155588, 1.8224048429109774, 0.6001541290065803),\\n (-0.7643941488427433, 2.603963636650936, -0.21014179435545444),\\n (-1.160550573561837, 2.7172772321690157, -1.5709018700345496),\\n (-2.2541573481252226, -0.7309807530987936, -1.3543442072197793),\\n (-2.624019853257233, -0.1650323593077789, 0.7836186270202112),\\n (-0.885183881101036, -0.13905038811734782, 0.4040751763466029),\\n (-2.7832838991835382, 2.2645645737400506, 0.427600829327477),\\n (-1.562954529947271, 1.9038127448704014, 1.6674459390753227),\\n (0.21794991835079414, 2.1225495495404854, -0.17169963700580776),\\n (-0.6621011473071822, 3.6174144955483376, 0.18956005497753062),\\n (-1.544021016988015, 1.848253867499642, -1.8191893347265315))}\\n self.assertTrue(converter.compare_confs(occco_1, occco_3))\\n self.assertAlmostEqual(converter.compare_confs(occco_1, occco_3, rmsd_score=True), 0.0, places=4)\\n\\n occco_4 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\\n 'coords': ((-2.149273800177333, 0.3878561319363274, -1.067994536622897),\\n (-1.8905197406541119, 0.16731522843849514, 0.3147948930907352),\\n (-2.2871289950009728, 1.398560959638718, 1.1233863494505283),\\n (-1.5514369216953363, 2.6559008371480006, 0.6687883285931627),\\n (-1.97578532983754, 3.0584350426221696, -0.6271056229665328),\\n (-1.9649535743321451, -0.45216201833697967, -1.5265252922322194),\\n (-2.469954748031858, -0.7010050003658949, 0.6439649209501082),\\n (-0.8252433635746496, -0.052453567250684806, 0.43760424203717113),\\n (-3.36502756891988, 1.5624906595612045, 1.0051865026931144),\\n (-2.0814509723064316, 1.2139856682154606, 2.18312358675434),\\n (-0.4688204416342118, 2.495542838358611, 0.6472342384223745),\\n (-1.759053255113454, 3.4819317812208626, 1.3557210162758644),\\n (-2.0785703072969466, 2.2346795710060765, -1.151280970188824))}\\n self.assertTrue(converter.compare_confs(occco_1, occco_4))\\n self.assertAlmostEqual(converter.compare_confs(occco_1, occco_4, rmsd_score=True), 0.0, places=4)\\n\\n occco_5 = {'symbols': ('O', 'C', 'C', 'C', 'O', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H'),\\n 'isotopes': (16, 12, 12, 12, 16, 1, 1, 1, 1, 1, 1, 1, 1),\\n 'coords': (\\n (-2.4583263702786398, 0.0718285271168676, -1.073208642860504),\\n (-1.937015981064579, 0.0890930305098165, 0.25179580507902494),\\n (-2.348398726927575, 1.376978495961958, 0.9583384372437844),\\n (-1.8770165015484177, 2.6245423907540975, 0.21659228399276115),\\n (-2.569162634647042, 2.781228761671768, -1.015409941988472),\\n (-2.2457141392131477, -0.8022675472463131, -1.4481857725877),\\n (-2.3295554159352525, -0.7779156570908263, 0.792431073403323),\\n (-0.8469088168553344, 0.006439213644634347, 0.19675913068823528),\\n (-3.4425715658912557, 1.4040720104173179, 1.0273387203822888),\\n (-1.939605978646692, 1.375711847707625, 1.9743606178959265),\\n (-0.8029097001610058, 2.5806396078360736, 0.010183430117069694),\\n (-2.07095189274604, 3.516416462694289, 0.8201309204066712),\\n (-2.650256378769789, 1.8823222470150054, -1.4017891959903757))}\\n self.assertTrue(converter.compare_confs(occco_1, occco_5))\\n self.assertAlmostEqual(converter.compare_confs(occco_1, occco_5, rmsd_score=True), 0.0, places=4)\",\n \"def test_compare_genomes_1(self):\\n import_genome.compare_genomes(self.genome_pair, self.eval_flags)\\n count = count_status(self.genome_pair, \\\"error\\\", \\\"warning\\\")\\n with self.subTest():\\n self.assertEqual(len(self.genome_pair.evaluations), 12)\\n with self.subTest():\\n self.assertEqual(count, 0)\",\n \"def test_matching_tracks(self):\\n\\n # 5037: Pop 101 (feat. Anami Vice) by Marianas Trench\\n # 8755 : Satisfied (feat. Miguel & Queen Latifah) by Sia\\n # 6699 : Un Besito Mas (feat. Juan Luis Guerra) by Jesse & Joy\\n targets = {5037: '2fGFaTDbE8aS4f31fM0XE4',\\n 8755: '1ybJ2itxCxPCPkcA9sOgTO',\\n 6699: '1182pxG4uNxr3QqIH8b8k0',\\n }\\n\\n matches = {track.i_id: track.id\\n for track in self.tracks\\n if track.i_id in targets}\\n\\n for i_id, s_id in targets.iteritems():\\n self.assertEqual(s_id, matches[i_id])\",\n \"def test_residential_to_secondary():\\n assert query_row(db_conf, 'osm_roads', 40001)['type'] == 'residential'\\n assert not query_row(db_conf, 'osm_roads_gen0', 40001)\\n assert not query_row(db_conf, 'osm_roads_gen1', 40001)\",\n \"def testGetsPresentRichness(self):\\n self.assertEqual(37, self.coal.get_species_richness(1))\\n self.assertEqual(53, self.coal.get_species_richness(3))\",\n \"def test_eq(self):\\n r1 = Resources(4, 2, {\\\"Hadamard\\\": 1, \\\"CNOT\\\": 1}, {1: 1, 2: 1}, 2, Shots(100))\\n r2 = Resources(4, 2, {\\\"Hadamard\\\": 1, \\\"CNOT\\\": 1}, {1: 1, 2: 1}, 2, Shots(100))\\n r3 = Resources(4, 2, {\\\"CNOT\\\": 1, \\\"Hadamard\\\": 1}, {2: 1, 1: 1}, 2, Shots(100)) # all equal\\n\\n r4 = Resources(1, 2, {\\\"Hadamard\\\": 1, \\\"CNOT\\\": 1}, {1: 1, 2: 1}, 2, Shots(100)) # diff wires\\n r5 = Resources(\\n 4, 1, {\\\"Hadamard\\\": 1, \\\"CNOT\\\": 1}, {1: 1, 2: 1}, 2, Shots(100)\\n ) # diff num_gates\\n r6 = Resources(4, 2, {\\\"CNOT\\\": 1}, {1: 1, 2: 1}, 2, Shots(100)) # diff gate_types\\n r7 = Resources(\\n 4, 2, {\\\"Hadamard\\\": 1, \\\"CNOT\\\": 1}, {1: 3, 2: 2}, 2, Shots(100)\\n ) # diff gate_sizes\\n r8 = Resources(4, 2, {\\\"Hadamard\\\": 1, \\\"CNOT\\\": 1}, {1: 1, 2: 1}, 1, Shots(100)) # diff depth\\n r9 = Resources(\\n 4, 2, {\\\"Hadamard\\\": 1, \\\"CNOT\\\": 1}, {1: 1, 2: 1}, 2, Shots((10, 10))\\n ) # diff shots\\n\\n assert r1.__eq__(r1)\\n assert r1.__eq__(r2)\\n assert r1.__eq__(r3)\\n\\n assert not r1.__eq__(r4)\\n assert not r1.__eq__(r5)\\n assert not r1.__eq__(r6)\\n assert not r1.__eq__(r7)\\n assert not r1.__eq__(r8)\\n assert not r1.__eq__(r9)\",\n \"def test_reviewer_capacity(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.reviewers[0].matching = range(match.reviewers[0].capacity + 1)\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_capacity()\",\n \"def matches(self, tgt_residence_dir: str) -> bool:\",\n \"def findMatches3(personDict,matches,skepticalMatches,additionalMatches,personDict2):\\n dictConsidered = personDict['ALIAS']\\n for alias in dictConsidered:\\n if alias == \\\"\\\":\\n continue\\n pairs = itertools.combinations(dictConsidered[alias],2)\\n for p in pairs:\\n k = tuple(sorted(p))\\n if (k not in matches) and (k not in skepticalMatches) and (k not in additionalMatches):\\n info1 = personDict['EnterpriseID'][p[0]]\\n info2 = personDict['EnterpriseID'][p[1]]\\n \\n info1b = personDict2['EnterpriseID'][p[0]]\\n info2b = personDict2['EnterpriseID'][p[1]]\\n score = getScorePair(info1b,info2b)\\n if score>=7:\\n additionalMatches[k] = score\\n\\n return additionalMatches\",\n \"def has_matches(self):\\n for rank, rank_count in self._rank_counts.items():\\n if rank_count > 1:\\n return True\\n return False\",\n \"def test_fuzzy_matching_basic(nlp: Language, countries: List[Dict[str, Any]]) -> None:\\n ruler = SpaczzRuler(nlp)\\n ruler.add_patterns(countries)\\n doc = nlp(\\\"This is a test that should find Egypt and Argentina\\\")\\n doc = ruler(doc)\\n matches = [(ent.ent_id_, ent.text) for ent in doc.ents if ent.label_ == \\\"COUNTRY\\\"]\\n assert matches == [(\\\"Egypt\\\", \\\"Egypt\\\"), (\\\"Argentina\\\", \\\"Argentina\\\")]\",\n \"def partial_match_score(\\n truth: List[Rationale], pred: List[Rationale], thresholds: List[float]\\n) -> List[PartialMatchScore]:\\n\\n ann_to_rat = _keyed_rationale_from_list(truth)\\n pred_to_rat = _keyed_rationale_from_list(pred)\\n\\n num_classifications = {k: len(v) for k, v in pred_to_rat.items()}\\n num_truth = {k: len(v) for k, v in ann_to_rat.items()}\\n ious: Dict[str, Dict[str, float]] = defaultdict(dict)\\n for k in set(ann_to_rat.keys()) | set(pred_to_rat.keys()):\\n for p in pred_to_rat.get(k, []):\\n best_iou = 0.0\\n for t in ann_to_rat.get(k, []):\\n num = len(\\n set(range(p.start_token, p.end_token))\\n & set(range(t.start_token, t.end_token))\\n )\\n denom = len(\\n set(range(p.start_token, p.end_token))\\n | set(range(t.start_token, t.end_token))\\n )\\n iou = 0 if denom == 0 else num / denom\\n if iou > best_iou:\\n best_iou = iou\\n ious[k][p] = best_iou\\n\\n scores: List[PartialMatchScore] = []\\n for threshold in thresholds:\\n threshold_tps: Dict[str, float] = {}\\n for k, vs in ious.items():\\n threshold_tps[k] = sum(int(x >= threshold) for x in vs.values())\\n micro_r = (\\n sum(threshold_tps.values()) / sum(num_truth.values())\\n if sum(num_truth.values()) > 0\\n else 0\\n )\\n micro_p = (\\n sum(threshold_tps.values()) / sum(num_classifications.values())\\n if sum(num_classifications.values()) > 0\\n else 0\\n )\\n micro_f1 = _f1(micro_r, micro_p)\\n macro_rs = list(\\n threshold_tps.get(k, 0.0) / n if n > 0 else 0 for k, n in num_truth.items()\\n )\\n macro_ps = list(\\n threshold_tps.get(k, 0.0) / n if n > 0 else 0\\n for k, n in num_classifications.items()\\n )\\n macro_r = sum(macro_rs) / len(macro_rs) if len(macro_rs) > 0 else 0\\n macro_p = sum(macro_ps) / len(macro_ps) if len(macro_ps) > 0 else 0\\n macro_f1 = _f1(macro_r, macro_p)\\n\\n scores.append(\\n PartialMatchScore(\\n threshold=threshold,\\n micro=InstanceScore(p=micro_p, r=micro_r, f1=micro_f1),\\n macro=InstanceScore(p=macro_p, r=macro_r, f1=macro_f1),\\n )\\n )\\n\\n return scores\",\n \"def precision_score_candidate_match(standard, selected_types, candidate_docs):\\n\\tscore = 0\\n\\tfor cdoc in candidate_docs:\\n\\t\\tcounts, all_events = standard[cdoc]\\n\\n\\t\\tevent_match = True\\n\\t\\tfor event_type in selected_types:\\n\\t\\t\\tif counts[EVENT_TYPES[event_type]] == 0:\\n\\t\\t\\t\\tevent_match = False\\n\\n\\t\\tif event_match:\\n\\t\\t\\tscore += 1\\n\\n\\tassert(score <= TOP_K), str(score)\\n\\treturn score\",\n \"def matches_p2(info: Mapping[str, int],\\n result: Mapping[str, int]) -> bool:\\n for key, value in info.items():\\n if key in ['cats', 'trees']:\\n if value <= result[key]:\\n return False\\n elif key in ['pomeranians', 'goldfish']:\\n if value >= result[key]:\\n return False\\n else:\\n if result[key] != value:\\n return False\\n return True\",\n \"def test_boolean_complex_selection(self):\\n\\n # The residue selection loop.\\n sel = list(mol_res_spin.residue_loop(\\\"#Ap4Aase:4 & :Pro | #RNA\\\"))\\n\\n # Residue names and numbers.\\n names = ['Pro', None, None]\\n numbers = [4, -5, -4]\\n\\n # The residues.\\n self.assertEqual(len(sel), 3)\\n for i in range(3):\\n self.assertEqual(sel[i].name, names[i])\\n self.assertEqual(sel[i].num, numbers[i])\",\n \"def test_compare_taxa_summaries_paired_sample_id_map(self):\\r\\n exp = ('Taxon\\\\tS1\\\\tS2\\\\nArchaea\\\\t0.4\\\\t0.4\\\\nBacteria\\\\t0.5\\\\t'\\r\\n '0.7\\\\nEukarya\\\\t0.4\\\\t0.5\\\\n', 'Taxon\\\\tE1\\\\tE2\\\\nArchaea\\\\t0.5\\\\t'\\r\\n '0.6\\\\nBacteria\\\\t0.7\\\\t0.8\\\\nEukarya\\\\t0.5\\\\t0.6\\\\n',\\r\\n '# Correlation coefficient: pearson.\\\\n# The parametric p-value(s) '\\r\\n 'were calculated using a two-sided test of significance using a '\\r\\n 't-distribution.\\\\n# The nonparametric p-value(s) were calculated '\\r\\n 'using a two-sided permutation test with permutations.\\\\n# The '\\r\\n 'confidence interval(s) were constructed at a confidence level of '\\r\\n '.% using Fisher\\\\'s z-transformation (see Sokal and Rohlf rd '\\r\\n 'edition pg. ). The confidence interval(s) are two-sided.\\\\n# '\\r\\n 'Number of samples that matched between the taxa summary files: '\\r\\n '\\\\nCorrelation coefficient\\\\tParametric p-value\\\\tNonparametric '\\r\\n 'p-value\\\\tCI (lower)\\\\tCI (upper)\\\\n.\\\\t.\\\\t.\\\\t.\\\\t.\\\\n', None)\\r\\n obs = compare_taxa_summaries(self.taxa_summary_paired1,\\r\\n self.taxa_summary_paired4, 'paired', 'pearson',\\r\\n perform_detailed_comparisons=False,\\r\\n sample_id_map=self.taxa_summary_paired_samp_id_map1)\\r\\n self.assertEqual(obs[0], exp[0])\\r\\n self.assertEqual(obs[1], exp[1])\\r\\n self.assertEqual(self.remove_nums(obs[2]), exp[2])\",\n \"def evaluate_rouge_match(ref_data, pred_data, align=\\\"rouge\\\"):\\n\\n scorer = RougeScorer([\\\"rougeL\\\"])\\n agg = scoring.BootstrapAggregator(confidence_interval=0.95)\\n sample_evaluated = 0\\n\\n for tid in tqdm(pred_data):\\n cur_sys = pred_data[tid][\\\"pred\\\"]\\n if not tid in ref_data: continue\\n cur_ref_lst = [x[\\\"tgt_words\\\"] for x in ref_data[tid]]\\n if len(cur_ref_lst) > 0:\\n if align == \\\"rouge\\\":\\n best_rouge_scrs = __rouge_pairs(cur_sys, cur_ref_lst, scorer)\\n agg.add_scores(best_rouge_scrs)\\n else:\\n best_paired_ref, _ = utils.pair_ref_sys_by_coverage(cur_sys, cur_ref_lst)\\n agg.add_scores(scorer.score(best_paired_ref, cur_sys))\\n sample_evaluated += 1\\n\\n print(sample_evaluated)\\n result = agg.aggregate()\\n for rouge_type in result:\\n print(rouge_type)\\n print(result[rouge_type].mid)\\n\\n print(agg.aggregate())\\n return\",\n \"def test_counts(contrib_data):\\n ## Find the probands - they should always contribute their own alleles\\n P = ped.Pedigree(args.pedfile)\\n\\n ## TODO: Test individuals with multiple offspring +p2 id:112\\n ## Case when no regions are specified - only all probands\\n regions = contrib_data.keys()\\n if len(regions) == 1:\\n all_contribs = contrib_data[regions[0]]\\n for prob in P.probands:\\n ## Probands should always contribute 1 allele\\n assert all_contribs[prob, 1] == args.iterations\\n\\n ## Test specific individuals\\n counts = Counter(all_contribs[11])\\n binomial_sd = np.sqrt(args.iterations * 0.25)\\n mean = np.mean(counts.values())\\n conf95 = (mean - 2 * binomial_sd, mean + 2 * binomial_sd)\\n\\n for count in counts.values():\\n assert conf95[0] < count < conf95[1]\",\n \"def test_recommendation_evaluation_7(model, interactions_ds):\\n assert recommendation_evaluation(model, interactions_ds[1], cn_test_users=None, k=2, n_pos_interactions=None,\\n novelty=False, interaction_threshold=2) == \\\\\\n {'HitRatio@2': 0.0069, 'NDCG@2': 0.0116, 'Precision@2': 0.0104, 'Recall@2': 0.0069}\",\n \"def PredictMatch(yourName, tgtName, teamModels, db):\\n\\n teamTgtPipe, teamYourPipe = QueryTeamData(tgtName, yourName, db)\\n featureCoefTgt, probTgt = PredictOp(teamTgtPipe, teamYourPipe, tgtName, teamModels)\\n featureCoefYour, probYour = PredictOp(teamYourPipe, teamTgtPipe, yourName, teamModels)\\n odds = round(probYour / (probYour + probTgt), 2)\\n\\n # In featureCoefYour, you want INCREASE those with POSTIVE COEF, DECREASE those with NEGATIVE COEF\\n # In featureCoefTgt, you want to do the opposite\\n\\n # reverse both the sign of the coef, and '_op' in features so as to be the same with featureCoefYour\\n featureCoefTgt['coef'] = - featureCoefTgt['coef']\\n featureCoefTgt.features = [ii[:-3] if \\\"_op\\\" in ii else ii + '_op' for ii in featureCoefTgt.features]\\n\\n # Combine only the most important 10 features\\n # featureBoth = featureCoefTgt[11:].append(featureCoefYour[11:])\\n\\n # Combine only all the most important features\\n featureBoth = featureCoefTgt.append(featureCoefYour)\\n\\n # get action recommendations\\n # Somehow the pandas here uses a deprecated para cols, instaed of the new one subset\\n #featureBoth.drop_duplicates(subset = 'features', take_last = True, inplace = True)\\n featureBoth.drop_duplicates(cols = 'features', take_last = True, inplace = True)\\n actions, featureImprove = GetActions(featureBoth)\\n Imp = 0.1\\n oddsNew = ImprovedScore(tgtName, yourName, teamModels, featureImprove, teamTgtPipe, teamYourPipe, Imp)\\n\\n return odds, oddsNew, actions\",\n \"def reportMatch(winner, loser):\\n conn = connect()\\n cursor = conn.cursor()\\n cursor.execute(\\\"INSERT INTO results VALUES (%s, %s, true)\\\",\\n (winner, loser))\\n cursor.execute(\\\"INSERT INTO results VALUES (%s, %s, false)\\\",\\n (loser, winner))\\n conn.commit()\\n conn.close()\",\n \"def check_matches(known, match): \\n #array containing tuples of corrected spellings\\n out_vals = []\\n\\n #clear screen\\n os.system('clear')\\n \\n for k,v in known.iteritems():\\n matched_vals = [ik for ik, iv in match.iteritems() if iv == v]\\n cur_matches = process.extract(k, matched_vals, limit = MATCH_LIMIT)\\n \\n #append a none option and option for actual val\\n cur_matches.insert(0,(\\\"NONE\\\",0))\\n cur_matches.append((k,100))\\n\\n\\n #if there is a match of 100, skip\\n if cur_matches[1][1] == 86 or cur_matches[1][1] < 50:\\n #special case where probably means there really isn't a match\\n choice = 'NONE'\\n print 'No match!'\\n\\n elif cur_matches[1][1] < 95:\\n #print out options for each word\\n print_options(k, cur_matches)\\n\\n #cycle through prompt for input if valid (numeric, within MATCH_LIMIT)\\n choice = None\\n while choice == None:\\n try:\\n choice = cur_matches[get_choice()][0]\\n except:\\n print '!Invalid match! Try again'\\n print_options(k, cur_matches)\\n \\n print choice\\n\\n else:\\n #exact match\\n choice = cur_matches[1][0]\\n print '%s - Match!'%choice\\n\\n print \\n print\\n #create a new tuple with correct and incorrect spelling\\n out_vals.append((k, choice))\\n output(out_vals)\\n\\n #we're done, return vals array\\n return out_vals\",\n \"def compute_matches(self):\\n\\t\\tself.local_database[\\\"figure_number\\\"] = [0] * len(self.local_database[\\\"feature_vectors\\\"])\\n\\t\\tForensics = wbForensicsHOG(Database=self.local_database)\\n\\t\\tForensics.KDTree_pairs(leaf_size = len(self.local_database)+1)\\n\\t\\tForensics.d_rank(pairs=Forensics.pairs, distances=Forensics.dists, ratios=Forensics.ratios)\\n\\n\\t\\tself.local_matches = Forensics.Dist_Rank\",\n \"def test4(self):\\n tgts = [ ('CCC(O)C(=O)O',\\n ('CCC','OCC','OCC=O','OCCO','CCCC','OC=O','CC(O)C')),\\n ]\\n for smi,matches in tgts:\\n m = Chem.MolFromSmiles(smi)\\n fp1 = Chem.RDKFingerprint(m,2,7,9192,4,0)\\n obs = fp1.GetOnBits()\\n for match in matches:\\n m2 = Chem.MolFromSmiles(match)\\n fp2 = Chem.RDKFingerprint(m2,2,7,9192,4,0)\\n v1,v2 = DataStructs.OnBitProjSimilarity(fp2,fp1)\\n assert feq(v1,1.0000),'substruct %s not properly contained in %s'%(match,smi)\",\n \"def _prediction_match(self, thermo, ref_values, eps=0.05):\\n singlet_array = self._get_singlet_array(thermo)\\n for cur_array, ref_array in zip(singlet_array, ref_values):\\n for cur_val, ref_val in zip(cur_array, ref_array):\\n if abs(cur_val - ref_val) > eps:\\n return False\\n return True\",\n \"def test_check_validity(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n assert match.check_validity()\",\n \"def test_check_inputs_resident_prefs_all_hospitals(game):\\n\\n resident = game.residents[0]\\n resident.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert resident.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert resident.prefs == []\",\n \"def test_perceive_residues_cyclic_peptide_chirality(self, strict_chirality):\\n smiles = (\\n \\\"[H]c1c(c(c(c(c1[H])[H])C([H])([H])[C@]2(C(=O)N([C@](C(=O)N([C@@](C(=O)N3[C@@](C(=O)N2[H])\\\"\\n \\\"(C(C(C3([H])[H])([H])[H])([H])[H])[H])([H])C([H])([H])[H])[H])([H])C([H])([H])C4=C(N(c5c4c(c(c(c5[H])\\\"\\n \\\"[H])[H])[H])[H])[H])[H])[H])[H])[H]\\\"\\n )\\n offmol = Molecule.from_smiles(smiles)\\n # perceive residues\\n offmol.perceive_residues(strict_chirality=strict_chirality)\\n counter = 0 # matched atom counter\\n for atom in offmol.atoms:\\n if atom.metadata:\\n counter += 1\\n if strict_chirality:\\n # Make sure it fails if strict_chirality=True\\n with pytest.raises(AssertionError):\\n assert counter == offmol.n_atoms\\n else:\\n assert counter == offmol.n_atoms\",\n \"def refuse_matches(self):\\n return self._refuse_matches\",\n \"def testSpeciesRichnessCorrect(self):\\n self.assertEqual(136, self.coal.get_species_richness(1))\\n self.assertNotEqual(self.coal.get_species_richness(2), self.coal.get_species_richness(1))\\n self.assertNotEqual(self.coal.get_species_richness(3), self.coal.get_species_richness(2))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6435155","0.6338223","0.63054293","0.6247562","0.6155224","0.5968124","0.5949417","0.59368134","0.5935081","0.59329754","0.5906463","0.58899987","0.58429325","0.58374435","0.5827494","0.5767351","0.575049","0.5748831","0.57429886","0.56962484","0.56914806","0.5687966","0.5669713","0.565566","0.5647429","0.5637684","0.56364655","0.56221795","0.55818486","0.55650026","0.55605876","0.55468357","0.5513709","0.5510507","0.55004454","0.5499511","0.54917353","0.54878205","0.5473831","0.5470311","0.54689425","0.544576","0.54417187","0.54410356","0.5432664","0.5432664","0.54323196","0.54302603","0.5426756","0.5426095","0.54183966","0.54175717","0.54086447","0.5401427","0.5400315","0.53901994","0.5371341","0.53639054","0.53625417","0.53568465","0.5353615","0.5342796","0.5342722","0.53424704","0.53412336","0.5338081","0.53376997","0.53184915","0.5316811","0.53057003","0.5298146","0.5293607","0.5291988","0.5288532","0.5285043","0.52806556","0.5278112","0.52678907","0.52640355","0.5254969","0.5254858","0.5251716","0.5251391","0.5248119","0.5246247","0.524386","0.5239195","0.52367353","0.52363026","0.5232432","0.5227924","0.522758","0.5226402","0.52181196","0.52120924","0.52116394","0.5207838","0.5201456","0.5200729","0.51991737"],"string":"[\n \"0.6435155\",\n \"0.6338223\",\n \"0.63054293\",\n \"0.6247562\",\n \"0.6155224\",\n \"0.5968124\",\n \"0.5949417\",\n \"0.59368134\",\n \"0.5935081\",\n \"0.59329754\",\n \"0.5906463\",\n \"0.58899987\",\n \"0.58429325\",\n \"0.58374435\",\n \"0.5827494\",\n \"0.5767351\",\n \"0.575049\",\n \"0.5748831\",\n \"0.57429886\",\n \"0.56962484\",\n \"0.56914806\",\n \"0.5687966\",\n \"0.5669713\",\n \"0.565566\",\n \"0.5647429\",\n \"0.5637684\",\n \"0.56364655\",\n \"0.56221795\",\n \"0.55818486\",\n \"0.55650026\",\n \"0.55605876\",\n \"0.55468357\",\n \"0.5513709\",\n \"0.5510507\",\n \"0.55004454\",\n \"0.5499511\",\n \"0.54917353\",\n \"0.54878205\",\n \"0.5473831\",\n \"0.5470311\",\n \"0.54689425\",\n \"0.544576\",\n \"0.54417187\",\n \"0.54410356\",\n \"0.5432664\",\n \"0.5432664\",\n \"0.54323196\",\n \"0.54302603\",\n \"0.5426756\",\n \"0.5426095\",\n \"0.54183966\",\n \"0.54175717\",\n \"0.54086447\",\n \"0.5401427\",\n \"0.5400315\",\n \"0.53901994\",\n \"0.5371341\",\n \"0.53639054\",\n \"0.53625417\",\n \"0.53568465\",\n \"0.5353615\",\n \"0.5342796\",\n \"0.5342722\",\n \"0.53424704\",\n \"0.53412336\",\n \"0.5338081\",\n \"0.53376997\",\n \"0.53184915\",\n \"0.5316811\",\n \"0.53057003\",\n \"0.5298146\",\n \"0.5293607\",\n \"0.5291988\",\n \"0.5288532\",\n \"0.5285043\",\n \"0.52806556\",\n \"0.5278112\",\n \"0.52678907\",\n \"0.52640355\",\n \"0.5254969\",\n \"0.5254858\",\n \"0.5251716\",\n \"0.5251391\",\n \"0.5248119\",\n \"0.5246247\",\n \"0.524386\",\n \"0.5239195\",\n \"0.52367353\",\n \"0.52363026\",\n \"0.5232432\",\n \"0.5227924\",\n \"0.522758\",\n \"0.5226402\",\n \"0.52181196\",\n \"0.52120924\",\n \"0.52116394\",\n \"0.5207838\",\n \"0.5201456\",\n \"0.5200729\",\n \"0.51991737\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53893685"},"document_rank":{"kind":"string","value":"56"}}},{"rowIdx":93798,"cells":{"query":{"kind":"string","value":"Test that each hospital has a preference of all its matches."},"document":{"kind":"string","value":"def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"hospital\")\n\n assert set(hospitals) == set(matching.keys())\n\n for hospital, matches in matching.items():\n old_idx = -np.infty\n for resident in matches:\n idx = hospital.pref_names.index(resident.name)\n assert idx >= old_idx\n old_idx = idx","def test_check_inputs_hospital_reciprocated_all_prefs(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n hospital._forget(resident)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"hospitals\", \"residents\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert hospital not in resident.prefs","def test_check_inputs_hospital_prefs_all_residents(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert hospital.prefs == []","def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert hospital.name in message\n assert resident.name in message\n\n if game.clean:\n assert resident not in hospital.prefs","def test_resident_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"resident\")\n\n assert set(hospitals) == set(matching.keys())\n assert all(\n [\n r in set(residents)\n for r in set(\n [\n r_match\n for matches in matching.values()\n for r_match in matches\n ]\n )\n ]\n )\n\n for resident in residents:\n if resident.matching:\n assert resident.pref_names.index(resident.matching.name) == 0","def test_check_stability():\n\n residents = [Resident(\"A\"), Resident(\"B\"), Resident(\"C\")]\n hospitals = [Hospital(\"X\", 2), Hospital(\"Y\", 2)]\n\n (a, b, c), (x, y) = residents, hospitals\n\n a.set_prefs([x, y])\n b.set_prefs([y])\n c.set_prefs([y, x])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = HospitalResident(residents, hospitals)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (x, y) = game.residents, game.hospitals\n matching[x] = [c]\n matching[y] = [a, b]\n\n assert not game.check_stability()","def test_check_inputs_resident_prefs_all_hospitals(game):\n\n resident = game.residents[0]\n resident.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert resident.name in message\n assert \"foo\" in message\n\n if game.clean:\n assert resident.prefs == []","def test_solve(game, optimal):\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_hospital, hospital in zip(matching_keys, hospitals):\n assert game_hospital.name == hospital.name\n assert game_hospital._pref_names == hospital._pref_names\n assert game_hospital.capacity == hospital.capacity\n\n matched_residents = [\n resident for match in matching.values() for resident in match\n ]\n\n assert matched_residents != [] and set(matched_residents).issubset(\n set(game.residents)\n )\n\n for resident in set(game.residents) - set(matched_residents):\n assert resident.matching is None","def test_check_stability():\n\n residents = [Player(\"A\", [1, 2]), Player(\"B\", [2]), Player(\"C\", [2, 1])]\n hospitals = [Player(1, [\"C\", \"A\"], 3), Player(2, [\"A\", \"B\", \"C\"], 3)]\n match = HospitalResident(residents, hospitals)\n\n matching = match.solve()\n assert match.check_stability()\n\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\n matching[P_1] = [P_C]\n matching[P_2] = [P_A, P_B]\n\n assert not match.check_stability()","def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.append(\n ValueError(\n f\"{hospital} has not ranked all the residents that \"\n f\"ranked it: {set(hospital.prefs)} != \"\n f\"{set(residents_that_ranked)}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def _check_hospital_matching(self):\n\n errors = []\n for hospital in self.hospitals:\n for resident in hospital.matching:\n if resident not in hospital.prefs:\n errors.append(\n ValueError(\n f\"{hospital} has {resident} in their matching but \"\n \"they do not appear in their preference list: \"\n f\"{hospital.prefs}.\"\n )\n )\n\n if errors:\n raise Exception(*errors)\n\n return True","def test_solve(resident_names, hospital_names, capacities, seed):\n\n for optimal in [\"suitor\", \"resident\", \"reviewer\", \"hospital\"]:\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n matching = match.solve(optimal)\n assert isinstance(matching, Matching)\n assert set(matching.keys()) == set(hospitals)\n matched_residents = [\n res for match in matching.values() for res in match\n ]\n assert matched_residents != [] and set(matched_residents).issubset(\n set(residents)\n )\n\n for resident in set(residents) - set(matched_residents):\n assert resident.matching is None","def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_suitor_prefs()\n\n match.suitors[0].pref_names = [1, 2, 3]\n\n with pytest.raises(Exception):\n match._check_suitor_prefs()","def test_init(players, clean):\n\n residents, hospitals = players\n\n game = HospitalResident(residents, hospitals, clean)\n\n for resident, game_resident in zip(residents, game.residents):\n assert resident.name == game_resident.name\n assert resident._pref_names == game_resident._pref_names\n\n for hospital, game_hospital in zip(hospitals, game.hospitals):\n assert hospital.name == game_hospital.name\n assert hospital._pref_names == game_hospital._pref_names\n assert hospital.capacity == game_hospital.capacity\n\n assert all([resident.matching is None for resident in game.residents])\n assert all([hospital.matching == [] for hospital in game.hospitals])\n assert game.matching is None","def test_get_state_comparison_stats_penmanship_contest(self):\n\n key = 'Left-hand penmanship contest entrants'\n\n # Mississippi: 0 employees who entered the contest, 3 who didn't\n for _ in range(3):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.mississippi)\n\n # Texas: 2 employees who entered the contest, 2 who didn't\n for _ in range(2):\n employee = EmployeeFactory(penmanship_contest=True)\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 3 employees who entered the contest, 1 who didn't\n for _ in range(3):\n employee = EmployeeFactory(penmanship_contest=True)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n expected_output = [('Kentucky', 3), ('Texas', 2)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x number of employees who entered contest\"\n )","def test_fuzzy_matching_multi_match(\n nlp: Language, countries: List[Dict[str, Any]]\n) -> None:\n ruler = SpaczzRuler(nlp, spaczz_fuzzy_defaults={\"min_r2\": 85})\n ruler.add_patterns(countries)\n doc = nlp(\"This is a test that should find Northern Ireland and Ireland\")\n doc = ruler(doc)\n matches = [(ent.ent_id_, ent.text) for ent in doc.ents if ent.label_ == \"COUNTRY\"]\n assert matches == [(\"Northern Ireland\", \"Northern Ireland\"), (\"Ireland\", \"Ireland\")]","def test_check_inputs_hospital_prefs_all_nonempty(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def test_when_opponent_all_Ds(self):\n self.responses_test([C, C, C, C], [D, D, D, D], [D, D, D], random_seed=5)","def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching.append(Player(name=\"foo\", pref_names=[]))\n\n with pytest.raises(Exception):\n match._check_reviewer_matching()","def test_when_opponent_all_Ds(self):\n self.responses_test([D, D, D, D], [D, D, D, D], [D, D, D],\n random_seed=5)","def test_init(resident_names, hospital_names, capacities, seed):\n\n residents, hospitals, match = _make_match(\n resident_names, hospital_names, capacities, seed\n )\n\n assert match.suitors == residents\n assert match.reviewers == hospitals\n assert all([suitor.matching is None for suitor in match.suitors])\n assert all([reviewer.matching == [] for reviewer in match.reviewers])\n assert match.matching is None","def hospital_optimal(hospitals):\n\n free_hospitals = hospitals[:]\n while free_hospitals:\n\n hospital = free_hospitals.pop()\n resident = hospital.get_favourite()\n\n if resident.matching:\n curr_match = resident.matching\n unmatch_pair(resident, curr_match)\n if curr_match not in free_hospitals:\n free_hospitals.append(curr_match)\n\n match_pair(resident, hospital)\n if len(hospital.matching) < hospital.capacity and [\n res for res in hospital.prefs if res not in hospital.matching\n ]:\n free_hospitals.append(hospital)\n\n successors = resident.get_successors()\n for successor in successors:\n delete_pair(resident, successor)\n if (\n not [\n res\n for res in successor.prefs\n if res not in successor.matching\n ]\n and successor in free_hospitals\n ):\n free_hospitals.remove(successor)\n\n return {r: r.matching for r in hospitals}","def test_student_optimal(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n np.random.seed(seed)\n students, projects, _ = make_players(\n student_names, project_names, supervisor_names, capacities\n )\n matching = student_optimal(students, projects)\n\n assert set(projects) == set(matching.keys())\n assert all(\n [\n s in set(students)\n for s in {\n match for matches in matching.values() for match in matches\n }\n ]\n )\n\n for student in students:\n if student.matching:\n assert student.prefs.index(student.matching) == 0","def galeshapley(suitor_pref_dict, reviewer_pref_dict, max_iteration):\n suitors = list(suitor_pref_dict.keys())\n matching = dict()\n rev_matching = dict()\n\n for i in range(max_iteration):\n if len(suitors) <= 0:\n break\n for s in suitors:\n r = suitor_pref_dict[s][0]\n if r not in matching.values():\n matching[s] = r\n rev_matching[r] = s\n else:\n r_partner = rev_matching.get(r)\n if reviewer_pref_dict[r].index(s) < reviewer_pref_dict[r].index(r_partner):\n del matching[r_partner]\n matching[s] = r\n rev_matching[r] = s\n else:\n suitor_pref_dict[s].remove(r)\n suitors = list(set(suitor_pref_dict.keys()) - set(matching.keys()))\n return matching","def test_affect_of_strategy(self):\n self.responses_test([C, C, C], [C, C, C], [C, C, C])\n # Make sure that the retaliations are increasing\n # Retaliate once and forgive\n self.responses_test([C], [D], [D])\n self.responses_test([C, D], [D, C], [C])\n self.responses_test([C, D, C], [D, C, C], [C])\n # Retaliate twice and forgive\n self.responses_test([C, D, C], [D, C, D], [D, D])\n self.responses_test([C, D, C, D, D], [D, C, D, C, C], [C])\n # Opponent defection during retaliation doesn't increase retaliation period\n self.responses_test([C, D, C, D, D], [D, C, D, D, C], [C])\n # Retaliate thrice and forgive\n self.responses_test([C, D, C, D, D, C], [D, C, D, C, C, D], [D, D, D])\n history_1 = [C, D, C, D, D, C, D, D, D]\n history_2 = [D, C, D, C, C, D, C, C, C]\n self.responses_test(history_1, history_2, [C])","def _check_mutual_preference(resident, hospital):\n\n return resident in hospital.prefs and hospital in resident.prefs","def test_21_29(self):\r\n husband = Person()\r\n husband.id = \"I01\"\r\n husband.name = \"Jotaro /Kujo/\"\r\n husband.gender = \"F\"\r\n husband.birthDate = \"29 SEP 1918\"\r\n husband.age = 101\r\n husband.alive = True\r\n husband.death = \"N/A\"\r\n husband.child = [\"I03\"]\r\n husband.spouse = [\"I02\"]\r\n\r\n wife = Person()\r\n wife.id = \"I02\"\r\n wife.name = \"Marry Sue\"\r\n wife.gender = \"M\"\r\n wife.birthDate = \"12 Jan 1988\"\r\n wife.age = 80\r\n wife.alive = False\r\n wife.death = \"23 JAN 2020\"\r\n wife.child = []\r\n wife.spouse = [\"I01\"]\r\n\r\n test_family = Family()\r\n test_family.id = \"F01\"\r\n test_family.married = \"29 SEP 1993\"\r\n test_family.divorce = \"25 JAN 2020\"\r\n test_family.husbandID = \"I01\"\r\n test_family.husbandName = \"Morgan Sue\"\r\n test_family.wifeID = \"I02\"\r\n test_family.wifeName = \"Marry Sue\"\r\n test_family.chidren = [\"I03\"]\r\n\r\n personList = [husband, wife]\r\n familiList = [test_family]\r\n\r\n self.assertEqual(us21(personList, familiList), \"Husband in family is not male or wife in family is not female\")\r\n self.assertEqual(us29(personList), ['I02'])","def test_inputs_reviewer_prefs(\n resident_names, hospital_names, capacities, seed\n):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n assert match._check_reviewer_prefs()\n\n match.reviewers[0].pref_names.pop()\n\n with pytest.raises(Exception):\n match._check_reviewer_prefs()","def athlete_match(athlete, athlete_criteria):\n for key, value in athlete_criteria.items():\n if key not in athlete or athlete[key] not in value:\n return False\n return True","def test_review_phams_1(self):\n self.db_filter.values = self.db_filter.build_values(\n where=self.db_filter.build_where_clauses())\n\n review.review_phams(self.db_filter)\n\n self.assertFalse(39854 in self.db_filter.values)\n self.assertTrue(40481 in self.db_filter.values)","def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error","def test_multiple_players_return_own_results(self):\r\n observed = highscore.highscore(self.fixture_player1, self.fixture_score_high1)\r\n self.assertAlmostEquals(observed, self.fixture_score_high1)\r\n observed = highscore.highscore(self.fixture_player2, self.fixture_score_high2)\r\n self.assertAlmostEquals(observed, self.fixture_score_high2)","def test_phs_test_queries_with_phs_in_string(self):\n url = self.get_url()\n for query in self.TEST_PHS_QUERIES:\n response = self.client.get(url, {'q': 'phs' + query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = self.TEST_PHS_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phs in expected_matches:\n expected_pk = models.Study.objects.get(i_accession=expected_phs).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected phs {} with query '{}'\".format(expected_phs, query))","def test_phs_test_queries_with_phs_in_string(self):\n url = self.get_url()\n for query in self.TEST_PHS_QUERIES:\n response = self.client.get(url, {'q': 'phs' + query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = self.TEST_PHS_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phs in expected_matches:\n expected_pk = models.Study.objects.get(i_accession=expected_phs).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected phs {} with query '{}'\".format(expected_phs, query))","def test_counts(contrib_data):\n ## Find the probands - they should always contribute their own alleles\n P = ped.Pedigree(args.pedfile)\n\n ## TODO: Test individuals with multiple offspring +p2 id:112\n ## Case when no regions are specified - only all probands\n regions = contrib_data.keys()\n if len(regions) == 1:\n all_contribs = contrib_data[regions[0]]\n for prob in P.probands:\n ## Probands should always contribute 1 allele\n assert all_contribs[prob, 1] == args.iterations\n\n ## Test specific individuals\n counts = Counter(all_contribs[11])\n binomial_sd = np.sqrt(args.iterations * 0.25)\n mean = np.mean(counts.values())\n conf95 = (mean - 2 * binomial_sd, mean + 2 * binomial_sd)\n\n for count in counts.values():\n assert conf95[0] < count < conf95[1]","def analyze_similarities():\r\n print('Total number of candidate pairs:', len(pairs))\r\n print(f'\\nNumber of actual item pairs in the train set: {pairs[\"true_match\"].sum()}\\n')\r\n\r\n for feature in ['text_score', 'image_score', 'txt_img_score', 'words_ratio', 'txt_img_words']:\r\n\r\n # Check distribution of True and False predictions for various similarity scores\r\n print('-' * 50)\r\n print(f'\\nDistribution of True/False predictions for {feature}')\r\n for thr in (0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95):\r\n print('-' * 50)\r\n print(f'Similarity score over {thr}')\r\n pairs_sample = pairs[pairs[feature] >= thr]\r\n print(f'Number of similar item pairs: {len(pairs_sample)}')\r\n print(pairs_sample['true_match'].value_counts(normalize=True))\r\n\r\n # Check if identical phash can be used to improve the accuracy\r\n same_phash = pairs[pairs['phash_match'] == 1]\r\n different_phash = pairs[pairs['phash_match'] == 0]\r\n\r\n print('\\nFor item pairs with the same phash:')\r\n print(same_phash['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(same_phash))\r\n\r\n print('\\nFor item pairs with different phash:')\r\n print(different_phash['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(different_phash))\r\n\r\n # Check if numbers in titles can be used to improve the accuracy\r\n same_numbers = pairs[pairs['nums_match'] == 1]\r\n different_numbers = pairs[pairs['nums_match'] == 0]\r\n\r\n print('\\nFor item pairs with the same numbers:')\r\n print(same_numbers['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(same_numbers))\r\n\r\n print('\\nFor item pairs with different numbers:')\r\n print(different_numbers['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(different_numbers))","def test_query_all(test_store, andy, pandy, candy):\n items = list(test_store.get_by())\n assert len(items) == 3\n assert andy in items\n assert pandy in items\n assert candy in items","def suggestionexpposteriors(self,character,room,weapon):\n # Some helpful phrasings of the suggestion:\n triadList = [character,room,weapon]\n triadSet = set(triadList)\n #\n #### PHASE 1\n # Sort the hypotheses by their murder scenario, and by the answer(s) \n # they provoke to this suggestion. A player may hold multiple of the\n # cards suggested and hence have a choice regarding which to show us.\n #\n # Therefore, we sort into 7 bins:\n # Bins 1,3,5,7 (binary 1 bit set) => Player holds character card\n # Bins 2,3,6,7 (binary 2 bit set) => Player holds room card\n # Bins 4,5,6,7 (binary 4 bit set) => Player holds weapon card\n # Note for zero based indexing, we must subtract one from these sort \n # indices.\n hypCountsSorted = np.zeros(\n (self.nPlayers-1, # Players that could answer\n 7, # Cards this player could hold\n game.NCHARS,game.NROOMS,game.NWEAPS # Suggestion murder scenario\n ))\n # Lastly, we could have no answer from anyone\n hypCountsNoAnswer = np.zeros((game.NCHARS,game.NROOMS,game.NWEAPS))\n #\n # Sort the hypotheses:\n for hypothesis in self.hypotheses:\n for ixPlayer in range(1,self.nPlayers):\n ixAnswer = 0\n if character in hypothesis[ixPlayer]:\n ixAnswer = 1\n if room in hypothesis[ixPlayer]:\n ixAnswer |= 2\n if weapon in hypothesis[ixPlayer]:\n ixAnswer |= 4\n if ixAnswer > 0:\n hypCountsSorted[ixPlayer-1,ixAnswer-1,\n hypothesis[-1][0],hypothesis[-1][1],\n hypothesis[-1][2]] += 1\n break\n else:\n # Else executed when for loop terminates without break => none\n # of the players held any of the cards suggested:\n hypCountsNoAnswer[hypothesis[-1][0],hypothesis[-1][1],\n hypothesis[-1][2]] += 1\n #\n #### PHASE 2\n # Calculate the scenario entropy we're left with after receiving each\n # possible answer from each possible player (or no answers).\n #\n # Receiving an answer from a player invalidates all hypotheses in which\n # that player does NOT hold that card. For example, if player X shows us\n # the character, we will be left with the matrix of hypothesis counts\n # by scenario as follows:\n # self.hypCountsByScenario[:,:,:] = \n # hypCountsSorted[X-1,{ix & 1},:,:,:]\n #\n # let HypCountsAfterAnswer be the hypothesis counts after reduction (but\n # before any rebuilding) following each possible answer:\n # nPlayersAsked * 3 (Possible Answers) * NCHARS * NROOMS * NWEAPS\n #\n # Showing the character card keeps bins (1,3,5,7)-1 = (0,2,4,6)\n # Showing the room card keeps bins (2,3,6,7)-1 = (1,2,5,6)\n # Showing the weapon card keeps bins (4,5,6,7)-1 = (3,4,5,6)\n #\n # This matrix represents the distribution over scenarios after each \n # answer is received - where deals allowing multiple answers are counted\n # multiple times\n hypCountsAfterAnswer = (\n hypCountsSorted[:,(0,1,3),:,:,:] + # One card only\n hypCountsSorted[:,(2,2,4),:,:,:] + \n hypCountsSorted[:,(4,5,5),:,:,:] +\n hypCountsSorted[:,(6,6,6),:,:,:]) # All three cards\n #\n # This matrix represents the marginal distribution over murder rooms \n # after each answer is received.\n hypCountsByRoomAfterAnswer = np.sum(np.sum(\n hypCountsAfterAnswer,4),2)\n #\n # To calculate probabilities (and so entropies) from our hypothesis \n # counts, we need to divide by the total hypotheses remaining after each\n # answer. Only the room axis must be summed over, since we already\n # produced the marginal over rooms above. Append three singleton \n # dimensions to allow direct division of the scenario count matrices by\n # (slices of) this matrix:\n hypCountSumsAfterAnswer = np.sum(\n hypCountsByRoomAfterAnswer,2)[:,:,None,None,None]\n pAfterAnswer = hypCountsAfterAnswer/hypCountSumsAfterAnswer\n #\n # pAfterAnswer will have np.infs in it where certain player,answer \n # combinations are completely impossible \n # (hypCountSumAfterAnswer[p,ans] = 0). These will appear as np.nans in\n # the entropy calculation below and must be dealt with. Note we don't \n # bother dealing with the infs in p separately because:\n # a) 0log(0) would still map to non-finite entropy so need correction\n # b) We don't use pAfterAnswer after entropy calculation so may as \n # well save our time.\n entsAfterAnswer = -pAfterAnswer*np.log(pAfterAnswer)\n entsAfterAnswer[np.isnan(entsAfterAnswer)] = 0.\n # Distribution entropy is sum of entropies over distribution axes:\n # (NCHARS, NROOMS, NWEAPS)\n entAfterAnswer = np.sum(np.sum(np.sum(entsAfterAnswer,4),3),2)\n #\n # With all that out of the way, dealing with no-answer case is easier:\n hypCountsByRoomNoAnswer = np.sum(np.sum(hypCountsNoAnswer,2),0)\n hypCountSumNoAnswer = np.sum(hypCountsByRoomNoAnswer)\n if hypCountSumNoAnswer > 0.:\n # pNoAnswer will contain no infs...\n pNoAnswer = hypCountsNoAnswer/hypCountSumNoAnswer\n # ...But it will still have zeros so we must remove nan entropies.\n entsNoAnswer = -pNoAnswer*np.log(pNoAnswer)\n entsNoAnswer[np.isnan(entsNoAnswer)] = 0.\n entNoAnswer = np.sum(entsNoAnswer)\n else:\n entNoAnswer = 0.\n #\n #### PHASE 3\n # Calculate the probability of receiving each answer; given opponents'\n # expected strategy for choosing cards to show us where multiple \n # possibilities exist.\n #\n # We assume players maximise the Detective's turn-end entropy when \n # choosing cards to show; and hence provide the minimum amount of \n # information to our distribution.\n #\n # let playerDecisions[ply,:] be player ply's order of preference for\n # each response (2=weap, 1=room, 0=character) starting with favourite:\n playerDecisions = np.argsort(entAfterAnswer)[:,::-1]\n #\n # Hence, the most preferred response will be given for all hypotheses \n # where ply holds that card. The least preferred response will be given\n # for hypotheses where ply holds the least preferred card only.\n # Example: 0,1,2 => Character-holding bins 1,3,5,7 count to response 0\n # Bin 4 only counts towards response 2 (weapon)\n # Bins 2,6 count towards response 1 (room)\n #\n # For starters, allocate bins corresponding to holding only one card:\n # (don't worry - this creates a new copy not a view)\n receivedHypCounts = hypCountsSorted[:,(0,1,3),:,:,:]\n #\n # Because each player will have a different preference order, we must\n # loop through the player slices assigning the bins separately for each.\n # Note we refrain from use of 'ixPlayer' since we're off by one, not \n # having the usual ixPlayer=0=Detective.\n for ixOPlayer in range(self.nPlayers-1):\n # The three-card case (bin 7) is fairly easily allocated - to the \n # most preferred response:\n receivedHypCounts[ixOPlayer,playerDecisions[ixOPlayer,0]] += (\n hypCountsSorted[ixOPlayer,6,:,:,:])\n #\n # The three two-card bins require a little more negotiating:\n binsForAllocation = [3,5,6] # indexes 2,4 and 5\n mostPreferredBit = 2 ** playerDecisions[ixOPlayer,0]\n for thisBin in binsForAllocation:\n if thisBin & mostPreferredBit:\n # Goes to most preferred answer\n receivedHypCounts[ixOPlayer,playerDecisions[ixOPlayer,0]] \\\n += hypCountsSorted[ixOPlayer,thisBin-1,:,:,:]\n else:\n # Goes to second preferred answer\n receivedHypCounts[ixOPlayer,playerDecisions[ixOPlayer,1]] \\\n += hypCountsSorted[ixOPlayer,thisBin-1,:,:,:]\n #\n # We don't actually care about these distributions; only the total \n # number of hypotheses in each for weighting our earlier entropies:\n totalReceivedCounts = np.sum(np.sum(np.sum(receivedHypCounts,4),3),2)\n # The sum over all given answers plus the no-answer case will, of course\n # be the total number of hypotheses we have:\n totalHypCount = len(self.hypotheses)\n # TODO: Remove debug check\n assert totalHypCount == \\\n np.sum(totalReceivedCounts) + hypCountSumNoAnswer\n #\n # Hence the expected entropy after this suggestion is the sum of \n # scenario entropies after each possible answer (and no answer); \n # weighted by the number of hypotheses for which each answer is received\n expEntropy = ((entNoAnswer * hypCountSumNoAnswer) + \n np.sum(entAfterAnswer * totalReceivedCounts)) / totalHypCount\n #\n # The expected room distribution works similarly, but needs to be\n # normalised by whatever constant necessary to represent a valid \n # probability distribution. (totalHypCount^2 does not work)\n expRoomDist = ((hypCountsByRoomNoAnswer * hypCountSumNoAnswer) +\n np.sum(np.sum(\n (hypCountsByRoomAfterAnswer * totalReceivedCounts[:,:,None]),1),0))\n expRoomDist = expRoomDist / np.sum(expRoomDist)\n #\n return {'expEntropy':expEntropy,'expRoomDist':expRoomDist}","def test_strategy(self):\n self.responses_test([C, C, C, C], [C, C, C, C], [C])\n self.responses_test([C, C, C, C, D], [C, C, C, D, C], [D])\n self.responses_test([C] * 11, [C] * 10 + [D], [C])","def test_check_stability():\n\n students = [Student(\"A\"), Student(\"B\"), Student(\"C\")]\n projects = [Project(\"P\", 2), Project(\"Q\", 2)]\n supervisors = [Supervisor(\"X\", 2), Supervisor(\"Y\", 2)]\n\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\n\n p.set_supervisor(x)\n q.set_supervisor(y)\n\n a.set_prefs([p, q])\n b.set_prefs([q])\n c.set_prefs([q, p])\n\n x.set_prefs([c, a])\n y.set_prefs([a, b, c])\n\n game = StudentAllocation(students, projects, supervisors)\n\n matching = game.solve()\n assert game.check_stability()\n\n (a, b, c), (p, q) = game.students, game.projects\n\n matching[p] = [c]\n matching[q] = [a, b]\n\n assert not game.check_stability()","def profesionalRecommendation(user_preferences: dict, matcher: NodeMatcher):\r\n profesional = user_preferences[\"vida_profesional\"]\r\n equal_styles = list(matcher.match(\"User\", prof = profesional))\r\n return equal_styles","def test_pl_many(k, l, th_list):\n patterns = build_pattern_dict(k, l)\n best_factor = 0\n for x_th, y_th in th_list:\n try:\n factor = test_pl(k, l, x_th, y_th, patterns=patterns)\n if factor > best_factor:\n print(\"New best factor {} @ (x_th,y_th) = ({},{})\".format(factor, x_th, y_th))\n best_factor = factor\n except:\n print(\"An error occured while solving the LP\")\n print(\"Final best factor {}\".format(best_factor))","def test_strategy(self):\n\n axelrod.seed(8)\n opponent = axelrod.Cooperator()\n player = axelrod.WorseAndWorse()\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'D'),\n ('C', 'C'),\n ('C', 'C'),\n ('C', 'C')])\n\n # Test that behaviour does not depend on opponent\n opponent = axelrod.Defector()\n player = axelrod.WorseAndWorse()\n axelrod.seed(8)\n match = axelrod.Match((opponent, player), turns=10)\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'D'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C')])","def make_fuzzy_comparable_member_list(all_households):\n\n heads_and_spouses = []\n for house in all_households:\n for m in ['headOfHouse', 'spouse']:\n person = house[m] if m in house else None\n if person:\n member = FuzzyDict({\n 'first_name': person['preferredName'].split(',')[1].strip() if ',' in person['preferredName'] else '',\n 'last_name': person['surname'] if 'surname' in house[m] else '', \n 'full_name': person['fullName'] if 'fullName' in house[m] else 'fail',\n 'street': house['desc1'] if 'desc1' in house else '',\n 'phones': {\n 'phone1': utils.normalize_phone_number(person['phone']) if 'phone' in person else 0,\n 'phone2': utils.normalize_phone_number( house['phone']) if 'phone' in house else 0\n },\n 'emails': {\n 'email1' : person['email'] if 'email' in person else '',\n 'email2': house['email'] if 'email' in house else ''\n },\n 'ward': house['ward'],\n 'match': False\n })\n # print member\n heads_and_spouses.append(member)\n\n return heads_and_spouses","def habits(user_preferences: dict, matcher: NodeMatcher):\r\n goodhabits = user_preferences[\"Buenos_habitos\"]\r\n equal_styles = list(matcher.match(\"User\", Habits = goodhabits))\r\n return equal_styles","def test_two_and_three_card_petitions(self):\n f = gtrutils.check_petition_combos\n\n self.assertTrue( f( 0, 0, [], True, True))\n\n self.assertFalse( f( 1, 0, [], True, True))\n self.assertFalse( f( 1, 0, [1], True, True))\n self.assertTrue( f( 1, 0, [2], True, True))\n self.assertTrue( f( 1, 0, [3], True, True))\n self.assertFalse( f( 1, 0, [4], True, True))\n self.assertTrue( f( 1, 1, [], True, True))\n self.assertTrue( f( 1, 2, [], True, True))\n self.assertTrue( f( 1, 3, [], True, True))\n self.assertFalse( f( 1, 4, [], True, True))\n\n self.assertFalse( f( 1, 1, [2], True, True))\n self.assertFalse( f( 1, 1, [3], True, True))\n self.assertFalse( f( 1, 2, [2], True, True))\n self.assertFalse( f( 1, 3, [2], True, True))\n self.assertFalse( f( 1, 3, [3], True, True))\n\n self.assertTrue( f( 2, 1, [2], True, True))\n self.assertTrue( f( 2, 1, [3], True, True))\n self.assertTrue( f( 2, 0, [4], True, True))\n self.assertTrue( f( 2, 0, [5], True, True))\n self.assertTrue( f( 2, 0, [6], True, True))\n self.assertTrue( f( 2, 4, [], True, True))\n self.assertTrue( f( 2, 5, [], True, True))\n self.assertTrue( f( 2, 6, [], True, True))\n \n self.assertTrue( f(13, 26, [], True, True))\n self.assertTrue( f(13, 39, [], True, True))\n self.assertTrue( f(13, 0, [26], True, True))\n self.assertTrue( f(13, 14, [12], True, True))\n self.assertTrue( f(13, 13, [10], True, True))\n self.assertTrue( f(13, 15, [11], True, True))\n self.assertFalse( f(13, 40, [], True, True))\n self.assertFalse( f(13, 11, [3], True, True))\n\n self.assertFalse( f(4, 1, [2,3,6], True, True))\n self.assertTrue( f(5, 1, [2,3,6], True, True))\n self.assertTrue( f(6, 1, [2,3,6], True, True))\n self.assertFalse( f(7, 1, [2,3,6], True, True))","def test_tally_Schulze(self):\n self.populate_database(election_type=\"Schulze\")\n\n uAvote1 = models.Vote(\n user = self.userA,\n candidate = self.candidateBA,\n value =5)\n uAvote2 = models.Vote(\n user = self.userA,\n candidate = self.candidateBB,\n value = 0)\n uAvote3 = models.Vote(\n user = self.userA,\n candidate = self.candidateBC,\n value = 3)\n uAvote4 = models.Vote(\n user = self.userA,\n candidate = self.candidateBD,\n value = -2)\n\n uBvote1 = models.Vote(\n user = self.userB,\n candidate = self.candidateBA,\n value = 6)\n uBvote2 = models.Vote(\n user = self.userB,\n candidate = self.candidateBB,\n value = 1)\n uBvote3 = models.Vote(\n user = self.userB,\n candidate = self.candidateBC,\n value = -2)\n uBvote4 = models.Vote(\n user = self.userB,\n candidate = self.candidateBD,\n value = 5)\n\n uCvote1 = models.Vote(\n user = self.userC,\n candidate = self.candidateBA,\n value = -2)\n uCvote2 = models.Vote(\n user = self.userC,\n candidate = self.candidateBB,\n value = 5)\n uCvote3 = models.Vote(\n user = self.userC,\n candidate = self.candidateBC,\n value = 2)\n uCvote4 = models.Vote(\n user = self.userC,\n candidate = self.candidateBD,\n value = 3)\n # Check gen_pair_results() method in Schulze()\n cand_pair_results = self.schulze.gen_pair_results(self.raceB)\n # Generate expected pair_results dict for comparitive purposes\n vote2 = aliased(models.Vote, name=\"vote2\")\n expected_pair_results = {}\n for cand1, cand2 in cand_pair_results.keys():\n preferred_expected = 0\n for user in [self.userA, self.userB, self.userC]:\n v1, v2 = session.query(\n models.Vote.value.label(\"vote1\"),\n vote2.value.label(\"vote2\")).filter(\n models.Vote.user_id == user.id,\n vote2.user_id == user.id,\n models.Vote.candidate_id == cand1,\n vote2.candidate_id == cand2).all()[0]\n if v1 > v2:\n preferred_expected += 1\n expected_pair_results[(cand1, cand2)] = preferred_expected\n self.assertEqual(cand_pair_results[(cand1, cand2)],\n expected_pair_results[(cand1, cand2)])\n\n final_result = self.schulze.tally_race(self.raceB.id)\n\n self.dbresults = models.Results(\n race_id = self.raceB.id,\n results = final_result) \n session.add(self.dbresults)\n session.commit()\n\n # JSON doesn't allow dict keys as anything but strings, so \n # the original model's keys must be converted for comparative\n # purposes\n print(\"results\", self.dbresults.results)\n final_result_keys_to_str = utils.dict_keys_to_str(final_result.items())\n\n self.assertEqual(final_result, {3:True, 4:False, 5:False, 6:False})\n self.assertEqual(self.dbresults.results, final_result_keys_to_str)\n self.assertEqual(self.dbresults.election_type, self.raceB.election_type)\n # self.assertEqual(1,0)","def test_solve(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n for optimal in [\"student\", \"supervisor\"]:\n students, projects, _, game = make_game(\n student_names,\n project_names,\n supervisor_names,\n capacities,\n seed,\n clean,\n )\n\n matching = game.solve(optimal)\n assert isinstance(matching, MultipleMatching)\n\n projects = sorted(projects, key=lambda p: p.name)\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\n for game_project, project in zip(matching_keys, projects):\n assert game_project.name == project.name\n assert game_project._pref_names == project._pref_names\n assert game_project.capacity == project.capacity\n assert game_project.supervisor.name == project.supervisor.name\n\n matched_students = [\n stud for match in matching.values() for stud in match\n ]\n assert matched_students != [] and set(matched_students).issubset(\n set(game.students)\n )\n\n for student in matched_students:\n supervisor = student.matching.supervisor\n assert student in supervisor.matching\n\n for student in set(game.students) - set(matched_students):\n assert student.matching is None","def test_candidates_list(self):\n pass","def test_known_common_stable_isotopes_cases():\n assert \"H-1\" in known_isotopes(\"H\")\n assert \"D\" in known_isotopes(\"H\")\n assert \"T\" in known_isotopes(\"H\")\n assert \"Be-8\" in known_isotopes(\"Be\")\n assert \"Og-294\" in known_isotopes(118)\n assert \"H-1\" in common_isotopes(\"H\")\n assert \"H-4\" not in common_isotopes(1)\n assert \"H-1\" in stable_isotopes(\"H\")\n assert \"D\" in stable_isotopes(\"H\")\n assert \"T\" not in stable_isotopes(\"H\")\n assert \"Fe-56\" in common_isotopes(\"Fe\", most_common_only=True)\n assert \"He-4\" in common_isotopes(\"He\", most_common_only=True)","def matchClasses(self, probable, actual = None):\r\n if actual == None:\r\n actual = self.classes\r\n return not probable or len([x for x in probable if x in actual]) == len(probable)","def test_prefilter_exact_matches(self):\r\n seqs = [('s1 comment1', 'ACCTTGTTACTTT'), # three copies\r\n ('s2 comment2', 'ACCTTGTTACTTTC'), # one copy\r\n ('s3 comment3', 'ACCTTGTTACTTTCC'), # two copies\r\n ('s4 comment4', 'ACCTTGTTACTTT'),\r\n ('s5 comment5', 'ACCTTGTTACTTTCC'),\r\n ('s6 comment6', 'ACCTTGTTACTTT')]\r\n expected0 = [('QiimeExactMatch.s1', 'ACCTTGTTACTTT'),\r\n ('QiimeExactMatch.s2', 'ACCTTGTTACTTTC'),\r\n ('QiimeExactMatch.s3', 'ACCTTGTTACTTTCC')]\r\n expected1 = {'QiimeExactMatch.s1': ['s1', 's4', 's6'],\r\n 'QiimeExactMatch.s2': ['s2'],\r\n 'QiimeExactMatch.s3': ['s3', 's5']}\r\n expected = (expected0, expected1)\r\n p = OtuPicker({})\r\n actual = p._prefilter_exact_matches(seqs)\r\n self.assertEqual(actual, expected)","def test_get_state_comparison_stats_confederate(self):\n\n key = '% Ex-Confederate employees'\n\n # 1 ex-Confederate employee in Texas, and 3 not\n for _ in range(1):\n employee = EmployeeFactory(confederate_veteran=True)\n employee.bureau_states.add(self.texas)\n for _ in range(3):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.texas)\n\n # 1 ex-Confederate employee in Kentucky, and 1 not\n for _ in range(1):\n employee = EmployeeFactory(confederate_veteran=True)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n # 3 ex-Confederate employees in Mississippi\n for _ in range(3):\n employee = EmployeeFactory(confederate_veteran=True)\n employee.bureau_states.add(self.mississippi)\n\n expected_output = [('Mississippi', 100), ('Kentucky', 50)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(top_states, expected_output,\n f\"'{key}' should contain states with the top x % of Confederate veteran employees\")","def test_strategy(self):\n axelrod.seed(1)\n opponent = axelrod.Cooperator()\n player = axelrod.KnowledgeableWorseAndWorse()\n match = axelrod.Match((opponent, player), turns=5)\n self.assertEqual(match.play(), [('C', 'C'),\n ('C', 'D'),\n ('C', 'D'),\n ('C', 'D'),\n ('C', 'D')])\n\n # Test that behaviour does not depend on opponent\n opponent = axelrod.Defector()\n player = axelrod.KnowledgeableWorseAndWorse()\n axelrod.seed(1)\n match = axelrod.Match((opponent, player), turns=5)\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'D'),\n ('D', 'D'),\n ('D', 'D'),\n ('D', 'D')])\n\n # Test that behaviour changes when does not know length.\n axelrod.seed(1)\n match = axelrod.Match((opponent, player), turns=5,\n match_attributes={'length': float('inf')})\n self.assertEqual(match.play(), [('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C'),\n ('D', 'C')])","def test_fuzzy_matching_basic(nlp: Language, countries: List[Dict[str, Any]]) -> None:\n ruler = SpaczzRuler(nlp)\n ruler.add_patterns(countries)\n doc = nlp(\"This is a test that should find Egypt and Argentina\")\n doc = ruler(doc)\n matches = [(ent.ent_id_, ent.text) for ent in doc.ents if ent.label_ == \"COUNTRY\"]\n assert matches == [(\"Egypt\", \"Egypt\"), (\"Argentina\", \"Argentina\")]","def test_supervisor_optimal(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n np.random.seed(seed)\n students, projects, supervisors = make_players(\n student_names, project_names, supervisor_names, capacities\n )\n matching = supervisor_optimal(projects, supervisors)\n\n assert set(projects) == set(matching.keys())\n assert all(\n [\n s in set(students)\n for s in {\n match for matches in matching.values() for match in matches\n }\n ]\n )\n\n for supervisor in supervisors:\n old_idx = -np.infty\n for student in supervisor.matching:\n idx = supervisor.prefs.index(student)\n assert idx >= old_idx\n old_idx = idx","def test_search_multiresults(self):\n self.assertEquals(len(self.t['Scrubs'].search('my first')) >= 3, True)","def test_phs_test_queries_without_phs_in_string(self):\n url = self.get_url()\n for query in self.TEST_PHS_QUERIES:\n response = self.client.get(url, {'q': query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = self.TEST_PHS_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phs in expected_matches:\n expected_pk = models.Study.objects.get(i_accession=expected_phs).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected phs {} with query '{}'\".format(expected_phs, query))","def test_get_matches(self):\n\n results = GenomePropertiesResultsWithMatches(*self.test_genome_property_results, properties_tree=self.test_tree)\n\n self.assertEqual(len(\n results.step_matches.reset_index()[['Property_Identifier', 'Step_Number']].drop_duplicates()), 1)\n self.assertEqual(len(results.get_sample_matches('C_chlorochromatii_CaD3')), 5)\n self.assertEqual(len(results.get_sample_matches('C_luteolum_DSM_273')), 4)\n\n self.assertEqual(results.get_sample_matches('Your moms house'), None)","def test_four_kind(self):\n self.assertEqual(poker([self.fk, self.fh]), [self.fk])","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def testProtractedPostApplicationSanityChecks(self):\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(1))\n self.assertEqual(4, self.c.get_species_richness(2))\n self.assertEqual(7, self.c.get_species_richness(3))\n self.assertEqual(7, self.c.get_species_richness(4))\n self.assertEqual(4, self.c.get_species_richness(5))\n self.assertEqual(4, self.c.get_species_richness(6))\n self.assertEqual(21, self.c.get_species_richness(7))\n self.assertEqual(38, self.c.get_species_richness(8))","def test_store_three_responses(self):\n question = \"What language did you first learn to speak?\"\n my_survey = AnonymousSurvey(question)\n responses = ['English','German','French']\n for response in responses:\n my_survey.store_response(response)\n\n for response in responses:\n self.assertIn(response, my_survey.responses)","def tests_in(strategy):\n retval = []\n for pri, test, args in strategy:\n if test not in retval:\n retval.append(test)\n return retval","def test_phs_test_queries_without_phs_in_string(self):\n url = self.get_url()\n for query in self.TEST_PHS_QUERIES:\n response = self.client.get(url, {'q': query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = self.TEST_PHS_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phs in expected_matches:\n expected_pk = models.Study.objects.get(i_accession=expected_phs).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected pht {} with query '{}'\".format(expected_phs, query))","def test_suitor_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.suitors[0].matching = Player(name=\"foo\", pref_names=[])\n\n with pytest.raises(Exception):\n match._check_suitor_matching()","def main():\n args = parse_arguments()\n\n ##### READ AND PREPROCESS DATA #####\n mentors, candidates, mentors_years, candidates_years = read_and_preprocess(args.mentor_file, args.candidate_file, args.num_preferences)\n\n # Store a copy of the initially declared preferences (after filtering)\n # This is used later to see how many people didn't get someone in their top \n candidate_dict_initial = list_to_dictionary(candidates)\n mentor_dict_initial = list_to_dictionary(mentors)\n\n ##### PAIR BASED ON FIRST CHOICES #####\n successful_pairings = match_top_choices(list_to_dictionary(mentors), list_to_dictionary(candidates))\n print(\"Paired {} people based on first choices\".format(len(successful_pairings)))\n remove_pairings(successful_pairings, mentors, candidates)\n\n ##### PAIR BASED ON ONE-SIDED PREFERENCE #####\n ## ONE SIDED CANDIDATES\n one_sided_candidate_pairings = match_no_preference(candidates, mentors)\n print(\"Paired {} people based on candidates with no mentor preferences\".format(len(one_sided_candidate_pairings)))\n remove_pairings(one_sided_candidate_pairings, candidates, mentors)\n\n ## ONE SIDED MENTORS\n one_sided_mentor_pairings = match_no_preference(mentors, candidates)\n print(\"Paired {} people based on mentors with no candidate preferences\".format(len(one_sided_mentor_pairings)))\n remove_pairings(one_sided_mentor_pairings, mentors, candidates)\n\n # Fill the remaining preference list with the valid unspecified mentors/candidates\n mentors_filled, candidates_filled = fill_with_valid(mentors, candidates, mentors_years, candidates_years)\n\n candidate_dict = list_to_dictionary(candidates_filled)\n mentor_dict = list_to_dictionary(mentors_filled)\n\n stable_matches = stable_matching.stable_marriage(mentor_dict, candidate_dict)\n stable_match_success = (len(stable_matches) != 0)\n if stable_match_success:\n print(\"Paired {} people based on stable matching\".format(len(stable_matches)))\n else:\n print(\"Warning: Could not determine a stable match with the optimizations.\")\n print(\"Attempting to stable match without...\")\n mentors, candidates, mentors_years, candidates_years = read_and_preprocess(args.mentor_file, args.candidate_file, args.num_preferences)\n # Fill the remaining preference list with the valid unspecified mentors/candidates\n mentors_filled, candidates_filled = fill_with_valid(mentors, candidates, mentors_years, candidates_years)\n\n candidate_dict = list_to_dictionary(candidates_filled)\n mentor_dict = list_to_dictionary(mentors_filled)\n\n # Stable match immediately\n stable_matches = stable_matching.stable_marriage(mentor_dict, candidate_dict)\n if len(stable_matches) == 0:\n print(\"Error: Could not stable match these preference lists.\")\n return\n\n # Combine the pairings from all sources into a single list\n if stable_match_success:\n all_pairings = []\n for mentor, candidate in successful_pairings:\n all_pairings.append([mentor, candidate, \"Paired based on first choice\"])\n for mentor, candidate in one_sided_mentor_pairings:\n all_pairings.append([mentor, candidate, \"Paired based on one-sided mentors\"])\n for candidate, mentor in one_sided_candidate_pairings:\n all_pairings.append([mentor, candidate, \"Paired based on one-sided candidates\"])\n for mentor, candidate in stable_matches:\n mentor_name = mentor\n if mentor is None:\n mentor_name = \"No mentor\"\n candidate_name = candidate\n if candidate is None:\n candidate_name = \"No candidate\"\n all_pairings.append([mentor_name, candidate_name, \"Paired based on stable matching\"])\n else:\n all_pairings = stable_matches\n\n output_file = \"pairings.csv\"\n if args.output_file:\n output_file = args.output_file\n with open(output_file, \"w+\") as f:\n f.write(\"Mentor,Candidate,Notes\\n\")\n for mentor, candidate, notes in all_pairings:\n f.write(\"{},{},{}\\n\".format(mentor, candidate, notes))","def test_reasonable_auction(self):\n # I have no preferences\n bids = [Cost(ITEM1, ACTOR1, 1000),\n Cost(ITEM2, ACTOR1, 1000),\n Cost(ITEM3, ACTOR1, 1000),\n Cost(ITEM4, ACTOR1, 1000),\n Cost(ITEM5, ACTOR1, 1000),\n\n # I have linear preferences\n Cost(ITEM1, ACTOR2, 700),\n Cost(ITEM2, ACTOR2, 800),\n Cost(ITEM3, ACTOR2, 1000),\n Cost(ITEM4, ACTOR2, 1200),\n Cost(ITEM5, ACTOR2, 1300),\n\n # I have non-linear preferences\n Cost(ITEM1, ACTOR3, 400),\n Cost(ITEM2, ACTOR3, 800),\n Cost(ITEM3, ACTOR3, 1000),\n Cost(ITEM4, ACTOR3, 1200),\n Cost(ITEM5, ACTOR3, 1600),\n\n # I have arbitrary preference\n Cost(ITEM1, ACTOR4, 2435),\n Cost(ITEM2, ACTOR4, 305),\n Cost(ITEM3, ACTOR4, 310),\n Cost(ITEM4, ACTOR4, 1725),\n Cost(ITEM5, ACTOR4, 225),\n\n # I have strong preferences\n Cost(ITEM1, ACTOR5, 0),\n Cost(ITEM2, ACTOR5, 0),\n Cost(ITEM3, ACTOR5, 0),\n Cost(ITEM4, ACTOR5, 0),\n Cost(ITEM5, ACTOR5, 5000)]\n result = self.splitter.split(ITEMS[:5], ACTORS[:5], bids)\n expected = [(ITEM1, ACTOR4, None),\n (ITEM2, ACTOR1, None),\n (ITEM3, ACTOR3, None),\n (ITEM4, ACTOR2, None),\n (ITEM5, ACTOR5, None)]\n item_assignments_present(self, result, expected)","def test_results_prefit() -> None:\n X_train_val, X_test, y_train_val, y_test = train_test_split(\n X, y, test_size=1/10, random_state=1\n )\n X_train, X_val, y_train, y_val = train_test_split(\n X_train_val, y_train_val, test_size=1/9, random_state=1\n )\n estimator = LinearRegression().fit(X_train, y_train)\n mapie = MapieRegressor(estimator=estimator, cv=\"prefit\")\n mapie.fit(X_val, y_val)\n _, y_pis = mapie.predict(X_test, alpha=0.05)\n width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()\n coverage = regression_coverage_score(\n y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]\n )\n np.testing.assert_allclose(width_mean, WIDTHS[\"prefit\"], rtol=1e-2)\n np.testing.assert_allclose(coverage, COVERAGES[\"prefit\"], rtol=1e-2)","def stable_roommates(players):\n\n players = first_phase(players)\n\n if any(p.prefs == [] for p in players):\n warnings.warn(\n NoStableMatchingWarning(\n \"The following players have been rejected by all others, \"\n \"emptying their preference list: \"\n f\"{[p for p in players if not p.prefs]}\"\n )\n )\n\n if any(len(p.prefs) > 1 for p in players):\n players = second_phase(players)\n\n return {player: player.matching for player in players}","def property_apply(x): \n p_lst = ['Arson',\n 'Attempt to Rob',\n 'Attempted Arson',\n 'Attempted Robbery', \n 'Burglary', \n 'Defacing Brands',\n 'Effacing and Defacing Brands',\n 'Having Tools with Intent to Commit Burglary',\n 'Having Tools with Intent to Enter Building',\n 'Larceny',\n 'Larceny as Bailee',\n 'Larceny from Person',\n 'Receiving Stolen Goods',\n 'Stealing / Killing Cattle',\n 'Stealing Stock',\n 'Killing Cattle',\n 'Killing Stock',\n 'Robbery',\n 'Having Burgular Tools',\n 'Felony, Branding Stock',\n 'Malicious Mischief',\n 'Grand Larceny',\n 'Obstructing of Railroad Track',\n 'Train Wrecking',\n 'Maliciously Destroying Check']\n for elem in x:\n if elem in p_lst:\n return 1\n return 0","def top_matches(self, prefs, p1):\n #print 'top_matches', prefs, p1\n #print '\\n'\n return [(p2, self.similarity(prefs[p1], prefs[p2])) for p2 in prefs if p2 != p1]","def findMatches3(personDict,matches,skepticalMatches,additionalMatches,personDict2):\n dictConsidered = personDict['ALIAS']\n for alias in dictConsidered:\n if alias == \"\":\n continue\n pairs = itertools.combinations(dictConsidered[alias],2)\n for p in pairs:\n k = tuple(sorted(p))\n if (k not in matches) and (k not in skepticalMatches) and (k not in additionalMatches):\n info1 = personDict['EnterpriseID'][p[0]]\n info2 = personDict['EnterpriseID'][p[1]]\n \n info1b = personDict2['EnterpriseID'][p[0]]\n info2b = personDict2['EnterpriseID'][p[1]]\n score = getScorePair(info1b,info2b)\n if score>=7:\n additionalMatches[k] = score\n\n return additionalMatches","def test_only_three_card_petitions(self):\n f = gtrutils.check_petition_combos\n\n self.assertTrue( f( 0, 0, [0], False, True))\n\n self.assertFalse( f( 1, 0, [0], False, True))\n self.assertTrue( f( 1, 1, [0], False, True))\n self.assertTrue( f( 1, 0, [3], False, True))\n self.assertTrue( f( 1, 3, [0], False, True))\n\n self.assertFalse( f( 1, 1, [2], False, True))\n self.assertFalse( f( 1, 1, [3], False, True))\n self.assertFalse( f( 1, 1, [4], False, True))\n\n self.assertTrue( f( 2, 2, [0], False, True))\n self.assertTrue( f( 2, 1, [3], False, True))\n self.assertTrue( f( 2, 3, [3], False, True))\n self.assertTrue( f( 2, 6, [0], False, True))\n self.assertTrue( f( 2, 0, [6], False, True))\n self.assertFalse( f( 2, 4, [3], False, True))\n\n self.assertFalse( f( 3, 1, [], False, True))\n self.assertFalse( f( 3, 2, [], False, True))\n self.assertFalse( f( 3, 0, [3], False, True))\n self.assertFalse( f( 3, 0, [6], False, True))\n self.assertTrue( f( 3, 3, [], False, True))\n self.assertTrue( f( 3, 2, [3], False, True))\n self.assertTrue( f( 3, 3, [6], False, True))\n self.assertTrue( f( 3, 1, [6], False, True))\n self.assertTrue( f( 3, 0, [9], False, True))\n\n self.assertTrue( f(13,13, [], False, True))\n self.assertTrue( f(13,39, [], False, True))\n self.assertTrue( f(13, 0, [39], False, True))\n self.assertTrue( f(13,15, [24], False, True))\n self.assertTrue( f(13,15, [], False, True))\n self.assertTrue( f(13,12, [3], False, True))\n self.assertFalse( f(13,14, [], False, True))\n\n self.assertFalse( f( 6, 1, [3,6,9], False, True))\n self.assertTrue( f( 7, 1, [3,6,9], False, True))\n self.assertFalse( f( 8, 1, [3,6,9], False, True))","def test_match(self):\n self.assertEqual(solution( \"\"\"p=<-6,0,0>, v=< 3,0,0>, a=< 0,0,0>\np=<-4,0,0>, v=< 2,0,0>, a=< 0,0,0>\np=<-2,0,0>, v=< 1,0,0>, a=< 0,0,0>\np=< 3,0,0>, v=<-1,0,0>, a=< 0,0,0>\"\"\"), 1)","def test_check_inputs_supervisor_prefs_all_reciprocated(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n projects = supervisor.projects\n for project in student.prefs:\n if project in projects:\n student._forget(project)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"supervisors\")\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(supervisor.name)\n assert student.name in message\n\n if clean:\n assert student not in supervisor.prefs\n assert all(\n student not in project.prefs for project in supervisor.projects\n )","def test_assigned_match(self):\n user1 = get_user_model().objects.get(username='test1@example.com')\n self.client.login(username='test1@example.com', password='1')\n\n office = OfficeLocation.objects.all()[0]\n org = OrgGroup.objects.filter(parent__isnull=True)[0]\n\n submission1 = Interest()\n submission1.owner = user1\n submission1.for_coffee = True\n submission1.save()\n submission1.locations.add(office)\n submission1.departments.add(org)\n\n resp = self.client.get(reverse('mystery:mystery'))\n self.assertContains(resp, \"Cancel this\", status_code=200)\n\n user2 = random_user()\n submission2 = Interest()\n submission2.owner = user2\n submission2.is_active = False\n submission2.save()\n submission2.for_coffee = True\n submission2.locations.add(office)\n submission2.departments.add(org)\n submission2.is_active = True\n submission2.save()\n\n resp = self.client.get(reverse('mystery:mystery'))\n self.assertContains(resp, reverse(\"mystery:close_incomplete\", args=(submission1.id,)))\n self.assertContains(resp, reverse(\"mystery:close_complete\", args=(submission1.id,)))\n\n # verify assigned match page requires login\n self.client.logout()\n resp = self.client.get(reverse('mystery:mystery'))\n self.assertEqual(resp.status_code, 302)\n self.assertIn('login', resp['Location'])","def compareWithAll(lijst, previouslist, feedback = 0):\n\n global usedcombos\n\n results = []\n\n\n if feedback == 2: #to make sure there's a 2 letter combination with gaps\n for i in previouslist:\n for letter1, letter2 in lijst:\n if letter1 in i and letter2 in i:\n results.append(i)\n\n elif feedback == 3: #to make sure there's a 3 letter combination with gaps\n for i in previouslist:\n for letter1, letter2, letter3 in lijst:\n if letter1 in i and letter2 in i and letter3 in i:\n results.append(i)\n else:\n for i in previouslist:\n\n for j in range(len(lijst)):\n\n if lijst[j] in i:\n\n results.append(i)\n\n results = [item for item in results if item not in usedcombos]\n results = list(dict.fromkeys(results))\n\n print(f\"It seems I only {len(results)} options left!\")\n\n return AIguessing(results)","def test_query_multiple(test_store, andy, pandy):\n items = list(test_store.get_by(age=12))\n assert len(items) == 2\n assert andy in items\n assert pandy in items","def test_sqpp_distributed_ands_equivalent(self):\n self.assertEqual(sorted(perform_request_search(p='ellis and (kaluza-klein or r-parity)')),\n sorted(perform_request_search(p='ellis and (r-parity or kaluza-klein)')))","def partial_match_score(\n truth: List[Rationale], pred: List[Rationale], thresholds: List[float]\n) -> List[PartialMatchScore]:\n\n ann_to_rat = _keyed_rationale_from_list(truth)\n pred_to_rat = _keyed_rationale_from_list(pred)\n\n num_classifications = {k: len(v) for k, v in pred_to_rat.items()}\n num_truth = {k: len(v) for k, v in ann_to_rat.items()}\n ious: Dict[str, Dict[str, float]] = defaultdict(dict)\n for k in set(ann_to_rat.keys()) | set(pred_to_rat.keys()):\n for p in pred_to_rat.get(k, []):\n best_iou = 0.0\n for t in ann_to_rat.get(k, []):\n num = len(\n set(range(p.start_token, p.end_token))\n & set(range(t.start_token, t.end_token))\n )\n denom = len(\n set(range(p.start_token, p.end_token))\n | set(range(t.start_token, t.end_token))\n )\n iou = 0 if denom == 0 else num / denom\n if iou > best_iou:\n best_iou = iou\n ious[k][p] = best_iou\n\n scores: List[PartialMatchScore] = []\n for threshold in thresholds:\n threshold_tps: Dict[str, float] = {}\n for k, vs in ious.items():\n threshold_tps[k] = sum(int(x >= threshold) for x in vs.values())\n micro_r = (\n sum(threshold_tps.values()) / sum(num_truth.values())\n if sum(num_truth.values()) > 0\n else 0\n )\n micro_p = (\n sum(threshold_tps.values()) / sum(num_classifications.values())\n if sum(num_classifications.values()) > 0\n else 0\n )\n micro_f1 = _f1(micro_r, micro_p)\n macro_rs = list(\n threshold_tps.get(k, 0.0) / n if n > 0 else 0 for k, n in num_truth.items()\n )\n macro_ps = list(\n threshold_tps.get(k, 0.0) / n if n > 0 else 0\n for k, n in num_classifications.items()\n )\n macro_r = sum(macro_rs) / len(macro_rs) if len(macro_rs) > 0 else 0\n macro_p = sum(macro_ps) / len(macro_ps) if len(macro_ps) > 0 else 0\n macro_f1 = _f1(macro_r, macro_p)\n\n scores.append(\n PartialMatchScore(\n threshold=threshold,\n micro=InstanceScore(p=micro_p, r=micro_r, f1=micro_f1),\n macro=InstanceScore(p=macro_p, r=macro_r, f1=macro_f1),\n )\n )\n\n return scores","def check_matches(known, match): \n #array containing tuples of corrected spellings\n out_vals = []\n\n #clear screen\n os.system('clear')\n \n for k,v in known.iteritems():\n matched_vals = [ik for ik, iv in match.iteritems() if iv == v]\n cur_matches = process.extract(k, matched_vals, limit = MATCH_LIMIT)\n \n #append a none option and option for actual val\n cur_matches.insert(0,(\"NONE\",0))\n cur_matches.append((k,100))\n\n\n #if there is a match of 100, skip\n if cur_matches[1][1] == 86 or cur_matches[1][1] < 50:\n #special case where probably means there really isn't a match\n choice = 'NONE'\n print 'No match!'\n\n elif cur_matches[1][1] < 95:\n #print out options for each word\n print_options(k, cur_matches)\n\n #cycle through prompt for input if valid (numeric, within MATCH_LIMIT)\n choice = None\n while choice == None:\n try:\n choice = cur_matches[get_choice()][0]\n except:\n print '!Invalid match! Try again'\n print_options(k, cur_matches)\n \n print choice\n\n else:\n #exact match\n choice = cur_matches[1][0]\n print '%s - Match!'%choice\n\n print \n print\n #create a new tuple with correct and incorrect spelling\n out_vals.append((k, choice))\n output(out_vals)\n\n #we're done, return vals array\n return out_vals","def test_list_occurrences(self):\n pass","def test_strategy(self):\n self.first_play_test(C)\n for i in range(10):\n history_1 = [C] * i\n history_2 = [C] * i\n self.responses_test(history_1, history_2, [C])\n # Now cooperate 10% less than opponent\n history_1 = [C] * 11\n history_2 = [D] * 11\n self.responses_test(history_1, history_2, [D], random_seed=10)\n history_1 = [C] * 11\n history_2 = [D] * 10 + [C]\n self.responses_test(history_1, history_2, [D], random_seed=10)\n # Test beyond 10 rounds\n history_1 = [C] * 11\n history_2 = [D] * 5 + [C] * 6\n self.responses_test(history_1, history_2, [D, D, D, D], random_seed=20)\n history_1 = [C] * 11\n history_2 = [C] * 9 + [D] * 2\n self.responses_test(history_1, history_2, [C, D, D, C], random_seed=25)","def test_homeland_defense(self):\n self.conf[\"game\"][\"homeland_defense\"] = \"100/50/25\"\n self.assertEqual(None, self.sapphire.owner)\n battle = self.battle\n sess = self.sess\n\n # Skirmish 1\n s1 = battle.create_skirmish(self.alice, 10) # Attack 10\n s1a = s1.react(self.carol, 4, hinder=False) # --Support 4\n s1a.react(self.bob, 3) # ----Attack 3\n s1.react(self.dave, 8) # --Attack 8\n # Winner will be team orangered, 11 VP\n\n # Skirmish 2\n battle.create_skirmish(self.bob, 5) # Attack 5\n # Winner will be team periwinkle, 10 VP for unopposed\n\n # End fight\n self.battle.ends = self.battle.begins\n sess.commit()\n self.assert_(battle.past_end_time())\n\n updates = Battle.update_all(sess, conf=self.conf)\n sess.commit()\n\n # Overall winner would ordinarily be orangered, but this should get\n # modified by the homeland defense buffs - Sapphire is right next to\n # the test capital, and so will get an extra 50%\n self.assertEqual(battle.victor, 1)\n self.assertEqual(battle.score1, 15)\n # OR should get 25% bonus\n self.assertEqual(battle.score0, 13)","def match_all_phrases(self, inphrases):\n# temporary - attempted matches\n attempted_matches = []\n phrase_attempts = {}\n phrase = \"\"\n step = \"A\"\n # ALL full phrases \n for phrase in inphrases:\n phrase_attempts[phrase] = 1\n attempted_matches.append(phrase + ':' + step)\n if phrase in self.cls_phrases:\n match_choices = self.cls_phrases[phrase]\n #return match_choices, attempted_matches, phrase\n return (self.get_list_counts(match_choices), attempted_matches, \n phrase, self.get_most_common(match_choices))\n\n # Normalised version of ALL all full phrases \n phrases = [self.get_normalised_phrase(p) for p in inphrases]\n\n # 3 all prefix trigrams \n step = \"3\"\n for ngram in [p.split()[0:3] for p in phrases if len(p.split()) > 2]:\n phrase = ' '.join(ngram)\n phrase_attempts[phrase] = 1\n attempted_matches.append(phrase + ':' + step)\n if phrase in self.cls_phrases:\n match_choices = self.cls_phrases[phrase]\n return (self.get_list_counts(match_choices), attempted_matches, \n phrase, self.get_most_common(match_choices))\n\n # 2 all prefix bigrams \n step = \"2\"\n for ngram in [p.split()[0:2] for p in phrases if len(p.split()) > 1]:\n phrase = ' '.join(ngram)\n phrase_attempts[phrase] = 1\n attempted_matches.append(phrase + ':' + step)\n if phrase in self.cls_phrases:\n match_choices = self.cls_phrases[phrase]\n return (self.get_list_counts(match_choices), attempted_matches, \n phrase, self.get_most_common(match_choices))\n\n # 1 all valid words \n step = \"1\"\n for phr_elem in phrases:\n #print phr_elem.split()\n for phrase in [w.strip() for w in phr_elem.split() \n if self.isExcluded(w.strip()) == False and w.strip() not in phrase_attempts]:\n #print \"***\", phrase\n phrase_attempts[phrase] = 1\n attempted_matches.append(phrase + ':' + step)\n if phrase in self.cls_phrases:\n match_choices = self.cls_phrases[phrase]\n return (self.get_list_counts(match_choices), attempted_matches, \n phrase, self.get_most_common(match_choices))\n\n return [], attempted_matches, phrase, None","def test_check_full_house_true(self):\n full_house_fixtures = [[1, 1, 2, 2, 2],\n [1, 2, 2, 2, 1],\n [1, 2, 1, 2, 2],\n [1, 2, 2, 1, 2],\n [2, 1, 2, 2, 1],\n [2, 2, 1, 2, 1],\n [2, 2, 2, 1, 1],\n ]\n\n for fixture in full_house_fixtures:\n score = self.roll.check_full_house(fixture)\n\n self.assertEqual(score, 25)\n self.assertEqual(len(fixture), 5)","def test_get_pf_data_1(self):\n self.db_filter.values = [40481]\n\n pf_data = review.get_pf_data(self.alchemist, self.db_filter)\n\n self.assertTrue(isinstance(pf_data, list))\n\n for header in review.PF_HEADER:\n with self.subTest(header=header):\n self.assertTrue(header in pf_data[0].keys())\n self.assertFalse(isinstance(pf_data[0][header], list))","def test_check_inputs_resident_prefs_all_nonempty(game):\n\n resident = game.residents[0]\n resident.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n assert resident.name in str(record[0].message)\n\n if game.clean:\n assert resident not in game.residents","def test_check_inputs_supervisor_reciprocated_all_prefs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n supervisor = game.supervisors[0]\n student = supervisor.prefs[0]\n supervisor.prefs.remove(student)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_reciprocated_all_prefs(\n \"supervisors\", \"students\"\n )\n\n assert len(record) == 1\n\n message = str(record[0].message)\n\n assert message.startswith(student.name)\n assert supervisor.name in message\n\n if clean:\n assert supervisor not in student.prefs\n assert all(\n project not in student.prefs for project in supervisor.projects\n )","def fuzzy_matching(mapper, fav_parks, verbose=True):\n match_tuple = []\n # get match\n for title, idx in mapper.items():\n ratio = fuzz.ratio(title.lower(), fav_parks.lower())\n if ratio >= 100:\n match_tuple.append((title, idx, ratio))\n # sort\n match_tuple = sorted(match_tuple, key=lambda x: x[2])[::-1]\n if not match_tuple:\n #print('Oops! No match is found')\n return\n if verbose:\n #print('Found possible matches in our database: {0}\\n'.format([x[0] for x in match_tuple]))\n return match_tuple[0][1]","def test_rating_to_happy(self):\n data = {\n 1: False,\n 2: False,\n 3: False,\n 4: True,\n 5: True\n }\n for rat, expected in data.items():\n # Create the response, but DON'T save it to the db.\n resp = ResponseFactory.build(happy=None, rating=rat)\n resp.save()\n eq_(resp.happy, expected)","def _find_homeless_mps(self):\n mps = Person.objects.filter(\n active=True,\n house__name=HOUSE_OF_COMMONS,\n constituency=None,\n )\n\n self.stdout('MPs with missing constituency:')\n for mp in mps:\n self.stdout(f' [{mp.parliamentdotuk}] {mp.name} has no constituency')","def test_poker_one_sf(self):\n self.assertEqual(poker([self.sf1, self.fk, self.fh]), [self.sf1])","def test_phv_test_queries_with_phv_in_string(self):\n url = self.get_url()\n for query in TEST_PHV_QUERIES:\n response = self.client.get(url, {'q': 'phv' + query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = TEST_PHV_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phv in expected_matches:\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected phv {} with query '{}'\".format(expected_phv, query))","def test_phv_test_queries_with_phv_in_string(self):\n url = self.get_url()\n for query in TEST_PHV_QUERIES:\n response = self.client.get(url, {'q': 'phv' + query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = TEST_PHV_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phv in expected_matches:\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected phv {} with query '{}'\".format(expected_phv, query))","def test_phv_test_queries_with_phv_in_string(self):\n url = self.get_url()\n for query in TEST_PHV_QUERIES:\n response = self.client.get(url, {'q': 'phv' + query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = TEST_PHV_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phv in expected_matches:\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected phv {} with query '{}'\".format(expected_phv, query))","def test_phv_test_queries_with_phv_in_string(self):\n url = self.get_url()\n for query in TEST_PHV_QUERIES:\n response = self.client.get(url, {'q': 'phv' + query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = TEST_PHV_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phv in expected_matches:\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected phv {} with query '{}'\".format(expected_phv, query))","def test_phv_test_queries_with_phv_in_string(self):\n url = self.get_url()\n for query in TEST_PHV_QUERIES:\n response = self.client.get(url, {'q': 'phv' + query})\n returned_pks = get_autocomplete_view_ids(response)\n expected_matches = TEST_PHV_QUERIES[query]\n # Make sure number of matches is as expected.\n self.assertEqual(len(returned_pks), len(expected_matches))\n # Make sure the matches that are found are the ones expected.\n for expected_phv in expected_matches:\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\n self.assertIn(expected_pk, returned_pks,\n msg=\"Could not find expected phv {} with query '{}'\".format(expected_phv, query))"],"string":"[\n \"def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"hospital\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n\\n for hospital, matches in matching.items():\\n old_idx = -np.infty\\n for resident in matches:\\n idx = hospital.pref_names.index(resident.name)\\n assert idx >= old_idx\\n old_idx = idx\",\n \"def test_check_inputs_hospital_reciprocated_all_prefs(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n hospital._forget(resident)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"hospitals\\\", \\\"residents\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert hospital not in resident.prefs\",\n \"def test_check_inputs_hospital_prefs_all_residents(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert hospital.prefs == []\",\n \"def test_check_inputs_hospital_prefs_all_reciprocated(game):\\n\\n hospital = game.hospitals[0]\\n resident = hospital.prefs[0]\\n resident._forget(hospital)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert hospital.name in message\\n assert resident.name in message\\n\\n if game.clean:\\n assert resident not in hospital.prefs\",\n \"def test_resident_optimal(resident_names, hospital_names, capacities, seed):\\n\\n np.random.seed(seed)\\n residents = _make_residents(resident_names, hospital_names)\\n hospitals = _make_hospitals(residents, capacities)\\n matching = hospital_resident(residents, hospitals, optimal=\\\"resident\\\")\\n\\n assert set(hospitals) == set(matching.keys())\\n assert all(\\n [\\n r in set(residents)\\n for r in set(\\n [\\n r_match\\n for matches in matching.values()\\n for r_match in matches\\n ]\\n )\\n ]\\n )\\n\\n for resident in residents:\\n if resident.matching:\\n assert resident.pref_names.index(resident.matching.name) == 0\",\n \"def test_check_stability():\\n\\n residents = [Resident(\\\"A\\\"), Resident(\\\"B\\\"), Resident(\\\"C\\\")]\\n hospitals = [Hospital(\\\"X\\\", 2), Hospital(\\\"Y\\\", 2)]\\n\\n (a, b, c), (x, y) = residents, hospitals\\n\\n a.set_prefs([x, y])\\n b.set_prefs([y])\\n c.set_prefs([y, x])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = HospitalResident(residents, hospitals)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (x, y) = game.residents, game.hospitals\\n matching[x] = [c]\\n matching[y] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def test_check_inputs_resident_prefs_all_hospitals(game):\\n\\n resident = game.residents[0]\\n resident.prefs = [Resident(\\\"foo\\\")]\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_in_party(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert resident.name in message\\n assert \\\"foo\\\" in message\\n\\n if game.clean:\\n assert resident.prefs == []\",\n \"def test_solve(game, optimal):\\n\\n matching = game.solve(optimal)\\n assert isinstance(matching, MultipleMatching)\\n\\n hospitals = sorted(game.hospitals, key=lambda h: h.name)\\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\\n for game_hospital, hospital in zip(matching_keys, hospitals):\\n assert game_hospital.name == hospital.name\\n assert game_hospital._pref_names == hospital._pref_names\\n assert game_hospital.capacity == hospital.capacity\\n\\n matched_residents = [\\n resident for match in matching.values() for resident in match\\n ]\\n\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(game.residents)\\n )\\n\\n for resident in set(game.residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def test_check_stability():\\n\\n residents = [Player(\\\"A\\\", [1, 2]), Player(\\\"B\\\", [2]), Player(\\\"C\\\", [2, 1])]\\n hospitals = [Player(1, [\\\"C\\\", \\\"A\\\"], 3), Player(2, [\\\"A\\\", \\\"B\\\", \\\"C\\\"], 3)]\\n match = HospitalResident(residents, hospitals)\\n\\n matching = match.solve()\\n assert match.check_stability()\\n\\n (P_A, P_B, P_C), (P_1, P_2) = match.suitors, match.reviewers\\n matching[P_1] = [P_C]\\n matching[P_2] = [P_A, P_B]\\n\\n assert not match.check_stability()\",\n \"def _check_hospital_prefs(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n residents_that_ranked = [\\n res for res in self.residents if hospital in res.prefs\\n ]\\n if set(hospital.prefs) != set(residents_that_ranked):\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has not ranked all the residents that \\\"\\n f\\\"ranked it: {set(hospital.prefs)} != \\\"\\n f\\\"{set(residents_that_ranked)}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def _check_hospital_matching(self):\\n\\n errors = []\\n for hospital in self.hospitals:\\n for resident in hospital.matching:\\n if resident not in hospital.prefs:\\n errors.append(\\n ValueError(\\n f\\\"{hospital} has {resident} in their matching but \\\"\\n \\\"they do not appear in their preference list: \\\"\\n f\\\"{hospital.prefs}.\\\"\\n )\\n )\\n\\n if errors:\\n raise Exception(*errors)\\n\\n return True\",\n \"def test_solve(resident_names, hospital_names, capacities, seed):\\n\\n for optimal in [\\\"suitor\\\", \\\"resident\\\", \\\"reviewer\\\", \\\"hospital\\\"]:\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n matching = match.solve(optimal)\\n assert isinstance(matching, Matching)\\n assert set(matching.keys()) == set(hospitals)\\n matched_residents = [\\n res for match in matching.values() for res in match\\n ]\\n assert matched_residents != [] and set(matched_residents).issubset(\\n set(residents)\\n )\\n\\n for resident in set(residents) - set(matched_residents):\\n assert resident.matching is None\",\n \"def test_inputs_suitor_prefs(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_suitor_prefs()\\n\\n match.suitors[0].pref_names = [1, 2, 3]\\n\\n with pytest.raises(Exception):\\n match._check_suitor_prefs()\",\n \"def test_init(players, clean):\\n\\n residents, hospitals = players\\n\\n game = HospitalResident(residents, hospitals, clean)\\n\\n for resident, game_resident in zip(residents, game.residents):\\n assert resident.name == game_resident.name\\n assert resident._pref_names == game_resident._pref_names\\n\\n for hospital, game_hospital in zip(hospitals, game.hospitals):\\n assert hospital.name == game_hospital.name\\n assert hospital._pref_names == game_hospital._pref_names\\n assert hospital.capacity == game_hospital.capacity\\n\\n assert all([resident.matching is None for resident in game.residents])\\n assert all([hospital.matching == [] for hospital in game.hospitals])\\n assert game.matching is None\",\n \"def test_get_state_comparison_stats_penmanship_contest(self):\\n\\n key = 'Left-hand penmanship contest entrants'\\n\\n # Mississippi: 0 employees who entered the contest, 3 who didn't\\n for _ in range(3):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.mississippi)\\n\\n # Texas: 2 employees who entered the contest, 2 who didn't\\n for _ in range(2):\\n employee = EmployeeFactory(penmanship_contest=True)\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 3 employees who entered the contest, 1 who didn't\\n for _ in range(3):\\n employee = EmployeeFactory(penmanship_contest=True)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n expected_output = [('Kentucky', 3), ('Texas', 2)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x number of employees who entered contest\\\"\\n )\",\n \"def test_fuzzy_matching_multi_match(\\n nlp: Language, countries: List[Dict[str, Any]]\\n) -> None:\\n ruler = SpaczzRuler(nlp, spaczz_fuzzy_defaults={\\\"min_r2\\\": 85})\\n ruler.add_patterns(countries)\\n doc = nlp(\\\"This is a test that should find Northern Ireland and Ireland\\\")\\n doc = ruler(doc)\\n matches = [(ent.ent_id_, ent.text) for ent in doc.ents if ent.label_ == \\\"COUNTRY\\\"]\\n assert matches == [(\\\"Northern Ireland\\\", \\\"Northern Ireland\\\"), (\\\"Ireland\\\", \\\"Ireland\\\")]\",\n \"def test_check_inputs_hospital_prefs_all_nonempty(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def test_when_opponent_all_Ds(self):\\n self.responses_test([C, C, C, C], [D, D, D, D], [D, D, D], random_seed=5)\",\n \"def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.reviewers[0].matching.append(Player(name=\\\"foo\\\", pref_names=[]))\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_matching()\",\n \"def test_when_opponent_all_Ds(self):\\n self.responses_test([D, D, D, D], [D, D, D, D], [D, D, D],\\n random_seed=5)\",\n \"def test_init(resident_names, hospital_names, capacities, seed):\\n\\n residents, hospitals, match = _make_match(\\n resident_names, hospital_names, capacities, seed\\n )\\n\\n assert match.suitors == residents\\n assert match.reviewers == hospitals\\n assert all([suitor.matching is None for suitor in match.suitors])\\n assert all([reviewer.matching == [] for reviewer in match.reviewers])\\n assert match.matching is None\",\n \"def hospital_optimal(hospitals):\\n\\n free_hospitals = hospitals[:]\\n while free_hospitals:\\n\\n hospital = free_hospitals.pop()\\n resident = hospital.get_favourite()\\n\\n if resident.matching:\\n curr_match = resident.matching\\n unmatch_pair(resident, curr_match)\\n if curr_match not in free_hospitals:\\n free_hospitals.append(curr_match)\\n\\n match_pair(resident, hospital)\\n if len(hospital.matching) < hospital.capacity and [\\n res for res in hospital.prefs if res not in hospital.matching\\n ]:\\n free_hospitals.append(hospital)\\n\\n successors = resident.get_successors()\\n for successor in successors:\\n delete_pair(resident, successor)\\n if (\\n not [\\n res\\n for res in successor.prefs\\n if res not in successor.matching\\n ]\\n and successor in free_hospitals\\n ):\\n free_hospitals.remove(successor)\\n\\n return {r: r.matching for r in hospitals}\",\n \"def test_student_optimal(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n np.random.seed(seed)\\n students, projects, _ = make_players(\\n student_names, project_names, supervisor_names, capacities\\n )\\n matching = student_optimal(students, projects)\\n\\n assert set(projects) == set(matching.keys())\\n assert all(\\n [\\n s in set(students)\\n for s in {\\n match for matches in matching.values() for match in matches\\n }\\n ]\\n )\\n\\n for student in students:\\n if student.matching:\\n assert student.prefs.index(student.matching) == 0\",\n \"def galeshapley(suitor_pref_dict, reviewer_pref_dict, max_iteration):\\n suitors = list(suitor_pref_dict.keys())\\n matching = dict()\\n rev_matching = dict()\\n\\n for i in range(max_iteration):\\n if len(suitors) <= 0:\\n break\\n for s in suitors:\\n r = suitor_pref_dict[s][0]\\n if r not in matching.values():\\n matching[s] = r\\n rev_matching[r] = s\\n else:\\n r_partner = rev_matching.get(r)\\n if reviewer_pref_dict[r].index(s) < reviewer_pref_dict[r].index(r_partner):\\n del matching[r_partner]\\n matching[s] = r\\n rev_matching[r] = s\\n else:\\n suitor_pref_dict[s].remove(r)\\n suitors = list(set(suitor_pref_dict.keys()) - set(matching.keys()))\\n return matching\",\n \"def test_affect_of_strategy(self):\\n self.responses_test([C, C, C], [C, C, C], [C, C, C])\\n # Make sure that the retaliations are increasing\\n # Retaliate once and forgive\\n self.responses_test([C], [D], [D])\\n self.responses_test([C, D], [D, C], [C])\\n self.responses_test([C, D, C], [D, C, C], [C])\\n # Retaliate twice and forgive\\n self.responses_test([C, D, C], [D, C, D], [D, D])\\n self.responses_test([C, D, C, D, D], [D, C, D, C, C], [C])\\n # Opponent defection during retaliation doesn't increase retaliation period\\n self.responses_test([C, D, C, D, D], [D, C, D, D, C], [C])\\n # Retaliate thrice and forgive\\n self.responses_test([C, D, C, D, D, C], [D, C, D, C, C, D], [D, D, D])\\n history_1 = [C, D, C, D, D, C, D, D, D]\\n history_2 = [D, C, D, C, C, D, C, C, C]\\n self.responses_test(history_1, history_2, [C])\",\n \"def _check_mutual_preference(resident, hospital):\\n\\n return resident in hospital.prefs and hospital in resident.prefs\",\n \"def test_21_29(self):\\r\\n husband = Person()\\r\\n husband.id = \\\"I01\\\"\\r\\n husband.name = \\\"Jotaro /Kujo/\\\"\\r\\n husband.gender = \\\"F\\\"\\r\\n husband.birthDate = \\\"29 SEP 1918\\\"\\r\\n husband.age = 101\\r\\n husband.alive = True\\r\\n husband.death = \\\"N/A\\\"\\r\\n husband.child = [\\\"I03\\\"]\\r\\n husband.spouse = [\\\"I02\\\"]\\r\\n\\r\\n wife = Person()\\r\\n wife.id = \\\"I02\\\"\\r\\n wife.name = \\\"Marry Sue\\\"\\r\\n wife.gender = \\\"M\\\"\\r\\n wife.birthDate = \\\"12 Jan 1988\\\"\\r\\n wife.age = 80\\r\\n wife.alive = False\\r\\n wife.death = \\\"23 JAN 2020\\\"\\r\\n wife.child = []\\r\\n wife.spouse = [\\\"I01\\\"]\\r\\n\\r\\n test_family = Family()\\r\\n test_family.id = \\\"F01\\\"\\r\\n test_family.married = \\\"29 SEP 1993\\\"\\r\\n test_family.divorce = \\\"25 JAN 2020\\\"\\r\\n test_family.husbandID = \\\"I01\\\"\\r\\n test_family.husbandName = \\\"Morgan Sue\\\"\\r\\n test_family.wifeID = \\\"I02\\\"\\r\\n test_family.wifeName = \\\"Marry Sue\\\"\\r\\n test_family.chidren = [\\\"I03\\\"]\\r\\n\\r\\n personList = [husband, wife]\\r\\n familiList = [test_family]\\r\\n\\r\\n self.assertEqual(us21(personList, familiList), \\\"Husband in family is not male or wife in family is not female\\\")\\r\\n self.assertEqual(us29(personList), ['I02'])\",\n \"def test_inputs_reviewer_prefs(\\n resident_names, hospital_names, capacities, seed\\n):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n assert match._check_reviewer_prefs()\\n\\n match.reviewers[0].pref_names.pop()\\n\\n with pytest.raises(Exception):\\n match._check_reviewer_prefs()\",\n \"def athlete_match(athlete, athlete_criteria):\\n for key, value in athlete_criteria.items():\\n if key not in athlete or athlete[key] not in value:\\n return False\\n return True\",\n \"def test_review_phams_1(self):\\n self.db_filter.values = self.db_filter.build_values(\\n where=self.db_filter.build_where_clauses())\\n\\n review.review_phams(self.db_filter)\\n\\n self.assertFalse(39854 in self.db_filter.values)\\n self.assertTrue(40481 in self.db_filter.values)\",\n \"def test_check_for_oversubscribed_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n hospital.matching = range(hospital.capacity + 1)\\n\\n issues = game._check_for_oversubscribed_players(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.capacity}.\\\")\\n assert str(hospital.matching) in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_hospitals[0]\\n assert issue == error\",\n \"def test_multiple_players_return_own_results(self):\\r\\n observed = highscore.highscore(self.fixture_player1, self.fixture_score_high1)\\r\\n self.assertAlmostEquals(observed, self.fixture_score_high1)\\r\\n observed = highscore.highscore(self.fixture_player2, self.fixture_score_high2)\\r\\n self.assertAlmostEquals(observed, self.fixture_score_high2)\",\n \"def test_phs_test_queries_with_phs_in_string(self):\\n url = self.get_url()\\n for query in self.TEST_PHS_QUERIES:\\n response = self.client.get(url, {'q': 'phs' + query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = self.TEST_PHS_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phs in expected_matches:\\n expected_pk = models.Study.objects.get(i_accession=expected_phs).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected phs {} with query '{}'\\\".format(expected_phs, query))\",\n \"def test_phs_test_queries_with_phs_in_string(self):\\n url = self.get_url()\\n for query in self.TEST_PHS_QUERIES:\\n response = self.client.get(url, {'q': 'phs' + query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = self.TEST_PHS_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phs in expected_matches:\\n expected_pk = models.Study.objects.get(i_accession=expected_phs).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected phs {} with query '{}'\\\".format(expected_phs, query))\",\n \"def test_counts(contrib_data):\\n ## Find the probands - they should always contribute their own alleles\\n P = ped.Pedigree(args.pedfile)\\n\\n ## TODO: Test individuals with multiple offspring +p2 id:112\\n ## Case when no regions are specified - only all probands\\n regions = contrib_data.keys()\\n if len(regions) == 1:\\n all_contribs = contrib_data[regions[0]]\\n for prob in P.probands:\\n ## Probands should always contribute 1 allele\\n assert all_contribs[prob, 1] == args.iterations\\n\\n ## Test specific individuals\\n counts = Counter(all_contribs[11])\\n binomial_sd = np.sqrt(args.iterations * 0.25)\\n mean = np.mean(counts.values())\\n conf95 = (mean - 2 * binomial_sd, mean + 2 * binomial_sd)\\n\\n for count in counts.values():\\n assert conf95[0] < count < conf95[1]\",\n \"def analyze_similarities():\\r\\n print('Total number of candidate pairs:', len(pairs))\\r\\n print(f'\\\\nNumber of actual item pairs in the train set: {pairs[\\\"true_match\\\"].sum()}\\\\n')\\r\\n\\r\\n for feature in ['text_score', 'image_score', 'txt_img_score', 'words_ratio', 'txt_img_words']:\\r\\n\\r\\n # Check distribution of True and False predictions for various similarity scores\\r\\n print('-' * 50)\\r\\n print(f'\\\\nDistribution of True/False predictions for {feature}')\\r\\n for thr in (0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95):\\r\\n print('-' * 50)\\r\\n print(f'Similarity score over {thr}')\\r\\n pairs_sample = pairs[pairs[feature] >= thr]\\r\\n print(f'Number of similar item pairs: {len(pairs_sample)}')\\r\\n print(pairs_sample['true_match'].value_counts(normalize=True))\\r\\n\\r\\n # Check if identical phash can be used to improve the accuracy\\r\\n same_phash = pairs[pairs['phash_match'] == 1]\\r\\n different_phash = pairs[pairs['phash_match'] == 0]\\r\\n\\r\\n print('\\\\nFor item pairs with the same phash:')\\r\\n print(same_phash['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(same_phash))\\r\\n\\r\\n print('\\\\nFor item pairs with different phash:')\\r\\n print(different_phash['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(different_phash))\\r\\n\\r\\n # Check if numbers in titles can be used to improve the accuracy\\r\\n same_numbers = pairs[pairs['nums_match'] == 1]\\r\\n different_numbers = pairs[pairs['nums_match'] == 0]\\r\\n\\r\\n print('\\\\nFor item pairs with the same numbers:')\\r\\n print(same_numbers['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(same_numbers))\\r\\n\\r\\n print('\\\\nFor item pairs with different numbers:')\\r\\n print(different_numbers['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(different_numbers))\",\n \"def test_query_all(test_store, andy, pandy, candy):\\n items = list(test_store.get_by())\\n assert len(items) == 3\\n assert andy in items\\n assert pandy in items\\n assert candy in items\",\n \"def suggestionexpposteriors(self,character,room,weapon):\\n # Some helpful phrasings of the suggestion:\\n triadList = [character,room,weapon]\\n triadSet = set(triadList)\\n #\\n #### PHASE 1\\n # Sort the hypotheses by their murder scenario, and by the answer(s) \\n # they provoke to this suggestion. A player may hold multiple of the\\n # cards suggested and hence have a choice regarding which to show us.\\n #\\n # Therefore, we sort into 7 bins:\\n # Bins 1,3,5,7 (binary 1 bit set) => Player holds character card\\n # Bins 2,3,6,7 (binary 2 bit set) => Player holds room card\\n # Bins 4,5,6,7 (binary 4 bit set) => Player holds weapon card\\n # Note for zero based indexing, we must subtract one from these sort \\n # indices.\\n hypCountsSorted = np.zeros(\\n (self.nPlayers-1, # Players that could answer\\n 7, # Cards this player could hold\\n game.NCHARS,game.NROOMS,game.NWEAPS # Suggestion murder scenario\\n ))\\n # Lastly, we could have no answer from anyone\\n hypCountsNoAnswer = np.zeros((game.NCHARS,game.NROOMS,game.NWEAPS))\\n #\\n # Sort the hypotheses:\\n for hypothesis in self.hypotheses:\\n for ixPlayer in range(1,self.nPlayers):\\n ixAnswer = 0\\n if character in hypothesis[ixPlayer]:\\n ixAnswer = 1\\n if room in hypothesis[ixPlayer]:\\n ixAnswer |= 2\\n if weapon in hypothesis[ixPlayer]:\\n ixAnswer |= 4\\n if ixAnswer > 0:\\n hypCountsSorted[ixPlayer-1,ixAnswer-1,\\n hypothesis[-1][0],hypothesis[-1][1],\\n hypothesis[-1][2]] += 1\\n break\\n else:\\n # Else executed when for loop terminates without break => none\\n # of the players held any of the cards suggested:\\n hypCountsNoAnswer[hypothesis[-1][0],hypothesis[-1][1],\\n hypothesis[-1][2]] += 1\\n #\\n #### PHASE 2\\n # Calculate the scenario entropy we're left with after receiving each\\n # possible answer from each possible player (or no answers).\\n #\\n # Receiving an answer from a player invalidates all hypotheses in which\\n # that player does NOT hold that card. For example, if player X shows us\\n # the character, we will be left with the matrix of hypothesis counts\\n # by scenario as follows:\\n # self.hypCountsByScenario[:,:,:] = \\n # hypCountsSorted[X-1,{ix & 1},:,:,:]\\n #\\n # let HypCountsAfterAnswer be the hypothesis counts after reduction (but\\n # before any rebuilding) following each possible answer:\\n # nPlayersAsked * 3 (Possible Answers) * NCHARS * NROOMS * NWEAPS\\n #\\n # Showing the character card keeps bins (1,3,5,7)-1 = (0,2,4,6)\\n # Showing the room card keeps bins (2,3,6,7)-1 = (1,2,5,6)\\n # Showing the weapon card keeps bins (4,5,6,7)-1 = (3,4,5,6)\\n #\\n # This matrix represents the distribution over scenarios after each \\n # answer is received - where deals allowing multiple answers are counted\\n # multiple times\\n hypCountsAfterAnswer = (\\n hypCountsSorted[:,(0,1,3),:,:,:] + # One card only\\n hypCountsSorted[:,(2,2,4),:,:,:] + \\n hypCountsSorted[:,(4,5,5),:,:,:] +\\n hypCountsSorted[:,(6,6,6),:,:,:]) # All three cards\\n #\\n # This matrix represents the marginal distribution over murder rooms \\n # after each answer is received.\\n hypCountsByRoomAfterAnswer = np.sum(np.sum(\\n hypCountsAfterAnswer,4),2)\\n #\\n # To calculate probabilities (and so entropies) from our hypothesis \\n # counts, we need to divide by the total hypotheses remaining after each\\n # answer. Only the room axis must be summed over, since we already\\n # produced the marginal over rooms above. Append three singleton \\n # dimensions to allow direct division of the scenario count matrices by\\n # (slices of) this matrix:\\n hypCountSumsAfterAnswer = np.sum(\\n hypCountsByRoomAfterAnswer,2)[:,:,None,None,None]\\n pAfterAnswer = hypCountsAfterAnswer/hypCountSumsAfterAnswer\\n #\\n # pAfterAnswer will have np.infs in it where certain player,answer \\n # combinations are completely impossible \\n # (hypCountSumAfterAnswer[p,ans] = 0). These will appear as np.nans in\\n # the entropy calculation below and must be dealt with. Note we don't \\n # bother dealing with the infs in p separately because:\\n # a) 0log(0) would still map to non-finite entropy so need correction\\n # b) We don't use pAfterAnswer after entropy calculation so may as \\n # well save our time.\\n entsAfterAnswer = -pAfterAnswer*np.log(pAfterAnswer)\\n entsAfterAnswer[np.isnan(entsAfterAnswer)] = 0.\\n # Distribution entropy is sum of entropies over distribution axes:\\n # (NCHARS, NROOMS, NWEAPS)\\n entAfterAnswer = np.sum(np.sum(np.sum(entsAfterAnswer,4),3),2)\\n #\\n # With all that out of the way, dealing with no-answer case is easier:\\n hypCountsByRoomNoAnswer = np.sum(np.sum(hypCountsNoAnswer,2),0)\\n hypCountSumNoAnswer = np.sum(hypCountsByRoomNoAnswer)\\n if hypCountSumNoAnswer > 0.:\\n # pNoAnswer will contain no infs...\\n pNoAnswer = hypCountsNoAnswer/hypCountSumNoAnswer\\n # ...But it will still have zeros so we must remove nan entropies.\\n entsNoAnswer = -pNoAnswer*np.log(pNoAnswer)\\n entsNoAnswer[np.isnan(entsNoAnswer)] = 0.\\n entNoAnswer = np.sum(entsNoAnswer)\\n else:\\n entNoAnswer = 0.\\n #\\n #### PHASE 3\\n # Calculate the probability of receiving each answer; given opponents'\\n # expected strategy for choosing cards to show us where multiple \\n # possibilities exist.\\n #\\n # We assume players maximise the Detective's turn-end entropy when \\n # choosing cards to show; and hence provide the minimum amount of \\n # information to our distribution.\\n #\\n # let playerDecisions[ply,:] be player ply's order of preference for\\n # each response (2=weap, 1=room, 0=character) starting with favourite:\\n playerDecisions = np.argsort(entAfterAnswer)[:,::-1]\\n #\\n # Hence, the most preferred response will be given for all hypotheses \\n # where ply holds that card. The least preferred response will be given\\n # for hypotheses where ply holds the least preferred card only.\\n # Example: 0,1,2 => Character-holding bins 1,3,5,7 count to response 0\\n # Bin 4 only counts towards response 2 (weapon)\\n # Bins 2,6 count towards response 1 (room)\\n #\\n # For starters, allocate bins corresponding to holding only one card:\\n # (don't worry - this creates a new copy not a view)\\n receivedHypCounts = hypCountsSorted[:,(0,1,3),:,:,:]\\n #\\n # Because each player will have a different preference order, we must\\n # loop through the player slices assigning the bins separately for each.\\n # Note we refrain from use of 'ixPlayer' since we're off by one, not \\n # having the usual ixPlayer=0=Detective.\\n for ixOPlayer in range(self.nPlayers-1):\\n # The three-card case (bin 7) is fairly easily allocated - to the \\n # most preferred response:\\n receivedHypCounts[ixOPlayer,playerDecisions[ixOPlayer,0]] += (\\n hypCountsSorted[ixOPlayer,6,:,:,:])\\n #\\n # The three two-card bins require a little more negotiating:\\n binsForAllocation = [3,5,6] # indexes 2,4 and 5\\n mostPreferredBit = 2 ** playerDecisions[ixOPlayer,0]\\n for thisBin in binsForAllocation:\\n if thisBin & mostPreferredBit:\\n # Goes to most preferred answer\\n receivedHypCounts[ixOPlayer,playerDecisions[ixOPlayer,0]] \\\\\\n += hypCountsSorted[ixOPlayer,thisBin-1,:,:,:]\\n else:\\n # Goes to second preferred answer\\n receivedHypCounts[ixOPlayer,playerDecisions[ixOPlayer,1]] \\\\\\n += hypCountsSorted[ixOPlayer,thisBin-1,:,:,:]\\n #\\n # We don't actually care about these distributions; only the total \\n # number of hypotheses in each for weighting our earlier entropies:\\n totalReceivedCounts = np.sum(np.sum(np.sum(receivedHypCounts,4),3),2)\\n # The sum over all given answers plus the no-answer case will, of course\\n # be the total number of hypotheses we have:\\n totalHypCount = len(self.hypotheses)\\n # TODO: Remove debug check\\n assert totalHypCount == \\\\\\n np.sum(totalReceivedCounts) + hypCountSumNoAnswer\\n #\\n # Hence the expected entropy after this suggestion is the sum of \\n # scenario entropies after each possible answer (and no answer); \\n # weighted by the number of hypotheses for which each answer is received\\n expEntropy = ((entNoAnswer * hypCountSumNoAnswer) + \\n np.sum(entAfterAnswer * totalReceivedCounts)) / totalHypCount\\n #\\n # The expected room distribution works similarly, but needs to be\\n # normalised by whatever constant necessary to represent a valid \\n # probability distribution. (totalHypCount^2 does not work)\\n expRoomDist = ((hypCountsByRoomNoAnswer * hypCountSumNoAnswer) +\\n np.sum(np.sum(\\n (hypCountsByRoomAfterAnswer * totalReceivedCounts[:,:,None]),1),0))\\n expRoomDist = expRoomDist / np.sum(expRoomDist)\\n #\\n return {'expEntropy':expEntropy,'expRoomDist':expRoomDist}\",\n \"def test_strategy(self):\\n self.responses_test([C, C, C, C], [C, C, C, C], [C])\\n self.responses_test([C, C, C, C, D], [C, C, C, D, C], [D])\\n self.responses_test([C] * 11, [C] * 10 + [D], [C])\",\n \"def test_check_stability():\\n\\n students = [Student(\\\"A\\\"), Student(\\\"B\\\"), Student(\\\"C\\\")]\\n projects = [Project(\\\"P\\\", 2), Project(\\\"Q\\\", 2)]\\n supervisors = [Supervisor(\\\"X\\\", 2), Supervisor(\\\"Y\\\", 2)]\\n\\n (a, b, c), (p, q), (x, y) = students, projects, supervisors\\n\\n p.set_supervisor(x)\\n q.set_supervisor(y)\\n\\n a.set_prefs([p, q])\\n b.set_prefs([q])\\n c.set_prefs([q, p])\\n\\n x.set_prefs([c, a])\\n y.set_prefs([a, b, c])\\n\\n game = StudentAllocation(students, projects, supervisors)\\n\\n matching = game.solve()\\n assert game.check_stability()\\n\\n (a, b, c), (p, q) = game.students, game.projects\\n\\n matching[p] = [c]\\n matching[q] = [a, b]\\n\\n assert not game.check_stability()\",\n \"def profesionalRecommendation(user_preferences: dict, matcher: NodeMatcher):\\r\\n profesional = user_preferences[\\\"vida_profesional\\\"]\\r\\n equal_styles = list(matcher.match(\\\"User\\\", prof = profesional))\\r\\n return equal_styles\",\n \"def test_pl_many(k, l, th_list):\\n patterns = build_pattern_dict(k, l)\\n best_factor = 0\\n for x_th, y_th in th_list:\\n try:\\n factor = test_pl(k, l, x_th, y_th, patterns=patterns)\\n if factor > best_factor:\\n print(\\\"New best factor {} @ (x_th,y_th) = ({},{})\\\".format(factor, x_th, y_th))\\n best_factor = factor\\n except:\\n print(\\\"An error occured while solving the LP\\\")\\n print(\\\"Final best factor {}\\\".format(best_factor))\",\n \"def test_strategy(self):\\n\\n axelrod.seed(8)\\n opponent = axelrod.Cooperator()\\n player = axelrod.WorseAndWorse()\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'D'),\\n ('C', 'C'),\\n ('C', 'C'),\\n ('C', 'C')])\\n\\n # Test that behaviour does not depend on opponent\\n opponent = axelrod.Defector()\\n player = axelrod.WorseAndWorse()\\n axelrod.seed(8)\\n match = axelrod.Match((opponent, player), turns=10)\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'D'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C')])\",\n \"def make_fuzzy_comparable_member_list(all_households):\\n\\n heads_and_spouses = []\\n for house in all_households:\\n for m in ['headOfHouse', 'spouse']:\\n person = house[m] if m in house else None\\n if person:\\n member = FuzzyDict({\\n 'first_name': person['preferredName'].split(',')[1].strip() if ',' in person['preferredName'] else '',\\n 'last_name': person['surname'] if 'surname' in house[m] else '', \\n 'full_name': person['fullName'] if 'fullName' in house[m] else 'fail',\\n 'street': house['desc1'] if 'desc1' in house else '',\\n 'phones': {\\n 'phone1': utils.normalize_phone_number(person['phone']) if 'phone' in person else 0,\\n 'phone2': utils.normalize_phone_number( house['phone']) if 'phone' in house else 0\\n },\\n 'emails': {\\n 'email1' : person['email'] if 'email' in person else '',\\n 'email2': house['email'] if 'email' in house else ''\\n },\\n 'ward': house['ward'],\\n 'match': False\\n })\\n # print member\\n heads_and_spouses.append(member)\\n\\n return heads_and_spouses\",\n \"def habits(user_preferences: dict, matcher: NodeMatcher):\\r\\n goodhabits = user_preferences[\\\"Buenos_habitos\\\"]\\r\\n equal_styles = list(matcher.match(\\\"User\\\", Habits = goodhabits))\\r\\n return equal_styles\",\n \"def test_two_and_three_card_petitions(self):\\n f = gtrutils.check_petition_combos\\n\\n self.assertTrue( f( 0, 0, [], True, True))\\n\\n self.assertFalse( f( 1, 0, [], True, True))\\n self.assertFalse( f( 1, 0, [1], True, True))\\n self.assertTrue( f( 1, 0, [2], True, True))\\n self.assertTrue( f( 1, 0, [3], True, True))\\n self.assertFalse( f( 1, 0, [4], True, True))\\n self.assertTrue( f( 1, 1, [], True, True))\\n self.assertTrue( f( 1, 2, [], True, True))\\n self.assertTrue( f( 1, 3, [], True, True))\\n self.assertFalse( f( 1, 4, [], True, True))\\n\\n self.assertFalse( f( 1, 1, [2], True, True))\\n self.assertFalse( f( 1, 1, [3], True, True))\\n self.assertFalse( f( 1, 2, [2], True, True))\\n self.assertFalse( f( 1, 3, [2], True, True))\\n self.assertFalse( f( 1, 3, [3], True, True))\\n\\n self.assertTrue( f( 2, 1, [2], True, True))\\n self.assertTrue( f( 2, 1, [3], True, True))\\n self.assertTrue( f( 2, 0, [4], True, True))\\n self.assertTrue( f( 2, 0, [5], True, True))\\n self.assertTrue( f( 2, 0, [6], True, True))\\n self.assertTrue( f( 2, 4, [], True, True))\\n self.assertTrue( f( 2, 5, [], True, True))\\n self.assertTrue( f( 2, 6, [], True, True))\\n \\n self.assertTrue( f(13, 26, [], True, True))\\n self.assertTrue( f(13, 39, [], True, True))\\n self.assertTrue( f(13, 0, [26], True, True))\\n self.assertTrue( f(13, 14, [12], True, True))\\n self.assertTrue( f(13, 13, [10], True, True))\\n self.assertTrue( f(13, 15, [11], True, True))\\n self.assertFalse( f(13, 40, [], True, True))\\n self.assertFalse( f(13, 11, [3], True, True))\\n\\n self.assertFalse( f(4, 1, [2,3,6], True, True))\\n self.assertTrue( f(5, 1, [2,3,6], True, True))\\n self.assertTrue( f(6, 1, [2,3,6], True, True))\\n self.assertFalse( f(7, 1, [2,3,6], True, True))\",\n \"def test_tally_Schulze(self):\\n self.populate_database(election_type=\\\"Schulze\\\")\\n\\n uAvote1 = models.Vote(\\n user = self.userA,\\n candidate = self.candidateBA,\\n value =5)\\n uAvote2 = models.Vote(\\n user = self.userA,\\n candidate = self.candidateBB,\\n value = 0)\\n uAvote3 = models.Vote(\\n user = self.userA,\\n candidate = self.candidateBC,\\n value = 3)\\n uAvote4 = models.Vote(\\n user = self.userA,\\n candidate = self.candidateBD,\\n value = -2)\\n\\n uBvote1 = models.Vote(\\n user = self.userB,\\n candidate = self.candidateBA,\\n value = 6)\\n uBvote2 = models.Vote(\\n user = self.userB,\\n candidate = self.candidateBB,\\n value = 1)\\n uBvote3 = models.Vote(\\n user = self.userB,\\n candidate = self.candidateBC,\\n value = -2)\\n uBvote4 = models.Vote(\\n user = self.userB,\\n candidate = self.candidateBD,\\n value = 5)\\n\\n uCvote1 = models.Vote(\\n user = self.userC,\\n candidate = self.candidateBA,\\n value = -2)\\n uCvote2 = models.Vote(\\n user = self.userC,\\n candidate = self.candidateBB,\\n value = 5)\\n uCvote3 = models.Vote(\\n user = self.userC,\\n candidate = self.candidateBC,\\n value = 2)\\n uCvote4 = models.Vote(\\n user = self.userC,\\n candidate = self.candidateBD,\\n value = 3)\\n # Check gen_pair_results() method in Schulze()\\n cand_pair_results = self.schulze.gen_pair_results(self.raceB)\\n # Generate expected pair_results dict for comparitive purposes\\n vote2 = aliased(models.Vote, name=\\\"vote2\\\")\\n expected_pair_results = {}\\n for cand1, cand2 in cand_pair_results.keys():\\n preferred_expected = 0\\n for user in [self.userA, self.userB, self.userC]:\\n v1, v2 = session.query(\\n models.Vote.value.label(\\\"vote1\\\"),\\n vote2.value.label(\\\"vote2\\\")).filter(\\n models.Vote.user_id == user.id,\\n vote2.user_id == user.id,\\n models.Vote.candidate_id == cand1,\\n vote2.candidate_id == cand2).all()[0]\\n if v1 > v2:\\n preferred_expected += 1\\n expected_pair_results[(cand1, cand2)] = preferred_expected\\n self.assertEqual(cand_pair_results[(cand1, cand2)],\\n expected_pair_results[(cand1, cand2)])\\n\\n final_result = self.schulze.tally_race(self.raceB.id)\\n\\n self.dbresults = models.Results(\\n race_id = self.raceB.id,\\n results = final_result) \\n session.add(self.dbresults)\\n session.commit()\\n\\n # JSON doesn't allow dict keys as anything but strings, so \\n # the original model's keys must be converted for comparative\\n # purposes\\n print(\\\"results\\\", self.dbresults.results)\\n final_result_keys_to_str = utils.dict_keys_to_str(final_result.items())\\n\\n self.assertEqual(final_result, {3:True, 4:False, 5:False, 6:False})\\n self.assertEqual(self.dbresults.results, final_result_keys_to_str)\\n self.assertEqual(self.dbresults.election_type, self.raceB.election_type)\\n # self.assertEqual(1,0)\",\n \"def test_solve(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n for optimal in [\\\"student\\\", \\\"supervisor\\\"]:\\n students, projects, _, game = make_game(\\n student_names,\\n project_names,\\n supervisor_names,\\n capacities,\\n seed,\\n clean,\\n )\\n\\n matching = game.solve(optimal)\\n assert isinstance(matching, MultipleMatching)\\n\\n projects = sorted(projects, key=lambda p: p.name)\\n matching_keys = sorted(matching.keys(), key=lambda k: k.name)\\n for game_project, project in zip(matching_keys, projects):\\n assert game_project.name == project.name\\n assert game_project._pref_names == project._pref_names\\n assert game_project.capacity == project.capacity\\n assert game_project.supervisor.name == project.supervisor.name\\n\\n matched_students = [\\n stud for match in matching.values() for stud in match\\n ]\\n assert matched_students != [] and set(matched_students).issubset(\\n set(game.students)\\n )\\n\\n for student in matched_students:\\n supervisor = student.matching.supervisor\\n assert student in supervisor.matching\\n\\n for student in set(game.students) - set(matched_students):\\n assert student.matching is None\",\n \"def test_candidates_list(self):\\n pass\",\n \"def test_known_common_stable_isotopes_cases():\\n assert \\\"H-1\\\" in known_isotopes(\\\"H\\\")\\n assert \\\"D\\\" in known_isotopes(\\\"H\\\")\\n assert \\\"T\\\" in known_isotopes(\\\"H\\\")\\n assert \\\"Be-8\\\" in known_isotopes(\\\"Be\\\")\\n assert \\\"Og-294\\\" in known_isotopes(118)\\n assert \\\"H-1\\\" in common_isotopes(\\\"H\\\")\\n assert \\\"H-4\\\" not in common_isotopes(1)\\n assert \\\"H-1\\\" in stable_isotopes(\\\"H\\\")\\n assert \\\"D\\\" in stable_isotopes(\\\"H\\\")\\n assert \\\"T\\\" not in stable_isotopes(\\\"H\\\")\\n assert \\\"Fe-56\\\" in common_isotopes(\\\"Fe\\\", most_common_only=True)\\n assert \\\"He-4\\\" in common_isotopes(\\\"He\\\", most_common_only=True)\",\n \"def matchClasses(self, probable, actual = None):\\r\\n if actual == None:\\r\\n actual = self.classes\\r\\n return not probable or len([x for x in probable if x in actual]) == len(probable)\",\n \"def test_prefilter_exact_matches(self):\\r\\n seqs = [('s1 comment1', 'ACCTTGTTACTTT'), # three copies\\r\\n ('s2 comment2', 'ACCTTGTTACTTTC'), # one copy\\r\\n ('s3 comment3', 'ACCTTGTTACTTTCC'), # two copies\\r\\n ('s4 comment4', 'ACCTTGTTACTTT'),\\r\\n ('s5 comment5', 'ACCTTGTTACTTTCC'),\\r\\n ('s6 comment6', 'ACCTTGTTACTTT')]\\r\\n expected0 = [('QiimeExactMatch.s1', 'ACCTTGTTACTTT'),\\r\\n ('QiimeExactMatch.s2', 'ACCTTGTTACTTTC'),\\r\\n ('QiimeExactMatch.s3', 'ACCTTGTTACTTTCC')]\\r\\n expected1 = {'QiimeExactMatch.s1': ['s1', 's4', 's6'],\\r\\n 'QiimeExactMatch.s2': ['s2'],\\r\\n 'QiimeExactMatch.s3': ['s3', 's5']}\\r\\n expected = (expected0, expected1)\\r\\n p = OtuPicker({})\\r\\n actual = p._prefilter_exact_matches(seqs)\\r\\n self.assertEqual(actual, expected)\",\n \"def test_get_state_comparison_stats_confederate(self):\\n\\n key = '% Ex-Confederate employees'\\n\\n # 1 ex-Confederate employee in Texas, and 3 not\\n for _ in range(1):\\n employee = EmployeeFactory(confederate_veteran=True)\\n employee.bureau_states.add(self.texas)\\n for _ in range(3):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.texas)\\n\\n # 1 ex-Confederate employee in Kentucky, and 1 not\\n for _ in range(1):\\n employee = EmployeeFactory(confederate_veteran=True)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n # 3 ex-Confederate employees in Mississippi\\n for _ in range(3):\\n employee = EmployeeFactory(confederate_veteran=True)\\n employee.bureau_states.add(self.mississippi)\\n\\n expected_output = [('Mississippi', 100), ('Kentucky', 50)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of Confederate veteran employees\\\")\",\n \"def test_strategy(self):\\n axelrod.seed(1)\\n opponent = axelrod.Cooperator()\\n player = axelrod.KnowledgeableWorseAndWorse()\\n match = axelrod.Match((opponent, player), turns=5)\\n self.assertEqual(match.play(), [('C', 'C'),\\n ('C', 'D'),\\n ('C', 'D'),\\n ('C', 'D'),\\n ('C', 'D')])\\n\\n # Test that behaviour does not depend on opponent\\n opponent = axelrod.Defector()\\n player = axelrod.KnowledgeableWorseAndWorse()\\n axelrod.seed(1)\\n match = axelrod.Match((opponent, player), turns=5)\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'D'),\\n ('D', 'D'),\\n ('D', 'D'),\\n ('D', 'D')])\\n\\n # Test that behaviour changes when does not know length.\\n axelrod.seed(1)\\n match = axelrod.Match((opponent, player), turns=5,\\n match_attributes={'length': float('inf')})\\n self.assertEqual(match.play(), [('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C'),\\n ('D', 'C')])\",\n \"def test_fuzzy_matching_basic(nlp: Language, countries: List[Dict[str, Any]]) -> None:\\n ruler = SpaczzRuler(nlp)\\n ruler.add_patterns(countries)\\n doc = nlp(\\\"This is a test that should find Egypt and Argentina\\\")\\n doc = ruler(doc)\\n matches = [(ent.ent_id_, ent.text) for ent in doc.ents if ent.label_ == \\\"COUNTRY\\\"]\\n assert matches == [(\\\"Egypt\\\", \\\"Egypt\\\"), (\\\"Argentina\\\", \\\"Argentina\\\")]\",\n \"def test_supervisor_optimal(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n np.random.seed(seed)\\n students, projects, supervisors = make_players(\\n student_names, project_names, supervisor_names, capacities\\n )\\n matching = supervisor_optimal(projects, supervisors)\\n\\n assert set(projects) == set(matching.keys())\\n assert all(\\n [\\n s in set(students)\\n for s in {\\n match for matches in matching.values() for match in matches\\n }\\n ]\\n )\\n\\n for supervisor in supervisors:\\n old_idx = -np.infty\\n for student in supervisor.matching:\\n idx = supervisor.prefs.index(student)\\n assert idx >= old_idx\\n old_idx = idx\",\n \"def test_search_multiresults(self):\\n self.assertEquals(len(self.t['Scrubs'].search('my first')) >= 3, True)\",\n \"def test_phs_test_queries_without_phs_in_string(self):\\n url = self.get_url()\\n for query in self.TEST_PHS_QUERIES:\\n response = self.client.get(url, {'q': query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = self.TEST_PHS_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phs in expected_matches:\\n expected_pk = models.Study.objects.get(i_accession=expected_phs).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected phs {} with query '{}'\\\".format(expected_phs, query))\",\n \"def test_get_matches(self):\\n\\n results = GenomePropertiesResultsWithMatches(*self.test_genome_property_results, properties_tree=self.test_tree)\\n\\n self.assertEqual(len(\\n results.step_matches.reset_index()[['Property_Identifier', 'Step_Number']].drop_duplicates()), 1)\\n self.assertEqual(len(results.get_sample_matches('C_chlorochromatii_CaD3')), 5)\\n self.assertEqual(len(results.get_sample_matches('C_luteolum_DSM_273')), 4)\\n\\n self.assertEqual(results.get_sample_matches('Your moms house'), None)\",\n \"def test_four_kind(self):\\n self.assertEqual(poker([self.fk, self.fh]), [self.fk])\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def testProtractedPostApplicationSanityChecks(self):\\n self.assertLess(self.c.get_species_richness(1), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(2), self.c.get_species_richness(4))\\n self.assertLess(self.c.get_species_richness(5), self.c.get_species_richness(3))\\n self.assertLess(self.c.get_species_richness(6), self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(1))\\n self.assertEqual(4, self.c.get_species_richness(2))\\n self.assertEqual(7, self.c.get_species_richness(3))\\n self.assertEqual(7, self.c.get_species_richness(4))\\n self.assertEqual(4, self.c.get_species_richness(5))\\n self.assertEqual(4, self.c.get_species_richness(6))\\n self.assertEqual(21, self.c.get_species_richness(7))\\n self.assertEqual(38, self.c.get_species_richness(8))\",\n \"def test_store_three_responses(self):\\n question = \\\"What language did you first learn to speak?\\\"\\n my_survey = AnonymousSurvey(question)\\n responses = ['English','German','French']\\n for response in responses:\\n my_survey.store_response(response)\\n\\n for response in responses:\\n self.assertIn(response, my_survey.responses)\",\n \"def tests_in(strategy):\\n retval = []\\n for pri, test, args in strategy:\\n if test not in retval:\\n retval.append(test)\\n return retval\",\n \"def test_phs_test_queries_without_phs_in_string(self):\\n url = self.get_url()\\n for query in self.TEST_PHS_QUERIES:\\n response = self.client.get(url, {'q': query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = self.TEST_PHS_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phs in expected_matches:\\n expected_pk = models.Study.objects.get(i_accession=expected_phs).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected pht {} with query '{}'\\\".format(expected_phs, query))\",\n \"def test_suitor_matching(resident_names, hospital_names, capacities, seed):\\n\\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\\n\\n match.solve()\\n match.suitors[0].matching = Player(name=\\\"foo\\\", pref_names=[])\\n\\n with pytest.raises(Exception):\\n match._check_suitor_matching()\",\n \"def main():\\n args = parse_arguments()\\n\\n ##### READ AND PREPROCESS DATA #####\\n mentors, candidates, mentors_years, candidates_years = read_and_preprocess(args.mentor_file, args.candidate_file, args.num_preferences)\\n\\n # Store a copy of the initially declared preferences (after filtering)\\n # This is used later to see how many people didn't get someone in their top \\n candidate_dict_initial = list_to_dictionary(candidates)\\n mentor_dict_initial = list_to_dictionary(mentors)\\n\\n ##### PAIR BASED ON FIRST CHOICES #####\\n successful_pairings = match_top_choices(list_to_dictionary(mentors), list_to_dictionary(candidates))\\n print(\\\"Paired {} people based on first choices\\\".format(len(successful_pairings)))\\n remove_pairings(successful_pairings, mentors, candidates)\\n\\n ##### PAIR BASED ON ONE-SIDED PREFERENCE #####\\n ## ONE SIDED CANDIDATES\\n one_sided_candidate_pairings = match_no_preference(candidates, mentors)\\n print(\\\"Paired {} people based on candidates with no mentor preferences\\\".format(len(one_sided_candidate_pairings)))\\n remove_pairings(one_sided_candidate_pairings, candidates, mentors)\\n\\n ## ONE SIDED MENTORS\\n one_sided_mentor_pairings = match_no_preference(mentors, candidates)\\n print(\\\"Paired {} people based on mentors with no candidate preferences\\\".format(len(one_sided_mentor_pairings)))\\n remove_pairings(one_sided_mentor_pairings, mentors, candidates)\\n\\n # Fill the remaining preference list with the valid unspecified mentors/candidates\\n mentors_filled, candidates_filled = fill_with_valid(mentors, candidates, mentors_years, candidates_years)\\n\\n candidate_dict = list_to_dictionary(candidates_filled)\\n mentor_dict = list_to_dictionary(mentors_filled)\\n\\n stable_matches = stable_matching.stable_marriage(mentor_dict, candidate_dict)\\n stable_match_success = (len(stable_matches) != 0)\\n if stable_match_success:\\n print(\\\"Paired {} people based on stable matching\\\".format(len(stable_matches)))\\n else:\\n print(\\\"Warning: Could not determine a stable match with the optimizations.\\\")\\n print(\\\"Attempting to stable match without...\\\")\\n mentors, candidates, mentors_years, candidates_years = read_and_preprocess(args.mentor_file, args.candidate_file, args.num_preferences)\\n # Fill the remaining preference list with the valid unspecified mentors/candidates\\n mentors_filled, candidates_filled = fill_with_valid(mentors, candidates, mentors_years, candidates_years)\\n\\n candidate_dict = list_to_dictionary(candidates_filled)\\n mentor_dict = list_to_dictionary(mentors_filled)\\n\\n # Stable match immediately\\n stable_matches = stable_matching.stable_marriage(mentor_dict, candidate_dict)\\n if len(stable_matches) == 0:\\n print(\\\"Error: Could not stable match these preference lists.\\\")\\n return\\n\\n # Combine the pairings from all sources into a single list\\n if stable_match_success:\\n all_pairings = []\\n for mentor, candidate in successful_pairings:\\n all_pairings.append([mentor, candidate, \\\"Paired based on first choice\\\"])\\n for mentor, candidate in one_sided_mentor_pairings:\\n all_pairings.append([mentor, candidate, \\\"Paired based on one-sided mentors\\\"])\\n for candidate, mentor in one_sided_candidate_pairings:\\n all_pairings.append([mentor, candidate, \\\"Paired based on one-sided candidates\\\"])\\n for mentor, candidate in stable_matches:\\n mentor_name = mentor\\n if mentor is None:\\n mentor_name = \\\"No mentor\\\"\\n candidate_name = candidate\\n if candidate is None:\\n candidate_name = \\\"No candidate\\\"\\n all_pairings.append([mentor_name, candidate_name, \\\"Paired based on stable matching\\\"])\\n else:\\n all_pairings = stable_matches\\n\\n output_file = \\\"pairings.csv\\\"\\n if args.output_file:\\n output_file = args.output_file\\n with open(output_file, \\\"w+\\\") as f:\\n f.write(\\\"Mentor,Candidate,Notes\\\\n\\\")\\n for mentor, candidate, notes in all_pairings:\\n f.write(\\\"{},{},{}\\\\n\\\".format(mentor, candidate, notes))\",\n \"def test_reasonable_auction(self):\\n # I have no preferences\\n bids = [Cost(ITEM1, ACTOR1, 1000),\\n Cost(ITEM2, ACTOR1, 1000),\\n Cost(ITEM3, ACTOR1, 1000),\\n Cost(ITEM4, ACTOR1, 1000),\\n Cost(ITEM5, ACTOR1, 1000),\\n\\n # I have linear preferences\\n Cost(ITEM1, ACTOR2, 700),\\n Cost(ITEM2, ACTOR2, 800),\\n Cost(ITEM3, ACTOR2, 1000),\\n Cost(ITEM4, ACTOR2, 1200),\\n Cost(ITEM5, ACTOR2, 1300),\\n\\n # I have non-linear preferences\\n Cost(ITEM1, ACTOR3, 400),\\n Cost(ITEM2, ACTOR3, 800),\\n Cost(ITEM3, ACTOR3, 1000),\\n Cost(ITEM4, ACTOR3, 1200),\\n Cost(ITEM5, ACTOR3, 1600),\\n\\n # I have arbitrary preference\\n Cost(ITEM1, ACTOR4, 2435),\\n Cost(ITEM2, ACTOR4, 305),\\n Cost(ITEM3, ACTOR4, 310),\\n Cost(ITEM4, ACTOR4, 1725),\\n Cost(ITEM5, ACTOR4, 225),\\n\\n # I have strong preferences\\n Cost(ITEM1, ACTOR5, 0),\\n Cost(ITEM2, ACTOR5, 0),\\n Cost(ITEM3, ACTOR5, 0),\\n Cost(ITEM4, ACTOR5, 0),\\n Cost(ITEM5, ACTOR5, 5000)]\\n result = self.splitter.split(ITEMS[:5], ACTORS[:5], bids)\\n expected = [(ITEM1, ACTOR4, None),\\n (ITEM2, ACTOR1, None),\\n (ITEM3, ACTOR3, None),\\n (ITEM4, ACTOR2, None),\\n (ITEM5, ACTOR5, None)]\\n item_assignments_present(self, result, expected)\",\n \"def test_results_prefit() -> None:\\n X_train_val, X_test, y_train_val, y_test = train_test_split(\\n X, y, test_size=1/10, random_state=1\\n )\\n X_train, X_val, y_train, y_val = train_test_split(\\n X_train_val, y_train_val, test_size=1/9, random_state=1\\n )\\n estimator = LinearRegression().fit(X_train, y_train)\\n mapie = MapieRegressor(estimator=estimator, cv=\\\"prefit\\\")\\n mapie.fit(X_val, y_val)\\n _, y_pis = mapie.predict(X_test, alpha=0.05)\\n width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()\\n coverage = regression_coverage_score(\\n y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]\\n )\\n np.testing.assert_allclose(width_mean, WIDTHS[\\\"prefit\\\"], rtol=1e-2)\\n np.testing.assert_allclose(coverage, COVERAGES[\\\"prefit\\\"], rtol=1e-2)\",\n \"def stable_roommates(players):\\n\\n players = first_phase(players)\\n\\n if any(p.prefs == [] for p in players):\\n warnings.warn(\\n NoStableMatchingWarning(\\n \\\"The following players have been rejected by all others, \\\"\\n \\\"emptying their preference list: \\\"\\n f\\\"{[p for p in players if not p.prefs]}\\\"\\n )\\n )\\n\\n if any(len(p.prefs) > 1 for p in players):\\n players = second_phase(players)\\n\\n return {player: player.matching for player in players}\",\n \"def property_apply(x): \\n p_lst = ['Arson',\\n 'Attempt to Rob',\\n 'Attempted Arson',\\n 'Attempted Robbery', \\n 'Burglary', \\n 'Defacing Brands',\\n 'Effacing and Defacing Brands',\\n 'Having Tools with Intent to Commit Burglary',\\n 'Having Tools with Intent to Enter Building',\\n 'Larceny',\\n 'Larceny as Bailee',\\n 'Larceny from Person',\\n 'Receiving Stolen Goods',\\n 'Stealing / Killing Cattle',\\n 'Stealing Stock',\\n 'Killing Cattle',\\n 'Killing Stock',\\n 'Robbery',\\n 'Having Burgular Tools',\\n 'Felony, Branding Stock',\\n 'Malicious Mischief',\\n 'Grand Larceny',\\n 'Obstructing of Railroad Track',\\n 'Train Wrecking',\\n 'Maliciously Destroying Check']\\n for elem in x:\\n if elem in p_lst:\\n return 1\\n return 0\",\n \"def top_matches(self, prefs, p1):\\n #print 'top_matches', prefs, p1\\n #print '\\\\n'\\n return [(p2, self.similarity(prefs[p1], prefs[p2])) for p2 in prefs if p2 != p1]\",\n \"def findMatches3(personDict,matches,skepticalMatches,additionalMatches,personDict2):\\n dictConsidered = personDict['ALIAS']\\n for alias in dictConsidered:\\n if alias == \\\"\\\":\\n continue\\n pairs = itertools.combinations(dictConsidered[alias],2)\\n for p in pairs:\\n k = tuple(sorted(p))\\n if (k not in matches) and (k not in skepticalMatches) and (k not in additionalMatches):\\n info1 = personDict['EnterpriseID'][p[0]]\\n info2 = personDict['EnterpriseID'][p[1]]\\n \\n info1b = personDict2['EnterpriseID'][p[0]]\\n info2b = personDict2['EnterpriseID'][p[1]]\\n score = getScorePair(info1b,info2b)\\n if score>=7:\\n additionalMatches[k] = score\\n\\n return additionalMatches\",\n \"def test_only_three_card_petitions(self):\\n f = gtrutils.check_petition_combos\\n\\n self.assertTrue( f( 0, 0, [0], False, True))\\n\\n self.assertFalse( f( 1, 0, [0], False, True))\\n self.assertTrue( f( 1, 1, [0], False, True))\\n self.assertTrue( f( 1, 0, [3], False, True))\\n self.assertTrue( f( 1, 3, [0], False, True))\\n\\n self.assertFalse( f( 1, 1, [2], False, True))\\n self.assertFalse( f( 1, 1, [3], False, True))\\n self.assertFalse( f( 1, 1, [4], False, True))\\n\\n self.assertTrue( f( 2, 2, [0], False, True))\\n self.assertTrue( f( 2, 1, [3], False, True))\\n self.assertTrue( f( 2, 3, [3], False, True))\\n self.assertTrue( f( 2, 6, [0], False, True))\\n self.assertTrue( f( 2, 0, [6], False, True))\\n self.assertFalse( f( 2, 4, [3], False, True))\\n\\n self.assertFalse( f( 3, 1, [], False, True))\\n self.assertFalse( f( 3, 2, [], False, True))\\n self.assertFalse( f( 3, 0, [3], False, True))\\n self.assertFalse( f( 3, 0, [6], False, True))\\n self.assertTrue( f( 3, 3, [], False, True))\\n self.assertTrue( f( 3, 2, [3], False, True))\\n self.assertTrue( f( 3, 3, [6], False, True))\\n self.assertTrue( f( 3, 1, [6], False, True))\\n self.assertTrue( f( 3, 0, [9], False, True))\\n\\n self.assertTrue( f(13,13, [], False, True))\\n self.assertTrue( f(13,39, [], False, True))\\n self.assertTrue( f(13, 0, [39], False, True))\\n self.assertTrue( f(13,15, [24], False, True))\\n self.assertTrue( f(13,15, [], False, True))\\n self.assertTrue( f(13,12, [3], False, True))\\n self.assertFalse( f(13,14, [], False, True))\\n\\n self.assertFalse( f( 6, 1, [3,6,9], False, True))\\n self.assertTrue( f( 7, 1, [3,6,9], False, True))\\n self.assertFalse( f( 8, 1, [3,6,9], False, True))\",\n \"def test_match(self):\\n self.assertEqual(solution( \\\"\\\"\\\"p=<-6,0,0>, v=< 3,0,0>, a=< 0,0,0>\\np=<-4,0,0>, v=< 2,0,0>, a=< 0,0,0>\\np=<-2,0,0>, v=< 1,0,0>, a=< 0,0,0>\\np=< 3,0,0>, v=<-1,0,0>, a=< 0,0,0>\\\"\\\"\\\"), 1)\",\n \"def test_check_inputs_supervisor_prefs_all_reciprocated(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n projects = supervisor.projects\\n for project in student.prefs:\\n if project in projects:\\n student._forget(project)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_prefs_all_reciprocated(\\\"supervisors\\\")\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(supervisor.name)\\n assert student.name in message\\n\\n if clean:\\n assert student not in supervisor.prefs\\n assert all(\\n student not in project.prefs for project in supervisor.projects\\n )\",\n \"def test_assigned_match(self):\\n user1 = get_user_model().objects.get(username='test1@example.com')\\n self.client.login(username='test1@example.com', password='1')\\n\\n office = OfficeLocation.objects.all()[0]\\n org = OrgGroup.objects.filter(parent__isnull=True)[0]\\n\\n submission1 = Interest()\\n submission1.owner = user1\\n submission1.for_coffee = True\\n submission1.save()\\n submission1.locations.add(office)\\n submission1.departments.add(org)\\n\\n resp = self.client.get(reverse('mystery:mystery'))\\n self.assertContains(resp, \\\"Cancel this\\\", status_code=200)\\n\\n user2 = random_user()\\n submission2 = Interest()\\n submission2.owner = user2\\n submission2.is_active = False\\n submission2.save()\\n submission2.for_coffee = True\\n submission2.locations.add(office)\\n submission2.departments.add(org)\\n submission2.is_active = True\\n submission2.save()\\n\\n resp = self.client.get(reverse('mystery:mystery'))\\n self.assertContains(resp, reverse(\\\"mystery:close_incomplete\\\", args=(submission1.id,)))\\n self.assertContains(resp, reverse(\\\"mystery:close_complete\\\", args=(submission1.id,)))\\n\\n # verify assigned match page requires login\\n self.client.logout()\\n resp = self.client.get(reverse('mystery:mystery'))\\n self.assertEqual(resp.status_code, 302)\\n self.assertIn('login', resp['Location'])\",\n \"def compareWithAll(lijst, previouslist, feedback = 0):\\n\\n global usedcombos\\n\\n results = []\\n\\n\\n if feedback == 2: #to make sure there's a 2 letter combination with gaps\\n for i in previouslist:\\n for letter1, letter2 in lijst:\\n if letter1 in i and letter2 in i:\\n results.append(i)\\n\\n elif feedback == 3: #to make sure there's a 3 letter combination with gaps\\n for i in previouslist:\\n for letter1, letter2, letter3 in lijst:\\n if letter1 in i and letter2 in i and letter3 in i:\\n results.append(i)\\n else:\\n for i in previouslist:\\n\\n for j in range(len(lijst)):\\n\\n if lijst[j] in i:\\n\\n results.append(i)\\n\\n results = [item for item in results if item not in usedcombos]\\n results = list(dict.fromkeys(results))\\n\\n print(f\\\"It seems I only {len(results)} options left!\\\")\\n\\n return AIguessing(results)\",\n \"def test_query_multiple(test_store, andy, pandy):\\n items = list(test_store.get_by(age=12))\\n assert len(items) == 2\\n assert andy in items\\n assert pandy in items\",\n \"def test_sqpp_distributed_ands_equivalent(self):\\n self.assertEqual(sorted(perform_request_search(p='ellis and (kaluza-klein or r-parity)')),\\n sorted(perform_request_search(p='ellis and (r-parity or kaluza-klein)')))\",\n \"def partial_match_score(\\n truth: List[Rationale], pred: List[Rationale], thresholds: List[float]\\n) -> List[PartialMatchScore]:\\n\\n ann_to_rat = _keyed_rationale_from_list(truth)\\n pred_to_rat = _keyed_rationale_from_list(pred)\\n\\n num_classifications = {k: len(v) for k, v in pred_to_rat.items()}\\n num_truth = {k: len(v) for k, v in ann_to_rat.items()}\\n ious: Dict[str, Dict[str, float]] = defaultdict(dict)\\n for k in set(ann_to_rat.keys()) | set(pred_to_rat.keys()):\\n for p in pred_to_rat.get(k, []):\\n best_iou = 0.0\\n for t in ann_to_rat.get(k, []):\\n num = len(\\n set(range(p.start_token, p.end_token))\\n & set(range(t.start_token, t.end_token))\\n )\\n denom = len(\\n set(range(p.start_token, p.end_token))\\n | set(range(t.start_token, t.end_token))\\n )\\n iou = 0 if denom == 0 else num / denom\\n if iou > best_iou:\\n best_iou = iou\\n ious[k][p] = best_iou\\n\\n scores: List[PartialMatchScore] = []\\n for threshold in thresholds:\\n threshold_tps: Dict[str, float] = {}\\n for k, vs in ious.items():\\n threshold_tps[k] = sum(int(x >= threshold) for x in vs.values())\\n micro_r = (\\n sum(threshold_tps.values()) / sum(num_truth.values())\\n if sum(num_truth.values()) > 0\\n else 0\\n )\\n micro_p = (\\n sum(threshold_tps.values()) / sum(num_classifications.values())\\n if sum(num_classifications.values()) > 0\\n else 0\\n )\\n micro_f1 = _f1(micro_r, micro_p)\\n macro_rs = list(\\n threshold_tps.get(k, 0.0) / n if n > 0 else 0 for k, n in num_truth.items()\\n )\\n macro_ps = list(\\n threshold_tps.get(k, 0.0) / n if n > 0 else 0\\n for k, n in num_classifications.items()\\n )\\n macro_r = sum(macro_rs) / len(macro_rs) if len(macro_rs) > 0 else 0\\n macro_p = sum(macro_ps) / len(macro_ps) if len(macro_ps) > 0 else 0\\n macro_f1 = _f1(macro_r, macro_p)\\n\\n scores.append(\\n PartialMatchScore(\\n threshold=threshold,\\n micro=InstanceScore(p=micro_p, r=micro_r, f1=micro_f1),\\n macro=InstanceScore(p=macro_p, r=macro_r, f1=macro_f1),\\n )\\n )\\n\\n return scores\",\n \"def check_matches(known, match): \\n #array containing tuples of corrected spellings\\n out_vals = []\\n\\n #clear screen\\n os.system('clear')\\n \\n for k,v in known.iteritems():\\n matched_vals = [ik for ik, iv in match.iteritems() if iv == v]\\n cur_matches = process.extract(k, matched_vals, limit = MATCH_LIMIT)\\n \\n #append a none option and option for actual val\\n cur_matches.insert(0,(\\\"NONE\\\",0))\\n cur_matches.append((k,100))\\n\\n\\n #if there is a match of 100, skip\\n if cur_matches[1][1] == 86 or cur_matches[1][1] < 50:\\n #special case where probably means there really isn't a match\\n choice = 'NONE'\\n print 'No match!'\\n\\n elif cur_matches[1][1] < 95:\\n #print out options for each word\\n print_options(k, cur_matches)\\n\\n #cycle through prompt for input if valid (numeric, within MATCH_LIMIT)\\n choice = None\\n while choice == None:\\n try:\\n choice = cur_matches[get_choice()][0]\\n except:\\n print '!Invalid match! Try again'\\n print_options(k, cur_matches)\\n \\n print choice\\n\\n else:\\n #exact match\\n choice = cur_matches[1][0]\\n print '%s - Match!'%choice\\n\\n print \\n print\\n #create a new tuple with correct and incorrect spelling\\n out_vals.append((k, choice))\\n output(out_vals)\\n\\n #we're done, return vals array\\n return out_vals\",\n \"def test_list_occurrences(self):\\n pass\",\n \"def test_strategy(self):\\n self.first_play_test(C)\\n for i in range(10):\\n history_1 = [C] * i\\n history_2 = [C] * i\\n self.responses_test(history_1, history_2, [C])\\n # Now cooperate 10% less than opponent\\n history_1 = [C] * 11\\n history_2 = [D] * 11\\n self.responses_test(history_1, history_2, [D], random_seed=10)\\n history_1 = [C] * 11\\n history_2 = [D] * 10 + [C]\\n self.responses_test(history_1, history_2, [D], random_seed=10)\\n # Test beyond 10 rounds\\n history_1 = [C] * 11\\n history_2 = [D] * 5 + [C] * 6\\n self.responses_test(history_1, history_2, [D, D, D, D], random_seed=20)\\n history_1 = [C] * 11\\n history_2 = [C] * 9 + [D] * 2\\n self.responses_test(history_1, history_2, [C, D, D, C], random_seed=25)\",\n \"def test_homeland_defense(self):\\n self.conf[\\\"game\\\"][\\\"homeland_defense\\\"] = \\\"100/50/25\\\"\\n self.assertEqual(None, self.sapphire.owner)\\n battle = self.battle\\n sess = self.sess\\n\\n # Skirmish 1\\n s1 = battle.create_skirmish(self.alice, 10) # Attack 10\\n s1a = s1.react(self.carol, 4, hinder=False) # --Support 4\\n s1a.react(self.bob, 3) # ----Attack 3\\n s1.react(self.dave, 8) # --Attack 8\\n # Winner will be team orangered, 11 VP\\n\\n # Skirmish 2\\n battle.create_skirmish(self.bob, 5) # Attack 5\\n # Winner will be team periwinkle, 10 VP for unopposed\\n\\n # End fight\\n self.battle.ends = self.battle.begins\\n sess.commit()\\n self.assert_(battle.past_end_time())\\n\\n updates = Battle.update_all(sess, conf=self.conf)\\n sess.commit()\\n\\n # Overall winner would ordinarily be orangered, but this should get\\n # modified by the homeland defense buffs - Sapphire is right next to\\n # the test capital, and so will get an extra 50%\\n self.assertEqual(battle.victor, 1)\\n self.assertEqual(battle.score1, 15)\\n # OR should get 25% bonus\\n self.assertEqual(battle.score0, 13)\",\n \"def match_all_phrases(self, inphrases):\\n# temporary - attempted matches\\n attempted_matches = []\\n phrase_attempts = {}\\n phrase = \\\"\\\"\\n step = \\\"A\\\"\\n # ALL full phrases \\n for phrase in inphrases:\\n phrase_attempts[phrase] = 1\\n attempted_matches.append(phrase + ':' + step)\\n if phrase in self.cls_phrases:\\n match_choices = self.cls_phrases[phrase]\\n #return match_choices, attempted_matches, phrase\\n return (self.get_list_counts(match_choices), attempted_matches, \\n phrase, self.get_most_common(match_choices))\\n\\n # Normalised version of ALL all full phrases \\n phrases = [self.get_normalised_phrase(p) for p in inphrases]\\n\\n # 3 all prefix trigrams \\n step = \\\"3\\\"\\n for ngram in [p.split()[0:3] for p in phrases if len(p.split()) > 2]:\\n phrase = ' '.join(ngram)\\n phrase_attempts[phrase] = 1\\n attempted_matches.append(phrase + ':' + step)\\n if phrase in self.cls_phrases:\\n match_choices = self.cls_phrases[phrase]\\n return (self.get_list_counts(match_choices), attempted_matches, \\n phrase, self.get_most_common(match_choices))\\n\\n # 2 all prefix bigrams \\n step = \\\"2\\\"\\n for ngram in [p.split()[0:2] for p in phrases if len(p.split()) > 1]:\\n phrase = ' '.join(ngram)\\n phrase_attempts[phrase] = 1\\n attempted_matches.append(phrase + ':' + step)\\n if phrase in self.cls_phrases:\\n match_choices = self.cls_phrases[phrase]\\n return (self.get_list_counts(match_choices), attempted_matches, \\n phrase, self.get_most_common(match_choices))\\n\\n # 1 all valid words \\n step = \\\"1\\\"\\n for phr_elem in phrases:\\n #print phr_elem.split()\\n for phrase in [w.strip() for w in phr_elem.split() \\n if self.isExcluded(w.strip()) == False and w.strip() not in phrase_attempts]:\\n #print \\\"***\\\", phrase\\n phrase_attempts[phrase] = 1\\n attempted_matches.append(phrase + ':' + step)\\n if phrase in self.cls_phrases:\\n match_choices = self.cls_phrases[phrase]\\n return (self.get_list_counts(match_choices), attempted_matches, \\n phrase, self.get_most_common(match_choices))\\n\\n return [], attempted_matches, phrase, None\",\n \"def test_check_full_house_true(self):\\n full_house_fixtures = [[1, 1, 2, 2, 2],\\n [1, 2, 2, 2, 1],\\n [1, 2, 1, 2, 2],\\n [1, 2, 2, 1, 2],\\n [2, 1, 2, 2, 1],\\n [2, 2, 1, 2, 1],\\n [2, 2, 2, 1, 1],\\n ]\\n\\n for fixture in full_house_fixtures:\\n score = self.roll.check_full_house(fixture)\\n\\n self.assertEqual(score, 25)\\n self.assertEqual(len(fixture), 5)\",\n \"def test_get_pf_data_1(self):\\n self.db_filter.values = [40481]\\n\\n pf_data = review.get_pf_data(self.alchemist, self.db_filter)\\n\\n self.assertTrue(isinstance(pf_data, list))\\n\\n for header in review.PF_HEADER:\\n with self.subTest(header=header):\\n self.assertTrue(header in pf_data[0].keys())\\n self.assertFalse(isinstance(pf_data[0][header], list))\",\n \"def test_check_inputs_resident_prefs_all_nonempty(game):\\n\\n resident = game.residents[0]\\n resident.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n assert resident.name in str(record[0].message)\\n\\n if game.clean:\\n assert resident not in game.residents\",\n \"def test_check_inputs_supervisor_reciprocated_all_prefs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n supervisor = game.supervisors[0]\\n student = supervisor.prefs[0]\\n supervisor.prefs.remove(student)\\n\\n with pytest.warns(PreferencesChangedWarning) as record:\\n game._check_inputs_player_reciprocated_all_prefs(\\n \\\"supervisors\\\", \\\"students\\\"\\n )\\n\\n assert len(record) == 1\\n\\n message = str(record[0].message)\\n\\n assert message.startswith(student.name)\\n assert supervisor.name in message\\n\\n if clean:\\n assert supervisor not in student.prefs\\n assert all(\\n project not in student.prefs for project in supervisor.projects\\n )\",\n \"def fuzzy_matching(mapper, fav_parks, verbose=True):\\n match_tuple = []\\n # get match\\n for title, idx in mapper.items():\\n ratio = fuzz.ratio(title.lower(), fav_parks.lower())\\n if ratio >= 100:\\n match_tuple.append((title, idx, ratio))\\n # sort\\n match_tuple = sorted(match_tuple, key=lambda x: x[2])[::-1]\\n if not match_tuple:\\n #print('Oops! No match is found')\\n return\\n if verbose:\\n #print('Found possible matches in our database: {0}\\\\n'.format([x[0] for x in match_tuple]))\\n return match_tuple[0][1]\",\n \"def test_rating_to_happy(self):\\n data = {\\n 1: False,\\n 2: False,\\n 3: False,\\n 4: True,\\n 5: True\\n }\\n for rat, expected in data.items():\\n # Create the response, but DON'T save it to the db.\\n resp = ResponseFactory.build(happy=None, rating=rat)\\n resp.save()\\n eq_(resp.happy, expected)\",\n \"def _find_homeless_mps(self):\\n mps = Person.objects.filter(\\n active=True,\\n house__name=HOUSE_OF_COMMONS,\\n constituency=None,\\n )\\n\\n self.stdout('MPs with missing constituency:')\\n for mp in mps:\\n self.stdout(f' [{mp.parliamentdotuk}] {mp.name} has no constituency')\",\n \"def test_poker_one_sf(self):\\n self.assertEqual(poker([self.sf1, self.fk, self.fh]), [self.sf1])\",\n \"def test_phv_test_queries_with_phv_in_string(self):\\n url = self.get_url()\\n for query in TEST_PHV_QUERIES:\\n response = self.client.get(url, {'q': 'phv' + query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = TEST_PHV_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phv in expected_matches:\\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected phv {} with query '{}'\\\".format(expected_phv, query))\",\n \"def test_phv_test_queries_with_phv_in_string(self):\\n url = self.get_url()\\n for query in TEST_PHV_QUERIES:\\n response = self.client.get(url, {'q': 'phv' + query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = TEST_PHV_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phv in expected_matches:\\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected phv {} with query '{}'\\\".format(expected_phv, query))\",\n \"def test_phv_test_queries_with_phv_in_string(self):\\n url = self.get_url()\\n for query in TEST_PHV_QUERIES:\\n response = self.client.get(url, {'q': 'phv' + query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = TEST_PHV_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phv in expected_matches:\\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected phv {} with query '{}'\\\".format(expected_phv, query))\",\n \"def test_phv_test_queries_with_phv_in_string(self):\\n url = self.get_url()\\n for query in TEST_PHV_QUERIES:\\n response = self.client.get(url, {'q': 'phv' + query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = TEST_PHV_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phv in expected_matches:\\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected phv {} with query '{}'\\\".format(expected_phv, query))\",\n \"def test_phv_test_queries_with_phv_in_string(self):\\n url = self.get_url()\\n for query in TEST_PHV_QUERIES:\\n response = self.client.get(url, {'q': 'phv' + query})\\n returned_pks = get_autocomplete_view_ids(response)\\n expected_matches = TEST_PHV_QUERIES[query]\\n # Make sure number of matches is as expected.\\n self.assertEqual(len(returned_pks), len(expected_matches))\\n # Make sure the matches that are found are the ones expected.\\n for expected_phv in expected_matches:\\n expected_pk = models.SourceTrait.objects.get(i_dbgap_variable_accession=expected_phv).pk\\n self.assertIn(expected_pk, returned_pks,\\n msg=\\\"Could not find expected phv {} with query '{}'\\\".format(expected_phv, query))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6493731","0.62789917","0.625827","0.6236417","0.6157629","0.61469007","0.60858804","0.6017746","0.601711","0.5959501","0.59545827","0.58850694","0.58698004","0.5841389","0.5817565","0.576249","0.5716353","0.5706714","0.56535304","0.5595088","0.5543368","0.5538924","0.5524462","0.5508094","0.5490586","0.5490581","0.547564","0.5458821","0.5456357","0.5454265","0.5432758","0.543167","0.5404304","0.5404304","0.538596","0.53824955","0.5375688","0.53732955","0.536984","0.5362928","0.53611493","0.5354431","0.53376746","0.533474","0.53261846","0.5320164","0.5304066","0.5302878","0.52976507","0.5281336","0.5272889","0.52588904","0.52369267","0.5226398","0.52257216","0.52220327","0.5221341","0.52182263","0.52140117","0.52074426","0.52067184","0.52067184","0.52067184","0.5206139","0.5205064","0.51987463","0.5172724","0.51656663","0.51651835","0.51647145","0.5161228","0.512893","0.5122303","0.511996","0.5118348","0.51114404","0.5106308","0.51018864","0.5089318","0.5088609","0.5085985","0.50853384","0.50848955","0.5084785","0.50846326","0.50829065","0.5081213","0.5079773","0.50676864","0.5067297","0.5063821","0.50628513","0.5060012","0.5057643","0.505682","0.50552654","0.50552654","0.50552654","0.50552654","0.50552654"],"string":"[\n \"0.6493731\",\n \"0.62789917\",\n \"0.625827\",\n \"0.6236417\",\n \"0.6157629\",\n \"0.61469007\",\n \"0.60858804\",\n \"0.6017746\",\n \"0.601711\",\n \"0.5959501\",\n \"0.59545827\",\n \"0.58850694\",\n \"0.58698004\",\n \"0.5841389\",\n \"0.5817565\",\n \"0.576249\",\n \"0.5716353\",\n \"0.5706714\",\n \"0.56535304\",\n \"0.5595088\",\n \"0.5543368\",\n \"0.5538924\",\n \"0.5524462\",\n \"0.5508094\",\n \"0.5490586\",\n \"0.5490581\",\n \"0.547564\",\n \"0.5458821\",\n \"0.5456357\",\n \"0.5454265\",\n \"0.5432758\",\n \"0.543167\",\n \"0.5404304\",\n \"0.5404304\",\n \"0.538596\",\n \"0.53824955\",\n \"0.5375688\",\n \"0.53732955\",\n \"0.536984\",\n \"0.5362928\",\n \"0.53611493\",\n \"0.5354431\",\n \"0.53376746\",\n \"0.533474\",\n \"0.53261846\",\n \"0.5320164\",\n \"0.5304066\",\n \"0.5302878\",\n \"0.52976507\",\n \"0.5281336\",\n \"0.5272889\",\n \"0.52588904\",\n \"0.52369267\",\n \"0.5226398\",\n \"0.52257216\",\n \"0.52220327\",\n \"0.5221341\",\n \"0.52182263\",\n \"0.52140117\",\n \"0.52074426\",\n \"0.52067184\",\n \"0.52067184\",\n \"0.52067184\",\n \"0.5206139\",\n \"0.5205064\",\n \"0.51987463\",\n \"0.5172724\",\n \"0.51656663\",\n \"0.51651835\",\n \"0.51647145\",\n \"0.5161228\",\n \"0.512893\",\n \"0.5122303\",\n \"0.511996\",\n \"0.5118348\",\n \"0.51114404\",\n \"0.5106308\",\n \"0.51018864\",\n \"0.5089318\",\n \"0.5088609\",\n \"0.5085985\",\n \"0.50853384\",\n \"0.50848955\",\n \"0.5084785\",\n \"0.50846326\",\n \"0.50829065\",\n \"0.5081213\",\n \"0.5079773\",\n \"0.50676864\",\n \"0.5067297\",\n \"0.5063821\",\n \"0.50628513\",\n \"0.5060012\",\n \"0.5057643\",\n \"0.505682\",\n \"0.50552654\",\n \"0.50552654\",\n \"0.50552654\",\n \"0.50552654\",\n \"0.50552654\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5400718"},"document_rank":{"kind":"string","value":"34"}}},{"rowIdx":93799,"cells":{"query":{"kind":"string","value":"Test that no hospitals can be oversubscribed."},"document":{"kind":"string","value":"def test_check_for_oversubscribed_hospitals(game):\n\n hospital = game.hospitals[0]\n hospital.matching = range(hospital.capacity + 1)\n\n issues = game._check_for_oversubscribed_players(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.capacity}.\")\n assert str(hospital.matching) in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_hospitals[0]\n assert issue == error"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_nonsubscriber(self) -> None:\n # Create a stream for which Hamlet is the only subscriber.\n stream_name = \"Saxony\"\n self.common_subscribe_to_streams(self.user_profile, [stream_name])\n other_user = self.example_user(\"othello\")\n\n # Fetch the subscriber list as a non-member.\n self.login_user(other_user)\n self.make_successful_subscriber_request(stream_name)","def test_subscriptions_does_not_exist(self) -> None:\n random_streams = self.make_random_stream_names(self.streams)\n self.assertNotEqual(len(random_streams), 0) # necessary for full test coverage\n self.helper_subscriptions_exists(random_streams[0], False, False)","def test_successful_subscriptions_exists_not_subbed(self) -> None:\n all_stream_names = [stream.name for stream in Stream.objects.filter(realm=self.test_realm)]\n streams_not_subbed = list(set(all_stream_names) - set(self.streams))\n self.assertNotEqual(len(streams_not_subbed), 0) # necessary for full test coverage\n self.helper_subscriptions_exists(streams_not_subbed[0], True, False)","def test_check_inputs_hospital_prefs_all_nonempty(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n assert hospital.name in str(record[0].message)\n\n if game.clean:\n assert hospital not in game.hospitals","def testNoSpecialties(self):\n self.failUnlessEqual(self.person.getSpecialties(), [])","def test_never_subscribed_streams(self) -> None:\n realm = get_realm(\"zulip\")\n users_to_subscribe = [\n self.example_user(\"othello\").id,\n self.example_user(\"cordelia\").id,\n ]\n\n public_streams = [\n \"test_stream_public_1\",\n \"test_stream_public_2\",\n \"test_stream_public_3\",\n \"test_stream_public_4\",\n \"test_stream_public_5\",\n ]\n\n private_streams = [\n \"test_stream_invite_only_1\",\n \"test_stream_invite_only_2\",\n ]\n\n web_public_streams = [\n \"test_stream_web_public_1\",\n \"test_stream_web_public_2\",\n ]\n\n def create_public_streams() -> None:\n for stream_name in public_streams:\n self.make_stream(stream_name, realm=realm)\n\n self.common_subscribe_to_streams(\n self.user_profile,\n public_streams,\n dict(principals=orjson.dumps(users_to_subscribe).decode()),\n )\n\n create_public_streams()\n\n def create_web_public_streams() -> None:\n for stream_name in web_public_streams:\n self.make_stream(stream_name, realm=realm, is_web_public=True)\n\n ret = self.common_subscribe_to_streams(\n self.user_profile,\n web_public_streams,\n dict(principals=orjson.dumps(users_to_subscribe).decode()),\n )\n self.assert_json_success(ret)\n\n create_web_public_streams()\n\n def create_private_streams() -> None:\n self.common_subscribe_to_streams(\n self.user_profile,\n private_streams,\n dict(principals=orjson.dumps(users_to_subscribe).decode()),\n invite_only=True,\n )\n\n create_private_streams()\n\n def get_never_subscribed() -> List[NeverSubscribedStreamDict]:\n with self.assert_database_query_count(4):\n sub_data = gather_subscriptions_helper(self.user_profile)\n self.verify_sub_fields(sub_data)\n never_subscribed = sub_data.never_subscribed\n\n # Ignore old streams.\n never_subscribed = [dct for dct in never_subscribed if dct[\"name\"].startswith(\"test_\")]\n return never_subscribed\n\n never_subscribed = get_never_subscribed()\n\n # Invite only stream should not be there in never_subscribed streams\n self.assert_length(never_subscribed, len(public_streams) + len(web_public_streams))\n for stream_dict in never_subscribed:\n name = stream_dict[\"name\"]\n self.assertFalse(\"invite_only\" in name)\n self.assert_length(stream_dict[\"subscribers\"], len(users_to_subscribe))\n\n # Send private stream subscribers to all realm admins.\n def test_admin_case() -> None:\n self.user_profile.role = UserProfile.ROLE_REALM_ADMINISTRATOR\n # Test realm admins can get never subscribed private stream's subscribers.\n never_subscribed = get_never_subscribed()\n\n self.assertEqual(\n len(never_subscribed),\n len(public_streams) + len(private_streams) + len(web_public_streams),\n )\n for stream_dict in never_subscribed:\n self.assert_length(stream_dict[\"subscribers\"], len(users_to_subscribe))\n\n test_admin_case()\n\n def test_guest_user_case() -> None:\n self.user_profile.role = UserProfile.ROLE_GUEST\n helper_result = gather_subscriptions_helper(self.user_profile)\n self.verify_sub_fields(helper_result)\n sub = helper_result.subscriptions\n unsub = helper_result.unsubscribed\n never_sub = helper_result.never_subscribed\n\n # It's +1 because of the stream Rome.\n self.assert_length(never_sub, len(web_public_streams) + 1)\n sub_ids = [stream[\"stream_id\"] for stream in sub]\n unsub_ids = [stream[\"stream_id\"] for stream in unsub]\n\n for stream_dict in never_sub:\n self.assertTrue(stream_dict[\"is_web_public\"])\n self.assertTrue(stream_dict[\"stream_id\"] not in sub_ids)\n self.assertTrue(stream_dict[\"stream_id\"] not in unsub_ids)\n\n # The Rome stream has is_web_public=True, with default\n # subscribers not set up by this test, so we do the\n # following check only for the streams we created.\n if stream_dict[\"name\"] in web_public_streams:\n self.assert_length(stream_dict[\"subscribers\"], len(users_to_subscribe))\n\n test_guest_user_case()","def test_no_email(self):\n\n a = Agency(name=\"Broadcasting Board of Governors\", slug=\"brodcasting\")\n a.save()\n\n response = self.client.get(\n reverse('contact_landing', args=['broadcasting']))\n self.assertTrue(200, response.status_code)\n content = response.content.decode('utf-8')\n self.assertTrue('Request online' not in content)","def test_issue_subscriptions(self):\n pass","def test_list_respects_invite_only_bit(self) -> None:\n\n user = self.example_user(\"hamlet\")\n self.login_user(user)\n\n self.common_subscribe_to_streams(user, [\"Saxony\"], invite_only=True)\n self.common_subscribe_to_streams(user, [\"Normandy\"], invite_only=False)\n result = self.api_get(user, \"/api/v1/users/me/subscriptions\")\n response_dict = self.assert_json_success(result)\n self.assertIn(\"subscriptions\", response_dict)\n for sub in response_dict[\"subscriptions\"]:\n if sub[\"name\"] == \"Normandy\":\n self.assertEqual(\n sub[\"invite_only\"], False, \"Normandy was mistakenly marked private\"\n )\n if sub[\"name\"] == \"Saxony\":\n self.assertEqual(sub[\"invite_only\"], True, \"Saxony was not properly marked private\")","def testHasNoInvites(self):\r\n u = User()\r\n u.invite_ct = 0\r\n self.assertFalse(u.has_invites(), 'User should have no invites')\r\n self.assertFalse(\r\n u.invite('me@you.com'), 'Should not be able to invite a user')","def test_skip_if_no_patients(self):\n\n appt_date = datetime.date.today() + datetime.timedelta(days=5)\n confirmed = self.create_confirmed_notification(self.test_patient, appt_date)\n\n # run email job\n from aremind.apps.reminders.app import daily_email_callback\n daily_email_callback(self.router)\n\n self.assertEqual(len(mail.outbox), 0)","def test_check_for_oversubscribed_supervisors(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n supervisor = game.supervisors[0]\n supervisor.matching = range(supervisor.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(supervisor.name)\n assert error.endswith(str(supervisor.capacity))\n assert str(supervisor.matching) in e","def test_unsubscribe_offer(self):\n pass","def test_can_subscribe_other_users(self) -> None:\n\n def validation_func(user_profile: UserProfile) -> bool:\n user_profile.refresh_from_db()\n return user_profile.can_subscribe_other_users()\n\n self.check_has_permission_policies(\"invite_to_stream_policy\", validation_func)","def test_gather_subscriptions_mit(self) -> None:\n # Subscribe only ourself because invites are disabled on mit.edu\n mit_user_profile = self.mit_user(\"starnine\")\n user_id = mit_user_profile.id\n users_to_subscribe = [user_id, self.mit_user(\"espuser\").id]\n for email in users_to_subscribe:\n stream = self.subscribe(mit_user_profile, \"mit_stream\")\n self.assertTrue(stream.is_in_zephyr_realm)\n\n self.common_subscribe_to_streams(\n mit_user_profile,\n [\"mit_invite_only\"],\n dict(principals=orjson.dumps(users_to_subscribe).decode()),\n invite_only=True,\n subdomain=\"zephyr\",\n )\n\n with self.assert_database_query_count(3):\n subscribed_streams, _ = gather_subscriptions(mit_user_profile, include_subscribers=True)\n\n self.assertGreaterEqual(len(subscribed_streams), 2)\n for sub in subscribed_streams:\n if not sub[\"name\"].startswith(\"mit_\"):\n raise AssertionError(\"Unexpected stream!\")\n if sub[\"name\"] == \"mit_invite_only\":\n self.assert_length(sub[\"subscribers\"], len(users_to_subscribe))\n else:\n self.assert_length(sub[\"subscribers\"], 0)\n self.assertIsNone(sub[\"stream_weekly_traffic\"])\n\n # Create a web-public stream to test never_subscried data.\n self.make_stream(\"mit_stream_2\", realm=mit_user_profile.realm, is_web_public=True)\n self.make_stream(\"mit_stream_3\", realm=mit_user_profile.realm)\n\n sub_info = gather_subscriptions_helper(mit_user_profile, include_subscribers=True)\n never_subscribed_streams = sub_info.never_subscribed\n # Users in zephyr mirror realm can only access web-public never subscribed streams.\n self.assert_length(never_subscribed_streams, 1)\n self.assertEqual(never_subscribed_streams[0][\"name\"], \"mit_stream_2\")\n self.assertTrue(never_subscribed_streams[0][\"is_web_public\"])\n self.assertIsNone(never_subscribed_streams[0][\"stream_weekly_traffic\"])","def test_negative_is_active_of_homework():\n assert not expired_hw.is_active()","def test_registered_no_notifications(self):\n msg = self._send(self.reg_conn, '1')\n self.assertEqual(len(msg.responses), 1)\n self.assertEqual(msg.responses[0].text,\n self.app.no_reminders)","def test_registered_no_notifications(self):\n msg = self._send(self.reg_conn, '1')\n self.assertEqual(len(msg.responses), 1)\n self.assertEqual(msg.responses[0].text,\n self.app.no_reminders)","def test_healer_not_confirmed(self):\n\t\tself.signup('healer')\n\n\t\tresponse = self.rest_client.get(reverse('provider_setup_intro'))\n\t\tself.assertEqual(response.status_code, 200)\n\n\t\t# check if any other page except setup is available\n\t\tresponse = self.rest_client.get(reverse('notes'))\n\t\tself.assertEqual(response.status_code, 302)","def test_cant_remove_other_users_from_stream(self) -> None:\n result = self.attempt_unsubscribe_of_principal(\n query_count=8,\n target_users=[self.example_user(\"cordelia\")],\n is_realm_admin=False,\n is_subbed=True,\n invite_only=False,\n target_users_subbed=True,\n )\n self.assert_json_error(result, \"Insufficient permission\")","def test_subscriptions_add_notification_default_none(self) -> None:\n user_profile = self.example_user(\"iago\")\n invitee_user_id = user_profile.id\n invitee_realm = user_profile.realm\n user_profile.enable_stream_desktop_notifications = True\n user_profile.enable_stream_push_notifications = True\n user_profile.enable_stream_audible_notifications = True\n user_profile.enable_stream_email_notifications = True\n user_profile.save()\n current_stream = self.get_streams(user_profile)[0]\n invite_streams = self.make_random_stream_names([current_stream])\n self.assert_adding_subscriptions_for_principal(\n invitee_user_id, invitee_realm, invite_streams, policy_name=\"Public\"\n )\n subscription = self.get_subscription(user_profile, invite_streams[0])\n\n with mock.patch(\"zerver.models.Recipient.__repr__\", return_value=\"recip\"):\n self.assertEqual(\n repr(subscription),\n \"<Subscription: \"\n f\"<UserProfile: {user_profile.email} {user_profile.realm!r}> -> recip>\",\n )\n\n self.assertIsNone(subscription.desktop_notifications)\n self.assertIsNone(subscription.push_notifications)\n self.assertIsNone(subscription.audible_notifications)\n self.assertIsNone(subscription.email_notifications)","def test_check_for_unacceptable_matches_hospitals(game):\n\n hospital = game.hospitals[0]\n resident = Resident(name=\"foo\")\n hospital.matching.append(resident)\n\n issues = game._check_for_unacceptable_matches(\"hospitals\")\n assert len(issues) == 1\n\n issue = issues[0]\n assert issue.startswith(hospital.name)\n assert issue.endswith(f\"{hospital.prefs}.\")\n assert resident.name in issue\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.unacceptable_matches[0]\n assert issue == error","def test_subscriber_access_if_vsg1_goes_down(self):","def test_hubs_fail(self):\n with self.assertRaises(AssertionError):\n self.resource.hubs(-1)","def test_no_events(woodshop, caplog):\n caplog.set_level(logging.INFO)\n woodshop.log_conflicts(\"0000-00-00 00:00:00\")\n assert caplog.text == \"\"","def test_no_other_enrollments(self):\n user = self.make_user()\n EnrollmentFactory()\n\n with self.login(user):\n self.get_check_200(\"reports:index\")\n\n assert list(self.get_context(\"enrollments\")) == []","def test_check_inputs_resident_prefs_all_nonempty(game):\n\n resident = game.residents[0]\n resident.prefs = []\n\n with pytest.warns(PlayerExcludedWarning) as record:\n game._check_inputs_player_prefs_nonempty(\"residents\", \"hospitals\")\n\n assert len(record) == 1\n assert resident.name in str(record[0].message)\n\n if game.clean:\n assert resident not in game.residents","def test_skip_if_no_patients(self):\n\n appt_date = datetime.date.today() + datetime.timedelta(days=5)\n reminders.Patient.objects.filter(\n pk__in=[self.test_patient.pk, self.other_patient.pk]\n ).update(next_visit=appt_date)\n confirmed = self.create_confirmed_notification(self.test_patient, appt_date)\n\n self.startRouter()\n self.router.logger.setLevel(logging.DEBUG)\n # run email job\n from afrims.apps.reminders.app import daily_email_callback\n daily_email_callback(self.router)\n\n self.assertEqual(len(mail.outbox), 0)\n self.stopRouter()","def test_nonsubscriber_private_stream(self) -> None:\n # Create a private stream for which Hamlet is the only subscriber.\n stream_name = \"NewStream\"\n self.common_subscribe_to_streams(self.user_profile, [stream_name], invite_only=True)\n user_profile = self.example_user(\"othello\")\n\n # Try to fetch the subscriber list as a non-member & non-realm-admin-user.\n stream_id = get_stream(stream_name, user_profile.realm).id\n result = self.make_subscriber_request(stream_id, user=user_profile)\n self.assert_json_error(result, \"Invalid stream ID\")\n\n # Try to fetch the subscriber list as a non-member & realm-admin-user.\n self.login(\"iago\")\n self.make_successful_subscriber_request(stream_name)","def test_get_users_eligible_for_fist_notification_with_no_result(self):\n # Given:\n self.batch_setup()\n # When:\n response = self.client.get(\"/api/batch/account/users/eligible-for-first-notification\", headers=self.headers)\n # Then:\n self.assertTrue(200, response.status_code)\n users = response.get_json()\n self.assertEqual(0, len(users))\n self.assertNotIn(self.user_0, users)\n self.assertNotIn(self.user_2, users)\n self.assertNotIn(self.user_1, users)\n self.assertNotIn(self.user_3, users)","def test_check_for_oversubscribed_projects(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n game.solve()\n\n project = game.projects[0]\n project.matching = range(project.capacity + 1)\n\n with pytest.raises(MatchingError) as e:\n game.check_validity()\n error = e.oversubscribed_players[0]\n assert error.startswith(project.name)\n assert error.endswith(str(project.capacity))\n assert str(project.matching) in e","def test_existing_subscriptions_autosubscription_private_stream(self) -> None:\n stream_name = \"Saxony\"\n cordelia = self.example_user(\"cordelia\")\n self.common_subscribe_to_streams(cordelia, [stream_name], invite_only=True)\n stream = get_stream(stream_name, self.test_realm)\n\n result = self.client_post(\n \"/json/subscriptions/exists\", {\"stream\": stream_name, \"autosubscribe\": \"true\"}\n )\n # We can't see invite-only streams here\n self.assert_json_error(result, \"Invalid stream name 'Saxony'\", status_code=404)\n # Importantly, we are not now subscribed\n self.assertEqual(num_subscribers_for_stream_id(stream.id), 1)\n\n # A user who is subscribed still sees the stream exists\n self.login(\"cordelia\")\n result = self.client_post(\n \"/json/subscriptions/exists\", {\"stream\": stream_name, \"autosubscribe\": \"false\"}\n )\n response_dict = self.assert_json_success(result)\n self.assertIn(\"subscribed\", response_dict)\n self.assertTrue(response_dict)","def test_private_rooms_do_not_have_profiles_collected(self) -> None:\n room_id = self.helper.create_room_as(\n self.alice, is_public=False, tok=self.alice_tok\n )\n self.get_success(\n event_injection.inject_member_event(\n self.hs,\n room_id,\n \"@bruce:remote\",\n \"join\",\n \"@bruce:remote\",\n extra_content={\n \"displayname\": \"super-duper bruce\",\n \"avatar_url\": \"mxc://remote/456\",\n },\n )\n )\n # Sending this event makes the streams move forward after the injection...\n self.helper.send(room_id, \"Test\", tok=self.alice_tok)\n self.pump(0.1)\n\n profiles = self.get_success(\n self.user_dir_helper.get_profiles_in_user_directory()\n )\n self.assertNotIn(\"@bruce:remote\", profiles)","def test_successful_subscriptions_exists_subbed(self) -> None:\n self.assertNotEqual(len(self.streams), 0) # necessary for full test coverage\n self.helper_subscriptions_exists(self.streams[0], True, True)","def test_subscriber_access_if_vsg2_goes_down(self):","def test_unverified_subscriber(self):\n self.prep_consumer()\n subscriber = Subscriber.objects.get(id=7)\n self.consumer.subscriber = subscriber\n self.consumer.save()\n UnqualifiedConsumerEmailTask().run(test_mode=self.consumer)\n self.common_asserts()\n self.assertTrue('71010' in mail.outbox[0].alternatives[0][0])\n self.assertTrue('71010' in mail.outbox[0].body)","def test_subscriptions_add_for_principal_deactivated(self) -> None:\n target_profile = self.example_user(\"cordelia\")\n post_data = dict(\n principals=orjson.dumps([target_profile.id]).decode(),\n )\n self.common_subscribe_to_streams(self.test_user, \"Verona\", post_data)\n\n do_deactivate_user(target_profile, acting_user=None)\n result = self.common_subscribe_to_streams(\n self.test_user, \"Denmark\", post_data, allow_fail=True\n )\n self.assert_json_error(\n result,\n f\"User not authorized to execute queries on behalf of '{target_profile.id}'\",\n status_code=403,\n )","def test_empty_shared(self):\n self.do_sharable(False, 'pattieblack', None, is_admin=True)\n self.do_sharable(False, 'pattieblack', FakeMembership(True),\n is_admin=True)","def test_get_subscriptions(self):\n pass","async def test_skipped_already_unsilenced(self):\n self.cog.scheduler.__contains__.return_value = False\n self.cog.previous_overwrites.get.return_value = None\n\n for channel in (MockVoiceChannel(), MockTextChannel()):\n with self.subTest(channel=channel):\n self.assertFalse(await self.cog._unsilence(channel))\n channel.set_permissions.assert_not_called()","def test_incompatible_subscription_and_tenant():\n pass","def test_unsubscribe_any_listener(self):\n with self.assertRaises(AssertionError):\n EVENT_MANAGER.unsubscribe()","def test_no_other_school_years(self):\n user = self.make_user()\n SchoolYearFactory()\n\n with self.login(user):\n self.get_check_200(\"reports:index\")\n\n assert list(self.get_context(\"school_years\")) == []","def not_test_without_user(self):\n # TODO","def test_empty_case(self):\n a_user = User.objects.create()\n user = VSBUser.objects.create(user=a_user)\n\n received = util.bucket_calenderevents(user.calenderevent_set)\n\n self.assertEqual(received, [], msg=\"CalenderEvents.empty_case: Non-empty list returned.\")","def test_user_not_in_users_table(self) -> None:\n user1 = self.register_user(\"user1\", \"pass\")\n token1 = self.login(user1, \"pass\")\n room = self.helper.create_room_as(user1, is_public=True, tok=token1)\n\n # Inject a join event for a user who doesn't exist\n self.get_success(inject_member_event(self.hs, room, \"@not-a-user:test\", \"join\"))\n\n # Another new user registers and joins the room\n user2 = self.register_user(\"user2\", \"pass\")\n token2 = self.login(user2, \"pass\")\n self.helper.join(room, user2, tok=token2)\n\n # The dodgy event should not have stopped us from processing user2's join.\n in_public = self.get_success(self.user_dir_helper.get_users_in_public_rooms())\n self.assertEqual(set(in_public), {(user1, room), (user2, room)})","def no_save_individual_subscription(sender, instance, **kwargs):\n try:\n Subscription.objects.get(pk=instance.pk) # looking if the subscription exist, if the case, we assume here is updating active status or email status\n except:\n if instance.user is not None:\n subs_ids = instance.user.groups.values_list('subscription')\n for sub in subs_ids:\n if None not in sub:\n alarm = Subscription.objects.get(id=sub[0]).alarm\n if alarm == instance.alarm:\n raise ValidationError('The user is subscribed to the same alarm for a group')\n\n subs = Subscription.objects.filter(user=instance.user)\n for sub in subs:\n if sub.alarm == instance.alarm:\n raise ValidationError('The user is subscribed to this alarm')","def test_overlapping_events_unchecked(sample_events, lobby, caplog):\n caplog.set_level(logging.INFO)\n event1, event2 = sample_events.make_overlapping_events()\n overlap_events(event1, event2, lobby, lobby, [lobby])\n assert len(caplog.messages) == 0","def test_simple_unconfirmed(self):\n appt_date = datetime.date.today()\n self.create_confirmed_notification(self.test_patient, appt_date)\n self.create_unconfirmed_notification(self.other_patient, appt_date)\n qs = Patient.objects.unconfirmed_for_date(appt_date)\n self.assertFalse(self.test_patient in qs)\n self.assertTrue(self.other_patient in qs)\n self.assertFalse(self.unrelated_patient in qs)","def test_validate_user_access_to_subscribers_helper(self) -> None:\n user_profile = self.example_user(\"othello\")\n realm_name = \"no_othello_allowed\"\n realm = do_create_realm(realm_name, \"Everyone but Othello is allowed\")\n stream_dict = {\n \"name\": \"publicstream\",\n \"description\": \"Public stream with public history\",\n \"realm_id\": realm.id,\n }\n\n # For this test to work, othello can't be in the no_othello_here realm\n self.assertNotEqual(\n user_profile.realm.id, realm.id, \"Expected othello user to not be in this realm.\"\n )\n\n # This should result in missing user\n with self.assertRaises(ValidationError):\n validate_user_access_to_subscribers_helper(None, stream_dict, lambda user_profile: True)\n\n # This should result in user not in realm\n with self.assertRaises(ValidationError):\n validate_user_access_to_subscribers_helper(\n user_profile, stream_dict, lambda user_profile: True\n )","def test_process_subscriptions(self):\n pass","def test_no_data(self):\n response = self.client.get(reverse('education:states'))\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.context.get(\"states\").count(), 0)\n self.assertContains(response, \"No Data Available\")\n self.assertNotContains(response, \"Number of Public High Schools\")","def test_disallow_absent_fighting(self):\n londo = self.get_region(\"Orange Londo\")\n self.alice.region = londo\n self.sess.commit()\n\n with self.assertRaises(db.NotPresentException):\n self.battle.create_skirmish(self.alice, 1)\n\n n = (self.sess.query(db.SkirmishAction).filter_by(parent_id=None).\n filter_by(participant=self.alice)).count()\n self.assertEqual(n, 0)","def test_transaction_is_affordable_there_is_no_wealth(self):\n currency_endowment = {\"FET\": 0}\n good_endowment = {\"good_id\": 0}\n self.ownership_state.init(\n amount_by_currency_id=currency_endowment,\n quantities_by_good_id=good_endowment,\n )\n tx_message = TransactionMessage(\n performative=TransactionMessage.Performative.PROPOSE_FOR_SETTLEMENT,\n skill_callback_ids=[PublicId(\"author\", \"a_skill\", \"0.1.0\")],\n tx_id=\"transaction0\",\n tx_sender_addr=\"agent_1\",\n tx_counterparty_addr=\"pk\",\n tx_amount_by_currency_id={\"FET\": 0},\n tx_sender_fee=0,\n tx_counterparty_fee=0,\n tx_quantities_by_good_id={\"good_id\": 0},\n info={\"some_info_key\": \"some_info_value\"},\n ledger_id=\"fetchai\",\n tx_nonce=\"transaction nonce\",\n )\n\n assert not self.ownership_state.is_affordable_transaction(\n tx_message=tx_message\n ), \"We must reject the transaction.\"","def test_must_be_subbed_to_send(self) -> None:\n user = self.example_user(\"hamlet\")\n self.login_user(user)\n # Create Saxony as an invite-only stream.\n self.assert_json_success(\n self.common_subscribe_to_streams(user, [\"Saxony\"], invite_only=True)\n )\n\n cordelia = self.example_user(\"cordelia\")\n with self.assertRaises(JsonableError):\n self.send_stream_message(cordelia, \"Saxony\")","def test_unlisted_addon(self):\n addon = Addon.objects.get(pk=3615)\n self.make_addon_unlisted(addon)\n\n up = self.get(self.good_data)\n assert up.is_valid()","def test_alert_create_for_site_members(self):\n pass","def test_empty_events(self, client, site, homepage):\n response = client.get(homepage.relative_url(site))\n assertTemplateNotUsed(response, \"events/snippets/event_card.html\")\n assertContains(response, \"Next semester's events are being scheduled.\")","def test_not_unopposed(self):\n s1 = self.battle.create_skirmish(self.alice, 2) # Attack 2\n s1.react(self.bob, 1) # --Attack 1\n s1.resolve()\n self.assertFalse(s1.unopposed)","def test_user_can_not_enroll_to_unpublished_project(self):\n self.client.force_authenticate(self.global_user_1)\n\n # Make sure nothing is found - not enrolled.\n resp = self.client.get(self.api_project_state_not_enrolled_unpublished_url)\n self.assertEqual(resp.status_code, 404)\n\n #NOTE: Allowed!\n # # POST and make sure the user is enrolled\n # resp = self.client.post(self.api_project_state_not_enrolled_unpublished_url)\n # self.assertEqual(resp.status_code, 403)\n # resp = self.client.get(self.api_project_state_not_enrolled_unpublished_url)\n # self.assertEqual(resp.status_code, 404)","def test_user_can_not_enroll_to_unpublished_lesson(self):\n self.client.force_authenticate(self.global_user_1)\n\n # Make the unpublished project owned by the authenticated user:\n self.project_unpublished.owner = self.global_user_1\n self.project_unpublished.save()\n\n # Make sure nothing is found - not enrolled.\n resp = self.client.get(self.api_lesson_state_not_enrolled_unpublished_url)\n self.assertEqual(resp.status_code, 404)\n\n #NOTE: Allowed!\n # # POST and make sure the user is enrolled\n # resp = self.client.post(self.api_lesson_state_not_enrolled_unpublished_url)\n # self.assertEqual(resp.status_code, 403)\n # resp = self.client.get(self.api_lesson_state_not_enrolled_unpublished_url)\n # self.assertEqual(resp.status_code, 404)\n\n # Other user (that do not have access to the project)\n\n # Make the unpublished project owned by a user that is not child or delegator of the authenticated user:\n new_owner = get_user_model().objects.exclude(\n Q(pk=self.global_user_1.pk) |\n Q(pk__in=self.global_user_1.delegators.all()) |\n Q(pk__in=self.global_user_1.children.all())\n )[0]\n self.project_unpublished.owner = new_owner\n self.project_unpublished.save()\n\n # Make sure nothing is found - not enrolled.\n resp = self.client.get(self.api_lesson_state_not_enrolled_unpublished_url)\n self.assertEqual(resp.status_code, 404)\n\n # POST and make sure the user is enrolled\n resp = self.client.post(self.api_lesson_state_not_enrolled_unpublished_url)\n self.assertEqual(resp.status_code, 404)\n resp = self.client.get(self.api_lesson_state_not_enrolled_unpublished_url)\n self.assertEqual(resp.status_code, 404)","def test_un_member_status(self):\n\n # Get Hong Kong\n hong_kong = Country.objects.get(iso3=\"HKG\")\n\n # Assert that is_un_member_at is None\n self.assertEqual(hong_kong.is_un_member_at, None)\n\n # Initialize assertRaises block\n with self.assertRaises(ValidationError):\n\n # Set is UN member to True\n hong_kong.is_un_member = True\n\n # Attempt to clean the Hong Kong object\n hong_kong.clean()\n\n # Should fail because no corresponding is UN member at date is set\n\n # Get Thailand\n thailand = Country.objects.get(iso3=\"THA\")\n\n # Assert that Thailand is a UN member state\n self.assertEqual(thailand.is_un_member, True)\n\n # Assert that Thailand is UN member at is not None\n self.assertIsNotNone(thailand.is_un_member_at)\n\n # Set Thailand is UN member at to False\n thailand.is_un_member = False\n\n # Clean Thailand object\n thailand.clean()\n\n # Asser that Thailand is UN member at is now None\n self.assertIsNone(thailand.is_un_member_at)","def test_case_user_not_yet_customer(self):\n pass","def no_save_subscription_if_dont_have_permissions(sender, instance, **kwargs):\n user = instance.user if instance.user else None\n group = instance.group if instance.group else None\n\n if user:\n if 'can_subscribe' not in get_user_perms(user, instance.alarm):\n raise ValidationError(\"User don't have permissions to subscribe to this alarm\")\n elif group:\n if 'can_subscribe' not in get_group_perms(group, instance.alarm):\n raise ValidationError(\"Group don't have permissions to subscribe to this alarm\")\n else:\n raise ValidationError(\"BAD REQUEST. Check your selections.\")","def test_meeting_registrants(self):\n pass","def test_not_notified(scraper):\n\n assert scraper.scrape_profile('https://www.punters.com.au/') is None","def test_remove_endorsement(self):\n self.client.login(username='user', password='test')\n p = self.make('Prescription')\n\n url = reverse('admin:prescription_prescription_changelist')\n response = self.client.get(url)\n\n self.assertContains(response, 'No prescribing officer')\n\n u = User.objects.create(username='test2', password='test',\n first_name='Test', last_name='McTest')\n p.prescribing_officer = u\n p.save()\n\n response = self.client.get(url)","def test_subscribe_offer(self):\n pass","def test_qry_unqualified_eligible(self):\n from_date = datetime.strptime('2008-01-01', '%Y-%m-%d')\n to_date = datetime.today().date()\n consumer = Consumer.objects.get(id=106)\n subscriber = Subscriber.objects.get(id=6)\n consumer.subscriber = subscriber\n consumer.save()\n result = UnqualifiedConsumerEmailTask().qry_unqualified_consumers(\n from_date, to_date)\n self.assertEqual(result.filter(id=106).count(), 0)","def test_website_companies_remove_additions(self):\n pass","def test_get_users_eligible_for_second_notification_with_no_result(self):\n # Given:\n self.batch_setup()\n # When:\n response = self.client.get(\"/api/batch/account/users/eligible-for-second-notification\", headers=self.headers)\n # Then:\n self.assertTrue(200, response.status_code)\n users = response.get_json()\n self.assertEqual(0, len(users))\n self.assertNotIn(self.user_0, users)\n self.assertNotIn(self.user_2, users)\n self.assertNotIn(self.user_1, users)\n self.assertNotIn(self.user_3, users)","def test_no_repost(self, mock_slack):\n from mail.management.commands.monitor_imap_folder import get_messages\n mock_slack.return_value.api_call.return_value = {'ok': True, 'ts': '1234'}\n get_messages(folder='INBOX', channel='#mailtest')\n mock_slack.return_value.api_call.assert_not_called()","def test_get_newsletter_statistics_division_by_zero(self):\n get_newsletter_statistics(self.newsletter)\n\n self.mailinglist.unsubscribers.add(*self.contacts)\n ContactMailingStatus.objects.create(\n newsletter=self.newsletter,\n contact=self.contacts[0],\n status=ContactMailingStatus.OPENED\n )\n get_newsletter_statistics(self.newsletter)","def test_listing_from_wall_when_blocked_some_users(self):","def test_get_household_not_successful(self):\n res = self.client.get(HOUSEHOLD_URL)\n\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)","def test_inactive_account(self):","def is_unsubscribed(self, source, medium, entity):\n return self.filter(source=source, medium=medium, entity=entity).exists()","def test_meeting_polls(self):\n pass","def test_leave_oversubscribe_off(self):\n user = mommy.make(\"auth.User\", first_name=\"Bob\", last_name=\"Ndoe\")\n staffprofile = mommy.make(\"small_small_hr.StaffProfile\", user=user)\n staffprofile.leave_days = 21\n staffprofile.sick_days = 10\n staffprofile.save()\n\n request = self.factory.get(\"/\")\n request.session = {}\n request.user = AnonymousUser()\n\n # 40 days of leave\n start = datetime(2017, 6, 1, 7, 0, 0, tzinfo=pytz.timezone(settings.TIME_ZONE))\n end = datetime(2017, 7, 10, 7, 0, 0, tzinfo=pytz.timezone(settings.TIME_ZONE))\n\n mommy.make(\n \"small_small_hr.AnnualLeave\",\n staff=staffprofile,\n year=2017,\n leave_type=Leave.REGULAR,\n carried_over_days=0,\n )\n\n data = {\n \"staff\": staffprofile.id,\n \"leave_type\": Leave.REGULAR,\n \"start\": start,\n \"end\": end,\n \"review_reason\": \"Mini retirement\",\n }\n\n form = ApplyLeaveForm(data=data)\n self.assertFalse(form.is_valid())\n self.assertEqual(2, len(form.errors.keys()))\n self.assertEqual(\n \"Not enough leave days. Available leave days are 21.00\",\n form.errors[\"start\"][0],\n )\n self.assertEqual(\n \"Not enough leave days. Available leave days are 21.00\",\n form.errors[\"end\"][0],\n )","def test_not_owner(self):\n creating_user = create_user()\n creating_user.save()\n festival = create_festival('test', creating_user)\n festival.save()\n\n concert = create_concert(festival, 'test')\n concert.save()\n\n login(self.client)\n\n client = create_client('test')\n client.delete_access = True\n client.save()\n\n response = self.client.post('/backend/u/conc/', {'client': 'test', 'id': concert.pk})\n self.assertEqual(response.status_code, 200)\n self.assertEqual('Permission not granted', response.content.decode('utf-8'))","def test_successful_subscriptions_notifies_pm(self) -> None:\n invitee = self.example_user(\"iago\")\n\n current_stream = self.get_streams(invitee)[0]\n invite_streams = self.make_random_stream_names([current_stream])[:1]\n self.common_subscribe_to_streams(\n invitee,\n invite_streams,\n extra_post_data={\n \"announce\": \"true\",\n \"principals\": orjson.dumps([self.user_profile.id]).decode(),\n },\n )","def test_disallow_betrayal(self):\n s1 = self.battle.create_skirmish(self.alice, 1)\n with self.assertRaises(db.TeamException):\n s1.react(self.bob, 1, hinder=False)\n\n n = (self.sess.query(db.SkirmishAction).filter_by(parent_id=None).\n filter_by(participant=self.alice)).count()\n self.assertEqual(n, 1)","def test_auth_public_unowned(self):\n self.do_visible(True, 'pattieblack', True, tenant='froggy')","def test_startup_defensive(self) -> None:\n self.assertFalse(self.client.triggers, 'predefined triggers')\n self.assertIsNone(self.client.websocket, 'websocket without triggers')","def test_existing_subscriptions_autosubscription(self) -> None:\n stream_name = \"new_public_stream\"\n cordelia = self.example_user(\"cordelia\")\n self.common_subscribe_to_streams(cordelia, [stream_name], invite_only=False)\n result = self.client_post(\n \"/json/subscriptions/exists\", {\"stream\": stream_name, \"autosubscribe\": \"false\"}\n )\n response_dict = self.assert_json_success(result)\n self.assertIn(\"subscribed\", response_dict)\n self.assertFalse(response_dict[\"subscribed\"])\n\n result = self.client_post(\n \"/json/subscriptions/exists\", {\"stream\": stream_name, \"autosubscribe\": \"true\"}\n )\n response_dict = self.assert_json_success(result)\n self.assertIn(\"subscribed\", response_dict)\n self.assertTrue(response_dict)","def test_post_to_unregistered_event_does_not_call_other_funcs(self):\n mock_subscription = Mock()\n bus = event_bus._event_bus\n mock_type = Mock()\n mock_subscription.event_type = mock_type\n bus._subscriptions[mock_type] = [mock_subscription]\n\n event_bus.post(Mock())\n\n self.assertEqual(mock_subscription.deliver.call_count, 0)","def test_negative_conditions(self):\r\n outline_url = reverse_course_url('course_handler', self.course.id)\r\n # register a non-staff member and try to delete the course branch\r\n non_staff_client, _ = self.create_non_staff_authed_user_client()\r\n response = non_staff_client.delete(outline_url, {}, HTTP_ACCEPT='application/json')\r\n self.assertEqual(response.status_code, 403)","def test_is_subscribed(self):\n manager_root = ISubscriptionManager(self.root)\n manager_root.subscribability = SUBSCRIBABLE\n manager_root.subscribe('wim@example.com')\n self.assertEqual(manager_root.is_subscribed('wim@example.com'), True)\n self.assertEqual(manager_root.is_subscribed('unknown@u.com'), False)\n\n manager = ISubscriptionManager(self.root.folder)\n self.assertEqual(manager.is_subscribed('wim@example.com'), True)\n self.assertEqual(manager.is_subscribed('unknown@u.com'), False)\n\n # If you turn off subscription off at the folder level, you\n # are no longer subscribed\n manager.subscribability = NOT_SUBSCRIBABLE\n self.assertEqual(manager.is_subscribed('wim@example.com'), False)\n\n # That didn't changed anything on the parent\n self.assertEqual(manager_root.is_subscribed('wim@example.com'), True)","def test_no_registration_admins_registered(self):\n new_user = self.registration_profile.objects.create_inactive_user(\n site=Site.objects.get_current(), **self.user_info)\n\n with warnings.catch_warnings(record=True) as _warning:\n self.registration_profile.objects.send_admin_approve_email(\n new_user, Site.objects.get_current())\n\n assertion_error = '''No warning triggered for unregistered\n REGISTRATION_ADMINS'''\n self.assertTrue(len(_warning) > 0, assertion_error)\n self.assertTrue('REGISTRATION_ADMINS' in str(_warning[-1].message),\n assertion_error)","def test_not_approved_user(self):\n (_,\n joining_user_id,\n conversation_id,\n _) = self.setup_invites(is_approved=None)\n self.set_session_cookie(joining_user_id, conversation_id)\n self.set_user_cookie(joining_user_id, conversation_id)\n uri = '/status/{}/{}'.format(conversation_id, joining_user_id)\n rsp = self.client.post(uri,\n data={'public_key': '',\n 'last_message_seen_id': 0})\n\n rsp_json = json.loads(rsp.data)\n self.assertFalse(rsp_json['success'])","def test_non_matching_active(self):\n user1 = get_user_model().objects.get(username='test1@example.com')\n self.client.login(username='test1@example.com', password='1')\n\n office = OfficeLocation.objects.all()[0]\n org = OrgGroup.objects.filter(parent__isnull=True)[0]\n\n submission1 = Interest()\n submission1.owner = user1\n submission1.for_coffee = True\n submission1.save()\n submission1.locations.add(office)\n submission1.departments.add(org)\n\n resp = self.client.get(reverse('mystery:mystery'))\n self.assertContains(resp, \"Cancel this\", status_code=200)\n\n user2 = random_user()\n submission2 = Interest()\n submission2.owner = user2\n submission2.is_active = False\n submission2.save()\n submission2.for_coffee = True\n submission2.locations.add(office)\n submission2.departments.add(org)\n submission2.save()\n\n resp = self.client.get(reverse('mystery:mystery'))\n self.assertContains(resp, \"Cancel this\", status_code=200)","def test_auth_private_unowned(self):\n self.do_visible(False, 'pattieblack', False, tenant='froggy')","def test_no_email_to_studio_if_setting_not_on(self, mock_tz):\n mock_tz.now.return_value = datetime(2015, 2, 11, 10, tzinfo=dt_timezone.utc)\n for i in range(5):\n baker.make(\n TicketBooking, ticketed_event=self.ticketed_event,\n cancelled=False, paid=False,\n user__email=\"unpaid_user{}@test.com\".format(i),\n date_booked= datetime(2015, 2, 9, tzinfo=dt_timezone.utc),\n warning_sent=True,\n date_warning_sent= datetime(2015, 2, 9, tzinfo=dt_timezone.utc),\n )\n for booking in TicketBooking.objects.all():\n baker.make(Ticket, ticket_booking=booking)\n\n management.call_command('cancel_unpaid_ticket_bookings')\n # emails are sent to user per cancelled booking (6) (these 5 plus\n # self.unpaid); none to studio\n self.assertEqual(len(mail.outbox), 6)\n cancelled_booking_emails = [\n booking.user.email for booking\n in TicketBooking.objects.filter(cancelled=True)\n ]\n self.assertEqual(\n cancelled_booking_emails, [email.to[0] for email in mail.outbox]\n )","def test_not_approved_user(self):\n (_,\n joining_user_id,\n conversation_id,\n _) = self.setup_invites(is_approved=False)\n self.set_session_cookie(joining_user_id, conversation_id)\n self.set_user_cookie(joining_user_id, conversation_id)\n uri = '/status/{}/{}'.format(conversation_id, joining_user_id)\n rsp = self.client.post(uri,\n data={'public_key': '',\n 'last_message_seen_id': 0})\n\n rsp_json = json.loads(rsp.data)\n self.assertFalse(rsp_json['success'])","def test_subscriptions_anonymous(self):\n self.go200('forum_index')\n self.notfind('Оновити підписки')\n self.go200('forum_subscriptions')\n self.url(self.build_url('auth_login')+'.*')","def test_sinus_does_not_trigger_alert(sim_sampler, events, data):\n for _ in range(SIMULATION_SAMPLES):\n sim_sampler.sampling_iteration()\n\n assert len(events.alerts_queue) == 0,\\\n f\"Unexpected alerts: {events.alerts_queue.active_alerts}\"","def test_permission_denied_no_permission(self):\n\n # Remove user from group ( added in setUpClass ) -> no permission\n self.group.user_set.remove(self.user)\n\n c = suds.client.Client(\n self.wsdl, username=self.username, password=self.password)\n result_code, result_msg = c.service.getRecentAuditTrails()\n self.assertEqual(result_code, 401)","def test_resend_activation_email_nonexistent_user(self):\n self.assertFalse(self.registration_profile.objects.resend_activation_mail(\n email=self.user_info['email'],\n site=Site.objects.get_current(),\n ))\n self.assertEqual(len(mail.outbox), 0)","def test_no_program_user_response(self, *args): # pylint: disable=unused-argument\n with mute_signals(post_save):\n no_permissions_profile = ProfileFactory.create()\n self.client.force_login(no_permissions_profile.user)\n resp_post = self.client.post(self.mail_url, data=self.request_data, format='json')\n assert resp_post.status_code == HTTP_403_FORBIDDEN","def test_meeting_registrant_status(self):\n pass"],"string":"[\n \"def test_nonsubscriber(self) -> None:\\n # Create a stream for which Hamlet is the only subscriber.\\n stream_name = \\\"Saxony\\\"\\n self.common_subscribe_to_streams(self.user_profile, [stream_name])\\n other_user = self.example_user(\\\"othello\\\")\\n\\n # Fetch the subscriber list as a non-member.\\n self.login_user(other_user)\\n self.make_successful_subscriber_request(stream_name)\",\n \"def test_subscriptions_does_not_exist(self) -> None:\\n random_streams = self.make_random_stream_names(self.streams)\\n self.assertNotEqual(len(random_streams), 0) # necessary for full test coverage\\n self.helper_subscriptions_exists(random_streams[0], False, False)\",\n \"def test_successful_subscriptions_exists_not_subbed(self) -> None:\\n all_stream_names = [stream.name for stream in Stream.objects.filter(realm=self.test_realm)]\\n streams_not_subbed = list(set(all_stream_names) - set(self.streams))\\n self.assertNotEqual(len(streams_not_subbed), 0) # necessary for full test coverage\\n self.helper_subscriptions_exists(streams_not_subbed[0], True, False)\",\n \"def test_check_inputs_hospital_prefs_all_nonempty(game):\\n\\n hospital = game.hospitals[0]\\n hospital.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"hospitals\\\", \\\"residents\\\")\\n\\n assert len(record) == 1\\n assert hospital.name in str(record[0].message)\\n\\n if game.clean:\\n assert hospital not in game.hospitals\",\n \"def testNoSpecialties(self):\\n self.failUnlessEqual(self.person.getSpecialties(), [])\",\n \"def test_never_subscribed_streams(self) -> None:\\n realm = get_realm(\\\"zulip\\\")\\n users_to_subscribe = [\\n self.example_user(\\\"othello\\\").id,\\n self.example_user(\\\"cordelia\\\").id,\\n ]\\n\\n public_streams = [\\n \\\"test_stream_public_1\\\",\\n \\\"test_stream_public_2\\\",\\n \\\"test_stream_public_3\\\",\\n \\\"test_stream_public_4\\\",\\n \\\"test_stream_public_5\\\",\\n ]\\n\\n private_streams = [\\n \\\"test_stream_invite_only_1\\\",\\n \\\"test_stream_invite_only_2\\\",\\n ]\\n\\n web_public_streams = [\\n \\\"test_stream_web_public_1\\\",\\n \\\"test_stream_web_public_2\\\",\\n ]\\n\\n def create_public_streams() -> None:\\n for stream_name in public_streams:\\n self.make_stream(stream_name, realm=realm)\\n\\n self.common_subscribe_to_streams(\\n self.user_profile,\\n public_streams,\\n dict(principals=orjson.dumps(users_to_subscribe).decode()),\\n )\\n\\n create_public_streams()\\n\\n def create_web_public_streams() -> None:\\n for stream_name in web_public_streams:\\n self.make_stream(stream_name, realm=realm, is_web_public=True)\\n\\n ret = self.common_subscribe_to_streams(\\n self.user_profile,\\n web_public_streams,\\n dict(principals=orjson.dumps(users_to_subscribe).decode()),\\n )\\n self.assert_json_success(ret)\\n\\n create_web_public_streams()\\n\\n def create_private_streams() -> None:\\n self.common_subscribe_to_streams(\\n self.user_profile,\\n private_streams,\\n dict(principals=orjson.dumps(users_to_subscribe).decode()),\\n invite_only=True,\\n )\\n\\n create_private_streams()\\n\\n def get_never_subscribed() -> List[NeverSubscribedStreamDict]:\\n with self.assert_database_query_count(4):\\n sub_data = gather_subscriptions_helper(self.user_profile)\\n self.verify_sub_fields(sub_data)\\n never_subscribed = sub_data.never_subscribed\\n\\n # Ignore old streams.\\n never_subscribed = [dct for dct in never_subscribed if dct[\\\"name\\\"].startswith(\\\"test_\\\")]\\n return never_subscribed\\n\\n never_subscribed = get_never_subscribed()\\n\\n # Invite only stream should not be there in never_subscribed streams\\n self.assert_length(never_subscribed, len(public_streams) + len(web_public_streams))\\n for stream_dict in never_subscribed:\\n name = stream_dict[\\\"name\\\"]\\n self.assertFalse(\\\"invite_only\\\" in name)\\n self.assert_length(stream_dict[\\\"subscribers\\\"], len(users_to_subscribe))\\n\\n # Send private stream subscribers to all realm admins.\\n def test_admin_case() -> None:\\n self.user_profile.role = UserProfile.ROLE_REALM_ADMINISTRATOR\\n # Test realm admins can get never subscribed private stream's subscribers.\\n never_subscribed = get_never_subscribed()\\n\\n self.assertEqual(\\n len(never_subscribed),\\n len(public_streams) + len(private_streams) + len(web_public_streams),\\n )\\n for stream_dict in never_subscribed:\\n self.assert_length(stream_dict[\\\"subscribers\\\"], len(users_to_subscribe))\\n\\n test_admin_case()\\n\\n def test_guest_user_case() -> None:\\n self.user_profile.role = UserProfile.ROLE_GUEST\\n helper_result = gather_subscriptions_helper(self.user_profile)\\n self.verify_sub_fields(helper_result)\\n sub = helper_result.subscriptions\\n unsub = helper_result.unsubscribed\\n never_sub = helper_result.never_subscribed\\n\\n # It's +1 because of the stream Rome.\\n self.assert_length(never_sub, len(web_public_streams) + 1)\\n sub_ids = [stream[\\\"stream_id\\\"] for stream in sub]\\n unsub_ids = [stream[\\\"stream_id\\\"] for stream in unsub]\\n\\n for stream_dict in never_sub:\\n self.assertTrue(stream_dict[\\\"is_web_public\\\"])\\n self.assertTrue(stream_dict[\\\"stream_id\\\"] not in sub_ids)\\n self.assertTrue(stream_dict[\\\"stream_id\\\"] not in unsub_ids)\\n\\n # The Rome stream has is_web_public=True, with default\\n # subscribers not set up by this test, so we do the\\n # following check only for the streams we created.\\n if stream_dict[\\\"name\\\"] in web_public_streams:\\n self.assert_length(stream_dict[\\\"subscribers\\\"], len(users_to_subscribe))\\n\\n test_guest_user_case()\",\n \"def test_no_email(self):\\n\\n a = Agency(name=\\\"Broadcasting Board of Governors\\\", slug=\\\"brodcasting\\\")\\n a.save()\\n\\n response = self.client.get(\\n reverse('contact_landing', args=['broadcasting']))\\n self.assertTrue(200, response.status_code)\\n content = response.content.decode('utf-8')\\n self.assertTrue('Request online' not in content)\",\n \"def test_issue_subscriptions(self):\\n pass\",\n \"def test_list_respects_invite_only_bit(self) -> None:\\n\\n user = self.example_user(\\\"hamlet\\\")\\n self.login_user(user)\\n\\n self.common_subscribe_to_streams(user, [\\\"Saxony\\\"], invite_only=True)\\n self.common_subscribe_to_streams(user, [\\\"Normandy\\\"], invite_only=False)\\n result = self.api_get(user, \\\"/api/v1/users/me/subscriptions\\\")\\n response_dict = self.assert_json_success(result)\\n self.assertIn(\\\"subscriptions\\\", response_dict)\\n for sub in response_dict[\\\"subscriptions\\\"]:\\n if sub[\\\"name\\\"] == \\\"Normandy\\\":\\n self.assertEqual(\\n sub[\\\"invite_only\\\"], False, \\\"Normandy was mistakenly marked private\\\"\\n )\\n if sub[\\\"name\\\"] == \\\"Saxony\\\":\\n self.assertEqual(sub[\\\"invite_only\\\"], True, \\\"Saxony was not properly marked private\\\")\",\n \"def testHasNoInvites(self):\\r\\n u = User()\\r\\n u.invite_ct = 0\\r\\n self.assertFalse(u.has_invites(), 'User should have no invites')\\r\\n self.assertFalse(\\r\\n u.invite('me@you.com'), 'Should not be able to invite a user')\",\n \"def test_skip_if_no_patients(self):\\n\\n appt_date = datetime.date.today() + datetime.timedelta(days=5)\\n confirmed = self.create_confirmed_notification(self.test_patient, appt_date)\\n\\n # run email job\\n from aremind.apps.reminders.app import daily_email_callback\\n daily_email_callback(self.router)\\n\\n self.assertEqual(len(mail.outbox), 0)\",\n \"def test_check_for_oversubscribed_supervisors(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n supervisor = game.supervisors[0]\\n supervisor.matching = range(supervisor.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(supervisor.name)\\n assert error.endswith(str(supervisor.capacity))\\n assert str(supervisor.matching) in e\",\n \"def test_unsubscribe_offer(self):\\n pass\",\n \"def test_can_subscribe_other_users(self) -> None:\\n\\n def validation_func(user_profile: UserProfile) -> bool:\\n user_profile.refresh_from_db()\\n return user_profile.can_subscribe_other_users()\\n\\n self.check_has_permission_policies(\\\"invite_to_stream_policy\\\", validation_func)\",\n \"def test_gather_subscriptions_mit(self) -> None:\\n # Subscribe only ourself because invites are disabled on mit.edu\\n mit_user_profile = self.mit_user(\\\"starnine\\\")\\n user_id = mit_user_profile.id\\n users_to_subscribe = [user_id, self.mit_user(\\\"espuser\\\").id]\\n for email in users_to_subscribe:\\n stream = self.subscribe(mit_user_profile, \\\"mit_stream\\\")\\n self.assertTrue(stream.is_in_zephyr_realm)\\n\\n self.common_subscribe_to_streams(\\n mit_user_profile,\\n [\\\"mit_invite_only\\\"],\\n dict(principals=orjson.dumps(users_to_subscribe).decode()),\\n invite_only=True,\\n subdomain=\\\"zephyr\\\",\\n )\\n\\n with self.assert_database_query_count(3):\\n subscribed_streams, _ = gather_subscriptions(mit_user_profile, include_subscribers=True)\\n\\n self.assertGreaterEqual(len(subscribed_streams), 2)\\n for sub in subscribed_streams:\\n if not sub[\\\"name\\\"].startswith(\\\"mit_\\\"):\\n raise AssertionError(\\\"Unexpected stream!\\\")\\n if sub[\\\"name\\\"] == \\\"mit_invite_only\\\":\\n self.assert_length(sub[\\\"subscribers\\\"], len(users_to_subscribe))\\n else:\\n self.assert_length(sub[\\\"subscribers\\\"], 0)\\n self.assertIsNone(sub[\\\"stream_weekly_traffic\\\"])\\n\\n # Create a web-public stream to test never_subscried data.\\n self.make_stream(\\\"mit_stream_2\\\", realm=mit_user_profile.realm, is_web_public=True)\\n self.make_stream(\\\"mit_stream_3\\\", realm=mit_user_profile.realm)\\n\\n sub_info = gather_subscriptions_helper(mit_user_profile, include_subscribers=True)\\n never_subscribed_streams = sub_info.never_subscribed\\n # Users in zephyr mirror realm can only access web-public never subscribed streams.\\n self.assert_length(never_subscribed_streams, 1)\\n self.assertEqual(never_subscribed_streams[0][\\\"name\\\"], \\\"mit_stream_2\\\")\\n self.assertTrue(never_subscribed_streams[0][\\\"is_web_public\\\"])\\n self.assertIsNone(never_subscribed_streams[0][\\\"stream_weekly_traffic\\\"])\",\n \"def test_negative_is_active_of_homework():\\n assert not expired_hw.is_active()\",\n \"def test_registered_no_notifications(self):\\n msg = self._send(self.reg_conn, '1')\\n self.assertEqual(len(msg.responses), 1)\\n self.assertEqual(msg.responses[0].text,\\n self.app.no_reminders)\",\n \"def test_registered_no_notifications(self):\\n msg = self._send(self.reg_conn, '1')\\n self.assertEqual(len(msg.responses), 1)\\n self.assertEqual(msg.responses[0].text,\\n self.app.no_reminders)\",\n \"def test_healer_not_confirmed(self):\\n\\t\\tself.signup('healer')\\n\\n\\t\\tresponse = self.rest_client.get(reverse('provider_setup_intro'))\\n\\t\\tself.assertEqual(response.status_code, 200)\\n\\n\\t\\t# check if any other page except setup is available\\n\\t\\tresponse = self.rest_client.get(reverse('notes'))\\n\\t\\tself.assertEqual(response.status_code, 302)\",\n \"def test_cant_remove_other_users_from_stream(self) -> None:\\n result = self.attempt_unsubscribe_of_principal(\\n query_count=8,\\n target_users=[self.example_user(\\\"cordelia\\\")],\\n is_realm_admin=False,\\n is_subbed=True,\\n invite_only=False,\\n target_users_subbed=True,\\n )\\n self.assert_json_error(result, \\\"Insufficient permission\\\")\",\n \"def test_subscriptions_add_notification_default_none(self) -> None:\\n user_profile = self.example_user(\\\"iago\\\")\\n invitee_user_id = user_profile.id\\n invitee_realm = user_profile.realm\\n user_profile.enable_stream_desktop_notifications = True\\n user_profile.enable_stream_push_notifications = True\\n user_profile.enable_stream_audible_notifications = True\\n user_profile.enable_stream_email_notifications = True\\n user_profile.save()\\n current_stream = self.get_streams(user_profile)[0]\\n invite_streams = self.make_random_stream_names([current_stream])\\n self.assert_adding_subscriptions_for_principal(\\n invitee_user_id, invitee_realm, invite_streams, policy_name=\\\"Public\\\"\\n )\\n subscription = self.get_subscription(user_profile, invite_streams[0])\\n\\n with mock.patch(\\\"zerver.models.Recipient.__repr__\\\", return_value=\\\"recip\\\"):\\n self.assertEqual(\\n repr(subscription),\\n \\\"<Subscription: \\\"\\n f\\\"<UserProfile: {user_profile.email} {user_profile.realm!r}> -> recip>\\\",\\n )\\n\\n self.assertIsNone(subscription.desktop_notifications)\\n self.assertIsNone(subscription.push_notifications)\\n self.assertIsNone(subscription.audible_notifications)\\n self.assertIsNone(subscription.email_notifications)\",\n \"def test_check_for_unacceptable_matches_hospitals(game):\\n\\n hospital = game.hospitals[0]\\n resident = Resident(name=\\\"foo\\\")\\n hospital.matching.append(resident)\\n\\n issues = game._check_for_unacceptable_matches(\\\"hospitals\\\")\\n assert len(issues) == 1\\n\\n issue = issues[0]\\n assert issue.startswith(hospital.name)\\n assert issue.endswith(f\\\"{hospital.prefs}.\\\")\\n assert resident.name in issue\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.unacceptable_matches[0]\\n assert issue == error\",\n \"def test_subscriber_access_if_vsg1_goes_down(self):\",\n \"def test_hubs_fail(self):\\n with self.assertRaises(AssertionError):\\n self.resource.hubs(-1)\",\n \"def test_no_events(woodshop, caplog):\\n caplog.set_level(logging.INFO)\\n woodshop.log_conflicts(\\\"0000-00-00 00:00:00\\\")\\n assert caplog.text == \\\"\\\"\",\n \"def test_no_other_enrollments(self):\\n user = self.make_user()\\n EnrollmentFactory()\\n\\n with self.login(user):\\n self.get_check_200(\\\"reports:index\\\")\\n\\n assert list(self.get_context(\\\"enrollments\\\")) == []\",\n \"def test_check_inputs_resident_prefs_all_nonempty(game):\\n\\n resident = game.residents[0]\\n resident.prefs = []\\n\\n with pytest.warns(PlayerExcludedWarning) as record:\\n game._check_inputs_player_prefs_nonempty(\\\"residents\\\", \\\"hospitals\\\")\\n\\n assert len(record) == 1\\n assert resident.name in str(record[0].message)\\n\\n if game.clean:\\n assert resident not in game.residents\",\n \"def test_skip_if_no_patients(self):\\n\\n appt_date = datetime.date.today() + datetime.timedelta(days=5)\\n reminders.Patient.objects.filter(\\n pk__in=[self.test_patient.pk, self.other_patient.pk]\\n ).update(next_visit=appt_date)\\n confirmed = self.create_confirmed_notification(self.test_patient, appt_date)\\n\\n self.startRouter()\\n self.router.logger.setLevel(logging.DEBUG)\\n # run email job\\n from afrims.apps.reminders.app import daily_email_callback\\n daily_email_callback(self.router)\\n\\n self.assertEqual(len(mail.outbox), 0)\\n self.stopRouter()\",\n \"def test_nonsubscriber_private_stream(self) -> None:\\n # Create a private stream for which Hamlet is the only subscriber.\\n stream_name = \\\"NewStream\\\"\\n self.common_subscribe_to_streams(self.user_profile, [stream_name], invite_only=True)\\n user_profile = self.example_user(\\\"othello\\\")\\n\\n # Try to fetch the subscriber list as a non-member & non-realm-admin-user.\\n stream_id = get_stream(stream_name, user_profile.realm).id\\n result = self.make_subscriber_request(stream_id, user=user_profile)\\n self.assert_json_error(result, \\\"Invalid stream ID\\\")\\n\\n # Try to fetch the subscriber list as a non-member & realm-admin-user.\\n self.login(\\\"iago\\\")\\n self.make_successful_subscriber_request(stream_name)\",\n \"def test_get_users_eligible_for_fist_notification_with_no_result(self):\\n # Given:\\n self.batch_setup()\\n # When:\\n response = self.client.get(\\\"/api/batch/account/users/eligible-for-first-notification\\\", headers=self.headers)\\n # Then:\\n self.assertTrue(200, response.status_code)\\n users = response.get_json()\\n self.assertEqual(0, len(users))\\n self.assertNotIn(self.user_0, users)\\n self.assertNotIn(self.user_2, users)\\n self.assertNotIn(self.user_1, users)\\n self.assertNotIn(self.user_3, users)\",\n \"def test_check_for_oversubscribed_projects(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n game.solve()\\n\\n project = game.projects[0]\\n project.matching = range(project.capacity + 1)\\n\\n with pytest.raises(MatchingError) as e:\\n game.check_validity()\\n error = e.oversubscribed_players[0]\\n assert error.startswith(project.name)\\n assert error.endswith(str(project.capacity))\\n assert str(project.matching) in e\",\n \"def test_existing_subscriptions_autosubscription_private_stream(self) -> None:\\n stream_name = \\\"Saxony\\\"\\n cordelia = self.example_user(\\\"cordelia\\\")\\n self.common_subscribe_to_streams(cordelia, [stream_name], invite_only=True)\\n stream = get_stream(stream_name, self.test_realm)\\n\\n result = self.client_post(\\n \\\"/json/subscriptions/exists\\\", {\\\"stream\\\": stream_name, \\\"autosubscribe\\\": \\\"true\\\"}\\n )\\n # We can't see invite-only streams here\\n self.assert_json_error(result, \\\"Invalid stream name 'Saxony'\\\", status_code=404)\\n # Importantly, we are not now subscribed\\n self.assertEqual(num_subscribers_for_stream_id(stream.id), 1)\\n\\n # A user who is subscribed still sees the stream exists\\n self.login(\\\"cordelia\\\")\\n result = self.client_post(\\n \\\"/json/subscriptions/exists\\\", {\\\"stream\\\": stream_name, \\\"autosubscribe\\\": \\\"false\\\"}\\n )\\n response_dict = self.assert_json_success(result)\\n self.assertIn(\\\"subscribed\\\", response_dict)\\n self.assertTrue(response_dict)\",\n \"def test_private_rooms_do_not_have_profiles_collected(self) -> None:\\n room_id = self.helper.create_room_as(\\n self.alice, is_public=False, tok=self.alice_tok\\n )\\n self.get_success(\\n event_injection.inject_member_event(\\n self.hs,\\n room_id,\\n \\\"@bruce:remote\\\",\\n \\\"join\\\",\\n \\\"@bruce:remote\\\",\\n extra_content={\\n \\\"displayname\\\": \\\"super-duper bruce\\\",\\n \\\"avatar_url\\\": \\\"mxc://remote/456\\\",\\n },\\n )\\n )\\n # Sending this event makes the streams move forward after the injection...\\n self.helper.send(room_id, \\\"Test\\\", tok=self.alice_tok)\\n self.pump(0.1)\\n\\n profiles = self.get_success(\\n self.user_dir_helper.get_profiles_in_user_directory()\\n )\\n self.assertNotIn(\\\"@bruce:remote\\\", profiles)\",\n \"def test_successful_subscriptions_exists_subbed(self) -> None:\\n self.assertNotEqual(len(self.streams), 0) # necessary for full test coverage\\n self.helper_subscriptions_exists(self.streams[0], True, True)\",\n \"def test_subscriber_access_if_vsg2_goes_down(self):\",\n \"def test_unverified_subscriber(self):\\n self.prep_consumer()\\n subscriber = Subscriber.objects.get(id=7)\\n self.consumer.subscriber = subscriber\\n self.consumer.save()\\n UnqualifiedConsumerEmailTask().run(test_mode=self.consumer)\\n self.common_asserts()\\n self.assertTrue('71010' in mail.outbox[0].alternatives[0][0])\\n self.assertTrue('71010' in mail.outbox[0].body)\",\n \"def test_subscriptions_add_for_principal_deactivated(self) -> None:\\n target_profile = self.example_user(\\\"cordelia\\\")\\n post_data = dict(\\n principals=orjson.dumps([target_profile.id]).decode(),\\n )\\n self.common_subscribe_to_streams(self.test_user, \\\"Verona\\\", post_data)\\n\\n do_deactivate_user(target_profile, acting_user=None)\\n result = self.common_subscribe_to_streams(\\n self.test_user, \\\"Denmark\\\", post_data, allow_fail=True\\n )\\n self.assert_json_error(\\n result,\\n f\\\"User not authorized to execute queries on behalf of '{target_profile.id}'\\\",\\n status_code=403,\\n )\",\n \"def test_empty_shared(self):\\n self.do_sharable(False, 'pattieblack', None, is_admin=True)\\n self.do_sharable(False, 'pattieblack', FakeMembership(True),\\n is_admin=True)\",\n \"def test_get_subscriptions(self):\\n pass\",\n \"async def test_skipped_already_unsilenced(self):\\n self.cog.scheduler.__contains__.return_value = False\\n self.cog.previous_overwrites.get.return_value = None\\n\\n for channel in (MockVoiceChannel(), MockTextChannel()):\\n with self.subTest(channel=channel):\\n self.assertFalse(await self.cog._unsilence(channel))\\n channel.set_permissions.assert_not_called()\",\n \"def test_incompatible_subscription_and_tenant():\\n pass\",\n \"def test_unsubscribe_any_listener(self):\\n with self.assertRaises(AssertionError):\\n EVENT_MANAGER.unsubscribe()\",\n \"def test_no_other_school_years(self):\\n user = self.make_user()\\n SchoolYearFactory()\\n\\n with self.login(user):\\n self.get_check_200(\\\"reports:index\\\")\\n\\n assert list(self.get_context(\\\"school_years\\\")) == []\",\n \"def not_test_without_user(self):\\n # TODO\",\n \"def test_empty_case(self):\\n a_user = User.objects.create()\\n user = VSBUser.objects.create(user=a_user)\\n\\n received = util.bucket_calenderevents(user.calenderevent_set)\\n\\n self.assertEqual(received, [], msg=\\\"CalenderEvents.empty_case: Non-empty list returned.\\\")\",\n \"def test_user_not_in_users_table(self) -> None:\\n user1 = self.register_user(\\\"user1\\\", \\\"pass\\\")\\n token1 = self.login(user1, \\\"pass\\\")\\n room = self.helper.create_room_as(user1, is_public=True, tok=token1)\\n\\n # Inject a join event for a user who doesn't exist\\n self.get_success(inject_member_event(self.hs, room, \\\"@not-a-user:test\\\", \\\"join\\\"))\\n\\n # Another new user registers and joins the room\\n user2 = self.register_user(\\\"user2\\\", \\\"pass\\\")\\n token2 = self.login(user2, \\\"pass\\\")\\n self.helper.join(room, user2, tok=token2)\\n\\n # The dodgy event should not have stopped us from processing user2's join.\\n in_public = self.get_success(self.user_dir_helper.get_users_in_public_rooms())\\n self.assertEqual(set(in_public), {(user1, room), (user2, room)})\",\n \"def no_save_individual_subscription(sender, instance, **kwargs):\\n try:\\n Subscription.objects.get(pk=instance.pk) # looking if the subscription exist, if the case, we assume here is updating active status or email status\\n except:\\n if instance.user is not None:\\n subs_ids = instance.user.groups.values_list('subscription')\\n for sub in subs_ids:\\n if None not in sub:\\n alarm = Subscription.objects.get(id=sub[0]).alarm\\n if alarm == instance.alarm:\\n raise ValidationError('The user is subscribed to the same alarm for a group')\\n\\n subs = Subscription.objects.filter(user=instance.user)\\n for sub in subs:\\n if sub.alarm == instance.alarm:\\n raise ValidationError('The user is subscribed to this alarm')\",\n \"def test_overlapping_events_unchecked(sample_events, lobby, caplog):\\n caplog.set_level(logging.INFO)\\n event1, event2 = sample_events.make_overlapping_events()\\n overlap_events(event1, event2, lobby, lobby, [lobby])\\n assert len(caplog.messages) == 0\",\n \"def test_simple_unconfirmed(self):\\n appt_date = datetime.date.today()\\n self.create_confirmed_notification(self.test_patient, appt_date)\\n self.create_unconfirmed_notification(self.other_patient, appt_date)\\n qs = Patient.objects.unconfirmed_for_date(appt_date)\\n self.assertFalse(self.test_patient in qs)\\n self.assertTrue(self.other_patient in qs)\\n self.assertFalse(self.unrelated_patient in qs)\",\n \"def test_validate_user_access_to_subscribers_helper(self) -> None:\\n user_profile = self.example_user(\\\"othello\\\")\\n realm_name = \\\"no_othello_allowed\\\"\\n realm = do_create_realm(realm_name, \\\"Everyone but Othello is allowed\\\")\\n stream_dict = {\\n \\\"name\\\": \\\"publicstream\\\",\\n \\\"description\\\": \\\"Public stream with public history\\\",\\n \\\"realm_id\\\": realm.id,\\n }\\n\\n # For this test to work, othello can't be in the no_othello_here realm\\n self.assertNotEqual(\\n user_profile.realm.id, realm.id, \\\"Expected othello user to not be in this realm.\\\"\\n )\\n\\n # This should result in missing user\\n with self.assertRaises(ValidationError):\\n validate_user_access_to_subscribers_helper(None, stream_dict, lambda user_profile: True)\\n\\n # This should result in user not in realm\\n with self.assertRaises(ValidationError):\\n validate_user_access_to_subscribers_helper(\\n user_profile, stream_dict, lambda user_profile: True\\n )\",\n \"def test_process_subscriptions(self):\\n pass\",\n \"def test_no_data(self):\\n response = self.client.get(reverse('education:states'))\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(response.context.get(\\\"states\\\").count(), 0)\\n self.assertContains(response, \\\"No Data Available\\\")\\n self.assertNotContains(response, \\\"Number of Public High Schools\\\")\",\n \"def test_disallow_absent_fighting(self):\\n londo = self.get_region(\\\"Orange Londo\\\")\\n self.alice.region = londo\\n self.sess.commit()\\n\\n with self.assertRaises(db.NotPresentException):\\n self.battle.create_skirmish(self.alice, 1)\\n\\n n = (self.sess.query(db.SkirmishAction).filter_by(parent_id=None).\\n filter_by(participant=self.alice)).count()\\n self.assertEqual(n, 0)\",\n \"def test_transaction_is_affordable_there_is_no_wealth(self):\\n currency_endowment = {\\\"FET\\\": 0}\\n good_endowment = {\\\"good_id\\\": 0}\\n self.ownership_state.init(\\n amount_by_currency_id=currency_endowment,\\n quantities_by_good_id=good_endowment,\\n )\\n tx_message = TransactionMessage(\\n performative=TransactionMessage.Performative.PROPOSE_FOR_SETTLEMENT,\\n skill_callback_ids=[PublicId(\\\"author\\\", \\\"a_skill\\\", \\\"0.1.0\\\")],\\n tx_id=\\\"transaction0\\\",\\n tx_sender_addr=\\\"agent_1\\\",\\n tx_counterparty_addr=\\\"pk\\\",\\n tx_amount_by_currency_id={\\\"FET\\\": 0},\\n tx_sender_fee=0,\\n tx_counterparty_fee=0,\\n tx_quantities_by_good_id={\\\"good_id\\\": 0},\\n info={\\\"some_info_key\\\": \\\"some_info_value\\\"},\\n ledger_id=\\\"fetchai\\\",\\n tx_nonce=\\\"transaction nonce\\\",\\n )\\n\\n assert not self.ownership_state.is_affordable_transaction(\\n tx_message=tx_message\\n ), \\\"We must reject the transaction.\\\"\",\n \"def test_must_be_subbed_to_send(self) -> None:\\n user = self.example_user(\\\"hamlet\\\")\\n self.login_user(user)\\n # Create Saxony as an invite-only stream.\\n self.assert_json_success(\\n self.common_subscribe_to_streams(user, [\\\"Saxony\\\"], invite_only=True)\\n )\\n\\n cordelia = self.example_user(\\\"cordelia\\\")\\n with self.assertRaises(JsonableError):\\n self.send_stream_message(cordelia, \\\"Saxony\\\")\",\n \"def test_unlisted_addon(self):\\n addon = Addon.objects.get(pk=3615)\\n self.make_addon_unlisted(addon)\\n\\n up = self.get(self.good_data)\\n assert up.is_valid()\",\n \"def test_alert_create_for_site_members(self):\\n pass\",\n \"def test_empty_events(self, client, site, homepage):\\n response = client.get(homepage.relative_url(site))\\n assertTemplateNotUsed(response, \\\"events/snippets/event_card.html\\\")\\n assertContains(response, \\\"Next semester's events are being scheduled.\\\")\",\n \"def test_not_unopposed(self):\\n s1 = self.battle.create_skirmish(self.alice, 2) # Attack 2\\n s1.react(self.bob, 1) # --Attack 1\\n s1.resolve()\\n self.assertFalse(s1.unopposed)\",\n \"def test_user_can_not_enroll_to_unpublished_project(self):\\n self.client.force_authenticate(self.global_user_1)\\n\\n # Make sure nothing is found - not enrolled.\\n resp = self.client.get(self.api_project_state_not_enrolled_unpublished_url)\\n self.assertEqual(resp.status_code, 404)\\n\\n #NOTE: Allowed!\\n # # POST and make sure the user is enrolled\\n # resp = self.client.post(self.api_project_state_not_enrolled_unpublished_url)\\n # self.assertEqual(resp.status_code, 403)\\n # resp = self.client.get(self.api_project_state_not_enrolled_unpublished_url)\\n # self.assertEqual(resp.status_code, 404)\",\n \"def test_user_can_not_enroll_to_unpublished_lesson(self):\\n self.client.force_authenticate(self.global_user_1)\\n\\n # Make the unpublished project owned by the authenticated user:\\n self.project_unpublished.owner = self.global_user_1\\n self.project_unpublished.save()\\n\\n # Make sure nothing is found - not enrolled.\\n resp = self.client.get(self.api_lesson_state_not_enrolled_unpublished_url)\\n self.assertEqual(resp.status_code, 404)\\n\\n #NOTE: Allowed!\\n # # POST and make sure the user is enrolled\\n # resp = self.client.post(self.api_lesson_state_not_enrolled_unpublished_url)\\n # self.assertEqual(resp.status_code, 403)\\n # resp = self.client.get(self.api_lesson_state_not_enrolled_unpublished_url)\\n # self.assertEqual(resp.status_code, 404)\\n\\n # Other user (that do not have access to the project)\\n\\n # Make the unpublished project owned by a user that is not child or delegator of the authenticated user:\\n new_owner = get_user_model().objects.exclude(\\n Q(pk=self.global_user_1.pk) |\\n Q(pk__in=self.global_user_1.delegators.all()) |\\n Q(pk__in=self.global_user_1.children.all())\\n )[0]\\n self.project_unpublished.owner = new_owner\\n self.project_unpublished.save()\\n\\n # Make sure nothing is found - not enrolled.\\n resp = self.client.get(self.api_lesson_state_not_enrolled_unpublished_url)\\n self.assertEqual(resp.status_code, 404)\\n\\n # POST and make sure the user is enrolled\\n resp = self.client.post(self.api_lesson_state_not_enrolled_unpublished_url)\\n self.assertEqual(resp.status_code, 404)\\n resp = self.client.get(self.api_lesson_state_not_enrolled_unpublished_url)\\n self.assertEqual(resp.status_code, 404)\",\n \"def test_un_member_status(self):\\n\\n # Get Hong Kong\\n hong_kong = Country.objects.get(iso3=\\\"HKG\\\")\\n\\n # Assert that is_un_member_at is None\\n self.assertEqual(hong_kong.is_un_member_at, None)\\n\\n # Initialize assertRaises block\\n with self.assertRaises(ValidationError):\\n\\n # Set is UN member to True\\n hong_kong.is_un_member = True\\n\\n # Attempt to clean the Hong Kong object\\n hong_kong.clean()\\n\\n # Should fail because no corresponding is UN member at date is set\\n\\n # Get Thailand\\n thailand = Country.objects.get(iso3=\\\"THA\\\")\\n\\n # Assert that Thailand is a UN member state\\n self.assertEqual(thailand.is_un_member, True)\\n\\n # Assert that Thailand is UN member at is not None\\n self.assertIsNotNone(thailand.is_un_member_at)\\n\\n # Set Thailand is UN member at to False\\n thailand.is_un_member = False\\n\\n # Clean Thailand object\\n thailand.clean()\\n\\n # Asser that Thailand is UN member at is now None\\n self.assertIsNone(thailand.is_un_member_at)\",\n \"def test_case_user_not_yet_customer(self):\\n pass\",\n \"def no_save_subscription_if_dont_have_permissions(sender, instance, **kwargs):\\n user = instance.user if instance.user else None\\n group = instance.group if instance.group else None\\n\\n if user:\\n if 'can_subscribe' not in get_user_perms(user, instance.alarm):\\n raise ValidationError(\\\"User don't have permissions to subscribe to this alarm\\\")\\n elif group:\\n if 'can_subscribe' not in get_group_perms(group, instance.alarm):\\n raise ValidationError(\\\"Group don't have permissions to subscribe to this alarm\\\")\\n else:\\n raise ValidationError(\\\"BAD REQUEST. Check your selections.\\\")\",\n \"def test_meeting_registrants(self):\\n pass\",\n \"def test_not_notified(scraper):\\n\\n assert scraper.scrape_profile('https://www.punters.com.au/') is None\",\n \"def test_remove_endorsement(self):\\n self.client.login(username='user', password='test')\\n p = self.make('Prescription')\\n\\n url = reverse('admin:prescription_prescription_changelist')\\n response = self.client.get(url)\\n\\n self.assertContains(response, 'No prescribing officer')\\n\\n u = User.objects.create(username='test2', password='test',\\n first_name='Test', last_name='McTest')\\n p.prescribing_officer = u\\n p.save()\\n\\n response = self.client.get(url)\",\n \"def test_subscribe_offer(self):\\n pass\",\n \"def test_qry_unqualified_eligible(self):\\n from_date = datetime.strptime('2008-01-01', '%Y-%m-%d')\\n to_date = datetime.today().date()\\n consumer = Consumer.objects.get(id=106)\\n subscriber = Subscriber.objects.get(id=6)\\n consumer.subscriber = subscriber\\n consumer.save()\\n result = UnqualifiedConsumerEmailTask().qry_unqualified_consumers(\\n from_date, to_date)\\n self.assertEqual(result.filter(id=106).count(), 0)\",\n \"def test_website_companies_remove_additions(self):\\n pass\",\n \"def test_get_users_eligible_for_second_notification_with_no_result(self):\\n # Given:\\n self.batch_setup()\\n # When:\\n response = self.client.get(\\\"/api/batch/account/users/eligible-for-second-notification\\\", headers=self.headers)\\n # Then:\\n self.assertTrue(200, response.status_code)\\n users = response.get_json()\\n self.assertEqual(0, len(users))\\n self.assertNotIn(self.user_0, users)\\n self.assertNotIn(self.user_2, users)\\n self.assertNotIn(self.user_1, users)\\n self.assertNotIn(self.user_3, users)\",\n \"def test_no_repost(self, mock_slack):\\n from mail.management.commands.monitor_imap_folder import get_messages\\n mock_slack.return_value.api_call.return_value = {'ok': True, 'ts': '1234'}\\n get_messages(folder='INBOX', channel='#mailtest')\\n mock_slack.return_value.api_call.assert_not_called()\",\n \"def test_get_newsletter_statistics_division_by_zero(self):\\n get_newsletter_statistics(self.newsletter)\\n\\n self.mailinglist.unsubscribers.add(*self.contacts)\\n ContactMailingStatus.objects.create(\\n newsletter=self.newsletter,\\n contact=self.contacts[0],\\n status=ContactMailingStatus.OPENED\\n )\\n get_newsletter_statistics(self.newsletter)\",\n \"def test_listing_from_wall_when_blocked_some_users(self):\",\n \"def test_get_household_not_successful(self):\\n res = self.client.get(HOUSEHOLD_URL)\\n\\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def test_inactive_account(self):\",\n \"def is_unsubscribed(self, source, medium, entity):\\n return self.filter(source=source, medium=medium, entity=entity).exists()\",\n \"def test_meeting_polls(self):\\n pass\",\n \"def test_leave_oversubscribe_off(self):\\n user = mommy.make(\\\"auth.User\\\", first_name=\\\"Bob\\\", last_name=\\\"Ndoe\\\")\\n staffprofile = mommy.make(\\\"small_small_hr.StaffProfile\\\", user=user)\\n staffprofile.leave_days = 21\\n staffprofile.sick_days = 10\\n staffprofile.save()\\n\\n request = self.factory.get(\\\"/\\\")\\n request.session = {}\\n request.user = AnonymousUser()\\n\\n # 40 days of leave\\n start = datetime(2017, 6, 1, 7, 0, 0, tzinfo=pytz.timezone(settings.TIME_ZONE))\\n end = datetime(2017, 7, 10, 7, 0, 0, tzinfo=pytz.timezone(settings.TIME_ZONE))\\n\\n mommy.make(\\n \\\"small_small_hr.AnnualLeave\\\",\\n staff=staffprofile,\\n year=2017,\\n leave_type=Leave.REGULAR,\\n carried_over_days=0,\\n )\\n\\n data = {\\n \\\"staff\\\": staffprofile.id,\\n \\\"leave_type\\\": Leave.REGULAR,\\n \\\"start\\\": start,\\n \\\"end\\\": end,\\n \\\"review_reason\\\": \\\"Mini retirement\\\",\\n }\\n\\n form = ApplyLeaveForm(data=data)\\n self.assertFalse(form.is_valid())\\n self.assertEqual(2, len(form.errors.keys()))\\n self.assertEqual(\\n \\\"Not enough leave days. Available leave days are 21.00\\\",\\n form.errors[\\\"start\\\"][0],\\n )\\n self.assertEqual(\\n \\\"Not enough leave days. Available leave days are 21.00\\\",\\n form.errors[\\\"end\\\"][0],\\n )\",\n \"def test_not_owner(self):\\n creating_user = create_user()\\n creating_user.save()\\n festival = create_festival('test', creating_user)\\n festival.save()\\n\\n concert = create_concert(festival, 'test')\\n concert.save()\\n\\n login(self.client)\\n\\n client = create_client('test')\\n client.delete_access = True\\n client.save()\\n\\n response = self.client.post('/backend/u/conc/', {'client': 'test', 'id': concert.pk})\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual('Permission not granted', response.content.decode('utf-8'))\",\n \"def test_successful_subscriptions_notifies_pm(self) -> None:\\n invitee = self.example_user(\\\"iago\\\")\\n\\n current_stream = self.get_streams(invitee)[0]\\n invite_streams = self.make_random_stream_names([current_stream])[:1]\\n self.common_subscribe_to_streams(\\n invitee,\\n invite_streams,\\n extra_post_data={\\n \\\"announce\\\": \\\"true\\\",\\n \\\"principals\\\": orjson.dumps([self.user_profile.id]).decode(),\\n },\\n )\",\n \"def test_disallow_betrayal(self):\\n s1 = self.battle.create_skirmish(self.alice, 1)\\n with self.assertRaises(db.TeamException):\\n s1.react(self.bob, 1, hinder=False)\\n\\n n = (self.sess.query(db.SkirmishAction).filter_by(parent_id=None).\\n filter_by(participant=self.alice)).count()\\n self.assertEqual(n, 1)\",\n \"def test_auth_public_unowned(self):\\n self.do_visible(True, 'pattieblack', True, tenant='froggy')\",\n \"def test_startup_defensive(self) -> None:\\n self.assertFalse(self.client.triggers, 'predefined triggers')\\n self.assertIsNone(self.client.websocket, 'websocket without triggers')\",\n \"def test_existing_subscriptions_autosubscription(self) -> None:\\n stream_name = \\\"new_public_stream\\\"\\n cordelia = self.example_user(\\\"cordelia\\\")\\n self.common_subscribe_to_streams(cordelia, [stream_name], invite_only=False)\\n result = self.client_post(\\n \\\"/json/subscriptions/exists\\\", {\\\"stream\\\": stream_name, \\\"autosubscribe\\\": \\\"false\\\"}\\n )\\n response_dict = self.assert_json_success(result)\\n self.assertIn(\\\"subscribed\\\", response_dict)\\n self.assertFalse(response_dict[\\\"subscribed\\\"])\\n\\n result = self.client_post(\\n \\\"/json/subscriptions/exists\\\", {\\\"stream\\\": stream_name, \\\"autosubscribe\\\": \\\"true\\\"}\\n )\\n response_dict = self.assert_json_success(result)\\n self.assertIn(\\\"subscribed\\\", response_dict)\\n self.assertTrue(response_dict)\",\n \"def test_post_to_unregistered_event_does_not_call_other_funcs(self):\\n mock_subscription = Mock()\\n bus = event_bus._event_bus\\n mock_type = Mock()\\n mock_subscription.event_type = mock_type\\n bus._subscriptions[mock_type] = [mock_subscription]\\n\\n event_bus.post(Mock())\\n\\n self.assertEqual(mock_subscription.deliver.call_count, 0)\",\n \"def test_negative_conditions(self):\\r\\n outline_url = reverse_course_url('course_handler', self.course.id)\\r\\n # register a non-staff member and try to delete the course branch\\r\\n non_staff_client, _ = self.create_non_staff_authed_user_client()\\r\\n response = non_staff_client.delete(outline_url, {}, HTTP_ACCEPT='application/json')\\r\\n self.assertEqual(response.status_code, 403)\",\n \"def test_is_subscribed(self):\\n manager_root = ISubscriptionManager(self.root)\\n manager_root.subscribability = SUBSCRIBABLE\\n manager_root.subscribe('wim@example.com')\\n self.assertEqual(manager_root.is_subscribed('wim@example.com'), True)\\n self.assertEqual(manager_root.is_subscribed('unknown@u.com'), False)\\n\\n manager = ISubscriptionManager(self.root.folder)\\n self.assertEqual(manager.is_subscribed('wim@example.com'), True)\\n self.assertEqual(manager.is_subscribed('unknown@u.com'), False)\\n\\n # If you turn off subscription off at the folder level, you\\n # are no longer subscribed\\n manager.subscribability = NOT_SUBSCRIBABLE\\n self.assertEqual(manager.is_subscribed('wim@example.com'), False)\\n\\n # That didn't changed anything on the parent\\n self.assertEqual(manager_root.is_subscribed('wim@example.com'), True)\",\n \"def test_no_registration_admins_registered(self):\\n new_user = self.registration_profile.objects.create_inactive_user(\\n site=Site.objects.get_current(), **self.user_info)\\n\\n with warnings.catch_warnings(record=True) as _warning:\\n self.registration_profile.objects.send_admin_approve_email(\\n new_user, Site.objects.get_current())\\n\\n assertion_error = '''No warning triggered for unregistered\\n REGISTRATION_ADMINS'''\\n self.assertTrue(len(_warning) > 0, assertion_error)\\n self.assertTrue('REGISTRATION_ADMINS' in str(_warning[-1].message),\\n assertion_error)\",\n \"def test_not_approved_user(self):\\n (_,\\n joining_user_id,\\n conversation_id,\\n _) = self.setup_invites(is_approved=None)\\n self.set_session_cookie(joining_user_id, conversation_id)\\n self.set_user_cookie(joining_user_id, conversation_id)\\n uri = '/status/{}/{}'.format(conversation_id, joining_user_id)\\n rsp = self.client.post(uri,\\n data={'public_key': '',\\n 'last_message_seen_id': 0})\\n\\n rsp_json = json.loads(rsp.data)\\n self.assertFalse(rsp_json['success'])\",\n \"def test_non_matching_active(self):\\n user1 = get_user_model().objects.get(username='test1@example.com')\\n self.client.login(username='test1@example.com', password='1')\\n\\n office = OfficeLocation.objects.all()[0]\\n org = OrgGroup.objects.filter(parent__isnull=True)[0]\\n\\n submission1 = Interest()\\n submission1.owner = user1\\n submission1.for_coffee = True\\n submission1.save()\\n submission1.locations.add(office)\\n submission1.departments.add(org)\\n\\n resp = self.client.get(reverse('mystery:mystery'))\\n self.assertContains(resp, \\\"Cancel this\\\", status_code=200)\\n\\n user2 = random_user()\\n submission2 = Interest()\\n submission2.owner = user2\\n submission2.is_active = False\\n submission2.save()\\n submission2.for_coffee = True\\n submission2.locations.add(office)\\n submission2.departments.add(org)\\n submission2.save()\\n\\n resp = self.client.get(reverse('mystery:mystery'))\\n self.assertContains(resp, \\\"Cancel this\\\", status_code=200)\",\n \"def test_auth_private_unowned(self):\\n self.do_visible(False, 'pattieblack', False, tenant='froggy')\",\n \"def test_no_email_to_studio_if_setting_not_on(self, mock_tz):\\n mock_tz.now.return_value = datetime(2015, 2, 11, 10, tzinfo=dt_timezone.utc)\\n for i in range(5):\\n baker.make(\\n TicketBooking, ticketed_event=self.ticketed_event,\\n cancelled=False, paid=False,\\n user__email=\\\"unpaid_user{}@test.com\\\".format(i),\\n date_booked= datetime(2015, 2, 9, tzinfo=dt_timezone.utc),\\n warning_sent=True,\\n date_warning_sent= datetime(2015, 2, 9, tzinfo=dt_timezone.utc),\\n )\\n for booking in TicketBooking.objects.all():\\n baker.make(Ticket, ticket_booking=booking)\\n\\n management.call_command('cancel_unpaid_ticket_bookings')\\n # emails are sent to user per cancelled booking (6) (these 5 plus\\n # self.unpaid); none to studio\\n self.assertEqual(len(mail.outbox), 6)\\n cancelled_booking_emails = [\\n booking.user.email for booking\\n in TicketBooking.objects.filter(cancelled=True)\\n ]\\n self.assertEqual(\\n cancelled_booking_emails, [email.to[0] for email in mail.outbox]\\n )\",\n \"def test_not_approved_user(self):\\n (_,\\n joining_user_id,\\n conversation_id,\\n _) = self.setup_invites(is_approved=False)\\n self.set_session_cookie(joining_user_id, conversation_id)\\n self.set_user_cookie(joining_user_id, conversation_id)\\n uri = '/status/{}/{}'.format(conversation_id, joining_user_id)\\n rsp = self.client.post(uri,\\n data={'public_key': '',\\n 'last_message_seen_id': 0})\\n\\n rsp_json = json.loads(rsp.data)\\n self.assertFalse(rsp_json['success'])\",\n \"def test_subscriptions_anonymous(self):\\n self.go200('forum_index')\\n self.notfind('Оновити підписки')\\n self.go200('forum_subscriptions')\\n self.url(self.build_url('auth_login')+'.*')\",\n \"def test_sinus_does_not_trigger_alert(sim_sampler, events, data):\\n for _ in range(SIMULATION_SAMPLES):\\n sim_sampler.sampling_iteration()\\n\\n assert len(events.alerts_queue) == 0,\\\\\\n f\\\"Unexpected alerts: {events.alerts_queue.active_alerts}\\\"\",\n \"def test_permission_denied_no_permission(self):\\n\\n # Remove user from group ( added in setUpClass ) -> no permission\\n self.group.user_set.remove(self.user)\\n\\n c = suds.client.Client(\\n self.wsdl, username=self.username, password=self.password)\\n result_code, result_msg = c.service.getRecentAuditTrails()\\n self.assertEqual(result_code, 401)\",\n \"def test_resend_activation_email_nonexistent_user(self):\\n self.assertFalse(self.registration_profile.objects.resend_activation_mail(\\n email=self.user_info['email'],\\n site=Site.objects.get_current(),\\n ))\\n self.assertEqual(len(mail.outbox), 0)\",\n \"def test_no_program_user_response(self, *args): # pylint: disable=unused-argument\\n with mute_signals(post_save):\\n no_permissions_profile = ProfileFactory.create()\\n self.client.force_login(no_permissions_profile.user)\\n resp_post = self.client.post(self.mail_url, data=self.request_data, format='json')\\n assert resp_post.status_code == HTTP_403_FORBIDDEN\",\n \"def test_meeting_registrant_status(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.707774","0.66021943","0.6580524","0.6333144","0.63258743","0.62714976","0.62273604","0.62074196","0.6164765","0.61497414","0.61130273","0.6111483","0.60828555","0.60759217","0.607235","0.6040785","0.60398805","0.60398805","0.6023948","0.6017094","0.59793305","0.5976312","0.5967551","0.5961837","0.59615964","0.5954754","0.59546053","0.59438354","0.59429973","0.5909212","0.5887684","0.5883613","0.5879358","0.5869988","0.58690673","0.5862134","0.58574075","0.5854309","0.58490354","0.58455","0.5843379","0.58278835","0.5801592","0.5800637","0.5798984","0.57938737","0.5793481","0.57846326","0.5783149","0.5767809","0.57654935","0.5758312","0.5752816","0.5742017","0.5734352","0.5730326","0.5730088","0.57272756","0.571645","0.571086","0.5709913","0.5708688","0.5706556","0.57031214","0.5701604","0.57002944","0.5694899","0.5694009","0.5692323","0.568924","0.56784976","0.56753194","0.56740427","0.56727767","0.5668424","0.56647205","0.5660574","0.56604576","0.56597126","0.5659559","0.56552595","0.5647823","0.56445503","0.5640219","0.56281304","0.5624158","0.56210834","0.5618937","0.5615382","0.56153363","0.56093806","0.5596736","0.55956614","0.55919826","0.5591755","0.5591295","0.55884874","0.558501","0.5579932","0.55677587"],"string":"[\n \"0.707774\",\n \"0.66021943\",\n \"0.6580524\",\n \"0.6333144\",\n \"0.63258743\",\n \"0.62714976\",\n \"0.62273604\",\n \"0.62074196\",\n \"0.6164765\",\n \"0.61497414\",\n \"0.61130273\",\n \"0.6111483\",\n \"0.60828555\",\n \"0.60759217\",\n \"0.607235\",\n \"0.6040785\",\n \"0.60398805\",\n \"0.60398805\",\n \"0.6023948\",\n \"0.6017094\",\n \"0.59793305\",\n \"0.5976312\",\n \"0.5967551\",\n \"0.5961837\",\n \"0.59615964\",\n \"0.5954754\",\n \"0.59546053\",\n \"0.59438354\",\n \"0.59429973\",\n \"0.5909212\",\n \"0.5887684\",\n \"0.5883613\",\n \"0.5879358\",\n \"0.5869988\",\n \"0.58690673\",\n \"0.5862134\",\n \"0.58574075\",\n \"0.5854309\",\n \"0.58490354\",\n \"0.58455\",\n \"0.5843379\",\n \"0.58278835\",\n \"0.5801592\",\n \"0.5800637\",\n \"0.5798984\",\n \"0.57938737\",\n \"0.5793481\",\n \"0.57846326\",\n \"0.5783149\",\n \"0.5767809\",\n \"0.57654935\",\n \"0.5758312\",\n \"0.5752816\",\n \"0.5742017\",\n \"0.5734352\",\n \"0.5730326\",\n \"0.5730088\",\n \"0.57272756\",\n \"0.571645\",\n \"0.571086\",\n \"0.5709913\",\n \"0.5708688\",\n \"0.5706556\",\n \"0.57031214\",\n \"0.5701604\",\n \"0.57002944\",\n \"0.5694899\",\n \"0.5694009\",\n \"0.5692323\",\n \"0.568924\",\n \"0.56784976\",\n \"0.56753194\",\n \"0.56740427\",\n \"0.56727767\",\n \"0.5668424\",\n \"0.56647205\",\n \"0.5660574\",\n \"0.56604576\",\n \"0.56597126\",\n \"0.5659559\",\n \"0.56552595\",\n \"0.5647823\",\n \"0.56445503\",\n \"0.5640219\",\n \"0.56281304\",\n \"0.5624158\",\n \"0.56210834\",\n \"0.5618937\",\n \"0.5615382\",\n \"0.56153363\",\n \"0.56093806\",\n \"0.5596736\",\n \"0.55956614\",\n \"0.55919826\",\n \"0.5591755\",\n \"0.5591295\",\n \"0.55884874\",\n \"0.558501\",\n \"0.5579932\",\n \"0.55677587\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7428499"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":937,"numItemsPerPage":100,"numTotalItems":95772,"offset":93700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTUxODY5OSwianRpIjoiZGI1YWFmYWUtZTYxYy00OGM2LTg3OGYtZmJiMzdhNjQxOGM3Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU1MjIyOTksImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.pPihWtkfLomSaVkH6wX0If9DSv2G8mFGll1oUbvSWDl2_Wmu1OQRZ3Pd0_WhEsCZ21fu_jxl8yksqrDrIO4pDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Runs num_iters steps of SC steepest descent on the dictionary elements The a comment on axis ordering and the padding necessary to deal with boundary affects, see the docstrings in analysis_transforms/convolutional | def run(images_padded, dictionary, codes, kernel_stride, padding_dims,
stepsize=0.001, num_iters=1, normalize_dictionary=True):
reconstruction_mask = create_mask(images_padded, padding_dims)
codes_temp_transposed = codes.transpose(dim0=1, dim1=0)
# TODO: Figure out if I can remove the double-transpose in ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_conv_code_unroll_batch_kern(d, unroll_bsize=1, unroll_ksize=1):\r\n assert unroll_bsize > 0 and unroll_ksize > 0\r\n if \"unroll_bsize\" in d or \"unroll_ksize\" in d or \"unroll_iter\" in d or \"unroll_biter\" in d or \"unroll_kiter\" in d:\r\n raise Exception(\"We can't use this dictionnary ... | [
"0.561682",
"0.5455139",
"0.54320246",
"0.5400106",
"0.53414565",
"0.53230274",
"0.52961946",
"0.52878106",
"0.52821034",
"0.5278173",
"0.52781266",
"0.52665293",
"0.52234095",
"0.5221753",
"0.5208205",
"0.52052426",
"0.5204347",
"0.5190821",
"0.5174756",
"0.5170999",
"0.5153... | 0.6045369 | 0 |
Perform multiple consecutive attacks | def multiattack(self, sentence, attacks_with_severity):
for i,(attack, severity) in enumerate(attacks_with_severity):
if attack=="rand":
attack = random.choice(self.methods)
while attack=="intrude" and i!=len(attacks_with_severity)-1:
attack = rand... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def attacking_logic(self):\n if len(self.units(UnitTypeId.ZERGLING)) >= 6:\n for zergling in self.units(UnitTypeId.ZERGLING):\n self.do(zergling.attack(self.enemy_start_locations[0]))",
"def attack(self, robot):\n pass",
"def attack(self):\n\n if not self.at... | [
"0.5993577",
"0.5946337",
"0.576245",
"0.5705413",
"0.56642854",
"0.5558414",
"0.55434275",
"0.5485012",
"0.54479545",
"0.53891695",
"0.537023",
"0.53190684",
"0.5313921",
"0.53021246",
"0.5299953",
"0.5299419",
"0.5256593",
"0.5190473",
"0.5189436",
"0.51798266",
"0.51435095... | 0.6846764 | 0 |
Attack all sentences in document with a set of attacks | def multiattack_document(self, infile, outfile, attacks_with_severity):
scores,first_sentences, second_sentences = read_labeled_data(infile,do_float=False)
pert_first = [self.multiattack(sentence,attacks_with_severity) for sentence in tqdm(first_sentences)]
pert_second = [self.multiattack(senten... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def multiattack(self, sentence, attacks_with_severity):\n for i,(attack, severity) in enumerate(attacks_with_severity):\n if attack==\"rand\":\n attack = random.choice(self.methods)\n while attack==\"intrude\" and i!=len(attacks_with_severity)-1:\n ... | [
"0.66755813",
"0.5918671",
"0.58177257",
"0.56210095",
"0.561726",
"0.558743",
"0.55699134",
"0.556751",
"0.5551098",
"0.5545578",
"0.5531936",
"0.5516357",
"0.55113065",
"0.54990625",
"0.5497921",
"0.5471788",
"0.5453732",
"0.54429746",
"0.5412646",
"0.53980833",
"0.53979903... | 0.5592882 | 5 |
Generate a temporary location. | def create_base_temp_dir(cls):
if cls._thread_local.state.temp_dirs:
base_temp_dir = os.path.join(cls._thread_local.state.temp_dirs[-1],
cls._TEMP_SUBDIR)
else:
raise ValueError(
'A tf.Transform function that required a temp dir was called but no '
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def location_fixture():\n return _create_location()",
"def location():\n return _locate_or_create()",
"def get_temp_path():\n return os.getcwd() + \"/temp/\"",
"def mktemp(self):\n if self.dryrun:\n return os.path.expandvars(\"$TEMP/build\")\n\n return tempfile.mkdtemp()",
... | [
"0.64077693",
"0.62422216",
"0.62217087",
"0.61807305",
"0.6160167",
"0.6148715",
"0.6127639",
"0.6066422",
"0.60584307",
"0.6011024",
"0.59502506",
"0.592658",
"0.59185654",
"0.5910147",
"0.5893831",
"0.5826466",
"0.5786726",
"0.5777028",
"0.57585794",
"0.57543325",
"0.57349... | 0.55394065 | 40 |
Runs the given graph to realize the output tensors (i.e. features). Runs the graph in a TF session for computing the output values of the tensors, given an input row of data (input tensors). Due to the recordby record nature of beam we are operating sess.run() on individual record tensors vs batched tensors. | def process(self, element, saved_model_dir):
if self._graph_state is None:
if (getattr(self._thread_local, 'graph_state', None) is None or
self._thread_local.graph_state.saved_model_dir != saved_model_dir):
start = datetime.datetime.now()
self._thread_local.graph_state = self._GraphS... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(self, input_graph, dataloader, postprocess=None,\n metric=None, measurer=None, iteration=-1,\n tensorboard=False, fp32_baseline=False):\n sess_options = ort.SessionOptions()\n if measurer:\n # https://github.com/microsoft/onnxruntime/issues/7347... | [
"0.6365999",
"0.6352293",
"0.6220024",
"0.61351186",
"0.61189103",
"0.61167485",
"0.6106582",
"0.60465544",
"0.60121274",
"0.5962098",
"0.59156436",
"0.5904605",
"0.5866814",
"0.5846091",
"0.5842682",
"0.58306944",
"0.58295",
"0.58167785",
"0.581451",
"0.58137304",
"0.58033",... | 0.0 | -1 |
Create path to a unique temp dir from given base temp dir. | def _make_unique_temp_dir(base_temp_dir):
return os.path.join(base_temp_dir, uuid.uuid4().hex) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_base_temp_dir(cls):\n if cls._thread_local.state.temp_dirs:\n base_temp_dir = os.path.join(cls._thread_local.state.temp_dirs[-1],\n cls._TEMP_SUBDIR)\n else:\n raise ValueError(\n 'A tf.Transform function that required a temp dir was called but ... | [
"0.7639535",
"0.75858647",
"0.75725234",
"0.7204607",
"0.7200416",
"0.71961796",
"0.7147817",
"0.71170455",
"0.7116208",
"0.71064216",
"0.7062529",
"0.6970509",
"0.695506",
"0.6905613",
"0.689262",
"0.68787223",
"0.68727803",
"0.681926",
"0.6812259",
"0.68059057",
"0.6804259"... | 0.885131 | 0 |
Converts a dict of statistics to a transform function. | def expand(self, analyzer_outputs_to_pcoll):
# Create a transform_fn with unbound values.
unbound_transform_fn_dir = _make_unique_temp_dir(base_temp_dir)
input_columns_to_statistics = impl_helper.make_transform_fn_def(
input_schema, self._input_columns, self._output_columns,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def map_values(function, dictionary):\n return {k: function(dictionary[k]) for k in dictionary}",
"def map_values(fun, a_dict):\n return dict((k, fun(v)) for (k, v) in a_dict.items())",
"def transform_fn(transforms, params, invert=False):\n if invert:\n transforms = {k: v.inv for k, v in transf... | [
"0.6108298",
"0.60716486",
"0.60059154",
"0.5998334",
"0.59223676",
"0.5902528",
"0.5645412",
"0.55915976",
"0.55356425",
"0.54930705",
"0.5493034",
"0.54446834",
"0.54181",
"0.5416377",
"0.53979397",
"0.53423756",
"0.5333607",
"0.5317512",
"0.5307221",
"0.5295512",
"0.525213... | 0.48021248 | 90 |
Returns a list of lists, containing analyzers by level. We need to run analyzers in order so that when running the TF graph to get the inputs of an analyzer, we only rely on analyzers that have already been computed. This is acheived by running analyzers in order of their `level` where the level of an analyzer is defin... | def _analyzers_by_level(self, outputs):
memoized_column_levels = {}
analyzers_by_level = collections.defaultdict(list)
def column_level(column):
"""Adds all analyzers above this column to analyzers_by_level.
Visits all parents of this column or statistic in the `Column`/`Statistic`
graph,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_analyzers(cls):\n for analyzer_name, analyzer_class in iter(cls._class_registry.items()):\n yield analyzer_name, analyzer_class",
"def list_analyzers(args: argparse.Namespace):\n first = True\n queue = [tuple(c) + (\"lookout.\",) for c in pkgutil.iter_modules(lookout.__path__)]\n ... | [
"0.5864481",
"0.53452533",
"0.50972193",
"0.5097106",
"0.506351",
"0.49360263",
"0.49226353",
"0.4915473",
"0.48669732",
"0.48573586",
"0.475987",
"0.47398412",
"0.46899807",
"0.46852565",
"0.46842974",
"0.4634878",
"0.46315",
"0.46094146",
"0.4595435",
"0.45916668",
"0.45753... | 0.7261228 | 0 |
Adds all analyzers above this column to analyzers_by_level. Visits all parents of this column or statistic in the `Column`/`Statistic` graph, and adds each _AnalyzerOutput to analyzers_by_level according to its level. | def column_level(column):
if column in memoized_column_levels:
return memoized_column_levels[column]
# pylint: disable=protected-access
if isinstance(column, api._AnalyzerOutput):
level = max(
[column_level(input_column) for input_column in column.inputs]) + 1
anal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _analyzers_by_level(self, outputs):\n memoized_column_levels = {}\n analyzers_by_level = collections.defaultdict(list)\n def column_level(column):\n \"\"\"Adds all analyzers above this column to analyzers_by_level.\n\n Visits all parents of this column or statistic in the `Column`/`Statistic... | [
"0.78771067",
"0.48176712",
"0.45828122",
"0.44564846",
"0.44539464",
"0.4452111",
"0.44325805",
"0.43774885",
"0.4376514",
"0.42762798",
"0.4257025",
"0.42525667",
"0.42483383",
"0.42271966",
"0.42109933",
"0.42009965",
"0.41943592",
"0.41868788",
"0.41585994",
"0.41352066",
... | 0.45502272 | 3 |
Transform the dataset by applying the preprocessing_fn. | def expand(self, dataset):
# Expand is currently implemented by composing AnalyzeDataset and
# TransformDataset. Future versions however could do somthing more optimal,
# e.g. caching the values of expensive computations done in AnalyzeDataset.
transform_fn = dataset | AnalyzeDataset(self._preprocessin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess_data(self):\n\n self._preprocess_train_data()\n self._preprocess_test_data()",
"def _preprocess(self, data, normalize=False) -> np.ndarray:\n \n preprocessor = StandardScaler() if not normalize else Normalizer()\n\n data = preprocessor.fit_transform(data)\n ... | [
"0.73838943",
"0.72549456",
"0.7252309",
"0.7228141",
"0.71974176",
"0.7074556",
"0.7052995",
"0.7013554",
"0.69532084",
"0.69424987",
"0.6872287",
"0.68646985",
"0.6855033",
"0.68126386",
"0.6806819",
"0.6798322",
"0.67914605",
"0.6788149",
"0.67493486",
"0.6716809",
"0.6714... | 0.0 | -1 |
Transforms the dataset using the transform_fn. | def expand(self, dataset_and_transform_fn):
(input_values, input_metadata), (transform_fn, output_metadata) = (
dataset_and_transform_fn)
# If exclude_outputs is set, update the output metadata, which will also
# cause _RunMetaGraphDoFn not to create the excluded outputs.
if self._exclude_outpu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _transform(self, dataset):\n raise NotImplementedError()",
"def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\n # checks here\n super().transform(dataset)\n # convert to accepted dtype and get attributes\n dataset = dataset.to_pandas()\n df = dataset.data\... | [
"0.80232185",
"0.7545224",
"0.7201159",
"0.7166387",
"0.7132256",
"0.71175015",
"0.70721376",
"0.70721376",
"0.70721376",
"0.7071818",
"0.703013",
"0.70073336",
"0.69790024",
"0.6948974",
"0.6937917",
"0.6937917",
"0.6937917",
"0.6937917",
"0.6937917",
"0.6937917",
"0.6937917... | 0.0 | -1 |
Replaces a wit b and b with a in my_str | def inverse_replacer(my_str:str, a:str, b:str) -> str:
my_str = list(my_str)
for i in range(len(my_str)):
if my_str[i] == a:
my_str[i] = b
elif my_str[i] == b:
my_str[i] = a
return(''.join(my_str[::-1])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_substitutions_in_word(self):\n m = strutils.MultiReplace({r'cat': 'kedi', r'purple': 'mor', })\n self.assertEqual(m.sub('Thecatispurple'), 'Thekediismor')",
"def myreplace(old, new, s):\n\n s = s.split()\n s = \" \".join(s)\n\n return new.join(s.split(old))",
"def test_simple_su... | [
"0.6426957",
"0.6371966",
"0.62510085",
"0.6172532",
"0.605283",
"0.60364926",
"0.5999566",
"0.591037",
"0.5850798",
"0.58500206",
"0.58080274",
"0.5771347",
"0.57628804",
"0.56756216",
"0.56451476",
"0.56305456",
"0.56139046",
"0.5609791",
"0.5602158",
"0.55999005",
"0.55693... | 0.7641272 | 0 |
Return a list of all audited data. | def _audited_log(self, request, pk=None, format=None, id=None):
if not self.queryset:
raise LookupError("You must override the queryset param!")
model = self.queryset.model
primary_key = pk
if not primary_key:
primary_key = id
if not id:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def directory_audits(self):\n return self.properties.get('directoryAudits',\n EntityCollection(self.context, DirectoryAudit,\n ResourcePath(\"directoryAudits\", self.resource_path)))",
"def audiences(self) -> pulumi.Outpu... | [
"0.6826847",
"0.61051714",
"0.60854363",
"0.60486275",
"0.6005237",
"0.5997306",
"0.5969678",
"0.59662133",
"0.59562707",
"0.59453773",
"0.59410286",
"0.5906567",
"0.59025174",
"0.58759016",
"0.58625376",
"0.58387715",
"0.5833064",
"0.5813266",
"0.58034986",
"0.5798717",
"0.5... | 0.0 | -1 |
Return an array of n logspaces values from min to max. | def rangeLog(min, max, n):
logmin, logmax = log(min), log(max)
return np.exp( np.arange( logmin, logmax, (logmax-logmin)/float(n) ) ) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logspace(min, max, num, **kwargs):\n if isinstance(min, datetime.datetime):\n from matplotlib.dates import date2num, num2date\n ans = num2date(np.logspace(np.log10(date2num(min)), np.log10(date2num(max)), num, **kwargs))\n ans = spt.no_tzinfo(ans)\n return np.array(ans)\n else... | [
"0.70716333",
"0.70391375",
"0.67055446",
"0.66992354",
"0.6579034",
"0.6477024",
"0.64768785",
"0.6472296",
"0.64413065",
"0.6438916",
"0.64164",
"0.6350058",
"0.61967665",
"0.6185765",
"0.6154829",
"0.6042712",
"0.601705",
"0.59243286",
"0.5922405",
"0.59168375",
"0.5911305... | 0.6757458 | 2 |
Return an array of n linearspaced values from min to max. | def rangeLin(min, max, n):
return np.arange( min, max, (max-min)/n ) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linspace(start, stop, n, istart=True, istop=True):\r\n n = n-1\r\n arr = [start + ((stop-start)/n) * i for i in range(n+1)]\r\n return arr",
"def create_array(n, bound):\n array = [np.random.randint(0, bound) for x in range(n)]\n return array",
"def x_uniform1(xlim, n):\n return linspace... | [
"0.71289086",
"0.7006669",
"0.66802406",
"0.65675193",
"0.6554845",
"0.6424489",
"0.63840723",
"0.6360398",
"0.63580406",
"0.6333735",
"0.6289109",
"0.6270997",
"0.6265102",
"0.6229819",
"0.6141139",
"0.6074155",
"0.6066987",
"0.6065085",
"0.60578597",
"0.6039758",
"0.6018827... | 0.7715625 | 0 |
Return X matrix Xij = [ exp(k_jt_i); Iw^(1/2) ] from arrays k and t. | def Xmatrix(k,t,w, standardizeData=True):
K, N = len(k), len(t)
X = np.zeros( (N+K,K) )
for i in range(N):
for j in range(K):
X[i,j] = np.exp(-1.0*k[j]*t[i])
for i in range(K):
X[i+len(t),i] = w**0.5
Xmean = (X[0:N,:]).mean(axis=0)
if standardizeData:
for j in r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Xsubmatrix(k,t, standardizeData=True):\n\n K, N = len(k), len(t)\n X = np.zeros( (N,K) )\n for i in range(N):\n for j in range(K):\n X[i,j] = np.exp(-1.0*k[j]*t[i])\n\n Xmean = X.mean(axis=0)\n if standardizeData:\n for j in range(K):\n X[:,j] = X[:,j] - Xmean[j]\n\n r... | [
"0.654792",
"0.62343216",
"0.59998757",
"0.59954584",
"0.58997905",
"0.5833076",
"0.58315307",
"0.5817481",
"0.57968336",
"0.57816815",
"0.5695442",
"0.56352943",
"0.5633382",
"0.55761397",
"0.55315673",
"0.5512695",
"0.55079854",
"0.5452388",
"0.545135",
"0.542148",
"0.53979... | 0.6576722 | 0 |
Return X matrix Xij = [ exp(k_jt_i)] from arrays k and t. | def Xsubmatrix(k,t, standardizeData=True):
K, N = len(k), len(t)
X = np.zeros( (N,K) )
for i in range(N):
for j in range(K):
X[i,j] = np.exp(-1.0*k[j]*t[i])
Xmean = X.mean(axis=0)
if standardizeData:
for j in range(K):
X[:,j] = X[:,j] - Xmean[j]
return X, Xmean | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Xmatrix(k,t,w, standardizeData=True):\n \n K, N = len(k), len(t)\n X = np.zeros( (N+K,K) )\n for i in range(N):\n for j in range(K):\n X[i,j] = np.exp(-1.0*k[j]*t[i])\n for i in range(K):\n X[i+len(t),i] = w**0.5\n\n Xmean = (X[0:N,:]).mean(axis=0) \n if standardizeData:\... | [
"0.64807385",
"0.62325656",
"0.6106175",
"0.6067337",
"0.5988209",
"0.59643847",
"0.58780324",
"0.586434",
"0.58161384",
"0.5789269",
"0.5753004",
"0.57186216",
"0.5696763",
"0.56853116",
"0.56414497",
"0.5595421",
"0.5574128",
"0.55618346",
"0.5512092",
"0.5468021",
"0.54500... | 0.66882664 | 0 |
Return the sum of the exponential relaxations. If the data is standardized, an offset constant zero rate must be provided. | def SumSpectra(A, Rates, Times, offset=0.0):
#print '*** in SumSpectra: ***'
result = np.zeros( Times.shape )
for i in range(len(Rates)):
#print '***', Rates[i]
result += A[i]*np.exp( -1.0*Rates[i]*Times)
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def energy(data):\n return sum(pow(data, 2))",
"def epfromdata(data, krdel, kexp, fp):\n balance = compute_balance(data, krdel)\n return weighted_energy(balance, fp, kexp)",
"def energy(data):\n\n return np.real(np.mean(np.abs(data)**2, axis=1))",
"def sum_exp(self,time):\n sum = np.sum([s... | [
"0.591776",
"0.56907016",
"0.5573584",
"0.550739",
"0.54849076",
"0.5483118",
"0.5482724",
"0.5391489",
"0.5375844",
"0.5359106",
"0.53125805",
"0.529915",
"0.5268953",
"0.5262514",
"0.5254501",
"0.523982",
"0.52286243",
"0.52204746",
"0.5184274",
"0.51719624",
"0.5151444",
... | 0.50822574 | 24 |
nTimes = 727 same number as W55F dataset taus = [1.0e6, 1.0e4, 5.0e3] timescales for a test data curve amps = [0.3, 0.3, 0.4] ampltudes of each relaxation sigma = 0.05 add artifical noise to the data | def testData(nTimes = 1000, taus = [1.0e-6, 1.0e-4, 5.0e-3], amps = [0.3, 0.3, 0.4], sigma = 0.05, linear=False):
if linear:
Times = rangeLin(1.0e-7, 1.0e-3, nTimes) # in seconds
else:
Times = rangeLog(1.0e-9, 1.0, nTimes) # in seconds
Data = np.zeros( Times.shape )
for i in range(len(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_plt_mag_time():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n # create the arrays per filter and readout pattern\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\n nrsra... | [
"0.6373813",
"0.6351109",
"0.6140943",
"0.613694",
"0.6108043",
"0.6039261",
"0.60305786",
"0.596345",
"0.59240603",
"0.58829147",
"0.5873695",
"0.5839105",
"0.58336765",
"0.58019346",
"0.5791312",
"0.57601404",
"0.57052803",
"0.5704976",
"0.569301",
"0.5687342",
"0.5664945",... | 0.6944508 | 0 |
nTimes = 727 same number as W55F dataset beta = the stretching exponenent (should be between 0 and 1) sigma = 0.05 add artifical noise to the data | def testStretchedExpData(nTimes = 1000, beta = 0.5, sigma = 0.05, linear=False):
if linear:
Times = rangeLin(1.0e-3, 1.0e+3, nTimes) # in seconds
else:
Times = rangeLog(1.0e-3, 1.0e+3, nTimes) # in seconds
Data = np.exp(-1.*(Times**beta))
Data += sigma*np.random.randn( len(Data) )
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def noise_generator(n, mean, std, fractindex):\n if fractindex not in VALID_FRACT:\n raise ValueError(\"results: status must be one of %r.\" % VALID_FRACT)\n \n stdev = std\n \n b = 2*fractindex-1\n print('beta: ', b)\n \n bdis = np.zeros(n)\n\n bdis[0] = 1\n for i in range(1,n... | [
"0.64249635",
"0.6127469",
"0.59821934",
"0.5905486",
"0.5902299",
"0.5896128",
"0.5882188",
"0.5829961",
"0.58168125",
"0.5813827",
"0.5785464",
"0.57696134",
"0.5728573",
"0.5718349",
"0.57028085",
"0.56930923",
"0.56703866",
"0.5651287",
"0.5642848",
"0.5610342",
"0.559990... | 0.623264 | 1 |
Using pseudoinverse, with Tikhonov regularization (w parameter) to solve the inverse lapace tranform. Returns coefficients A_k, residual sum of squares (rss), and number of degrees of freedom, for each relaxation rate. | def fitRateSpectrum(Times, Data, Rates, w, Lnorm='ridge', standardizeData=True, CalcNdof=False, rho=0.5):
if Lnorm == 'lasso':
# Use L1-norm Lasso regression
try:
from scikits.learn.linear_model import Lasso
except:
print 'Error: could NOT import Lasso from sci... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solver(u_init, eta_0, eta, eta_lin, T, H, L_lhs, L_rhs, alpha, gamma, B, D, C, ftol = 1e-3, max_iter = 5000, verbose = 0, nnls_max_iter=30):\n\n # Raise('NotImplementedError: only adjusted the arguments.')\n #Need to incorporate L_lhs into stacked and appropriate w_lin updates, u_update and eta_lin incre... | [
"0.5710855",
"0.5675077",
"0.5650308",
"0.5553886",
"0.5542864",
"0.5486818",
"0.54824346",
"0.5439341",
"0.5427962",
"0.5392118",
"0.5376725",
"0.537398",
"0.53675294",
"0.5363947",
"0.5357868",
"0.5353982",
"0.5346847",
"0.53347623",
"0.5315761",
"0.53112704",
"0.53029287",... | 0.0 | -1 |
Scale a numpy array of values so that (min, max) = (0,1). | def scaleValues(values):
values = values - values.min()
return values/values.max() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scale01(arr):\r\n walk_arr_01 = numpy.interp(arr, (numpy.amin(arr), numpy.amax(arr)), (-1, +1)) # linear scaling\r\n return walk_arr_01 #return the scaled array\r",
"def scale_1d(x):\n return (min(x), max(x), len(x))",
"def _scale_array(arr, clip=True):\n if clip:\n scaled = np.c... | [
"0.77611136",
"0.7545397",
"0.74959666",
"0.7466113",
"0.7265087",
"0.72355026",
"0.7218419",
"0.7183934",
"0.71745336",
"0.7156415",
"0.70853466",
"0.7025777",
"0.6994783",
"0.6936765",
"0.69095606",
"0.69059616",
"0.6852354",
"0.6841582",
"0.6785465",
"0.6775435",
"0.675613... | 0.72233146 | 6 |
Scale a numpy array of values so that (min, max) = (0,1). | def scaleValuesWithInfo(values):
Shiftby = values.min()
values = values - Shiftby
Scaleby = values.max()
return values/Scaleby, Scaleby, Shiftby | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scale01(arr):\r\n walk_arr_01 = numpy.interp(arr, (numpy.amin(arr), numpy.amax(arr)), (-1, +1)) # linear scaling\r\n return walk_arr_01 #return the scaled array\r",
"def scale_1d(x):\n return (min(x), max(x), len(x))",
"def _scale_array(arr, clip=True):\n if clip:\n scaled = np.c... | [
"0.7762404",
"0.75432307",
"0.749825",
"0.74669933",
"0.72655994",
"0.72361785",
"0.72225434",
"0.72195363",
"0.7185347",
"0.7177425",
"0.71583474",
"0.7086495",
"0.7026466",
"0.69962096",
"0.69380826",
"0.6910129",
"0.69072604",
"0.6853833",
"0.68411666",
"0.67867655",
"0.67... | 0.0 | -1 |
Apply a passthrough filter to a cloud | def apply_passthrough_filter(cloud, axis, axis_min, axis_max):
# Create a PassThrough filter object.
passthrough = cloud.make_passthrough_filter()
# Assign axis and range to the passthrough filter object.
filter_axis = axis
passthrough.set_filter_field_name(filter_axis)
#axis_min = 0.6
#axi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply_filter(self, image):\n pass",
"def filter(self, *args, **kwargs):",
"def apply(self, *args):\n return _ida_hexrays.udc_filter_t_apply(self, *args)",
"def apply_filters(self, new_filters):\n\t\tself.filters = new_filters",
"def apply(self, *args):\n return _ida_hexrays.microco... | [
"0.6539141",
"0.60534006",
"0.6045387",
"0.593166",
"0.5931561",
"0.5887304",
"0.58217114",
"0.581077",
"0.5772639",
"0.57151353",
"0.5696424",
"0.5690819",
"0.56490076",
"0.563207",
"0.5628303",
"0.56268173",
"0.561666",
"0.5591821",
"0.5530589",
"0.5467814",
"0.5466966",
... | 0.74443835 | 0 |
Segments a cloud using a sac model | def ransac(cloud, sacmodel):
# Create the segmentation object
seg = cloud.make_segmenter()
# Set the model you wish to fit
seg.set_model_type(sacmodel)
seg.set_method_type(pcl.SAC_RANSAC)
# Max distance for a point to be considered fitting the model
# Experiment with different values for ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def segment(points, model=pclpy.pcl.sample_consensus.SACMODEL_LINE, method=pclpy.pcl.sample_consensus.SAC_RANSAC, miter=1000, distance=0.5, rlim=[0,0.5]):\n pointcloud = pclpy.pcl.PointCloud.PointXYZ(points)\n segmenter = pclpy.pcl.segmentation.SACSegmentation.PointXYZ()\n\n segmenter.setInputCloud(pointc... | [
"0.5972947",
"0.57596177",
"0.5705211",
"0.55908954",
"0.5576257",
"0.55459577",
"0.55090237",
"0.54882836",
"0.54302746",
"0.53734714",
"0.52986604",
"0.528243",
"0.5271106",
"0.52643365",
"0.5247393",
"0.5247393",
"0.5171636",
"0.51356274",
"0.51081306",
"0.50940233",
"0.50... | 0.6150639 | 0 |
Generates training data in the CRF++ format for the ingredient tagging task | def run(self):
with open(self.opts.data_path, encoding='utf-8') as data_file:
data_reader = labelled_data.Reader(data_file)
for row in data_reader:
# Write the utf-8 encoded data directly to stdout instead of using print
# because print() will output a byt... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def easydatagen():\n\n # Reading in the training file\n data = pd.read_json('train.json')\n\n # The set of different cuisines\n cuisines = data.cuisine.unique()\n\n # To find the different ingredients, we need to clean them up a little.\n def clean(string) :\n s = string.replace('-',' ') #... | [
"0.71794796",
"0.6370519",
"0.63217074",
"0.6288061",
"0.6260432",
"0.61992145",
"0.61916214",
"0.61623365",
"0.6151186",
"0.61314774",
"0.6079185",
"0.6071901",
"0.60713",
"0.6058677",
"0.60482717",
"0.6039198",
"0.60251206",
"0.60112286",
"0.6009894",
"0.60096776",
"0.60037... | 0.0 | -1 |
Parse the commandline arguments into a dict. | def _parse_args(self, argv):
opts = optparse.OptionParser()
opts.add_option("--data-path",
default="nyt-ingredients-snapshot-2015.csv",
help="(%default)")
(options, args) = opts.parse_args(argv)
return options | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def retrieve_args_dict():\n process_args = sys.argv[1:]\n dictionary = dict()\n for process_arg in process_args:\n splitted = process_arg.split(\":\")\n if len(splitted) > 1:\n key = splitted[0]\n value = \"\".join(splitted[1:])\n dictionary[key] = value\n ... | [
"0.805954",
"0.7853354",
"0.748627",
"0.7477624",
"0.742788",
"0.73723",
"0.7298476",
"0.7266872",
"0.7208575",
"0.7201047",
"0.71894705",
"0.71479756",
"0.7123694",
"0.7111748",
"0.70769215",
"0.70635056",
"0.7010335",
"0.7010014",
"0.6994027",
"0.6871115",
"0.6870057",
"0... | 0.0 | -1 |
Return the results of a data request based on the configuration UID, options include search, pagination and sorting | def fetch():
req_data= request.get_json()
## ddb uses text files, using this as to eat my own dogfoor and improve
## no service sql client. No daemon, low cpu.
e=load_db()
try:
res=e.query(req_data['query'])
serialized = jsonpickle.encode( res,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query(self,\n template: Union[list, str, pd.Series, pd.DataFrame, None] = None,\n title: Optional[str] = None,\n keyword: Union[str, list, None] = None,\n mongoquery: Union[str, dict, None] = None,\n page: Optional[int] = None,\n parse_dates: bool = True,\n... | [
"0.5923269",
"0.5864445",
"0.5786591",
"0.57578933",
"0.57529306",
"0.57469153",
"0.5704843",
"0.5696772",
"0.56865305",
"0.5661509",
"0.5644907",
"0.56123847",
"0.55941075",
"0.5550665",
"0.55397886",
"0.55127746",
"0.55086654",
"0.5501796",
"0.55009973",
"0.54982543",
"0.54... | 0.0 | -1 |
Returns an OptionParser object with all the default options. | def MakeOpts():
parser = ArgumentParser()
parser.add_argument("-o", "--host", dest="host", default="hldbv02",
help="The hostname for the MySQL database")
parser.add_argument('-d', '--debug', action='store_true', default=False,
help='debug mode, store results ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_default_options():\n parser = OptionParser()\n add_option_group(parser)\n options, args = parser.parse_args([])\n return options",
"def initCmdLineParser():\n\n # Init parser and all general flags\n logging.debug(\"initiating command line option parser\")\n usage = \"usage: %prog [options... | [
"0.84064525",
"0.7035872",
"0.69315475",
"0.6762891",
"0.6612723",
"0.65971667",
"0.657549",
"0.65095645",
"0.65095645",
"0.63397056",
"0.6329244",
"0.63176024",
"0.6288814",
"0.6283019",
"0.6267587",
"0.6220962",
"0.6210075",
"0.6204305",
"0.6200668",
"0.619317",
"0.61901367... | 0.0 | -1 |
Make a terminal loader/spinner animation using the imports above. Takes seconds argument = time for the spinner to run. Does not return anything, only prints to stdout. | def spinner(seconds):
timeout = time()
for state in cycle(SPINNER_STATES):
if time() - timeout > seconds: break
else:
print(state, end= '\r', flush= True)
sleep(STATE_TRANSITION_TIME) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loading(delay):\r\n\r\n for i in range(3):\r\n\r\n print \".\",\r\n sys.stdout.flush()\r\n time.sleep(delay)\r\n\r\n print(\"\")",
"def animate():\n for c in itertools.cycle(['|', '/', '-', '\\\\']):\n if done:\n break\n sys.stdout.wr... | [
"0.66631764",
"0.65671074",
"0.6313429",
"0.62911284",
"0.57967776",
"0.57249594",
"0.5722175",
"0.5686955",
"0.5641586",
"0.56180584",
"0.5608205",
"0.5562148",
"0.55549806",
"0.5531149",
"0.5498166",
"0.5469922",
"0.54436475",
"0.54220825",
"0.54170036",
"0.5410249",
"0.540... | 0.68003017 | 0 |
Removes punctuation & excess whitespace, sets to lowercase, and stems tweets. Returns a list of stemmed tokens. | def tokenize(tweet):
tweet = " ".join(re.split("[^a-zA-Z]*", tweet.lower())).strip()
tokens = [stemmer.stem(t) for t in tweet.split()]
return tokens | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stem_tokens(tokens, stemmer):\n stemmed = []\n # going through tokens stem where needed\n for token in tokens:\n stemmed.append(stemmer.stem(token))\n return stemmed",
"def tokenizeAndStem(phrase, ignore = []):\n # Final list of stems\n stems = []\n # Loop through tokenized items\... | [
"0.7678134",
"0.7551364",
"0.75347817",
"0.7524167",
"0.750994",
"0.750994",
"0.750994",
"0.750994",
"0.750994",
"0.7473676",
"0.7467269",
"0.7444527",
"0.74240965",
"0.7400697",
"0.7393167",
"0.7378211",
"0.7346575",
"0.7345031",
"0.73118025",
"0.7284678",
"0.72558844",
"0... | 0.8219763 | 1 |
Same as tokenize but without the stemming | def basic_tokenize(tweet):
tweet = " ".join(re.split("[^a-zA-Z.,!?]*", tweet.lower())).strip()
return tweet.split() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def my_tokenize(sentence):\n sentence = sentence.lower()\n ll = word_tokenize(sentence)\n lls = [stemmer.stem(ii) for ii in ll if re.search(r'[a-z0-9]+', ii)]\n\n return lls",
"def tokenize(tweet):\n tweet = \" \".join(re.split(\"[^a-zA-Z]*\", tweet.lower())).strip()\n tokens = [stemmer.stem(t)... | [
"0.7751562",
"0.7710764",
"0.7710764",
"0.76984525",
"0.76871353",
"0.7683495",
"0.7663285",
"0.7655944",
"0.7632767",
"0.7604723",
"0.7599857",
"0.75302166",
"0.75051105",
"0.7499571",
"0.7489158",
"0.73736125",
"0.7353079",
"0.7338581",
"0.7311257",
"0.7308638",
"0.7302826"... | 0.69179165 | 60 |
Tally the statistics in the given HDF5 | def tally_stats(hdf5_file):
Stat = namedtuple('Stat', ['cr_count',
'img_count',
'total_exptime'])
with h5py.File(hdf5_file,mode='r') as f:
instr = list(f.keys())[0]
print(instr)
grp = f['/{}/sizes'.format(instr)]
num_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def summarize_hdf5(hdf5_file):\n # characterize the h5file in a mostly content-agnostic way\n summary = {\n 'sums': {},\n 'shapes': {}\n }\n\n def characterize_object(obj_name, obj_data):\n \"\"\"retain some properties of each dataset in an hdf5 file\"\"\"\n if isinstance(ob... | [
"0.65691614",
"0.65687823",
"0.647717",
"0.61742824",
"0.6162718",
"0.5820011",
"0.58141285",
"0.5803666",
"0.5748079",
"0.57440376",
"0.5700492",
"0.5689688",
"0.5675805",
"0.5675805",
"0.56419486",
"0.56329083",
"0.55740833",
"0.55623597",
"0.55504835",
"0.5539534",
"0.5532... | 0.8037474 | 0 |
Parse all files in the results directory and tally the statistics | def compile_global_stats(results_dir='./../data/*/*cr_sizes*hdf5'):
flist = glob.glob(results_dir)
output = defaultdict(list)
flist = [f for f in flist if 'nicmos' not in f]
print(flist)
flist.append('./../data/STIS/stis_cr_sizes.hdf5')
results = [dask.delayed(tally_stats)(f) for f in flist]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def analyze_files(self):\n for file in os.listdir(self.directory):\n if file[-3:] == (\".py\"):\n fopen = open(os.path.join(self.directory, file), \"r\")\n try:\n if not (py_file := fopen):\n raise FileNotFoundError\n\n ... | [
"0.7124748",
"0.69089925",
"0.68689764",
"0.6803728",
"0.67018825",
"0.64793134",
"0.6475737",
"0.64586645",
"0.6270642",
"0.62664855",
"0.6229735",
"0.62132794",
"0.61848545",
"0.6163296",
"0.61612755",
"0.615097",
"0.6112404",
"0.60943806",
"0.6093197",
"0.60524863",
"0.604... | 0.594888 | 30 |
Queries a given endpoint on the Eskom loadshedding API with the specified payload | async def async_query_api(self, endpoint, payload=None):
async with RetryClient() as client:
# The Eskom API occasionally drops incoming connections, implement reies
async with client.get(
url=self.base_url + endpoint,
headers=self.headers,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_call_api(url, payload, headers):\n return requests.request(\"GET\", url, headers=headers, data=payload)",
"def _query(self, url, payload=None, action='GET'):\r\n full_url = self._base + url\r\n\r\n headers = {\r\n 'Content-Type': 'application/json'\r\n }\r\n\r\n ... | [
"0.6770775",
"0.6607444",
"0.6537534",
"0.6452347",
"0.6452142",
"0.6295583",
"0.61789805",
"0.6112436",
"0.6107325",
"0.60884076",
"0.59610105",
"0.59126604",
"0.5908381",
"0.587297",
"0.586031",
"0.58270234",
"0.5821195",
"0.5821195",
"0.5802803",
"0.57767856",
"0.57263523"... | 0.7321024 | 0 |
Fetches the current loadshedding stage from the Eskom API | async def async_get_stage(self, attempts=50):
# Query the API until a sensible (> 0) value is received, or the number of attempts is exceeded
for attempt in range(attempts):
res = await self.async_query_api("/GetStatus")
# Return the current loadshedding stage by subtracting 1 ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_stage():\n try:\n filename = os.path.join(get_var('SITE'), \".stage\")\n f = open(filename, \"r\")\n stage = f.readline().strip()\n f.close()\n logger.debug(\"get stage: %s\" % (stage))\n return stage\n except:\n return reset_stage()",
"def get_stage... | [
"0.5896602",
"0.55642724",
"0.54953194",
"0.54792047",
"0.5462125",
"0.54313517",
"0.53262895",
"0.53185606",
"0.5314176",
"0.5314081",
"0.5314081",
"0.5295862",
"0.5229258",
"0.5218341",
"0.52007157",
"0.5197444",
"0.5197444",
"0.5197444",
"0.5137261",
"0.512215",
"0.5114277... | 0.71549255 | 0 |
Fetches data from the loadshedding API | async def async_get_data(self):
stage = await self.async_get_stage()
data = {
"data": {"stage": stage},
}
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fetch_data(self):\n pass",
"def fetch_data(self):",
"def _load_data(self):\n if self._api_response.status_code == 200:\n self._dataset = self._api_response.json()\n self._fill_day_dicts()",
"def fetch_data():\n data.fetch_data()\n data.start_updating()",
"def ... | [
"0.73392963",
"0.72956276",
"0.70448846",
"0.6874289",
"0.6612632",
"0.65690875",
"0.6422691",
"0.6422691",
"0.64019233",
"0.6395586",
"0.6293553",
"0.62517023",
"0.6210572",
"0.61741084",
"0.6149246",
"0.6116018",
"0.61003584",
"0.6090419",
"0.607891",
"0.60308963",
"0.60286... | 0.0 | -1 |
Check whether each file listed in the changes file is present. ```changes_file``` The changes file to parse for the orig.tar (note the dsc file referenced must exist) | def test_files_present(self, changes_file):
for filename in changes_file.get_files():
log.debug('Looking whether %s was actually uploaded' % filename)
if os.path.isfile(os.path.join(pylons.config['debexpo.upload.incoming'], filename)):
log.debug('%s is present' % filename... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_native_package(self, changes_file):\n\n for file in changes_file['Files']:\n if file['name'].endswith('.diff.gz'):\n return False\n if file['name'].endswith(('.debian.tar.gz','.debian.tar.bz2','.debian.tar.xz')):\n return False\n return True"... | [
"0.6562452",
"0.61490893",
"0.5917509",
"0.5785546",
"0.5767901",
"0.5733005",
"0.5689596",
"0.5616368",
"0.5590855",
"0.55259246",
"0.54963535",
"0.5468433",
"0.5468134",
"0.5463716",
"0.5461302",
"0.54606396",
"0.5443999",
"0.5441099",
"0.542676",
"0.53501844",
"0.5344584",... | 0.7056379 | 0 |
Check each file's md5sum and make sure the md5sum in the changes file is the same as the actual file's md5sum. ```changes_file``` The changes file to parse for the orig.tar (note the dsc file referenced must exist) | def test_md5sum(self, changes_file):
for file in changes_file['Files']:
log.debug('Checking md5sum of %s' % file['name'])
filename = os.path.join(pylons.config['debexpo.upload.incoming'], file['name'])
if not os.path.isfile(filename):
raise OSError("Missing fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_sum(file_path, md5_sum):\n file_md5_sum = generate_sum(file_path)\n return (file_md5_sum == md5_sum)",
"def svn_fs_file_md5_checksum(*args):\r\n return _fs.svn_fs_file_md5_checksum(*args)",
"def test_source_package_checksum_changes(self):\n response = self.client.head(\n f'/... | [
"0.61215234",
"0.6010805",
"0.59985346",
"0.59703654",
"0.59538466",
"0.5940276",
"0.59365785",
"0.5863028",
"0.58563745",
"0.5855248",
"0.581167",
"0.5796914",
"0.5796634",
"0.57454133",
"0.57304496",
"0.5703791",
"0.5699861",
"0.56995535",
"0.5671011",
"0.5628634",
"0.56136... | 0.8076951 | 0 |
Guess based on the changes file and files being uploaded, whether a package is considered native of not ```changes_file``` The changes file to parse for the orig.tar (note the dsc file referenced must exist) | def is_native_package(self, changes_file):
for file in changes_file['Files']:
if file['name'].endswith('.diff.gz'):
return False
if file['name'].endswith(('.debian.tar.gz','.debian.tar.bz2','.debian.tar.xz')):
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_orig_tarball(self, changes_file):\n\n orig_name = None\n if not changes_file.get_dsc() or open(changes_file.get_dsc()) == None:\n return (orig_name, constants.ORIG_TARBALL_LOCATION_NOT_FOUND)\n\n dscfile = open(changes_file.get_dsc())\n dsc = deb822.Dsc(dscfile)\n ... | [
"0.7022202",
"0.5861582",
"0.5830605",
"0.57546717",
"0.5683719",
"0.5522598",
"0.55078954",
"0.5425292",
"0.5394608",
"0.53885627",
"0.537539",
"0.5327455",
"0.5298978",
"0.52786845",
"0.524849",
"0.51662666",
"0.5139674",
"0.51386774",
"0.5101697",
"0.508725",
"0.507584",
... | 0.7332381 | 0 |
Look to see whether there is an orig tarball present, if the dsc refers to one. This method returns a triple (filename, file_found, location_hint), returning the (expected) name of the original tarball, and whether it was found in the local repository ```changes_file``` The changes file to parse for the orig.tar (note ... | def find_orig_tarball(self, changes_file):
orig_name = None
if not changes_file.get_dsc() or open(changes_file.get_dsc()) == None:
return (orig_name, constants.ORIG_TARBALL_LOCATION_NOT_FOUND)
dscfile = open(changes_file.get_dsc())
dsc = deb822.Dsc(dscfile)
for file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_applicable(result: Result, original_file_dict, file_diff_dict):\n\n if len(result.affected_code) == 0:\n return 'The result is not associated with any source code.'\n\n filenames = set(src.renamed_file(file_diff_dict)\n for src in result.affected_code)\n ... | [
"0.5856133",
"0.5586155",
"0.5303869",
"0.52426755",
"0.5206382",
"0.5184854",
"0.5141354",
"0.5130958",
"0.509906",
"0.506888",
"0.5050227",
"0.50488764",
"0.49886075",
"0.49809384",
"0.49780205",
"0.49768654",
"0.49239424",
"0.4915758",
"0.48938805",
"0.48809808",
"0.487459... | 0.806148 | 0 |
Returns all unique paths for files associated with the supplied package version ```packageversion``` The package version to be scanned ```absolute_path``` if set to True, returns the absolute path instead of a path relative to the repository root | def find_files_for_packageversion(self, packageversion, absolute_path=False):
package_files = []
for attr in ('binary_packages', 'source_packages'):
if hasattr(packageversion, attr):
for bp in getattr(packageversion, attr):
for files in bp.package_files:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_files_for_package(self, package, absolute_path=False):\n package_files = []\n for p in package.package_versions:\n package_files.extend(self.find_files_for_packageversion(p, absolute_path))\n return package_files",
"def full_find(self, file, version):\n matches = [... | [
"0.6776081",
"0.57738966",
"0.57006",
"0.5643437",
"0.5551925",
"0.5528448",
"0.54549044",
"0.5375594",
"0.53727055",
"0.5351883",
"0.5307413",
"0.5246328",
"0.5221049",
"0.51959926",
"0.5160953",
"0.51521415",
"0.5149688",
"0.51018757",
"0.51011",
"0.5095191",
"0.5087868",
... | 0.7598108 | 0 |
Returns all unique paths for files associated with the supplied packages ```package``` The package to be scanned ```absolute_path``` if set to True, returns the absolute path instead of a path relative to the repository root | def find_files_for_package(self, package, absolute_path=False):
package_files = []
for p in package.package_versions:
package_files.extend(self.find_files_for_packageversion(p, absolute_path))
return package_files | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_files_for_packageversion(self, packageversion, absolute_path=False):\n package_files = []\n for attr in ('binary_packages', 'source_packages'):\n if hasattr(packageversion, attr):\n for bp in getattr(packageversion, attr):\n for files in bp.packag... | [
"0.65744954",
"0.6538738",
"0.6327214",
"0.60860723",
"0.6070535",
"0.5906358",
"0.5833705",
"0.5832187",
"0.58272886",
"0.58078057",
"0.5749993",
"0.57397157",
"0.57397157",
"0.57397157",
"0.5738294",
"0.56959873",
"0.565308",
"0.563047",
"0.56227636",
"0.56075794",
"0.55792... | 0.7131917 | 0 |
Removes all files associated with the package version supplied ```packageversion``` PackageVersion object whose files are supposed to be removed | def delete_files_for_packageversion(self, packageversion):
files = self.find_files_for_packageversion(packageversion, absolute_path=True)
if not files:
return
path = os.path.dirname(files[0])
for file in files:
if os.path.exists(file):
log.debu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_files_for_package(self, package):\n files = self.find_files_for_package(package, absolute_path=True)\n if not files:\n return\n path = os.path.dirname(files[0])\n for file in files:\n if os.path.exists(file):\n log.debug(\"Removing fil... | [
"0.7006803",
"0.6729492",
"0.66909456",
"0.62634903",
"0.62541205",
"0.6170387",
"0.61650103",
"0.6109658",
"0.6072664",
"0.6071761",
"0.6042147",
"0.600773",
"0.60031897",
"0.5883982",
"0.58733577",
"0.5864497",
"0.58045745",
"0.57851744",
"0.57476383",
"0.57243997",
"0.5702... | 0.8144159 | 0 |
Removes all files associated with the package supplied ```package``` package object whose files are supposed to be removed | def delete_files_for_package(self, package):
files = self.find_files_for_package(package, absolute_path=True)
if not files:
return
path = os.path.dirname(files[0])
for file in files:
if os.path.exists(file):
log.debug("Removing file '%s'" % (fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_packages(self, packages):",
"def clean(self) -> None:\n # remove all *.py and *.pyi files in the folder\n for wc in [\"*.py\", \"*.pyi\", \"modules.json\"]:\n for f in (self.package_path).rglob(wc):\n f.unlink()",
"def remove_module_files(self, base_dir):\n ... | [
"0.7546989",
"0.73516136",
"0.71049297",
"0.70153433",
"0.7001211",
"0.6886907",
"0.68704784",
"0.6867759",
"0.6712021",
"0.66684604",
"0.6525831",
"0.6513985",
"0.64653736",
"0.6430455",
"0.641628",
"0.63933665",
"0.6354628",
"0.63250524",
"0.63172334",
"0.6308882",
"0.62891... | 0.7580052 | 0 |
Looks at a filename's extension and decides whether to accept it. We only want package files to be uploaded, after all. It returns a boolean of whether to accept the file or not. ``filename`` File to test. | def allowed_upload(self, filename):
for suffix in ['.changes', '.dsc', '.tar.gz', '.diff.gz', '.deb', '.udeb', '.tar.bz2', ".tar.xz"]:
if filename.endswith(suffix):
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allowed_file(filename):\n \n if not '.' in filename:\n return False\n \n ext = filename.rsplit('.', 1)[1]\n \n if ext.lower() in ALLOWED_FILE_EXTENSIONS:\n return True\n else:\n return False",
"def allowed_file(filename: str) -> bool:\n if filename is None or file... | [
"0.82098633",
"0.7922775",
"0.790487",
"0.787731",
"0.7795147",
"0.7711532",
"0.7709475",
"0.7709475",
"0.77041215",
"0.77041215",
"0.76879025",
"0.76774913",
"0.76774913",
"0.76774913",
"0.76774913",
"0.76774913",
"0.76774913",
"0.76774913",
"0.76774913",
"0.76774913",
"0.76... | 0.74530524 | 36 |
Sets the personal available time slot in calendar | def set_available_time_slot():
if request.content_type != 'application/json':
error = json.dumps({'error': 'Invalid Content Type'})
return make_response(error, 400, InterviewCalendarApi.HEADERS)
data = request.json
# For Temporary purpose, stored in flat file database
with open(InterviewCal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_time_available(self, new_value):\n\n self.available_at = new_value\n self.save()",
"def time_slot(self):\n pass",
"def timeslot(self, timeslot: List[TimeslotTimeslot]):\n\n self._timeslot = timeslot",
"def choose_time_slot():\n req = request.json\n firebase_uid = req... | [
"0.6769007",
"0.6269106",
"0.6212529",
"0.61314505",
"0.6038733",
"0.6016706",
"0.59351456",
"0.5850808",
"0.580659",
"0.5770316",
"0.5760114",
"0.57599235",
"0.5728663",
"0.5717891",
"0.57096136",
"0.5688027",
"0.5682681",
"0.56790507",
"0.5677591",
"0.56367344",
"0.56291395... | 0.78393424 | 0 |
Gets the ideal time slot to arrange interview between different parties. | def get_available_time_slot():
try:
time_slot_set_list = list()
# Read all time slot from database
with open(InterviewCalendarApi.DB_FILE, "r") as fd:
for line in fd:
time_slot_list = list()
(_,_,_, time_slots) = line.strip().split("|")
for time_slot in time_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def time_slot(self):\n min_time = self.introduction_time or 0\n current_time = self.agent.evaluation_context.get_current_time()\n assert isinstance(current_time, Number)\n if current_time < min_time:\n return None\n num = divmod(current_time - min_time, self.__period)[... | [
"0.63423276",
"0.57650554",
"0.571941",
"0.5690644",
"0.5626049",
"0.55934006",
"0.5564088",
"0.55332726",
"0.5426856",
"0.542664",
"0.5384493",
"0.5384436",
"0.5369141",
"0.5359809",
"0.53586596",
"0.5353438",
"0.5322902",
"0.5312281",
"0.5306348",
"0.5242515",
"0.52214307",... | 0.5513882 | 8 |
Converts a timestamp into a human readable date format >>> convert_timestamp(1601104029) | def convert_timestamp():
ts = int(sys.argv[1])
human_readable_time = '%m/%d/%Y @ %I:%M %p (UTC)'
print(datetime.utcfromtimestamp(ts).strftime(human_readable_time)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_timestamp(stamp):\n date = datetime.fromtimestamp(float(stamp))\n return date.strftime(\"%m/%d/%y %I:%M:%S %p\")",
"def convert_timestamp_to_readable_date(timestamp: int) -> str:\n return datetime.utcfromtimestamp(timestamp).isoformat()",
"def convert_epoch_to_timestamp(cls, timestamp, tsf... | [
"0.76768744",
"0.7368898",
"0.714151",
"0.708612",
"0.7062975",
"0.6758006",
"0.67139393",
"0.6706629",
"0.6660264",
"0.6614045",
"0.66062516",
"0.65518653",
"0.64964134",
"0.6483565",
"0.6470719",
"0.6469389",
"0.6456289",
"0.6430389",
"0.64229417",
"0.63479745",
"0.62924296... | 0.71047884 | 3 |
update the current state based on action The working states, as oppose to 'idle' include that of 'pending', 'testing', 'countingDown', 'writing' | def update_state(self, act):
# check the checkbox logic
if act in ['follow', 'not_follow', 'locate', 'not_locate']:
self.check_locate_follow_logic(act)
# test/record logic
print("update function not implemented") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def take_action(self, state):",
"def doAction(self,state,action):\n self.lastState = state\n self.lastAction = action",
"def state_update(self, last_action, action):\n s_t = self._get_state(last_action)\n r= self._get_reward(last_action,action)\n self._move_user()\n s_t1 = sel... | [
"0.70931727",
"0.70630455",
"0.7048988",
"0.6953234",
"0.6851414",
"0.68310285",
"0.6781109",
"0.6740664",
"0.671901",
"0.666701",
"0.6620263",
"0.66010344",
"0.6586356",
"0.6544292",
"0.65192556",
"0.6504407",
"0.64934057",
"0.6481681",
"0.6471958",
"0.6464481",
"0.645273",
... | 0.67353684 | 8 |
can only choose one | def check_locate_follow_logic(self, act):
if act == 'freq_low':
# if locate is chosen, remove it
if 'freq_high' in self.state:
self.state.remove('freq_high')
self.state.append(act)
elif act == 'not_freq_low':
if 'freq_low' in self.state:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decide():",
"def choose(self, choice):\n if self.available(choice):\n self.select(choice)",
"def _choose_best_option(self):",
"def choose(self, event):\n return self.can(event)[0]",
"def select(self):\r\n pass",
"def select(self):\n pass",
"def select(self):\n... | [
"0.66341513",
"0.6275091",
"0.625221",
"0.6161598",
"0.61244404",
"0.5988319",
"0.5988319",
"0.5933216",
"0.5883137",
"0.5878196",
"0.5851665",
"0.58068585",
"0.5729175",
"0.5729175",
"0.5727388",
"0.5708148",
"0.5706344",
"0.568923",
"0.56353563",
"0.5628979",
"0.56164134",
... | 0.0 | -1 |
Parse a date string and convert to a naive ``datetime`` object in UTC | def parse(date_string: str):
# parse the date string
date = dateutil.parser.parse(date_string)
# convert to UTC if containing time-zone information
# then drop the timezone information to prevent unsupported errors
if date.tzinfo:
date = date.astimezone(dateutil.tz.UTC).replace(tzinfo=None)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_date(s):\n return parse(s).astimezone(pytz.utc)",
"def parse_datetime(datestr):\r\n try:\r\n return dateutil.parser.parse(datestr).replace(tzinfo=utc)\r\n except ValueError:\r\n raise DashboardError(_(\"Unable to parse date: \") + datestr)",
"def parse_datetime(date_string):\n... | [
"0.7907918",
"0.7845481",
"0.76971775",
"0.7675037",
"0.7473908",
"0.7440517",
"0.735506",
"0.7350854",
"0.72948474",
"0.72435045",
"0.7241617",
"0.72313863",
"0.7218996",
"0.7172882",
"0.71403486",
"0.71389437",
"0.71222574",
"0.71187925",
"0.71035874",
"0.70936656",
"0.7073... | 0.8085187 | 1 |
Parse a date string of the form | def parse_date_string(date_string: str):
# try parsing the original date string as a date
try:
epoch = parse(date_string)
except ValueError:
pass
else:
# return the epoch (as list)
return (datetime_to_list(epoch), 0.0)
# split the date string into units and epoch
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def date_parse(date_string) -> datetime:\n return datetime.strptime(date_string, DATE_FMT)",
"def parse_date(str_date):\n return ciso8601.parse_datetime(str_date)",
"def parse(self, str):\n values = self._exp.findall(str)\n if values is None or len(values) == 0:\n return None\n\n... | [
"0.7754825",
"0.7681746",
"0.7619202",
"0.7595637",
"0.7554202",
"0.7522775",
"0.7512194",
"0.7293273",
"0.7285541",
"0.7257252",
"0.7254006",
"0.7244446",
"0.7221628",
"0.7219147",
"0.72051275",
"0.720416",
"0.7189553",
"0.71871907",
"0.71790546",
"0.71494836",
"0.7117056",
... | 0.6474711 | 81 |
Split a date string into units and epoch | def split_date_string(date_string: str):
try:
units,_,epoch = date_string.split(None, 2)
except ValueError:
raise ValueError(f'Invalid format: {date_string}')
else:
return (units.lower(), parse(epoch)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_date_string(date_string: str):\n # try parsing the original date string as a date\n try:\n epoch = parse(date_string)\n except ValueError:\n pass\n else:\n # return the epoch (as list)\n return (datetime_to_list(epoch), 0.0)\n # split the date string into units ... | [
"0.725681",
"0.72525626",
"0.6715245",
"0.64701724",
"0.6382786",
"0.619452",
"0.61797726",
"0.6074394",
"0.6071968",
"0.60437346",
"0.601686",
"0.6001186",
"0.59548134",
"0.5884169",
"0.5833305",
"0.58276343",
"0.5821117",
"0.581139",
"0.5810008",
"0.5795588",
"0.57780796",
... | 0.7802953 | 1 |
Convert a ``datetime`` object into a list | def datetime_to_list(date):
return [date.year, date.month, date.day,
date.hour, date.minute, date.second] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_datetime_objs(list_of_dates):\n datetime_list = []\n for date in list_of_dates:\n date_obj = datetime.datetime.strptime(date, '%d.%m.%Y')\n datetime_list.append(date_obj)\n return datetime_list",
"def deconstruct_datetime(self, date: datetime) -> List[int]:\n year, month... | [
"0.72293544",
"0.7085231",
"0.70122313",
"0.64888054",
"0.6427361",
"0.6376207",
"0.62667364",
"0.6259938",
"0.62293786",
"0.61688745",
"0.6106284",
"0.6053032",
"0.6039443",
"0.6039443",
"0.6039443",
"0.6039443",
"0.602236",
"0.60170233",
"0.601256",
"0.5993775",
"0.59911925... | 0.8505954 | 1 |
Calculates the number of days per month for a given year | def calendar_days(year: int | float | np.ndarray) -> np.ndarray:
# Rules in the Gregorian calendar for a year to be a leap year:
# divisible by 4, but not by 100 unless divisible by 400
# True length of the year is about 365.2422 days
# Adding a leap day every four years ==> average 365.25
# Subtrac... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def numDays(month, year):\n\tif month in [9, 4, 6, 11]:\n\t\treturn 30\n\telif month == 2 and year % 4 == 0 and (year % 100 != 0 or year % 400 == 0):\n\t\treturn 29\n\telif month == 2:\n\t\treturn 28\n\telse:\n\t\treturn 31",
"def dayInYear(month, day, year):\n current = 1\n numberOfDays = day\n while (... | [
"0.7755178",
"0.77529544",
"0.73326933",
"0.7312692",
"0.71782887",
"0.71756566",
"0.71731204",
"0.7086364",
"0.69122785",
"0.6904365",
"0.68112487",
"0.67785037",
"0.6743583",
"0.6650364",
"0.662489",
"0.6611992",
"0.6603074",
"0.6593793",
"0.65065837",
"0.64749384",
"0.6471... | 0.7056173 | 9 |
Convert a ``numpy`` ``datetime`` array to seconds since ``epoch`` | def convert_datetime(
date: float | np.ndarray,
epoch: str | tuple | list | np.datetime64 = _unix_epoch
):
# convert epoch to datetime variables
if isinstance(epoch, (tuple, list)):
epoch = np.datetime64(datetime.datetime(*epoch))
elif isinstance(epoch, str):
epoch = np.d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ts_to_epoch_seconds(t) -> float:\n return t.astype(int) / 1e9",
"def epoch2datetime(t):\n return datetime.fromtimestamp(t/1000.0)",
"def seconds_since_epoch(date_time, epoch=None):\n return microseconds_since_epoch(date_time) / 10.0**6",
"def epoch_seconds(date):\n td = date - epoch\n retu... | [
"0.7119384",
"0.70471823",
"0.67733616",
"0.6677283",
"0.6677283",
"0.66770333",
"0.65791225",
"0.6510859",
"0.64249825",
"0.62467736",
"0.62348825",
"0.62348825",
"0.61904925",
"0.6188793",
"0.6172504",
"0.5990658",
"0.5972928",
"0.5956192",
"0.59386444",
"0.59345615",
"0.59... | 0.72007585 | 1 |
Convert delta time from seconds since ``epoch1`` to time since ``epoch2`` | def convert_delta_time(
delta_time: np.ndarray,
epoch1: str | tuple | list | np.datetime64 | None = None,
epoch2: str | tuple | list | np.datetime64 | None = None,
scale: float = 1.0
):
# convert epochs to datetime variables
if isinstance(epoch1, (tuple, list)):
epoch... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def epoch2time(time):\n\tvalue = datetime.datetime.fromtimestamp(time)\n\tNormal = value.strftime('%Y-%m-%d %H:%M:%S')\n\tprint(normal)\n\treturn normal",
"def pcr_delta_time_ms(pcr_t1, pcr_t2, offset = 0):\n return float(pcr_t2-pcr_t1)/90000.0 + offset",
"def to_deltatime(self,\n epoch: str | tu... | [
"0.65060514",
"0.6424808",
"0.6152584",
"0.60279953",
"0.59376085",
"0.5886172",
"0.5869834",
"0.58575207",
"0.5820404",
"0.58052814",
"0.5757795",
"0.57485944",
"0.56638455",
"0.56527215",
"0.56485754",
"0.56349045",
"0.56349045",
"0.5627923",
"0.5625807",
"0.5594252",
"0.55... | 0.7332065 | 1 |
Calculate the time in time units since ``epoch`` from calendar dates | def convert_calendar_dates(
year: np.ndarray,
month: np.ndarray,
day: np.ndarray,
hour: np.ndarray | float = 0.0,
minute: np.ndarray | float = 0.0,
second: np.ndarray | float = 0.0,
epoch: tuple | list | np.datetime64 = _tide_epoch,
scale: float = 1.0
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def epoch_time(when):\n if not when: return 0\n epoch = datetime.utcfromtimestamp(0)\n delta = when - epoch\n return int(delta.total_seconds())",
"def seconds_since_epoch(date_time, epoch=None):\n return microseconds_since_epoch(date_time) / 10.0**6",
"def epoch_seconds(date):\r\n td = date -... | [
"0.65561664",
"0.6340998",
"0.63183016",
"0.6301214",
"0.6301214",
"0.62795186",
"0.6216668",
"0.62102425",
"0.61283773",
"0.6122344",
"0.61176836",
"0.6072185",
"0.6036625",
"0.59966165",
"0.59800243",
"0.5976523",
"0.59661543",
"0.5852806",
"0.57929045",
"0.5781751",
"0.577... | 0.5230787 | 57 |
Converts from calendar date into decimal years taking into account leap years | def convert_calendar_decimal(
year: np.ndarray,
month: np.ndarray,
day: np.ndarray,
hour: np.ndarray | float | None = None,
minute: np.ndarray | float | None = None,
second: np.ndarray | float | None = None,
DofY: np.ndarray | float | None = None,
) -> np.ndar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def days_to_years(datum):\n return datum/DAYS_PER_YEAR",
"def _days_before_year(year):\n y = year - 1\n return y * 365 + y // 4 - y // 100 + y // 400",
"def year(self) -> int:\n if self.is_old_style:\n yy = int(self.split('/', 1)[1][0:2])\n else:\n yy = int(self[:2])... | [
"0.82051677",
"0.7126889",
"0.71093416",
"0.71062267",
"0.70007455",
"0.695518",
"0.68835115",
"0.68746775",
"0.6820882",
"0.6783935",
"0.6728416",
"0.66622645",
"0.66086173",
"0.6600496",
"0.65989643",
"0.6592281",
"0.65849185",
"0.6562183",
"0.65444785",
"0.6497388",
"0.649... | 0.0 | -1 |
Converts from Julian day to calendar date and time | def convert_julian(JD: np.ndarray, **kwargs):
# set default keyword arguments
kwargs.setdefault('astype', None)
kwargs.setdefault('format', 'dict')
# raise warnings for deprecated keyword arguments
deprecated_keywords = dict(ASTYPE='astype', FORMAT='format')
for old,new in deprecated_keywords.it... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def JulianDay(self):\n a = (14 - self._month) // 12\n y = self._year + 4800 - a\n m = self._month + (12 * a) - 3\n return (self._day + (153 * m + 2) // 5 + 365 * y +\n y // 4 - y // 100 + y // 400 - 32045)",
"def _gregorian_to_julian_day(year, month, day):\n if month... | [
"0.6966466",
"0.6876087",
"0.6870243",
"0.68287796",
"0.67212313",
"0.6684041",
"0.6644245",
"0.664008",
"0.66230804",
"0.65871185",
"0.6576862",
"0.6546014",
"0.6470467",
"0.645271",
"0.645271",
"0.64524555",
"0.64404553",
"0.6387761",
"0.63858074",
"0.62827337",
"0.6244979"... | 0.6714327 | 6 |
Counts the number of leap seconds between a given GPS time and UTC | def count_leap_seconds(
GPS_Time: np.ndarray | float,
truncate: bool = True
):
# get the valid leap seconds
leaps = get_leap_seconds(truncate=truncate)
# number of leap seconds prior to GPS_Time
n_leaps = np.zeros_like(GPS_Time,dtype=np.float64)
for i,leap in enumerate(leaps):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def utctoweekseconds(utc, leapseconds=18):\n datetimeformat = \"%Y-%m-%d %H:%M:%S\"\n epoch = datetime.datetime.strptime(\"1980-01-06 00:00:00\", datetimeformat)\n tdiff = utc - epoch + datetime.timedelta(seconds=leapseconds)\n gpsweek = tdiff.days // 7\n gpsdays = tdiff.days - 7 * gpsweek\n gpss... | [
"0.652139",
"0.58384943",
"0.57290035",
"0.56942356",
"0.56746274",
"0.5595739",
"0.55098593",
"0.5501737",
"0.53909945",
"0.5376595",
"0.5324631",
"0.5324156",
"0.5290026",
"0.5261761",
"0.5184121",
"0.5171609",
"0.51585203",
"0.5151989",
"0.51178706",
"0.5116599",
"0.505507... | 0.7145605 | 1 |
Gets a list of GPS times for when leap seconds occurred | def get_leap_seconds(truncate: bool = True):
leap_secs = icesat2_toolkit.utilities.get_data_path(['data','leap-seconds.list'])
# find line with file expiration as delta time
with leap_secs.open(mode='r', encoding='utf8') as fid:
secs, = [re.findall(r'\d+',i).pop() for i in fid.read().splitlines()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_leap_seconds(truncate: bool = True):\n leap_secs = pyTMD.utilities.get_data_path(['data','leap-seconds.list'])\n # find line with file expiration as delta time\n with leap_secs.open(mode='r', encoding='utf8') as fid:\n secs, = [re.findall(r'\\d+',i).pop() for i in fid.read().splitlines()\n ... | [
"0.6662289",
"0.63633394",
"0.6226151",
"0.6171691",
"0.61506224",
"0.61460394",
"0.6143277",
"0.6093351",
"0.5990384",
"0.5958879",
"0.5958879",
"0.5958879",
"0.5945612",
"0.5896451",
"0.5885215",
"0.588292",
"0.58812433",
"0.5852047",
"0.581152",
"0.57902336",
"0.5767987",
... | 0.676572 | 0 |
Connects to servers to download leapseconds.list files from NIST servers | def update_leap_seconds(
timeout: int | None = 20,
verbose: bool = False,
mode: oct = 0o775
):
# local version of file
FILE = 'leap-seconds.list'
LOCAL = icesat2_toolkit.utilities.get_data_path(['data',FILE])
HASH = icesat2_toolkit.utilities.get_hash(LOCAL)
# try downloa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createFileListHTTP(self, server, directory):\r\n conn = httplib.HTTPConnection(server)\r\n conn.request(\"GET\",directory)\r\n r1 = conn.getresponse()\r\n '''if r1.status==200:\r\n print \"status200 received ok\"\r\n else:\r\n print \"oh no = status=%d %... | [
"0.630811",
"0.5879872",
"0.5738498",
"0.5700458",
"0.56712866",
"0.5523393",
"0.5522576",
"0.54847145",
"0.54457563",
"0.54361594",
"0.5377989",
"0.5363726",
"0.5337887",
"0.5322642",
"0.5293564",
"0.52784735",
"0.5269241",
"0.52584785",
"0.52491146",
"0.5248196",
"0.5207827... | 0.572799 | 3 |
generate label vectors from collected images | def get_label_vectors():
print("Retrieving label vectors...")
label_dict = {} # instantiate dict for labels:vectors
categories = sorted([c for c in os.listdir('images/') if c[0] != '.']) # ignore hidden files
x = np.zeros(len(categories)) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_labels(pics):\r\n return []",
"def extract_labels(filename, num_images, starting_id, context_factor):\n gt_imgs = []\n for i in range(starting_id, num_images+starting_id):\n imageid = \"satImage_%.3d\" % i\n image_filename = filename + imageid + \".png\"\n if os.path.is... | [
"0.7303702",
"0.7006394",
"0.68937486",
"0.6873022",
"0.68646514",
"0.6803414",
"0.6724581",
"0.6620808",
"0.66204023",
"0.6591786",
"0.6567913",
"0.65644395",
"0.65557486",
"0.6555063",
"0.6541309",
"0.6527798",
"0.65208435",
"0.65085703",
"0.6501463",
"0.6480988",
"0.646566... | 0.78073823 | 0 |
load image data from collected categories | def load_image_data():
print("Loading image data...")
label_dict = get_label_vectors()
categories = [c for c in os.listdir('images/') if c[0] != '.'] # ignore
labels = [] # instantiate list for image labels
data = [] ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prep_image_data(arg_dict):\n cat_df = pd.read_csv(arg_dict['category_file'],\n skiprows=1,\n sep='\\s+')\n bbox_df = pd.read_csv(arg_dict['bbox_file'],\n skiprows=1,\n sep='\\s+')\n img_dir = arg_dict['im... | [
"0.73744714",
"0.70992035",
"0.6866402",
"0.6855467",
"0.67352",
"0.67105335",
"0.6656234",
"0.66058004",
"0.65050745",
"0.65049785",
"0.6448114",
"0.6420186",
"0.6376639",
"0.63688254",
"0.6314507",
"0.63099957",
"0.6244066",
"0.6235248",
"0.62325114",
"0.6227874",
"0.621148... | 0.75711864 | 0 |
main method of image_mgmt.py | def main():
labels, data = load_image_data()
print(labels.shape, data.shape) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n images = Images()\n #print images.create_image_urls()\n print images.get_image_random()\n print images.get_image(12)",
"def main():\n \n # for inserting other images, add tem to /input folder and list them here\n images = (\n 'image-0',\n 'image-1',\n 'image-2... | [
"0.7724663",
"0.7417523",
"0.72567064",
"0.7056425",
"0.6892278",
"0.67639",
"0.66935354",
"0.6682261",
"0.66802955",
"0.66739655",
"0.6670762",
"0.6670762",
"0.6670762",
"0.6636167",
"0.65970665",
"0.6594695",
"0.65808475",
"0.657597",
"0.6567467",
"0.6554589",
"0.6533119",
... | 0.65013987 | 23 |
Detect posedge or negedge on input signal, default to posedge(mode=0) | def edge_detect(
clk,
rst_n,
din,
valid,
mode=0
):
din_r = UBool(0)
@always_ff(clk.posedge, reset=rst_n)
def _latch():
din_r.next = din
# TODO: conditional parameter on generate
@always_comb
def _valid():
if mode == 0:
valid.next = not din and d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sensed(self):\n return not self.pin.read() if self.pinishigh else self.pin.read()",
"async def handle_less(message: types.Message):\n await handle_change_threshold(message, 1 / 1.5)",
"def nearest_test_pulse(self):",
"def is_ringing(self) -> bool:",
"def forward(self, input):\r\n retur... | [
"0.5264356",
"0.5113618",
"0.5000561",
"0.49808434",
"0.49234867",
"0.49224997",
"0.49052876",
"0.4896278",
"0.4894377",
"0.4853233",
"0.47944462",
"0.47820812",
"0.47796014",
"0.47683772",
"0.47345206",
"0.47341648",
"0.47143006",
"0.4695682",
"0.46904302",
"0.4684524",
"0.4... | 0.0 | -1 |
Binary to onehot conversion | def bin2ohot(din, dout):
IN_W = len(din)
OUT_W = 2**IN_W
@always_comb
def _conv():
for i in range(OUT_W):
if din == i:
dout[i] = 1
else:
dout[i] = 0
return instances() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binary_to_one_hot(arr: np.ndarray) -> np.ndarray:\n res = np.zeros((arr.shape[0], 2))\n res[np.where(arr == 1)[0], 0] = 1\n res[np.where(arr == 0)[0], 1] = 1\n return res",
"def one_hot_encode(x):\n # TODO: Implement Function\n output = np.zeros((len(x), 10))\n \n for i, j in enumerat... | [
"0.8336464",
"0.8243893",
"0.8077957",
"0.8006649",
"0.792823",
"0.79020256",
"0.7887456",
"0.7865496",
"0.78533834",
"0.7835221",
"0.7825861",
"0.7818072",
"0.7791989",
"0.7786955",
"0.77542466",
"0.77538735",
"0.77049476",
"0.7704875",
"0.76757723",
"0.76493716",
"0.7637535... | 0.0 | -1 |
Generates a bot object | def __init__(self, order, timer):
super().__init__(order)
self.name = 'Bot' + str(order + 1)
self.is_checked = False
self.timer = timer
# vpip, pfr and agg stored as integer% (/ 100)
# TODO
# Dictionary?
# Translate to playable hand ratings
self.vp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, bot=BNBot):\n self.bot = bot",
"def __init__(self, bot):\n self.bot = bot",
"def __init__(self, bot):\n self.bot = bot",
"def bot_factory(url):\n from dallinger_experiment.experiment import Bot\n\n return Bot(url)",
"def makebot(self, name, cfg=None):\n\n ... | [
"0.6956568",
"0.6869598",
"0.6869598",
"0.6731655",
"0.66259605",
"0.6554135",
"0.65377176",
"0.65377176",
"0.6139103",
"0.60624367",
"0.6005556",
"0.58768237",
"0.5853435",
"0.58470196",
"0.5823871",
"0.57866377",
"0.5736363",
"0.5711528",
"0.56844354",
"0.5640654",
"0.56065... | 0.0 | -1 |
Decides whether to bet or fold at this point in time | def bet_fold_algorithm(self, current_top_bid,
pot_size, community_cards, position, number_of_players, is_first_cycle, total_value):
# If pre-flop
if len(community_cards) == 0:
rank = self.hand.opening_hand_ranking()
# Cut-off rank = 0.00 right now
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fold(self):\n self.active = False\n self.hand = None\n self.bet = 0\n return True",
"def get_learn_after_each_decision(self):\r\n return 0",
"def notFold(self):\n return not self._fold",
"def rough_outcome(self) -> float:\n # HUYNH YOU PRICK WHY THE FUCK D... | [
"0.72265923",
"0.6000676",
"0.5944416",
"0.56944317",
"0.5582698",
"0.5582698",
"0.5582698",
"0.55106276",
"0.5448707",
"0.54409707",
"0.54199135",
"0.5411135",
"0.5394675",
"0.5384363",
"0.53631586",
"0.536092",
"0.5355963",
"0.5352496",
"0.5329533",
"0.5312051",
"0.530132",... | 0.61763084 | 1 |
Decides amount to bet | def amount_algorithm(self, current_top_bid, pot_size,
community_cards, position, number_of_players, is_first_cycle, total_value):
# If pre-flop
if len(community_cards) == 0:
if is_first_cycle is False:
return current_top_bid - self.current_bet
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bet(self, amount):\n if amount >self.budget:\n print 'you cannot bet because of little money'\n else:\n self.bet_amount = amount\n print 'you bet %s' % (amount)",
"def get_bet(self) -> int:\n return int(input(f\"How much money would you like to place? \"\... | [
"0.80275404",
"0.7679025",
"0.7675108",
"0.7609656",
"0.74898267",
"0.7448841",
"0.733221",
"0.7229419",
"0.69224185",
"0.69190437",
"0.69124156",
"0.6872131",
"0.68597424",
"0.67960364",
"0.6715273",
"0.66506547",
"0.6604676",
"0.6566986",
"0.65195584",
"0.65036714",
"0.6471... | 0.66638166 | 15 |
Used to remove any possible empty module params passed in from a task file. | def filter_empty_subparams(self, param_name):
param = self.module.params.get(param_name)
filtered = []
if isinstance(param, list):
for subparam in param:
if isinstance(subparam, dict):
filtered.append(
dict(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_parameters(self):\n self.parameters = []",
"def purge(root=os.path.join(\"~\", \".daisykit\", \"assets\")):\n root = os.path.expanduser(root)\n files = os.listdir(root)\n for f in files:\n if f.endswith(\".params\"):\n os.remove(os.path.join(root, f))",
"def buildWi... | [
"0.57616585",
"0.5588865",
"0.5586321",
"0.5513312",
"0.5493854",
"0.5449779",
"0.54313815",
"0.5419195",
"0.53861827",
"0.53727466",
"0.53104013",
"0.53044254",
"0.52886343",
"0.5246557",
"0.52453816",
"0.52363384",
"0.52314204",
"0.51972383",
"0.51937133",
"0.5189336",
"0.5... | 0.5294682 | 12 |
Sets up other parameters for the api call that may not be specified in the modules from tasks file. | def api_params(self):
params = dict(
(key, self.module.params.get(key))
for key, value in RECORD_KEYS_MAP.items()
if key != "answers" and self.module.params.get(key) is not None
)
return params | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_params():\n global module \n global ora_inst\n global response_loc\n\n module_args=dict(\n ora_inst=dict(type='str', required=True),\n response_loc=dict(type='str', required=True)\n )\n\n module=AnsibleModule(\n argument_spec=module_args\n )\n\n ora_inst = modul... | [
"0.6986048",
"0.64589137",
"0.63455117",
"0.62272775",
"0.60726595",
"0.6069171",
"0.5985259",
"0.5889219",
"0.5888519",
"0.5883398",
"0.58747536",
"0.58570075",
"0.5845327",
"0.5829871",
"0.5793331",
"0.5773644",
"0.5773644",
"0.57736164",
"0.5762372",
"0.5761211",
"0.573227... | 0.0 | -1 |
Remove fields from the APIreturned record that we don't want to pass back, or consider when diffing. | def sanitize_record(self, record):
def remove_ids(d):
if isinstance(d, dict):
if 'id' in d:
del d['id']
for key, val in d.items():
if isinstance(val, (dict, list)):
remove_ids(val)
if isinstan... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strip_unwanted_fields(self, data, many, **kwargs):\n unwanted_fields = [\"resource_type\"]\n for field in unwanted_fields:\n if field in data:\n data.pop(field)\n return data",
"def prune(record: cfg.OpenAPI, outformat: cfg.Format = None) -> NoReturn:\n conte... | [
"0.6754392",
"0.66556805",
"0.62742776",
"0.62539446",
"0.6249638",
"0.6215634",
"0.6152936",
"0.60918176",
"0.60912836",
"0.6078683",
"0.602921",
"0.59926665",
"0.59876364",
"0.59813493",
"0.5973602",
"0.5944134",
"0.5934237",
"0.5881215",
"0.58473516",
"0.5843913",
"0.58421... | 0.5617131 | 33 |
Used to get the zone associated with the record being worked on. | def get_zone(self):
to_return = None
try:
to_return = self.ns1.loadZone(self.module.params.get('zone'))
except ResourceException as re:
if re.response.code == 404:
if (
self.module.params.get('ignore_missing_zone')
a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zone(self):\n return self._zone",
"def get_zone(self):\n return self.project.get_flow().get_zone_of_object(self)",
"def zone(self) -> str:\n return pulumi.get(self, \"zone\")",
"def zone(self) -> str:\n return pulumi.get(self, \"zone\")",
"def zone(self) -> str:\n ret... | [
"0.80801183",
"0.80179244",
"0.78354365",
"0.78354365",
"0.7710645",
"0.7473004",
"0.7473004",
"0.7236832",
"0.7219368",
"0.7201566",
"0.7194352",
"0.71658826",
"0.7155139",
"0.7087766",
"0.7087766",
"0.7087766",
"0.7087766",
"0.7087766",
"0.7074198",
"0.70047337",
"0.6966433... | 0.710795 | 13 |
Used to look up the record name and type from the specified zone. | def get_record(self, zone):
to_return = None
try:
to_return = zone.loadRecord(self.module.params.get('name'),
self.module.params.get('type').upper())
except ResourceException as re:
if re.response.code != 404:
self.m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_r53_record_by_name ( route53_conn, dns_name, record_type = 'A' ) :\n dns_name = dns_name.lower( )\n zone_info = route53_conn.get_hosted_zone( route_53_hosted_zoneid )\n zone_name = zone_info[ 'GetHostedZoneResponse' ][ 'HostedZone' ][ 'Name' ]\n zone = route53_conn.get_zone( zone_name )\n return ... | [
"0.684966",
"0.6636891",
"0.6613396",
"0.6532525",
"0.64089847",
"0.6375922",
"0.6265335",
"0.6205971",
"0.61214995",
"0.6064319",
"0.6040061",
"0.60173184",
"0.60097045",
"0.5822029",
"0.5814325",
"0.5788017",
"0.5685655",
"0.56776536",
"0.56634295",
"0.56493497",
"0.5640705... | 0.74746597 | 0 |
Used to handle records with values changing. | def update(self, record):
# clean copy of record to preserve IDs for response if no update required
record_data = self.sanitize_record(copy.deepcopy(record.data))
changed = False
args = {}
for key in RECORD_KEYS_MAP:
input_data = self.filter_empty_subparams(key)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_record(self, record):\n raise NotImplementedError",
"def process_record(self, record):\n raise NotImplementedError('Process record needs to be customized')",
"def visit_record(self, syrecord):\n for other_key, other_value in syrecord.items():\n try:\n g... | [
"0.6425481",
"0.60396576",
"0.5966757",
"0.5945433",
"0.58991665",
"0.58991665",
"0.58991665",
"0.5884155",
"0.58602935",
"0.58449084",
"0.57962346",
"0.57869387",
"0.5782773",
"0.57684565",
"0.57654285",
"0.5758463",
"0.57228583",
"0.57219404",
"0.5706814",
"0.5650283",
"0.5... | 0.54137516 | 40 |
Central exit point for the module. | def record_exit(self, after=None, before=None, changed=None, record=None):
# convert dictionaries to yaml txt dump
before_yaml = yaml.safe_dump(before, default_flow_style=False)
after_yaml = yaml.safe_dump(after, default_flow_style=False)
# build the final dict to pass into exit_json
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exit(self):\n pass",
"def __exit(self, *args):\n sys.exit(0)",
"def exit(self):\n super(Agent, self).exit()\n\n if self._module is not None:\n self._module.exit()",
"def __exit__(self, *args):\n pass",
"def exit(self):\n logger.debug(\"EXIT\")",
"def... | [
"0.7896138",
"0.78592885",
"0.77071196",
"0.7696922",
"0.7685185",
"0.756464",
"0.7539602",
"0.7297987",
"0.7291299",
"0.7278677",
"0.7160537",
"0.714784",
"0.71462065",
"0.7087686",
"0.7087392",
"0.70799416",
"0.70055544",
"0.69983804",
"0.6993124",
"0.6984299",
"0.69821894"... | 0.0 | -1 |
Method called by main to handle record state logic handling. | def exec_module(self):
state = self.module.params.get('state')
zone = self.get_zone()
if zone is None:
self.module.fail_json(msg='zone "{}" not found'.format(
self.module.params.get('zone')
))
record = self.get_record(zone)
# record found
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_record(self, record):\n raise NotImplementedError",
"def process_record(self, record):\n raise NotImplementedError('Process record needs to be customized')",
"def _post_init(self):\n self.set_new_record_state(False)",
"def record(self, state):\r\n\r\n if state: # Start... | [
"0.6171262",
"0.6028878",
"0.5895539",
"0.5820173",
"0.57114506",
"0.5640522",
"0.56325686",
"0.5569406",
"0.55691856",
"0.5568817",
"0.555975",
"0.5550883",
"0.55424047",
"0.5522525",
"0.5502867",
"0.54969925",
"0.5492289",
"0.5486894",
"0.5483148",
"0.54682165",
"0.54612875... | 0.51843923 | 45 |
write an NPZ file (Numpy binary files) containing only the preprocessed data needed for running Component Contribution estimations | def save_equilibrator_bin_data(self, npz_file_name):
preprocess_dict = {'cids': self.params['cids']}
for k, v in self.params.items():
if k.find('preprocess_') != -1:
preprocess_dict[k.replace('preprocess_', '')] = v
np.savez_compressed(npz_file_name, **preprocess_dict... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate(self):\n super().generate()\n records = random.random((self._dimension, self._dimension, self.num_samples))\n record_labels = [0] * self.num_samples\n prev_out_spec =\"\"\n count = 0\n for i in range(0, int(self.num_files)):\n if i % self.comm_size ... | [
"0.66370714",
"0.65234995",
"0.63456404",
"0.6172922",
"0.6125077",
"0.60102993",
"0.59520173",
"0.5935673",
"0.59053946",
"0.58978695",
"0.58922994",
"0.5873275",
"0.58675355",
"0.58388406",
"0.58206254",
"0.5806083",
"0.57888585",
"0.57863545",
"0.57822645",
"0.5781454",
"0... | 0.70493346 | 0 |
Returns the chemical formation energy of the major MS at pH 7. If the compound is part of the training set, returns the value that was calculated during training. Otherwise, we use pure group contribution (if possible) on the groups of the major MS. | def get_major_ms_dG0_f(self, compound_id):
if compound_id is None:
raise ValueError('given compound ID is None')
if self.params is None:
self.train()
if compound_id in self.cids:
i = self.cids.index(compound_id)
return self.params['dG0_cc'][i, 0]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_energy(self):\n return self.momentum*self.momentum/(2*self.mass)",
"def get_hoomd_energy(key, hoomd_force_groups, calc_group):\n\n total_energy = 0\n total_energy = sum([a.get_energy(calc_group) \n for a in hoomd_force_groups[key]])\n return total_energy",
"def co... | [
"0.620082",
"0.5894757",
"0.58618146",
"0.5841101",
"0.5817328",
"0.578734",
"0.5772486",
"0.57676405",
"0.5644884",
"0.5638012",
"0.56097746",
"0.5535166",
"0.5525179",
"0.5514855",
"0.5491314",
"0.54726696",
"0.5452643",
"0.5449478",
"0.54388803",
"0.54244787",
"0.54218894"... | 0.591733 | 1 |
adds the componentcontribution estimation to the JSON | def to_dict(self, compound_id):
if compound_id is None:
raise ValueError('given compound ID is None')
if self.params is None:
self.train()
comp = ccache.get_compound(compound_id)
d = {'compound_id': compound_id, 'inchi_key': comp.inchi_key}
gv = None
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, contribution):\n self._contribution = contribution",
"def add_comp(self, component, mole_fraction=np.nan):\n self['pore.mole_fraction.' + component.name] = mole_fraction",
"def execute_contribution(self):\n contributions = [v for v in self.action_register if v['action'] ... | [
"0.5488856",
"0.546339",
"0.5305696",
"0.524301",
"0.5230317",
"0.5165457",
"0.51322114",
"0.5111415",
"0.50932914",
"0.5080458",
"0.50602216",
"0.503721",
"0.50131637",
"0.4996776",
"0.499471",
"0.497496",
"0.49216732",
"0.4919991",
"0.48574156",
"0.48565018",
"0.4780565",
... | 0.46383762 | 30 |
Initialize G matrix, and then use the python script "inchi2gv.py" to decompose each of the compounds that has an InChI and save the decomposition as a row in the G matrix. | def create_group_incidence_matrix(self):
gv_data = []
# decompose the compounds in the training_data and add to G
for compound_id in self.cids:
smiles = ccache.get_compound(compound_id).smiles
try:
gv_data.append(
list(self.decompo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def invcell2Gmat(invcell):\n G = fillgmat(invcell)\n g = nl.inv(G)\n return G, g",
"def complete_disp(IBC, nodes, UG):\r\n nnodes = nodes.shape[0]\r\n UC = np.zeros([nnodes, 3], dtype=np.float)\r\n for row in range(nnodes):\r\n for col in range(3):\r\n cons = IBC[row, col]\r\n... | [
"0.59091425",
"0.57651687",
"0.5696586",
"0.5588323",
"0.5519537",
"0.54216594",
"0.54123414",
"0.538351",
"0.5372897",
"0.5361422",
"0.5356955",
"0.5349356",
"0.53209347",
"0.53064805",
"0.5252595",
"0.5252",
"0.5212284",
"0.52063763",
"0.51806784",
"0.51663315",
"0.5142872"... | 0.5412369 | 6 |
Estimate standard Gibbs energies of formation | def train(S, G, b, w):
assert type(S) == np.ndarray
assert type(G) == np.ndarray
assert type(b) == np.ndarray
assert type(w) == np.ndarray
m, n = S.shape
assert G.shape[0] == m
assert b.shape == (n,)
assert w.shape == (n,)
# Apply weighing
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bse_fe(self):\n p = self.model.exog.shape[1]\n return np.sqrt(np.diag(self.cov_params())[0:p])",
"def gibbs_init(self, sigma2_s_param=None, sigma2_g_param=None):\n #Gibbs : Initialization step\n self.gibbs_init_step(self.nb_days, self.nb_particles, sigma2_s_param, sigma2_g_param)\... | [
"0.6456396",
"0.6389554",
"0.6329877",
"0.62599355",
"0.6148615",
"0.60690296",
"0.60667825",
"0.60188264",
"0.5966948",
"0.59658873",
"0.5957548",
"0.595523",
"0.5916523",
"0.5912427",
"0.58843017",
"0.58783764",
"0.58764386",
"0.5825013",
"0.5818016",
"0.57995355",
"0.57661... | 0.0 | -1 |
Test for correct instantiation given a set of players. | def test_init(players, clean):
residents, hospitals = players
game = HospitalResident(residents, hospitals, clean)
for resident, game_resident in zip(residents, game.residents):
assert resident.name == game_resident.name
assert resident._pref_names == game_resident._pref_names
for ho... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_players(self, players):\n\t\tself.players.clear()\n\t\tids = set([p.get_player_id() for p in players])\n\t\tfor p in self.state.get_players():\n\t\t\tif p not in ids:\n\t\t\t\traise PlayerException(p)\n\t\tfor p in players:\n\t\t\tself.players[p.get_player_id()] = p",
"def test_valid_game_setup(self):\... | [
"0.68950206",
"0.68166816",
"0.6779744",
"0.6726286",
"0.6673796",
"0.6636629",
"0.65848154",
"0.6571941",
"0.6493531",
"0.64693403",
"0.6440882",
"0.64096856",
"0.6371468",
"0.63653827",
"0.6354527",
"0.6353987",
"0.6349084",
"0.63259184",
"0.6288197",
"0.6285662",
"0.626197... | 0.7017496 | 0 |
Test for correct instantiation given a set of dictionaries. | def test_create_from_dictionaries(connections, clean):
resident_prefs, hospital_prefs, capacities = connections
game = HospitalResident.create_from_dictionaries(
resident_prefs, hospital_prefs, capacities, clean
)
for resident in game.residents:
assert resident._pref_names == resident... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_from_dictionaries(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n stud_prefs, sup_prefs, proj_sups, proj_caps, sup_caps = make_connections(\n student_names, project_names, supervisor_names, capacities, seed\n )\n\n game = StudentAllocation.creat... | [
"0.64251405",
"0.61764544",
"0.599205",
"0.5967179",
"0.5964097",
"0.59410644",
"0.57628375",
"0.5736472",
"0.57099533",
"0.56787115",
"0.56783843",
"0.56684375",
"0.56655174",
"0.5664207",
"0.5655415",
"0.5654512",
"0.5649937",
"0.5634078",
"0.5615306",
"0.55827117",
"0.5577... | 0.6566189 | 0 |
Test that inputs to an instance of HR can be verified. | def test_check_inputs(game):
with warnings.catch_warnings():
warnings.simplefilter("error")
game.check_inputs()
assert game.residents == game._all_residents
assert game.hospitals == game._all_hospitals | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_validParameters(self):\n # stupid/simple inputs\n h = Helix(\"thing\", \"Cu\", 0, 0, 1, 1, 1)\n self.assertEqual(h.getDimension(\"axialPitch\"), 1)\n\n # standard case / inputs ordered well\n h = Helix(\n \"what\",\n \"Cu\",\n Tinput=25.0... | [
"0.6665209",
"0.6371906",
"0.627438",
"0.62270224",
"0.6090711",
"0.6064556",
"0.6048889",
"0.603993",
"0.6019733",
"0.5987716",
"0.59819835",
"0.5966513",
"0.5957849",
"0.5943022",
"0.5917704",
"0.5901547",
"0.5900447",
"0.5894026",
"0.5893552",
"0.58609515",
"0.58448863",
... | 0.62387663 | 3 |
Test each resident has only hospitals in its preference list. If not, check that a warning is caught and the resident's preferences are changed. | def test_check_inputs_resident_prefs_all_hospitals(game):
resident = game.residents[0]
resident.prefs = [Resident("foo")]
with pytest.warns(PreferencesChangedWarning) as record:
game._check_inputs_player_prefs_all_in_party("residents", "hospitals")
assert len(record) == 1
message = str(r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_check_inputs_hospital_prefs_all_residents(game):\n\n hospital = game.hospitals[0]\n hospital.prefs = [Resident(\"foo\")]\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_in_party(\"hospitals\", \"residents\")\n\n assert len(record) == 1\n... | [
"0.76461375",
"0.7579265",
"0.75171703",
"0.75103134",
"0.7466917",
"0.71831375",
"0.70516497",
"0.7038354",
"0.64109606",
"0.62843776",
"0.6155221",
"0.6151795",
"0.608665",
"0.6074421",
"0.59615344",
"0.5961042",
"0.587259",
"0.5870157",
"0.5802495",
"0.5735709",
"0.5553829... | 0.7821524 | 0 |
Test each hospital has only residents in its preference list. If not, check that a warning is caught and the hospitals's preferences are changed. | def test_check_inputs_hospital_prefs_all_residents(game):
hospital = game.hospitals[0]
hospital.prefs = [Resident("foo")]
with pytest.warns(PreferencesChangedWarning) as record:
game._check_inputs_player_prefs_all_in_party("hospitals", "residents")
assert len(record) == 1
message = str(r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.a... | [
"0.7820969",
"0.76278716",
"0.75781184",
"0.74795306",
"0.73008245",
"0.70282596",
"0.69267523",
"0.67586416",
"0.6459601",
"0.62566453",
"0.6184107",
"0.6075766",
"0.6060278",
"0.600085",
"0.5896375",
"0.5779385",
"0.5753072",
"0.5702106",
"0.56878966",
"0.5652447",
"0.55289... | 0.77083474 | 1 |
Test each hospital has ranked only residents that have ranked it. If not, check that a warning is caught and the hospital has forgotten any such residents. | def test_check_inputs_hospital_prefs_all_reciprocated(game):
hospital = game.hospitals[0]
resident = hospital.prefs[0]
resident._forget(hospital)
with pytest.warns(PreferencesChangedWarning) as record:
game._check_inputs_player_prefs_all_reciprocated("hospitals")
assert len(record) == 1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.a... | [
"0.65927255",
"0.62228805",
"0.6033156",
"0.5816217",
"0.57702166",
"0.5740605",
"0.569085",
"0.56001204",
"0.552595",
"0.5460149",
"0.54297745",
"0.53983676",
"0.5360775",
"0.5351841",
"0.5335599",
"0.5331168",
"0.528455",
"0.5271201",
"0.52700424",
"0.52670234",
"0.5262435"... | 0.5287138 | 16 |
Test each hospital has ranked all residents that have ranked it. If not, check that a warning is caught and any such resident has forgotten the hospital. | def test_check_inputs_hospital_reciprocated_all_prefs(game):
hospital = game.hospitals[0]
resident = hospital.prefs[0]
hospital._forget(resident)
with pytest.warns(PreferencesChangedWarning) as record:
game._check_inputs_player_reciprocated_all_prefs(
"hospitals", "residents"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_hospital_prefs(self):\n\n errors = []\n for hospital in self.hospitals:\n residents_that_ranked = [\n res for res in self.residents if hospital in res.prefs\n ]\n if set(hospital.prefs) != set(residents_that_ranked):\n errors.a... | [
"0.6707456",
"0.6244367",
"0.6030781",
"0.5817202",
"0.57967025",
"0.56785023",
"0.5596258",
"0.554864",
"0.5504701",
"0.5493291",
"0.54924923",
"0.5488279",
"0.5485713",
"0.54585725",
"0.5428704",
"0.5420342",
"0.5397366",
"0.5381196",
"0.53527856",
"0.53451216",
"0.53155655... | 0.58555126 | 3 |
Test that each resident has a nonempty preference list. If not, check that a warning is caught and the resident has been removed from the game. | def test_check_inputs_resident_prefs_all_nonempty(game):
resident = game.residents[0]
resident.prefs = []
with pytest.warns(PlayerExcludedWarning) as record:
game._check_inputs_player_prefs_nonempty("residents", "hospitals")
assert len(record) == 1
assert resident.name in str(record[0].me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert le... | [
"0.7198795",
"0.71749365",
"0.71210027",
"0.6954522",
"0.6899493",
"0.6747819",
"0.6641792",
"0.6345878",
"0.61837107",
"0.6050496",
"0.5979725",
"0.59595793",
"0.5754744",
"0.57290685",
"0.5725816",
"0.56024337",
"0.5517876",
"0.5488233",
"0.54230016",
"0.5410084",
"0.539465... | 0.7836128 | 0 |
Test that each hospital has a nonempty preference list. If not, check that a warning is caught and the player has been removed from the game. | def test_check_inputs_hospital_prefs_all_nonempty(game):
hospital = game.hospitals[0]
hospital.prefs = []
with pytest.warns(PlayerExcludedWarning) as record:
game._check_inputs_player_prefs_nonempty("hospitals", "residents")
assert len(record) == 1
assert hospital.name in str(record[0].me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_check_inputs_hospital_prefs_all_reciprocated(game):\n\n hospital = game.hospitals[0]\n resident = hospital.prefs[0]\n resident._forget(hospital)\n\n with pytest.warns(PreferencesChangedWarning) as record:\n game._check_inputs_player_prefs_all_reciprocated(\"hospitals\")\n\n assert le... | [
"0.74706024",
"0.7364109",
"0.7355341",
"0.7349609",
"0.7254875",
"0.6616572",
"0.62527543",
"0.6152463",
"0.61230946",
"0.60876256",
"0.6085861",
"0.6065636",
"0.5856437",
"0.58475614",
"0.58438146",
"0.58048016",
"0.5630108",
"0.56236094",
"0.5622575",
"0.5621679",
"0.56021... | 0.81164616 | 0 |
Test each hospital has a positive integer capacity. If not, raise an Exception detailing the hospital. | def test_check_inputs_hospital_capacity(game):
hospital = game.hospitals[0]
capacity = hospital.capacity
hospital.capacity = 0
assert hospital._original_capacity == capacity
with pytest.warns(PlayerExcludedWarning) as record:
game._check_inputs_player_capacity("hospitals", "residents")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_hospital_capacity(self):\n\n errors = []\n for hospital in self.hospitals:\n if len(hospital.matching) > hospital.capacity:\n errors.append(\n ValueError(\n f\"{hospital} is matched to {hospital.matching} which \"\n ... | [
"0.7667984",
"0.6397596",
"0.62737614",
"0.6177663",
"0.60829747",
"0.6006602",
"0.59802",
"0.5944431",
"0.5858093",
"0.58271384",
"0.58146626",
"0.5800991",
"0.57918227",
"0.5761161",
"0.5714757",
"0.5673754",
"0.56648004",
"0.5643236",
"0.56302327",
"0.56134015",
"0.5581795... | 0.73591745 | 1 |
Test for the correct solving of games. | def test_solve(game, optimal):
matching = game.solve(optimal)
assert isinstance(matching, MultipleMatching)
hospitals = sorted(game.hospitals, key=lambda h: h.name)
matching_keys = sorted(matching.keys(), key=lambda k: k.name)
for game_hospital, hospital in zip(matching_keys, hospitals):
a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_check_validity(game):\n\n game.solve()\n assert game.check_validity()",
"def test_check_solved():\n game = Game()\n game.word = 'word'\n game.pattern = 'word'\n game.check_solved()\n assert game.solved is True",
"def test_is_solved_when_puzzle_is_solved(self):\n self.assert... | [
"0.7135749",
"0.711226",
"0.70119655",
"0.6942161",
"0.69317514",
"0.68992364",
"0.68689203",
"0.68571925",
"0.68241847",
"0.6820517",
"0.6752076",
"0.6728036",
"0.6698164",
"0.6654949",
"0.6626608",
"0.6581106",
"0.65573555",
"0.65466815",
"0.6539139",
"0.6468664",
"0.642725... | 0.68758893 | 6 |
Test for a valid matching when the game is solved. | def test_check_validity(game):
game.solve()
assert game.check_validity() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_match(self):\n self.assertEqual(solution( \"\"\"p=<-6,0,0>, v=< 3,0,0>, a=< 0,0,0>\np=<-4,0,0>, v=< 2,0,0>, a=< 0,0,0>\np=<-2,0,0>, v=< 1,0,0>, a=< 0,0,0>\np=< 3,0,0>, v=<-1,0,0>, a=< 0,0,0>\"\"\"), 1)",
"def test_check_validity(resident_names, hospital_names, capacities, seed):\n\n _, _, matc... | [
"0.6855574",
"0.6748413",
"0.67361814",
"0.66506636",
"0.65528977",
"0.6500697",
"0.64891684",
"0.64536655",
"0.6449834",
"0.6446524",
"0.64406365",
"0.6418722",
"0.6375467",
"0.6332083",
"0.6327593",
"0.6303921",
"0.6292576",
"0.6269725",
"0.62239105",
"0.62134755",
"0.62047... | 0.5917138 | 45 |
Test that matched residents have a preference of their match. | def test_check_for_unacceptable_matches_residents(game):
resident = game.residents[0]
hospital = Hospital(name="foo", capacity=1)
resident.matching = hospital
issues = game._check_for_unacceptable_matches("residents")
assert len(issues) == 1
issue = issues[0]
assert issue.startswith(resid... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reviewer_matching(resident_names, hospital_names, capacities, seed):\n\n _, _, match = _make_match(resident_names, hospital_names, capacities, seed)\n\n match.solve()\n match.reviewers[0].matching.append(Player(name=\"foo\", pref_names=[]))\n\n with pytest.raises(Exception):\n match._ch... | [
"0.6435155",
"0.6338223",
"0.63054293",
"0.6247562",
"0.6155224",
"0.5968124",
"0.5949417",
"0.59368134",
"0.5935081",
"0.59329754",
"0.5906463",
"0.58899987",
"0.58429325",
"0.58374435",
"0.5827494",
"0.5767351",
"0.575049",
"0.5748831",
"0.57429886",
"0.56962484",
"0.569148... | 0.53893685 | 56 |
Test that each hospital has a preference of all its matches. | def test_check_for_unacceptable_matches_hospitals(game):
hospital = game.hospitals[0]
resident = Resident(name="foo")
hospital.matching.append(resident)
issues = game._check_for_unacceptable_matches("hospitals")
assert len(issues) == 1
issue = issues[0]
assert issue.startswith(hospital.na... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_hospital_optimal(resident_names, hospital_names, capacities, seed):\n\n np.random.seed(seed)\n residents = _make_residents(resident_names, hospital_names)\n hospitals = _make_hospitals(residents, capacities)\n matching = hospital_resident(residents, hospitals, optimal=\"hospital\")\n\n asse... | [
"0.6493731",
"0.62789917",
"0.625827",
"0.6236417",
"0.6157629",
"0.61469007",
"0.60858804",
"0.6017746",
"0.601711",
"0.5959501",
"0.59545827",
"0.58850694",
"0.58698004",
"0.5841389",
"0.5817565",
"0.576249",
"0.5716353",
"0.5706714",
"0.56535304",
"0.5595088",
"0.5543368",... | 0.5400718 | 34 |
Test that no hospitals can be oversubscribed. | def test_check_for_oversubscribed_hospitals(game):
hospital = game.hospitals[0]
hospital.matching = range(hospital.capacity + 1)
issues = game._check_for_oversubscribed_players("hospitals")
assert len(issues) == 1
issue = issues[0]
assert issue.startswith(hospital.name)
assert issue.endsw... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_nonsubscriber(self) -> None:\n # Create a stream for which Hamlet is the only subscriber.\n stream_name = \"Saxony\"\n self.common_subscribe_to_streams(self.user_profile, [stream_name])\n other_user = self.example_user(\"othello\")\n\n # Fetch the subscriber list as a no... | [
"0.707774",
"0.66021943",
"0.6580524",
"0.6333144",
"0.63258743",
"0.62714976",
"0.62273604",
"0.62074196",
"0.6164765",
"0.61497414",
"0.61130273",
"0.6111483",
"0.60828555",
"0.60759217",
"0.607235",
"0.6040785",
"0.60398805",
"0.60398805",
"0.6023948",
"0.6017094",
"0.5979... | 0.7428499 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.