= volume_shape[i]):\n return output_volume\n r0max = [int(x/2) for x in patch_shape]\n r1max = [patch_shape[i] - r0max[i] for i in range(len(r0max))]\n r0 = [min(r0max[i], center[i]) for i in range(len(r0max))]\n r1 = [min(r1max[i], volume_shape[i] - center[i]) for i in range(len(r0max))]\n patch_center = r0max\n\n if(len(center) == 3):\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\n range(center[1] - r0[1], center[1] + r1[1]),\n range(center[2] - r0[2], center[2] + r1[2]))] = \\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]))]\n elif(len(center) == 4):\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\n range(center[1] - r0[1], center[1] + r1[1]),\n range(center[2] - r0[2], center[2] + r1[2]),\n range(center[3] - r0[3], center[3] + r1[3]))] = \\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]),\n range(patch_center[3] - r0[3], patch_center[3] + r1[3]))]\n else:\n raise ValueError(\"array dimension should be 3 or 4\") \n return output_volume","def set_roi_to_volume(volume, center, sub_volume):\n volume_shape = volume.shape \n patch_shape = sub_volume.shape\n output_volume = volume\n for i in range(len(center)):\n if(center[i] >= volume_shape[i]):\n return output_volume\n r0max = [int(x/2) for x in patch_shape]\n r1max = [patch_shape[i] - r0max[i] for i in range(len(r0max))]\n r0 = [min(r0max[i], center[i]) for i in range(len(r0max))]\n r1 = [min(r1max[i], volume_shape[i] - center[i]) for i in range(len(r0max))]\n patch_center = r0max\n\n if(len(center) == 3):\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\n range(center[1] - r0[1], center[1] + r1[1]),\n range(center[2] - r0[2], center[2] + r1[2]))] = \\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]))]\n elif(len(center) == 4):\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\n range(center[1] - r0[1], center[1] + r1[1]),\n range(center[2] - r0[2], center[2] + r1[2]),\n range(center[3] - r0[3], center[3] + r1[3]))] = \\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]),\n range(patch_center[3] - r0[3], patch_center[3] + r1[3]))]\n else:\n raise ValueError(\"array dimension should be 3 or 4\") \n return output_volume","def __init__(\n self,\n folder_path: FolderPathType,\n streams: Optional[List[str]] = None,\n verbose: bool = False,\n ):\n folder_path = Path(folder_path)\n\n streams = streams or self.get_streams(folder_path=folder_path)\n\n data_interfaces = dict()\n for stream in streams:\n if \"ap\" in stream:\n probe_name = stream[:5]\n file_path = (\n folder_path / f\"{folder_path.stem}_{probe_name}\" / f\"{folder_path.stem}_t0.{probe_name}.ap.bin\"\n )\n interface = SpikeGLXRecordingInterface(file_path=file_path)\n if \"lf\" in stream:\n probe_name = stream[:5]\n file_path = (\n folder_path / f\"{folder_path.stem}_{probe_name}\" / f\"{folder_path.stem}_t0.{probe_name}.lf.bin\"\n )\n interface = SpikeGLXRecordingInterface(file_path=file_path)\n if \"nidq\" in stream:\n file_path = folder_path / f\"{folder_path.stem}_t0.nidq.bin\"\n interface = SpikeGLXNIDQInterface(file_path=file_path)\n num_channels = interface.recording_extractor.get_num_channels()\n # To avoid warning/error turing write\n # TODO: When PyNWB supports other more proper AUX electrode types, remove\n interface.recording_extractor.set_property(key=\"shank_electrode_number\", values=[np.nan] * num_channels)\n interface.recording_extractor.set_property(key=\"contact_shapes\", values=[np.nan] * num_channels)\n data_interfaces.update({stream: interface})\n\n super().__init__(data_interfaces=data_interfaces, verbose=verbose)","def register_shapes():\n turtle.Screen().register_shape(\"saphire.gif\")\n turtle.Screen().register_shape(\"player_right.gif\")\n turtle.Screen().register_shape(\"player_left.gif\")\n turtle.Screen().register_shape(\"walls.gif\")","def testEdgeMarkerRaster1(self):\n outname = self.prefix+'.asap'\n self.res = sdcal(infile=self.rawfile,calmode='otfraster',noff=1,markonly=True,outfile=outname,outform='ASAP')\n refdir = self._readref( self.reffiles[1] )\n self._checkfile( outname ) \n self._checkmarker( outname, refdir )","def ndvi(in_nir_band, in_colour_band, in_rows, in_cols, in_geotransform, out_tiff, data_type=gdal.GDT_Float32):\r\n\r\n # Read the input bands as numpy arrays.\r\n np_nir = in_nir_band.ReadAsArray(0, 0, in_cols, in_rows)\r\n np_colour = in_colour_band.ReadAsArray(0, 0, in_cols, in_rows)\r\n\r\n # Convert the np arrays to 32-bit floating point to make sure division will occur properly.\r\n np_nir_as32 = np_nir.astype(np.float32)\r\n np_colour_as32 = np_colour.astype(np.float32)\r\n\r\n # Calculate the NDVI formula.\r\n numerator = subtract(np_nir_as32, np_colour_as32)\r\n denominator = add(np_nir_as32, np_colour_as32)\r\n result = divide(numerator, denominator)\r\n\r\n # Remove any NaNs cause by division by zero.\r\n ndvi_float32 = nan_to_num(result)\r\n\r\n # Initialize a geotiff driver.\r\n geotiff = GetDriverByName('GTiff')\r\n\r\n # If the desired output is an int16, map the domain [-1,1] to [0,255], create an int16 geotiff with one band and\r\n # write the contents of the int16 NDVI calculation to it. Otherwise, create a float32 geotiff with one band and\r\n # write the contents of the float32 NDVI calculation to it.\r\n if data_type == gdal.GDT_UInt16:\r\n ndvi_int16 = multiply((ndvi_float32 + 1), (2**7 - 1))\r\n output = geotiff.Create(out_tiff, in_cols, in_rows, 1, gdal.GDT_UInt16)\r\n output.GetRasterBand(1).WriteArray(ndvi_int16)\r\n elif data_type == gdal.GDT_Float32:\r\n output = geotiff.Create(out_tiff, in_cols, in_rows, 1, gdal.GDT_Float32)\r\n output.GetRasterBand(1).WriteArray(ndvi_float32)\r\n else:\r\n raise ValueError('Invalid output data type. Valid types are gdal.UInt16 or gdal.Float32.')\r\n\r\n # Set the geographic transformation as the input.\r\n output.SetGeoTransform(in_geotransform)\r\n\r\n # return the output image in case you want to do something else with it.\r\n return output","def region_sets(self,listA,listB):\n self.setA = GenomicRegionSet('for Unit Test')\n for i in range(len(listA)):\n self.setA.add(GenomicRegion(chrom=listA[i][0], initial=listA[i][1], final=listA[i][2]))\n \n self.setB = GenomicRegionSet('for Unit Test')\n for i in range(len(listB)):\n self.setB.add(GenomicRegion(chrom=listB[i][0], initial=listB[i][1], final=listB[i][2]))","def volumes(self):","def test_CreateROI1(self):\r\n\r\n self.delayDisplay(\"Starting the test\")\r\n #\r\n # first, get some data\r\n #\r\n import urllib\r\n downloads = (\r\n ('http://slicer.kitware.com/midas3/download?items=5767', 'FA.nrrd', slicer.util.loadVolume),\r\n )\r\n\r\n for url,name,loader in downloads:\r\n filePath = slicer.app.temporaryPath + '/' + name\r\n if not os.path.exists(filePath) or os.stat(filePath).st_size == 0:\r\n logging.info('Requesting download %s from %s...\\n' % (name, url))\r\n urllib.urlretrieve(url, filePath)\r\n if loader:\r\n logging.info('Loading %s...' % (name,))\r\n loader(filePath)\r\n self.delayDisplay('Finished with download and loading')\r\n\r\n volumeNode = slicer.util.getNode(pattern=\"FA\")\r\n logic = CreateROILogic()\r\n self.assertIsNotNone( logic.hasImageData(volumeNode) )\r\n self.delayDisplay('Test passed!')","def unpack_landsat(inpath, outpath, bands=None, clouds=None):\r\n tfile = tarfile.open(inpath,'r:gz')\r\n if bands is None:\r\n #Extract all of the bands.\r\n tfile.extractall(outpath);\r\n else:\r\n #Extract just the bands specified.\r\n\r\n #Get the names of the members of the archive\r\n names = tfile.getnames()\r\n\r\n #Make sure the bands are given as a list\r\n bands = list(bands)\r\n\r\n #Find those elements of the archive that match the bands specified\r\n #\t(plus metadata file)\r\n members = tfile.getmembers()\r\n elements = []\r\n blist = ''.join(str(i) for i in bands)\r\n if clouds:\r\n patternStr = '.*_B[' + blist + ']\\.TIF|.*_MTL.txt|.*_band[' + blist + \\\r\n ']\\.tif|.*_cfmask.*\\.tif|.*\\.xml|.*cloud.*\\.tif'\r\n else:\r\n patternStr = '.*_B[' + blist + ']\\.TIF|.*_MTL.txt|.*_band[' + blist + \\\r\n ']\\.tif|.*_cfmask.*\\.tif|.*\\.xml'\r\n\r\n pattern = re.compile(patternStr)\r\n for i, name in enumerate(names):\r\n if pattern.match(name):\r\n elements.append(members[i])\r\n tfile.extractall(outpath,members = elements)\r\n\r\n '''\r\n Note: the below section was written with the intention of\r\n adding valuable metadata info to the .TIF files themselves.\r\n This theoretically allows easy retreival of key metadata info,\r\n but the method to set new metadata seems unreliable. Appears to\r\n be an open issue, but there may be a solution. For now, assume\r\n this does not work.\r\n\r\n #Now we loop through each output, adding important metadata.\r\n #Fetch metadata and make sure there is only one file.\r\n meta = glob.glob(outpath + '/*_MTL.txt')\r\n if len(meta) > 1:\r\n raise('MORE THAN ONE METADATA FILE FOUND!')\r\n\r\n metadata = read_metadata(meta[0])\r\n\r\n #Add the metadata.\r\n for tif in glob.glob(outpath + '*.TIF'):\r\n #Open the tif file\r\n ras = gdal.Open(tif)\r\n\r\n #Preserve any existing metadata & add important metadata from file\r\n meta = ras.GetMetadata()\r\n meta['SCENE_CENTER_TIME'] = metadata['SCENE_CENTER_TIME']\r\n meta['DATE_ACQUIRED'] = metadata['DATE_ACQUIRED']\r\n meta['LANDSAT_SCENE_ID'] = metadata['LANDSAT_SCENE_ID']\r\n\r\n #Now write the new metadata back to the tif.\r\n ras.SetMetadata(meta)\r\n ras.FlushCache()\r\n ras = None\r\n '''","def gen_landmark_data(src_txt_path, net, augmet=False):\r\n print(\">>>>>> Start landmark data create...Stage: %s\" % net)\r\n save_folder = os.path.join(root_path, '../DATA/12/')\r\n save_image_folder = os.path.join(save_folder, 'train_%s_landmark_aug' % net)\r\n size_of_net = {'PNet': 12, 'RNet': 24, 'ONet': 48}\r\n if net not in size_of_net:\r\n raise Exception(\"The net type error!\")\r\n if not os.path.isdir(save_image_folder):\r\n os.makedirs(save_image_folder)\r\n print('create folder: ', save_image_folder)\r\n save_f = open(os.path.join(save_folder, 'landmark_%s_aug.txt' % size_of_net[net]), 'w')\r\n image_count = 0\r\n # image_path bbox landmark(5*2)\r\n bbox_landmark_info = get_bbox_landmark_from_txt(src_txt_path, data_path='../DATA/landmarks_traindata', with_landmark=True)\r\n for img_path, bbox, landmark_gt in bbox_landmark_info:\r\n f_imgs = list()\r\n f_landmarks = list()\r\n img = cv2.imread(img_path)\r\n assert(img is not None)\r\n img_h, img_w, img_c = img.shape\r\n gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])\r\n f_face = img[bbox.top: bbox.bottom+1, bbox.left: bbox.right+1]\r\n f_face = cv2.resize(f_face, (size_of_net[net], size_of_net[net]))\r\n landmark = np.zeros((5, 2))\r\n # normalize\r\n for index, one in enumerate(landmark_gt):\r\n rv = ((one[0]-gt_box[0])/(gt_box[2]-gt_box[0]), (one[1]-gt_box[1])/(gt_box[3]-gt_box[1]))\r\n landmark[index] = rv\r\n f_imgs.append(f_face)\r\n f_landmarks.append(landmark.reshape(10))\r\n landmark = np.zeros((5, 2))\r\n if augmet:\r\n x1, y1, x2, y2 = gt_box\r\n gt_width = x2 - x1 + 1\r\n gt_height = y2 - y1 + 1\r\n if max(gt_width, gt_height) < 40 or x1 < 0 or y1 < 0:\r\n continue\r\n # random shift\r\n for i in range(10):\r\n bbox_size = np.random.randint(int(min(gt_width, gt_height) * 0.8), np.ceil(1.25 * max(gt_width, gt_height)))\r\n # delta_x and delta_y are offsets of (x1, y1)\r\n # max can make sure if the delta is a negative number , x1+delta_x >0\r\n # parameter high of randint make sure there will be intersection between bbox and cropped_box\r\n delta_x = np.random.randint(-gt_width*0.2, gt_width*0.2)\r\n delta_y = np.random.randint(-gt_height*0.2, gt_height*0.2)\r\n nx1 = int(max(x1+gt_width/2 - bbox_size/2 + delta_x, 0))\r\n ny1 = int(max(y1+gt_height/2 - bbox_size/2 + delta_y, 0))\r\n nx2 = nx1 + bbox_size\r\n ny2 = ny1 + bbox_size\r\n if nx2 > img_w or ny2 > img_h:\r\n continue\r\n # print(nx1, ny1, nx2, ny2)\r\n crop_box = np.array([nx1, ny1, nx2, ny2])\r\n cropped_img = img[ny1: ny2+1, nx1: nx2+1, :]\r\n resized_img = cv2.resize(cropped_img, (size_of_net[net], size_of_net[net]))\r\n iou = calc_iou(crop_box, np.expand_dims(gt_box, 0))\r\n if iou <= 0.65:\r\n continue\r\n f_imgs.append(resized_img)\r\n # normalize\r\n for index, one in enumerate(landmark_gt):\r\n rv = ((one[0]-nx1)/bbox_size, (one[1]-ny1/bbox_size))\r\n landmark[index] = rv\r\n f_landmarks.append(landmark.reshape(10))\r\n landmark = np.zeros((5, 2))\r\n # get last landmark from list\r\n landmark_ = f_landmarks[-1].reshape((-1, 2))\r\n bbox = BBox([nx1, ny1, nx2, ny2])\r\n\r\n # mirror\r\n if random.choice([0, 1]) > 0:\r\n face_flipped, landmark_flipped = flip(resized_img, landmark_)\r\n face_flipped = cv2.resize(face_flipped, (size_of_net[net], size_of_net[net]))\r\n # c*h*w\r\n f_imgs.append(face_flipped)\r\n f_landmarks.append(landmark_flipped.reshape(10))\r\n # rotate\r\n if random.choice([0, 1]) > 0:\r\n face_rotated_by_alpha, landmark_rotated = rotate(img, bbox,\r\n bbox.reproject_landmark(landmark_), 5)\r\n # landmark offset\r\n landmark_rotated = bbox.project_landmark(landmark_rotated)\r\n face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (size_of_net[net], size_of_net[net]))\r\n f_imgs.append(face_rotated_by_alpha)\r\n f_landmarks.append(landmark_rotated.reshape(10))\r\n\r\n # flip\r\n face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)\r\n face_flipped = cv2.resize(face_flipped, (size_of_net[net], size_of_net[net]))\r\n f_imgs.append(face_flipped)\r\n f_landmarks.append(landmark_flipped.reshape(10))\r\n # anti-clockwise rotation\r\n if random.choice([0, 1]) > 0:\r\n face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, bbox.reproject_landmark(landmark_), -5)\r\n landmark_rotated = bbox.project_landmark(landmark_rotated)\r\n face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (size_of_net[net], size_of_net[net]))\r\n f_imgs.append(face_rotated_by_alpha)\r\n f_landmarks.append(landmark_rotated.reshape(10))\r\n\r\n face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)\r\n face_flipped = cv2.resize(face_flipped, (size_of_net[net], size_of_net[net]))\r\n f_imgs.append(face_flipped)\r\n f_landmarks.append(landmark_flipped.reshape(10))\r\n f_imgs, f_landmarks = np.asarray(f_imgs), np.asarray(f_landmarks)\r\n for i in range(len(f_imgs)):\r\n # if np.sum(np.where(f_landmarks[i] <= 0, 1, 0)) > 0:\r\n # print('skip image: %d' % i)\r\n # print(f_landmarks[i])\r\n # continue\r\n # if np.sum(np.where(f_landmarks[i] >= 1, 1, 0)) > 0:\r\n # print('skip image: %d', i)\r\n # print(f_landmarks[i])\r\n # continue\r\n path = os.path.join(save_image_folder, '%d.jpg' % image_count)\r\n cv2.imwrite(path, f_imgs[i])\r\n landmarks = map(str, list(f_landmarks[i]))\r\n save_f.write(path + ' -2 ' + ' '.join(landmarks) + '\\n')\r\n image_count += 1\r\n print_str = \"\\rCount: {}\".format(image_count)\r\n sys.stdout.write(print_str)\r\n sys.stdout.flush()\r\n save_f.close()\r\n print('\\n Landmark create done!')","def identify_landmarks(image: numpy.ndarray) -> Dict[int, Tuple[int, int]]:\n copy_img = image.copy()\n\n detector = dlib.get_frontal_face_detector()\n predictor = dlib.shape_predictor(os.path.join(\n project_dir, \"shufflealgos\", \"image\", \"landmarks\",\n \"shape_predictor_68_face_landmarks.dat\"))\n\n gray_img_array = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n found_face = detector(gray_img_array)\n\n face_lmarks: Dict[int, Tuple[int, int]] = dict()\n\n # Assume only one face is found for our purposes\n for idx, face in enumerate(found_face, start=1):\n landmarks = predictor(gray_img_array, face)\n\n for n in range(68):\n lmx = landmarks.part(n).x\n lmy = landmarks.part(n).y\n\n face_lmarks[n + 1] = (lmx, lmy)\n\n cv2.circle(copy_img, (lmx, lmy), 1, (50, 50, 255), -1)\n\n cv2.putText(\n copy_img, str(n), (lmx, lmy - 5),\n cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.6, (0, 0, 255), 1)\n\n return face_lmarks","def register_mount(self, app, path, variables, converters, required,\n get_converters, mount_name, app_factory):\n self.register_path(app, path, variables,\n converters, required, get_converters, False,\n app_factory)\n\n self.mounted[app] = app_factory\n mount_name = mount_name or path\n self.named_mounted[mount_name] = app_factory","def _mri_scanner_ras_to_mri_voxels(ras_landmarks, img_mgh):\n # Get landmarks in voxel space, using the T1 data\n vox2ras = img_mgh.header.get_vox2ras()\n ras2vox = linalg.inv(vox2ras)\n vox_landmarks = apply_trans(ras2vox, ras_landmarks) # in vox\n return vox_landmarks","def do_stuff(self):\n self.create_tourism_raster()","def test_register_fov(self):\n task = MesoscopeFOV(self.session_path, device_collection='raw_imaging_data', one=self.one)\n mlapdv = {'topLeft': [2317.2, -1599.8, -535.5], 'topRight': [2862.7, -1625.2, -748.7],\n 'bottomLeft': [2317.3, -2181.4, -466.3], 'bottomRight': [2862.7, -2206.9, -679.4],\n 'center': [2596.1, -1900.5, -588.6]}\n meta = {'FOV': [{'MLAPDV': mlapdv, 'nXnYnZ': [512, 512, 1], 'roiUUID': 0}]}\n with unittest.mock.patch.object(self.one.alyx, 'rest') as mock_rest:\n task.register_fov(meta, 'estimate')\n calls = mock_rest.call_args_list\n self.assertEqual(3, len(calls))\n\n args, kwargs = calls[1]\n self.assertEqual(('fields-of-view', 'create'), args)\n expected = {'data': {'session': None, 'imaging_type': 'mesoscope', 'name': 'FOV_00', 'stack': None}}\n self.assertEqual(expected, kwargs)\n\n args, kwargs = calls[2]\n self.assertEqual(('fov-location', 'create'), args)\n expected = ['field_of_view', 'default_provenance', 'coordinate_system', 'n_xyz', 'provenance', 'x', 'y', 'z',\n 'brain_region']\n self.assertCountEqual(expected, kwargs.get('data', {}).keys())\n self.assertEqual(5, len(kwargs['data']['brain_region']))\n self.assertEqual([512, 512, 1], kwargs['data']['n_xyz'])\n self.assertIs(kwargs['data']['field_of_view'], mock_rest().get('id'))\n self.assertEqual('E', kwargs['data']['provenance'])\n self.assertEqual([2317.2, 2862.7, 2317.3, 2862.7], kwargs['data']['x'])\n\n # Check dry mode with suffix input = None\n for file in self.session_path.joinpath('alf', 'FOV_00').glob('mpciMeanImage.*'):\n file.replace(file.with_name(file.name.replace('_estimate', '')))\n self.one.mode = 'local'\n with unittest.mock.patch.object(self.one.alyx, 'rest') as mock_rest:\n out = task.register_fov(meta, None)\n mock_rest.assert_not_called()\n self.assertEqual(1, len(out))\n self.assertEqual('FOV_00', out[0].get('name'))\n locations = out[0]['location']\n self.assertEqual(1, len(locations))\n self.assertEqual('L', locations[0].get('provenance', 'L'))","def showSS(self, show):\n\t\tfrom chimera.Sequence import defHelixColor, defStrandColor\n\t\thelixReg = self.getRegion(\"structure helices\", create=1,\n\t\t\t\tfill=(1.0, 1.0, 0.8), outline=defHelixColor)\n\t\tstrandReg = self.getRegion(\"structure strands\", create=1,\n\t\t\t\tfill=(0.8, 1.0, 0.8), outline=defStrandColor)\n\t\thelixReg.shown = show\n\t\tstrandReg.shown = show\n\t\tif not show:\n\t\t\treturn\n\t\thelixReg.clear(makeCB=False) # callback will happen in\n\t\tstrandReg.clear(makeCB=False) # addBlocks below\n\n\t\tassocSeqs = {}\n\t\thelices = []\n\t\tstrands = []\n\t\tfor aseq in self.seqCanvas.mav.associations.values():\n\t\t\tassocSeqs[aseq] = 1\n\t\tfor aseq in assocSeqs.keys():\n\t\t\tinHelix = inStrand = 0\n\t\t\tfor pos in range(len(aseq.ungapped())):\n\t\t\t\tisHelix = isStrand = 0\n\t\t\t\tfor matchMap in aseq.matchMaps.values():\n\t\t\t\t\ttry:\n\t\t\t\t\t\tres = matchMap[pos]\n\t\t\t\t\texcept KeyError:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif res.isHelix:\n\t\t\t\t\t\tisHelix = 1\n\t\t\t\t\telif res.isStrand:\n\t\t\t\t\t\tisStrand = 1\n\t\t\t\tgapped = aseq.ungapped2gapped(pos)\n\t\t\t\tif isHelix:\n\t\t\t\t\tif inHelix:\n\t\t\t\t\t\thelices[-1][-1] = gapped\n\t\t\t\t\telse:\n\t\t\t\t\t\thelices.append([aseq, aseq,\n\t\t\t\t\t\t\t\tgapped, gapped])\n\t\t\t\t\t\tinHelix = 1\n\t\t\t\telse:\n\t\t\t\t\tif inHelix:\n\t\t\t\t\t\tinHelix = 0\n\t\t\t\tif isStrand:\n\t\t\t\t\tif inStrand:\n\t\t\t\t\t\tstrands[-1][-1] = gapped\n\t\t\t\t\telse:\n\t\t\t\t\t\tstrands.append([aseq, aseq,\n\t\t\t\t\t\t\t\tgapped, gapped])\n\t\t\t\t\t\tinStrand = 1\n\t\t\t\telse:\n\t\t\t\t\tif inStrand:\n\t\t\t\t\t\tinStrand = 0\n\t\thelixReg.addBlocks(helices)\n\t\tstrandReg.addBlocks(strands)","def __init__(self, inst: ghidra.program.model.listing.Instruction, bl: ghidra.program.model.correlate.Block, ind: int):\n ...","def testingData(self):\n\n #import SampleData\n #sampleDataLogic = SampleData.SampleDataLogic()\n #mrHead = sampleDataLogic.downloadMRHead()\n #dtiBrain = sampleDataLogic.downloadDTIBrain()\n \n # w = slicer.modules.SteeredFluidRegistrationWidget\n # w.fixedSelector.setCurrentNode(mrHead)\n # w.movingSelector.setCurrentNode(dtiBrain)\n \n if not slicer.util.getNodes('testbrain1*'):\n import os\n fileName = \"C:\\\\Work\\\\testbrain1.nrrd\"\n vl = slicer.modules.volumes.logic()\n brain1Node = vl.AddArchetypeVolume(fileName, \"testbrain1\", 0)\n else:\n nodes = slicer.util.getNodes('testbrain1.nrrd')\n brain1Node = nodes[0]\n\n if not slicer.util.getNodes('testbrain2*'):\n import os\n fileName = \"C:\\\\Work\\\\testbrain2.nrrd\"\n vl = slicer.modules.volumes.logic()\n brain2Node = vl.AddArchetypeVolume(fileName, \"testbrain2\", 0)\n #TODO else assign from list\n\n # if not slicer.util.getNodes('movingToFixed*'):\n # # Create transform node\n # transform = slicer.vtkMRMLLinearTransformNode()\n # transform.SetName('movingToFixed')\n # slicer.mrmlScene.AddNode(transform)\n\n # transform = slicer.util.getNode('movingToFixed')\n \n # ###\n # # neutral.SetAndObserveTransformNodeID(transform.GetID())\n # ###\n \n compositeNodes = slicer.util.getNodes('vtkMRMLSliceCompositeNode*')\n for compositeNode in compositeNodes.values():\n compositeNode.SetBackgroundVolumeID(brain1Node.GetID())\n compositeNode.SetForegroundVolumeID(brain2Node.GetID())\n compositeNode.SetForegroundOpacity(0.5)\n applicationLogic = slicer.app.applicationLogic()\n applicationLogic.FitSliceToAll()","def map_to_homo_nid(self, ids, ntype): # -> None:\n ...","def add_microstructures_to_arbor(self,arbor,mbranches,bbranches,myelingeometry=[1,1,3,8,3,1],boutongeometry=[28,4]):\n\t\tarbor_labels = dict(zip([key for key in arbor.keys()],[[] for key in arbor.keys()]))\n\t\tarbor,arbor_labels = self.myelinate_branches(arbor,arbor_labels,mbranches,myelin_geometry=myelingeometry) #myelin_geometry=[1,1,3,8,3,1]\n\t\tarbor,arbor_labels = self.bouton_branches(arbor,arbor_labels,bbranches,bouton_geometry=boutongeometry) #bouton_geometry=[28,4]\n\t\tarbor = self.ensure_precision(arbor)\n\t\treturn(arbor,arbor_labels)","def show_landmarks(image, landmarks, save_fig=False, save_path=None):\n\n plt.imshow(image)\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')\n if save_fig and save_path is not None:\n plt.savefig(save_path)\n plt.pause(0.001) # pause a bit so that plots are updated","def monolayer_4band():\n a = 0.222\n ax = 0.438\n ay = 0.332\n theta = 96.79 * (pi / 180)\n phi = 103.69 * (pi / 180)\n\n lat = pb.Lattice(a1=[ax, 0], a2=[0, ay])\n\n h = a * sin(phi - pi / 2)\n s = 0.5 * ax - a * cos(theta / 2)\n lat.add_sublattices(\n ('A', [0, 0, h], 0),\n ('B', [s, 0, 0], 0),\n ('C', [ax/2, ay/2, 0], 0),\n ('D', [ax/2 + s, ay/2, h], 0)\n )\n\n lat.register_hopping_energies({\n 't1': -1.22,\n 't2': 3.665,\n 't3': -0.205,\n 't4': -0.105,\n 't5': -0.055\n })\n\n lat.add_hoppings(\n # t1\n ([-1, 0], 'A', 'D', 't1'),\n ([-1, -1], 'A', 'D', 't1'),\n ([ 0, 0], 'B', 'C', 't1'),\n ([ 0, -1], 'B', 'C', 't1'),\n # t2\n ([ 0, 0], 'A', 'B', 't2'),\n ([ 0, 0], 'C', 'D', 't2'),\n # t3\n ([ 0, 0], 'A', 'D', 't3'),\n ([ 0, -1], 'A', 'D', 't3'),\n ([ 1, 1], 'C', 'B', 't3'),\n ([ 1, 0], 'C', 'B', 't3'),\n # t4\n ([ 0, 0], 'A', 'C', 't4'),\n ([ 0, -1], 'A', 'C', 't4'),\n ([-1, 0], 'A', 'C', 't4'),\n ([-1, -1], 'A', 'C', 't4'),\n ([ 0, 0], 'B', 'D', 't4'),\n ([ 0, -1], 'B', 'D', 't4'),\n ([-1, 0], 'B', 'D', 't4'),\n ([-1, -1], 'B', 'D', 't4'),\n # t5\n ([-1, 0], 'A', 'B', 't5'),\n ([ 0, 1], 'A', 'B', 't5'),\n ([ 0, -1], 'A', 'B', 't5'),\n ([-1, 0], 'C', 'D', 't5'),\n ([ 0, 1], 'C', 'D', 't5'),\n ([ 0, -1], 'C', 'D', 't5'),\n )\n\n return lat","def region_setup(self, slices, ipa_regions):\n self.ipa_regions = ipa_regions\n self.slices = slices","def __init__(self, manager, name):\n Online.DatapointLoader.DatapointLoader.__init__(self,manager,name)\n self.strerror = ''\n self.inUse = self.dp('InUse')\n self.subfarms = self.dp('SubFarms')\n self.writer = self.manager.devWriter()\n self.reader.add(self.inUse)\n self.reader.add(self.subfarms)","def get_landmarks(self):\n lm_x = [x for x, y in self.landmarks]\n lm_y = [y for x, y in self.landmarks]\n return [lm_x, lm_y]","def __init__(self, config, ll=None, osimmodel=None, landmarks=None):\n self.config = config\n self.ll = ll\n self.trcdata = landmarks\n self.gias_osimmodel = None\n if osimmodel is not None:\n self.set_osim_model(osimmodel)\n self._unit_scaling = dim_unit_scaling(\n self.config['in_unit'], self.config['out_unit']\n )","def show_2gr(img1,img2,title1='',title2=''):\n\tf, (ax1,ax2) = plt.subplots(1,2,figsize=(20,10))\n\tax1.imshow(img1, cmap='gray')\n\tax1.set_title(title1)\n\tax2.imshow(img2, cmap='gray')\n\tax2.set_title(title2)\n\tplt.show()","def vis_mechanically_coupled_regions(img_dir,output_dir,data,dbscn_length,dbscn_min_size,display_not_save=False):\n #Read in the image that is segmented/labelled for nuclei\n img=imread(img_dir)\n\n #save plots to show clusters\n fig = plt.figure(figsize=(6, 2))\n ax0 = fig.add_subplot(131)\n ax1 = fig.add_subplot(132)\n ax3 = fig.add_subplot(133)\n #show segmented image labels\n ax0.imshow(img,aspect='auto') \n ax0.axis('off')\n #nuclear centroid color-coded by their orientation\n img1=ax1.scatter(data[\"Y\"], data[\"X\"], c=data[\"angles\"],s=1)\n ax1.set_xlim(0,img.shape[0])\n ax1.set_ylim(img.shape[1],0)\n plt.colorbar(img1)\n ax1.axis('off')\n\n # plot the cluster assignments\n img3=ax3.scatter(data[data[\"clusters\"]> -1][\"Y\"], data[data[\"clusters\"]> -1][\"X\"], \n c=data[data[\"clusters\"]> -1][\"clusters\"],cmap=\"plasma\",s=1)\n ax3.set_xlim(0,img.shape[0])\n ax3.set_ylim(img.shape[1],0)\n ax3.axis('off')\n\n #add titles\n ax0.title.set_text('Segmented Image')\n ax1.title.set_text('Filtered Orientation')\n ax3.title.set_text('Clusters')\n\n if display_not_save:\n plt.show()\n else: \n plt.savefig((output_dir+\"/\"+img_dir.rsplit('/', 1)[-1][:-4]+\"_\"+str(dbscn_length)+\"_\"+ str(dbscn_min_size)+\".png\"),dpi=600, bbox_inches = 'tight',pad_inches = 0)\n fig.clf()\n plt.close(fig)\n plt.close('all')\n \n \n del fig,ax0,ax1,ax3,img1,img3"],"string":"[\n \"def add_registered_image_layers(\\n viewer: napari.Viewer, *, registration_directory: pathlib.Path\\n) -> Tuple[napari.layers.Image, napari.layers.Labels]:\\n layers: List[LayerDataTuple] = []\\n\\n meta_file = (registration_directory / \\\"brainreg.json\\\").resolve()\\n if meta_file.exists():\\n with open(meta_file) as json_file:\\n metadata = json.load(json_file)\\n layers = load_registration(layers, registration_directory, metadata)\\n else:\\n raise FileNotFoundError(\\n f\\\"'brainreg.json' file not found in {registration_directory}\\\"\\n )\\n\\n boundaries = viewer.add_layer(napari.layers.Layer.create(*layers[0]))\\n labels = viewer.add_layer(napari.layers.Layer.create(*layers[1]))\\n return boundaries, labels\",\n \"def initial_representations():\\n cmd.hide('everything', 'all')\\n cmd.show('cartoon', 'all')\\n cmd.select('ligand', 'resn NFT')\\n cmd.deselect()\\n cmd.show(\\\"sticks\\\", \\\"ligand\\\")\",\n \"def register ():\\n dsf_geom_import.register ()\\n dsf_morph_import.register ()\\n dsf_morph_export.register ()\\n dsf_uvset_import.register ()\\n dsf_arm_import.register ()\\n dsf_pose_import.register ()\\n dsf_wm_import.register ()\\n dsf_geom_export.register ()\\n dsf_prop_export.register ()\",\n \"def __init__(self):\\n DetectLandmarks.__init__(self)\\n self.red_l = 0\\n self.green_l = 0\\n self.blue_l = 0\\n self.red_e = 0\\n self.green_e = 0\\n self.blue_e = 0\\n self.debug = 0\\n self.image = 0\\n self.width = 0\\n self.height = 0\\n self.im_copy = 0\\n self.lip_x = []\\n self.lip_y = []\",\n \"def register(blk):\\n pass\",\n \"def land(self, n, e):\\n pass\",\n \"def test_regref(self):\\n dset1 = self.f.create_dataset('x', (10,10))\\n regref = dset1.regionref[...]\\n dset2 = self.f.create_dataset('y', (1,), dtype=h5py.regionref_dtype)\\n dset2[0] = regref\\n self.assertEqual(type(dset2[0]), h5py.RegionReference)\",\n \"def get_anat_landmarks(image, info, trans, fs_subject, fs_subjects_dir=None):\\n nib = _import_nibabel(\\\"get anatomical landmarks\\\")\\n coords_dict, coord_frame = _get_fid_coords(info[\\\"dig\\\"])\\n if coord_frame != FIFF.FIFFV_COORD_HEAD:\\n raise ValueError(\\n \\\"Fiducial coordinates in `info` must be in \\\"\\n f\\\"the head coordinate frame, got {coord_frame}\\\"\\n )\\n landmarks = np.asarray(\\n (coords_dict[\\\"lpa\\\"], coords_dict[\\\"nasion\\\"], coords_dict[\\\"rpa\\\"])\\n )\\n\\n # get trans and ensure it is from head to MRI\\n trans, _ = _get_trans(trans, fro=\\\"head\\\", to=\\\"mri\\\")\\n landmarks = _meg_landmarks_to_mri_landmarks(landmarks, trans)\\n\\n # Get FS T1 image in MGH format\\n t1w_mgh = _get_t1w_mgh(fs_subject, fs_subjects_dir)\\n\\n # FS MGH image: go to T1 voxel space from surface RAS/TkReg RAS/freesurfer\\n landmarks = _mri_landmarks_to_mri_voxels(landmarks, t1w_mgh)\\n\\n # FS MGH image: go to T1 scanner space from T1 voxel space\\n landmarks = _mri_voxels_to_mri_scanner_ras(landmarks, t1w_mgh)\\n\\n # Input image: go to T1 voxel space from T1 scanner space\\n if isinstance(image, BIDSPath):\\n image = image.fpath\\n img_nii = _load_image(image, name=\\\"image\\\")\\n img_mgh = nib.MGHImage(img_nii.dataobj, img_nii.affine)\\n landmarks = _mri_scanner_ras_to_mri_voxels(landmarks, img_mgh)\\n\\n landmarks = mne.channels.make_dig_montage(\\n lpa=landmarks[0], nasion=landmarks[1], rpa=landmarks[2], coord_frame=\\\"mri_voxel\\\"\\n )\\n\\n return landmarks\",\n \"def test_BiplaneRegistration1(self):\\n\\n self.delayDisplay(\\\"Starting the test\\\")\\n #\\n # first, get some data\\n #\\n import urllib\\n downloads = (\\n ('http://slicer.kitware.com/midas3/download?items=5767', 'FA.nrrd', slicer.util.loadVolume),\\n )\\n\\n for url,name,loader in downloads:\\n filePath = slicer.app.temporaryPath + '/' + name\\n if not os.path.exists(filePath) or os.stat(filePath).st_size == 0:\\n logging.info('Requesting download %s from %s...\\\\n' % (name, url))\\n urllib.urlretrieve(url, filePath)\\n if loader:\\n logging.info('Loading %s...' % (name,))\\n loader(filePath)\\n self.delayDisplay('Finished with download and loading')\\n\\n volumeNode = slicer.util.getNode(pattern=\\\"FA\\\")\\n logic = BiplaneRegistrationLogic()\\n self.assertIsNotNone( logic.hasImageData(volumeNode) )\\n self.delayDisplay('Test passed!')\",\n \"def get_landmarks(self, image): # from https://www.paulvangent.com/2016/08/05/emotion-recognition-using-facial-landmarks/\\n # Ask the detector to find the bounding boxes of each face. The 1 in the\\n # second argument indicates that we should upsample the image 1 time. This\\n # will make everything bigger and allow us to detect more faces.\\n detections = self.detector(image, 1)\\n if len(detections) < 1: # Number of faces detected = 0\\n # print(\\\"Number of faces detected: {}\\\".format(len(detections)))\\n return None\\n # Draw Facial Landmarks with the predictor class\\n shape = self.predictor(image, detections[0])\\n xlist = []\\n ylist = []\\n for i in range(68): # Store X and Y coordinates in two lists\\n xlist.append(float(shape.part(i).x))\\n ylist.append(float(shape.part(i).y))\\n\\n landmarks_vectorised = []\\n landmarks_vectorised = self.our_ft_landmark(xlist, ylist)# Extaraction des features\\n\\n xmean = np.mean(xlist)\\n ymean = np.mean(ylist)\\n xcentral = [(x-xmean) for x in xlist]\\n ycentral = [(y-ymean) for y in ylist]\\n \\n for x, y, w, z in zip(xcentral, ycentral, xlist, ylist):\\n landmarks_vectorised.append(w)\\n landmarks_vectorised.append(z)\\n # landmarks_vectorised.append(x)\\n # landmarks_vectorised.append(y)\\n meannp = np.asarray((ymean, xmean))\\n coornp = np.asarray((z, w))\\n dist = np.linalg.norm(coornp-meannp)# Distance euclidienne\\n landmarks_vectorised.append(dist)\\n landmarks_vectorised.append((math.atan2(y, x)*360)/(2*math.pi))# Calcule de l'ongle entre le moyenne et un point\\n\\n return landmarks_vectorised\",\n \"def add_layout_pins(self):\\n en_offset = self.dc_inst.get_pin(\\\"in\\\").ll()\\n self.add_layout_pin(text=\\\"en\\\",\\n layer=\\\"metal1\\\",\\n offset=en_offset.scale(1,0),\\n width=self.m1_width,\\n height=en_offset.y)\\n\\n out_offset = self.rbl_inv_inst.get_pin(\\\"Z\\\").ll()\\n self.add_layout_pin(text=\\\"out\\\",\\n layer=\\\"metal1\\\",\\n offset=out_offset.scale(1,0),\\n width=self.m1_width,\\n height=out_offset.y)\",\n \"def show_landmarks(image, landmarks):\\n\\n if isinstance(image, str):\\n # image = io.imread(image)\\n image = cv2.cvtColor(cv2.imread(image), cv2.COLOR_BGR2RGB)\\n\\n plt.imshow(image)\\n\\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')\\n plt.pause(0.001) # pause a bit so that plots are updated\",\n \"def createAddOrSelectLabelMapNode(self, script=False):\\r\\n # productive\\r\\n profprint()\\r\\n print \\\"creating label map for working intensity volume\\\"\\r\\n # create, select label map\\r\\n volLogic = slicer.modules.volumes.logic()\\r\\n sliceLogic = slicer.app.layoutManager().sliceWidget(\\\"Red\\\").sliceLogic()\\r\\n vn = sliceLogic.GetBackgroundLayer().GetVolumeNode()\\r\\n self.labelMapNode = slicer.util.getNode(vn.GetName() + \\\"-label\\\")\\r\\n if not self.labelMapNode:\\r\\n self.labelMapNode = volLogic.CreateAndAddLabelVolume(slicer.mrmlScene, vn, vn.GetName() + \\\"-label\\\")\\r\\n # select label volume\\r\\n if not script: #TODO guess there is a bug here (at least while testing with parSearch): also changes the main volume!!\\r\\n selectionNode = slicer.app.applicationLogic().GetSelectionNode()\\r\\n selectionNode.SetReferenceActiveLabelVolumeID(self.labelMapNode.GetID())\\r\\n #slicer.app.applicationLogic().PropagateVolumeSelection() #<<<this line causes unpredictable volume switching\\r\\n #set half transparency\\r\\n scRed=slicer.app.layoutManager().sliceWidget(\\\"Red\\\").sliceController()\\r\\n scRed.setLabelMapOpacity(.5)\\r\\n scYel=slicer.app.layoutManager().sliceWidget(\\\"Yellow\\\").sliceController()\\r\\n scYel.setLabelMapOpacity(.5)\\r\\n scGrn=slicer.app.layoutManager().sliceWidget(\\\"Green\\\").sliceController()\\r\\n scGrn.setLabelMapOpacity(.5)\\r\\n #enable label map outline display mode\\r\\n sRed = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNodeRed\\\")\\r\\n if sRed == None :\\r\\n sRed = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNode1\\\")\\r\\n sRed.SetUseLabelOutline(1)\\r\\n sYel = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNodeYellow\\\")\\r\\n if sYel == None :\\r\\n sYel = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNode2\\\")\\r\\n sYel.SetUseLabelOutline(1)\\r\\n sGrn = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNodeGreen\\\")\\r\\n if sGrn == None :\\r\\n sGrn = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSliceNode3\\\")\\r\\n sGrn.SetUseLabelOutline(1)\\r\\n self.editorWidget.setMasterNode(vn)\\r\\n self.editorWidget.setMergeNode(self.labelMapNode)\",\n \"def _mri_landmarks_to_mri_voxels(mri_landmarks, t1_mgh):\\n # Get landmarks in voxel space, using the T1 data\\n vox2ras_tkr_t = t1_mgh.header.get_vox2ras_tkr()\\n ras_tkr2vox_t = linalg.inv(vox2ras_tkr_t)\\n vox_landmarks = apply_trans(ras_tkr2vox_t, mri_landmarks)\\n\\n return vox_landmarks\",\n \"def registration(im1, im2, num = 10, opt = 'py', outputPath = 'None'):\\n\\n # determin which one is the right side of the breast\\n b_size = 5\\n n_row, n_col = im1.shape\\n side = 0\\n if np.sum(im1[0:b_size,0:b_size]) < np.sum(im1[0:b_size,n_col-b_size:n_col]):\\n side = 1 \\n\\n # flip the right side image\\n if side == 1:\\n im1 = np.fliplr(im1)\\n else:\\n im2 = np.fliplr(im2) \\n\\n # find edges of both images\\n edge1 = findEdge(im1)\\n edge2 = findEdge(im2)\\n\\n # tune edges of both side\\n edge1 = tuneEdge(edge1,im1.shape)\\n edge2 = tuneEdge(edge2,im2.shape)\\n\\n # samping from both side\\n points1 = contour_sampling(edge1, num)\\n points2 = contour_sampling(edge2, num)\\n\\n # for debugging .........................\\n sam_im1 = np.zeros(im1.shape,np.float32)\\n for point in points1:\\n sam_im1[point[0],point[1]] = 1\\n\\n sam_im2 = np.zeros(im2.shape,np.float32)\\n for point in points2:\\n sam_im2[point[0],point[1]] = 1\\n \\n selem = disk(15)\\n dilated1 = ndimage.convolve(sam_im1, selem, mode='constant', cval=0)\\n dilated2 = ndimage.convolve(sam_im2, selem, mode='constant', cval=0)\\n\\n points1 = np.asarray(points1)\\n points2 = np.asarray(points2)\\n \\n # Thin Plate Spline interpolation\\n dst = np.zeros(im1.shape)\\n # im1 as source\\n if opt == 'py': \\n tps = TPSpline.TPSpline()\\n tps.setCorrespondences(points1, points2)\\n dst = tps.warpImage(im1)\\n return dst\\n\\n if opt == 'c':\\n print \\\"Please run the interpolation with C++ exe file!\\\"\\n print \\\"./TPSpline /home/yanbin/Tomosynthesis/libs/TPSpline/test/ps.txt /home/yanbin/Tomosynthesis/libs/TPSpline/test/pd.txt /home/yanbin/Tomosynthesis/libs/TPSpline/test/5016_test.tif /home/yanbin/Tomosynthesis/libs/TPSpline/test/dst.tif\\\"\\n np.savetxt(outputPath + 'ps.txt', points1, '%d', delimiter=' ') # X is an array\\n np.savetxt(outputPath + 'pd.txt', points2, '%d', delimiter=' ') # X is an array\\n tiffLib.imsave(outputPath + 'im1.tif',im1)\\n return None\",\n \"def create_ds9_regions(cat_name,output_fname,color,radius,Type):\\n cat=ascii.read('%s.txt' % cat_name)\\n with open('%s.reg' % output_fname, 'w') as f:\\n f.write('global color=%s dashlist=8 3 width=1\\\\n' % color)\\n f.write('image\\\\n')\\n if Type == 'input':\\n for x,y,mag in zip(cat['COORD_XPIXEL'],cat['COORD_YPIXEL'],cat['MAG']):\\n f.write('circle(%d,%d,%d) # text = {%.2f}\\\\n' %(x,y,radius,mag))\\n elif Type=='detected':\\n for x,y,mag in zip(cat['x_coord_det'],cat['y_coord_det'],cat['mag_det']):\\n f.write('circle(%d,%d,%d) # text = { %.2f}\\\\n' %(x,y,radius,mag))\\n elif Type=='false_detection':\\n for x,y,mag in zip(cat['x_coord'],cat['y_coord'],cat['mag_det']):\\n f.write('circle(%d,%d,%d) # text = { %.2f}\\\\n' %(x,y,radius,mag))\",\n \"def show_landmarks(image, landmarks):\\n if Image.isImageType(image):\\n image = np.asarray(image)\\n plt.imshow(image)\\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')\\n plt.pause(0.001) # pause a bit so that plots are updated\",\n \"def register_com(vol_a: Volume, vol_b: Volume) -> Tuple[Volume, Volume]:\\n from dipy.align.imaffine import transform_centers_of_mass\\n\\n affine = transform_centers_of_mass(vol_a, vol_a.grid_to_world, vol_b, vol_b.grid_to_world)\\n\\n vol_b.world_transform[:] = np.array(affine.affine)\\n return vol_a, vol_b\",\n \"def show_landmarks(image, landmarks):\\n plt.imshow(image)\\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')\\n plt.pause(0.001) # pause a bit so that plots are updated\",\n \"def show_landmarks(image, landmarks):\\n plt.imshow(image)\\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')\\n plt.pause(0.001) # pause a bit so that plots are updated\",\n \"def show_landmarks(image, landmarks):\\n plt.imshow(image)\\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')\\n plt.pause(0.001) # pause a bit so that plots are updated\",\n \"def __init__(self):\\n self.regions = []\",\n \"def register(cls, L):\\r\\n ...\",\n \"def nine_regions(self):\\n\\n coordinateList = []\\n\\n # Top left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\\n coordinateList.append( [x, y] )\\n\\n # Top center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] ) \\n coordinateList.append( [x, y] )\\n\\n # Top right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * ( 1.0 - self.ratioTopLeft[IDX_X] ) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\\n coordinateList.append( [x, y] )\\n\\n # Center left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Center right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Bottom left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n # Bottom center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n # Bottom right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n return coordinateList\",\n \"def __init__(self, *args, **kwargs):\\n _gdi_.Region_swiginit(self,_gdi_.new_Region(*args, **kwargs))\",\n \"def output_ds9_region_file(reference_image_prefix, object_ids, x, y,\\n r_aper, rsi=None, rso=None, index_offset=1.0):\\n outfile = '{}.reg'.format(reference_image_prefix)\\n if os.path.exists(outfile):\\n print('WARNING: CLOBBERING {}'.format(outfile))\\n f = open(outfile, 'w')\\n region_header = ('# Region file format: DS9 version 4.1\\\\n'\\n 'global color=red dashlist=8 3 width=1 '\\n 'font=\\\"helvetica 10 normal roman\\\" select=1 '\\n 'highlite=1 dash=0 fixed=0 edit=1 move=1 '\\n 'delete=1 include=1 source=1\\\\nphysical\\\\n')\\n f.write(region_header)\\n if not rso or not rsi:\\n rsi = np.array([20]*len(x))\\n rso = np.array([25]*len(x))\\n for i, j, k, l, m in zip(x, y, rsi, rso, object_ids):\\n line = 'annulus({},{},{},{}) # text={{{}}}\\\\n'.format(i+index_offset,\\n j+index_offset,\\n k, l, m)\\n line2 = 'circle({},{},{}) \\\\n'.format(i+index_offset,\\n j+index_offset,\\n r_aper)\\n f.write(line)\\n f.write(line2)\\n f.close()\",\n \"def visualize_results_same_landmark(img_1: str, img_2: str) -> None:\\n\\n similarity_percentage = same_landmark_images(img_1, img_2)\\n print(similarity_percentage)\\n same_landmark = 'YES' if similarity_percentage > THRESHOLD else 'NO'\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(121)\\n ax.imshow(imread(img_1))\\n ax.set_title('')\\n\\n ax = fig.add_subplot(122)\\n ax.set_title('')\\n ax.imshow(imread(img_2))\\n\\n fig.suptitle(SAME_LANDMARK + same_landmark, fontsize=16)\\n fig.tight_layout()\\n plt.show()\",\n \"def plug_vifs(self, instance, network_info):\\n raise NotImplementedError()\",\n \"def __init__(self, source, label_config_center, name=None, header=None, \\n view_min=None, view_max=None, alpha=255, colormap='gray',\\n cross_pos=None):\\n if isinstance(source, np.ndarray):\\n self._data = np.rot90(source)\\n if name == None:\\n self._name = 'new_image'\\n else:\\n self._name = str(name)\\n if not isinstance(header, nib.nifti1.Nifti1Header):\\n raise ValueError(\\\"Parameter header must be specified!\\\")\\n elif header.get_data_shape() == source.shape:\\n self._header = header\\n self._img = None\\n else:\\n raise ValueError(\\\"Data dimension does not match.\\\")\\n else:\\n self._img = nib.load(source)\\n self._header = self._img.get_header()\\n basename = os.path.basename(source.strip('/'))\\n self._name = re.sub(r'(.*)\\\\.nii(\\\\.gz)?', r'\\\\1', basename)\\n self.save_mem_load()\\n\\n # For convenience, define a shift variable\\n self._y_shift = self.get_data_shape()[1] - 1\\n\\n if view_min == None:\\n self._view_min = self._data.min()\\n else:\\n self._view_min = view_min\\n\\n if view_max == None:\\n self._view_max = self._data.max()\\n else:\\n self._view_max = view_max\\n\\n self._alpha = alpha\\n self._colormap = colormap\\n self._rgba_list = range(self.get_data_shape()[2])\\n \\n # bool status for the item\\n self._visible = True\\n if len(self.get_data_shape()) == 3:\\n self._4d = False\\n else:\\n self._4d = True\\n self._time_point = 0\\n\\n # temporal variant for OrthView\\n self._cross_pos = cross_pos\\n\\n # define a dictionary \\n self.label_config_center = label_config_center\\n self.label_config_center.single_roi_view_update.connect(self.update_single_roi)\\n \\n # undo redo stacks\\n self.undo_stack = DoStack()\\n self.redo_stack = DoStack()\\n\\n self.update_rgba()\\n if self._cross_pos:\\n self.update_orth_rgba()\",\n \"def CreateNorthSymbols(color1=(0.0,0.0,0.0,1.0),color2=(1.0,1.0,1.0,1.0),\\n scale=1.0,symbol_manager=None):\\n\\n if symbol_manager is None:\\n sm=gview.GvSymbolManager()\\n else:\\n sm=symbol_manager\\n\\n refnames=[]\\n for item in [GVNORTHSYM1]:\\n rname,junk=CreateNorthSymbol(color1,color2,scale,sm)\\n refnames.append(rname)\\n\\n return (refnames,sm)\",\n \"def itkLabelShapeOpeningImageFilterIUS2_cast(*args):\\n return _itkLabelShapeOpeningImageFilterPython.itkLabelShapeOpeningImageFilterIUS2_cast(*args)\",\n \"def load_registration_as_layers() -> None:\\n viewer = getattr(widget, \\\"viewer\\\").value\\n registration_directory = pathlib.Path(\\n getattr(widget, \\\"registration_output_folder\\\").value\\n )\\n add_registered_image_layers(\\n viewer, registration_directory=registration_directory\\n )\",\n \"def draw_and_show_landmarks_and_head_pose(landmarks, image, yaw='unknown', pitch='unknown', roll='unknown', info_text=''):\\n for pos in landmarks:\\n cv2.circle(image, (int(pos[0]), int(pos[1])), 5, (0, 0, 255), -1)\\n cv2.putText(image, 'Yaw: {}'.format(yaw), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\\n cv2.putText(image, 'Pitch: {}'.format(pitch), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\\n cv2.putText(image, 'Roll: {}'.format(roll), (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\\n cv2.putText(image, info_text, (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)\\n\\n if not isinstance(yaw, str) and not isinstance(pitch, str) and not isinstance(roll, str):\\n # Create rotation matrix\\n rotation = Rotation.from_euler('yxz', [-yaw, pitch, roll], degrees=True)\\n rotation_matrix = rotation.as_dcm()\\n # offsets to axis end-points\\n axis_points = np.float32([[50, 0, 0],\\n [0, -50, 0],\\n [0, 0, 50],\\n ])\\n\\n # position in image\\n position = np.array([60, image.shape[0] - 60, 0])\\n\\n axis = np.zeros((3, 3), dtype=float)\\n axis[0] = np.dot(rotation_matrix, axis_points[0]) + position\\n axis[1] = np.dot(rotation_matrix, axis_points[1]) + position\\n axis[2] = np.dot(rotation_matrix, axis_points[2]) + position\\n\\n cv2.rectangle(image,\\n (0, image.shape[0] - 120),\\n (120, image.shape[0]),\\n (255, 255, 255),\\n -1)\\n cv2.line(image, (int(position[0]), int(position[1])), (int(axis[1][0]), int(axis[1][1])), (0, 255, 0), 3)\\n cv2.line(image, (int(position[0]), int(position[1])), (int(axis[0][0]), int(axis[0][1])), (255, 0, 0), 3)\\n cv2.line(image, (int(position[0]), int(position[1])), (int(axis[2][0]), int(axis[2][1])), (0, 0, 255), 3)\\n\\n cv2.imshow('Out', image)\",\n \"def __init__(self, island, x = 0, y = 0, s=\\\"A\\\"):\\n self.island = island\\n self.name = s\\n self.x = x\\n self.y = y\",\n \"def add_new_region(self, image_name: str, region_text: str, region_position: RegionPosition, region_type: str):\\n pass\",\n \"def __init__(self, app):\\n super(LandmarkDetector, self).__init__()\\n app.start()\\n session = app.session\\n # Get the service ALMemory.\\n self.memory = session.service(\\\"ALMemory\\\")\\n # Connect the event callback.\\n self.subscriber = self.memory.subscriber(\\\"LandmarkDetected\\\")\\n self.subscriber.signal.connect(self.on_landmark_detected)\\n # Get the services ALTextToSpeech and ALLandMarkDetection.\\n self.tts = session.service(\\\"ALTextToSpeech\\\")\\n self.landmark_detection = session.service(\\\"ALLandMarkDetection\\\")\\n self.landmark_detection.subscribe(\\\"LandmarkDetector\\\", 500, 0.0 )\\n self.got_landmark = False\",\n \"def test_assign_to_regions(self):\\n \\n tool = pybedtools.BedTool(clipper.test_file(\\\"FOX2Brain-05.15.09.polyATrim.adapterTrim.rmRep.sorted.rmDup.peaks.bed\\\"))\\n \\n assign_to_regions(tool=tool, \\n clusters=\\\"test\\\", \\n speciesFA= clipper.test_file(\\\"mm9.fa\\\"), \\n regions_dir=os.path.join(clipper.test_dir(), \\\"regions\\\"), \\n regions={\\\"exons\\\" : \\\"Exon\\\", \\\"utr3\\\" : \\\"3' UTR\\\", \\n \\\"utr5\\\" : \\\"5' UTR\\\", \\\"proxintron500\\\" : \\\"Proximal Intron\\\", \\n \\\"distintron500\\\" : \\\"Distal Intron\\\"} ,\\n assigned_dir = clipper.test_dir(),\\n fasta_dir = clipper.test_dir(),\\n species=\\\"mm9\\\", \\n nrand = 3, \\n getseq=False)\",\n \"def test_enterprise_vpg_annotations(self):\\n proj_obj, fabric_obj, pr_obj = self._create_prerequisites()\\n\\n esi_id = '00:11:22:33:44:55:66:77:88:99'\\n vlan_1 = 42\\n vlan_2 = '4094'\\n pi_name = self.id() + '_physical_interface1'\\n pi = PhysicalInterface(name=pi_name,\\n parent_obj=pr_obj,\\n ethernet_segment_identifier=esi_id)\\n pi_uuid = self._vnc_lib.physical_interface_create(pi)\\n pi_obj = self._vnc_lib.physical_interface_read(id=pi_uuid)\\n\\n fabric_name = fabric_obj.get_fq_name()\\n pi_fq_name = pi_obj.get_fq_name()\\n\\n # Create VPG\\n vpg_name = \\\"vpg-1\\\"\\n vpg = VirtualPortGroup(vpg_name, parent_obj=fabric_obj)\\n vpg_uuid = self.api.virtual_port_group_create(vpg)\\n vpg_obj = self._vnc_lib.virtual_port_group_read(id=vpg_uuid)\\n vpg_name = vpg_obj.get_fq_name()\\n\\n # Create single VN\\n vn1 = VirtualNetwork('vn1-%s' % (self.id()), parent_obj=proj_obj)\\n self.api.virtual_network_create(vn1)\\n\\n # Create a VMI that's attached to vpg-1 and having reference\\n # to vn1\\n vmi_obj_1 = VirtualMachineInterface(self.id() + \\\"1\\\",\\n parent_obj=proj_obj)\\n vmi_obj_1.set_virtual_network(vn1)\\n\\n # Create KV_Pairs for this VMI\\n kv_pairs = self._create_kv_pairs(pi_fq_name,\\n fabric_name,\\n vpg_name)\\n\\n vmi_obj_1.set_virtual_machine_interface_bindings(kv_pairs)\\n\\n vmi_obj_1.set_virtual_machine_interface_properties(\\n VirtualMachineInterfacePropertiesType(\\n sub_interface_vlan_tag=vlan_1))\\n vmi_uuid_1 = self.api.virtual_machine_interface_create(vmi_obj_1)\\n vpg_obj.add_virtual_machine_interface(vmi_obj_1)\\n self.api.virtual_port_group_update(vpg_obj)\\n\\n mock_zk = self._api_server._db_conn._zk_db\\n # Verify if Znode are created for VMI1\\n vlan_validation_node1 = os.path.join(\\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\\n 'virtual-port-group:%s' % vpg_uuid,\\n 'virtual-network:%s' % vn1.uuid)\\n znode_vlan_1_id = mock_zk._zk_client.read_node(\\n vlan_validation_node1)\\n tagged_validation_path = os.path.join(\\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\\n 'virtual-port-group:%s' % vpg_uuid,\\n 'vlan:%s' % znode_vlan_1_id)\\n\\n # Read Znode\\n znode_vmi_1_uuid = mock_zk._zk_client.read_node(\\n tagged_validation_path)\\n # Verify if correct Znodes are created\\n assert znode_vmi_1_uuid == vmi_uuid_1, \\\\\\n \\\"Znode for VMI_1 (%s) doesn't exist\\\" % vmi_uuid_1\\n\\n # Attach Second VMI with untagged vlan\\n vn2 = VirtualNetwork('vn2-%s' % (self.id()), parent_obj=proj_obj)\\n self.api.virtual_network_create(vn2)\\n\\n # Create first untagged VMI and attach it to Virtual Port Group\\n vmi_obj_2 = VirtualMachineInterface(self.id() + \\\"2\\\",\\n parent_obj=proj_obj)\\n vmi_obj_2.set_virtual_network(vn2)\\n\\n # Create KV_Pairs for this VMI with an untagged VLAN\\n # If tor_port_vlan_id is set, then it signifies a untagged VMI\\n kv_pairs = self._create_kv_pairs(pi_fq_name,\\n fabric_name,\\n vpg_name,\\n tor_port_vlan_id=vlan_2)\\n\\n vmi_obj_2.set_virtual_machine_interface_bindings(kv_pairs)\\n vmi_uuid_2 = self.api.virtual_machine_interface_create(vmi_obj_2)\\n vpg_obj.add_virtual_machine_interface(vmi_obj_2)\\n self.api.virtual_port_group_update(vpg_obj)\\n\\n vpg_obj = self.api.virtual_port_group_read(id=vpg_obj.uuid)\\n # Verify if validation Znodes are created for both VMIs\\n # Path for ZNode creation\\n untagged_validation_path = os.path.join(\\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\\n 'virtual-port-group:%s' % vpg_obj.uuid,\\n 'untagged')\\n\\n # Read Znode2\\n znode_vmi_2_uuid = mock_zk._zk_client.read_node(\\n untagged_validation_path)\\n # Verify if correct Znode is created\\n assert znode_vmi_2_uuid == vmi_uuid_2, \\\\\\n \\\"Znode for VMI_2 (%s) doesn't exist\\\" % vmi_uuid_2\\n\\n # Delete VMIs from VPG\\n self.api.virtual_machine_interface_delete(id=vmi_uuid_1)\\n self.api.virtual_machine_interface_delete(id=vmi_uuid_2)\\n\\n # Read Znodes\\n znode_vmi_1_uuid = mock_zk._zk_client.read_node(tagged_validation_path)\\n znode_vmi_2_uuid = mock_zk._zk_client.read_node(\\n untagged_validation_path)\\n # Verify if Znodes are deleted\\n assert znode_vmi_1_uuid is None, \\\\\\n \\\"Znode for VMI_1 (%s) exists\\\" % vmi_uuid_1\\n assert znode_vmi_2_uuid is None, \\\\\\n \\\"Znode for VMI_2 (%s) exists\\\" % vmi_uuid_2\\n\\n self.api.virtual_port_group_delete(id=vpg_obj.uuid)\\n self.api.physical_interface_delete(id=pi_uuid)\\n self.api.physical_router_delete(id=pr_obj.uuid)\\n self.api.fabric_delete(id=fabric_obj.uuid)\",\n \"def CreateNorthSymbol(ntype=GVNORTHSYM1,color1=(0.0,0.0,0.0,1.0),\\n color2=(1.0,1.0,1.0,1.0),scale=1.0,symbol_manager=None):\\n\\n if symbol_manager is None:\\n sm=gview.GvSymbolManager()\\n else:\\n sm=symbol_manager\\n\\n cstr1=gvogrfs.gv_to_ogr_color(color1)\\n if len(cstr1) < 9:\\n cstr1=cstr1+\\\"FF\\\"\\n\\n cstr2=gvogrfs.gv_to_ogr_color(color2)\\n if len(cstr2) < 9:\\n cstr2=cstr2+\\\"FF\\\"\\n\\n sstr = str(scale).replace('.','_')\\n\\n refname=ntype+cstr1[1:]+cstr2[1:]+sstr\\n if ntype==GVNORTHSYM1: \\n shape=gview.GvShape(type=gview.GVSHAPE_AREA)\\n shape.set_node(1.0*scale,-2.6*scale,node=0)\\n shape.set_node(0.0,-0.8*scale,node=1)\\n shape.set_node(-1.0*scale,-2.6*scale,node=2)\\n shape.set_node(0.0,2.6*scale,node=3)\\n shape.set_node(1.0*scale,-2.6*scale,node=4)\\n shape.set_property('_gv_ogrfs','PEN(c:'+cstr1+');BRUSH(c:'+\\\\\\n cstr2+')')\\n sm.inject_vector_symbol(refname,shape)\\n\\n return (refname,sm)\",\n \"def draw_annotations(self, image, results):\\n image.flags.writeable = True\\n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\\n for face_landmarks in results.multi_face_landmarks:\\n # draw face landmark net\\n mp_drawing.draw_landmarks(\\n image=image,\\n landmark_list=face_landmarks,\\n connections=mp_face_mesh.FACE_CONNECTIONS,\\n landmark_drawing_spec=self.drawing_spec,\\n connection_drawing_spec=self.drawing_spec)\\n cv2.imshow('MediaPipe FaceMesh', image)\",\n \"def add_karyotype_rank_from_bands_info(self,\\n regions_with_karyotype_bands: list, # [ (seq_region_name, seq_region_id, unversioned_name) ]\\n other_regions_with_ranks: list, # [ (seq_region_name, seq_region_id, unversioned_name) ] \\n attrib_type_map: dict,\\n work_dir: str):\\n # form set of used seq_region_id(s)\\n regions_with_ranks = frozenset( map(lambda el: el[1], other_regions_with_ranks) )\\n\\n # get set of seq_region_ids with bands\\n regions_with_bands = set( map(lambda el: el[1], regions_with_karyotype_bands) )\\n\\n # seq_region_ids list to add ranks for\\n region_ids_with_bands_but_no_karyotype_ranks = sorted( list( regions_with_bands - regions_with_ranks ) )\\n\\n # return if nothing to add\\n if not region_ids_with_bands_but_no_karyotype_ranks:\\n return\\n\\n # technical / optimization. get external_db_id for \\\"karyotype_rank\\\"\\n karyotype_rank_attrib_id = self.id_from_map_or_die(\\\"karyotype_rank\\\", attrib_type_map, \\\"attrib_type_map\\\")\\n\\n # set/update proper attributes for \\\"chromomosome\\\" regions\\n rank_insertions_trios = []\\n for seq_region_id in region_ids_with_bands_but_no_karyotype_ranks:\\n rank_insertions_trios.append( (seq_region_id, karyotype_rank_attrib_id, len(rank_insertions_trios) + 1 + len(regions_with_ranks)) )\\n\\n # run insertion SQL for \\\"karyotype_rank\\\"\\n # do not alter \\\"coord_system_tag\\\" anyhow in the case of the \\\"chromosome\\\" coord_system\\n self.insert_to_db(\\n rank_insertions_trios,\\n \\\"seq_region_attrib\\\",\\n [\\\"seq_region_id\\\", \\\"attrib_type_id\\\", \\\"value\\\"],\\n self.pjc(work_dir, \\\"karyotype_rank_insertion\\\"),\\n ignore = True\\n )\",\n \"def plot_sensors_2d_intra(epo1: mne.Epochs, epo2: mne.Epochs, lab: bool = False):\\n\\n # extract sensor info and transform loc to fit with headmodel\\n loc1 = copy(np.array([ch['loc'][:3] for ch in epo1.info['chs']]))\\n loc1 = transform_2d_intra(loc1, traX=-0.178, traY=0.012, traZ=0, rotZ=(-np.pi/2))\\n lab1 = [ch for ch in epo1.ch_names]\\n\\n loc2 = copy(np.array([ch['loc'][:3] for ch in epo2.info['chs']]))\\n loc2 = transform_2d_intra(loc2, traX=0.178, traY=0.012, traZ=0, rotZ=(-np.pi/2))\\n lab2 = [ch for ch in epo2.ch_names]\\n\\n bads_epo1 = []\\n bads_epo1 = epo1.info['bads']\\n bads_epo2 = []\\n bads_epo2 = epo2.info['bads']\\n\\n # plot sensors\\n for ch in epo1.ch_names:\\n if ch in bads_epo1:\\n index_ch = epo1.ch_names.index(ch)\\n x1, y1, z1 = loc1[index_ch, :]\\n plt.plot(x1, y1, marker='x', color='dimgrey')\\n if lab:\\n plt.text(x1+0.012, y1+0.012, lab1[index_ch],\\n horizontalalignment='center',\\n verticalalignment='center')\\n else:\\n index_ch = epo1.ch_names.index(ch)\\n x1, y1, z1 = loc1[index_ch, :]\\n plt.plot(x1, y1, marker='o', color='dimgrey')\\n if lab:\\n plt.text(x1+0.012, y1+0.012, lab1[index_ch],\\n horizontalalignment='center',\\n verticalalignment='center')\\n\\n \\n for ch in epo2.ch_names:\\n if ch in bads_epo2:\\n index_ch = epo2.ch_names.index(ch)\\n x2, y2, z2 = loc2[index_ch, :]\\n plt.plot(x2, y2, marker='x', color='dimgrey')\\n if lab:\\n plt.text(x2+0.012, y2+0.012, lab2[index_ch],\\n horizontalalignment='center',\\n verticalalignment='center')\\n else:\\n index_ch = epo2.ch_names.index(ch)\\n x2, y2, z2 = loc2[index_ch, :]\\n plt.plot(x2, y2, marker='o', color='dimgrey')\\n if lab:\\n plt.text(x2+0.012, y2+0.012, lab2[index_ch],\\n horizontalalignment='center',\\n verticalalignment='center')\",\n \"def place_cat(self, ox1, ox2, nb_atoms, r1, r2, rs):\\n \\\"\\\"\\\"rotation angle\\\"\\\"\\\"\\n rot = pi * rs / 180\\n \\\"\\\"\\\"first oxygen\\\"\\\"\\\"\\n v1 = ox1[1]\\n \\\"\\\"\\\"second oxygen\\\"\\\"\\\"\\n v2 = ox2[1]\\n d = v2 - v1\\n k = (r1 - r2 + Vector.length(d)) / (Vector.length(d) * 2)\\n \\\"\\\"\\\"center of ox1-ox2\\\"\\\"\\\"\\n hd = self.cartesian_to_spherical(d * k)\\n corr = acos( Vector.length(d * k) / r1)\\n \\\"\\\"\\\"cross point of two spheres\\\"\\\"\\\"\\n cp = self.spherical_to_cartesian(r1, hd[1], (hd[2] - corr))\\n ev = Vector.normal(d)\\n ka = Vector.cross(ev, cp)\\n kb = ev*(ev*cp)\\n for rt in range(360 / rs):\\n ang = rt*rot\\n flag = True\\n \\\"\\\"\\\"Rodrigues' formula for vector rotation\\\"\\\"\\\"\\n cat_vector = cp * cos(ang) + ka*sin(ang) + kb*(1-cos(ang)) + v1\\n for test_nb in nb_atoms:\\n \\\"\\\"\\\"distance between cation and other neighbours has to be more\\n than 2A\\\"\\\"\\\"\\n if Vector.length(cat_vector - nb_atoms[test_nb][1]) <= 2.:\\n flag = False\\n break\\n if flag:\\n return cat_vector\",\n \"def register():\\n minerl_rllib.models.torch.baseline.register()\",\n \"def create_type_area_object(): \\n RefValues()\\n RefValues().add_ref_parameter_from_file('DIN_winter', 'D:/Utveckling/g_EKOSTAT_tool/test_data/din_vinter.txt')\\n RefValues().add_ref_parameter_from_file('TOTN_winter', 'D:/Utveckling/g_EKOSTAT_tool/test_data/totn_vinter.txt')\",\n \"def islands(\\n self,\\n corners=False\\n ):\\n\\n if self.linked_mask == None:\\n print \\\"Island assignment requires a mask.\\\"\\n return\\n \\n structure = (Struct(\\n \\\"simple\\\", \\n ndim=self.linked_mask.data.ndim, \\n corners=corners)).struct\\n\\n labels, nlabels = ndimage.label(self.linked_mask.data,\\n structure=structure)\\n \\n self.data = labels\\n self.nclouds = nlabels\\n self.slices = ndimage.find_objects(self.data)\",\n \"def plot_detections(self, draw_landmarks=True, draw_facelines=True, muscle=False, pose=False):\\n\\n from PIL import Image\\n import matplotlib.pyplot as plt\\n from matplotlib.patches import Rectangle\\n import seaborn as sns\\n from textwrap import wrap\\n sns.set_context(\\\"paper\\\", font_scale=2.0)\\n\\n # check how many images.\\n inputs = self.input().unique()\\n all_axes = []\\n for imagefile in inputs:\\n f, axes = plt.subplots(1, 3, figsize=(15, 7))\\n ax = axes[0]\\n try:\\n if os.path.exists(imagefile):\\n color = \\\"w\\\"\\n # draw base image\\n im = Image.open(imagefile)\\n ax.imshow(im)\\n image_exists = True\\n else:\\n image_exists = False\\n color = \\\"k\\\"\\n except:\\n color = \\\"k\\\"\\n print(f\\\"Input image {imagefile} not found.\\\")\\n image_exists = False\\n\\n sub_data = self.query(\\\"input==@imagefile\\\")\\n for i in range(len(sub_data)):\\n # draw landmarks\\n row = sub_data.iloc[[i]]\\n landmark = row.landmark().values[0]\\n currx = landmark[:68]\\n curry = landmark[68:]\\n if draw_landmarks:\\n if draw_facelines:\\n draw_lineface(currx, curry, ax=ax, color=color, linewidth=3)\\n else:\\n draw_lineface(currx, curry, ax=ax, color=color, linewidth=0)\\n # muscle\\n if muscle:\\n au20index = [\\n f\\\"AU{str(i).zfill(2)}\\\"\\n for i in [\\n 1,\\n 2,\\n 4,\\n 5,\\n 6,\\n 7,\\n 9,\\n 10,\\n 12,\\n 14,\\n 15,\\n 17,\\n 18,\\n 20,\\n 23,\\n 24,\\n 25,\\n 26,\\n 28,\\n 43,\\n ]\\n ]\\n aus = row.aus().T.reindex(index=au20index).fillna(0).T.values[0]\\n draw_muscles(currx, curry, au=aus, ax=ax, all=\\\"heatmap\\\")\\n # facebox\\n facebox = row.facebox().values[0]\\n rect = Rectangle(\\n (facebox[0], facebox[1]),\\n facebox[2],\\n facebox[3],\\n linewidth=2,\\n edgecolor=\\\"cyan\\\",\\n fill=False,\\n )\\n ax.add_patch(rect)\\n\\n # facepose\\n if pose:\\n draw_facepose(pose=row.facepose().values[0], facebox=facebox, ax=ax)\\n\\n if image_exists:\\n if sub_data.input().any():\\n ax.set_title(\\n \\\"\\\\n\\\".join(wrap(sub_data.input().unique()[0], 30)), loc=\\\"left\\\", wrap=True, fontsize=14\\n )\\n else:\\n ax.set_title(\\\"\\\\n\\\".join(wrap(imagefile, 30)), loc=\\\"left\\\", wrap=True, fontsize=14)\\n ax.set(ylim=ax.get_ylim()[::-1])\\n ax.set_aspect(\\\"equal\\\", \\\"box\\\")\\n\\n # plot AUs\\n sub_data.aus().T.plot(kind=\\\"barh\\\", ax=axes[1])\\n axes[1].invert_yaxis()\\n axes[1].get_legend().remove()\\n axes[1].set(xlim=[0, 1.1], title=\\\"Action Units\\\")\\n\\n # plot emotions\\n sub_data.emotions().T.plot(kind=\\\"barh\\\", ax=axes[2])\\n axes[2].invert_yaxis()\\n axes[2].get_legend().remove()\\n axes[2].set(xlim=[0, 1.1], title=\\\"Emotions\\\")\\n\\n plt.tight_layout()\\n plt.show()\\n all_axes.append(axes)\\n return axes\",\n \"def map_to_homo_nid(self, ids, ntype):\\n ...\",\n \"def map_to_homo_nid(self, ids, ntype):\\n ...\",\n \"def _add_opposing_boundaries(self,btype=[1,6]):\\n logger.info(\\\"add_opposing_boundaries: start of method\\\")\\n\\n if btype==[2,5]:\\n D=0\\n W=1\\n H=2\\n elif btype==[3,4]:\\n D=1\\n W=0\\n H=2\\n elif btype==[1,6]:\\n D=2\\n W=1\\n H=0\\n\\n Lx = self.domain_size[D]\\n Ly = self.domain_size[W]\\n Lz = self.domain_size[H]\\n #Rotate pore coordinates (use only internal pores)\\n pnum = self._net.pore_data['numbering'][self._net.pore_data['type']==0]\\n pcoords = np.zeros_like(self._net.pore_data['coords'][pnum,:])\\n pcoords[:,0] = self._net.pore_data['coords'][pnum,D]\\n pcoords[:,1] = self._net.pore_data['coords'][pnum,W]\\n pcoords[:,2] = self._net.pore_data['coords'][pnum,H]\\n\\n #Determine dimensions of image from dimensions of domain\\n f = 100 #minimum image dimension\\n im_dim = [0,0,0]\\n im_dim[0] = np.floor(f*Lx/np.min([Lx,Ly,Lz]))\\n im_dim[1] = np.floor(f*Ly/np.min([Lx,Ly,Lz]))\\n im_dim[2] = np.floor(f*Lz/np.min([Lx,Ly,Lz]))\\n im_dim = np.array(im_dim,dtype=int)\\n\\n #Convert pore coordinates into image subscripts\\n im_subs = np.zeros_like(pcoords,dtype=int)\\n im_subs[:,0] = pcoords[:,0]*im_dim[0]/Lx\\n im_subs[:,1] = pcoords[:,1]*im_dim[1]/Ly\\n im_subs[:,2] = pcoords[:,2]*im_dim[2]/Lz\\n #Find linear indices of each pore in the new image\\n im_inds = np.ravel_multi_index((im_subs[:,0], im_subs[:,1], im_subs[:,2]), dims=(im_dim), order='F')\\n\\n #Generate 3D image of points (place pore numbers at each site for use later)\\n img = np.zeros(im_dim,dtype=int)\\n img[im_subs[:,0],im_subs[:,1],im_subs[:,2]] = pnum\\n\\n #Perform distance transform on points and also get 'indicies' of each point\\n img_dt, ind_dt = spim.distance_transform_edt(img==0)\\n\\n #Project all* internal points to x face\\n #*Note that it's possible/likely that mutliple internal points map to the same boundary point\\n img_bd0 = np.zeros([im_dim[1],im_dim[2]],dtype=int)\\n img_bd1 = np.zeros([im_dim[1],im_dim[2]],dtype=int)\\n img_bd0[im_subs[:,1],im_subs[:,2]] = im_inds\\n img_bd1[im_subs[:,1],im_subs[:,2]] = im_inds\\n\\n #Create 2D array of distance transform indices for 0 and end faces\\n dt_D0 = ind_dt[0,0,:,:]*(img_bd0>0) #0 face\\n dt_D1 = ind_dt[0,-1,:,:]*(img_bd1>0) #end face\\n\\n #Create a 2D mask containing x coordinates of internal points and -1 elsewhere\\n img_D0 = -np.ones([im_dim[1],im_dim[2]],dtype=int)\\n img_D1 = -np.ones([im_dim[1],im_dim[2]],dtype=int)\\n img_D0[im_subs[:,1],im_subs[:,2]] = im_subs[:,0]\\n img_D1[im_subs[:,1],im_subs[:,2]] = im_subs[:,0]\\n\\n #Find where x value of internal points corresponds to x value of distance transform indices\\n img_bd0 = (img_D0 == dt_D0)*img_bd0\\n img_bd1 = (img_D1 == dt_D1)*img_bd1\\n\\n #Convert boundary sites to linear indices\\n inds_bd0 = img_bd0[np.nonzero(img_bd0)]\\n inds_bd1 = img_bd1[np.nonzero(img_bd1)]\\n\\n #Use linear indices to find pore ID nums\\n nums_bd0 = img[np.unravel_index(inds_bd0, dims=(im_dim), order='F')]\\n nums_bd1 = img[np.unravel_index(inds_bd1, dims=(im_dim), order='F')]\\n nums_bd = np.append(nums_bd0,nums_bd1)\\n types_bd = np.append(np.zeros_like(nums_bd0),np.ones_like(nums_bd1))\\n\\n #Add new boundary pores and throats to the network\\n Np = self._net.num_pores() #Get all pores including previously added boundaries\\n bp_numbering = np.r_[Np:Np+np.size(nums_bd)]\\n bp_type = (types_bd==0)*btype[0] + (types_bd==1)*btype[1]\\n bp_coords = np.zeros([np.size(nums_bd),3])\\n bp_coords[types_bd==0,D] = np.zeros_like(nums_bd0)-.0001\\n bp_coords[types_bd==0,W] = pcoords[nums_bd0,1]\\n bp_coords[types_bd==0,H] = pcoords[nums_bd0,2]\\n bp_coords[types_bd==1,D] = np.ones_like(nums_bd1)*Lx+0.0001\\n bp_coords[types_bd==1,W] = pcoords[nums_bd1,1]\\n bp_coords[types_bd==1,H] = pcoords[nums_bd1,2]\\n self._net.pore_data['numbering'] = np.append(self._net.pore_data['numbering'],bp_numbering)\\n self._net.pore_data['type'] = np.append(self._net.pore_data['type'],bp_type)\\n self._net.pore_data['coords'] = np.concatenate((self._net.pore_data['coords'],bp_coords))\\n Nt = self._net.num_throats()\\n bt_numbering = np.r_[Nt:Nt+np.size(nums_bd)]\\n bt_type = np.ones(np.size(nums_bd),dtype=int)*2\\n bt_connections = np.zeros([np.size(nums_bd),2],dtype=int)\\n bt_connections[:,0] = nums_bd\\n bt_connections[:,1] = bp_numbering\\n self._net.throat_data['numbering'] = np.append(self._net.throat_data['numbering'],bt_numbering)\\n self._net.throat_data['type'] = np.append(self._net.throat_data['type'],bt_type)\\n self._net.throat_data['conns'] = np.concatenate((self._net.throat_data['conns'],bt_connections))\",\n \"def visualize_landmarks_2d(\\n landmarks, figure_size=(7, 7), browser_style=\\\"buttons\\\", custom_info_callback=None\\n):\\n # Make sure that landmarks is a list even with one landmark manager member\\n if isinstance(landmarks, LandmarkManager):\\n landmarks = [landmarks]\\n\\n # Get the number of landmark managers\\n n_landmarks = len(landmarks)\\n\\n # Define the styling options\\n main_style = \\\"info\\\"\\n\\n output = ipywidgets.Output()\\n\\n @output.capture(clear_output=True, wait=True)\\n def render_function(change):\\n # get selected index and selected group\\n i = landmark_number_wid.selected_values if n_landmarks > 1 else 0\\n g = landmark_options_wid.selected_values[\\\"landmarks\\\"][\\\"group\\\"]\\n\\n if landmark_options_wid.selected_values[\\\"landmarks\\\"][\\\"render_landmarks\\\"]:\\n # get shape\\n shape = landmarks[i][g]\\n\\n # Create options dictionary\\n options = dict()\\n options.update(landmark_options_wid.selected_values[\\\"lines\\\"])\\n options.update(landmark_options_wid.selected_values[\\\"markers\\\"])\\n options[\\\"image_view\\\"] = landmark_options_wid.selected_values[\\\"image_view\\\"]\\n options.update(renderer_options_wid.selected_values[\\\"numbering_matplotlib\\\"])\\n options.update(renderer_options_wid.selected_values[\\\"axes\\\"])\\n\\n # Correct options based on the type of the shape\\n if hasattr(shape, \\\"labels\\\"):\\n # If the shape is a LabelledPointUndirectedGraph ...\\n # ...use the legend options\\n options.update(renderer_options_wid.selected_values[\\\"legend\\\"])\\n # ...use with_labels\\n options[\\\"with_labels\\\"] = landmark_options_wid.selected_values[\\n \\\"landmarks\\\"\\n ][\\\"with_labels\\\"]\\n # ...correct colours\\n line_colour = []\\n marker_face_colour = []\\n marker_edge_colour = []\\n for lbl in options[\\\"with_labels\\\"]:\\n id = shape.labels.index(lbl)\\n line_colour.append(options[\\\"line_colour\\\"][id])\\n marker_face_colour.append(options[\\\"marker_face_colour\\\"][id])\\n marker_edge_colour.append(options[\\\"marker_edge_colour\\\"][id])\\n options[\\\"line_colour\\\"] = line_colour\\n options[\\\"marker_face_colour\\\"] = marker_face_colour\\n options[\\\"marker_edge_colour\\\"] = marker_edge_colour\\n else:\\n # If shape is PointCloud, TriMesh or PointGraph\\n # ...correct colours\\n options[\\\"line_colour\\\"] = options[\\\"line_colour\\\"][0]\\n options[\\\"marker_face_colour\\\"] = options[\\\"marker_face_colour\\\"][0]\\n options[\\\"marker_edge_colour\\\"] = options[\\\"marker_edge_colour\\\"][0]\\n\\n # Get figure size\\n new_figure_size = (\\n renderer_options_wid.selected_values[\\\"zoom_one\\\"] * figure_size[0],\\n renderer_options_wid.selected_values[\\\"zoom_one\\\"] * figure_size[1],\\n )\\n\\n # Render shape with selected options\\n save_figure_wid.renderer = shape.view(\\n figure_id=save_figure_wid.renderer.figure_id,\\n new_figure=False,\\n figure_size=new_figure_size,\\n **options\\n )\\n\\n # Force rendering\\n save_figure_wid.renderer.force_draw()\\n else:\\n output.clear_output()\\n\\n # update info text widget\\n update_info(landmarks[i], g, custom_info_callback=custom_info_callback)\\n\\n # Define function that updates the info text\\n def update_info(landmarks, group, custom_info_callback=None):\\n if group is not None:\\n min_b, max_b = landmarks[group].bounds()\\n rang = landmarks[group].range()\\n cm = landmarks[group].centre()\\n text_per_line = [\\n \\\"> {} landmark points\\\".format(landmarks[group].n_points),\\n \\\"> {}\\\".format(name_of_callable(landmarks[group])),\\n \\\"> Bounds: [{0:.1f}-{1:.1f}]W, [{2:.1f}-{3:.1f}]H\\\".format(\\n min_b[0], max_b[0], min_b[1], max_b[1]\\n ),\\n \\\"> Range: {0:.1f}W, {1:.1f}H\\\".format(rang[0], rang[1]),\\n \\\"> Centre of mass: ({0:.1f}, {1:.1f})\\\".format(cm[0], cm[1]),\\n \\\"> Norm: {0:.2f}\\\".format(landmarks[group].norm()),\\n ]\\n if custom_info_callback is not None:\\n # iterate over the list of messages returned by the callback\\n # function and append them in the text_per_line.\\n for msg in custom_info_callback(landmarks[group]):\\n text_per_line.append(\\\"> {}\\\".format(msg))\\n else:\\n text_per_line = [\\\"No landmarks available.\\\"]\\n\\n info_wid.set_widget_state(text_per_line=text_per_line)\\n\\n # Create widgets\\n groups_keys, labels_keys = extract_group_labels_from_landmarks(landmarks[0])\\n first_label = labels_keys[0] if labels_keys else None\\n landmark_options_wid = LandmarkOptionsWidget(\\n group_keys=groups_keys,\\n labels_keys=labels_keys,\\n type=\\\"2D\\\",\\n render_function=render_function,\\n )\\n renderer_options_wid = RendererOptionsWidget(\\n options_tabs=[\\\"zoom_one\\\", \\\"axes\\\", \\\"numbering_matplotlib\\\", \\\"legend\\\"],\\n labels=first_label,\\n axes_x_limits=0.1,\\n axes_y_limits=0.1,\\n render_function=render_function,\\n )\\n info_wid = TextPrintWidget(text_per_line=[\\\"\\\"])\\n save_figure_wid = SaveMatplotlibFigureOptionsWidget()\\n\\n # Group widgets\\n if n_landmarks > 1:\\n # Define function that updates options' widgets state\\n def update_widgets(change):\\n # Get new groups and labels\\n i = landmark_number_wid.selected_values\\n g_keys, l_keys = extract_group_labels_from_landmarks(landmarks[i])\\n\\n # Update landmarks options\\n landmark_options_wid.set_widget_state(\\n group_keys=g_keys, labels_keys=l_keys, allow_callback=True\\n )\\n\\n # Landmark selection slider\\n index = {\\\"min\\\": 0, \\\"max\\\": n_landmarks - 1, \\\"step\\\": 1, \\\"index\\\": 0}\\n landmark_number_wid = AnimationOptionsWidget(\\n index,\\n render_function=update_widgets,\\n index_style=browser_style,\\n interval=0.2,\\n description=\\\"Shape\\\",\\n loop_enabled=True,\\n continuous_update=False,\\n )\\n\\n # Header widget\\n logo_wid = LogoWidget(style=main_style)\\n logo_wid.layout.margin = \\\"0px 10px 0px 0px\\\"\\n header_wid = ipywidgets.HBox([logo_wid, landmark_number_wid])\\n header_wid.layout.align_items = \\\"center\\\"\\n header_wid.layout.margin = \\\"0px 0px 10px 0px\\\"\\n else:\\n # Header widget\\n header_wid = LogoWidget(style=main_style)\\n header_wid.layout.margin = \\\"0px 10px 0px 0px\\\"\\n options_box = ipywidgets.Tab(\\n children=[info_wid, landmark_options_wid, renderer_options_wid, save_figure_wid]\\n )\\n tab_titles = [\\\"Info\\\", \\\"Landmarks\\\", \\\"Renderer\\\", \\\"Export\\\"]\\n for (k, tl) in enumerate(tab_titles):\\n options_box.set_title(k, tl)\\n\\n output.layout.align_self = \\\"center\\\"\\n wid = ipywidgets.VBox([header_wid, options_box, output])\\n\\n # Set widget's style\\n wid.box_style = main_style\\n wid.layout.border = \\\"2px solid \\\" + map_styles_to_hex_colours(main_style)\\n\\n # Display final widget\\n final_box = ipywidgets.Box([wid])\\n final_box.layout.display = \\\"flex\\\"\\n ipydisplay.display(final_box)\\n\\n # Trigger initial visualization\\n render_function({})\",\n \"def test_ipam_vrfs_create(self):\\n pass\",\n \"def AddRasterMap(self, name, firstMap = True, secondMap = True):\\n cmdlist = ['d.rast', 'map=%s' % name]\\n if firstMap:\\n self.GetFirstMap().AddLayer(type='raster', command=cmdlist, l_active=True,\\n name=name, l_hidden=False, l_opacity=1.0, l_render=False)\\n self.GetWindow().UpdateMap(render = True, renderVector = False)\",\n \"def plot_sensors_2d_inter(epo1: mne.Epochs, epo2: mne.Epochs, lab: bool = False):\\n\\n # extract sensor info and transform loc to fit with headmodel\\n loc1 = copy(np.array([ch['loc'][:3] for ch in epo1.info['chs']]))\\n loc1 = transform(loc1, traX=-0.17, traY=0, traZ=0.08, rotY=(-np.pi/12), rotZ=(-np.pi/2))\\n lab1 = [ch for ch in epo1.ch_names]\\n\\n loc2 = copy(np.array([ch['loc'][:3] for ch in epo2.info['chs']]))\\n loc2 = transform(loc2, traX=0.17, traY=0, traZ=0.08, rotY=(np.pi/12), rotZ=np.pi/2)\\n lab2 = [ch for ch in epo2.ch_names]\\n\\n bads_epo1 = []\\n bads_epo1 = epo1.info['bads']\\n bads_epo2 = []\\n bads_epo2 = epo2.info['bads']\\n \\n # plot sensors ('x' for bads)\\n for ch in epo1.ch_names:\\n if ch in bads_epo1:\\n index_ch = epo1.ch_names.index(ch)\\n x1, y1, z1 = loc1[index_ch, :]\\n plt.plot(x1, y1, marker='x', color='dimgrey')\\n if lab:\\n plt.text(x1+0.012, y1+0.012, lab1[index_ch],\\n horizontalalignment='center',\\n verticalalignment='center')\\n else:\\n index_ch = epo1.ch_names.index(ch)\\n x1, y1, z1 = loc1[index_ch, :]\\n plt.plot(x1, y1, marker='o', color='dimgrey')\\n if lab:\\n plt.text(x1+0.012, y1+0.012, lab1[index_ch],\\n horizontalalignment='center',\\n verticalalignment='center')\\n\\n \\n for ch in epo2.ch_names:\\n if ch in bads_epo2:\\n index_ch = epo2.ch_names.index(ch)\\n x2, y2, z2 = loc2[index_ch, :]\\n plt.plot(x2, y2, marker='x', color='dimgrey')\\n if lab:\\n plt.text(x2+0.012, y2+0.012, lab2[index_ch],\\n horizontalalignment='center',\\n verticalalignment='center')\\n else:\\n index_ch = epo2.ch_names.index(ch)\\n x2, y2, z2 = loc2[index_ch, :]\\n plt.plot(x2, y2, marker='o', color='dimgrey')\\n if lab:\\n plt.text(x2+0.012, y2+0.012, lab2[index_ch],\\n horizontalalignment='center',\\n verticalalignment='center')\",\n \"def test_verify_sp_reinit_remove_annotations(self):\\n proj_obj, fabric_obj, pr_obj = self._create_prerequisites(\\n enterprise_style_flag=False)\\n\\n esi_id = '00:11:22:33:44:55:66:77:88:99'\\n vlan_1 = 42\\n vlan_2 = '4094'\\n pi_name = self.id() + '_physical_interface1'\\n pi = PhysicalInterface(name=pi_name,\\n parent_obj=pr_obj,\\n ethernet_segment_identifier=esi_id)\\n pi_uuid = self._vnc_lib.physical_interface_create(pi)\\n pi_obj = self._vnc_lib.physical_interface_read(id=pi_uuid)\\n\\n fabric_name = fabric_obj.get_fq_name()\\n pi_fq_name = pi_obj.get_fq_name()\\n\\n # Create VPG\\n vpg_name = \\\"vpg-1\\\"\\n vpg = VirtualPortGroup(vpg_name, parent_obj=fabric_obj)\\n vpg_uuid = self.api.virtual_port_group_create(vpg)\\n vpg_obj = self._vnc_lib.virtual_port_group_read(id=vpg_uuid)\\n vpg_name = vpg_obj.get_fq_name()\\n\\n # Create single VN\\n vn1 = VirtualNetwork('vn1-%s' % (self.id()), parent_obj=proj_obj)\\n self.api.virtual_network_create(vn1)\\n\\n # Create a VMI that's attached to vpg-1 and having reference\\n # to vn1\\n vmi_obj_1 = VirtualMachineInterface(self.id() + \\\"1\\\",\\n parent_obj=proj_obj)\\n vmi_obj_1.set_virtual_network(vn1)\\n\\n # Create KV_Pairs for this VMI\\n kv_pairs = self._create_kv_pairs(pi_fq_name,\\n fabric_name,\\n vpg_name)\\n\\n vmi_obj_1.set_virtual_machine_interface_bindings(kv_pairs)\\n\\n vmi_obj_1.set_virtual_machine_interface_properties(\\n VirtualMachineInterfacePropertiesType(\\n sub_interface_vlan_tag=vlan_1))\\n vmi_uuid_1 = self.api.virtual_machine_interface_create(vmi_obj_1)\\n vpg_obj.add_virtual_machine_interface(vmi_obj_1)\\n\\n mock_zk = self._api_server._db_conn._zk_db\\n # Verify if Znode are created for VMI1\\n validation_node1 = os.path.join(\\n _DEFAULT_ZK_FABRIC_SP_PATH_PREFIX,\\n 'virtual-port-group:%s' % vpg_uuid,\\n 'virtual-network:%s' % vn1.uuid,\\n 'vlan:%s' % vlan_1)\\n\\n # Read Znode\\n znode_vmi_1_uuid = mock_zk._zk_client.read_node(validation_node1)\\n # Verify if correct Znodes are created\\n assert znode_vmi_1_uuid == vmi_uuid_1, \\\\\\n \\\"Znode for VMI_1 (%s) doesn't exist\\\" % vmi_uuid_1\\n\\n # Attach Second VMI with untagged vlan\\n vn2 = VirtualNetwork('vn2-%s' % (self.id()), parent_obj=proj_obj)\\n self.api.virtual_network_create(vn2)\\n\\n # Create first untagged VMI and attach it to Virtual Port Group\\n vmi_obj_2 = VirtualMachineInterface(self.id() + \\\"2\\\",\\n parent_obj=proj_obj)\\n vmi_obj_2.set_virtual_network(vn2)\\n\\n # Create KV_Pairs for this VMI with an untagged VLAN\\n # If tor_port_vlan_id is set, then it signifies a untagged VMI\\n kv_pairs = self._create_kv_pairs(pi_fq_name,\\n fabric_name,\\n vpg_name,\\n tor_port_vlan_id=vlan_2)\\n\\n vmi_obj_2.set_virtual_machine_interface_bindings(kv_pairs)\\n vmi_uuid_2 = self.api.virtual_machine_interface_create(vmi_obj_2)\\n vpg_obj = self.api.virtual_port_group_read(id=vpg_obj.uuid)\\n vpg_obj.add_virtual_machine_interface(vmi_obj_2)\\n\\n # Verify if Znode are created for VMI2\\n validation_node2 = os.path.join(\\n _DEFAULT_ZK_FABRIC_SP_PATH_PREFIX,\\n 'virtual-port-group:%s' % vpg_uuid,\\n 'untagged')\\n\\n # Set annotations in VPG\\n kvp_array = []\\n kvp = KeyValuePair(validation_node1, vmi_uuid_1)\\n kvp_array.append(kvp)\\n kvp = KeyValuePair(validation_node2, vmi_uuid_2)\\n kvp_array.append(kvp)\\n kvps = KeyValuePairs()\\n kvps.set_key_value_pair(kvp_array)\\n vpg_obj.set_annotations(kvps)\\n self.api.virtual_port_group_update(vpg_obj)\\n\\n # Read Znode\\n znode_vmi_2_uuid = mock_zk._zk_client.read_node(validation_node2)\\n # Verify if correct Znodes are created\\n assert znode_vmi_2_uuid == vmi_uuid_2, \\\\\\n \\\"Znode for VMI_2 (%s) doesn't exist\\\" % vmi_uuid_2\\n\\n # Delete all Znodes for VMI1, VMI2\\n mock_zk._zk_client.delete_node(validation_node1, True)\\n mock_zk._zk_client.delete_node(validation_node2, True)\\n\\n # Verify if annotations are added in VPG object\\n vpg_obj = self._vnc_lib.virtual_port_group_read(id=vpg_uuid)\\n vpg_annotations = vpg_obj.get_annotations()\\n assert vpg_annotations, \\\\\\n \\\"Annotations not added for VPG (%s)\\\" % vpg_obj.uuid\\n\\n # manually setting contrail_version to 21.4\\n # so db_resync is run as part of upgrade scenario\\n self._api_server._args.contrail_version = '21.4'\\n\\n self._api_server._db_conn._db_resync_done.clear()\\n # API server DB reinit\\n self._api_server._db_init_entries()\\n self._api_server._db_conn.wait_for_resync_done()\\n\\n # Verify if Znodes are added back\\n znode_vmi_1_uuid = mock_zk._zk_client.read_node(validation_node1)\\n znode_vmi_2_uuid = mock_zk._zk_client.read_node(validation_node2)\\n\\n # Verify if correct Znodes are created\\n assert znode_vmi_1_uuid == vmi_uuid_1, \\\\\\n \\\"Znode for VMI_1 (%s) doesn't exist\\\" % vmi_uuid_1\\n assert znode_vmi_2_uuid == vmi_uuid_2, \\\\\\n \\\"Znode for VMI_2 (%s) doesn't exist\\\" % vmi_uuid_2\\n\\n # Verify if annotations are removed from VPG object\\n vpg_obj = self._vnc_lib.virtual_port_group_read(id=vpg_uuid)\\n vpg_annotations = vpg_obj.get_annotations()\\n assert vpg_annotations is None, \\\\\\n \\\"Annotations not removed for VPG (%s)\\\" % vpg_obj.uuid\\n\\n # Delete VMIs from VPG\\n self.api.virtual_machine_interface_delete(id=vmi_uuid_1)\\n self.api.virtual_machine_interface_delete(id=vmi_uuid_2)\\n self.api.virtual_port_group_delete(id=vpg_obj.uuid)\\n self.api.physical_interface_delete(id=pi_uuid)\\n self.api.physical_router_delete(id=pr_obj.uuid)\\n self.api.fabric_delete(id=fabric_obj.uuid)\\n # adding back zknode to original version\\n # so other test cases runs from the begining\\n mock_zk._zk_client.update_node(PATH_SYNC, '2011')\",\n \"def add_sidedef(self, x_offset, y_offset, upper_texture, lower_texture, middle_texture, sector):\\n self.append({ 'x_offset': x_offset, 'y_offset': y_offset, 'upper_texture': upper_texture, 'lower_texture': lower_texture, 'middle_texture': middle_texture, 'sector': sector})\\n return len(self)-1\",\n \"def add_bilink(self, nodeport_a, nodeport_b, bilink):\",\n \"def nifti2dicom(seg_nifti, bk_nifti, ref_dicom_dir, save_dir, description, mode_RGB=False, zoom_num=4, watermarks=True): \\n #Load nifti, here is segmentation and background\\n seg_image = sitk.ReadImage(seg_nifti)\\n seg_image = sitk.GetArrayFromImage(seg_image)\\n seg_image = seg_image.astype(np.uint8)\\n \\n # print(nifti_image.shape)\\n bk_image = sitk.ReadImage(bk_nifti)\\n bk_image = sitk.GetArrayFromImage(bk_image)\\n\\n #Get Volume report from the seg_image, cubic ml, and the 95% CI:\\n v_nonenhancing = round(seg_image[seg_image==1].sum()/1000,1)\\n ci_nonenhancing = round(v_nonenhancing*0.2,1)\\n v_enhancing = round(seg_image[seg_image==4].sum()/1000,1)\\n ci_enhancing = round(v_enhancing*0.3,1)\\n v_edema = round(seg_image[seg_image==2].sum()/1000,1)\\n ci_edema = round(v_edema*0.1,1)\\n\\n #Loading the reference dicom, in order to get the headers of each slice. \\n series_IDs = sitk.ImageSeriesReader.GetGDCMSeriesIDs(ref_dicom_dir)\\n if not series_IDs:\\n print(\\\"ERROR: given directory \\\\\\\"\\\"+data_directory+\\\"\\\\\\\" does not contain a DICOM series.\\\")\\n sys.exit(1)\\n\\n series_file_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(ref_dicom_dir, series_IDs[0])\\n\\n series_reader = sitk.ImageSeriesReader()\\n series_reader.SetFileNames(series_file_names)\\n\\n # Configure the reader to load all of the DICOM tags (public+private):\\n # By default tags are not loaded (saves time).\\n # By default if tags are loaded, the private tags are not loaded.\\n # We explicitly configure the reader to load tags, including the private ones.\\n series_reader.MetaDataDictionaryArrayUpdateOn()\\n series_reader.LoadPrivateTagsOn()\\n ref_image = series_reader.Execute()\\n \\n #set reader for slice \\n reader = sitk.ImageFileReader()\\n reader.LoadPrivateTagsOn()\\n \\n writer = sitk.ImageFileWriter()\\n # Use the study/series/frame of reference information given in the meta-data\\n # dictionary and not the automatically generated information from the file IO\\n writer.KeepOriginalImageUIDOn()\\n\\n # Copy some of the tags and add the relevant tags indicating the change.\\n # For the series instance UID (0020|000e), each of the components is a number, cannot start\\n # with zero, and separated by a '.' We create a unique series ID using the date and time. tags of interest:\\n \\n castFilter = sitk.CastImageFilter()\\n castFilter.SetOutputPixelType(sitk.sitkInt16)\\n ORG_ROOT=\\\"1.3.12.2\\\"\\n #create SeriesInstanceUID and StudyInstanceUID\\n SeriesInstanceUID = generateUID(org_root=ORG_ROOT)\\n StudyInstanceUID = generateUID(org_root=ORG_ROOT)\\n #create a prefix for the accession number\\n acc='BTS'+series_reader.GetMetaData(0,\\\"0008|0050\\\")\\n #changing spacing\\n reader.SetFileName(series_file_names[0])\\n reader.ReadImageInformation()\\n\\n if mode_RGB:\\n customized_tag_values = [(\\\"0008|103e\\\", description),\\n (\\\"0020|000e\\\", SeriesInstanceUID),\\n (\\\"0008|0050\\\", acc),\\n (\\\"0020|000d\\\", StudyInstanceUID), \\n (\\\"0028|0004\\\", 'RGB'),\\n (\\\"0028|0002\\\", \\\"3\\\")]\\n else:\\n customized_tag_values = [(\\\"0008|103e\\\", description),\\n (\\\"0020|000e\\\", SeriesInstanceUID), \\n (\\\"0008|0050\\\", acc),\\n (\\\"0020|000d\\\", StudyInstanceUID)] \\n\\n os.makedirs(save_dir, exist_ok = True)\\n\\n #for nifti, the main axis is the first one, while for dicoms it is the last one\\n for i in range(ref_image.GetDepth()):\\n #zoom 2 times, todo need to figure out which axis to zoom, post is the 3rd\\n #pre assume the first axis is the slice numbers\\n bk_slice = ndimage.zoom(bk_image[i,:,:], zoom_num, order=0)\\n seg_slice = ndimage.zoom(seg_image[i,:,:], zoom_num, order=0)\\n \\n #Due to the DICOM saving coordinate system is different with nifti,i.e mirrored, it is easier to flip array\\n bk_slice = np.flip(bk_slice, (0, 1)) \\n seg_slice = np.flip(seg_slice, (0, 1)) \\n\\n #get contours\\n seg_idx = get_contours(seg_slice)\\n \\n #add watermarks\\n if watermarks:\\n canvas_tmp = np.zeros(list(bk_slice.shape), dtype=np.uint8)\\n font = cv2.FONT_HERSHEY_PLAIN\\n cv2.putText(canvas_tmp,'FOR RESEARCH ONLY;REFER TO OFFICIAL REPORT FOR DETAILS',(10,30), \\n font,2,255,1)\\n cv2.putText(canvas_tmp,'(This tool is intended for evaluation of gliomas, and results may be unreliable for other pathologies)',(90,50), \\n font,1,255,1) \\n #add Legend and volumes \\n cv2.putText(canvas_tmp, 'Legend Volume(+/-95% CI)',(10,900), font,0.8,255,1)\\n cv2.putText(canvas_tmp, f'Edema {v_edema}+/-{ci_edema} mL',(30,920), font,0.8,255,1)\\n cv2.putText(canvas_tmp, f'Enhancing {v_enhancing}+/-{ci_enhancing} mL',(30,940), font,0.8,255,1)\\n cv2.putText(canvas_tmp, f'Non- {v_nonenhancing}+/-{ci_nonenhancing} mL',(30,960), font,0.8,255,1)\\n cv2.putText(canvas_tmp,'Enhancing', (30,975), font,0.8,255,1)\\n cv2.putText(canvas_tmp,'(The error is based on testing of algorithm performance vs. manual segmentation)', (150,1000), font,1,255,1)\\n\\n \\n \\n #burning segmentation contour into slices\\n cv2.line(seg_idx, (10,915), (20,915), 2, 2)\\n cv2.line(seg_idx, (10,935), (20,935), 4, 2)\\n cv2.line(seg_idx, (10,955), (20,955), 1, 2)\\n \\n if mode_RGB:\\n #burning the watermarks\\n bk_slice[canvas_tmp==255]=bk_slice.max()\\n #convert dicom from nomogram to RGB\\n bk_slice = toRGB(bk_slice)\\n #colorize the bk_slice according to seg_idx\\n bk_slice[0,:,:,0][seg_idx==1] = 255\\n bk_slice[0,:,:,1][seg_idx==4] = 255\\n bk_slice[0,:,:,2][seg_idx==2] = 255 \\n else:\\n #grey the ori_image_slice according to seg_idx\\n bk_slice[canvas_tmp==255]=bk_slice.max()//2\\n bk_slice[seg_idx==1] = bk_slice.max()*2//50\\n bk_slice[seg_idx==2] = bk_slice.max()*1//50\\n bk_slice[seg_idx==4] = bk_slice.max()*3//50\\n\\n converted_slice = sitk.GetImageFromArray(bk_slice)\\n reader.SetFileName(series_file_names[i])\\n reader.ReadImageInformation()\\n spacing_new = [i/zoom_num for i in reader.GetSpacing()[:-1]] + [reader.GetSpacing()[-1]]\\n \\n #generate SOPInstanceUID\\n SOPInstanceUID = generateUID(org_root=ORG_ROOT)\\n series_tag_values = [(k, reader.GetMetaData(k)) for k in reader.GetMetaDataKeys()] + customized_tag_values + [(\\\"0008|0018\\\", SOPInstanceUID)]\\n# print(series_tag_values)\\n if '_seg_' in description:\\n converted_slice = converted_slice \\n \\n # Tags shared by the series.\\n for tag, value in series_tag_values:\\n converted_slice.SetMetaData(tag, value)\\n \\n # especially set spacing tags\\n # Image Position (Patient)\\n converted_slice.SetMetaData(\\\"0020|0013\\\", str(i)) # Instance Number\\n converted_slice.SetSpacing(spacing_new)\\n \\n # Write to the output directory and add the extension dcm, to force writing in DICOM format \\n writer.SetFileName(os.path.join(save_dir, str(i)+'.dcm'))\\n writer.Execute(converted_slice)\",\n \"def _mri_voxels_to_mri_scanner_ras(mri_landmarks, img_mgh):\\n # Get landmarks in voxel space, using the T1 data\\n vox2ras = img_mgh.header.get_vox2ras()\\n ras_landmarks = apply_trans(vox2ras, mri_landmarks) # in scanner RAS\\n return ras_landmarks\",\n \"def setup_localxyzs(self):\\n self.localxyzs = [sp.vscl(1.0/v[2],v) for v in self.uvlclxyzs]\\n self.fovsides = list()\\n lastxyz = self.localxyzs[-1]\\n for xyz in self.localxyzs:\\n self.fovsides.append(FOVSIDE(xyz,lastxyz))\\n lastxyz = xyz\",\n \"def add_pins(self):\\n\\n for bit in range(self.addr_size):\\n self.add_pin(\\\"addr_{0}\\\".format(bit),\\\"INPUT\\\")\\n \\n self.add_pin(\\\"wl_en\\\", \\\"INPUT\\\")\\n\\n for bit in range(self.num_rows):\\n self.add_pin(\\\"wl_{0}\\\".format(bit),\\\"OUTPUT\\\")\\n \\n self.add_pin(\\\"vdd\\\",\\\"POWER\\\")\\n self.add_pin(\\\"gnd\\\",\\\"GROUND\\\")\",\n \"def setupLL_Native(self):\\n self.LLN_Selector = slicer.qMRMLNodeComboBox()\\n self.LLN_Selector.nodeTypes = ['vtkMRMLMultiVolumeNode']\\n self.LLN_Selector.noneEnabled = True\\n self.LLN_Selector.setMRMLScene(slicer.mrmlScene)\\n self.LLN_Selector.addEnabled = 0\\n self.LLN_SelectorLabel = qt.QLabel('Native Look Locker')\\n self.LLN_Selector.setToolTip(\\\"Select the pre contrast Look Locker to create the T1 Mapping\\\")\\n self.InputOutput_Layout.addRow(self.LLN_SelectorLabel, self.LLN_Selector)\",\n \"def show_landmarks(image, landmarks, ground_truth=None):\\n plt.imshow(image, cmap='gray')\\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r', label=\\\"Prediction\\\")\\n if ground_truth is not None:\\n plt.scatter(ground_truth[:, 0], ground_truth[:, 1], s=10, marker='.', c='b', label=\\\"Ground Truth\\\")\\n # plt.figlegend('', ('Red', 'Blue'), 'center left')\\n plt.pause(0.001) # pause a bit so that plots are updated\",\n \"def __init__(self, world, location, elevation):\\n LandCell.__init__(self, world, location, elevation)\",\n \"def boron_nitride():\\n\\n positions = [[1/3, 2/3, 1/4],\\n [2/3, 1/4, 3/4],\\n [1/3, 2/3, 3/4],\\n [2/3, 1/3, 1/4]]\\n species = ['B', 'B', 'N', 'N']\\n bravais = 'hexagonal'\\n space_group = 194\\n lattice_parameters = {'a': Set(2.51242804, 'angstrom'), 'c': Set(7.70726501, 'angstrom')}\\n data = {'fractional':positions,\\n 'species':species,\\n 'lattice_parameters':lattice_parameters,\\n 'space_group': ('', space_group),\\n 'bravais': bravais,\\n 'n_atoms': 4}\\n return data\",\n \"def register_manual_translation(vol_a: Volume, vol_b: Volume) -> np.ndarray:\\n plt.ion()\\n fig, ax = plt.subplots(1, 2)\\n w, h, l = vol_a.data.shape\\n ax[0].imshow(\\n np.mean(vol_a.data[w // 4:-w // 4, h // 4:-h // 4, l // 4:-l // 4], axis=2, dtype=np.float32).T / 2 ** 16)\\n w, h, l = vol_b.data.shape\\n ax[1].imshow(\\n np.mean(vol_b.data[w // 4:-w // 4, h // 4:-h // 4, l // 4:-l // 4], axis=2, dtype=np.float32).T / 2 ** 16)\\n\\n points = plt.ginput(2, timeout=0)\\n plt.close()\\n\\n fig, ax = plt.subplots(1, 2)\\n w, h, l = vol_a.data.shape\\n ax[0].imshow(\\n np.mean(vol_a.data[w // 4:-w // 4, h // 4:-h // 4, l // 4:-l // 4], axis=1, dtype=np.float32).T / 2 ** 16)\\n w, h, l = vol_b.data.shape\\n ax[1].imshow(\\n np.mean(vol_b.data[w // 4:-w // 4, h // 4:-h // 4, l // 4:-l // 4], axis=1, dtype=np.float32).T / 2 ** 16)\\n\\n points2 = plt.ginput(2, timeout=0)\\n plt.close()\\n\\n return np.array([points[1][0] - points[0][0], points[1][1] - points[0][1], points2[1][1] - points2[0][1]])\",\n \"def register_pair(self, t1, t2):\\n key = tuple(sorted((t1, t2)))\\n try:\\n shift, error = self._cache[key]\\n except KeyError:\\n # We test a series of increasing overlap window sizes to help avoid\\n # missing alignments when the stage position error is large relative\\n # to the tile overlap. Simply using a large overlap in all cases\\n # limits the maximum achievable correlation thus increasing the\\n # error metric, leading to worse overall results. The window size\\n # starts at the nominal size and doubles until it's at least 10% of\\n # the tile size. If the nominal overlap is already 10% or greater,\\n # we only use that one size.\\n smin = self.intersection(key[0], key[1]).shape\\n smax = np.round(self.metadata.size * 0.1)\\n sizes = [smin]\\n while any(sizes[-1] < smax):\\n sizes.append(sizes[-1] * 2)\\n results = [self._register(key[0], key[1], s) for s in sizes]\\n # Use the shift from the window size that gave the lowest error.\\n shift, _ = min(results, key=lambda r: r[1])\\n # Extract the images from the nominal overlap window but with the\\n # shift applied to the second tile's position, and compute the error\\n # metric on these images. This should be even lower than the error\\n # computed above.\\n _, o1, o2 = self.overlap(key[0], key[1], shift=shift)\\n error = utils.nccw(o1, o2, self.filter_sigma)\\n self._cache[key] = (shift, error)\\n if t1 > t2:\\n shift = -shift\\n # Return copy of shift to prevent corruption of cached values.\\n return shift.copy(), error\",\n \"def presenetCar():\",\n \"def registerVertices(self,vl):\\n self.set('patchmesh.vertices',FuzzList(vl))\",\n \"def LabelDisks(self):\\n pass\",\n \"def __init__(self, coord1, coord2, typekey='normal', offset=None, offset_max_dist=None):\\n self.start, self.stop, self.type = list(map(round,coord1, repeat(7))),\\\\\\n list(map(round,coord2, repeat(7))), typekey\\n if offset != None:\\n self.df = pd.DataFrame(self._get_alt_profile(pole='n'), columns=['lat', 'long', 'alt', 'descr'])\\n self.addOffset(offset, offset_max_dist)\\n else:\\n self.df = pd.DataFrame(self._get_alt_profile(pole='y'), columns=['lat', 'long', 'alt', 'descr'])\\n self._set_prev_azi_angles()\",\n \"def set_roi_to_volume(volume, center, sub_volume):\\n volume_shape = volume.shape \\n patch_shape = sub_volume.shape\\n output_volume = volume\\n for i in range(len(center)):\\n if(center[i] >= volume_shape[i]):\\n return output_volume\\n r0max = [int(x/2) for x in patch_shape]\\n r1max = [patch_shape[i] - r0max[i] for i in range(len(r0max))]\\n r0 = [min(r0max[i], center[i]) for i in range(len(r0max))]\\n r1 = [min(r1max[i], volume_shape[i] - center[i]) for i in range(len(r0max))]\\n patch_center = r0max\\n\\n if(len(center) == 3):\\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\\n range(center[1] - r0[1], center[1] + r1[1]),\\n range(center[2] - r0[2], center[2] + r1[2]))] = \\\\\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]))]\\n elif(len(center) == 4):\\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\\n range(center[1] - r0[1], center[1] + r1[1]),\\n range(center[2] - r0[2], center[2] + r1[2]),\\n range(center[3] - r0[3], center[3] + r1[3]))] = \\\\\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]),\\n range(patch_center[3] - r0[3], patch_center[3] + r1[3]))]\\n else:\\n raise ValueError(\\\"array dimension should be 3 or 4\\\") \\n return output_volume\",\n \"def set_roi_to_volume(volume, center, sub_volume):\\n volume_shape = volume.shape \\n patch_shape = sub_volume.shape\\n output_volume = volume\\n for i in range(len(center)):\\n if(center[i] >= volume_shape[i]):\\n return output_volume\\n r0max = [int(x/2) for x in patch_shape]\\n r1max = [patch_shape[i] - r0max[i] for i in range(len(r0max))]\\n r0 = [min(r0max[i], center[i]) for i in range(len(r0max))]\\n r1 = [min(r1max[i], volume_shape[i] - center[i]) for i in range(len(r0max))]\\n patch_center = r0max\\n\\n if(len(center) == 3):\\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\\n range(center[1] - r0[1], center[1] + r1[1]),\\n range(center[2] - r0[2], center[2] + r1[2]))] = \\\\\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]))]\\n elif(len(center) == 4):\\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\\n range(center[1] - r0[1], center[1] + r1[1]),\\n range(center[2] - r0[2], center[2] + r1[2]),\\n range(center[3] - r0[3], center[3] + r1[3]))] = \\\\\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]),\\n range(patch_center[3] - r0[3], patch_center[3] + r1[3]))]\\n else:\\n raise ValueError(\\\"array dimension should be 3 or 4\\\") \\n return output_volume\",\n \"def __init__(\\n self,\\n folder_path: FolderPathType,\\n streams: Optional[List[str]] = None,\\n verbose: bool = False,\\n ):\\n folder_path = Path(folder_path)\\n\\n streams = streams or self.get_streams(folder_path=folder_path)\\n\\n data_interfaces = dict()\\n for stream in streams:\\n if \\\"ap\\\" in stream:\\n probe_name = stream[:5]\\n file_path = (\\n folder_path / f\\\"{folder_path.stem}_{probe_name}\\\" / f\\\"{folder_path.stem}_t0.{probe_name}.ap.bin\\\"\\n )\\n interface = SpikeGLXRecordingInterface(file_path=file_path)\\n if \\\"lf\\\" in stream:\\n probe_name = stream[:5]\\n file_path = (\\n folder_path / f\\\"{folder_path.stem}_{probe_name}\\\" / f\\\"{folder_path.stem}_t0.{probe_name}.lf.bin\\\"\\n )\\n interface = SpikeGLXRecordingInterface(file_path=file_path)\\n if \\\"nidq\\\" in stream:\\n file_path = folder_path / f\\\"{folder_path.stem}_t0.nidq.bin\\\"\\n interface = SpikeGLXNIDQInterface(file_path=file_path)\\n num_channels = interface.recording_extractor.get_num_channels()\\n # To avoid warning/error turing write\\n # TODO: When PyNWB supports other more proper AUX electrode types, remove\\n interface.recording_extractor.set_property(key=\\\"shank_electrode_number\\\", values=[np.nan] * num_channels)\\n interface.recording_extractor.set_property(key=\\\"contact_shapes\\\", values=[np.nan] * num_channels)\\n data_interfaces.update({stream: interface})\\n\\n super().__init__(data_interfaces=data_interfaces, verbose=verbose)\",\n \"def register_shapes():\\n turtle.Screen().register_shape(\\\"saphire.gif\\\")\\n turtle.Screen().register_shape(\\\"player_right.gif\\\")\\n turtle.Screen().register_shape(\\\"player_left.gif\\\")\\n turtle.Screen().register_shape(\\\"walls.gif\\\")\",\n \"def testEdgeMarkerRaster1(self):\\n outname = self.prefix+'.asap'\\n self.res = sdcal(infile=self.rawfile,calmode='otfraster',noff=1,markonly=True,outfile=outname,outform='ASAP')\\n refdir = self._readref( self.reffiles[1] )\\n self._checkfile( outname ) \\n self._checkmarker( outname, refdir )\",\n \"def ndvi(in_nir_band, in_colour_band, in_rows, in_cols, in_geotransform, out_tiff, data_type=gdal.GDT_Float32):\\r\\n\\r\\n # Read the input bands as numpy arrays.\\r\\n np_nir = in_nir_band.ReadAsArray(0, 0, in_cols, in_rows)\\r\\n np_colour = in_colour_band.ReadAsArray(0, 0, in_cols, in_rows)\\r\\n\\r\\n # Convert the np arrays to 32-bit floating point to make sure division will occur properly.\\r\\n np_nir_as32 = np_nir.astype(np.float32)\\r\\n np_colour_as32 = np_colour.astype(np.float32)\\r\\n\\r\\n # Calculate the NDVI formula.\\r\\n numerator = subtract(np_nir_as32, np_colour_as32)\\r\\n denominator = add(np_nir_as32, np_colour_as32)\\r\\n result = divide(numerator, denominator)\\r\\n\\r\\n # Remove any NaNs cause by division by zero.\\r\\n ndvi_float32 = nan_to_num(result)\\r\\n\\r\\n # Initialize a geotiff driver.\\r\\n geotiff = GetDriverByName('GTiff')\\r\\n\\r\\n # If the desired output is an int16, map the domain [-1,1] to [0,255], create an int16 geotiff with one band and\\r\\n # write the contents of the int16 NDVI calculation to it. Otherwise, create a float32 geotiff with one band and\\r\\n # write the contents of the float32 NDVI calculation to it.\\r\\n if data_type == gdal.GDT_UInt16:\\r\\n ndvi_int16 = multiply((ndvi_float32 + 1), (2**7 - 1))\\r\\n output = geotiff.Create(out_tiff, in_cols, in_rows, 1, gdal.GDT_UInt16)\\r\\n output.GetRasterBand(1).WriteArray(ndvi_int16)\\r\\n elif data_type == gdal.GDT_Float32:\\r\\n output = geotiff.Create(out_tiff, in_cols, in_rows, 1, gdal.GDT_Float32)\\r\\n output.GetRasterBand(1).WriteArray(ndvi_float32)\\r\\n else:\\r\\n raise ValueError('Invalid output data type. Valid types are gdal.UInt16 or gdal.Float32.')\\r\\n\\r\\n # Set the geographic transformation as the input.\\r\\n output.SetGeoTransform(in_geotransform)\\r\\n\\r\\n # return the output image in case you want to do something else with it.\\r\\n return output\",\n \"def region_sets(self,listA,listB):\\n self.setA = GenomicRegionSet('for Unit Test')\\n for i in range(len(listA)):\\n self.setA.add(GenomicRegion(chrom=listA[i][0], initial=listA[i][1], final=listA[i][2]))\\n \\n self.setB = GenomicRegionSet('for Unit Test')\\n for i in range(len(listB)):\\n self.setB.add(GenomicRegion(chrom=listB[i][0], initial=listB[i][1], final=listB[i][2]))\",\n \"def volumes(self):\",\n \"def test_CreateROI1(self):\\r\\n\\r\\n self.delayDisplay(\\\"Starting the test\\\")\\r\\n #\\r\\n # first, get some data\\r\\n #\\r\\n import urllib\\r\\n downloads = (\\r\\n ('http://slicer.kitware.com/midas3/download?items=5767', 'FA.nrrd', slicer.util.loadVolume),\\r\\n )\\r\\n\\r\\n for url,name,loader in downloads:\\r\\n filePath = slicer.app.temporaryPath + '/' + name\\r\\n if not os.path.exists(filePath) or os.stat(filePath).st_size == 0:\\r\\n logging.info('Requesting download %s from %s...\\\\n' % (name, url))\\r\\n urllib.urlretrieve(url, filePath)\\r\\n if loader:\\r\\n logging.info('Loading %s...' % (name,))\\r\\n loader(filePath)\\r\\n self.delayDisplay('Finished with download and loading')\\r\\n\\r\\n volumeNode = slicer.util.getNode(pattern=\\\"FA\\\")\\r\\n logic = CreateROILogic()\\r\\n self.assertIsNotNone( logic.hasImageData(volumeNode) )\\r\\n self.delayDisplay('Test passed!')\",\n \"def unpack_landsat(inpath, outpath, bands=None, clouds=None):\\r\\n tfile = tarfile.open(inpath,'r:gz')\\r\\n if bands is None:\\r\\n #Extract all of the bands.\\r\\n tfile.extractall(outpath);\\r\\n else:\\r\\n #Extract just the bands specified.\\r\\n\\r\\n #Get the names of the members of the archive\\r\\n names = tfile.getnames()\\r\\n\\r\\n #Make sure the bands are given as a list\\r\\n bands = list(bands)\\r\\n\\r\\n #Find those elements of the archive that match the bands specified\\r\\n #\\t(plus metadata file)\\r\\n members = tfile.getmembers()\\r\\n elements = []\\r\\n blist = ''.join(str(i) for i in bands)\\r\\n if clouds:\\r\\n patternStr = '.*_B[' + blist + ']\\\\.TIF|.*_MTL.txt|.*_band[' + blist + \\\\\\r\\n ']\\\\.tif|.*_cfmask.*\\\\.tif|.*\\\\.xml|.*cloud.*\\\\.tif'\\r\\n else:\\r\\n patternStr = '.*_B[' + blist + ']\\\\.TIF|.*_MTL.txt|.*_band[' + blist + \\\\\\r\\n ']\\\\.tif|.*_cfmask.*\\\\.tif|.*\\\\.xml'\\r\\n\\r\\n pattern = re.compile(patternStr)\\r\\n for i, name in enumerate(names):\\r\\n if pattern.match(name):\\r\\n elements.append(members[i])\\r\\n tfile.extractall(outpath,members = elements)\\r\\n\\r\\n '''\\r\\n Note: the below section was written with the intention of\\r\\n adding valuable metadata info to the .TIF files themselves.\\r\\n This theoretically allows easy retreival of key metadata info,\\r\\n but the method to set new metadata seems unreliable. Appears to\\r\\n be an open issue, but there may be a solution. For now, assume\\r\\n this does not work.\\r\\n\\r\\n #Now we loop through each output, adding important metadata.\\r\\n #Fetch metadata and make sure there is only one file.\\r\\n meta = glob.glob(outpath + '/*_MTL.txt')\\r\\n if len(meta) > 1:\\r\\n raise('MORE THAN ONE METADATA FILE FOUND!')\\r\\n\\r\\n metadata = read_metadata(meta[0])\\r\\n\\r\\n #Add the metadata.\\r\\n for tif in glob.glob(outpath + '*.TIF'):\\r\\n #Open the tif file\\r\\n ras = gdal.Open(tif)\\r\\n\\r\\n #Preserve any existing metadata & add important metadata from file\\r\\n meta = ras.GetMetadata()\\r\\n meta['SCENE_CENTER_TIME'] = metadata['SCENE_CENTER_TIME']\\r\\n meta['DATE_ACQUIRED'] = metadata['DATE_ACQUIRED']\\r\\n meta['LANDSAT_SCENE_ID'] = metadata['LANDSAT_SCENE_ID']\\r\\n\\r\\n #Now write the new metadata back to the tif.\\r\\n ras.SetMetadata(meta)\\r\\n ras.FlushCache()\\r\\n ras = None\\r\\n '''\",\n \"def gen_landmark_data(src_txt_path, net, augmet=False):\\r\\n print(\\\">>>>>> Start landmark data create...Stage: %s\\\" % net)\\r\\n save_folder = os.path.join(root_path, '../DATA/12/')\\r\\n save_image_folder = os.path.join(save_folder, 'train_%s_landmark_aug' % net)\\r\\n size_of_net = {'PNet': 12, 'RNet': 24, 'ONet': 48}\\r\\n if net not in size_of_net:\\r\\n raise Exception(\\\"The net type error!\\\")\\r\\n if not os.path.isdir(save_image_folder):\\r\\n os.makedirs(save_image_folder)\\r\\n print('create folder: ', save_image_folder)\\r\\n save_f = open(os.path.join(save_folder, 'landmark_%s_aug.txt' % size_of_net[net]), 'w')\\r\\n image_count = 0\\r\\n # image_path bbox landmark(5*2)\\r\\n bbox_landmark_info = get_bbox_landmark_from_txt(src_txt_path, data_path='../DATA/landmarks_traindata', with_landmark=True)\\r\\n for img_path, bbox, landmark_gt in bbox_landmark_info:\\r\\n f_imgs = list()\\r\\n f_landmarks = list()\\r\\n img = cv2.imread(img_path)\\r\\n assert(img is not None)\\r\\n img_h, img_w, img_c = img.shape\\r\\n gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])\\r\\n f_face = img[bbox.top: bbox.bottom+1, bbox.left: bbox.right+1]\\r\\n f_face = cv2.resize(f_face, (size_of_net[net], size_of_net[net]))\\r\\n landmark = np.zeros((5, 2))\\r\\n # normalize\\r\\n for index, one in enumerate(landmark_gt):\\r\\n rv = ((one[0]-gt_box[0])/(gt_box[2]-gt_box[0]), (one[1]-gt_box[1])/(gt_box[3]-gt_box[1]))\\r\\n landmark[index] = rv\\r\\n f_imgs.append(f_face)\\r\\n f_landmarks.append(landmark.reshape(10))\\r\\n landmark = np.zeros((5, 2))\\r\\n if augmet:\\r\\n x1, y1, x2, y2 = gt_box\\r\\n gt_width = x2 - x1 + 1\\r\\n gt_height = y2 - y1 + 1\\r\\n if max(gt_width, gt_height) < 40 or x1 < 0 or y1 < 0:\\r\\n continue\\r\\n # random shift\\r\\n for i in range(10):\\r\\n bbox_size = np.random.randint(int(min(gt_width, gt_height) * 0.8), np.ceil(1.25 * max(gt_width, gt_height)))\\r\\n # delta_x and delta_y are offsets of (x1, y1)\\r\\n # max can make sure if the delta is a negative number , x1+delta_x >0\\r\\n # parameter high of randint make sure there will be intersection between bbox and cropped_box\\r\\n delta_x = np.random.randint(-gt_width*0.2, gt_width*0.2)\\r\\n delta_y = np.random.randint(-gt_height*0.2, gt_height*0.2)\\r\\n nx1 = int(max(x1+gt_width/2 - bbox_size/2 + delta_x, 0))\\r\\n ny1 = int(max(y1+gt_height/2 - bbox_size/2 + delta_y, 0))\\r\\n nx2 = nx1 + bbox_size\\r\\n ny2 = ny1 + bbox_size\\r\\n if nx2 > img_w or ny2 > img_h:\\r\\n continue\\r\\n # print(nx1, ny1, nx2, ny2)\\r\\n crop_box = np.array([nx1, ny1, nx2, ny2])\\r\\n cropped_img = img[ny1: ny2+1, nx1: nx2+1, :]\\r\\n resized_img = cv2.resize(cropped_img, (size_of_net[net], size_of_net[net]))\\r\\n iou = calc_iou(crop_box, np.expand_dims(gt_box, 0))\\r\\n if iou <= 0.65:\\r\\n continue\\r\\n f_imgs.append(resized_img)\\r\\n # normalize\\r\\n for index, one in enumerate(landmark_gt):\\r\\n rv = ((one[0]-nx1)/bbox_size, (one[1]-ny1/bbox_size))\\r\\n landmark[index] = rv\\r\\n f_landmarks.append(landmark.reshape(10))\\r\\n landmark = np.zeros((5, 2))\\r\\n # get last landmark from list\\r\\n landmark_ = f_landmarks[-1].reshape((-1, 2))\\r\\n bbox = BBox([nx1, ny1, nx2, ny2])\\r\\n\\r\\n # mirror\\r\\n if random.choice([0, 1]) > 0:\\r\\n face_flipped, landmark_flipped = flip(resized_img, landmark_)\\r\\n face_flipped = cv2.resize(face_flipped, (size_of_net[net], size_of_net[net]))\\r\\n # c*h*w\\r\\n f_imgs.append(face_flipped)\\r\\n f_landmarks.append(landmark_flipped.reshape(10))\\r\\n # rotate\\r\\n if random.choice([0, 1]) > 0:\\r\\n face_rotated_by_alpha, landmark_rotated = rotate(img, bbox,\\r\\n bbox.reproject_landmark(landmark_), 5)\\r\\n # landmark offset\\r\\n landmark_rotated = bbox.project_landmark(landmark_rotated)\\r\\n face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (size_of_net[net], size_of_net[net]))\\r\\n f_imgs.append(face_rotated_by_alpha)\\r\\n f_landmarks.append(landmark_rotated.reshape(10))\\r\\n\\r\\n # flip\\r\\n face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)\\r\\n face_flipped = cv2.resize(face_flipped, (size_of_net[net], size_of_net[net]))\\r\\n f_imgs.append(face_flipped)\\r\\n f_landmarks.append(landmark_flipped.reshape(10))\\r\\n # anti-clockwise rotation\\r\\n if random.choice([0, 1]) > 0:\\r\\n face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, bbox.reproject_landmark(landmark_), -5)\\r\\n landmark_rotated = bbox.project_landmark(landmark_rotated)\\r\\n face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (size_of_net[net], size_of_net[net]))\\r\\n f_imgs.append(face_rotated_by_alpha)\\r\\n f_landmarks.append(landmark_rotated.reshape(10))\\r\\n\\r\\n face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)\\r\\n face_flipped = cv2.resize(face_flipped, (size_of_net[net], size_of_net[net]))\\r\\n f_imgs.append(face_flipped)\\r\\n f_landmarks.append(landmark_flipped.reshape(10))\\r\\n f_imgs, f_landmarks = np.asarray(f_imgs), np.asarray(f_landmarks)\\r\\n for i in range(len(f_imgs)):\\r\\n # if np.sum(np.where(f_landmarks[i] <= 0, 1, 0)) > 0:\\r\\n # print('skip image: %d' % i)\\r\\n # print(f_landmarks[i])\\r\\n # continue\\r\\n # if np.sum(np.where(f_landmarks[i] >= 1, 1, 0)) > 0:\\r\\n # print('skip image: %d', i)\\r\\n # print(f_landmarks[i])\\r\\n # continue\\r\\n path = os.path.join(save_image_folder, '%d.jpg' % image_count)\\r\\n cv2.imwrite(path, f_imgs[i])\\r\\n landmarks = map(str, list(f_landmarks[i]))\\r\\n save_f.write(path + ' -2 ' + ' '.join(landmarks) + '\\\\n')\\r\\n image_count += 1\\r\\n print_str = \\\"\\\\rCount: {}\\\".format(image_count)\\r\\n sys.stdout.write(print_str)\\r\\n sys.stdout.flush()\\r\\n save_f.close()\\r\\n print('\\\\n Landmark create done!')\",\n \"def identify_landmarks(image: numpy.ndarray) -> Dict[int, Tuple[int, int]]:\\n copy_img = image.copy()\\n\\n detector = dlib.get_frontal_face_detector()\\n predictor = dlib.shape_predictor(os.path.join(\\n project_dir, \\\"shufflealgos\\\", \\\"image\\\", \\\"landmarks\\\",\\n \\\"shape_predictor_68_face_landmarks.dat\\\"))\\n\\n gray_img_array = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\\n found_face = detector(gray_img_array)\\n\\n face_lmarks: Dict[int, Tuple[int, int]] = dict()\\n\\n # Assume only one face is found for our purposes\\n for idx, face in enumerate(found_face, start=1):\\n landmarks = predictor(gray_img_array, face)\\n\\n for n in range(68):\\n lmx = landmarks.part(n).x\\n lmy = landmarks.part(n).y\\n\\n face_lmarks[n + 1] = (lmx, lmy)\\n\\n cv2.circle(copy_img, (lmx, lmy), 1, (50, 50, 255), -1)\\n\\n cv2.putText(\\n copy_img, str(n), (lmx, lmy - 5),\\n cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.6, (0, 0, 255), 1)\\n\\n return face_lmarks\",\n \"def register_mount(self, app, path, variables, converters, required,\\n get_converters, mount_name, app_factory):\\n self.register_path(app, path, variables,\\n converters, required, get_converters, False,\\n app_factory)\\n\\n self.mounted[app] = app_factory\\n mount_name = mount_name or path\\n self.named_mounted[mount_name] = app_factory\",\n \"def _mri_scanner_ras_to_mri_voxels(ras_landmarks, img_mgh):\\n # Get landmarks in voxel space, using the T1 data\\n vox2ras = img_mgh.header.get_vox2ras()\\n ras2vox = linalg.inv(vox2ras)\\n vox_landmarks = apply_trans(ras2vox, ras_landmarks) # in vox\\n return vox_landmarks\",\n \"def do_stuff(self):\\n self.create_tourism_raster()\",\n \"def test_register_fov(self):\\n task = MesoscopeFOV(self.session_path, device_collection='raw_imaging_data', one=self.one)\\n mlapdv = {'topLeft': [2317.2, -1599.8, -535.5], 'topRight': [2862.7, -1625.2, -748.7],\\n 'bottomLeft': [2317.3, -2181.4, -466.3], 'bottomRight': [2862.7, -2206.9, -679.4],\\n 'center': [2596.1, -1900.5, -588.6]}\\n meta = {'FOV': [{'MLAPDV': mlapdv, 'nXnYnZ': [512, 512, 1], 'roiUUID': 0}]}\\n with unittest.mock.patch.object(self.one.alyx, 'rest') as mock_rest:\\n task.register_fov(meta, 'estimate')\\n calls = mock_rest.call_args_list\\n self.assertEqual(3, len(calls))\\n\\n args, kwargs = calls[1]\\n self.assertEqual(('fields-of-view', 'create'), args)\\n expected = {'data': {'session': None, 'imaging_type': 'mesoscope', 'name': 'FOV_00', 'stack': None}}\\n self.assertEqual(expected, kwargs)\\n\\n args, kwargs = calls[2]\\n self.assertEqual(('fov-location', 'create'), args)\\n expected = ['field_of_view', 'default_provenance', 'coordinate_system', 'n_xyz', 'provenance', 'x', 'y', 'z',\\n 'brain_region']\\n self.assertCountEqual(expected, kwargs.get('data', {}).keys())\\n self.assertEqual(5, len(kwargs['data']['brain_region']))\\n self.assertEqual([512, 512, 1], kwargs['data']['n_xyz'])\\n self.assertIs(kwargs['data']['field_of_view'], mock_rest().get('id'))\\n self.assertEqual('E', kwargs['data']['provenance'])\\n self.assertEqual([2317.2, 2862.7, 2317.3, 2862.7], kwargs['data']['x'])\\n\\n # Check dry mode with suffix input = None\\n for file in self.session_path.joinpath('alf', 'FOV_00').glob('mpciMeanImage.*'):\\n file.replace(file.with_name(file.name.replace('_estimate', '')))\\n self.one.mode = 'local'\\n with unittest.mock.patch.object(self.one.alyx, 'rest') as mock_rest:\\n out = task.register_fov(meta, None)\\n mock_rest.assert_not_called()\\n self.assertEqual(1, len(out))\\n self.assertEqual('FOV_00', out[0].get('name'))\\n locations = out[0]['location']\\n self.assertEqual(1, len(locations))\\n self.assertEqual('L', locations[0].get('provenance', 'L'))\",\n \"def showSS(self, show):\\n\\t\\tfrom chimera.Sequence import defHelixColor, defStrandColor\\n\\t\\thelixReg = self.getRegion(\\\"structure helices\\\", create=1,\\n\\t\\t\\t\\tfill=(1.0, 1.0, 0.8), outline=defHelixColor)\\n\\t\\tstrandReg = self.getRegion(\\\"structure strands\\\", create=1,\\n\\t\\t\\t\\tfill=(0.8, 1.0, 0.8), outline=defStrandColor)\\n\\t\\thelixReg.shown = show\\n\\t\\tstrandReg.shown = show\\n\\t\\tif not show:\\n\\t\\t\\treturn\\n\\t\\thelixReg.clear(makeCB=False) # callback will happen in\\n\\t\\tstrandReg.clear(makeCB=False) # addBlocks below\\n\\n\\t\\tassocSeqs = {}\\n\\t\\thelices = []\\n\\t\\tstrands = []\\n\\t\\tfor aseq in self.seqCanvas.mav.associations.values():\\n\\t\\t\\tassocSeqs[aseq] = 1\\n\\t\\tfor aseq in assocSeqs.keys():\\n\\t\\t\\tinHelix = inStrand = 0\\n\\t\\t\\tfor pos in range(len(aseq.ungapped())):\\n\\t\\t\\t\\tisHelix = isStrand = 0\\n\\t\\t\\t\\tfor matchMap in aseq.matchMaps.values():\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tres = matchMap[pos]\\n\\t\\t\\t\\t\\texcept KeyError:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif res.isHelix:\\n\\t\\t\\t\\t\\t\\tisHelix = 1\\n\\t\\t\\t\\t\\telif res.isStrand:\\n\\t\\t\\t\\t\\t\\tisStrand = 1\\n\\t\\t\\t\\tgapped = aseq.ungapped2gapped(pos)\\n\\t\\t\\t\\tif isHelix:\\n\\t\\t\\t\\t\\tif inHelix:\\n\\t\\t\\t\\t\\t\\thelices[-1][-1] = gapped\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\thelices.append([aseq, aseq,\\n\\t\\t\\t\\t\\t\\t\\t\\tgapped, gapped])\\n\\t\\t\\t\\t\\t\\tinHelix = 1\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif inHelix:\\n\\t\\t\\t\\t\\t\\tinHelix = 0\\n\\t\\t\\t\\tif isStrand:\\n\\t\\t\\t\\t\\tif inStrand:\\n\\t\\t\\t\\t\\t\\tstrands[-1][-1] = gapped\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tstrands.append([aseq, aseq,\\n\\t\\t\\t\\t\\t\\t\\t\\tgapped, gapped])\\n\\t\\t\\t\\t\\t\\tinStrand = 1\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif inStrand:\\n\\t\\t\\t\\t\\t\\tinStrand = 0\\n\\t\\thelixReg.addBlocks(helices)\\n\\t\\tstrandReg.addBlocks(strands)\",\n \"def __init__(self, inst: ghidra.program.model.listing.Instruction, bl: ghidra.program.model.correlate.Block, ind: int):\\n ...\",\n \"def testingData(self):\\n\\n #import SampleData\\n #sampleDataLogic = SampleData.SampleDataLogic()\\n #mrHead = sampleDataLogic.downloadMRHead()\\n #dtiBrain = sampleDataLogic.downloadDTIBrain()\\n \\n # w = slicer.modules.SteeredFluidRegistrationWidget\\n # w.fixedSelector.setCurrentNode(mrHead)\\n # w.movingSelector.setCurrentNode(dtiBrain)\\n \\n if not slicer.util.getNodes('testbrain1*'):\\n import os\\n fileName = \\\"C:\\\\\\\\Work\\\\\\\\testbrain1.nrrd\\\"\\n vl = slicer.modules.volumes.logic()\\n brain1Node = vl.AddArchetypeVolume(fileName, \\\"testbrain1\\\", 0)\\n else:\\n nodes = slicer.util.getNodes('testbrain1.nrrd')\\n brain1Node = nodes[0]\\n\\n if not slicer.util.getNodes('testbrain2*'):\\n import os\\n fileName = \\\"C:\\\\\\\\Work\\\\\\\\testbrain2.nrrd\\\"\\n vl = slicer.modules.volumes.logic()\\n brain2Node = vl.AddArchetypeVolume(fileName, \\\"testbrain2\\\", 0)\\n #TODO else assign from list\\n\\n # if not slicer.util.getNodes('movingToFixed*'):\\n # # Create transform node\\n # transform = slicer.vtkMRMLLinearTransformNode()\\n # transform.SetName('movingToFixed')\\n # slicer.mrmlScene.AddNode(transform)\\n\\n # transform = slicer.util.getNode('movingToFixed')\\n \\n # ###\\n # # neutral.SetAndObserveTransformNodeID(transform.GetID())\\n # ###\\n \\n compositeNodes = slicer.util.getNodes('vtkMRMLSliceCompositeNode*')\\n for compositeNode in compositeNodes.values():\\n compositeNode.SetBackgroundVolumeID(brain1Node.GetID())\\n compositeNode.SetForegroundVolumeID(brain2Node.GetID())\\n compositeNode.SetForegroundOpacity(0.5)\\n applicationLogic = slicer.app.applicationLogic()\\n applicationLogic.FitSliceToAll()\",\n \"def map_to_homo_nid(self, ids, ntype): # -> None:\\n ...\",\n \"def add_microstructures_to_arbor(self,arbor,mbranches,bbranches,myelingeometry=[1,1,3,8,3,1],boutongeometry=[28,4]):\\n\\t\\tarbor_labels = dict(zip([key for key in arbor.keys()],[[] for key in arbor.keys()]))\\n\\t\\tarbor,arbor_labels = self.myelinate_branches(arbor,arbor_labels,mbranches,myelin_geometry=myelingeometry) #myelin_geometry=[1,1,3,8,3,1]\\n\\t\\tarbor,arbor_labels = self.bouton_branches(arbor,arbor_labels,bbranches,bouton_geometry=boutongeometry) #bouton_geometry=[28,4]\\n\\t\\tarbor = self.ensure_precision(arbor)\\n\\t\\treturn(arbor,arbor_labels)\",\n \"def show_landmarks(image, landmarks, save_fig=False, save_path=None):\\n\\n plt.imshow(image)\\n plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')\\n if save_fig and save_path is not None:\\n plt.savefig(save_path)\\n plt.pause(0.001) # pause a bit so that plots are updated\",\n \"def monolayer_4band():\\n a = 0.222\\n ax = 0.438\\n ay = 0.332\\n theta = 96.79 * (pi / 180)\\n phi = 103.69 * (pi / 180)\\n\\n lat = pb.Lattice(a1=[ax, 0], a2=[0, ay])\\n\\n h = a * sin(phi - pi / 2)\\n s = 0.5 * ax - a * cos(theta / 2)\\n lat.add_sublattices(\\n ('A', [0, 0, h], 0),\\n ('B', [s, 0, 0], 0),\\n ('C', [ax/2, ay/2, 0], 0),\\n ('D', [ax/2 + s, ay/2, h], 0)\\n )\\n\\n lat.register_hopping_energies({\\n 't1': -1.22,\\n 't2': 3.665,\\n 't3': -0.205,\\n 't4': -0.105,\\n 't5': -0.055\\n })\\n\\n lat.add_hoppings(\\n # t1\\n ([-1, 0], 'A', 'D', 't1'),\\n ([-1, -1], 'A', 'D', 't1'),\\n ([ 0, 0], 'B', 'C', 't1'),\\n ([ 0, -1], 'B', 'C', 't1'),\\n # t2\\n ([ 0, 0], 'A', 'B', 't2'),\\n ([ 0, 0], 'C', 'D', 't2'),\\n # t3\\n ([ 0, 0], 'A', 'D', 't3'),\\n ([ 0, -1], 'A', 'D', 't3'),\\n ([ 1, 1], 'C', 'B', 't3'),\\n ([ 1, 0], 'C', 'B', 't3'),\\n # t4\\n ([ 0, 0], 'A', 'C', 't4'),\\n ([ 0, -1], 'A', 'C', 't4'),\\n ([-1, 0], 'A', 'C', 't4'),\\n ([-1, -1], 'A', 'C', 't4'),\\n ([ 0, 0], 'B', 'D', 't4'),\\n ([ 0, -1], 'B', 'D', 't4'),\\n ([-1, 0], 'B', 'D', 't4'),\\n ([-1, -1], 'B', 'D', 't4'),\\n # t5\\n ([-1, 0], 'A', 'B', 't5'),\\n ([ 0, 1], 'A', 'B', 't5'),\\n ([ 0, -1], 'A', 'B', 't5'),\\n ([-1, 0], 'C', 'D', 't5'),\\n ([ 0, 1], 'C', 'D', 't5'),\\n ([ 0, -1], 'C', 'D', 't5'),\\n )\\n\\n return lat\",\n \"def region_setup(self, slices, ipa_regions):\\n self.ipa_regions = ipa_regions\\n self.slices = slices\",\n \"def __init__(self, manager, name):\\n Online.DatapointLoader.DatapointLoader.__init__(self,manager,name)\\n self.strerror = ''\\n self.inUse = self.dp('InUse')\\n self.subfarms = self.dp('SubFarms')\\n self.writer = self.manager.devWriter()\\n self.reader.add(self.inUse)\\n self.reader.add(self.subfarms)\",\n \"def get_landmarks(self):\\n lm_x = [x for x, y in self.landmarks]\\n lm_y = [y for x, y in self.landmarks]\\n return [lm_x, lm_y]\",\n \"def __init__(self, config, ll=None, osimmodel=None, landmarks=None):\\n self.config = config\\n self.ll = ll\\n self.trcdata = landmarks\\n self.gias_osimmodel = None\\n if osimmodel is not None:\\n self.set_osim_model(osimmodel)\\n self._unit_scaling = dim_unit_scaling(\\n self.config['in_unit'], self.config['out_unit']\\n )\",\n \"def show_2gr(img1,img2,title1='',title2=''):\\n\\tf, (ax1,ax2) = plt.subplots(1,2,figsize=(20,10))\\n\\tax1.imshow(img1, cmap='gray')\\n\\tax1.set_title(title1)\\n\\tax2.imshow(img2, cmap='gray')\\n\\tax2.set_title(title2)\\n\\tplt.show()\",\n \"def vis_mechanically_coupled_regions(img_dir,output_dir,data,dbscn_length,dbscn_min_size,display_not_save=False):\\n #Read in the image that is segmented/labelled for nuclei\\n img=imread(img_dir)\\n\\n #save plots to show clusters\\n fig = plt.figure(figsize=(6, 2))\\n ax0 = fig.add_subplot(131)\\n ax1 = fig.add_subplot(132)\\n ax3 = fig.add_subplot(133)\\n #show segmented image labels\\n ax0.imshow(img,aspect='auto') \\n ax0.axis('off')\\n #nuclear centroid color-coded by their orientation\\n img1=ax1.scatter(data[\\\"Y\\\"], data[\\\"X\\\"], c=data[\\\"angles\\\"],s=1)\\n ax1.set_xlim(0,img.shape[0])\\n ax1.set_ylim(img.shape[1],0)\\n plt.colorbar(img1)\\n ax1.axis('off')\\n\\n # plot the cluster assignments\\n img3=ax3.scatter(data[data[\\\"clusters\\\"]> -1][\\\"Y\\\"], data[data[\\\"clusters\\\"]> -1][\\\"X\\\"], \\n c=data[data[\\\"clusters\\\"]> -1][\\\"clusters\\\"],cmap=\\\"plasma\\\",s=1)\\n ax3.set_xlim(0,img.shape[0])\\n ax3.set_ylim(img.shape[1],0)\\n ax3.axis('off')\\n\\n #add titles\\n ax0.title.set_text('Segmented Image')\\n ax1.title.set_text('Filtered Orientation')\\n ax3.title.set_text('Clusters')\\n\\n if display_not_save:\\n plt.show()\\n else: \\n plt.savefig((output_dir+\\\"/\\\"+img_dir.rsplit('/', 1)[-1][:-4]+\\\"_\\\"+str(dbscn_length)+\\\"_\\\"+ str(dbscn_min_size)+\\\".png\\\"),dpi=600, bbox_inches = 'tight',pad_inches = 0)\\n fig.clf()\\n plt.close(fig)\\n plt.close('all')\\n \\n \\n del fig,ax0,ax1,ax3,img1,img3\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.50881785","0.5056109","0.49519038","0.4912301","0.4879457","0.4813697","0.4787528","0.4785143","0.46455887","0.46359158","0.46346763","0.46140108","0.45900747","0.4589501","0.45878723","0.45546374","0.4543802","0.45378584","0.45247677","0.45247677","0.45247677","0.4521581","0.45196837","0.45185992","0.45091015","0.4508381","0.4500884","0.44913542","0.44738516","0.44708493","0.44700333","0.44590595","0.44577128","0.44536957","0.44455197","0.4442428","0.4440036","0.44339535","0.44223508","0.44128445","0.44126597","0.4411962","0.44113824","0.43991727","0.43945166","0.4393286","0.43916312","0.43855572","0.43855572","0.43847942","0.4383624","0.43831885","0.43828875","0.43708706","0.4369799","0.43660548","0.43639824","0.43593794","0.43564022","0.4352813","0.43525285","0.4352269","0.4347508","0.43402928","0.43380368","0.4335249","0.4333801","0.43324205","0.43318382","0.43253854","0.43219578","0.43203443","0.43203443","0.43137458","0.4311263","0.43068033","0.43056503","0.4304849","0.43037778","0.4300524","0.4298898","0.42984834","0.42945647","0.4294152","0.42936862","0.4291706","0.42893165","0.42823744","0.42683625","0.42637745","0.42636326","0.42623565","0.42618006","0.42581213","0.42572045","0.42539716","0.4243984","0.42438456","0.42384565","0.42379245"],"string":"[\n \"0.50881785\",\n \"0.5056109\",\n \"0.49519038\",\n \"0.4912301\",\n \"0.4879457\",\n \"0.4813697\",\n \"0.4787528\",\n \"0.4785143\",\n \"0.46455887\",\n \"0.46359158\",\n \"0.46346763\",\n \"0.46140108\",\n \"0.45900747\",\n \"0.4589501\",\n \"0.45878723\",\n \"0.45546374\",\n \"0.4543802\",\n \"0.45378584\",\n \"0.45247677\",\n \"0.45247677\",\n \"0.45247677\",\n \"0.4521581\",\n \"0.45196837\",\n \"0.45185992\",\n \"0.45091015\",\n \"0.4508381\",\n \"0.4500884\",\n \"0.44913542\",\n \"0.44738516\",\n \"0.44708493\",\n \"0.44700333\",\n \"0.44590595\",\n \"0.44577128\",\n \"0.44536957\",\n \"0.44455197\",\n \"0.4442428\",\n \"0.4440036\",\n \"0.44339535\",\n \"0.44223508\",\n \"0.44128445\",\n \"0.44126597\",\n \"0.4411962\",\n \"0.44113824\",\n \"0.43991727\",\n \"0.43945166\",\n \"0.4393286\",\n \"0.43916312\",\n \"0.43855572\",\n \"0.43855572\",\n \"0.43847942\",\n \"0.4383624\",\n \"0.43831885\",\n \"0.43828875\",\n \"0.43708706\",\n \"0.4369799\",\n \"0.43660548\",\n \"0.43639824\",\n \"0.43593794\",\n \"0.43564022\",\n \"0.4352813\",\n \"0.43525285\",\n \"0.4352269\",\n \"0.4347508\",\n \"0.43402928\",\n \"0.43380368\",\n \"0.4335249\",\n \"0.4333801\",\n \"0.43324205\",\n \"0.43318382\",\n \"0.43253854\",\n \"0.43219578\",\n \"0.43203443\",\n \"0.43203443\",\n \"0.43137458\",\n \"0.4311263\",\n \"0.43068033\",\n \"0.43056503\",\n \"0.4304849\",\n \"0.43037778\",\n \"0.4300524\",\n \"0.4298898\",\n \"0.42984834\",\n \"0.42945647\",\n \"0.4294152\",\n \"0.42936862\",\n \"0.4291706\",\n \"0.42893165\",\n \"0.42823744\",\n \"0.42683625\",\n \"0.42637745\",\n \"0.42636326\",\n \"0.42623565\",\n \"0.42618006\",\n \"0.42581213\",\n \"0.42572045\",\n \"0.42539716\",\n \"0.4243984\",\n \"0.42438456\",\n \"0.42384565\",\n \"0.42379245\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.54319185"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":80383,"cells":{"query":{"kind":"string","value":"Compute sum of squared error between pairwise landmarks"},"document":{"kind":"string","value":"def SSE(pointsA, pointsB):\n return sum(array(pointsA[:, 0:3]-pointsB[:, 0:3])**2.0)"},"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 mse(self):\n xs, ys = self.R.nonzero()\n predicted = self.full_matrix()\n error = 0\n for x, y in zip(xs, ys):\n # print(predicted[x, y], self.R[x, y] )\n error += pow(self.R[x, y] - predicted[x, y], 2)\n return np.sqrt(error)","def rmse2 (a, p) :\n s = len(a)\n z = zip(a, p)\n v = 0.0\n for x, y in z :\n v += sqre_diff(x, y)\n return math.sqrt(v / s)","def sum_of_squared_errors(P1,P2):\r\n return sum(euclidean_distance_3(p1,p2) for (p1,p2) in zip(P1,P2))","def rmse4 (a, p) :\n s = len(a)\n z = zip(a, p)\n v = sum(map(lambda (x, y) : sqre_diff(x, y), z), 0.0)\n return math.sqrt(v / s)","def sum_of_squared_errors(P1,P2):\r\n return sum(euclidean_distance_2(p1,p2) for (p1,p2) in zip(P1,P2))","def calcRMSE(labelsAndPreds):\n meanOfSqErrors = labelsAndPreds.map(lambda (x,y): squaredError(x,y)).mean()\n \n return math.sqrt(meanOfSqErrors)","def rmse_calc(arr1, arr2):\n assert arr1.shape==arr2.shape\n \n return np.sqrt(np.mean((arr2-arr1)**2))","def mse(result, expected):\n total_square_sum = 0\n for index1 in range(0, len(result)):\n total_square_sum += (result[index1] - expected[index1]) ** 2\n return total_square_sum / float(len(result))","def _ss_err(self):\n return np.sum(np.square(self._resids), axis=-2)","def lsumdiffsquared(x,y):\r\n sds = 0\r\n for i in range(len(x)):\r\n sds = sds + (x[i]-y[i])**2\r\n return sds","def rmse3 (a, p) :\n s = len(a)\n z = zip(a, p)\n v = reduce(lambda w, (x, y) : w + sqre_diff(x, y), z, 0.0)\n return math.sqrt(v / s)","def squaredError(label, prediction):\n return (label-prediction)*(label-prediction)","def rmse(predicted, actual):\n #maybe make some assertions, assume have same length & in right order\n interm_total = 0\n for i in range(len(predicted)):\n interm_total += (predicted[i] - actual[i]) ** 2\n return sqrt(interm_total / len(predicted))","def error(self, documents):\n ###TODO\n sum_1 = 0.0\n for c_id,clust in self.fin_clust.items():\n n = self.sqnorm(self.means[c_id]) \n sum_1 = sum_1 + sum([self.distance(self.docs[dc],self.means[c_id],n) for dc in clust]) \n return round(sum_1,2)","def error(self, trainset: ([], [])):\n # MSE = Σ | d – y |^2 / n\n error_sum = 0.0\n for index, example in enumerate(trainset):\n # | d – y |^2\n output = self.activate(example[0])\n\n target = example[1][0]\n\n error = target - output\n error_sum += error ** 2\n\n # Σ |error_sum| / n\n error_sum = error_sum / len(trainset)\n return error_sum","def calculate_error(d1, d2):\n square_sum = 0.0\n for i in range(len(d1)):\n square_sum += pow( distributions_EMD(d1[i], d2[i]), 2)\n return math.sqrt(square_sum)","def rmse(a, b):\n\n n = len(a)\n return np.linalg.norm(a - b) / np.sqrt(n)","def rmse (lmse) :\n return np.sqrt(2*lmse)","def rms_error(self, X, y) :\n ### ========== TODO : START ========== ###\n # part h: compute RMSE\n n, d = X.shape\n error = np.sqrt(self.cost(X,y)/n)\n ### ========== TODO : END ========== ###\n return error","def calc_centered_rms_error(x, y):\n xdat = x.flatten()\n ydat = y.flatten()\n xm = np.ma.mean(xdat)\n ym = np.ma.mean(ydat)\n\n anom = np.sqrt(np.ma.mean(((xdat - xm) - (ydat - ym)) ** 2.))\n\n return xm - ym, anom","def pairwise_score(l1, l2):\n theta1 = math.atan(slope(l1))\n theta2 = math.atan(slope(l2))\n return (theta1-theta2)**2","def calculate_mean_squared_error(self, X, y):\n mserror = 0\n results = self.predict(X)\n \n mserror = mean_squared_error(results,y)\n #print(error)\n return mserror","def rmse(self, X, y) -> float:\n return np.sqrt(np.sum(self.l2_error(X, y)))","def stderr(predicted, actual):\n return np.sqrt(mse(predicted, actual))","def calc_error_dist(self):\n pass","def mean_squared_error ( actuals, predictions ):\n return np.mean ( ( actuals - predictions ) ** 2 )\n # End mean_squared_error()","def mean_squared_error ( actuals, predictions ):\n return np.mean ( ( actuals - predictions ) ** 2 )\n # End mean_squared_error()","def rmse_metric(actual, predicted):\r\n sum_error = 0.0\r\n for i in range(len(actual)):\r\n prediction_error = predicted[i] - actual[i]\r\n sum_error += (prediction_error ** 2)\r\n mean_error = sum_error / float(len(actual))\r\n return sqrt(mean_error)","def rmse(actual: np.ndarray, predicted: np.ndarray):\n return np.sqrt(np.mean(np.square(_error(actual, predicted))))","def rmse5 (a, p) :\n s = len(a)\n v = sum(map(sqre_diff, a, p), 0.0)\n return math.sqrt(v / s)","def site_error_optimized(matrix,site):\n return score(matrix,site,ns=False) - C","def rmse1 (a, p) :\n s = len(a)\n i = 0\n v = 0.0\n while i != s :\n v += sqre_diff(a[i], p[i])\n i += 1\n return math.sqrt(v / s)","def mean_square_error(predictions: list, labels: list):\n temp = 0\n for i in range(len(predictions)):\n temp += (labels[i] - predictions[i]) ** 2\n\n return round(temp / len(predictions), 2)","def sse(matrix,motif):\n return sum([site_error(matrix,site)**2\n for site in motif])","def error(line, data): # error function\n # Metric: Sum of squared Y-axis differences\n err = np.sum((data[:, 1] - (line[0] * data[:, 0] + line[1])) ** 2)\n return err","def _calculate_anisoplatanism_error(self):\n\n self.sigma_anisoplatanism = np.sqrt((self.science_object_separation/self.isoplanatic_angle)**(5/3))*(self.parameter_wavelength /(2*np.pi))","def rmse(X, Y):\n\n assert X.shape == Y.shape\n\n N = X.shape[0]\n\n if N < 9:\n print(\"Not enough points. {} datapoints given. At least 9 is required\".format(N))\n return\n\n diff = X - Y\n diff = diff**2\n rmse = np.sqrt(diff.mean())\n\n le = rmse * (1.0 - np.sqrt(1-1.96*np.sqrt(2.0)/np.sqrt(N-1)))\n ue = rmse * (np.sqrt(1 + 1.96*np.sqrt(2.0)/np.sqrt(N-1))-1)\n\n return rmse, le, ue","def calculate_error(k_means_matrix):\n return sum([min(dist) for dist in k_means_matrix])","def rSquare(estimations, measureds):\n SEE = (( np.array(measureds) - np.array(estimations) )**2 ).sum()\n mMean = (np.array(measureds)).sum() / float(len(measureds))\n dErr = ((mMean - measureds)).sum()\n \n return 1 - (SEE / dErr)","def _mse_distance(feature_map_1: np.ndarray,\n feature_map_2: np.ndarray) -> np.ndarray:\n dist = np.mean((feature_map_1 - feature_map_2) ** 2, axis=-1)\n return dist","def sum_of_square_error(data_x, data_y, len_data, theta):\n error = 0.0\n #prod represent the out_result\n prod = np.dot(theta, data_x.transpose())\n # the error matrix\n prod -= data_y\n #np.square(prod) prod is a 1*n matrix, every_element get square\n sum_elem = np.sum(np.square(prod))\n # np.sum fet sum of the\n error = sum_elem / (2 * len_data)\n return error","def compute_error(data, user_features, item_features, nz):\n sum_err = 0\n for d, n in nz:\n err = data[d,n] - np.dot(item_features[d,:],user_features[:,n])\n sum_err += err**2\n rmse = 0.5*sum_err/len(nz)\n return rmse","def error_metric(phi_1, phi_2, spherical=False, xpts=None):\n if spherical:\n return sum(abs(phi_1-phi_2)*(xpts**2))/(2.0*sum(abs(phi_1)*(xpts**2)))\n else:\n return sum(abs(phi_1-phi_2))/(2.0*sum(phi_1))","def rmsd(x, y):\n\n # get the length of the dataset\n n, = x.shape\n\n return np.sqrt(np.sum((x-y)**2)/n)","def rmse(actual, predicted):\n rms = (actual-predicted)**2\n\n # Returning the sqaure root of the root mean square\n return float(np.sqrt(rms.mean()))","def calc_rmse(self, data):\n res= data.score- data[['userid','itemid']].apply(lambda row:self.calc_score(row[0], row[1]),axis=1)\n res=[el**2 for el in np.array(res)]\n return np.sqrt(np.sum(res)/data.shape[0])","def amasslinregress(*args):\r\n TINY = 1.0e-20\r\n if len(args) == 1: # more than 1D array?\r\n args = args[0]\r\n if len(args) == 2:\r\n x = N.ravel(args[0])\r\n y = args[1]\r\n else:\r\n x = N.ravel(args[:,0])\r\n y = args[:,1]\r\n else:\r\n x = args[0]\r\n y = args[1]\r\n x = x.astype(N.float_)\r\n y = y.astype(N.float_)\r\n n = len(x)\r\n xmean = amean(x)\r\n ymean = amean(y,0)\r\n shp = N.ones(len(y.shape))\r\n shp[0] = len(x)\r\n x.shape = shp\r\n print x.shape, y.shape\r\n r_num = n*(N.add.reduce(x*y,0)) - N.add.reduce(x)*N.add.reduce(y,0)\r\n r_den = N.sqrt((n*ass(x) - asquare_of_sums(x))*(n*ass(y,0)-asquare_of_sums(y,0)))\r\n zerodivproblem = N.equal(r_den,0)\r\n r_den = N.where(zerodivproblem,1,r_den) # avoid zero-division in 1st place\r\n r = r_num / r_den # need to do this nicely for matrix division\r\n r = N.where(zerodivproblem,0.0,r)\r\n z = 0.5*N.log((1.0+r+TINY)/(1.0-r+TINY))\r\n df = n-2\r\n t = r*N.sqrt(df/((1.0-r+TINY)*(1.0+r+TINY)))\r\n prob = abetai(0.5*df,0.5,df/(df+t*t))\r\n\r\n ss = float(n)*ass(x)-asquare_of_sums(x)\r\n s_den = N.where(ss==0,1,ss) # avoid zero-division in 1st place\r\n slope = r_num / s_den\r\n intercept = ymean - slope*xmean\r\n sterrest = N.sqrt(1-r*r)*asamplestdev(y,0)\r\n return slope, intercept, r, prob, sterrest, n","def mse(x, y):\n\n return (x - y).pow(2).sum(dim=1, keepdim=True).mean() / x.size(1)","def _mse(self):\n error = self._input * self._weights - self._label\n sum_ = 0.0\n for i in range(self._input.shape[0]):\n sum_ += error[i, 0]**2\n return sum_/self._input.shape[0]","def rmse(self):\n lam = self.lam()\n weights = lam / lam.sum()\n weighted_var = self.var() * weights\n rmse = np.sqrt(weighted_var.sum())\n return rmse","def mean_squared_error(predictions, actual):\n\n return ((predictions-actual)**2).sum() / len(actual)","def mse(a, b):\n a = numpy(a)\n b = numpy(b)\n return ((a - b) ** 2).sum()","def MeanSqError(self):\r\n\t\treturn self.mse","def mse(image1: np.ndarray, image2: np.ndarray) -> np.ndarray:\n return np.sqrt(np.power((image1 - image2), 2).mean(axis=(-1, -2)))","def rmspe(self) -> float:\n return float(np.sqrt(np.mean(np.square(((self.true - self.predicted) / self.true)), axis=0)))","def mse(A, B):\n return ((A - B) ** 2).mean(axis=0)","def _error(self, xy_tuple, coord_pairs, rcut_sq, kl_pairs):\n # set up target Bravais lattice\n kx1 = kl_pairs[:,0] * xy_tuple[0]\n lx2 = kl_pairs[:,1] * xy_tuple[2]\n ky1 = kl_pairs[:,0] * xy_tuple[1]\n ly2 = kl_pairs[:,1] * xy_tuple[3]\n bravais_pairs = np.vstack((kx1 + lx2, ky1 + ly2)).transpose()\n \n # get squared distance between every Bravais point and every coord point\n # sq_dists has shape (n_bravais_pairs, n_coord_pairs)\n sq_dists = spatial.distance.cdist(bravais_pairs, coord_pairs,\n 'sqeuclidean')\n # get min dist for each coord\n min_sq_dists = np.min(sq_dists, axis=0)\n \n # apply error function\n scaled_sq_dists = min_sq_dists / rcut_sq\n errors = np.where(scaled_sq_dists < 1.0, scaled_sq_dists, 1.0)\n error = np.mean(errors)\n \n # error = 0\n # for coord in coords:\n # find closest Bravais point to each actual particle\n # closest_dist_sq = min([(coord.x-bp.x)**2 + (coord.y-bp.y)**2 for bp in bravais])\n # piecewise error function\n # error += min(closest_dist_sq / rcut_sq, 1.0)\n # error /= len(coords)\n # error = sum([min(min([(coord.x-bp.x)**2 + (coord.y-bp.y)**2 for bp in bravais]) / rcut_sq, 1.0)]) / len(coords)\n \n return error","def rmse(x: np.ndarray, y: np.ndarray):\n x, y = np.copy(x), np.copy(y)\n if x.ndim > 1:\n return np.sqrt(np.nanmean((x-y)**2, axis=1))\n return np.sqrt(np.nanmean((x-y)**2))","def rmse(a,b):\n \n ### Import modules\n import numpy as np\n \n ### Calculate RMSE\n rmse_stat = np.sqrt(np.mean((a - b)**2))\n \n return rmse_stat","def mse(o, r):\r\n\r\n return np.mean(np.square((np.abs(o).astype(float) - np.abs(r).astype(float))))","def squared_error(a, b):\n return (a - b)**2","def calc_rmse(data1, data2):\n num_users = len(data1)\n\n SE = 0 #the accumulated Squared Error\n num_total = 0 #the accumulated number of ratings evaluated\n for i in range(num_users):\n data1_dict = dict(data1[i])\n for movie, rating2 in data2[i]:\n #Make one of the datasets into a dictionary to make the search more efficient\n rating1 = data1_dict.get(movie, -1)\n SE += (rating1-rating2)**2\n num_total += 1\n\n if rating1 == -1:\n print('Could not find rating for movie %i at user %i in data1'%(movie, i))\n rmse = np.sqrt(SE/num_total)\n return rmse","def rmse(targets: List[float], preds: List[float]) -> float:\n return math.sqrt(mean_squared_error(targets, preds))","def rmse(targets: List[float], preds: List[float]) -> float:\n return math.sqrt(mean_squared_error(targets, preds))","def get_residual_sum_of_squares(input_features, output, w):\n X = get_test_data (input_features)\n y = get_test_label (output)\n \n # Building Xbar\n one = np.ones ((X.shape[0], 1))\n Xbar = np.concatenate ((one, X), axis = 1)\n\n RSS = sum ((y - np.dot (Xbar, w)) ** 2)\n return RSS","def mse(mat1, mat2):\n\tmse = 0\n\tw, h = mat1.shape\n\tif mat1.shape != mat2.shape:\n\t\treturn -1\n\tprint(\"inside mse\")\n\tprint(mat1)\n\tprint(mat2)\n\tfor i in range(w):\n\t\tfor j in range(h):\n\t\t\tmse += \tpow((int(mat1[i,j]) - int(mat2[i,j])), 2)\n\treturn mse/ (w*h)","def _mean_squared_error(img1, img2):\n err = np.sum((img1.astype(\"float\") - img2.astype(\"float\")) ** 2)\n err /= float(img1.shape[0] * img1.shape[1])\n return err","def _std_err(self):\n return np.sqrt(np.sum(np.square(self._resids), axis=-2) / self._df_err)","def error_calculation_test(self):\n dataOrg = [[1,1], [2,2], [3,3], [4,4], [5,5], [6,6], [7,8], [7.3, 5], [8, 0], [9,10]]\n dataCalc = [[1,3], [2,5], [3,0], [4,3], [5,5], [6.1,6], [7,3], [7.3, 5], [8, 0], [9,9]]\n # abs difference: 2 3 3 1 0 NA 5 0 NA 1\n # local errors: 200 150 200 50 0 NA 125 0 NA 20\n # sum: 745\n\n tsOrg = TimeSeries.from_twodim_list(dataOrg)\n tsCalc = TimeSeries.from_twodim_list(dataCalc)\n\n wmape = WeightedMeanAbsolutePercentageError()\n wmape.initialize(tsOrg, tsCalc)\n assert str(wmape.get_error())[:6] == \"93.125\"","def rmse(y_hat, y):\n\tif type(y) == list:\n\t\tpass\n\telse:\n\t\ty = y.values.tolist()\n\tm = len(y)\n\tsum = 0\n\tfor i in range(m):\n\t\tsum += ((y_hat[i] - y[i]) ** 2 / m)\n\terror = np.sqrt(sum)\n\treturn error","def SSD(x,y):\n return np.sum((x-y)**2)","def site_error(matrix,site):\n return (score(matrix,site,ns=False)\n - C\n + 1/beta * sum([log(sum([exp(-beta*matrix[i][base_dict[b]])\n for b in \"ACGT\"]))\n for i in range(L)]))","def _calc_r2(self):\n sse = np.sum((self.data.y - self.predict(self.data.x))**2)\n sst = np.sum((self.data.y - self.data.y.mean())**2)\n return (1. - sse/sst)","def computeRmse(model, data, n):\n print \"RESULT_data:%s \" % ((data.map(lambda x: (x[0], x[1]))).take(50))\n predictions1 = model.predictAll(data.map(lambda x: (x[0], x[1])))\n print \"RESULT1: %s\" % predictions1\n predictionsAndRatings = predictions1.map(lambda x: ((x[0], x[1]), x[2])) \\\n .join(data.map(lambda x: ((x[0], x[1]), x[2]))) \\\n .values()\n #print \"RESULT2: %s\" % predictions1.take(11)\n return sqrt(predictionsAndRatings.map(lambda x: (x[0] - x[1]) ** 2).reduce(add) / float(n))","def abs_error(x, y):\n return np.sum(np.abs(x - y))","def root_mean_square_error(y, y_pred, w):\n return np.sqrt(np.average(((y_pred - y) ** 2), weights=w))","def rmse(self, weights=None) -> float:\n return sqrt(np.average((self.true - self.predicted) ** 2, axis=0, weights=weights))","def sum_squared_error(y_true, y_pred):\n return np.sum(np.square(y_true - y_pred))","def mse(observed, predicted):\n return np.sqrt(np.mean((observed - predicted)**2))","def mae(self, x_train, y_train):\n # number of training examples\n m = x_train.shape[0]\n error = 0\n for pair, r in zip(x_train, y_train):\n u, i = pair\n error += abs(r - np.dot(self.P[u], self.Q[i]))\n return error / m","def RMSE(observed: np.ndarray, targets: np.ndarray) -> np.float64:\n\treturn np.sqrt(np.mean((observed-targets)**2))","def d_mse(x, y):\n\n return 2 * (x - y) / x.size(0) / x.size(1)","def calculate_mse(img0, img1):\n mse = skm.mean_squared_error(img0, img1)\n return mse","def root_square_error(input_img, output_img):\n rse = np.sqrt(np.sum(np.power(output_img - input_img, 2)))\n\n return rse","def calc_rmse(y: np.ndarray, y_hat: np.ndarray) -> float:\n return np.sqrt(np.mean((y - y_hat)**2))","def get_residual_sum_of_squares(predicted_output, y):\n RSS = sum ((y - predicted_output) ** 2)\n return RSS[0]","def error_MSE(resid):\n if resid.ndim == 2:\n return (norm(np.asarray(resid).ravel())**2)/float(resid.shape[1])\n elif resid.ndim == 1:\n return (norm(np.asarray(resid).ravel())**2)\n else:\n raise Exception(\"array passed to error_MSE has incorrect shape\")","def overall_accuracy(error_matrix):\r\n\r\n return error_matrix.diagonal().sum() / error_matrix.sum()","def calcRMSE(inflow, demand):\n ssd = 0\n for i in range(len(inflow)):\n ssd += pow((inflow[i]-demand[i]),2)\n rmse = np.sqrt(ssd/len(inflow))\n return rmse","def _ss_reg(self):\n squares = np.square(self._predicted - np.expand_dims(self._ybar, axis=-2))\n if self.w is None:\n return np.sum(squares, axis=-2)\n else:\n return np.sum(np.matmul(self.w_diag, squares), axis=-2)","def rmse(y_preds: ndarray, y_actual: ndarray) -> float:\n\n return np.sqrt(np.mean(np.power(y_preds - y_actual, 2)))","def mse (vec1, vec2):\n sum = 0.0 #Initializes sum to 0\n count = len(vec1) #Number of total elements in each vector\n for i in range(count):\n sum += (vec2[i]-vec1[i])**2 #Adds the square of the difference between the values at each position in the two vectors\n return sum/count","def calculate_total_loss(self, train_x, train_y):\n return np.sum([self.calculate_error(x, y)\n for x, y in zip(train_x, train_y)])","def calc_score(pins_stats):\n count = 0\n new = pins_stats[:, :2] - ORIG_PINS_LOC\n for p in new:\n if np.linalg.norm(p) > R_PIN / 2:\n count += 1\n return count","def getRMSE(image1, image2):\n im1 = readImage(image1, grayscale=False)\n im2 = readImage(image2, grayscale=False)\n return np.sqrt( ((im1 - im2)**2).mean() )","def rmsd(array1, array2):\n total = 0\n for n1, n2 in zip(array1, array2):\n total += (n1 - n2) ** 2\n total /= len(array1)\n\n return math.sqrt(total)","def _rsq(self):\n return self._ss_reg / self._ss_tot","def rrse(self) -> float:\n return float(np.sqrt(self.rse()))","def euler_error(Rd, Re):\n\n nd = Rd[:, 0]\n sd = Rd[:, 1]\n ad = Rd[:, 2]\n\n ne = Re[:, 0]\n se = Re[:, 1]\n ae = Re[:, 2]\n\n return 0.5 * (np.cross(ne, nd) + np.cross(se, sd) + np.cross(ae, ad)) # eqn 3.85","def error(Y, X):\n return (Y - X) ** 2","def get_error(intercept, slope, points):\n error_value = 0\n for i in range(0, len(points)):\n error_value += (points[i].y - (slope * points[i].x + intercept)) ** 2\n return error_value / float(len(points))"],"string":"[\n \"def mse(self):\\n xs, ys = self.R.nonzero()\\n predicted = self.full_matrix()\\n error = 0\\n for x, y in zip(xs, ys):\\n # print(predicted[x, y], self.R[x, y] )\\n error += pow(self.R[x, y] - predicted[x, y], 2)\\n return np.sqrt(error)\",\n \"def rmse2 (a, p) :\\n s = len(a)\\n z = zip(a, p)\\n v = 0.0\\n for x, y in z :\\n v += sqre_diff(x, y)\\n return math.sqrt(v / s)\",\n \"def sum_of_squared_errors(P1,P2):\\r\\n return sum(euclidean_distance_3(p1,p2) for (p1,p2) in zip(P1,P2))\",\n \"def rmse4 (a, p) :\\n s = len(a)\\n z = zip(a, p)\\n v = sum(map(lambda (x, y) : sqre_diff(x, y), z), 0.0)\\n return math.sqrt(v / s)\",\n \"def sum_of_squared_errors(P1,P2):\\r\\n return sum(euclidean_distance_2(p1,p2) for (p1,p2) in zip(P1,P2))\",\n \"def calcRMSE(labelsAndPreds):\\n meanOfSqErrors = labelsAndPreds.map(lambda (x,y): squaredError(x,y)).mean()\\n \\n return math.sqrt(meanOfSqErrors)\",\n \"def rmse_calc(arr1, arr2):\\n assert arr1.shape==arr2.shape\\n \\n return np.sqrt(np.mean((arr2-arr1)**2))\",\n \"def mse(result, expected):\\n total_square_sum = 0\\n for index1 in range(0, len(result)):\\n total_square_sum += (result[index1] - expected[index1]) ** 2\\n return total_square_sum / float(len(result))\",\n \"def _ss_err(self):\\n return np.sum(np.square(self._resids), axis=-2)\",\n \"def lsumdiffsquared(x,y):\\r\\n sds = 0\\r\\n for i in range(len(x)):\\r\\n sds = sds + (x[i]-y[i])**2\\r\\n return sds\",\n \"def rmse3 (a, p) :\\n s = len(a)\\n z = zip(a, p)\\n v = reduce(lambda w, (x, y) : w + sqre_diff(x, y), z, 0.0)\\n return math.sqrt(v / s)\",\n \"def squaredError(label, prediction):\\n return (label-prediction)*(label-prediction)\",\n \"def rmse(predicted, actual):\\n #maybe make some assertions, assume have same length & in right order\\n interm_total = 0\\n for i in range(len(predicted)):\\n interm_total += (predicted[i] - actual[i]) ** 2\\n return sqrt(interm_total / len(predicted))\",\n \"def error(self, documents):\\n ###TODO\\n sum_1 = 0.0\\n for c_id,clust in self.fin_clust.items():\\n n = self.sqnorm(self.means[c_id]) \\n sum_1 = sum_1 + sum([self.distance(self.docs[dc],self.means[c_id],n) for dc in clust]) \\n return round(sum_1,2)\",\n \"def error(self, trainset: ([], [])):\\n # MSE = Σ | d – y |^2 / n\\n error_sum = 0.0\\n for index, example in enumerate(trainset):\\n # | d – y |^2\\n output = self.activate(example[0])\\n\\n target = example[1][0]\\n\\n error = target - output\\n error_sum += error ** 2\\n\\n # Σ |error_sum| / n\\n error_sum = error_sum / len(trainset)\\n return error_sum\",\n \"def calculate_error(d1, d2):\\n square_sum = 0.0\\n for i in range(len(d1)):\\n square_sum += pow( distributions_EMD(d1[i], d2[i]), 2)\\n return math.sqrt(square_sum)\",\n \"def rmse(a, b):\\n\\n n = len(a)\\n return np.linalg.norm(a - b) / np.sqrt(n)\",\n \"def rmse (lmse) :\\n return np.sqrt(2*lmse)\",\n \"def rms_error(self, X, y) :\\n ### ========== TODO : START ========== ###\\n # part h: compute RMSE\\n n, d = X.shape\\n error = np.sqrt(self.cost(X,y)/n)\\n ### ========== TODO : END ========== ###\\n return error\",\n \"def calc_centered_rms_error(x, y):\\n xdat = x.flatten()\\n ydat = y.flatten()\\n xm = np.ma.mean(xdat)\\n ym = np.ma.mean(ydat)\\n\\n anom = np.sqrt(np.ma.mean(((xdat - xm) - (ydat - ym)) ** 2.))\\n\\n return xm - ym, anom\",\n \"def pairwise_score(l1, l2):\\n theta1 = math.atan(slope(l1))\\n theta2 = math.atan(slope(l2))\\n return (theta1-theta2)**2\",\n \"def calculate_mean_squared_error(self, X, y):\\n mserror = 0\\n results = self.predict(X)\\n \\n mserror = mean_squared_error(results,y)\\n #print(error)\\n return mserror\",\n \"def rmse(self, X, y) -> float:\\n return np.sqrt(np.sum(self.l2_error(X, y)))\",\n \"def stderr(predicted, actual):\\n return np.sqrt(mse(predicted, actual))\",\n \"def calc_error_dist(self):\\n pass\",\n \"def mean_squared_error ( actuals, predictions ):\\n return np.mean ( ( actuals - predictions ) ** 2 )\\n # End mean_squared_error()\",\n \"def mean_squared_error ( actuals, predictions ):\\n return np.mean ( ( actuals - predictions ) ** 2 )\\n # End mean_squared_error()\",\n \"def rmse_metric(actual, predicted):\\r\\n sum_error = 0.0\\r\\n for i in range(len(actual)):\\r\\n prediction_error = predicted[i] - actual[i]\\r\\n sum_error += (prediction_error ** 2)\\r\\n mean_error = sum_error / float(len(actual))\\r\\n return sqrt(mean_error)\",\n \"def rmse(actual: np.ndarray, predicted: np.ndarray):\\n return np.sqrt(np.mean(np.square(_error(actual, predicted))))\",\n \"def rmse5 (a, p) :\\n s = len(a)\\n v = sum(map(sqre_diff, a, p), 0.0)\\n return math.sqrt(v / s)\",\n \"def site_error_optimized(matrix,site):\\n return score(matrix,site,ns=False) - C\",\n \"def rmse1 (a, p) :\\n s = len(a)\\n i = 0\\n v = 0.0\\n while i != s :\\n v += sqre_diff(a[i], p[i])\\n i += 1\\n return math.sqrt(v / s)\",\n \"def mean_square_error(predictions: list, labels: list):\\n temp = 0\\n for i in range(len(predictions)):\\n temp += (labels[i] - predictions[i]) ** 2\\n\\n return round(temp / len(predictions), 2)\",\n \"def sse(matrix,motif):\\n return sum([site_error(matrix,site)**2\\n for site in motif])\",\n \"def error(line, data): # error function\\n # Metric: Sum of squared Y-axis differences\\n err = np.sum((data[:, 1] - (line[0] * data[:, 0] + line[1])) ** 2)\\n return err\",\n \"def _calculate_anisoplatanism_error(self):\\n\\n self.sigma_anisoplatanism = np.sqrt((self.science_object_separation/self.isoplanatic_angle)**(5/3))*(self.parameter_wavelength /(2*np.pi))\",\n \"def rmse(X, Y):\\n\\n assert X.shape == Y.shape\\n\\n N = X.shape[0]\\n\\n if N < 9:\\n print(\\\"Not enough points. {} datapoints given. At least 9 is required\\\".format(N))\\n return\\n\\n diff = X - Y\\n diff = diff**2\\n rmse = np.sqrt(diff.mean())\\n\\n le = rmse * (1.0 - np.sqrt(1-1.96*np.sqrt(2.0)/np.sqrt(N-1)))\\n ue = rmse * (np.sqrt(1 + 1.96*np.sqrt(2.0)/np.sqrt(N-1))-1)\\n\\n return rmse, le, ue\",\n \"def calculate_error(k_means_matrix):\\n return sum([min(dist) for dist in k_means_matrix])\",\n \"def rSquare(estimations, measureds):\\n SEE = (( np.array(measureds) - np.array(estimations) )**2 ).sum()\\n mMean = (np.array(measureds)).sum() / float(len(measureds))\\n dErr = ((mMean - measureds)).sum()\\n \\n return 1 - (SEE / dErr)\",\n \"def _mse_distance(feature_map_1: np.ndarray,\\n feature_map_2: np.ndarray) -> np.ndarray:\\n dist = np.mean((feature_map_1 - feature_map_2) ** 2, axis=-1)\\n return dist\",\n \"def sum_of_square_error(data_x, data_y, len_data, theta):\\n error = 0.0\\n #prod represent the out_result\\n prod = np.dot(theta, data_x.transpose())\\n # the error matrix\\n prod -= data_y\\n #np.square(prod) prod is a 1*n matrix, every_element get square\\n sum_elem = np.sum(np.square(prod))\\n # np.sum fet sum of the\\n error = sum_elem / (2 * len_data)\\n return error\",\n \"def compute_error(data, user_features, item_features, nz):\\n sum_err = 0\\n for d, n in nz:\\n err = data[d,n] - np.dot(item_features[d,:],user_features[:,n])\\n sum_err += err**2\\n rmse = 0.5*sum_err/len(nz)\\n return rmse\",\n \"def error_metric(phi_1, phi_2, spherical=False, xpts=None):\\n if spherical:\\n return sum(abs(phi_1-phi_2)*(xpts**2))/(2.0*sum(abs(phi_1)*(xpts**2)))\\n else:\\n return sum(abs(phi_1-phi_2))/(2.0*sum(phi_1))\",\n \"def rmsd(x, y):\\n\\n # get the length of the dataset\\n n, = x.shape\\n\\n return np.sqrt(np.sum((x-y)**2)/n)\",\n \"def rmse(actual, predicted):\\n rms = (actual-predicted)**2\\n\\n # Returning the sqaure root of the root mean square\\n return float(np.sqrt(rms.mean()))\",\n \"def calc_rmse(self, data):\\n res= data.score- data[['userid','itemid']].apply(lambda row:self.calc_score(row[0], row[1]),axis=1)\\n res=[el**2 for el in np.array(res)]\\n return np.sqrt(np.sum(res)/data.shape[0])\",\n \"def amasslinregress(*args):\\r\\n TINY = 1.0e-20\\r\\n if len(args) == 1: # more than 1D array?\\r\\n args = args[0]\\r\\n if len(args) == 2:\\r\\n x = N.ravel(args[0])\\r\\n y = args[1]\\r\\n else:\\r\\n x = N.ravel(args[:,0])\\r\\n y = args[:,1]\\r\\n else:\\r\\n x = args[0]\\r\\n y = args[1]\\r\\n x = x.astype(N.float_)\\r\\n y = y.astype(N.float_)\\r\\n n = len(x)\\r\\n xmean = amean(x)\\r\\n ymean = amean(y,0)\\r\\n shp = N.ones(len(y.shape))\\r\\n shp[0] = len(x)\\r\\n x.shape = shp\\r\\n print x.shape, y.shape\\r\\n r_num = n*(N.add.reduce(x*y,0)) - N.add.reduce(x)*N.add.reduce(y,0)\\r\\n r_den = N.sqrt((n*ass(x) - asquare_of_sums(x))*(n*ass(y,0)-asquare_of_sums(y,0)))\\r\\n zerodivproblem = N.equal(r_den,0)\\r\\n r_den = N.where(zerodivproblem,1,r_den) # avoid zero-division in 1st place\\r\\n r = r_num / r_den # need to do this nicely for matrix division\\r\\n r = N.where(zerodivproblem,0.0,r)\\r\\n z = 0.5*N.log((1.0+r+TINY)/(1.0-r+TINY))\\r\\n df = n-2\\r\\n t = r*N.sqrt(df/((1.0-r+TINY)*(1.0+r+TINY)))\\r\\n prob = abetai(0.5*df,0.5,df/(df+t*t))\\r\\n\\r\\n ss = float(n)*ass(x)-asquare_of_sums(x)\\r\\n s_den = N.where(ss==0,1,ss) # avoid zero-division in 1st place\\r\\n slope = r_num / s_den\\r\\n intercept = ymean - slope*xmean\\r\\n sterrest = N.sqrt(1-r*r)*asamplestdev(y,0)\\r\\n return slope, intercept, r, prob, sterrest, n\",\n \"def mse(x, y):\\n\\n return (x - y).pow(2).sum(dim=1, keepdim=True).mean() / x.size(1)\",\n \"def _mse(self):\\n error = self._input * self._weights - self._label\\n sum_ = 0.0\\n for i in range(self._input.shape[0]):\\n sum_ += error[i, 0]**2\\n return sum_/self._input.shape[0]\",\n \"def rmse(self):\\n lam = self.lam()\\n weights = lam / lam.sum()\\n weighted_var = self.var() * weights\\n rmse = np.sqrt(weighted_var.sum())\\n return rmse\",\n \"def mean_squared_error(predictions, actual):\\n\\n return ((predictions-actual)**2).sum() / len(actual)\",\n \"def mse(a, b):\\n a = numpy(a)\\n b = numpy(b)\\n return ((a - b) ** 2).sum()\",\n \"def MeanSqError(self):\\r\\n\\t\\treturn self.mse\",\n \"def mse(image1: np.ndarray, image2: np.ndarray) -> np.ndarray:\\n return np.sqrt(np.power((image1 - image2), 2).mean(axis=(-1, -2)))\",\n \"def rmspe(self) -> float:\\n return float(np.sqrt(np.mean(np.square(((self.true - self.predicted) / self.true)), axis=0)))\",\n \"def mse(A, B):\\n return ((A - B) ** 2).mean(axis=0)\",\n \"def _error(self, xy_tuple, coord_pairs, rcut_sq, kl_pairs):\\n # set up target Bravais lattice\\n kx1 = kl_pairs[:,0] * xy_tuple[0]\\n lx2 = kl_pairs[:,1] * xy_tuple[2]\\n ky1 = kl_pairs[:,0] * xy_tuple[1]\\n ly2 = kl_pairs[:,1] * xy_tuple[3]\\n bravais_pairs = np.vstack((kx1 + lx2, ky1 + ly2)).transpose()\\n \\n # get squared distance between every Bravais point and every coord point\\n # sq_dists has shape (n_bravais_pairs, n_coord_pairs)\\n sq_dists = spatial.distance.cdist(bravais_pairs, coord_pairs,\\n 'sqeuclidean')\\n # get min dist for each coord\\n min_sq_dists = np.min(sq_dists, axis=0)\\n \\n # apply error function\\n scaled_sq_dists = min_sq_dists / rcut_sq\\n errors = np.where(scaled_sq_dists < 1.0, scaled_sq_dists, 1.0)\\n error = np.mean(errors)\\n \\n # error = 0\\n # for coord in coords:\\n # find closest Bravais point to each actual particle\\n # closest_dist_sq = min([(coord.x-bp.x)**2 + (coord.y-bp.y)**2 for bp in bravais])\\n # piecewise error function\\n # error += min(closest_dist_sq / rcut_sq, 1.0)\\n # error /= len(coords)\\n # error = sum([min(min([(coord.x-bp.x)**2 + (coord.y-bp.y)**2 for bp in bravais]) / rcut_sq, 1.0)]) / len(coords)\\n \\n return error\",\n \"def rmse(x: np.ndarray, y: np.ndarray):\\n x, y = np.copy(x), np.copy(y)\\n if x.ndim > 1:\\n return np.sqrt(np.nanmean((x-y)**2, axis=1))\\n return np.sqrt(np.nanmean((x-y)**2))\",\n \"def rmse(a,b):\\n \\n ### Import modules\\n import numpy as np\\n \\n ### Calculate RMSE\\n rmse_stat = np.sqrt(np.mean((a - b)**2))\\n \\n return rmse_stat\",\n \"def mse(o, r):\\r\\n\\r\\n return np.mean(np.square((np.abs(o).astype(float) - np.abs(r).astype(float))))\",\n \"def squared_error(a, b):\\n return (a - b)**2\",\n \"def calc_rmse(data1, data2):\\n num_users = len(data1)\\n\\n SE = 0 #the accumulated Squared Error\\n num_total = 0 #the accumulated number of ratings evaluated\\n for i in range(num_users):\\n data1_dict = dict(data1[i])\\n for movie, rating2 in data2[i]:\\n #Make one of the datasets into a dictionary to make the search more efficient\\n rating1 = data1_dict.get(movie, -1)\\n SE += (rating1-rating2)**2\\n num_total += 1\\n\\n if rating1 == -1:\\n print('Could not find rating for movie %i at user %i in data1'%(movie, i))\\n rmse = np.sqrt(SE/num_total)\\n return rmse\",\n \"def rmse(targets: List[float], preds: List[float]) -> float:\\n return math.sqrt(mean_squared_error(targets, preds))\",\n \"def rmse(targets: List[float], preds: List[float]) -> float:\\n return math.sqrt(mean_squared_error(targets, preds))\",\n \"def get_residual_sum_of_squares(input_features, output, w):\\n X = get_test_data (input_features)\\n y = get_test_label (output)\\n \\n # Building Xbar\\n one = np.ones ((X.shape[0], 1))\\n Xbar = np.concatenate ((one, X), axis = 1)\\n\\n RSS = sum ((y - np.dot (Xbar, w)) ** 2)\\n return RSS\",\n \"def mse(mat1, mat2):\\n\\tmse = 0\\n\\tw, h = mat1.shape\\n\\tif mat1.shape != mat2.shape:\\n\\t\\treturn -1\\n\\tprint(\\\"inside mse\\\")\\n\\tprint(mat1)\\n\\tprint(mat2)\\n\\tfor i in range(w):\\n\\t\\tfor j in range(h):\\n\\t\\t\\tmse += \\tpow((int(mat1[i,j]) - int(mat2[i,j])), 2)\\n\\treturn mse/ (w*h)\",\n \"def _mean_squared_error(img1, img2):\\n err = np.sum((img1.astype(\\\"float\\\") - img2.astype(\\\"float\\\")) ** 2)\\n err /= float(img1.shape[0] * img1.shape[1])\\n return err\",\n \"def _std_err(self):\\n return np.sqrt(np.sum(np.square(self._resids), axis=-2) / self._df_err)\",\n \"def error_calculation_test(self):\\n dataOrg = [[1,1], [2,2], [3,3], [4,4], [5,5], [6,6], [7,8], [7.3, 5], [8, 0], [9,10]]\\n dataCalc = [[1,3], [2,5], [3,0], [4,3], [5,5], [6.1,6], [7,3], [7.3, 5], [8, 0], [9,9]]\\n # abs difference: 2 3 3 1 0 NA 5 0 NA 1\\n # local errors: 200 150 200 50 0 NA 125 0 NA 20\\n # sum: 745\\n\\n tsOrg = TimeSeries.from_twodim_list(dataOrg)\\n tsCalc = TimeSeries.from_twodim_list(dataCalc)\\n\\n wmape = WeightedMeanAbsolutePercentageError()\\n wmape.initialize(tsOrg, tsCalc)\\n assert str(wmape.get_error())[:6] == \\\"93.125\\\"\",\n \"def rmse(y_hat, y):\\n\\tif type(y) == list:\\n\\t\\tpass\\n\\telse:\\n\\t\\ty = y.values.tolist()\\n\\tm = len(y)\\n\\tsum = 0\\n\\tfor i in range(m):\\n\\t\\tsum += ((y_hat[i] - y[i]) ** 2 / m)\\n\\terror = np.sqrt(sum)\\n\\treturn error\",\n \"def SSD(x,y):\\n return np.sum((x-y)**2)\",\n \"def site_error(matrix,site):\\n return (score(matrix,site,ns=False)\\n - C\\n + 1/beta * sum([log(sum([exp(-beta*matrix[i][base_dict[b]])\\n for b in \\\"ACGT\\\"]))\\n for i in range(L)]))\",\n \"def _calc_r2(self):\\n sse = np.sum((self.data.y - self.predict(self.data.x))**2)\\n sst = np.sum((self.data.y - self.data.y.mean())**2)\\n return (1. - sse/sst)\",\n \"def computeRmse(model, data, n):\\n print \\\"RESULT_data:%s \\\" % ((data.map(lambda x: (x[0], x[1]))).take(50))\\n predictions1 = model.predictAll(data.map(lambda x: (x[0], x[1])))\\n print \\\"RESULT1: %s\\\" % predictions1\\n predictionsAndRatings = predictions1.map(lambda x: ((x[0], x[1]), x[2])) \\\\\\n .join(data.map(lambda x: ((x[0], x[1]), x[2]))) \\\\\\n .values()\\n #print \\\"RESULT2: %s\\\" % predictions1.take(11)\\n return sqrt(predictionsAndRatings.map(lambda x: (x[0] - x[1]) ** 2).reduce(add) / float(n))\",\n \"def abs_error(x, y):\\n return np.sum(np.abs(x - y))\",\n \"def root_mean_square_error(y, y_pred, w):\\n return np.sqrt(np.average(((y_pred - y) ** 2), weights=w))\",\n \"def rmse(self, weights=None) -> float:\\n return sqrt(np.average((self.true - self.predicted) ** 2, axis=0, weights=weights))\",\n \"def sum_squared_error(y_true, y_pred):\\n return np.sum(np.square(y_true - y_pred))\",\n \"def mse(observed, predicted):\\n return np.sqrt(np.mean((observed - predicted)**2))\",\n \"def mae(self, x_train, y_train):\\n # number of training examples\\n m = x_train.shape[0]\\n error = 0\\n for pair, r in zip(x_train, y_train):\\n u, i = pair\\n error += abs(r - np.dot(self.P[u], self.Q[i]))\\n return error / m\",\n \"def RMSE(observed: np.ndarray, targets: np.ndarray) -> np.float64:\\n\\treturn np.sqrt(np.mean((observed-targets)**2))\",\n \"def d_mse(x, y):\\n\\n return 2 * (x - y) / x.size(0) / x.size(1)\",\n \"def calculate_mse(img0, img1):\\n mse = skm.mean_squared_error(img0, img1)\\n return mse\",\n \"def root_square_error(input_img, output_img):\\n rse = np.sqrt(np.sum(np.power(output_img - input_img, 2)))\\n\\n return rse\",\n \"def calc_rmse(y: np.ndarray, y_hat: np.ndarray) -> float:\\n return np.sqrt(np.mean((y - y_hat)**2))\",\n \"def get_residual_sum_of_squares(predicted_output, y):\\n RSS = sum ((y - predicted_output) ** 2)\\n return RSS[0]\",\n \"def error_MSE(resid):\\n if resid.ndim == 2:\\n return (norm(np.asarray(resid).ravel())**2)/float(resid.shape[1])\\n elif resid.ndim == 1:\\n return (norm(np.asarray(resid).ravel())**2)\\n else:\\n raise Exception(\\\"array passed to error_MSE has incorrect shape\\\")\",\n \"def overall_accuracy(error_matrix):\\r\\n\\r\\n return error_matrix.diagonal().sum() / error_matrix.sum()\",\n \"def calcRMSE(inflow, demand):\\n ssd = 0\\n for i in range(len(inflow)):\\n ssd += pow((inflow[i]-demand[i]),2)\\n rmse = np.sqrt(ssd/len(inflow))\\n return rmse\",\n \"def _ss_reg(self):\\n squares = np.square(self._predicted - np.expand_dims(self._ybar, axis=-2))\\n if self.w is None:\\n return np.sum(squares, axis=-2)\\n else:\\n return np.sum(np.matmul(self.w_diag, squares), axis=-2)\",\n \"def rmse(y_preds: ndarray, y_actual: ndarray) -> float:\\n\\n return np.sqrt(np.mean(np.power(y_preds - y_actual, 2)))\",\n \"def mse (vec1, vec2):\\n sum = 0.0 #Initializes sum to 0\\n count = len(vec1) #Number of total elements in each vector\\n for i in range(count):\\n sum += (vec2[i]-vec1[i])**2 #Adds the square of the difference between the values at each position in the two vectors\\n return sum/count\",\n \"def calculate_total_loss(self, train_x, train_y):\\n return np.sum([self.calculate_error(x, y)\\n for x, y in zip(train_x, train_y)])\",\n \"def calc_score(pins_stats):\\n count = 0\\n new = pins_stats[:, :2] - ORIG_PINS_LOC\\n for p in new:\\n if np.linalg.norm(p) > R_PIN / 2:\\n count += 1\\n return count\",\n \"def getRMSE(image1, image2):\\n im1 = readImage(image1, grayscale=False)\\n im2 = readImage(image2, grayscale=False)\\n return np.sqrt( ((im1 - im2)**2).mean() )\",\n \"def rmsd(array1, array2):\\n total = 0\\n for n1, n2 in zip(array1, array2):\\n total += (n1 - n2) ** 2\\n total /= len(array1)\\n\\n return math.sqrt(total)\",\n \"def _rsq(self):\\n return self._ss_reg / self._ss_tot\",\n \"def rrse(self) -> float:\\n return float(np.sqrt(self.rse()))\",\n \"def euler_error(Rd, Re):\\n\\n nd = Rd[:, 0]\\n sd = Rd[:, 1]\\n ad = Rd[:, 2]\\n\\n ne = Re[:, 0]\\n se = Re[:, 1]\\n ae = Re[:, 2]\\n\\n return 0.5 * (np.cross(ne, nd) + np.cross(se, sd) + np.cross(ae, ad)) # eqn 3.85\",\n \"def error(Y, X):\\n return (Y - X) ** 2\",\n \"def get_error(intercept, slope, points):\\n error_value = 0\\n for i in range(0, len(points)):\\n error_value += (points[i].y - (slope * points[i].x + intercept)) ** 2\\n return error_value / float(len(points))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65895677","0.6588133","0.6415059","0.6338098","0.6315696","0.631417","0.6205466","0.6133671","0.6129634","0.6095342","0.608772","0.6043297","0.60194534","0.6008844","0.5990452","0.59714705","0.59372824","0.59342045","0.5901768","0.58844507","0.5880896","0.587042","0.5867026","0.58612967","0.58542436","0.5854141","0.5854141","0.58484244","0.5834379","0.58325326","0.58111846","0.5809178","0.58082646","0.5807275","0.57927","0.57888216","0.57868475","0.5784396","0.5777175","0.57696676","0.5759347","0.57567453","0.5753842","0.57523495","0.57332116","0.57313865","0.57273614","0.57092005","0.56984013","0.5694875","0.56918204","0.56853217","0.56818897","0.5680747","0.5679111","0.5678643","0.5672174","0.56708974","0.56671077","0.56513387","0.5646862","0.564012","0.5637336","0.5637336","0.563611","0.5635578","0.5634438","0.5627825","0.5627108","0.5623339","0.5618978","0.5613597","0.5605922","0.56041324","0.56025195","0.55991936","0.55991924","0.55935746","0.55934554","0.5593219","0.55899155","0.558506","0.5578224","0.5578139","0.5572396","0.5571725","0.5568472","0.55657846","0.55613744","0.5553529","0.5551222","0.55439144","0.55383825","0.5535071","0.55346483","0.5530394","0.5528521","0.5526867","0.5518393","0.5516378","0.55156827"],"string":"[\n \"0.65895677\",\n \"0.6588133\",\n \"0.6415059\",\n \"0.6338098\",\n \"0.6315696\",\n \"0.631417\",\n \"0.6205466\",\n \"0.6133671\",\n \"0.6129634\",\n \"0.6095342\",\n \"0.608772\",\n \"0.6043297\",\n \"0.60194534\",\n \"0.6008844\",\n \"0.5990452\",\n \"0.59714705\",\n \"0.59372824\",\n \"0.59342045\",\n \"0.5901768\",\n \"0.58844507\",\n \"0.5880896\",\n \"0.587042\",\n \"0.5867026\",\n \"0.58612967\",\n \"0.58542436\",\n \"0.5854141\",\n \"0.5854141\",\n \"0.58484244\",\n \"0.5834379\",\n \"0.58325326\",\n \"0.58111846\",\n \"0.5809178\",\n \"0.58082646\",\n \"0.5807275\",\n \"0.57927\",\n \"0.57888216\",\n \"0.57868475\",\n \"0.5784396\",\n \"0.5777175\",\n \"0.57696676\",\n \"0.5759347\",\n \"0.57567453\",\n \"0.5753842\",\n \"0.57523495\",\n \"0.57332116\",\n \"0.57313865\",\n \"0.57273614\",\n \"0.57092005\",\n \"0.56984013\",\n \"0.5694875\",\n \"0.56918204\",\n \"0.56853217\",\n \"0.56818897\",\n \"0.5680747\",\n \"0.5679111\",\n \"0.5678643\",\n \"0.5672174\",\n \"0.56708974\",\n \"0.56671077\",\n \"0.56513387\",\n \"0.5646862\",\n \"0.564012\",\n \"0.5637336\",\n \"0.5637336\",\n \"0.563611\",\n \"0.5635578\",\n \"0.5634438\",\n \"0.5627825\",\n \"0.5627108\",\n \"0.5623339\",\n \"0.5618978\",\n \"0.5613597\",\n \"0.5605922\",\n \"0.56041324\",\n \"0.56025195\",\n \"0.55991936\",\n \"0.55991924\",\n \"0.55935746\",\n \"0.55934554\",\n \"0.5593219\",\n \"0.55899155\",\n \"0.558506\",\n \"0.5578224\",\n \"0.5578139\",\n \"0.5572396\",\n \"0.5571725\",\n \"0.5568472\",\n \"0.55657846\",\n \"0.55613744\",\n \"0.5553529\",\n \"0.5551222\",\n \"0.55439144\",\n \"0.55383825\",\n \"0.5535071\",\n \"0.55346483\",\n \"0.5530394\",\n \"0.5528521\",\n \"0.5526867\",\n \"0.5518393\",\n \"0.5516378\",\n \"0.55156827\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80384,"cells":{"query":{"kind":"string","value":"Cost function to minimize"},"document":{"kind":"string","value":"def minimize_transform(params, points_dest, points_src, constraints):\n # initialize dof\n dof = [0, 0, 0, 0, 0, 0, 1, 1, 1]\n # initialize dictionary to relate constraints index to dof\n dict_dof = {'Tx': 0, 'Ty': 1, 'Tz': 2, 'Rx': 3, 'Ry': 4, 'Rz': 5, 'Sx': 6, 'Sy': 7, 'Sz': 8}\n # extract constraints\n list_constraints = constraints.split('_')\n # loop across constraints and update dof\n for i in range(len(list_constraints)):\n dof[dict_dof[list_constraints[i]]] = params[i]\n # convert dof to more intuitive variables\n tx, ty, tz, alpha, beta, gamma, scx, scy, scz = dof[0], dof[1], dof[2], dof[3], dof[4], dof[5], dof[6], dof[7], dof[8]\n # build rotation matrix\n rotation_matrix = matrix([[cos(alpha)*cos(beta), cos(alpha)*sin(beta)*sin(gamma)-sin(alpha)*cos(gamma), cos(alpha)*sin(beta)*cos(gamma)+sin(alpha)*sin(gamma)],\n [sin(alpha)*cos(beta), sin(alpha)*sin(beta)*sin(gamma)+cos(alpha)*cos(gamma), sin(alpha)*sin(beta)*cos(gamma)-cos(alpha)*sin(gamma)],\n [-sin(beta), cos(beta)*sin(gamma), cos(beta)*cos(gamma)]])\n # build scaling matrix\n scaling_matrix = matrix([[scx, 0.0, 0.0], [0.0, scy, 0.0], [0.0, 0.0, scz]])\n # compute rotation+scaling matrix\n rotsc_matrix = scaling_matrix * rotation_matrix\n # compute center of mass from moving points (src)\n points_src_barycenter = mean(points_src, axis=0)\n # apply transformation to moving points (src)\n points_src_reg = ((rotsc_matrix * (matrix(points_src) - points_src_barycenter).T).T + points_src_barycenter) + matrix([tx, ty, tz])\n # record SSE for later display\n sse_results.append(SSE(matrix(points_dest), points_src_reg))\n # return SSE\n return SSE(matrix(points_dest), points_src_reg)"},"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 costFun(self, S, x):","def cost(self) -> float:","def compute_cost(AL, Y):\n pass","def compute_cost(AL, Y):\n pass","def costFun(self, x):\n\ttmp = x.reshape(self.inp_shape)\n\tc = np.float64(self.calcCost(np.asarray(tmp,dtype=np.float32))) + self.alpha * np.dot(x.T, x)\n\treturn c","def calcCostFun(self):\n\n self.start()\n F, K = self.model()\n \n return self.costFunction","def optimize(self, x0):\n (result,f,d) = fmin_l_bfgs_b(lambda x:self.costFun(x), np.ravel(x0),lambda x: self.gradFun(x))\n print(\"optimization completed with cost: \" + str(f))\n return result.reshape(self.inp_shape)","def minimize(self):\n raise NotImplementedError","def minimize_cost_function(m = no_of_training_exammples, x, y, theta_0, theta_1):\r\n var_x, var_y = np.array(x), np.array(y)\r\n compute_1, compute_2, compute_3 = (0,0,0)\r\n minimized_solution = []\r\n for i in range(m):\r\n compute_1 = var_y[i] - cost_function(var_x[i],theta_0, theta_1) # finds the computed Euclidean distance between\r\n # x and its corresponding value y\r\n compute_2 = pow(compute_1,2) # square of the distance\r\n minimized_solution.append(compute_2) # appends the corresponding solution to a list minimized_solution\r\n compute_3 = sum(minimized_solution)/ (2 * m) # compute the minimized cost function of x and y by dividing by\r\n # twice the number of training examples\r\n return compute_3","def final_cost(self, x):\n return self.x_M_x(x[-1,:,:],self.R)","def minimize(self):\n pass","def getCostFunctionSlow(self, evalpts, observations):\n #XXX: update interface to allow metric?\n def _(params):\n v = self.getVectorCostFunction(evalpts, observations)\n x = v(params)\n return sum(real((conjugate(x)*x)))\n return _","def cost_fun(x, problem):\n j = 0\n if problem['use_log_bar']:\n c = ineqconstr(x, problem)\n j += np.sum(logbarrierfunc(0.1, c, problem['use_sigma']))\n\n x, t_final = matrify(x, problem)\n if problem['T']!=0:\n j += np.sum([problem['cost_fun_single'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\n else:\n j = t_final\n return j","def cost_func(plist):\n\t\tgamma, alpha = plist\n\t\tk = ac.Moffat2DKernel(gamma, alpha, x_size=nx, y_size=ny)\n\n\t\tarr_out_predict = ac.convolve(arr_in, k)\n\n\t\tarr_out_fit, arr_out_predict_fit = match_dimension(arr_out, arr_out_predict)\n\t\tdiff = (arr_out_fit - arr_out_predict_fit)*scale_factor\n\n\t\treturn np.sum(diff**2)/diff.size","def compute_cost(im1, im2):\n assert im1.shape == im2.shape\n\n d = 0.0\n #\n # Your code here\n #\n\n # The minimized cost function should be SSD. Slide 36\n # Code taken from assignment 4\n d = np.sum((im2 - im1)**2)\n\n assert isinstance(d, float)\n return d","def eval_cost(self, params, **kwargs):\n raise NotImplementedError","def fixed_cost(self):\n return np.einsum('i->', self.c[self.f])","def cost(self,x):\n Mx = self.generate_vector()\n self.prior.M.mult(x,Mx)\n return .5*Mx.inner(x)","def objective(rp,n=5000,C=-2*10**11,a=300,b=1):\n l = log(rp)/n\n r = exp(l)\n rm1 = r-1\n return (rp-1)*((a-b*n)*rm1 + 1) - C*(rm1)*(rm1)\n #return rm1","def optimize(self):\n optimizer = tf.train.RMSPropOptimizer(self.config.learning_rate)\n return optimizer.minimize(self.cost)","def minimize(A, t, y0, function):\n return y0 - function(A, t)","def cost(self, cost_object, target):\n\n return cost_object.f(self.a[-1], target).mean(axis=0).sum()","def compute_cost(y, tx, w):\n # ***************************************************\n # INSERT YOUR CODE HERE\n # TODO: compute loss by MSE / MAE\n # ***************************************************\n \n # vector e\n e = compute_e(y, tx, w)\n N = compute_N(e)\n L_MSE = np.dot(np.matrix.transpose(e), e)\n L_MSE = L_MSE / (2 * N)\n \n return L_MSE","def cost_function(self, element):\n return self.maze.cost(element)","def compute_cost(self, del_u : list, u : list):\n print(\"ym: \", self.ym, \"yn: \", self.yn)\n self.cost = 0.0\n\n self.ym = self.d_model.ym\n self.yn = self.d_model.yn\n\n # FIXME : this is supposed to be from N1 to N2\n self.cost+= (self.ym[0] - self.yn[0])\n angle_diff = (self.ym[1] - self.yn[1])\n if angle_diff > np.pi:\n angle_diff -= 2*np.pi\n if angle_diff < -np.pi:\n angle_diff += 2*np.pi\n self.cost += angle_diff\n\n for j in range(self.Nu):\n self.cost += (self.ym[j] - self.yn[j])**2\n\n for j in range(self.Nu):\n self.cost += self.lambd[j]*(del_u[j])**2\n\n for j in range(self.Nu):\n self.cost += self.s / (u[j] + self.r / 2.0 - self.b) + self.s / (self.r/2.0 + self.b - u[j]) - 4.0 / self.r\n\n return self.cost","def total_cost(self):\n return np.einsum('i->', self.c[self.s])","def costFunction(self, x, y ):\n self.yEst = self.forward_propagate(x)\n sqErrors = ( self.yEst - y ) ** 2\n J = sqErrors.sum() / 2\n return J","def costFunction(R, W):\n costFunc = 0\n for i in range(0, len(R)):\n for j in range(i, len(R)):\n costFunc += costBetweenNodes(R, W, i, j)\n return costFunc","def fn(i, j):\n if i == len(costs): return 0 # no more houses to paint \n return costs[i][j] + min(fn(i+1, jj) for jj in range(3) if j != jj)","def get_expected_cost(self):","def _cost_method(self, *args, **kwargs):\n\n cost_val = 0.5 * np.linalg.norm(self.obs_data - self.op(args[0])) ** 2\n\n if 'verbose' in kwargs and kwargs['verbose']:\n print(' - DATA FIDELITY (X):', cost_val)\n\n return cost_val","def get_cost(self) -> float:\n return math.e / self.fitness","def calculateCost(self,sol,weights):\n\t\treturn sum([x.value*y if x != None else 0 \\\n\t\t\t\t\tfor x,y in zip(sol,weights)])","def objective(self, x):\n pass","def objective(self, x):\n pass","def calculate_cost(theta_values, data):\n population = data[:,0]\n prices = data[:,1]\n total_error = 0\n for i in range(0,len(population)):\n x = array([[1],[population[i]]])\n hypothesis = theta_values.dot(x).flatten() \n squared_error = (hypothesis - prices[i])**2\n total_error += squared_error\n return .5*total_error/len(population) #division by m is just a scaling factor since we're only interested in whether this function is minimized","def fn(i, cost):\n if cost >= target or i == len(toppingCosts): return cost\n return min(fn(i+1, cost), fn(i+1, cost+toppingCosts[i]), key=lambda x: (abs(x-target), x))","def calculate_cost(x, y, weights):\r\n predictions = compute_prediction(x, weights)\r\n cost = np.mean(-y * np.log(predictions) - (1 - y) * np.log(1 - predictions))\r\n return cost","def costFunction(self):\n priorDiff = np.matrix(self.model.stateVector - self.model.prior).T\n measurementDiff = np.matrix(self.model.observation\n - self.model.modelCalculation).T\n chisq = measurementDiff.T * self.errSinv * measurementDiff\n chisq += priorDiff.T * self.priorSinv * priorDiff\n \n return chisq[0,0]","def objective_function(x):\n return x * 1 # change this to our actual function","def wpCostFunc(P, *args):\n\tQ, beta = args\n\t\n\t# calculate the first item: closeness to the reference\n\tf1 = 0\n\tfor i in range(len(Q)):\n\t\tf1 += ( P[i]-Q[i] )**2\n\n\t# calculate the second item: misalignment penality\n\tf2 = 0\n\tn = len(P)/2\t# number of waypoints to be optmized\n\tfor k in range(2,n):\n\t\tp_kminus1 = ( P[2*(k-1)-2], P[2*(k-1)-1] )\n\t\tp_k = ( P[2*k-2], P[2*k-1] )\n\t\tp_kplus1 = ( P[2*(k+1)-2], P[2*(k+1)-1] )\n\n\t\tvec_cur = ( p_k[0]-p_kminus1[0], p_k[1]-p_kminus1[1] )\n\t\tvec_next = ( p_kplus1[0]-p_k[0], p_kplus1[1]-p_k[1] )\n\t\t\n\t\tf2 += np.dot(vec_cur, vec_next)/lg.norm(vec_cur)/lg.norm(vec_next)\n\n\tf = f1-beta*f2\n\treturn f","def add_objective(self): \n \n if \"CSS\" in self.algorithm:\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 normalizer_term = self.add_css_approximation(data_term)\n \n if \"CD\" in self.algorithm and self.num_hidden ==0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n normalizer_term = self.compute_energy(self.x_gibbs, \n self.batch_size)\n \n normalizer_term = -T.mean(normalizer_term)\n \n if \"CD\" in self.algorithm and self.num_hidden > 0:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\n \n normalizer_term = -T.mean(normalizer_term)\n \n # cost is negative log likelihood \n self.cost = T.mean(data_term) + normalizer_term","def sol_cost ( S, D, R ) :\n\tmask = array([0]* len(R))\n\tmask[R>0] = 1\n\tmaxerr = max(mask * abs(S - D))\n\tmeanerr = float(sum(mask * abs(S - D)))\n\tmeanerr /= sum(mask)\n\t\n\treturn (sum(R * (S - D)**2),maxerr,meanerr)","def cost(self):\n\t\treturn self.g + self.h","def critical_benefit_to_cost1(sigma_df,Z,benefit_function,*params):\n return sum(sigma_df.sigma)/(sigma_df.sigma*(benefit_function((sigma_df.j+1),(sigma_df.k+1),*params)-benefit_function((sigma_df.k-sigma_df.j),(sigma_df.k+1),*params))).sum()","def _cost_function(self, y_pred, y, m):\n sumatory = 0\n for x in range(m):\n sumatory += (y_pred[0][x] -y[0][x])**2\n\n cost = 1/(2*m) * sumatory\n return cost","def cost(lossvalues):\n return np.sum(lossvalues ** 2) / (2 * lossvalues.shape[1])","def objective(self):\n pass","def compute_cost(features, values, theta):\r\n m = len(values)\r\n sum_of_square_errors = numpy.square(numpy.dot(features, theta) - values).sum()\r\n cost = sum_of_square_errors / (2*m)\r\n\r\n return cost","def return_running_cost_func(RunningCost='Minimize Input Energy'):\n if type(RunningCost)==str:\n assert RunningCost in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\"\n else:\n assert type(RunningCost)==list, \"RunningCost must be a list of cost types.\"\n for el in RunningCost:\n assert type(el)==str, \"Each element of RunningCost must be a string. Not \" + str(type(el)) + \".\"\n assert el in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\" + el + \"' not accepted.\"\n\n if \"Minimize Input Energy\" in RunningCost:\n result1 = lambda X,U,dt: np.trapz((k3/2)*U**2,dx=dt)\n else:\n result1 = lambda X,U,dt: 0\n\n if \"Minimize time away from target angle\" in RunningCost:\n result2 = lambda X,U,dt: np.trapz(k1*(1/2)*(X[0,1:]-TargetAngle)**2,dx=dt)\n else:\n result2 = lambda X,U,dt: 0\n\n if \"Minimize time away from target angular velocity\" in RunningCost:\n result3 = lambda X,U,dt:\\\n np.trapz(k2*(1/2)*(X[1,1:]-TargetAngularVelocity)**2,dx=dt)\n else:\n result3 = lambda X,U,dt: 0\n\n result = lambda X,U,dt: result1(X,U,dt) \\\n + result2(X,U,dt) \\\n + result3(X,U,dt)\n return(result)","def compute_objective(X, C):\n # similar as mse\n\n acc = 0\n for x in range(len(X)):\n acc += (nearest_distance(X.iloc[x], C)) ** 2\n\n return acc","def calculate_total_cost(state):\n pass","def cost_function(self, u):\n # enforce that the input is a numpy array\n u = np.asarray(u)\n\n # Reshape the control input to (mxT). Vector input is required for some optimization libraries\n T = int(len(u)/2)\n u = u.reshape((2,T))\n\n J = - self.rho(u)\n\n return J","def mincost(L,m,n):\n # find the length of the strings\n # declaring the array for storing the dp values\n tc = [[0]*(n + 1) for i in range(m + 1)]\n tc[0][0]=L[0][0]\n for i in range(1,m + 1):\n tc[i][0]=tc[i-1][0]+L[i][0]\n for j in range(1,n + 1):\n tc[0][j]=tc[0][j-1]+L[0][j]\n for i in range(1,m+1):\n for j in range(1,n+1):\n tc[i][j]=min(tc[i-1][j-1],tc[i-1][j],tc[i][j-1])+L[i][j]\n\n\n # L[m][n] contains the length of LCS of X[0..n-1] & Y[0..m-1]\n return tc[m][n]","def __compute_cost(self, x, y):\n\n predictions = self.__compute_prediction(x)\n cost = np.mean(-y * np.log(predictions) - (1 - y) * np.log(1 - predictions))\n\n return cost","def get_cost(self, action: Action) -> N:\n pass","def _cost(A, n, estimate, eps=1e-6):\n p = A @ vec(estimate) # vectorized form of the probabilities of outcomes, p_ij\n # see appendix on \"stalling\"\n p = np.clip(p, a_min=eps, a_max=None)\n return - n.T @ np.log(p)","def compute_cost(x, y, theta=[[0], [0]]):\n m = y.size\n h = x.dot(theta)\n j = 1 / (2 * m) * np.sum(np.square(h - y))\n return j","def cofiCostFunc(self,params, *args):\n\t\tY, R, num_users, num_products, num_features,l = args[0], args[1],args[2], args[3],args[4],args[5]\n\n\t\taux = params.reshape((num_products + num_users, num_features))\n\n\t\tX = aux[0:num_products , :]\n\n\t\tTheta = aux[num_products:, :] \n\n\t\ttest = np.dot(X,Theta.transpose())\n\t\ttest = test - Y\n\t\ttest = np.multiply(test , R)\n\t\ttest = np.power(test,2)\n\t\ttest = test.sum()\n\t\ttest = 0.5 * test\n\n\t\tJ = 0;\n\t\tregularization = (l * 0.5) * np.power(X,2).sum() + np.power(Theta,2).sum()\n\n\t\tJ = test# + regularization\n\n\t\treturn J","def _objective_cost(self):\n\n def obj_expression_simple(model):\n total = model.A_total + model.A2_total + model.A3_total + \\\n model.A4_total\n return -total\n\n def obj_expression(model):\n total = model.A_total + model.A2_total + model.A3_total + \\\n model.A4_total\n total += model.Completion_total\n total += model.Affinity_cognitive_total\n total += model.CTu_total + model.CTl_total + model.S_total\n return -total\n\n # self.model.exp_cost = Expression(rule=obj_expression)\n # self.model.obj_cost = Objective(rule=self.model.exp_cost)\n # self.model.obj_cost = Objective(rule=obj_expression_simple)\n self.model.obj_cost = Objective(rule=obj_expression)","def compute_cost(AL, Y, parameters ,lambd):\n L = len(parameters) // 2\n m = Y.shape[1]\n cost = -1 / m * np.sum(np.nan_to_num(Y * np.log(AL) + (1-Y) * np.log(1-AL)))\n cost+= 0.5*(lambd/m)*sum(np.linalg.norm(parameters['W' + str(i)])**2 for i in range(1,L))\n return cost","def get_cost_updates(self):\n\n y = self.get_hidden_values()\n z = self.get_reconstructed_input(y)\n\n L = T.sum((self.x-z)**2, axis=1)\n\n cost = T.mean(L)\n\n return cost","def compute_cost(features, values, theta):\n m = len(values)\n sum_of_square_errors = np.square(np.dot(features, theta) - values).sum()\n cost = sum_of_square_errors / (2 * m)\n\n return cost","def _construct_reg_costs(self):\n param_reg_cost = sum([T.sum(p**2.0) for p in self.joint_params])\n return param_reg_cost","def objective(self, args: Dict[str, Any]) -> float:\n pass","def _local_cost(self, p, q):\n diagnol = q[0] == p[0] or q[1] == p[1]\n \n # c0, c1 and c2 are costs from Canny operator, gradient magnitude and gradient direction respectively\n if diagnol:\n c0 = self.cost_edges[p[0]][p[1]]-SQRT_0_5*(self.cost_edges[p[0]][p[1]]-self.cost_edges[q[0]][q[1]])\n c1 = self.cost_grad_mag[p[0]][p[1]]-SQRT_0_5*(self.cost_grad_mag[p[0]][p[1]]-self.cost_grad_mag[q[0]][q[1]])\n c2 = SQRT_0_5 * self._get_grad_direction_cost(p, q)\n else:\n c0 = self.cost_edges[q[0]][q[1]]\n c1 = self.cost_grad_mag[q[0]][q[1]]\n c2 = self._get_grad_direction_cost(p, q)\n \n if np.isnan(c2):\n c2 = 0.0\n \n w0, w1, w2 = self.weight\n cost_pq = w0*c0 + w1*c1 + w2*c2\n \n return cost_pq * cost_pq","def compute_cost(self, r):\n self.r_max = 1\n return np.exp(1 / (np.power(r, 2) - np.power(self.r_max, 2))) if r < self.r_max else 0","def _SD_optimal(t):","def calculate_total_cost(state):\r\n return state.cost()","def maximize(self, budget, optimizer):\n\n\t\tpass","def cost_(theta, X, Y):\n if X.shape[1] + 1 != theta.size or X.shape[0] != Y.size:\n print(\"Inc dim\")\n return\n c = cost_elem_(theta, X, Y)\n s = 0\n for i in c:\n s = s + i\n return(s)","def getCostFunction(self, evalpts, observations, sigma=None, metric=lambda x: sum(x*x)):\n #XXX: better interface for sigma?\n def _(params):\n ind = 0\n for F, n, ofilt, icheck in zip(self._forwardFactories, self._inputs, \\\n self._outputFilters, self._inputCheckers):\n # check input #XXX: is this worthwile to do?\n my_params = params[ind:ind+n]\n checkQ = icheck(my_params, evalpts)\n if checkQ is not None:\n # some parameters are out of range... returns \"cost\"\n return checkQ\n\n Gm = F(params[ind:ind+n])\n if ind == 0:\n x = ofilt(Gm(evalpts)) \n else:\n x = x + ofilt(Gm(evalpts)) \n ind = ind+n\n if sigma is None:\n x = x - observations\n else:\n x = (x - observations) / sigma\n #return sum(real((conjugate(x)*x)))\n #return sum(x*x) \n return metric(x)\n return _","def cost_function(H, n_qubits, p, params):\n ini_state=plus_state(n_qubits)\n for i in range(p):\n ini_state=qaoa_step(ini_state,H,n_qubits,params=[params[2*i],params[2*i+1]])\n return ((sparse.spmatrix.getH(ini_state)).dot(H.dot(ini_state))).real, ini_state","def uniformCostSearch(problem):\n \"*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***\"\n aStarSearch(problem)","def cost(self, X, y) :\n ### ========== TODO : START ========== ###\n # part d: compute J(theta)\n #we know for linear/polynomial regression, the cost is the square of the errors\n X = self.generate_polynomial_features(X)\n y_pred_vector = np.dot(X, self.coef_)\n cost = np.dot((y-y_pred_vector).transpose(),(y-y_pred_vector))#write in the matrix form\n ### ========== TODO : END ========== ###\n return cost","def compute_cost(features, values, theta):\n\n npoints = len(values)\n sum_of_square_errors = np.square(np.dot(features, theta) - values).sum()\n cost = sum_of_square_errors / (2*npoints)\n\n return cost","def objective(self,w):\n diffs = self.get_y_times_diffs(self.get_split_weights(w))\n #print diffs, sigmoid(diffs)\n obj = -np.sum(np.log(sigmoid(diffs))) #negative, since minimising\n # regularisation\n obj += 0.5 * self.alpha * np.dot(w[:self.interp_index[0]], w[:self.interp_index[0]])\n return obj","def cost_function(x, N, w, dt):\n yh = np.abs(fftkernel(x, w / dt)) # density\n # formula for density\n C = np.sum(yh ** 2) * dt - 2 * np.sum(yh * x) * \\\n dt + 2 / np.sqrt(2 * np.pi) / w / N\n C = C * N * N\n # formula for rate\n # C = dt*sum( yh.^2 - 2*yh.*y_hist + 2/sqrt(2*pi)/w*y_hist )\n return C, yh","def deriv_costFunction(self, x, y):\n self.yEst = self.forward_propagate(x)\n\n delta_o = np.multiply( ( self.yEst - y ), self.deriv_sigmoid(self.z_o) )\n #partial deriv of cost wrt hidden -> output weights\n partial_J_w_ho = np.dot( self.a_h.T, delta_o )\n\n ones_o = np.ones( delta_o.shape[0] )\n #partial deriv of cost wrt output biases\n partial_J_b_o = np.dot( ones_o, delta_o )\n\n delta_h = np.dot( delta_o, self.w_ho.T ) * self.deriv_sigmoid( self.z_h )\n #partial deriv of cost wrt input -> hidden weights\n partial_J_w_ih = np.dot( x.T, delta_h )\n \n ones_h = np.ones( delta_h.shape[0] )\n #partial deriv of cost wrt hidden biases\n partial_J_b_h = np.dot( ones_h, delta_h)\n\n return partial_J_w_ih, partial_J_w_ho, partial_J_b_h, partial_J_b_o","def compute_cost(features, values, theta):\r\n \r\n # your code here\r\n error = (values - features.dot(theta))\r\n cost = error.dot(error) \r\n return cost","def minimization_fun(params, x, y, sizelist, x_crit_fun,\n rescale_fun, rescale_type, return_data=False):\n #x, y = _pad_vals(x, y)\n\n x_rescaled = rescale_xvals(x, sizelist, x_crit_fun, rescale_fun,\n rescale_type,\n *params)\n\n x_min = x_rescaled.flatten()\n y_min = y.flatten()\n\n sort_args = np.argsort(x_min)\n y_min = y_min[sort_args]\n\n # define the cost function\n cost_fun = np.nansum(np.abs(np.diff(y_min))) / \\\n (np.nanmax(y_min) - np.nanmin(y_min)) - 1\n\n if not return_data:\n\n return cost_fun\n else:\n return np.array(x_min)[sort_args], y_min, cost_fun","def fn(x):\n if x == 0: return 0\n if x < 0: return -inf \n return max(fn(x - c) * 10 + i + 1 for i, c in enumerate(cost))","def minimize(self, func, grad, x0, args=()):\n learning_rate = self._learning_rate\n best_x = x = x0\n best_value = func(x, *args)\n iters_without_improve = 0\n\n for iteration in range(self._max_iterations):\n gradient = grad(x, *args)\n\n # If absolute values of all partial derivatives are equal to 0 with specified accuracy, then parameters are\n # close enough to the minimum and there is no need to continue gradient descent.\n if np.abs(gradient).max() <= self._accuracy:\n break\n\n x = x - learning_rate * gradient\n\n # If new values of x haven't lead to decrease of the function value for the specified number of iteration,\n # the x is reverted to its previous best value and the learning rate is reduced\n value = func(x, *args)\n if value > best_value:\n iters_without_improve += 1\n if iters_without_improve >= self._lr_reduce_patience:\n x = best_x\n learning_rate *= self._lr_reduce_factor\n else:\n iters_without_improve = 0\n best_value = value\n best_x = x\n\n return best_x","def calc_cost(y, yhat, cost_matrix):\n return np.mean(cost_matrix[list(y), list(yhat)])","def compute_cost(X, y, theta, lambd):\n assert(theta.shape[0] == X.shape[1])\n \n m = X.shape[0]\n grad = np.zeros(y.shape)\n J = 0\n \n output = sigmoid(np.dot(X, theta))\n\n J = np.sum(- y * np.log(output) - (1 - y) * np.log(1 - output)) / m + lambd / (2 * m) * np.sum(np.square(theta[1:]))\n\n grad = np.dot(X.T, (output - y)) / m\n \n grad[1:] = grad[1:] + lambd / m * theta[1:]\n\n return J, grad","def get_cost(self):\n\n\t\tx = self.symbolic_input\n\t\ty = self.get_reconstructed_input()\n\n\t\tnegative_log_loss = -theano.tensor.sum(x*theano.tensor.log(y) +\n\t\t\t(1-x)*theano.tensor.log(1-y), axis=1)\n\n\t\tmean_loss = theano.tensor.mean(negative_log_loss)\n\n\t\treturn mean_loss","def cost(self, state: Grid2D.State): # pylint: disable=no-self-use\n return 1","def minimize(self,x0=None):\n import time\n start_time = time.time()\n tmp,total_par,lik_grad = self.minimize_both_vers(numerical=False,x0=x0)\n if tmp['success']==False:\n print(\"Probably a problem with gradient, do numerical\")\n tmp,total_par,lik_grad = self.minimize_both_vers(x0=tmp['x'],numerical=True)\n print(\"--- %s seconds ---\" % (time.time() - start_time))\n self.lengthscale = total_par[0]\n self.variance = total_par[1]\n self.gstds = total_par[2]\n tmp['fx']=np.array([total_par[0],total_par[1],total_par[2]])\n return tmp,total_par,lik_grad","def cost_function(self, config_samples):\n cost = self.work_tracker(config_samples)\n return cost","def evaluate_cost(self, msg):\n raise NotImplementedError()","def cost(self,e1,e2):\n pass","def compute_cost_fake(df):\n\timport numpy as np\n\timport cPickle\n\tfrom nn import nn_param\n\tfrom matplotlib import pyplot as plt\n\tfrom theano import tensor as T\n\tf=file(\"nnparams.sav\")\n\tupdate=cPickle.load(f)\n\tsig=np.asarray(df.l0)\n\tsig_noise=np.asarray(df.l0+df.noise)\n\tsig/=np.max(sig)\n\tsig_noise/=np.max(sig_noise)\n\tyval=nn_param(update,sig_noise)\n\treturn T.mean(squared_error(yval,sig))","def compute_cost(AL, Y):\n m = Y.shape[1]\n log_val = np.multiply(Y,np.log(AL)) + np.multiply(1-Y,np.log(1-AL))\n cost = np.sum(log_val) * (-1/m)\n cost = np.squeeze(cost)\n return cost","def _grad_cost(A, n, estimate, eps=1e-6):\n p = A @ vec(estimate)\n # see appendix on \"stalling\"\n p = np.clip(p, a_min=eps, a_max=None)\n eta = n / p\n return unvec(-A.conj().T @ eta)","def compute_cost_derivative(AL, Y):\n pass","def objective(\n self,\n parameters: object\n ) -> float:\n pass","def compute_cost(self, chrome):\n return 1","def scenario_cost_rule(_m, y, s):\r\n\r\n return m.OP_T[y, s] + m.OP_H[y, s] + m.OP_W[y, s] + m.OP_S[y, s] + m.OP_Q[y, s] + m.OP_L[y, s]","def minimize(self, cost_function, initial_params):\n\n # Optimization Results Object\n history = []\n\n def wrapped_cost_function(params):\n value = cost_function.evaluate(params)\n history.append(cost_function.evaluations_history[-1])\n print(f'Function evaluation {len(history)}: {value}', flush=True)\n print(f'{params}', flush=True)\n return value\n\n strategy = cma.CMAEvolutionStrategy(initial_params, self.sigma_0, self.options)\n result = strategy.optimize(wrapped_cost_function).result\n\n optimization_results = {}\n optimization_results['opt_value'] = result.fbest\n optimization_results['opt_params'] = result.xbest\n optimization_results['history'] = history\n optimization_results['nfev'] = result.evaluations\n optimization_results['nit'] = result.iterations\n optimization_results['cma_xfavorite'] = list(result.xfavorite)\n\n return OptimizeResult(optimization_results)","def return_quadratic_cost_function_expansion_variables(\n X,U,dt,\n RunningCost=\"Minimize Input Energy\"):\n\n if type(RunningCost)==str:\n assert RunningCost in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\"\n else:\n assert type(RunningCost)==list, \"RunningCost must be a list of cost types.\"\n for el in RunningCost:\n assert type(el)==str, \"Each element of RunningCost must be a string. Not \" + str(type(el)) + \".\"\n assert el in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\" + el + \"' not accepted.\"\n\n # returns a list of length len(Time)-1, each element with shape (1,1), where n is the number of states.\n l_func = return_l_func(RunningCost=RunningCost)\n l = list(\n map(\n lambda X,U: l_func(X,U,dt),\n X[:,1:].T,\n U\n )\n )\n\n # returns a list of length len(Time)-1, each element with shape (n,1), where n is the number of states.\n lx_func = return_lx_func(RunningCost=RunningCost)\n lx = list(\n map(\n lambda X,U: lx_func(X,U,dt),\n X[:,1:].T,\n U\n )\n )\n\n # returns a list of length len(Time)-1, each element with shape (m,1), where n is the number of states.\n lu_func = return_lu_func(RunningCost=RunningCost)\n lu = list(\n map(\n lambda X,U: lu_func(X,U,dt),\n X[:,1:].T,\n U\n )\n )\n\n # returns a list of length len(Time)-1, each element with shape (m,n), where m is the number of inputs and n is the number of states.\n lux_func = return_lux_func(RunningCost=RunningCost)\n lux = list(\n map(\n lambda X,U: lux_func(X,U,dt),\n X[:,1:].T,\n U\n )\n )\n\n # returns a list of length len(Time)-1, each element with shape (n,m), where n is the number of states and m is the number of inputs.\n lxu_func = return_lxu_func(RunningCost=RunningCost)\n lxu = list(\n map(\n lambda X,U: lxu_func(X,U,dt),\n X[:,1:].T,\n U\n )\n )\n\n # returns a list of length len(Time)-1, each element with shape (m,m), where m is the number of inputs.\n luu_func = return_luu_func(RunningCost=RunningCost)\n luu = list(\n map(\n lambda X,U: luu_func(X,U,dt),\n X[:,1:].T,\n U\n )\n )\n\n # returns a list of length len(Time)-1, each element with shape (n,n), where n is the number of states.\n lxx_func = return_lxx_func(RunningCost=RunningCost)\n lxx = list(\n map(\n lambda X,U: lxx_func(X,U,dt),\n X[:,1:].T,\n U\n )\n )\n\n return(l,lx,lu,lux,lxu,luu,lxx)","def compute_cost(self,X, y):\n \n num_samples = len(X)\n # Do Forward propagation to calculate our predictions\n z1 = X.dot(self.W1) + self.b1\n a1 = np.tanh(z1)\n z2 = a1.dot(self.W2) + self.b2\n exp_z = np.exp(z2)\n a2 = exp_z / np.sum(exp_z, axis=1, keepdims=True)\n softmax_scores = a2\n # Calculate the cross-entropy loss\n cross_ent_err = -np.log(softmax_scores[range(num_samples), y])\n data_loss = np.sum(cross_ent_err)\n return 1./num_samples * data_loss"],"string":"[\n \"def costFun(self, S, x):\",\n \"def cost(self) -> float:\",\n \"def compute_cost(AL, Y):\\n pass\",\n \"def compute_cost(AL, Y):\\n pass\",\n \"def costFun(self, x):\\n\\ttmp = x.reshape(self.inp_shape)\\n\\tc = np.float64(self.calcCost(np.asarray(tmp,dtype=np.float32))) + self.alpha * np.dot(x.T, x)\\n\\treturn c\",\n \"def calcCostFun(self):\\n\\n self.start()\\n F, K = self.model()\\n \\n return self.costFunction\",\n \"def optimize(self, x0):\\n (result,f,d) = fmin_l_bfgs_b(lambda x:self.costFun(x), np.ravel(x0),lambda x: self.gradFun(x))\\n print(\\\"optimization completed with cost: \\\" + str(f))\\n return result.reshape(self.inp_shape)\",\n \"def minimize(self):\\n raise NotImplementedError\",\n \"def minimize_cost_function(m = no_of_training_exammples, x, y, theta_0, theta_1):\\r\\n var_x, var_y = np.array(x), np.array(y)\\r\\n compute_1, compute_2, compute_3 = (0,0,0)\\r\\n minimized_solution = []\\r\\n for i in range(m):\\r\\n compute_1 = var_y[i] - cost_function(var_x[i],theta_0, theta_1) # finds the computed Euclidean distance between\\r\\n # x and its corresponding value y\\r\\n compute_2 = pow(compute_1,2) # square of the distance\\r\\n minimized_solution.append(compute_2) # appends the corresponding solution to a list minimized_solution\\r\\n compute_3 = sum(minimized_solution)/ (2 * m) # compute the minimized cost function of x and y by dividing by\\r\\n # twice the number of training examples\\r\\n return compute_3\",\n \"def final_cost(self, x):\\n return self.x_M_x(x[-1,:,:],self.R)\",\n \"def minimize(self):\\n pass\",\n \"def getCostFunctionSlow(self, evalpts, observations):\\n #XXX: update interface to allow metric?\\n def _(params):\\n v = self.getVectorCostFunction(evalpts, observations)\\n x = v(params)\\n return sum(real((conjugate(x)*x)))\\n return _\",\n \"def cost_fun(x, problem):\\n j = 0\\n if problem['use_log_bar']:\\n c = ineqconstr(x, problem)\\n j += np.sum(logbarrierfunc(0.1, c, problem['use_sigma']))\\n\\n x, t_final = matrify(x, problem)\\n if problem['T']!=0:\\n j += np.sum([problem['cost_fun_single'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\\n else:\\n j = t_final\\n return j\",\n \"def cost_func(plist):\\n\\t\\tgamma, alpha = plist\\n\\t\\tk = ac.Moffat2DKernel(gamma, alpha, x_size=nx, y_size=ny)\\n\\n\\t\\tarr_out_predict = ac.convolve(arr_in, k)\\n\\n\\t\\tarr_out_fit, arr_out_predict_fit = match_dimension(arr_out, arr_out_predict)\\n\\t\\tdiff = (arr_out_fit - arr_out_predict_fit)*scale_factor\\n\\n\\t\\treturn np.sum(diff**2)/diff.size\",\n \"def compute_cost(im1, im2):\\n assert im1.shape == im2.shape\\n\\n d = 0.0\\n #\\n # Your code here\\n #\\n\\n # The minimized cost function should be SSD. Slide 36\\n # Code taken from assignment 4\\n d = np.sum((im2 - im1)**2)\\n\\n assert isinstance(d, float)\\n return d\",\n \"def eval_cost(self, params, **kwargs):\\n raise NotImplementedError\",\n \"def fixed_cost(self):\\n return np.einsum('i->', self.c[self.f])\",\n \"def cost(self,x):\\n Mx = self.generate_vector()\\n self.prior.M.mult(x,Mx)\\n return .5*Mx.inner(x)\",\n \"def objective(rp,n=5000,C=-2*10**11,a=300,b=1):\\n l = log(rp)/n\\n r = exp(l)\\n rm1 = r-1\\n return (rp-1)*((a-b*n)*rm1 + 1) - C*(rm1)*(rm1)\\n #return rm1\",\n \"def optimize(self):\\n optimizer = tf.train.RMSPropOptimizer(self.config.learning_rate)\\n return optimizer.minimize(self.cost)\",\n \"def minimize(A, t, y0, function):\\n return y0 - function(A, t)\",\n \"def cost(self, cost_object, target):\\n\\n return cost_object.f(self.a[-1], target).mean(axis=0).sum()\",\n \"def compute_cost(y, tx, w):\\n # ***************************************************\\n # INSERT YOUR CODE HERE\\n # TODO: compute loss by MSE / MAE\\n # ***************************************************\\n \\n # vector e\\n e = compute_e(y, tx, w)\\n N = compute_N(e)\\n L_MSE = np.dot(np.matrix.transpose(e), e)\\n L_MSE = L_MSE / (2 * N)\\n \\n return L_MSE\",\n \"def cost_function(self, element):\\n return self.maze.cost(element)\",\n \"def compute_cost(self, del_u : list, u : list):\\n print(\\\"ym: \\\", self.ym, \\\"yn: \\\", self.yn)\\n self.cost = 0.0\\n\\n self.ym = self.d_model.ym\\n self.yn = self.d_model.yn\\n\\n # FIXME : this is supposed to be from N1 to N2\\n self.cost+= (self.ym[0] - self.yn[0])\\n angle_diff = (self.ym[1] - self.yn[1])\\n if angle_diff > np.pi:\\n angle_diff -= 2*np.pi\\n if angle_diff < -np.pi:\\n angle_diff += 2*np.pi\\n self.cost += angle_diff\\n\\n for j in range(self.Nu):\\n self.cost += (self.ym[j] - self.yn[j])**2\\n\\n for j in range(self.Nu):\\n self.cost += self.lambd[j]*(del_u[j])**2\\n\\n for j in range(self.Nu):\\n self.cost += self.s / (u[j] + self.r / 2.0 - self.b) + self.s / (self.r/2.0 + self.b - u[j]) - 4.0 / self.r\\n\\n return self.cost\",\n \"def total_cost(self):\\n return np.einsum('i->', self.c[self.s])\",\n \"def costFunction(self, x, y ):\\n self.yEst = self.forward_propagate(x)\\n sqErrors = ( self.yEst - y ) ** 2\\n J = sqErrors.sum() / 2\\n return J\",\n \"def costFunction(R, W):\\n costFunc = 0\\n for i in range(0, len(R)):\\n for j in range(i, len(R)):\\n costFunc += costBetweenNodes(R, W, i, j)\\n return costFunc\",\n \"def fn(i, j):\\n if i == len(costs): return 0 # no more houses to paint \\n return costs[i][j] + min(fn(i+1, jj) for jj in range(3) if j != jj)\",\n \"def get_expected_cost(self):\",\n \"def _cost_method(self, *args, **kwargs):\\n\\n cost_val = 0.5 * np.linalg.norm(self.obs_data - self.op(args[0])) ** 2\\n\\n if 'verbose' in kwargs and kwargs['verbose']:\\n print(' - DATA FIDELITY (X):', cost_val)\\n\\n return cost_val\",\n \"def get_cost(self) -> float:\\n return math.e / self.fitness\",\n \"def calculateCost(self,sol,weights):\\n\\t\\treturn sum([x.value*y if x != None else 0 \\\\\\n\\t\\t\\t\\t\\tfor x,y in zip(sol,weights)])\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\",\n \"def calculate_cost(theta_values, data):\\n population = data[:,0]\\n prices = data[:,1]\\n total_error = 0\\n for i in range(0,len(population)):\\n x = array([[1],[population[i]]])\\n hypothesis = theta_values.dot(x).flatten() \\n squared_error = (hypothesis - prices[i])**2\\n total_error += squared_error\\n return .5*total_error/len(population) #division by m is just a scaling factor since we're only interested in whether this function is minimized\",\n \"def fn(i, cost):\\n if cost >= target or i == len(toppingCosts): return cost\\n return min(fn(i+1, cost), fn(i+1, cost+toppingCosts[i]), key=lambda x: (abs(x-target), x))\",\n \"def calculate_cost(x, y, weights):\\r\\n predictions = compute_prediction(x, weights)\\r\\n cost = np.mean(-y * np.log(predictions) - (1 - y) * np.log(1 - predictions))\\r\\n return cost\",\n \"def costFunction(self):\\n priorDiff = np.matrix(self.model.stateVector - self.model.prior).T\\n measurementDiff = np.matrix(self.model.observation\\n - self.model.modelCalculation).T\\n chisq = measurementDiff.T * self.errSinv * measurementDiff\\n chisq += priorDiff.T * self.priorSinv * priorDiff\\n \\n return chisq[0,0]\",\n \"def objective_function(x):\\n return x * 1 # change this to our actual function\",\n \"def wpCostFunc(P, *args):\\n\\tQ, beta = args\\n\\t\\n\\t# calculate the first item: closeness to the reference\\n\\tf1 = 0\\n\\tfor i in range(len(Q)):\\n\\t\\tf1 += ( P[i]-Q[i] )**2\\n\\n\\t# calculate the second item: misalignment penality\\n\\tf2 = 0\\n\\tn = len(P)/2\\t# number of waypoints to be optmized\\n\\tfor k in range(2,n):\\n\\t\\tp_kminus1 = ( P[2*(k-1)-2], P[2*(k-1)-1] )\\n\\t\\tp_k = ( P[2*k-2], P[2*k-1] )\\n\\t\\tp_kplus1 = ( P[2*(k+1)-2], P[2*(k+1)-1] )\\n\\n\\t\\tvec_cur = ( p_k[0]-p_kminus1[0], p_k[1]-p_kminus1[1] )\\n\\t\\tvec_next = ( p_kplus1[0]-p_k[0], p_kplus1[1]-p_k[1] )\\n\\t\\t\\n\\t\\tf2 += np.dot(vec_cur, vec_next)/lg.norm(vec_cur)/lg.norm(vec_next)\\n\\n\\tf = f1-beta*f2\\n\\treturn f\",\n \"def add_objective(self): \\n \\n if \\\"CSS\\\" in self.algorithm:\\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 normalizer_term = self.add_css_approximation(data_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden ==0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n normalizer_term = self.compute_energy(self.x_gibbs, \\n self.batch_size)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden > 0:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n # cost is negative log likelihood \\n self.cost = T.mean(data_term) + normalizer_term\",\n \"def sol_cost ( S, D, R ) :\\n\\tmask = array([0]* len(R))\\n\\tmask[R>0] = 1\\n\\tmaxerr = max(mask * abs(S - D))\\n\\tmeanerr = float(sum(mask * abs(S - D)))\\n\\tmeanerr /= sum(mask)\\n\\t\\n\\treturn (sum(R * (S - D)**2),maxerr,meanerr)\",\n \"def cost(self):\\n\\t\\treturn self.g + self.h\",\n \"def critical_benefit_to_cost1(sigma_df,Z,benefit_function,*params):\\n return sum(sigma_df.sigma)/(sigma_df.sigma*(benefit_function((sigma_df.j+1),(sigma_df.k+1),*params)-benefit_function((sigma_df.k-sigma_df.j),(sigma_df.k+1),*params))).sum()\",\n \"def _cost_function(self, y_pred, y, m):\\n sumatory = 0\\n for x in range(m):\\n sumatory += (y_pred[0][x] -y[0][x])**2\\n\\n cost = 1/(2*m) * sumatory\\n return cost\",\n \"def cost(lossvalues):\\n return np.sum(lossvalues ** 2) / (2 * lossvalues.shape[1])\",\n \"def objective(self):\\n pass\",\n \"def compute_cost(features, values, theta):\\r\\n m = len(values)\\r\\n sum_of_square_errors = numpy.square(numpy.dot(features, theta) - values).sum()\\r\\n cost = sum_of_square_errors / (2*m)\\r\\n\\r\\n return cost\",\n \"def return_running_cost_func(RunningCost='Minimize Input Energy'):\\n if type(RunningCost)==str:\\n assert RunningCost in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\\\"\\n else:\\n assert type(RunningCost)==list, \\\"RunningCost must be a list of cost types.\\\"\\n for el in RunningCost:\\n assert type(el)==str, \\\"Each element of RunningCost must be a string. Not \\\" + str(type(el)) + \\\".\\\"\\n assert el in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\\\" + el + \\\"' not accepted.\\\"\\n\\n if \\\"Minimize Input Energy\\\" in RunningCost:\\n result1 = lambda X,U,dt: np.trapz((k3/2)*U**2,dx=dt)\\n else:\\n result1 = lambda X,U,dt: 0\\n\\n if \\\"Minimize time away from target angle\\\" in RunningCost:\\n result2 = lambda X,U,dt: np.trapz(k1*(1/2)*(X[0,1:]-TargetAngle)**2,dx=dt)\\n else:\\n result2 = lambda X,U,dt: 0\\n\\n if \\\"Minimize time away from target angular velocity\\\" in RunningCost:\\n result3 = lambda X,U,dt:\\\\\\n np.trapz(k2*(1/2)*(X[1,1:]-TargetAngularVelocity)**2,dx=dt)\\n else:\\n result3 = lambda X,U,dt: 0\\n\\n result = lambda X,U,dt: result1(X,U,dt) \\\\\\n + result2(X,U,dt) \\\\\\n + result3(X,U,dt)\\n return(result)\",\n \"def compute_objective(X, C):\\n # similar as mse\\n\\n acc = 0\\n for x in range(len(X)):\\n acc += (nearest_distance(X.iloc[x], C)) ** 2\\n\\n return acc\",\n \"def calculate_total_cost(state):\\n pass\",\n \"def cost_function(self, u):\\n # enforce that the input is a numpy array\\n u = np.asarray(u)\\n\\n # Reshape the control input to (mxT). Vector input is required for some optimization libraries\\n T = int(len(u)/2)\\n u = u.reshape((2,T))\\n\\n J = - self.rho(u)\\n\\n return J\",\n \"def mincost(L,m,n):\\n # find the length of the strings\\n # declaring the array for storing the dp values\\n tc = [[0]*(n + 1) for i in range(m + 1)]\\n tc[0][0]=L[0][0]\\n for i in range(1,m + 1):\\n tc[i][0]=tc[i-1][0]+L[i][0]\\n for j in range(1,n + 1):\\n tc[0][j]=tc[0][j-1]+L[0][j]\\n for i in range(1,m+1):\\n for j in range(1,n+1):\\n tc[i][j]=min(tc[i-1][j-1],tc[i-1][j],tc[i][j-1])+L[i][j]\\n\\n\\n # L[m][n] contains the length of LCS of X[0..n-1] & Y[0..m-1]\\n return tc[m][n]\",\n \"def __compute_cost(self, x, y):\\n\\n predictions = self.__compute_prediction(x)\\n cost = np.mean(-y * np.log(predictions) - (1 - y) * np.log(1 - predictions))\\n\\n return cost\",\n \"def get_cost(self, action: Action) -> N:\\n pass\",\n \"def _cost(A, n, estimate, eps=1e-6):\\n p = A @ vec(estimate) # vectorized form of the probabilities of outcomes, p_ij\\n # see appendix on \\\"stalling\\\"\\n p = np.clip(p, a_min=eps, a_max=None)\\n return - n.T @ np.log(p)\",\n \"def compute_cost(x, y, theta=[[0], [0]]):\\n m = y.size\\n h = x.dot(theta)\\n j = 1 / (2 * m) * np.sum(np.square(h - y))\\n return j\",\n \"def cofiCostFunc(self,params, *args):\\n\\t\\tY, R, num_users, num_products, num_features,l = args[0], args[1],args[2], args[3],args[4],args[5]\\n\\n\\t\\taux = params.reshape((num_products + num_users, num_features))\\n\\n\\t\\tX = aux[0:num_products , :]\\n\\n\\t\\tTheta = aux[num_products:, :] \\n\\n\\t\\ttest = np.dot(X,Theta.transpose())\\n\\t\\ttest = test - Y\\n\\t\\ttest = np.multiply(test , R)\\n\\t\\ttest = np.power(test,2)\\n\\t\\ttest = test.sum()\\n\\t\\ttest = 0.5 * test\\n\\n\\t\\tJ = 0;\\n\\t\\tregularization = (l * 0.5) * np.power(X,2).sum() + np.power(Theta,2).sum()\\n\\n\\t\\tJ = test# + regularization\\n\\n\\t\\treturn J\",\n \"def _objective_cost(self):\\n\\n def obj_expression_simple(model):\\n total = model.A_total + model.A2_total + model.A3_total + \\\\\\n model.A4_total\\n return -total\\n\\n def obj_expression(model):\\n total = model.A_total + model.A2_total + model.A3_total + \\\\\\n model.A4_total\\n total += model.Completion_total\\n total += model.Affinity_cognitive_total\\n total += model.CTu_total + model.CTl_total + model.S_total\\n return -total\\n\\n # self.model.exp_cost = Expression(rule=obj_expression)\\n # self.model.obj_cost = Objective(rule=self.model.exp_cost)\\n # self.model.obj_cost = Objective(rule=obj_expression_simple)\\n self.model.obj_cost = Objective(rule=obj_expression)\",\n \"def compute_cost(AL, Y, parameters ,lambd):\\n L = len(parameters) // 2\\n m = Y.shape[1]\\n cost = -1 / m * np.sum(np.nan_to_num(Y * np.log(AL) + (1-Y) * np.log(1-AL)))\\n cost+= 0.5*(lambd/m)*sum(np.linalg.norm(parameters['W' + str(i)])**2 for i in range(1,L))\\n return cost\",\n \"def get_cost_updates(self):\\n\\n y = self.get_hidden_values()\\n z = self.get_reconstructed_input(y)\\n\\n L = T.sum((self.x-z)**2, axis=1)\\n\\n cost = T.mean(L)\\n\\n return cost\",\n \"def compute_cost(features, values, theta):\\n m = len(values)\\n sum_of_square_errors = np.square(np.dot(features, theta) - values).sum()\\n cost = sum_of_square_errors / (2 * m)\\n\\n return cost\",\n \"def _construct_reg_costs(self):\\n param_reg_cost = sum([T.sum(p**2.0) for p in self.joint_params])\\n return param_reg_cost\",\n \"def objective(self, args: Dict[str, Any]) -> float:\\n pass\",\n \"def _local_cost(self, p, q):\\n diagnol = q[0] == p[0] or q[1] == p[1]\\n \\n # c0, c1 and c2 are costs from Canny operator, gradient magnitude and gradient direction respectively\\n if diagnol:\\n c0 = self.cost_edges[p[0]][p[1]]-SQRT_0_5*(self.cost_edges[p[0]][p[1]]-self.cost_edges[q[0]][q[1]])\\n c1 = self.cost_grad_mag[p[0]][p[1]]-SQRT_0_5*(self.cost_grad_mag[p[0]][p[1]]-self.cost_grad_mag[q[0]][q[1]])\\n c2 = SQRT_0_5 * self._get_grad_direction_cost(p, q)\\n else:\\n c0 = self.cost_edges[q[0]][q[1]]\\n c1 = self.cost_grad_mag[q[0]][q[1]]\\n c2 = self._get_grad_direction_cost(p, q)\\n \\n if np.isnan(c2):\\n c2 = 0.0\\n \\n w0, w1, w2 = self.weight\\n cost_pq = w0*c0 + w1*c1 + w2*c2\\n \\n return cost_pq * cost_pq\",\n \"def compute_cost(self, r):\\n self.r_max = 1\\n return np.exp(1 / (np.power(r, 2) - np.power(self.r_max, 2))) if r < self.r_max else 0\",\n \"def _SD_optimal(t):\",\n \"def calculate_total_cost(state):\\r\\n return state.cost()\",\n \"def maximize(self, budget, optimizer):\\n\\n\\t\\tpass\",\n \"def cost_(theta, X, Y):\\n if X.shape[1] + 1 != theta.size or X.shape[0] != Y.size:\\n print(\\\"Inc dim\\\")\\n return\\n c = cost_elem_(theta, X, Y)\\n s = 0\\n for i in c:\\n s = s + i\\n return(s)\",\n \"def getCostFunction(self, evalpts, observations, sigma=None, metric=lambda x: sum(x*x)):\\n #XXX: better interface for sigma?\\n def _(params):\\n ind = 0\\n for F, n, ofilt, icheck in zip(self._forwardFactories, self._inputs, \\\\\\n self._outputFilters, self._inputCheckers):\\n # check input #XXX: is this worthwile to do?\\n my_params = params[ind:ind+n]\\n checkQ = icheck(my_params, evalpts)\\n if checkQ is not None:\\n # some parameters are out of range... returns \\\"cost\\\"\\n return checkQ\\n\\n Gm = F(params[ind:ind+n])\\n if ind == 0:\\n x = ofilt(Gm(evalpts)) \\n else:\\n x = x + ofilt(Gm(evalpts)) \\n ind = ind+n\\n if sigma is None:\\n x = x - observations\\n else:\\n x = (x - observations) / sigma\\n #return sum(real((conjugate(x)*x)))\\n #return sum(x*x) \\n return metric(x)\\n return _\",\n \"def cost_function(H, n_qubits, p, params):\\n ini_state=plus_state(n_qubits)\\n for i in range(p):\\n ini_state=qaoa_step(ini_state,H,n_qubits,params=[params[2*i],params[2*i+1]])\\n return ((sparse.spmatrix.getH(ini_state)).dot(H.dot(ini_state))).real, ini_state\",\n \"def uniformCostSearch(problem):\\n \\\"*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***\\\"\\n aStarSearch(problem)\",\n \"def cost(self, X, y) :\\n ### ========== TODO : START ========== ###\\n # part d: compute J(theta)\\n #we know for linear/polynomial regression, the cost is the square of the errors\\n X = self.generate_polynomial_features(X)\\n y_pred_vector = np.dot(X, self.coef_)\\n cost = np.dot((y-y_pred_vector).transpose(),(y-y_pred_vector))#write in the matrix form\\n ### ========== TODO : END ========== ###\\n return cost\",\n \"def compute_cost(features, values, theta):\\n\\n npoints = len(values)\\n sum_of_square_errors = np.square(np.dot(features, theta) - values).sum()\\n cost = sum_of_square_errors / (2*npoints)\\n\\n return cost\",\n \"def objective(self,w):\\n diffs = self.get_y_times_diffs(self.get_split_weights(w))\\n #print diffs, sigmoid(diffs)\\n obj = -np.sum(np.log(sigmoid(diffs))) #negative, since minimising\\n # regularisation\\n obj += 0.5 * self.alpha * np.dot(w[:self.interp_index[0]], w[:self.interp_index[0]])\\n return obj\",\n \"def cost_function(x, N, w, dt):\\n yh = np.abs(fftkernel(x, w / dt)) # density\\n # formula for density\\n C = np.sum(yh ** 2) * dt - 2 * np.sum(yh * x) * \\\\\\n dt + 2 / np.sqrt(2 * np.pi) / w / N\\n C = C * N * N\\n # formula for rate\\n # C = dt*sum( yh.^2 - 2*yh.*y_hist + 2/sqrt(2*pi)/w*y_hist )\\n return C, yh\",\n \"def deriv_costFunction(self, x, y):\\n self.yEst = self.forward_propagate(x)\\n\\n delta_o = np.multiply( ( self.yEst - y ), self.deriv_sigmoid(self.z_o) )\\n #partial deriv of cost wrt hidden -> output weights\\n partial_J_w_ho = np.dot( self.a_h.T, delta_o )\\n\\n ones_o = np.ones( delta_o.shape[0] )\\n #partial deriv of cost wrt output biases\\n partial_J_b_o = np.dot( ones_o, delta_o )\\n\\n delta_h = np.dot( delta_o, self.w_ho.T ) * self.deriv_sigmoid( self.z_h )\\n #partial deriv of cost wrt input -> hidden weights\\n partial_J_w_ih = np.dot( x.T, delta_h )\\n \\n ones_h = np.ones( delta_h.shape[0] )\\n #partial deriv of cost wrt hidden biases\\n partial_J_b_h = np.dot( ones_h, delta_h)\\n\\n return partial_J_w_ih, partial_J_w_ho, partial_J_b_h, partial_J_b_o\",\n \"def compute_cost(features, values, theta):\\r\\n \\r\\n # your code here\\r\\n error = (values - features.dot(theta))\\r\\n cost = error.dot(error) \\r\\n return cost\",\n \"def minimization_fun(params, x, y, sizelist, x_crit_fun,\\n rescale_fun, rescale_type, return_data=False):\\n #x, y = _pad_vals(x, y)\\n\\n x_rescaled = rescale_xvals(x, sizelist, x_crit_fun, rescale_fun,\\n rescale_type,\\n *params)\\n\\n x_min = x_rescaled.flatten()\\n y_min = y.flatten()\\n\\n sort_args = np.argsort(x_min)\\n y_min = y_min[sort_args]\\n\\n # define the cost function\\n cost_fun = np.nansum(np.abs(np.diff(y_min))) / \\\\\\n (np.nanmax(y_min) - np.nanmin(y_min)) - 1\\n\\n if not return_data:\\n\\n return cost_fun\\n else:\\n return np.array(x_min)[sort_args], y_min, cost_fun\",\n \"def fn(x):\\n if x == 0: return 0\\n if x < 0: return -inf \\n return max(fn(x - c) * 10 + i + 1 for i, c in enumerate(cost))\",\n \"def minimize(self, func, grad, x0, args=()):\\n learning_rate = self._learning_rate\\n best_x = x = x0\\n best_value = func(x, *args)\\n iters_without_improve = 0\\n\\n for iteration in range(self._max_iterations):\\n gradient = grad(x, *args)\\n\\n # If absolute values of all partial derivatives are equal to 0 with specified accuracy, then parameters are\\n # close enough to the minimum and there is no need to continue gradient descent.\\n if np.abs(gradient).max() <= self._accuracy:\\n break\\n\\n x = x - learning_rate * gradient\\n\\n # If new values of x haven't lead to decrease of the function value for the specified number of iteration,\\n # the x is reverted to its previous best value and the learning rate is reduced\\n value = func(x, *args)\\n if value > best_value:\\n iters_without_improve += 1\\n if iters_without_improve >= self._lr_reduce_patience:\\n x = best_x\\n learning_rate *= self._lr_reduce_factor\\n else:\\n iters_without_improve = 0\\n best_value = value\\n best_x = x\\n\\n return best_x\",\n \"def calc_cost(y, yhat, cost_matrix):\\n return np.mean(cost_matrix[list(y), list(yhat)])\",\n \"def compute_cost(X, y, theta, lambd):\\n assert(theta.shape[0] == X.shape[1])\\n \\n m = X.shape[0]\\n grad = np.zeros(y.shape)\\n J = 0\\n \\n output = sigmoid(np.dot(X, theta))\\n\\n J = np.sum(- y * np.log(output) - (1 - y) * np.log(1 - output)) / m + lambd / (2 * m) * np.sum(np.square(theta[1:]))\\n\\n grad = np.dot(X.T, (output - y)) / m\\n \\n grad[1:] = grad[1:] + lambd / m * theta[1:]\\n\\n return J, grad\",\n \"def get_cost(self):\\n\\n\\t\\tx = self.symbolic_input\\n\\t\\ty = self.get_reconstructed_input()\\n\\n\\t\\tnegative_log_loss = -theano.tensor.sum(x*theano.tensor.log(y) +\\n\\t\\t\\t(1-x)*theano.tensor.log(1-y), axis=1)\\n\\n\\t\\tmean_loss = theano.tensor.mean(negative_log_loss)\\n\\n\\t\\treturn mean_loss\",\n \"def cost(self, state: Grid2D.State): # pylint: disable=no-self-use\\n return 1\",\n \"def minimize(self,x0=None):\\n import time\\n start_time = time.time()\\n tmp,total_par,lik_grad = self.minimize_both_vers(numerical=False,x0=x0)\\n if tmp['success']==False:\\n print(\\\"Probably a problem with gradient, do numerical\\\")\\n tmp,total_par,lik_grad = self.minimize_both_vers(x0=tmp['x'],numerical=True)\\n print(\\\"--- %s seconds ---\\\" % (time.time() - start_time))\\n self.lengthscale = total_par[0]\\n self.variance = total_par[1]\\n self.gstds = total_par[2]\\n tmp['fx']=np.array([total_par[0],total_par[1],total_par[2]])\\n return tmp,total_par,lik_grad\",\n \"def cost_function(self, config_samples):\\n cost = self.work_tracker(config_samples)\\n return cost\",\n \"def evaluate_cost(self, msg):\\n raise NotImplementedError()\",\n \"def cost(self,e1,e2):\\n pass\",\n \"def compute_cost_fake(df):\\n\\timport numpy as np\\n\\timport cPickle\\n\\tfrom nn import nn_param\\n\\tfrom matplotlib import pyplot as plt\\n\\tfrom theano import tensor as T\\n\\tf=file(\\\"nnparams.sav\\\")\\n\\tupdate=cPickle.load(f)\\n\\tsig=np.asarray(df.l0)\\n\\tsig_noise=np.asarray(df.l0+df.noise)\\n\\tsig/=np.max(sig)\\n\\tsig_noise/=np.max(sig_noise)\\n\\tyval=nn_param(update,sig_noise)\\n\\treturn T.mean(squared_error(yval,sig))\",\n \"def compute_cost(AL, Y):\\n m = Y.shape[1]\\n log_val = np.multiply(Y,np.log(AL)) + np.multiply(1-Y,np.log(1-AL))\\n cost = np.sum(log_val) * (-1/m)\\n cost = np.squeeze(cost)\\n return cost\",\n \"def _grad_cost(A, n, estimate, eps=1e-6):\\n p = A @ vec(estimate)\\n # see appendix on \\\"stalling\\\"\\n p = np.clip(p, a_min=eps, a_max=None)\\n eta = n / p\\n return unvec(-A.conj().T @ eta)\",\n \"def compute_cost_derivative(AL, Y):\\n pass\",\n \"def objective(\\n self,\\n parameters: object\\n ) -> float:\\n pass\",\n \"def compute_cost(self, chrome):\\n return 1\",\n \"def scenario_cost_rule(_m, y, s):\\r\\n\\r\\n return m.OP_T[y, s] + m.OP_H[y, s] + m.OP_W[y, s] + m.OP_S[y, s] + m.OP_Q[y, s] + m.OP_L[y, s]\",\n \"def minimize(self, cost_function, initial_params):\\n\\n # Optimization Results Object\\n history = []\\n\\n def wrapped_cost_function(params):\\n value = cost_function.evaluate(params)\\n history.append(cost_function.evaluations_history[-1])\\n print(f'Function evaluation {len(history)}: {value}', flush=True)\\n print(f'{params}', flush=True)\\n return value\\n\\n strategy = cma.CMAEvolutionStrategy(initial_params, self.sigma_0, self.options)\\n result = strategy.optimize(wrapped_cost_function).result\\n\\n optimization_results = {}\\n optimization_results['opt_value'] = result.fbest\\n optimization_results['opt_params'] = result.xbest\\n optimization_results['history'] = history\\n optimization_results['nfev'] = result.evaluations\\n optimization_results['nit'] = result.iterations\\n optimization_results['cma_xfavorite'] = list(result.xfavorite)\\n\\n return OptimizeResult(optimization_results)\",\n \"def return_quadratic_cost_function_expansion_variables(\\n X,U,dt,\\n RunningCost=\\\"Minimize Input Energy\\\"):\\n\\n if type(RunningCost)==str:\\n assert RunningCost in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\\\"\\n else:\\n assert type(RunningCost)==list, \\\"RunningCost must be a list of cost types.\\\"\\n for el in RunningCost:\\n assert type(el)==str, \\\"Each element of RunningCost must be a string. Not \\\" + str(type(el)) + \\\".\\\"\\n assert el in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\\\" + el + \\\"' not accepted.\\\"\\n\\n # returns a list of length len(Time)-1, each element with shape (1,1), where n is the number of states.\\n l_func = return_l_func(RunningCost=RunningCost)\\n l = list(\\n map(\\n lambda X,U: l_func(X,U,dt),\\n X[:,1:].T,\\n U\\n )\\n )\\n\\n # returns a list of length len(Time)-1, each element with shape (n,1), where n is the number of states.\\n lx_func = return_lx_func(RunningCost=RunningCost)\\n lx = list(\\n map(\\n lambda X,U: lx_func(X,U,dt),\\n X[:,1:].T,\\n U\\n )\\n )\\n\\n # returns a list of length len(Time)-1, each element with shape (m,1), where n is the number of states.\\n lu_func = return_lu_func(RunningCost=RunningCost)\\n lu = list(\\n map(\\n lambda X,U: lu_func(X,U,dt),\\n X[:,1:].T,\\n U\\n )\\n )\\n\\n # returns a list of length len(Time)-1, each element with shape (m,n), where m is the number of inputs and n is the number of states.\\n lux_func = return_lux_func(RunningCost=RunningCost)\\n lux = list(\\n map(\\n lambda X,U: lux_func(X,U,dt),\\n X[:,1:].T,\\n U\\n )\\n )\\n\\n # returns a list of length len(Time)-1, each element with shape (n,m), where n is the number of states and m is the number of inputs.\\n lxu_func = return_lxu_func(RunningCost=RunningCost)\\n lxu = list(\\n map(\\n lambda X,U: lxu_func(X,U,dt),\\n X[:,1:].T,\\n U\\n )\\n )\\n\\n # returns a list of length len(Time)-1, each element with shape (m,m), where m is the number of inputs.\\n luu_func = return_luu_func(RunningCost=RunningCost)\\n luu = list(\\n map(\\n lambda X,U: luu_func(X,U,dt),\\n X[:,1:].T,\\n U\\n )\\n )\\n\\n # returns a list of length len(Time)-1, each element with shape (n,n), where n is the number of states.\\n lxx_func = return_lxx_func(RunningCost=RunningCost)\\n lxx = list(\\n map(\\n lambda X,U: lxx_func(X,U,dt),\\n X[:,1:].T,\\n U\\n )\\n )\\n\\n return(l,lx,lu,lux,lxu,luu,lxx)\",\n \"def compute_cost(self,X, y):\\n \\n num_samples = len(X)\\n # Do Forward propagation to calculate our predictions\\n z1 = X.dot(self.W1) + self.b1\\n a1 = np.tanh(z1)\\n z2 = a1.dot(self.W2) + self.b2\\n exp_z = np.exp(z2)\\n a2 = exp_z / np.sum(exp_z, axis=1, keepdims=True)\\n softmax_scores = a2\\n # Calculate the cross-entropy loss\\n cross_ent_err = -np.log(softmax_scores[range(num_samples), y])\\n data_loss = np.sum(cross_ent_err)\\n return 1./num_samples * data_loss\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7898446","0.71774364","0.71197855","0.7037681","0.7032501","0.6972743","0.69694555","0.6966542","0.6868792","0.68444115","0.68282765","0.6827418","0.67984587","0.6764421","0.6761392","0.6750837","0.67058235","0.66833335","0.66763395","0.6667288","0.6642287","0.6627729","0.6591342","0.65710247","0.654221","0.65295726","0.6512434","0.65028256","0.6492322","0.6486503","0.6483167","0.64830416","0.64802825","0.6480024","0.6480024","0.6478383","0.64591193","0.6457873","0.6435726","0.64238644","0.64214283","0.64142716","0.63844854","0.63811076","0.63537794","0.6312873","0.6304432","0.63028586","0.6301019","0.6296096","0.62903833","0.62854886","0.6258573","0.6255414","0.6254392","0.6251736","0.6244219","0.62369394","0.62346643","0.6234606","0.62261814","0.62256116","0.6224966","0.6215263","0.62133354","0.6211354","0.6202841","0.6201058","0.6200102","0.61978084","0.619202","0.61861914","0.6178275","0.61733997","0.6172529","0.61724555","0.6163213","0.6154427","0.61505026","0.61492884","0.6149069","0.61469626","0.61310554","0.6115256","0.611372","0.6106668","0.60951674","0.60907346","0.6083769","0.6079481","0.607168","0.6066446","0.60644615","0.6064178","0.60591847","0.6058613","0.6058352","0.6048614","0.6047355","0.6046936","0.604547"],"string":"[\n \"0.7898446\",\n \"0.71774364\",\n \"0.71197855\",\n \"0.7037681\",\n \"0.7032501\",\n \"0.6972743\",\n \"0.69694555\",\n \"0.6966542\",\n \"0.6868792\",\n \"0.68444115\",\n \"0.68282765\",\n \"0.6827418\",\n \"0.67984587\",\n \"0.6764421\",\n \"0.6761392\",\n \"0.6750837\",\n \"0.67058235\",\n \"0.66833335\",\n \"0.66763395\",\n \"0.6667288\",\n \"0.6642287\",\n \"0.6627729\",\n \"0.6591342\",\n \"0.65710247\",\n \"0.654221\",\n \"0.65295726\",\n \"0.6512434\",\n \"0.65028256\",\n \"0.6492322\",\n \"0.6486503\",\n \"0.6483167\",\n \"0.64830416\",\n \"0.64802825\",\n \"0.6480024\",\n \"0.6480024\",\n \"0.6478383\",\n \"0.64591193\",\n \"0.6457873\",\n \"0.6435726\",\n \"0.64238644\",\n \"0.64214283\",\n \"0.64142716\",\n \"0.63844854\",\n \"0.63811076\",\n \"0.63537794\",\n \"0.6312873\",\n \"0.6304432\",\n \"0.63028586\",\n \"0.6301019\",\n \"0.6296096\",\n \"0.62903833\",\n \"0.62854886\",\n \"0.6258573\",\n \"0.6255414\",\n \"0.6254392\",\n \"0.6251736\",\n \"0.6244219\",\n \"0.62369394\",\n \"0.62346643\",\n \"0.6234606\",\n \"0.62261814\",\n \"0.62256116\",\n \"0.6224966\",\n \"0.6215263\",\n \"0.62133354\",\n \"0.6211354\",\n \"0.6202841\",\n \"0.6201058\",\n \"0.6200102\",\n \"0.61978084\",\n \"0.619202\",\n \"0.61861914\",\n \"0.6178275\",\n \"0.61733997\",\n \"0.6172529\",\n \"0.61724555\",\n \"0.6163213\",\n \"0.6154427\",\n \"0.61505026\",\n \"0.61492884\",\n \"0.6149069\",\n \"0.61469626\",\n \"0.61310554\",\n \"0.6115256\",\n \"0.611372\",\n \"0.6106668\",\n \"0.60951674\",\n \"0.60907346\",\n \"0.6083769\",\n \"0.6079481\",\n \"0.607168\",\n \"0.6066446\",\n \"0.60644615\",\n \"0.6064178\",\n \"0.60591847\",\n \"0.6058613\",\n \"0.6058352\",\n \"0.6048614\",\n \"0.6047355\",\n \"0.6046936\",\n \"0.604547\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80385,"cells":{"query":{"kind":"string","value":"Compute affine transformation to register landmarks"},"document":{"kind":"string","value":"def getRigidTransformFromLandmarks(points_dest, points_src, constraints='Tx_Ty_Tz_Rx_Ry_Rz', verbose=0, path_qc='./'):\n # TODO: check input constraints\n from scipy.optimize import minimize\n\n # initialize default parameters\n init_param = [0, 0, 0, 0, 0, 0, 1, 1, 1]\n # initialize parameters for optimizer\n init_param_optimizer = []\n # initialize dictionary to relate constraints index to dof\n dict_dof = {'Tx': 0, 'Ty': 1, 'Tz': 2, 'Rx': 3, 'Ry': 4, 'Rz': 5, 'Sx': 6, 'Sy': 7, 'Sz': 8}\n # extract constraints\n list_constraints = constraints.split('_')\n # loop across constraints and build initial_parameters\n for i in range(len(list_constraints)):\n init_param_optimizer.append(init_param[dict_dof[list_constraints[i]]])\n\n # launch optimizer\n # res = minimize(minimize_transform, x0=init_param_optimizer, args=(points_src, points_dest, constraints), method='Nelder-Mead', tol=1e-8, options={'xtol': 1e-8, 'ftol': 1e-8, 'maxiter': 10000, 'maxfev': 10000, 'disp': show})\n res = minimize(minimize_transform, x0=init_param_optimizer, args=(points_dest, points_src, constraints), method='Powell', tol=1e-8, options={'xtol': 1e-8, 'ftol': 1e-8, 'maxiter': 100000, 'maxfev': 100000, 'disp': verbose})\n # res = minimize(minAffineTransform, x0=initial_parameters, args=points, method='COBYLA', tol=1e-8, options={'tol': 1e-8, 'rhobeg': 0.1, 'maxiter': 100000, 'catol': 0, 'disp': show})\n # loop across constraints and update dof\n dof = init_param\n for i in range(len(list_constraints)):\n dof[dict_dof[list_constraints[i]]] = res.x[i]\n # convert dof to more intuitive variables\n tx, ty, tz, alpha, beta, gamma, scx, scy, scz = dof[0], dof[1], dof[2], dof[3], dof[4], dof[5], dof[6], dof[7], dof[8]\n # convert results to intuitive variables\n # tx, ty, tz, alpha, beta, gamma, scx, scy, scz = res.x[0], res.x[1], res.x[2], res.x[3], res.x[4], res.x[5], res.x[6], res.x[7], res.x[8]\n # build translation matrix\n translation_array = matrix([tx, ty, tz])\n # build rotation matrix\n rotation_matrix = matrix([[cos(alpha)*cos(beta), cos(alpha)*sin(beta)*sin(gamma)-sin(alpha)*cos(gamma), cos(alpha)*sin(beta)*cos(gamma)+sin(alpha)*sin(gamma)],\n [sin(alpha)*cos(beta), sin(alpha)*sin(beta)*sin(gamma)+cos(alpha)*cos(gamma), sin(alpha)*sin(beta)*cos(gamma)-cos(alpha)*sin(gamma)],\n [-sin(beta), cos(beta)*sin(gamma), cos(beta)*cos(gamma)]])\n # build scaling matrix\n scaling_matrix = matrix([[scx, 0.0, 0.0], [0.0, scy, 0.0], [0.0, 0.0, scz]])\n # compute rotation+scaling matrix\n rotsc_matrix = scaling_matrix * rotation_matrix\n # compute center of mass from moving points (src)\n points_src_barycenter = mean(points_src, axis=0)\n # apply transformation to moving points (src)\n points_src_reg = ((rotsc_matrix * (matrix(points_src) - points_src_barycenter).T).T + points_src_barycenter) + translation_array\n # display results\n print 'Matrix:\\n'+str(rotation_matrix)\n print 'Center:\\n'+str(points_src_barycenter)\n print 'Translation:\\n'+str(translation_array)\n\n if verbose == 2:\n import matplotlib\n # use Agg to prevent display\n matplotlib.use('Agg')\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n points_src_matrix = matrix(points_src)\n points_dest_matrix = matrix(points_dest)\n\n number_points = len(points_dest)\n\n ax.scatter([points_dest_matrix[i, 0] for i in range(0, number_points)],\n [points_dest_matrix[i, 1] for i in range(0, number_points)],\n [points_dest_matrix[i, 2] for i in range(0, number_points)], c='g', marker='+', s=500, label='dest')\n ax.scatter([points_src_matrix[i, 0] for i in range(0, number_points)],\n [points_src_matrix[i, 1] for i in range(0, number_points)],\n [points_src_matrix[i, 2] for i in range(0, number_points)], c='r', label='src')\n ax.scatter([points_src_reg[i, 0] for i in range(0, number_points)],\n [points_src_reg[i, 1] for i in range(0, number_points)],\n [points_src_reg[i, 2] for i in range(0, number_points)], c='b', label='src_reg')\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n ax.set_aspect('auto')\n plt.legend()\n # plt.show()\n plt.savefig(path_qc + 'getRigidTransformFromLandmarks_plot.png')\n\n fig2 = plt.figure()\n plt.plot(sse_results)\n plt.grid()\n plt.title('#Iterations: ' + str(res.nit) + ', #FuncEval: ' + str(res.nfev) + ', Error: ' + str(res.fun))\n plt.show()\n plt.savefig(path_qc + 'getRigidTransformFromLandmarks_iterations.png')\n\n # transform numpy matrix to list structure because it is easier to handle\n points_src_reg = points_src_reg.tolist()\n\n return rotsc_matrix, translation_array, points_src_reg, points_src_barycenter"},"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 transform(self,image,landmarks,s0=None):\n if s0 is None:\n s0 = np.array([[127.6475, 227.8161], [79.1608, 87.0376], [176.8392, 87.0376]], np.float32)\n idx = [8,36,45] #\"\"\"Anchor points\"\"\"\n pts = np.float32(landmarks[idx,:])\n M = cv2.getAffineTransform(pts,s0)\n dst = cv2.warpAffine(image, M, (256,256))\n return dst","def transformAffine(self, path=None, src=None, dst=None):\n if path is not None:\n landmarks = pd.read_csv(path, skiprows=1,engine=\"c\", na_filter=False, header=None, delim_whitespace=True, dtype=np.float32).as_matrix()\n dst = landmarks[:,3:5]\n src = landmarks[:,1:3]\n affine = transform.estimate_transform(\"affine\",src,dst)\n data = self.stormData[0][:,0:2]\n data = affine(data)\n self.stormData[0][:,0:2] = data","def transformAffine(self, coords):\n coordsshape = coords.shape\n dims = coordsshape[0] + 1\n coords = coords.reshape((len(coords), -1))\n coords = np.concatenate((coords, np.ones((1, len(coords[0])))), 0)\n affine = np.eye(dims)\n # now transform first to center:\n meanvec = np.mean(coords, 1)\n center = np.eye(dims)\n center[:-1, -1] = -meanvec[:-1]\n affine = np.matmul(center, affine)\n\n if np.sum(self.shift):\n affine[:-1, -1] += (self.deformrandomstate.rand(dims - 1) - 0.5) * np.float32(self.shift)\n if np.max(self.scaling) > 1:\n scales = np.ones(dims)\n # scales[:-1] = (self.deformrandomstate.rand(dims-1)-0.5)*(self.scaling-1.0/self.scaling)+(self.scaling+1/self.scaling)/2\n scales[:-1] = self.scaling ** (self.deformrandomstate.rand(dims - 1) * 2 - 1)\n scales = np.diag(scales)\n # print(scales)\n affine = np.matmul(scales, affine)\n if np.sum(self.rotation):\n affine = self._rotate(affine)\n # move back to location:\n center[:-1, -1] = -center[:-1, -1]\n affine = np.matmul(center, affine)\n # now appyl to coords:\n coords = np.matmul(affine, coords)\n coords = coords[:-1]\n coords = coords.reshape(coordsshape)\n return coords","def calc_affine(df):\n\tx0 = df.columns[0]\n\ty0 = df.index[0]\n\tdx = df.columns[1] - df.columns[0]\n\tdy = df.index[1] - df.index[0]\n\t\n\tt = affine.Affine(dx, 0, x0 , 0, dy ,y0 - dy) \n\t# y0 - dy because anker point is in the south!\n\treturn t","def __affine_geo_transformation(x, y, gtr):\n\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\n # Affine transformation rewritten for rasterio:\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\n\n return gtr_x, gtr_y","def affine(self):\n return self._affine","def affine(self):\n return self._affine","def get_affine(x, m, c):\n x = m*x + c\n return x","def affine_mult(affine, coordinates):\n return np.dot(coordinates, affine[:3, :3].T) + affine[:3, -1]","def affine(params, x):\n return np.dot(params['w'], x) + params['b']","def getAffineTransform(self, coord1, coord2):\n num_coords = 2 * len(coord1)\n A = np.zeros((num_coords, 6))\n b = []\n for point2 in coord2:\n b.append(float(point2[0]))\n b.append(float(point2[1]))\n b = np.asarray(b)\n i = 0\n for point1 in coord1:\n A[i, 0:2] = point1[0:2]\n A[i, 2] = 1\n A[i+1, 3:5] = point1[0:2]\n A[i+1, 5] = 1\n i += 2\n A = np.asarray(A)\n b = np.asarray(b)\n x = np.matmul(np.matmul(np.linalg.inv(np.matmul(A.T, A)), A.T), b.T)\n self.depth2rgb_affine = np.reshape(x, (2, 3))\n csv.writer(open(\"depth2rgb_affine.cfg\", \"w+\", newline=''), delimiter=',').writerows(self.depth2rgb_affine)\n # else:\n # x = np.vstack([np.reshape(x,(2,3)),[0,0,1]])\n # self.cam_ext_mat = x\n # A = [point[i,j+0:j+3].astype(np.float32) for i,point in enumerate(coord1) if i%2 == 0]\n # pts1 = coord1[0:3].astype(np.float32)\n # pts2 = coord2[0:3].astype(np.float32)\n # print(cv2.getAffineTransform(pts1, pts2))\n # return cv2.getAffineTransform(pts1, pts2)","def affine_trans(self):\n h, w, _ = self.img.shape\n\n \"\"\"\n pts1 = np.float32(\n [\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n ]\n )\n pts2 = np.float32(\n [\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n ]\n )\n \"\"\"\n\n pts1 = np.float32([[50, 50], [200, 50], [50, 200]])\n pts2 = np.float32([[10, 100], [200, 50], [100, 250]])\n\n M = cv2.getAffineTransform(pts1, pts2)\n\n self.img = cv2.warpAffine(self.img, M, (w, h))\n\n self.edits.append(\"affine\")\n return self","def read_affine(df):\n SliceThickness = [df.SliceThickness]\n PixelSpacing = _string_to_list_of_floats(df.PixelSpacing)\n ImageOrientationPatient = _string_to_list_of_floats(df.ImageOrientationPatient)\n ImagePositionPatient = _string_to_list_of_floats(df.ImagePositionPatient)\n\n Zooms = np.array(PixelSpacing+SliceThickness, dtype=float)\n ImageOrientationPatient = np.array(ImageOrientationPatient, dtype=float)\n ImagePositionPatient = np.array(ImagePositionPatient, dtype=float)\n \n ijk2ras = extract_cosines(ImageOrientationPatient)\n\n ijk2ras = (ijk2ras*np.array([-1,-1,1])).T\n ImagePositionPatient = ImagePositionPatient*np.array([-1,-1,1])\n\n affine = np.stack((ijk2ras[:,0]*Zooms[0],\n ijk2ras[:,1]*Zooms[1],\n ijk2ras[:,2]*Zooms[2],\n ImagePositionPatient), axis=1)\n\n return np.vstack((affine,[[0,0,0,1]]))","def affine(self) -> torch.Tensor:\n return self.meta.get(MetaKeys.AFFINE, self.get_default_affine())","def __call__(self, affine, representations_list, aatype):\n act = [\n common_modules.Linear( # pylint: disable=g-complex-comprehension\n self.config.num_channel,\n name='input_projection')(jax.nn.relu(x))\n for x in representations_list\n ]\n # Sum the activation list (equivalent to concat then Linear).\n act = sum(act)\n\n final_init = 'zeros' if self.global_config.zero_init else 'linear'\n\n # Mapping with some residual blocks.\n for _ in range(self.config.num_residual_block):\n old_act = act\n act = common_modules.Linear(\n self.config.num_channel,\n initializer='relu',\n name='resblock1')(\n jax.nn.relu(act))\n act = common_modules.Linear(\n self.config.num_channel,\n initializer=final_init,\n name='resblock2')(\n jax.nn.relu(act))\n act += old_act\n\n # Map activations to torsion angles. Shape: (num_res, 14).\n num_res = act.shape[0]\n unnormalized_angles = common_modules.Linear(\n 14, name='unnormalized_angles')(\n jax.nn.relu(act))\n unnormalized_angles = jnp.reshape(\n unnormalized_angles, [num_res, 7, 2])\n angles = l2_normalize(unnormalized_angles, axis=-1)\n\n outputs = {\n 'angles_sin_cos': angles, # jnp.ndarray (N, 7, 2)\n 'unnormalized_angles_sin_cos':\n unnormalized_angles, # jnp.ndarray (N, 7, 2)\n }\n\n # Map torsion angles to frames.\n backb_to_global = r3.rigids_from_quataffine(affine)\n\n # Jumper et al. (2021) Suppl. Alg. 24 \"computeAllAtomCoordinates\"\n\n # r3.Rigids with shape (N, 8).\n all_frames_to_global = all_atom.torsion_angles_to_frames(\n aatype,\n backb_to_global,\n angles)\n\n # Use frames and literature positions to create the final atom coordinates.\n # r3.Vecs with shape (N, 14).\n pred_positions = all_atom.frames_and_literature_positions_to_atom14_pos(\n aatype, all_frames_to_global)\n\n outputs.update({\n 'atom_pos': pred_positions, # r3.Vecs (N, 14)\n 'frames': all_frames_to_global, # r3.Rigids (N, 8)\n })\n return outputs","def convert_affine(ref, t, out):\n args = [\n transform_exe,\n '-d', '3',\n '-r', ref,\n '-t', '[{},0]'.format(t),\n '-o', '[{},1]'.format(out),\n '--float'\n ]\n subprocess.check_call(args)","def get_affine_transform(gps_coords, pdr_coords):\n # Compute similarity Xp = s A X + b\n X = np.array(pdr_coords)\n Xp = np.array(gps_coords)\n T = tf.superimposition_matrix(X.T, Xp.T, scale=True)\n\n A, b = T[:3, :3], T[:3, 3]\n s = np.linalg.det(A)**(1. / 3)\n A /= s\n return s, A, b","def apply_affine_transform(x, M):\n is1d = len(x.shape) == 1\n if is1d:\n x = np.expand_dims(x, axis=0)\n\n x_hom = np.concatenate(\n [x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=-1\n )\n x_out = x_hom @ M.T\n if is1d:\n x_out = np.squeeze(x_out, axis=0)\n return x_out","def getAffineTransform(self, coord1, coord2):\n # generate coord1 into A\n mat_A = np.zeros((2*coord1.shape[0], 6))\n coord1 = np.hstack([coord1, np.ones((coord1.shape[0], 1))])\n for i in range(coord1.shape[0]):\n row = coord1[i,:]\n row_block = block_diag(row, row)\n assert(row_block.shape == (2,6))\n mat_A[2*i:2*i+2, :] = row_block\n \n # generate coord2 into b\n vec_b = coord2.reshape(-1,1)\n\n # solve the least square\n pseudo_inv = np.linalg.inv(np.matmul(mat_A.T, mat_A))\n pseudo_inv = np.matmul(pseudo_inv, mat_A.T)\n affine_mat = np.matmul(pseudo_inv, vec_b)\n assert(affine_mat.shape == (6,1))\n \n return affine_mat.reshape(2,-1)","def transform(self) -> Affine:\n transform = (\n Affine.translation(*self.origin)\n * Affine.rotation(self.rotation)\n * Affine.scale(*self.res)\n )\n return transform","def extract_affine(self):\n self.affine = self.ecat.affine.tolist()","def affine_map(self, W, b):\n self.base_vertices = np.dot(W, self.base_vertices) + b\n self.base_vectors = np.dot(W, self.base_vectors)","def affine_matrix(self) -> np.ndarray:\n return self._forward_link._inverse_tf_matrix","def _get_affine_matrix(center, angle, translate, scale, shear):\n angle = np.deg2rad(angle)\n shear = np.deg2rad(shear)\n C = np.matrix([[1., 0., center[0]],\n [0., 1., center[1]],\n [0., 0., 1.]])\n T = np.matrix([[1., 0., translate[0]],\n [0., 1., translate[1]],\n [0., 0., 1.]])\n RSS = np.matrix([[np.cos(angle), -np.sin(angle + shear), 0.],\n [np.sin(angle), np.cos(angle + shear), 0.],\n [ 0., 0., 1./scale]]) * scale\n M = T * C * RSS * np.linalg.inv(C)\n return M","def affineTransform(x,output_dim):\n w=tf.get_variable(\"w\", [x.get_shape()[1], output_dim])\n b=tf.get_variable(\"b\", [output_dim], initializer=tf.constant_initializer(0.0))\n return tf.matmul(x,w)+b","def affine_transform(trans_mat, p0):\r\n n_data, n_dim = np.shape(p0)\r\n p0 = np.hstack((p0, np.ones((n_data, 1))))\r\n #return np.transpose(np.dot(np.transpose(trans_mat), np.transpose(p0)))\r\n return np.dot(p0, trans_mat)","def apply_transform_to_image(self,img, transform, center=None):\n \n if center is None:\n center = (np.array(img.shape)[::-1]-1)/2.0\n \n displacement = np.dot(transform, center)\n shift = center - displacement\n \n img_tf = ndimage.interpolation.affine_transform(img, transform, offset=shift, mode=\"constant\", order=3, cval=0.0)\n return img_tf","def AffineTransform( from_pts, to_pts ):\n \n # check that there are match points\n if len(from_pts) != len(to_pts) or len(to_pts)<1:\n print \"from_pts and to_pts must be of same size.\"\n return False\n\n # check the dimensions\n dim = len(from_pts[0]) # num of dimensions\n if len(from_pts) < dim:\n print \"Too few points => under-determined system.\"\n return False\n elif len(from_pts) > dim + 1:\n print \"Too many points => over-determined system.\"\n return False\n\n \n #segregate the x and y coordinages\n from_pts_x, from_pts_y = zip(*from_pts)\n to_pts_x, to_pts_y = zip(*to_pts)\n \n #create the Matricies for processing\n I = np.matrix([from_pts_x, from_pts_y, [1,1,1]])\n P = np.matrix([to_pts_x, to_pts_y])\n \n #Calculate the 2D affine transform matrix (A)\n A = P * linalg.pinv(I) \n\n # Make a result object\n class Transformation:\n \"\"\"Result object that represents the transformation\n from affine fitter.\"\"\"\n\n def To_Str(self):\n res = \"\"\n for j in range(dim):\n str1 = \"x%d' = \" % j\n for i in range(dim):\n str1 +=\"x%d * %f + \" % (i, A[i][j+dim+1])\n str1 += \"%f\" % A[dim][j+dim+1]\n res += str1 + \"\\n\"\n return res\n\n def Transform(self, pt_x, pt_y):\n pt_vector = np.matrix([[pt_x], [pt_y], [1]])\n transformed_pt = A * pt_vector\n return map(itemgetter(0), transformed_pt.tolist())\n return Transformation()","def register_landmarks(fname_src, fname_dest, dof, fname_affine='affine.txt', verbose=1, path_qc='./'):\n from msct_image import Image\n # open src label\n im_src = Image(fname_src)\n # coord_src = im_src.getNonZeroCoordinates(sorting='value') # landmarks are sorted by value\n coord_src = im_src.getCoordinatesAveragedByValue() # landmarks are sorted by value\n # open dest labels\n im_dest = Image(fname_dest)\n # coord_dest = im_dest.getNonZeroCoordinates(sorting='value')\n coord_dest = im_dest.getCoordinatesAveragedByValue()\n # Reorganize landmarks\n\n points_src, points_dest = [], []\n for coord in coord_src:\n point_src = im_src.transfo_pix2phys([[coord.x, coord.y, coord.z]])\n # convert NIFTI to ITK world coordinate\n # points_src.append([point_src[0][0], point_src[0][1], point_src[0][2]])\n points_src.append([-point_src[0][0], -point_src[0][1], point_src[0][2]])\n for coord in coord_dest:\n point_dest = im_dest.transfo_pix2phys([[coord.x, coord.y, coord.z]])\n # convert NIFTI to ITK world coordinate\n # points_dest.append([point_dest[0][0], point_dest[0][1], point_dest[0][2]])\n points_dest.append([-point_dest[0][0], -point_dest[0][1], point_dest[0][2]])\n\n # display\n sct.printv('Labels src: ' + str(points_src), verbose)\n sct.printv('Labels dest: ' + str(points_dest), verbose)\n sct.printv('Degrees of freedom (dof): ' + dof, verbose)\n\n if len(coord_src) != len(coord_dest):\n raise Exception('Error: number of source and destination landmarks are not the same, so landmarks cannot be paired.')\n\n # estimate transformation\n # N.B. points_src and points_dest are inverted below, because ITK uses inverted transformation matrices, i.e., src->dest is defined in dest instead of src.\n # (rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = getRigidTransformFromLandmarks(points_dest, points_src, constraints=dof, verbose=verbose, path_qc=path_qc)\n (rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = getRigidTransformFromLandmarks(points_src, points_dest, constraints=dof, verbose=verbose, path_qc=path_qc)\n # writing rigid transformation file\n # N.B. x and y dimensions have a negative sign to ensure compatibility between Python and ITK transfo\n text_file = open(fname_affine, 'w')\n text_file.write(\"#Insight Transform File V1.0\\n\")\n text_file.write(\"#Transform 0\\n\")\n text_file.write(\"Transform: AffineTransform_double_3_3\\n\")\n text_file.write(\"Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\\n\" % (\n rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],\n rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2],\n rotation_matrix[2, 0], rotation_matrix[2, 1], rotation_matrix[2, 2],\n translation_array[0, 0], translation_array[0, 1], translation_array[0, 2]))\n text_file.write(\"FixedParameters: %.9f %.9f %.9f\\n\" % (points_moving_barycenter[0],\n points_moving_barycenter[1],\n points_moving_barycenter[2]))\n text_file.close()","def affine(self, image, random_state):\n shape = image.shape\n shape_size = shape[:2]\n transform_std = self.alpha_affine*image.shape[1]\n\n center_square = np.float32(shape[:2]) // 2\n square_size = min(shape[:2]) // 3\n\n source = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\n destination = source + random_state.uniform(-transform_std, transform_std, size=source.shape).astype(np.float32)\n M = cv2.getAffineTransform(source, destination)\n\n return cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE)","def affine(img, angle, translate, scale, shear):\n if not _is_numpy(img):\n raise TypeError('img should be Numpy Image. Got {}'.format(type(img)))\n\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\n \"Argument translate should be a list or tuple of length 2\"\n\n assert scale > 0.0, \"Argument scale should be positive\"\n\n aug = iaa.Affine(scale=scale, rotate=angle, translate_px=translate, shear=shear)\n return aug.augment_image(img)","def map(self,Affine,i):\n map_x = np.zeros([self.num,self.d])\n for k in range(self.num):\n map_x[k,:] = Affine.apply(i,self.pick(k))\n Mapped = Model_Points(map_x)\n return Mapped","def gen_affine_map(Ab, img_sz, dim=3):\n Ab = Ab.view(Ab.shape[0], dim+1, dim)\n phi = gen_identity_map(img_sz).to(Ab.device)\n phi_cp = phi.view(dim, -1)\n affine_map = torch.matmul(Ab[:, :dim, :], phi_cp)\n affine_map = Ab[:, dim, :].contiguous().view(-1, dim, 1) + affine_map\n affine_map = affine_map.view([Ab.shape[0]] + list(phi.shape))\n return affine_map","def apply_affine_transform(self, M):\n vertices = []\n for x,y in self.get_vertices():\n vertices.append(tuple(np.dot(M, [x, y, 1])[:2]))\n return type(self)(vertices, properties=self.properties, crs=self.crs)","def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1,\n row_axis=0, col_axis=1, channel_axis=2,\n fill_mode='nearest', cval=0., order=1):\n if scipy is None:\n raise ImportError('Image transformations require SciPy. '\n 'Install SciPy.')\n transform_matrix = None\n if theta != 0:\n theta = np.deg2rad(theta)\n rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],\n [np.sin(theta), np.cos(theta), 0],\n [0, 0, 1]])\n transform_matrix = rotation_matrix\n\n if tx != 0 or ty != 0:\n shift_matrix = np.array([[1, 0, tx],\n [0, 1, ty],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = shift_matrix\n else:\n transform_matrix = np.dot(transform_matrix, shift_matrix)\n\n if shear != 0:\n shear = np.deg2rad(shear)\n shear_matrix = np.array([[1, -np.sin(shear), 0],\n [0, np.cos(shear), 0],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = shear_matrix\n else:\n transform_matrix = np.dot(transform_matrix, shear_matrix)\n\n if zx != 1 or zy != 1:\n zoom_matrix = np.array([[zx, 0, 0],\n [0, zy, 0],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = zoom_matrix\n else:\n transform_matrix = np.dot(transform_matrix, zoom_matrix)\n\n if transform_matrix is not None:\n h, w = x.shape[row_axis], x.shape[col_axis]\n transform_matrix = transform_matrix_offset_center(\n transform_matrix, h, w)\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n\n channel_images = [ndimage.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=order,\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def affine_matrix(self) -> np.ndarray:\n return self._tf_matrix","def update_affine_param( cur_af, last_af): # A2(A1*x+b1) + b2 = A2A1*x + A2*b1+b2\n cur_af = cur_af.view(cur_af.shape[0], 4, 3)\n last_af = last_af.view(last_af.shape[0],4,3)\n updated_af = torch.zeros_like(cur_af.data).to(cur_af.device)\n dim =3\n updated_af[:,:3,:] = torch.matmul(cur_af[:,:3,:],last_af[:,:3,:])\n updated_af[:,3,:] = cur_af[:,3,:] + torch.squeeze(torch.matmul(cur_af[:,:3,:], torch.transpose(last_af[:,3:,:],1,2)),2)\n updated_af = updated_af.contiguous().view(cur_af.shape[0],-1)\n return updated_af","def affine_forward(x, w, b):\n #raise NotImplementedError\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################\n out=np.dot(x,w)+b\n cache=(x,w,b)\n return(out, cache)","def register_affine(self):\n try:\n safe_execute_command(self._prepare_affine_reg_cmd(),\n self.affine_log_file_path, self.affine_error_path)\n except SafeExecuteCommandError as err:\n raise RegistrationError('Affine registration failed; {}'.format(err))","def orientation_ras_lps(affine: NdarrayTensor) -> NdarrayTensor:\n sr = max(affine.shape[0] - 1, 1) # spatial rank is at least 1\n flip_d = [[-1, 1], [-1, -1, 1], [-1, -1, 1, 1]]\n flip_diag = flip_d[min(sr - 1, 2)] + [1] * (sr - 3)\n if isinstance(affine, torch.Tensor):\n return torch.diag(torch.as_tensor(flip_diag).to(affine)) @ affine # type: ignore\n return np.diag(flip_diag).astype(affine.dtype) @ affine # type: ignore","def translateToOriginXform(self):\n return np.array([[1, 0, 0, -self.eye[0]],\n [0, 1, 0, -self.eye[1]],\n [0, 0, 1, -self.eye[2]],\n [0, 0, 0, 1]])","def affine_transform(x, output_dim, name=None):\n\n w = tf.get_variable(name + \"_w\", [x.get_shape()[1], output_dim], initializer=tf.truncated_normal_initializer(stddev=0.02))\n b = tf.get_variable(name + \"_b\", [output_dim], initializer=tf.constant_initializer(0.0))\n\n return tf.matmul(x, w) + b","def augment_translate(image, mask, trans_range):\n\n tr_x = trans_range * np.random.uniform() - trans_range / 2\n tr_y = trans_range * np.random.uniform() - trans_range / 2\n trans_M = np.array([[1, 0, tr_x], [0, 1, tr_y]], dtype=np.float32)\n width, height, _ = image.shape\n image = cv2.warpAffine(image, trans_M, (width, height))\n if mask is not None:\n mask = cv2.warpAffine(mask, trans_M, (width, height))\n return image, mask","def getAffineTransform_deprecated(self, coord1, coord2):\n pts1 = coord1[0:3].astype(np.float32)\n pts2 = coord2[0:3].astype(np.float32)\n return cv2.getAffineTransform(pts1, pts2)","def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1):\n # transform_matrix = transform_matrix_offset_center()\n # asdihasid\n # asd\n\n x = np.rollaxis(x, channel_index, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [\n ndi.interpolation.affine_transform(\n x_channel, final_affine_matrix, final_offset, order=order, mode=fill_mode, cval=cval\n ) for x_channel in x\n ]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_index + 1)\n return x","def affine_forward(x, W, b):\r\n x2d = np.reshape(x, (x.shape[0], -1)) # convert 4D input matrix to 2D \r\n out = np.dot(x2d, W) + b # linear transformation\r\n cache = (x, W, b) # keep for backward step (stay with us)\r\n return out, cache","def affine_transform(geom, matrix):\n if geom.is_empty:\n return geom\n if len(matrix) == 6:\n ndim = 2\n a, b, d, e, xoff, yoff = matrix\n if geom.has_z:\n ndim = 3\n i = 1.0\n c = f = g = h = zoff = 0.0\n matrix = a, b, c, d, e, f, g, h, i, xoff, yoff, zoff\n elif len(matrix) == 12:\n ndim = 3\n a, b, c, d, e, f, g, h, i, xoff, yoff, zoff = matrix\n if not geom.has_z:\n ndim = 2\n matrix = a, b, d, e, xoff, yoff\n else:\n raise ValueError(\"'matrix' expects either 6 or 12 coefficients\")\n\n def affine_pts(pts):\n \"\"\"Internal function to yield affine transform of coordinate tuples\"\"\"\n if ndim == 2:\n for x, y in pts:\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n yield (xp, yp)\n elif ndim == 3:\n for x, y, z in pts:\n xp = a * x + b * y + c * z + xoff\n yp = d * x + e * y + f * z + yoff\n zp = g * x + h * y + i * z + zoff\n yield (xp, yp, zp)\n\n # Process coordinates from each supported geometry type\n if geom.type in ('Point', 'LineString', 'LinearRing'):\n return type(geom)(list(affine_pts(geom.coords)))\n elif geom.type == 'Polygon':\n ring = geom.exterior\n shell = type(ring)(list(affine_pts(ring.coords)))\n holes = list(geom.interiors)\n for pos, ring in enumerate(holes):\n holes[pos] = type(ring)(list(affine_pts(ring.coords)))\n return type(geom)(shell, holes)\n elif geom.type.startswith('Multi') or geom.type == 'GeometryCollection':\n # Recursive call\n # TODO: fix GeometryCollection constructor\n return type(geom)([affine_transform(part, matrix)\n for part in geom.geoms])\n else:\n raise ValueError('Type %r not recognized' % geom.type)","def encode_affine(msg, a, b):\n \n #Code to numbers\n encoded_message = [ RVALUES[(a * VALUES[i] + b) % 26] for i in msg ]\n \n return ''.join(encoded_message)","def read_affine(file):\n data = open(file, 'r').read()\n data = data.split('\\n')\n for i in range(1, 5):\n data[i] = data[i].split(':')\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\n Nlon = len(int_lon) - 1\n Nlat = len(int_lat) - 1\n data[3][1] = data[3][1].split(',')\n data[4][1] = data[4][1].split(',')\n lon_transform = np.zeros((Nlon, 2))\n lat_transform = np.zeros((Nlat, 2))\n for i in range(Nlon):\n data[3][1][i] = data[3][1][i].split(' ')\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\n for i in range(Nlat):\n data[4][1][i] = data[4][1][i].split(' ')\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\n lon_transform = np.array(lon_transform).astype('float')\n lat_transform = np.array(lat_transform).astype('float')\n return int_lon, int_lat, lon_transform, lat_transform","def affine(img, angle, translate, scale, shear, resample=0, fillcolor=None, border_mode=None):\n check_type(img)\n\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\n \"Argument translate should be a list or tuple of length 2\"\n\n assert scale > 0.0, \"Argument scale should be positive\"\n\n output_size = img.size\n center = (img.size[0] * 0.5 + 0.5, img.size[1] * 0.5 + 0.5)\n matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)\n kwargs = {\"fillcolor\": fillcolor} if PILLOW_VERSION[0] == '5' else {}\n return img.transform(output_size, Image.AFFINE, matrix, resample, **kwargs)","def AffineTz(dist):\n return np.mat([[1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 0, 1, dist],\n [0, 0, 0, 1]\n ],\n dtype='f8'\n )","def affine_transform(initialize=False, fixed=None, moments=True) :\n \n transform = itk.AffineTransform[itk.D, fixed.ndim].New()\n \n if initialize :\n fixed_itk = medipy.itk.medipy_image_to_itk_image(fixed, False)\n #moving_itk = medipy.itk.medipy_image_to_itk_image(moving, False)\n\n fixedIndex = fixed_itk.GetLargestPossibleRegion().GetIndex()\n fixedSize = fixed_itk.GetLargestPossibleRegion().GetSize()\n\n centerIndex = (int(fixedIndex[0] + fixedSize[0] / 2.0), \\\n int(fixedIndex[1] + fixedSize[1] / 2.0), \\\n int(fixedIndex[2] + fixedSize[2] / 2.0))\n\n rotationCenter = fixed_itk.TransformIndexToPhysicalPoint(centerIndex)\n\n transform.SetIdentity()\n transform.SetCenter(rotationCenter)\n\n #initial_transform = itk.VersorRigid3DTransform[itk.D].New()\n #initializer = itk.CenteredTransformInitializer[\n # initial_transform, fixed_itk, moving_itk].New(\n # Transform=initial_transform, FixedImage=fixed_itk, MovingImage=moving_itk) \n #if moments :\n # initializer.MomentsOn()\n #else :\n # initializer.GeometryOn()\n #initializer.InitializeTransform()\n #transform.SetCenter(initial_transform.GetCenter())\n #transform.SetOffset(initial_transform.GetOffset())\n \n return transform","def _compute_affine_cropper(bounding_box: BB, width_raw: int, height_raw: int) -> torch.Tensor: \n kx = (-1.0 + 2 * bounding_box.bx / width_raw).reshape(-1, 1)\n ky = (-1.0 + 2 * bounding_box.by / height_raw).reshape(-1, 1)\n sx = (bounding_box.bw / width_raw).reshape(-1, 1)\n sy = (bounding_box.bh / height_raw).reshape(-1, 1)\n zero = torch.zeros_like(kx)\n\n return torch.cat((sy, zero, ky, zero, sx, kx), dim=-1).reshape(-1, 2, 3)","def affine_computation(x, offset=None):\n if offset is not None:\n new_offset = x.mean(axis=1)\n y = 2 * x\n if offset is not None:\n y = y + offset[:, None]\n return y, new_offset\n return y","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n t = np.reshape(x,(x.shape[0],np.prod(np.shape(x)[1:])))\n \n\n out = np.dot(t,w) + b\n \n #print(np.shape(out))\n\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def estimate_stage_affine(t0, t1):\n src = np.array([t.tforms[0].translation for t in t0])\n dst = np.array([t.tforms[1].translation for t in t1])\n aff = renderapi.transform.AffineModel()\n aff.estimate(src, dst)\n return aff","def small_transf(x, y, a, xc, yc, ac, iref):\n x += xc - x[iref]\n y += yc - y[iref]\n x, y = rotate_xy(x, y, x[iref], y[iref], ac - a[iref])\n a += ac - a[iref]\n return x, y, a","def translateX(shift: float) -> Callable:\n return lambda img: TF.affine(img, 0, (shift * img.size[0], 0), 1, 0)","def affine_forward(X, W, b):\n return np.dot(X, W) + b","def augmentAffine(img_in, seg_in, strength=0.05):\n B,C,D,H,W = img_in.size()\n affine_matrix = (torch.eye(3,4).unsqueeze(0) + torch.randn(B, 3, 4) * strength).to(img_in.device)\n\n meshgrid = F.affine_grid(affine_matrix,torch.Size((B,1,D,H,W)))\n\n img_out = F.grid_sample(img_in, meshgrid,padding_mode='border')\n seg_out = F.grid_sample(seg_in.float().unsqueeze(1), meshgrid, mode='nearest').long().squeeze(1)\n\n return img_out, seg_out","def affine(img, angle=0, translate=(0, 0), scale=1, shear=0, resample='BILINEAR', fillcolor=(0,0,0)):\n if not _is_numpy_image(img):\n raise TypeError('img should be CV Image. Got {}'.format(type(img)))\n\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\n \"Argument translate should be a list or tuple of length 2\"\n\n assert scale > 0.0, \"Argument scale should be positive\"\n\n rows, cols, _ = img.shape\n center = (cols * 0.5, rows * 0.5)\n angle = math.radians(angle)\n shear = math.radians(shear)\n M00 = math.cos(angle)*scale\n M01 = -math.sin(angle+shear)*scale\n M10 = math.sin(angle)*scale\n M11 = math.cos(angle+shear)*scale\n M02 = center[0] - center[0]*M00 - center[1]*M01 + translate[0]\n M12 = center[1] - center[0]*M10 - center[1]*M11 + translate[1]\n affine_matrix = np.array([[M00, M01, M02], [M10, M11, M12]], dtype=np.float32)\n dst_img = cv2.warpAffine(img, affine_matrix, (cols, rows), flags=INTER_MODE[resample],\n borderMode=cv2.BORDER_CONSTANT, borderValue=fillcolor)\n return dst_img","def _inverse_affine_matrix(self) -> np.ndarray:\n raise NotImplementedError","def AffineRx(theta, units='deg'):\n if units == 'deg':\n theta = np.deg2rad(theta)\n\n return np.mat([[1, 0, 0, 0],\n [0, np.cos(theta), -np.sin(theta), 0],\n [0, np.sin(theta), np.cos(theta), 0],\n [0, 0, 0, 1]\n ],\n dtype='f8'\n )","def rotMatToAffine(rotmat, origin=None):\n return compose([1, 1, 1], [0, 0, 0], rotmat, origin)","def rotMatToAffine(rotmat, origin=None):\n return compose([1, 1, 1], [0, 0, 0], rotmat, origin)","def _mri_landmarks_to_mri_voxels(mri_landmarks, t1_mgh):\n # Get landmarks in voxel space, using the T1 data\n vox2ras_tkr_t = t1_mgh.header.get_vox2ras_tkr()\n ras_tkr2vox_t = linalg.inv(vox2ras_tkr_t)\n vox_landmarks = apply_trans(ras_tkr2vox_t, mri_landmarks)\n\n return vox_landmarks","def transform(self, affine_transformation):\n for i in range(len(self.poses)):\n self.poses[i].transform(affine_transformation)","def get_affine_reg_params(self):\n affine_params = [\n self.affine_reg_pyramid_steps,\n self.affine_reg_used_pyramid_steps,\n ]\n return affine_params","def encodeAffine(self, m, n):\n\n encode = Affine(m, n)\n cipherText = encode.encode(self.planeText)\n\n if (self.verbose == 1):\n print(cipherText)\n \n return(cipherText)","def eval_pos_affine():\n root_dir = \"/home/sdb/wangshentao/myspace/thesis/data/VisDrone2019-MOT-test-dev/\"\n seq_dir = root_dir + \"sequences/\"\n annotations_dir = root_dir + 'annotations/'\n affine_dir = root_dir + \"affine_orig/\"\n all_iou = []\n seqs_sample = '''\n uav0000249_00001_v\n uav0000249_02688_v\n '''\n seqs_str = seqs_sample\n seqs = [seq.strip() for seq in seqs_str.split()]\n for seq in seqs:\n image_file = os.listdir(os.path.join(seq_dir, seq))[0]\n image = cv2.imread(os.path.join(seq_dir, seq, image_file))\n orig_h, orig_w = image.shape[:2]\n\n with open(os.path.join(affine_dir, seq+'.pickle'), 'rb') as fin:\n affine_dict = pickle.load(fin)\n\n bbox, frame_id = get_frame_bbox(annotations_dir, seq + '.txt')\n predict_bbox = []\n for i in range(len(bbox)):\n # convert to std resolution\n bbox[i][:, 0] = bbox[i][:, 0]\n bbox[i][:, 1] = bbox[i][:, 1]\n bbox[i][:, 2] = bbox[i][:, 2]\n bbox[i][:, 3] = bbox[i][:, 3]\n\n # for j in range(bbox[i].shape[0]):\n # bbox[i][j] = tlwh_to_tlbr(bbox[i][j])\n for idx in range(len(bbox)):\n kalman_filter = KalmanFilter()\n trace_bbox = bbox[idx]\n trace_predict_bbox = []\n mean, covariance = kalman_filter.initiate(tlwh_to_xyah(trace_bbox[0]))\n for i in range(1, trace_bbox.shape[0]):\n # i-1 to i M\n frame_name = \"{:07d}.jpg\".format(int(frame_id[idx][i-1]))\n M = affine_dict[frame_name]\n bbox_infer = tlwh(mean)\n bbox_infer = tlwh_to_tlbr(bbox_infer)\n bbox_expand = np.ones((3, 4))\n bbox_expand[:2, 0] = bbox_infer[:2]\n bbox_expand[:2, 1] = bbox_infer[2:]\n # tr\n bbox_expand[:2, 2] = bbox_infer[2], bbox_infer[1]\n # bl\n bbox_expand[:2, 3] = bbox_infer[0], bbox_infer[3]\n bbox_expand = np.dot(M, bbox_expand)\n for t in range(bbox_expand.shape[1]):\n bbox_expand[:2, t] /= bbox_expand[2, t]\n # bbox_infer[:2] = bbox_expand[:2, 0]\n # bbox_infer[2:] = bbox_expand[:2, 1]\n # get the out bounding bbox\n bbox_infer[0] = min(bbox_expand[0, :])\n bbox_infer[1] = min(bbox_expand[1, :])\n bbox_infer[2] = max(bbox_expand[0, :])\n bbox_infer[3] = max(bbox_expand[1, :])\n bbox_infer = tlbr_to_tlwh(bbox_infer)\n # print(bbox_infer)\n trace_predict_bbox.append(bbox_infer)\n # move = mean[:4] - tlwh_to_xyah(bbox_infer)\n # if np.sum(np.square(move)[:2]) > 32*32:\n # print(move)\n # print(idx, frame_name)\n # print(mean)\n mean[:4] = tlwh_to_xyah(bbox_infer)\n # print(mean)\n mean, covariance = kalman_filter.predict(mean, covariance)\n mean, covariance = kalman_filter.update(mean, covariance, tlwh_to_xyah(trace_bbox[i]))\n\n trace_predict_bbox = np.array(trace_predict_bbox)\n for i in range(trace_predict_bbox.shape[0]):\n trace_predict_bbox[i] = tlwh_to_tlbr(trace_predict_bbox[i])\n for i in range(trace_bbox.shape[0]):\n trace_bbox[i] = tlwh_to_tlbr(trace_bbox[i])\n\n predict_bbox.append(trace_predict_bbox)\n bbox[idx] = bbox[idx][1:]\n frame_id[idx] = frame_id[idx][1:]\n assert bbox[idx].shape[0] == predict_bbox[idx].shape[0]\n iou = []\n for i in range(len(bbox)):\n trace_iou = []\n trace_bbox = bbox[i]\n trace_predict_bbx = predict_bbox[i]\n for j in range(trace_bbox.shape[0]):\n iou_val = bbox_ious(np.ascontiguousarray(trace_bbox[j][np.newaxis, :], dtype=np.float),\n np.ascontiguousarray(trace_predict_bbx[j][np.newaxis, :], dtype=np.float))\n trace_iou.append(iou_val)\n iou.append(np.array(trace_iou))\n iou = [int(np.mean(i) * 100) for i in iou]\n all_iou += iou\n bins = np.zeros(101)\n for i in all_iou:\n bins[i] += 1\n plt.bar(np.arange(101), bins)\n plt.ylabel('num')\n plt.xlabel('iou(*100)')\n plt.show()","def cast(*args):\n return _itkFixedCenterOfRotationAffineTransformPython.itkFixedCenterOfRotationAffineTransformD2_cast(*args)","def affine_forward(x,w,b):\n out=None\n N=x.shape[0]\n x_row=x.reshape(N,-1)\n out=np.dot(x_row,w)+b\n cache=(x,w,b)\n return out,cache","def get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False):\n assert len(center) == 2\n assert len(scale) == 2\n assert len(output_size) == 2\n assert len(shift) == 2\n scale_tmp = scale * 200.0\n shift = np.array(shift)\n src_w = scale_tmp[0]\n dst_w = output_size[0]\n dst_h = output_size[1]\n rot_rad = np.pi * rot / 180\n src_dir = rotate_point([0.0, src_w * -0.5], rot_rad)\n dst_dir = np.array([0.0, dst_w * -0.5])\n src = np.zeros((3, 2), dtype=np.float32)\n src[0, :] = center + scale_tmp * shift\n src[1, :] = center + src_dir + scale_tmp * shift\n src[2, :] = _get_3rd_point(src[0, :], src[1, :])\n dst = np.zeros((3, 2), dtype=np.float32)\n dst[0, :] = [dst_w * 0.5, dst_h * 0.5]\n dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir\n dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])\n if inv:\n trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))\n else:\n trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))\n return trans","def img_map_transforms(ts):\n # XXX TODO: unchecked textures give error of variable referenced before assignment XXX\n # POV-Ray \"scale\" is not a number of repetitions factor, but ,its\n # inverse, a standard scale factor.\n # 0.5 Offset is needed relatively to scale because center of the\n # scale is 0.5,0.5 in blender and 0,0 in POV\n # Strange that the translation factor for scale is not the same as for\n # translate.\n # TODO: verify both matches with other blender renderers / internal in previous versions.\n image_map_transforms = \"\"\n image_map_transforms = \"scale <%.4g,%.4g,%.4g> translate <%.4g,%.4g,%.4g>\" % (\n ts.scale[0],\n ts.scale[1],\n ts.scale[2],\n ts.offset[0],\n ts.offset[1],\n ts.offset[2],\n )\n # image_map_transforms = (\" translate <-0.5,-0.5,0.0> scale <%.4g,%.4g,%.4g> translate <%.4g,%.4g,%.4g>\" % \\\n # ( 1.0 / ts.scale.x,\n # 1.0 / ts.scale.y,\n # 1.0 / ts.scale.z,\n # (0.5 / ts.scale.x) + ts.offset.x,\n # (0.5 / ts.scale.y) + ts.offset.y,\n # ts.offset.z))\n # image_map_transforms = (\n # \"translate <-0.5,-0.5,0> \"\n # \"scale <-1,-1,1> * <%.4g,%.4g,%.4g> \"\n # \"translate <0.5,0.5,0> + <%.4g,%.4g,%.4g>\" % \\\n # (1.0 / ts.scale.x,\n # 1.0 / ts.scale.y,\n # 1.0 / ts.scale.z,\n # ts.offset.x,\n # ts.offset.y,\n # ts.offset.z)\n # )\n return image_map_transforms","def xyz_affine(coordmap, name2xyz=None):\n if name2xyz is None:\n name2xyz = known_names\n try:\n affine = coordmap.affine\n except AttributeError:\n raise SpaceTypeError('Need affine coordinate map')\n order = xyz_order(coordmap.function_range, name2xyz)\n affine = affine[order[:3]]\n # Check that dropped dimensions don't provide xyz coordinate info\n extra_cols = affine[:,3:-1]\n if not np.allclose(extra_cols, 0):\n raise AffineError('Dropped dimensions not orthogonal to xyz')\n return from_matrix_vector(affine[:3,:3], affine[:3,-1])","def geo_transform(self):\n pass","def _apply_transform(self, x, transform_parameters):\n # x is a single image, so it doesn't have image number at index 0\n img_row_axis = self.row_axis - 1\n img_col_axis = self.col_axis - 1\n img_channel_axis = self.channel_axis - 1\n\n x = apply_affine_transform(x, transform_parameters.get('theta', 0),\n transform_parameters.get('tx', 0),\n transform_parameters.get('ty', 0),\n transform_parameters.get('shear', 0),\n transform_parameters.get('zx', 1),\n transform_parameters.get('zy', 1),\n row_axis=img_row_axis,\n col_axis=img_col_axis,\n channel_axis=img_channel_axis,\n fill_mode=self.fill_mode,\n cval=self.cval)\n\n if transform_parameters.get('channel_shift_intensity') is not None:\n x = apply_channel_shift(x,\n transform_parameters['channel_shift_intensity'],\n img_channel_axis)\n\n if transform_parameters.get('flip_horizontal', False):\n x = self._flip_axis(x, img_col_axis)\n\n if transform_parameters.get('flip_vertical', False):\n x = self._flip_axis(x, img_row_axis)\n\n if transform_parameters.get('brightness') is not None:\n x = apply_brightness_shift(x, transform_parameters['brightness'])\n\n return x","def translate(geom, xoff=0.0, yoff=0.0, zoff=0.0):\n matrix = (1.0, 0.0, 0.0,\n 0.0, 1.0, 0.0,\n 0.0, 0.0, 1.0,\n xoff, yoff, zoff)\n return affine_transform(geom, matrix)","def affine_transformation(image, ratio = 0.2 ):\n random_degree = random.uniform(-15, 15) * np.pi / 180.\n random_translation_x = random.randint(0, int(image.shape[0] *ratio))\n random_translation_y = random.randint(0, int(image.shape[0] *ratio))\n \n transform = AffineTransform(translation=(random_translation_x,random_translation_y) , rotation = random_degree)\n wrapShift = warp(image,transform,mode= random.sample([\"constant\", \"edge\"], 1)[0])\n return wrapShift","def transform():","def translate(img, shift):\n\tgray = grayscale(img)\n\ttmp = img.copy()\n\trows, cols = gray.shape\n\tM = np.float32([[1, 0, shift[0]], [0, 1, shift[1]]]) # Translation Matrix\n\tdst = cv2.warpAffine(tmp, M, (cols, rows))\n\treturn dst","def apply_affine(A: Affine, x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\n\n shape = x.shape\n\n A = np.asarray(A).reshape(3, 3) # type: ignore[assignment]\n t = A[:2, -1].reshape((2, 1)) # type: ignore[index]\n A = A[:2, :2] # type: ignore[index]\n\n x, y = A @ np.vstack([x.ravel(), y.ravel()]) + t\n x, y = (a.reshape(shape) for a in (x, y))\n return (x, y)","def affine_field(ftr0, ftr1, cutoff=9, d2min_scale=1.0, fast=False, subset=None, dview=None):\n ftrcomp = ftr1[['particle', 'x', 'y']].join(ftr0.set_index('particle')[['x', 'y']], \n on='particle', rsuffix='0').dropna()\n NNE = NNEngine(ftrcomp, cutoff, fast=fast, subset=subset)\n return NNE._affine_field(d2min_scale=d2min_scale, dview=dview)","def get_transform(ds):\n\n if 'transform' in ds.attrs:\n ds_trans = ds.attrs['transform']\n if isinstance(ds_trans, Affine):\n return ds_trans\n else:\n return Affine(*ds_trans)\n\n elif 'crs' in ds.data_vars and 'i2m' in ds.data_vars['crs'].attrs:\n transf_str = ds.data_vars['crs'].attrs['i2m']\n a = list(map(float, transf_str.split(',')))\n return Affine(a[0], a[2], a[4], a[1], a[3], a[5])\n\n else:\n resx, resy = get_resolution(ds)\n xoff = ds['x'].values.min()\n yoff = ds['y'].values.max()\n return Affine(resx, 0, xoff, 0, resy, yoff)","def xy_to_rowcol(x, y, affine):\n # affine might be an Affine object, might be 3x3 array or\n # 9-element vector. Let's first make damn sure is a 9-element\n # vector\n aff_array = numpy.array(affine).reshape(9)\n\n # now be damn sure we have an Affine object, and reverse it to\n # go from coordinates to indices\n affine = ~Affine(*aff_array[:6])\n\n # compute the y, x values\n yx = numpy.floor(rowcol_to_xy(y, x, affine))\n\n # convert to int and flip to xy\n xy = yx.astype(int)[::-1]\n return xy","def translateY(shift: float) -> Callable:\n return lambda img: TF.affine(img, 0, (0, shift * img.size[1]), 1, 0)","def affine(img, matrix, interpolation=\"nearest\", fill=None, data_format='CHW'):\n ndim = len(img.shape)\n if ndim == 3:\n img = img.unsqueeze(0)\n\n img = img if data_format.lower() == 'chw' else img.transpose((0, 3, 1, 2))\n\n matrix = paddle.to_tensor(matrix, place=img.place)\n matrix = matrix.reshape((1, 2, 3))\n shape = img.shape\n\n grid = _affine_grid(\n matrix, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2]\n )\n\n if isinstance(fill, int):\n fill = tuple([fill] * 3)\n\n out = _grid_transform(img, grid, mode=interpolation, fill=fill)\n\n out = out if data_format.lower() == 'chw' else out.transpose((0, 2, 3, 1))\n out = out.squeeze(0) if ndim == 3 else out\n\n return out","def to_affine_nd(r, affine):\n affine_ = np.array(affine, dtype=np.float64)\n if affine_.ndim != 2:\n raise ValueError(f\"input affine matrix must have two dimensions, got {affine_.ndim}.\")\n new_affine = np.array(r, dtype=np.float64, copy=True)\n if new_affine.ndim == 0:\n sr = new_affine.astype(int)\n if not np.isfinite(sr) or sr < 0:\n raise ValueError(f\"r must be positive, got {sr}.\")\n new_affine = np.eye(sr + 1, dtype=np.float64)\n d = max(min(len(new_affine) - 1, len(affine_) - 1), 1)\n new_affine[:d, :d] = affine_[:d, :d]\n if d > 1:\n new_affine[:d, -1] = affine_[:d, -1]\n return new_affine","def __compose_transformation(self):\n s = self.scale\n rotR = self.rotation\n t = self.translation\n T = np.eye(4)\n T[0:3, 3] = t\n R = np.eye(4)\n R[0:3, 0:3] = rotR\n M = T.dot(R)\n if s == 1:\n M = T.dot(R)\n else:\n S = np.eye(4)\n S[0:3, 0:3] = np.diag([s, s, s])\n M = T.dot(R).dot(S)\n return M","def is_affine(self):\n if self.is_irreducible():\n return self._info['affine']\n else:\n return False","def affine_space(self):\n vertices = self.vertices()\n if not self.contains_origin():\n v0 = vertices[0]\n vertices = [v-v0 for v in vertices]\n return self.ambient_space().span(vertices).saturation()","def rotateAlignXform(self):\n r = np.concatenate(([self.x], [self.y], [self.z]), 0)\n r = np.concatenate((r, np.array([[0,0,0]])), 0)\n r = np.concatenate((r, np.array([0,0,0,1]).reshape(-1,1)), 1)\n return r","def get_affine(nii_file):\n\timg = nib.load(nii_file)\n\treturn img.get_affine()","def transform(self, x, y):\n # return self.transform_2D(x, y)\n return self.transform_perspective(x, y)","def recalculate_transform(self):\n bias_mat = wireframe.translationMatrix(*self.bias)\n rot_mat = wireframe.rotateMatrix(*self.view_angle)\n scale_mat = wireframe.scaleMatrix(*self.scale)\n\n self.tf_wireframe.nodes = (self.wireframe.nodes - self._center_half) @ \\\n rot_mat @ scale_mat @ bias_mat","def testTranslateAffine(self):\n affineClass = xyTransformRegistry[\"affine\"]\n affineConfig = affineClass.ConfigClass()\n affineConfig.translation = (1.2, -3.4)\n with lsst.utils.tests.getTempFilePath(\".py\") as filePath:\n self.checkConfig(affineClass, affineConfig, filePath)\n affine = affineClass(affineConfig)\n for fromPoint in self.fromIter():\n toPoint = affine.forwardTransform(fromPoint)\n predToPoint = fromPoint + Extent2D(*affineConfig.translation)\n for i in range(2):\n self.assertAlmostEqual(toPoint[i], predToPoint[i])","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n reshaped_inp = np.reshape(x,(int(x.shape[0]),int(np.prod(x.shape) / x.shape[0])))\n out = reshaped_inp.dot(w) + b\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def get_affine_transform_2d(gps_coords, pdr_coords):\n X = np.array(pdr_coords)\n Xp = np.array(gps_coords)\n\n # Estimate 2d similarity to align to GPS\n T = tf.affine_matrix_from_points(X.T[:2], Xp.T[:2], shear=False)\n s = np.linalg.det(T[:2, :2]) ** 0.5\n A = np.eye(3)\n A[:2, :2] = T[:2, :2] / s\n b = np.array([\n T[0, 2],\n T[1, 2],\n Xp[:, 2].mean() - s * X[:, 2].mean() # vertical alignment\n ])\n\n return s, A, b","def affine_forward(x, w, b):\n ############################################################################\n # TODO: Implement the affine forward pass. Store the result in 'out'. You #\n # will need to reshape the input into rows. #\n ############################################################################\n ############################################################################\n # START OF YOUR CODE #\n ############################################################################\n N = len(x)\n D,M = w.shape\n # reshape get a new x\n new_x = x.reshape(N,D)\n # get the output\n out = np.dot(new_x,w) + np.expand_dims(b,axis=0)\n \n ############################################################################\n # END OF YOUR CODE #\n ############################################################################\n return out","def affine_to_shift(affine_matrix, volshape, shift_center=True, indexing='ij'):\n\n if isinstance(volshape, (tf.compat.v1.Dimension, tf.TensorShape)):\n volshape = volshape.as_list()\n \n if affine_matrix.dtype != 'float32':\n affine_matrix = tf.cast(affine_matrix, 'float32')\n\n nb_dims = len(volshape)\n\n if len(affine_matrix.shape) == 1:\n if len(affine_matrix) != (nb_dims * (nb_dims + 1)):\n raise ValueError('transform is supposed a vector of len ndims * (ndims + 1).'\n 'Got len %d' % len(affine_matrix))\n\n affine_matrix = tf.reshape(affine_matrix, [nb_dims, nb_dims + 1])\n\n if not (affine_matrix.shape[0] in [nb_dims, nb_dims + 1] and affine_matrix.shape[1] == (nb_dims + 1)):\n shape1 = '(%d x %d)' % (nb_dims + 1, nb_dims + 1)\n shape2 = '(%d x %s)' % (nb_dims, nb_dims + 1)\n true_shape = str(affine_matrix.shape)\n raise Exception('Affine shape should match %s or %s, but got: %s' % (shape1, shape2, true_shape))\n\n # list of volume ndgrid\n # N-long list, each entry of shape volshape\n mesh = ne.utils.volshape_to_meshgrid(volshape, indexing=indexing) \n mesh = [tf.cast(f, 'float32') for f in mesh]\n \n if shift_center:\n mesh = [mesh[f] - (volshape[f]-1)/2 for f in range(len(volshape))]\n\n # add an all-ones entry and transform into a large matrix\n flat_mesh = [ne.utils.flatten(f) for f in mesh]\n flat_mesh.append(tf.ones(flat_mesh[0].shape, dtype='float32'))\n mesh_matrix = tf.transpose(tf.stack(flat_mesh, axis=1)) # 4 x nb_voxels\n\n # compute locations\n loc_matrix = tf.matmul(affine_matrix, mesh_matrix) # N+1 x nb_voxels\n loc_matrix = tf.transpose(loc_matrix[:nb_dims, :]) # nb_voxels x N\n loc = tf.reshape(loc_matrix, list(volshape) + [nb_dims]) # *volshape x N\n # loc = [loc[..., f] for f in range(nb_dims)] # N-long list, each entry of shape volshape\n\n # get shifts and return\n return loc - tf.stack(mesh, axis=nb_dims)","def _transform(\n self, x: \"torch.Tensor\", y: Optional[\"torch.Tensor\"], **kwargs\n ) -> Tuple[\"torch.Tensor\", Optional[\"torch.Tensor\"]]:\n import torch\n import torchvision.transforms.functional as F\n\n img_size = x.shape[:2]\n\n angle = float(\n torch.empty(1)\n .uniform_(float(self.degree_range[0]), float(self.degree_range[1]))\n .item()\n )\n\n max_dx = float(self.translate[0] * img_size[1])\n max_dy = float(self.translate[1] * img_size[0])\n tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))\n ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))\n translations = (tx, ty)\n\n scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())\n\n # x needs to have channel first\n x = x.permute(2, 0, 1)\n x = F.affine(\n img=x, angle=angle, translate=translations, scale=scale, shear=(0.0, 0.0)\n )\n x = x.permute(1, 2, 0)\n\n return torch.clamp(x, min=self.clip_values[0], max=self.clip_values[1]), y"],"string":"[\n \"def transform(self,image,landmarks,s0=None):\\n if s0 is None:\\n s0 = np.array([[127.6475, 227.8161], [79.1608, 87.0376], [176.8392, 87.0376]], np.float32)\\n idx = [8,36,45] #\\\"\\\"\\\"Anchor points\\\"\\\"\\\"\\n pts = np.float32(landmarks[idx,:])\\n M = cv2.getAffineTransform(pts,s0)\\n dst = cv2.warpAffine(image, M, (256,256))\\n return dst\",\n \"def transformAffine(self, path=None, src=None, dst=None):\\n if path is not None:\\n landmarks = pd.read_csv(path, skiprows=1,engine=\\\"c\\\", na_filter=False, header=None, delim_whitespace=True, dtype=np.float32).as_matrix()\\n dst = landmarks[:,3:5]\\n src = landmarks[:,1:3]\\n affine = transform.estimate_transform(\\\"affine\\\",src,dst)\\n data = self.stormData[0][:,0:2]\\n data = affine(data)\\n self.stormData[0][:,0:2] = data\",\n \"def transformAffine(self, coords):\\n coordsshape = coords.shape\\n dims = coordsshape[0] + 1\\n coords = coords.reshape((len(coords), -1))\\n coords = np.concatenate((coords, np.ones((1, len(coords[0])))), 0)\\n affine = np.eye(dims)\\n # now transform first to center:\\n meanvec = np.mean(coords, 1)\\n center = np.eye(dims)\\n center[:-1, -1] = -meanvec[:-1]\\n affine = np.matmul(center, affine)\\n\\n if np.sum(self.shift):\\n affine[:-1, -1] += (self.deformrandomstate.rand(dims - 1) - 0.5) * np.float32(self.shift)\\n if np.max(self.scaling) > 1:\\n scales = np.ones(dims)\\n # scales[:-1] = (self.deformrandomstate.rand(dims-1)-0.5)*(self.scaling-1.0/self.scaling)+(self.scaling+1/self.scaling)/2\\n scales[:-1] = self.scaling ** (self.deformrandomstate.rand(dims - 1) * 2 - 1)\\n scales = np.diag(scales)\\n # print(scales)\\n affine = np.matmul(scales, affine)\\n if np.sum(self.rotation):\\n affine = self._rotate(affine)\\n # move back to location:\\n center[:-1, -1] = -center[:-1, -1]\\n affine = np.matmul(center, affine)\\n # now appyl to coords:\\n coords = np.matmul(affine, coords)\\n coords = coords[:-1]\\n coords = coords.reshape(coordsshape)\\n return coords\",\n \"def calc_affine(df):\\n\\tx0 = df.columns[0]\\n\\ty0 = df.index[0]\\n\\tdx = df.columns[1] - df.columns[0]\\n\\tdy = df.index[1] - df.index[0]\\n\\t\\n\\tt = affine.Affine(dx, 0, x0 , 0, dy ,y0 - dy) \\n\\t# y0 - dy because anker point is in the south!\\n\\treturn t\",\n \"def __affine_geo_transformation(x, y, gtr):\\n\\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\\n # Affine transformation rewritten for rasterio:\\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\\n\\n return gtr_x, gtr_y\",\n \"def affine(self):\\n return self._affine\",\n \"def affine(self):\\n return self._affine\",\n \"def get_affine(x, m, c):\\n x = m*x + c\\n return x\",\n \"def affine_mult(affine, coordinates):\\n return np.dot(coordinates, affine[:3, :3].T) + affine[:3, -1]\",\n \"def affine(params, x):\\n return np.dot(params['w'], x) + params['b']\",\n \"def getAffineTransform(self, coord1, coord2):\\n num_coords = 2 * len(coord1)\\n A = np.zeros((num_coords, 6))\\n b = []\\n for point2 in coord2:\\n b.append(float(point2[0]))\\n b.append(float(point2[1]))\\n b = np.asarray(b)\\n i = 0\\n for point1 in coord1:\\n A[i, 0:2] = point1[0:2]\\n A[i, 2] = 1\\n A[i+1, 3:5] = point1[0:2]\\n A[i+1, 5] = 1\\n i += 2\\n A = np.asarray(A)\\n b = np.asarray(b)\\n x = np.matmul(np.matmul(np.linalg.inv(np.matmul(A.T, A)), A.T), b.T)\\n self.depth2rgb_affine = np.reshape(x, (2, 3))\\n csv.writer(open(\\\"depth2rgb_affine.cfg\\\", \\\"w+\\\", newline=''), delimiter=',').writerows(self.depth2rgb_affine)\\n # else:\\n # x = np.vstack([np.reshape(x,(2,3)),[0,0,1]])\\n # self.cam_ext_mat = x\\n # A = [point[i,j+0:j+3].astype(np.float32) for i,point in enumerate(coord1) if i%2 == 0]\\n # pts1 = coord1[0:3].astype(np.float32)\\n # pts2 = coord2[0:3].astype(np.float32)\\n # print(cv2.getAffineTransform(pts1, pts2))\\n # return cv2.getAffineTransform(pts1, pts2)\",\n \"def affine_trans(self):\\n h, w, _ = self.img.shape\\n\\n \\\"\\\"\\\"\\n pts1 = np.float32(\\n [\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n ]\\n )\\n pts2 = np.float32(\\n [\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n ]\\n )\\n \\\"\\\"\\\"\\n\\n pts1 = np.float32([[50, 50], [200, 50], [50, 200]])\\n pts2 = np.float32([[10, 100], [200, 50], [100, 250]])\\n\\n M = cv2.getAffineTransform(pts1, pts2)\\n\\n self.img = cv2.warpAffine(self.img, M, (w, h))\\n\\n self.edits.append(\\\"affine\\\")\\n return self\",\n \"def read_affine(df):\\n SliceThickness = [df.SliceThickness]\\n PixelSpacing = _string_to_list_of_floats(df.PixelSpacing)\\n ImageOrientationPatient = _string_to_list_of_floats(df.ImageOrientationPatient)\\n ImagePositionPatient = _string_to_list_of_floats(df.ImagePositionPatient)\\n\\n Zooms = np.array(PixelSpacing+SliceThickness, dtype=float)\\n ImageOrientationPatient = np.array(ImageOrientationPatient, dtype=float)\\n ImagePositionPatient = np.array(ImagePositionPatient, dtype=float)\\n \\n ijk2ras = extract_cosines(ImageOrientationPatient)\\n\\n ijk2ras = (ijk2ras*np.array([-1,-1,1])).T\\n ImagePositionPatient = ImagePositionPatient*np.array([-1,-1,1])\\n\\n affine = np.stack((ijk2ras[:,0]*Zooms[0],\\n ijk2ras[:,1]*Zooms[1],\\n ijk2ras[:,2]*Zooms[2],\\n ImagePositionPatient), axis=1)\\n\\n return np.vstack((affine,[[0,0,0,1]]))\",\n \"def affine(self) -> torch.Tensor:\\n return self.meta.get(MetaKeys.AFFINE, self.get_default_affine())\",\n \"def __call__(self, affine, representations_list, aatype):\\n act = [\\n common_modules.Linear( # pylint: disable=g-complex-comprehension\\n self.config.num_channel,\\n name='input_projection')(jax.nn.relu(x))\\n for x in representations_list\\n ]\\n # Sum the activation list (equivalent to concat then Linear).\\n act = sum(act)\\n\\n final_init = 'zeros' if self.global_config.zero_init else 'linear'\\n\\n # Mapping with some residual blocks.\\n for _ in range(self.config.num_residual_block):\\n old_act = act\\n act = common_modules.Linear(\\n self.config.num_channel,\\n initializer='relu',\\n name='resblock1')(\\n jax.nn.relu(act))\\n act = common_modules.Linear(\\n self.config.num_channel,\\n initializer=final_init,\\n name='resblock2')(\\n jax.nn.relu(act))\\n act += old_act\\n\\n # Map activations to torsion angles. Shape: (num_res, 14).\\n num_res = act.shape[0]\\n unnormalized_angles = common_modules.Linear(\\n 14, name='unnormalized_angles')(\\n jax.nn.relu(act))\\n unnormalized_angles = jnp.reshape(\\n unnormalized_angles, [num_res, 7, 2])\\n angles = l2_normalize(unnormalized_angles, axis=-1)\\n\\n outputs = {\\n 'angles_sin_cos': angles, # jnp.ndarray (N, 7, 2)\\n 'unnormalized_angles_sin_cos':\\n unnormalized_angles, # jnp.ndarray (N, 7, 2)\\n }\\n\\n # Map torsion angles to frames.\\n backb_to_global = r3.rigids_from_quataffine(affine)\\n\\n # Jumper et al. (2021) Suppl. Alg. 24 \\\"computeAllAtomCoordinates\\\"\\n\\n # r3.Rigids with shape (N, 8).\\n all_frames_to_global = all_atom.torsion_angles_to_frames(\\n aatype,\\n backb_to_global,\\n angles)\\n\\n # Use frames and literature positions to create the final atom coordinates.\\n # r3.Vecs with shape (N, 14).\\n pred_positions = all_atom.frames_and_literature_positions_to_atom14_pos(\\n aatype, all_frames_to_global)\\n\\n outputs.update({\\n 'atom_pos': pred_positions, # r3.Vecs (N, 14)\\n 'frames': all_frames_to_global, # r3.Rigids (N, 8)\\n })\\n return outputs\",\n \"def convert_affine(ref, t, out):\\n args = [\\n transform_exe,\\n '-d', '3',\\n '-r', ref,\\n '-t', '[{},0]'.format(t),\\n '-o', '[{},1]'.format(out),\\n '--float'\\n ]\\n subprocess.check_call(args)\",\n \"def get_affine_transform(gps_coords, pdr_coords):\\n # Compute similarity Xp = s A X + b\\n X = np.array(pdr_coords)\\n Xp = np.array(gps_coords)\\n T = tf.superimposition_matrix(X.T, Xp.T, scale=True)\\n\\n A, b = T[:3, :3], T[:3, 3]\\n s = np.linalg.det(A)**(1. / 3)\\n A /= s\\n return s, A, b\",\n \"def apply_affine_transform(x, M):\\n is1d = len(x.shape) == 1\\n if is1d:\\n x = np.expand_dims(x, axis=0)\\n\\n x_hom = np.concatenate(\\n [x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=-1\\n )\\n x_out = x_hom @ M.T\\n if is1d:\\n x_out = np.squeeze(x_out, axis=0)\\n return x_out\",\n \"def getAffineTransform(self, coord1, coord2):\\n # generate coord1 into A\\n mat_A = np.zeros((2*coord1.shape[0], 6))\\n coord1 = np.hstack([coord1, np.ones((coord1.shape[0], 1))])\\n for i in range(coord1.shape[0]):\\n row = coord1[i,:]\\n row_block = block_diag(row, row)\\n assert(row_block.shape == (2,6))\\n mat_A[2*i:2*i+2, :] = row_block\\n \\n # generate coord2 into b\\n vec_b = coord2.reshape(-1,1)\\n\\n # solve the least square\\n pseudo_inv = np.linalg.inv(np.matmul(mat_A.T, mat_A))\\n pseudo_inv = np.matmul(pseudo_inv, mat_A.T)\\n affine_mat = np.matmul(pseudo_inv, vec_b)\\n assert(affine_mat.shape == (6,1))\\n \\n return affine_mat.reshape(2,-1)\",\n \"def transform(self) -> Affine:\\n transform = (\\n Affine.translation(*self.origin)\\n * Affine.rotation(self.rotation)\\n * Affine.scale(*self.res)\\n )\\n return transform\",\n \"def extract_affine(self):\\n self.affine = self.ecat.affine.tolist()\",\n \"def affine_map(self, W, b):\\n self.base_vertices = np.dot(W, self.base_vertices) + b\\n self.base_vectors = np.dot(W, self.base_vectors)\",\n \"def affine_matrix(self) -> np.ndarray:\\n return self._forward_link._inverse_tf_matrix\",\n \"def _get_affine_matrix(center, angle, translate, scale, shear):\\n angle = np.deg2rad(angle)\\n shear = np.deg2rad(shear)\\n C = np.matrix([[1., 0., center[0]],\\n [0., 1., center[1]],\\n [0., 0., 1.]])\\n T = np.matrix([[1., 0., translate[0]],\\n [0., 1., translate[1]],\\n [0., 0., 1.]])\\n RSS = np.matrix([[np.cos(angle), -np.sin(angle + shear), 0.],\\n [np.sin(angle), np.cos(angle + shear), 0.],\\n [ 0., 0., 1./scale]]) * scale\\n M = T * C * RSS * np.linalg.inv(C)\\n return M\",\n \"def affineTransform(x,output_dim):\\n w=tf.get_variable(\\\"w\\\", [x.get_shape()[1], output_dim])\\n b=tf.get_variable(\\\"b\\\", [output_dim], initializer=tf.constant_initializer(0.0))\\n return tf.matmul(x,w)+b\",\n \"def affine_transform(trans_mat, p0):\\r\\n n_data, n_dim = np.shape(p0)\\r\\n p0 = np.hstack((p0, np.ones((n_data, 1))))\\r\\n #return np.transpose(np.dot(np.transpose(trans_mat), np.transpose(p0)))\\r\\n return np.dot(p0, trans_mat)\",\n \"def apply_transform_to_image(self,img, transform, center=None):\\n \\n if center is None:\\n center = (np.array(img.shape)[::-1]-1)/2.0\\n \\n displacement = np.dot(transform, center)\\n shift = center - displacement\\n \\n img_tf = ndimage.interpolation.affine_transform(img, transform, offset=shift, mode=\\\"constant\\\", order=3, cval=0.0)\\n return img_tf\",\n \"def AffineTransform( from_pts, to_pts ):\\n \\n # check that there are match points\\n if len(from_pts) != len(to_pts) or len(to_pts)<1:\\n print \\\"from_pts and to_pts must be of same size.\\\"\\n return False\\n\\n # check the dimensions\\n dim = len(from_pts[0]) # num of dimensions\\n if len(from_pts) < dim:\\n print \\\"Too few points => under-determined system.\\\"\\n return False\\n elif len(from_pts) > dim + 1:\\n print \\\"Too many points => over-determined system.\\\"\\n return False\\n\\n \\n #segregate the x and y coordinages\\n from_pts_x, from_pts_y = zip(*from_pts)\\n to_pts_x, to_pts_y = zip(*to_pts)\\n \\n #create the Matricies for processing\\n I = np.matrix([from_pts_x, from_pts_y, [1,1,1]])\\n P = np.matrix([to_pts_x, to_pts_y])\\n \\n #Calculate the 2D affine transform matrix (A)\\n A = P * linalg.pinv(I) \\n\\n # Make a result object\\n class Transformation:\\n \\\"\\\"\\\"Result object that represents the transformation\\n from affine fitter.\\\"\\\"\\\"\\n\\n def To_Str(self):\\n res = \\\"\\\"\\n for j in range(dim):\\n str1 = \\\"x%d' = \\\" % j\\n for i in range(dim):\\n str1 +=\\\"x%d * %f + \\\" % (i, A[i][j+dim+1])\\n str1 += \\\"%f\\\" % A[dim][j+dim+1]\\n res += str1 + \\\"\\\\n\\\"\\n return res\\n\\n def Transform(self, pt_x, pt_y):\\n pt_vector = np.matrix([[pt_x], [pt_y], [1]])\\n transformed_pt = A * pt_vector\\n return map(itemgetter(0), transformed_pt.tolist())\\n return Transformation()\",\n \"def register_landmarks(fname_src, fname_dest, dof, fname_affine='affine.txt', verbose=1, path_qc='./'):\\n from msct_image import Image\\n # open src label\\n im_src = Image(fname_src)\\n # coord_src = im_src.getNonZeroCoordinates(sorting='value') # landmarks are sorted by value\\n coord_src = im_src.getCoordinatesAveragedByValue() # landmarks are sorted by value\\n # open dest labels\\n im_dest = Image(fname_dest)\\n # coord_dest = im_dest.getNonZeroCoordinates(sorting='value')\\n coord_dest = im_dest.getCoordinatesAveragedByValue()\\n # Reorganize landmarks\\n\\n points_src, points_dest = [], []\\n for coord in coord_src:\\n point_src = im_src.transfo_pix2phys([[coord.x, coord.y, coord.z]])\\n # convert NIFTI to ITK world coordinate\\n # points_src.append([point_src[0][0], point_src[0][1], point_src[0][2]])\\n points_src.append([-point_src[0][0], -point_src[0][1], point_src[0][2]])\\n for coord in coord_dest:\\n point_dest = im_dest.transfo_pix2phys([[coord.x, coord.y, coord.z]])\\n # convert NIFTI to ITK world coordinate\\n # points_dest.append([point_dest[0][0], point_dest[0][1], point_dest[0][2]])\\n points_dest.append([-point_dest[0][0], -point_dest[0][1], point_dest[0][2]])\\n\\n # display\\n sct.printv('Labels src: ' + str(points_src), verbose)\\n sct.printv('Labels dest: ' + str(points_dest), verbose)\\n sct.printv('Degrees of freedom (dof): ' + dof, verbose)\\n\\n if len(coord_src) != len(coord_dest):\\n raise Exception('Error: number of source and destination landmarks are not the same, so landmarks cannot be paired.')\\n\\n # estimate transformation\\n # N.B. points_src and points_dest are inverted below, because ITK uses inverted transformation matrices, i.e., src->dest is defined in dest instead of src.\\n # (rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = getRigidTransformFromLandmarks(points_dest, points_src, constraints=dof, verbose=verbose, path_qc=path_qc)\\n (rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = getRigidTransformFromLandmarks(points_src, points_dest, constraints=dof, verbose=verbose, path_qc=path_qc)\\n # writing rigid transformation file\\n # N.B. x and y dimensions have a negative sign to ensure compatibility between Python and ITK transfo\\n text_file = open(fname_affine, 'w')\\n text_file.write(\\\"#Insight Transform File V1.0\\\\n\\\")\\n text_file.write(\\\"#Transform 0\\\\n\\\")\\n text_file.write(\\\"Transform: AffineTransform_double_3_3\\\\n\\\")\\n text_file.write(\\\"Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\\\\n\\\" % (\\n rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],\\n rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2],\\n rotation_matrix[2, 0], rotation_matrix[2, 1], rotation_matrix[2, 2],\\n translation_array[0, 0], translation_array[0, 1], translation_array[0, 2]))\\n text_file.write(\\\"FixedParameters: %.9f %.9f %.9f\\\\n\\\" % (points_moving_barycenter[0],\\n points_moving_barycenter[1],\\n points_moving_barycenter[2]))\\n text_file.close()\",\n \"def affine(self, image, random_state):\\n shape = image.shape\\n shape_size = shape[:2]\\n transform_std = self.alpha_affine*image.shape[1]\\n\\n center_square = np.float32(shape[:2]) // 2\\n square_size = min(shape[:2]) // 3\\n\\n source = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\\n destination = source + random_state.uniform(-transform_std, transform_std, size=source.shape).astype(np.float32)\\n M = cv2.getAffineTransform(source, destination)\\n\\n return cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE)\",\n \"def affine(img, angle, translate, scale, shear):\\n if not _is_numpy(img):\\n raise TypeError('img should be Numpy Image. Got {}'.format(type(img)))\\n\\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\\\\n \\\"Argument translate should be a list or tuple of length 2\\\"\\n\\n assert scale > 0.0, \\\"Argument scale should be positive\\\"\\n\\n aug = iaa.Affine(scale=scale, rotate=angle, translate_px=translate, shear=shear)\\n return aug.augment_image(img)\",\n \"def map(self,Affine,i):\\n map_x = np.zeros([self.num,self.d])\\n for k in range(self.num):\\n map_x[k,:] = Affine.apply(i,self.pick(k))\\n Mapped = Model_Points(map_x)\\n return Mapped\",\n \"def gen_affine_map(Ab, img_sz, dim=3):\\n Ab = Ab.view(Ab.shape[0], dim+1, dim)\\n phi = gen_identity_map(img_sz).to(Ab.device)\\n phi_cp = phi.view(dim, -1)\\n affine_map = torch.matmul(Ab[:, :dim, :], phi_cp)\\n affine_map = Ab[:, dim, :].contiguous().view(-1, dim, 1) + affine_map\\n affine_map = affine_map.view([Ab.shape[0]] + list(phi.shape))\\n return affine_map\",\n \"def apply_affine_transform(self, M):\\n vertices = []\\n for x,y in self.get_vertices():\\n vertices.append(tuple(np.dot(M, [x, y, 1])[:2]))\\n return type(self)(vertices, properties=self.properties, crs=self.crs)\",\n \"def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1,\\n row_axis=0, col_axis=1, channel_axis=2,\\n fill_mode='nearest', cval=0., order=1):\\n if scipy is None:\\n raise ImportError('Image transformations require SciPy. '\\n 'Install SciPy.')\\n transform_matrix = None\\n if theta != 0:\\n theta = np.deg2rad(theta)\\n rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],\\n [np.sin(theta), np.cos(theta), 0],\\n [0, 0, 1]])\\n transform_matrix = rotation_matrix\\n\\n if tx != 0 or ty != 0:\\n shift_matrix = np.array([[1, 0, tx],\\n [0, 1, ty],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = shift_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, shift_matrix)\\n\\n if shear != 0:\\n shear = np.deg2rad(shear)\\n shear_matrix = np.array([[1, -np.sin(shear), 0],\\n [0, np.cos(shear), 0],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = shear_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, shear_matrix)\\n\\n if zx != 1 or zy != 1:\\n zoom_matrix = np.array([[zx, 0, 0],\\n [0, zy, 0],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = zoom_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, zoom_matrix)\\n\\n if transform_matrix is not None:\\n h, w = x.shape[row_axis], x.shape[col_axis]\\n transform_matrix = transform_matrix_offset_center(\\n transform_matrix, h, w)\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n\\n channel_images = [ndimage.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=order,\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def affine_matrix(self) -> np.ndarray:\\n return self._tf_matrix\",\n \"def update_affine_param( cur_af, last_af): # A2(A1*x+b1) + b2 = A2A1*x + A2*b1+b2\\n cur_af = cur_af.view(cur_af.shape[0], 4, 3)\\n last_af = last_af.view(last_af.shape[0],4,3)\\n updated_af = torch.zeros_like(cur_af.data).to(cur_af.device)\\n dim =3\\n updated_af[:,:3,:] = torch.matmul(cur_af[:,:3,:],last_af[:,:3,:])\\n updated_af[:,3,:] = cur_af[:,3,:] + torch.squeeze(torch.matmul(cur_af[:,:3,:], torch.transpose(last_af[:,3:,:],1,2)),2)\\n updated_af = updated_af.contiguous().view(cur_af.shape[0],-1)\\n return updated_af\",\n \"def affine_forward(x, w, b):\\n #raise NotImplementedError\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\\n out=np.dot(x,w)+b\\n cache=(x,w,b)\\n return(out, cache)\",\n \"def register_affine(self):\\n try:\\n safe_execute_command(self._prepare_affine_reg_cmd(),\\n self.affine_log_file_path, self.affine_error_path)\\n except SafeExecuteCommandError as err:\\n raise RegistrationError('Affine registration failed; {}'.format(err))\",\n \"def orientation_ras_lps(affine: NdarrayTensor) -> NdarrayTensor:\\n sr = max(affine.shape[0] - 1, 1) # spatial rank is at least 1\\n flip_d = [[-1, 1], [-1, -1, 1], [-1, -1, 1, 1]]\\n flip_diag = flip_d[min(sr - 1, 2)] + [1] * (sr - 3)\\n if isinstance(affine, torch.Tensor):\\n return torch.diag(torch.as_tensor(flip_diag).to(affine)) @ affine # type: ignore\\n return np.diag(flip_diag).astype(affine.dtype) @ affine # type: ignore\",\n \"def translateToOriginXform(self):\\n return np.array([[1, 0, 0, -self.eye[0]],\\n [0, 1, 0, -self.eye[1]],\\n [0, 0, 1, -self.eye[2]],\\n [0, 0, 0, 1]])\",\n \"def affine_transform(x, output_dim, name=None):\\n\\n w = tf.get_variable(name + \\\"_w\\\", [x.get_shape()[1], output_dim], initializer=tf.truncated_normal_initializer(stddev=0.02))\\n b = tf.get_variable(name + \\\"_b\\\", [output_dim], initializer=tf.constant_initializer(0.0))\\n\\n return tf.matmul(x, w) + b\",\n \"def augment_translate(image, mask, trans_range):\\n\\n tr_x = trans_range * np.random.uniform() - trans_range / 2\\n tr_y = trans_range * np.random.uniform() - trans_range / 2\\n trans_M = np.array([[1, 0, tr_x], [0, 1, tr_y]], dtype=np.float32)\\n width, height, _ = image.shape\\n image = cv2.warpAffine(image, trans_M, (width, height))\\n if mask is not None:\\n mask = cv2.warpAffine(mask, trans_M, (width, height))\\n return image, mask\",\n \"def getAffineTransform_deprecated(self, coord1, coord2):\\n pts1 = coord1[0:3].astype(np.float32)\\n pts2 = coord2[0:3].astype(np.float32)\\n return cv2.getAffineTransform(pts1, pts2)\",\n \"def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1):\\n # transform_matrix = transform_matrix_offset_center()\\n # asdihasid\\n # asd\\n\\n x = np.rollaxis(x, channel_index, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [\\n ndi.interpolation.affine_transform(\\n x_channel, final_affine_matrix, final_offset, order=order, mode=fill_mode, cval=cval\\n ) for x_channel in x\\n ]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_index + 1)\\n return x\",\n \"def affine_forward(x, W, b):\\r\\n x2d = np.reshape(x, (x.shape[0], -1)) # convert 4D input matrix to 2D \\r\\n out = np.dot(x2d, W) + b # linear transformation\\r\\n cache = (x, W, b) # keep for backward step (stay with us)\\r\\n return out, cache\",\n \"def affine_transform(geom, matrix):\\n if geom.is_empty:\\n return geom\\n if len(matrix) == 6:\\n ndim = 2\\n a, b, d, e, xoff, yoff = matrix\\n if geom.has_z:\\n ndim = 3\\n i = 1.0\\n c = f = g = h = zoff = 0.0\\n matrix = a, b, c, d, e, f, g, h, i, xoff, yoff, zoff\\n elif len(matrix) == 12:\\n ndim = 3\\n a, b, c, d, e, f, g, h, i, xoff, yoff, zoff = matrix\\n if not geom.has_z:\\n ndim = 2\\n matrix = a, b, d, e, xoff, yoff\\n else:\\n raise ValueError(\\\"'matrix' expects either 6 or 12 coefficients\\\")\\n\\n def affine_pts(pts):\\n \\\"\\\"\\\"Internal function to yield affine transform of coordinate tuples\\\"\\\"\\\"\\n if ndim == 2:\\n for x, y in pts:\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n yield (xp, yp)\\n elif ndim == 3:\\n for x, y, z in pts:\\n xp = a * x + b * y + c * z + xoff\\n yp = d * x + e * y + f * z + yoff\\n zp = g * x + h * y + i * z + zoff\\n yield (xp, yp, zp)\\n\\n # Process coordinates from each supported geometry type\\n if geom.type in ('Point', 'LineString', 'LinearRing'):\\n return type(geom)(list(affine_pts(geom.coords)))\\n elif geom.type == 'Polygon':\\n ring = geom.exterior\\n shell = type(ring)(list(affine_pts(ring.coords)))\\n holes = list(geom.interiors)\\n for pos, ring in enumerate(holes):\\n holes[pos] = type(ring)(list(affine_pts(ring.coords)))\\n return type(geom)(shell, holes)\\n elif geom.type.startswith('Multi') or geom.type == 'GeometryCollection':\\n # Recursive call\\n # TODO: fix GeometryCollection constructor\\n return type(geom)([affine_transform(part, matrix)\\n for part in geom.geoms])\\n else:\\n raise ValueError('Type %r not recognized' % geom.type)\",\n \"def encode_affine(msg, a, b):\\n \\n #Code to numbers\\n encoded_message = [ RVALUES[(a * VALUES[i] + b) % 26] for i in msg ]\\n \\n return ''.join(encoded_message)\",\n \"def read_affine(file):\\n data = open(file, 'r').read()\\n data = data.split('\\\\n')\\n for i in range(1, 5):\\n data[i] = data[i].split(':')\\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\\n Nlon = len(int_lon) - 1\\n Nlat = len(int_lat) - 1\\n data[3][1] = data[3][1].split(',')\\n data[4][1] = data[4][1].split(',')\\n lon_transform = np.zeros((Nlon, 2))\\n lat_transform = np.zeros((Nlat, 2))\\n for i in range(Nlon):\\n data[3][1][i] = data[3][1][i].split(' ')\\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\\n for i in range(Nlat):\\n data[4][1][i] = data[4][1][i].split(' ')\\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\\n lon_transform = np.array(lon_transform).astype('float')\\n lat_transform = np.array(lat_transform).astype('float')\\n return int_lon, int_lat, lon_transform, lat_transform\",\n \"def affine(img, angle, translate, scale, shear, resample=0, fillcolor=None, border_mode=None):\\n check_type(img)\\n\\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\\\\n \\\"Argument translate should be a list or tuple of length 2\\\"\\n\\n assert scale > 0.0, \\\"Argument scale should be positive\\\"\\n\\n output_size = img.size\\n center = (img.size[0] * 0.5 + 0.5, img.size[1] * 0.5 + 0.5)\\n matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)\\n kwargs = {\\\"fillcolor\\\": fillcolor} if PILLOW_VERSION[0] == '5' else {}\\n return img.transform(output_size, Image.AFFINE, matrix, resample, **kwargs)\",\n \"def AffineTz(dist):\\n return np.mat([[1, 0, 0, 0],\\n [0, 1, 0, 0],\\n [0, 0, 1, dist],\\n [0, 0, 0, 1]\\n ],\\n dtype='f8'\\n )\",\n \"def affine_transform(initialize=False, fixed=None, moments=True) :\\n \\n transform = itk.AffineTransform[itk.D, fixed.ndim].New()\\n \\n if initialize :\\n fixed_itk = medipy.itk.medipy_image_to_itk_image(fixed, False)\\n #moving_itk = medipy.itk.medipy_image_to_itk_image(moving, False)\\n\\n fixedIndex = fixed_itk.GetLargestPossibleRegion().GetIndex()\\n fixedSize = fixed_itk.GetLargestPossibleRegion().GetSize()\\n\\n centerIndex = (int(fixedIndex[0] + fixedSize[0] / 2.0), \\\\\\n int(fixedIndex[1] + fixedSize[1] / 2.0), \\\\\\n int(fixedIndex[2] + fixedSize[2] / 2.0))\\n\\n rotationCenter = fixed_itk.TransformIndexToPhysicalPoint(centerIndex)\\n\\n transform.SetIdentity()\\n transform.SetCenter(rotationCenter)\\n\\n #initial_transform = itk.VersorRigid3DTransform[itk.D].New()\\n #initializer = itk.CenteredTransformInitializer[\\n # initial_transform, fixed_itk, moving_itk].New(\\n # Transform=initial_transform, FixedImage=fixed_itk, MovingImage=moving_itk) \\n #if moments :\\n # initializer.MomentsOn()\\n #else :\\n # initializer.GeometryOn()\\n #initializer.InitializeTransform()\\n #transform.SetCenter(initial_transform.GetCenter())\\n #transform.SetOffset(initial_transform.GetOffset())\\n \\n return transform\",\n \"def _compute_affine_cropper(bounding_box: BB, width_raw: int, height_raw: int) -> torch.Tensor: \\n kx = (-1.0 + 2 * bounding_box.bx / width_raw).reshape(-1, 1)\\n ky = (-1.0 + 2 * bounding_box.by / height_raw).reshape(-1, 1)\\n sx = (bounding_box.bw / width_raw).reshape(-1, 1)\\n sy = (bounding_box.bh / height_raw).reshape(-1, 1)\\n zero = torch.zeros_like(kx)\\n\\n return torch.cat((sy, zero, ky, zero, sx, kx), dim=-1).reshape(-1, 2, 3)\",\n \"def affine_computation(x, offset=None):\\n if offset is not None:\\n new_offset = x.mean(axis=1)\\n y = 2 * x\\n if offset is not None:\\n y = y + offset[:, None]\\n return y, new_offset\\n return y\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n t = np.reshape(x,(x.shape[0],np.prod(np.shape(x)[1:])))\\n \\n\\n out = np.dot(t,w) + b\\n \\n #print(np.shape(out))\\n\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def estimate_stage_affine(t0, t1):\\n src = np.array([t.tforms[0].translation for t in t0])\\n dst = np.array([t.tforms[1].translation for t in t1])\\n aff = renderapi.transform.AffineModel()\\n aff.estimate(src, dst)\\n return aff\",\n \"def small_transf(x, y, a, xc, yc, ac, iref):\\n x += xc - x[iref]\\n y += yc - y[iref]\\n x, y = rotate_xy(x, y, x[iref], y[iref], ac - a[iref])\\n a += ac - a[iref]\\n return x, y, a\",\n \"def translateX(shift: float) -> Callable:\\n return lambda img: TF.affine(img, 0, (shift * img.size[0], 0), 1, 0)\",\n \"def affine_forward(X, W, b):\\n return np.dot(X, W) + b\",\n \"def augmentAffine(img_in, seg_in, strength=0.05):\\n B,C,D,H,W = img_in.size()\\n affine_matrix = (torch.eye(3,4).unsqueeze(0) + torch.randn(B, 3, 4) * strength).to(img_in.device)\\n\\n meshgrid = F.affine_grid(affine_matrix,torch.Size((B,1,D,H,W)))\\n\\n img_out = F.grid_sample(img_in, meshgrid,padding_mode='border')\\n seg_out = F.grid_sample(seg_in.float().unsqueeze(1), meshgrid, mode='nearest').long().squeeze(1)\\n\\n return img_out, seg_out\",\n \"def affine(img, angle=0, translate=(0, 0), scale=1, shear=0, resample='BILINEAR', fillcolor=(0,0,0)):\\n if not _is_numpy_image(img):\\n raise TypeError('img should be CV Image. Got {}'.format(type(img)))\\n\\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\\\\n \\\"Argument translate should be a list or tuple of length 2\\\"\\n\\n assert scale > 0.0, \\\"Argument scale should be positive\\\"\\n\\n rows, cols, _ = img.shape\\n center = (cols * 0.5, rows * 0.5)\\n angle = math.radians(angle)\\n shear = math.radians(shear)\\n M00 = math.cos(angle)*scale\\n M01 = -math.sin(angle+shear)*scale\\n M10 = math.sin(angle)*scale\\n M11 = math.cos(angle+shear)*scale\\n M02 = center[0] - center[0]*M00 - center[1]*M01 + translate[0]\\n M12 = center[1] - center[0]*M10 - center[1]*M11 + translate[1]\\n affine_matrix = np.array([[M00, M01, M02], [M10, M11, M12]], dtype=np.float32)\\n dst_img = cv2.warpAffine(img, affine_matrix, (cols, rows), flags=INTER_MODE[resample],\\n borderMode=cv2.BORDER_CONSTANT, borderValue=fillcolor)\\n return dst_img\",\n \"def _inverse_affine_matrix(self) -> np.ndarray:\\n raise NotImplementedError\",\n \"def AffineRx(theta, units='deg'):\\n if units == 'deg':\\n theta = np.deg2rad(theta)\\n\\n return np.mat([[1, 0, 0, 0],\\n [0, np.cos(theta), -np.sin(theta), 0],\\n [0, np.sin(theta), np.cos(theta), 0],\\n [0, 0, 0, 1]\\n ],\\n dtype='f8'\\n )\",\n \"def rotMatToAffine(rotmat, origin=None):\\n return compose([1, 1, 1], [0, 0, 0], rotmat, origin)\",\n \"def rotMatToAffine(rotmat, origin=None):\\n return compose([1, 1, 1], [0, 0, 0], rotmat, origin)\",\n \"def _mri_landmarks_to_mri_voxels(mri_landmarks, t1_mgh):\\n # Get landmarks in voxel space, using the T1 data\\n vox2ras_tkr_t = t1_mgh.header.get_vox2ras_tkr()\\n ras_tkr2vox_t = linalg.inv(vox2ras_tkr_t)\\n vox_landmarks = apply_trans(ras_tkr2vox_t, mri_landmarks)\\n\\n return vox_landmarks\",\n \"def transform(self, affine_transformation):\\n for i in range(len(self.poses)):\\n self.poses[i].transform(affine_transformation)\",\n \"def get_affine_reg_params(self):\\n affine_params = [\\n self.affine_reg_pyramid_steps,\\n self.affine_reg_used_pyramid_steps,\\n ]\\n return affine_params\",\n \"def encodeAffine(self, m, n):\\n\\n encode = Affine(m, n)\\n cipherText = encode.encode(self.planeText)\\n\\n if (self.verbose == 1):\\n print(cipherText)\\n \\n return(cipherText)\",\n \"def eval_pos_affine():\\n root_dir = \\\"/home/sdb/wangshentao/myspace/thesis/data/VisDrone2019-MOT-test-dev/\\\"\\n seq_dir = root_dir + \\\"sequences/\\\"\\n annotations_dir = root_dir + 'annotations/'\\n affine_dir = root_dir + \\\"affine_orig/\\\"\\n all_iou = []\\n seqs_sample = '''\\n uav0000249_00001_v\\n uav0000249_02688_v\\n '''\\n seqs_str = seqs_sample\\n seqs = [seq.strip() for seq in seqs_str.split()]\\n for seq in seqs:\\n image_file = os.listdir(os.path.join(seq_dir, seq))[0]\\n image = cv2.imread(os.path.join(seq_dir, seq, image_file))\\n orig_h, orig_w = image.shape[:2]\\n\\n with open(os.path.join(affine_dir, seq+'.pickle'), 'rb') as fin:\\n affine_dict = pickle.load(fin)\\n\\n bbox, frame_id = get_frame_bbox(annotations_dir, seq + '.txt')\\n predict_bbox = []\\n for i in range(len(bbox)):\\n # convert to std resolution\\n bbox[i][:, 0] = bbox[i][:, 0]\\n bbox[i][:, 1] = bbox[i][:, 1]\\n bbox[i][:, 2] = bbox[i][:, 2]\\n bbox[i][:, 3] = bbox[i][:, 3]\\n\\n # for j in range(bbox[i].shape[0]):\\n # bbox[i][j] = tlwh_to_tlbr(bbox[i][j])\\n for idx in range(len(bbox)):\\n kalman_filter = KalmanFilter()\\n trace_bbox = bbox[idx]\\n trace_predict_bbox = []\\n mean, covariance = kalman_filter.initiate(tlwh_to_xyah(trace_bbox[0]))\\n for i in range(1, trace_bbox.shape[0]):\\n # i-1 to i M\\n frame_name = \\\"{:07d}.jpg\\\".format(int(frame_id[idx][i-1]))\\n M = affine_dict[frame_name]\\n bbox_infer = tlwh(mean)\\n bbox_infer = tlwh_to_tlbr(bbox_infer)\\n bbox_expand = np.ones((3, 4))\\n bbox_expand[:2, 0] = bbox_infer[:2]\\n bbox_expand[:2, 1] = bbox_infer[2:]\\n # tr\\n bbox_expand[:2, 2] = bbox_infer[2], bbox_infer[1]\\n # bl\\n bbox_expand[:2, 3] = bbox_infer[0], bbox_infer[3]\\n bbox_expand = np.dot(M, bbox_expand)\\n for t in range(bbox_expand.shape[1]):\\n bbox_expand[:2, t] /= bbox_expand[2, t]\\n # bbox_infer[:2] = bbox_expand[:2, 0]\\n # bbox_infer[2:] = bbox_expand[:2, 1]\\n # get the out bounding bbox\\n bbox_infer[0] = min(bbox_expand[0, :])\\n bbox_infer[1] = min(bbox_expand[1, :])\\n bbox_infer[2] = max(bbox_expand[0, :])\\n bbox_infer[3] = max(bbox_expand[1, :])\\n bbox_infer = tlbr_to_tlwh(bbox_infer)\\n # print(bbox_infer)\\n trace_predict_bbox.append(bbox_infer)\\n # move = mean[:4] - tlwh_to_xyah(bbox_infer)\\n # if np.sum(np.square(move)[:2]) > 32*32:\\n # print(move)\\n # print(idx, frame_name)\\n # print(mean)\\n mean[:4] = tlwh_to_xyah(bbox_infer)\\n # print(mean)\\n mean, covariance = kalman_filter.predict(mean, covariance)\\n mean, covariance = kalman_filter.update(mean, covariance, tlwh_to_xyah(trace_bbox[i]))\\n\\n trace_predict_bbox = np.array(trace_predict_bbox)\\n for i in range(trace_predict_bbox.shape[0]):\\n trace_predict_bbox[i] = tlwh_to_tlbr(trace_predict_bbox[i])\\n for i in range(trace_bbox.shape[0]):\\n trace_bbox[i] = tlwh_to_tlbr(trace_bbox[i])\\n\\n predict_bbox.append(trace_predict_bbox)\\n bbox[idx] = bbox[idx][1:]\\n frame_id[idx] = frame_id[idx][1:]\\n assert bbox[idx].shape[0] == predict_bbox[idx].shape[0]\\n iou = []\\n for i in range(len(bbox)):\\n trace_iou = []\\n trace_bbox = bbox[i]\\n trace_predict_bbx = predict_bbox[i]\\n for j in range(trace_bbox.shape[0]):\\n iou_val = bbox_ious(np.ascontiguousarray(trace_bbox[j][np.newaxis, :], dtype=np.float),\\n np.ascontiguousarray(trace_predict_bbx[j][np.newaxis, :], dtype=np.float))\\n trace_iou.append(iou_val)\\n iou.append(np.array(trace_iou))\\n iou = [int(np.mean(i) * 100) for i in iou]\\n all_iou += iou\\n bins = np.zeros(101)\\n for i in all_iou:\\n bins[i] += 1\\n plt.bar(np.arange(101), bins)\\n plt.ylabel('num')\\n plt.xlabel('iou(*100)')\\n plt.show()\",\n \"def cast(*args):\\n return _itkFixedCenterOfRotationAffineTransformPython.itkFixedCenterOfRotationAffineTransformD2_cast(*args)\",\n \"def affine_forward(x,w,b):\\n out=None\\n N=x.shape[0]\\n x_row=x.reshape(N,-1)\\n out=np.dot(x_row,w)+b\\n cache=(x,w,b)\\n return out,cache\",\n \"def get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False):\\n assert len(center) == 2\\n assert len(scale) == 2\\n assert len(output_size) == 2\\n assert len(shift) == 2\\n scale_tmp = scale * 200.0\\n shift = np.array(shift)\\n src_w = scale_tmp[0]\\n dst_w = output_size[0]\\n dst_h = output_size[1]\\n rot_rad = np.pi * rot / 180\\n src_dir = rotate_point([0.0, src_w * -0.5], rot_rad)\\n dst_dir = np.array([0.0, dst_w * -0.5])\\n src = np.zeros((3, 2), dtype=np.float32)\\n src[0, :] = center + scale_tmp * shift\\n src[1, :] = center + src_dir + scale_tmp * shift\\n src[2, :] = _get_3rd_point(src[0, :], src[1, :])\\n dst = np.zeros((3, 2), dtype=np.float32)\\n dst[0, :] = [dst_w * 0.5, dst_h * 0.5]\\n dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir\\n dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])\\n if inv:\\n trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))\\n else:\\n trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))\\n return trans\",\n \"def img_map_transforms(ts):\\n # XXX TODO: unchecked textures give error of variable referenced before assignment XXX\\n # POV-Ray \\\"scale\\\" is not a number of repetitions factor, but ,its\\n # inverse, a standard scale factor.\\n # 0.5 Offset is needed relatively to scale because center of the\\n # scale is 0.5,0.5 in blender and 0,0 in POV\\n # Strange that the translation factor for scale is not the same as for\\n # translate.\\n # TODO: verify both matches with other blender renderers / internal in previous versions.\\n image_map_transforms = \\\"\\\"\\n image_map_transforms = \\\"scale <%.4g,%.4g,%.4g> translate <%.4g,%.4g,%.4g>\\\" % (\\n ts.scale[0],\\n ts.scale[1],\\n ts.scale[2],\\n ts.offset[0],\\n ts.offset[1],\\n ts.offset[2],\\n )\\n # image_map_transforms = (\\\" translate <-0.5,-0.5,0.0> scale <%.4g,%.4g,%.4g> translate <%.4g,%.4g,%.4g>\\\" % \\\\\\n # ( 1.0 / ts.scale.x,\\n # 1.0 / ts.scale.y,\\n # 1.0 / ts.scale.z,\\n # (0.5 / ts.scale.x) + ts.offset.x,\\n # (0.5 / ts.scale.y) + ts.offset.y,\\n # ts.offset.z))\\n # image_map_transforms = (\\n # \\\"translate <-0.5,-0.5,0> \\\"\\n # \\\"scale <-1,-1,1> * <%.4g,%.4g,%.4g> \\\"\\n # \\\"translate <0.5,0.5,0> + <%.4g,%.4g,%.4g>\\\" % \\\\\\n # (1.0 / ts.scale.x,\\n # 1.0 / ts.scale.y,\\n # 1.0 / ts.scale.z,\\n # ts.offset.x,\\n # ts.offset.y,\\n # ts.offset.z)\\n # )\\n return image_map_transforms\",\n \"def xyz_affine(coordmap, name2xyz=None):\\n if name2xyz is None:\\n name2xyz = known_names\\n try:\\n affine = coordmap.affine\\n except AttributeError:\\n raise SpaceTypeError('Need affine coordinate map')\\n order = xyz_order(coordmap.function_range, name2xyz)\\n affine = affine[order[:3]]\\n # Check that dropped dimensions don't provide xyz coordinate info\\n extra_cols = affine[:,3:-1]\\n if not np.allclose(extra_cols, 0):\\n raise AffineError('Dropped dimensions not orthogonal to xyz')\\n return from_matrix_vector(affine[:3,:3], affine[:3,-1])\",\n \"def geo_transform(self):\\n pass\",\n \"def _apply_transform(self, x, transform_parameters):\\n # x is a single image, so it doesn't have image number at index 0\\n img_row_axis = self.row_axis - 1\\n img_col_axis = self.col_axis - 1\\n img_channel_axis = self.channel_axis - 1\\n\\n x = apply_affine_transform(x, transform_parameters.get('theta', 0),\\n transform_parameters.get('tx', 0),\\n transform_parameters.get('ty', 0),\\n transform_parameters.get('shear', 0),\\n transform_parameters.get('zx', 1),\\n transform_parameters.get('zy', 1),\\n row_axis=img_row_axis,\\n col_axis=img_col_axis,\\n channel_axis=img_channel_axis,\\n fill_mode=self.fill_mode,\\n cval=self.cval)\\n\\n if transform_parameters.get('channel_shift_intensity') is not None:\\n x = apply_channel_shift(x,\\n transform_parameters['channel_shift_intensity'],\\n img_channel_axis)\\n\\n if transform_parameters.get('flip_horizontal', False):\\n x = self._flip_axis(x, img_col_axis)\\n\\n if transform_parameters.get('flip_vertical', False):\\n x = self._flip_axis(x, img_row_axis)\\n\\n if transform_parameters.get('brightness') is not None:\\n x = apply_brightness_shift(x, transform_parameters['brightness'])\\n\\n return x\",\n \"def translate(geom, xoff=0.0, yoff=0.0, zoff=0.0):\\n matrix = (1.0, 0.0, 0.0,\\n 0.0, 1.0, 0.0,\\n 0.0, 0.0, 1.0,\\n xoff, yoff, zoff)\\n return affine_transform(geom, matrix)\",\n \"def affine_transformation(image, ratio = 0.2 ):\\n random_degree = random.uniform(-15, 15) * np.pi / 180.\\n random_translation_x = random.randint(0, int(image.shape[0] *ratio))\\n random_translation_y = random.randint(0, int(image.shape[0] *ratio))\\n \\n transform = AffineTransform(translation=(random_translation_x,random_translation_y) , rotation = random_degree)\\n wrapShift = warp(image,transform,mode= random.sample([\\\"constant\\\", \\\"edge\\\"], 1)[0])\\n return wrapShift\",\n \"def transform():\",\n \"def translate(img, shift):\\n\\tgray = grayscale(img)\\n\\ttmp = img.copy()\\n\\trows, cols = gray.shape\\n\\tM = np.float32([[1, 0, shift[0]], [0, 1, shift[1]]]) # Translation Matrix\\n\\tdst = cv2.warpAffine(tmp, M, (cols, rows))\\n\\treturn dst\",\n \"def apply_affine(A: Affine, x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\\n\\n shape = x.shape\\n\\n A = np.asarray(A).reshape(3, 3) # type: ignore[assignment]\\n t = A[:2, -1].reshape((2, 1)) # type: ignore[index]\\n A = A[:2, :2] # type: ignore[index]\\n\\n x, y = A @ np.vstack([x.ravel(), y.ravel()]) + t\\n x, y = (a.reshape(shape) for a in (x, y))\\n return (x, y)\",\n \"def affine_field(ftr0, ftr1, cutoff=9, d2min_scale=1.0, fast=False, subset=None, dview=None):\\n ftrcomp = ftr1[['particle', 'x', 'y']].join(ftr0.set_index('particle')[['x', 'y']], \\n on='particle', rsuffix='0').dropna()\\n NNE = NNEngine(ftrcomp, cutoff, fast=fast, subset=subset)\\n return NNE._affine_field(d2min_scale=d2min_scale, dview=dview)\",\n \"def get_transform(ds):\\n\\n if 'transform' in ds.attrs:\\n ds_trans = ds.attrs['transform']\\n if isinstance(ds_trans, Affine):\\n return ds_trans\\n else:\\n return Affine(*ds_trans)\\n\\n elif 'crs' in ds.data_vars and 'i2m' in ds.data_vars['crs'].attrs:\\n transf_str = ds.data_vars['crs'].attrs['i2m']\\n a = list(map(float, transf_str.split(',')))\\n return Affine(a[0], a[2], a[4], a[1], a[3], a[5])\\n\\n else:\\n resx, resy = get_resolution(ds)\\n xoff = ds['x'].values.min()\\n yoff = ds['y'].values.max()\\n return Affine(resx, 0, xoff, 0, resy, yoff)\",\n \"def xy_to_rowcol(x, y, affine):\\n # affine might be an Affine object, might be 3x3 array or\\n # 9-element vector. Let's first make damn sure is a 9-element\\n # vector\\n aff_array = numpy.array(affine).reshape(9)\\n\\n # now be damn sure we have an Affine object, and reverse it to\\n # go from coordinates to indices\\n affine = ~Affine(*aff_array[:6])\\n\\n # compute the y, x values\\n yx = numpy.floor(rowcol_to_xy(y, x, affine))\\n\\n # convert to int and flip to xy\\n xy = yx.astype(int)[::-1]\\n return xy\",\n \"def translateY(shift: float) -> Callable:\\n return lambda img: TF.affine(img, 0, (0, shift * img.size[1]), 1, 0)\",\n \"def affine(img, matrix, interpolation=\\\"nearest\\\", fill=None, data_format='CHW'):\\n ndim = len(img.shape)\\n if ndim == 3:\\n img = img.unsqueeze(0)\\n\\n img = img if data_format.lower() == 'chw' else img.transpose((0, 3, 1, 2))\\n\\n matrix = paddle.to_tensor(matrix, place=img.place)\\n matrix = matrix.reshape((1, 2, 3))\\n shape = img.shape\\n\\n grid = _affine_grid(\\n matrix, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2]\\n )\\n\\n if isinstance(fill, int):\\n fill = tuple([fill] * 3)\\n\\n out = _grid_transform(img, grid, mode=interpolation, fill=fill)\\n\\n out = out if data_format.lower() == 'chw' else out.transpose((0, 2, 3, 1))\\n out = out.squeeze(0) if ndim == 3 else out\\n\\n return out\",\n \"def to_affine_nd(r, affine):\\n affine_ = np.array(affine, dtype=np.float64)\\n if affine_.ndim != 2:\\n raise ValueError(f\\\"input affine matrix must have two dimensions, got {affine_.ndim}.\\\")\\n new_affine = np.array(r, dtype=np.float64, copy=True)\\n if new_affine.ndim == 0:\\n sr = new_affine.astype(int)\\n if not np.isfinite(sr) or sr < 0:\\n raise ValueError(f\\\"r must be positive, got {sr}.\\\")\\n new_affine = np.eye(sr + 1, dtype=np.float64)\\n d = max(min(len(new_affine) - 1, len(affine_) - 1), 1)\\n new_affine[:d, :d] = affine_[:d, :d]\\n if d > 1:\\n new_affine[:d, -1] = affine_[:d, -1]\\n return new_affine\",\n \"def __compose_transformation(self):\\n s = self.scale\\n rotR = self.rotation\\n t = self.translation\\n T = np.eye(4)\\n T[0:3, 3] = t\\n R = np.eye(4)\\n R[0:3, 0:3] = rotR\\n M = T.dot(R)\\n if s == 1:\\n M = T.dot(R)\\n else:\\n S = np.eye(4)\\n S[0:3, 0:3] = np.diag([s, s, s])\\n M = T.dot(R).dot(S)\\n return M\",\n \"def is_affine(self):\\n if self.is_irreducible():\\n return self._info['affine']\\n else:\\n return False\",\n \"def affine_space(self):\\n vertices = self.vertices()\\n if not self.contains_origin():\\n v0 = vertices[0]\\n vertices = [v-v0 for v in vertices]\\n return self.ambient_space().span(vertices).saturation()\",\n \"def rotateAlignXform(self):\\n r = np.concatenate(([self.x], [self.y], [self.z]), 0)\\n r = np.concatenate((r, np.array([[0,0,0]])), 0)\\n r = np.concatenate((r, np.array([0,0,0,1]).reshape(-1,1)), 1)\\n return r\",\n \"def get_affine(nii_file):\\n\\timg = nib.load(nii_file)\\n\\treturn img.get_affine()\",\n \"def transform(self, x, y):\\n # return self.transform_2D(x, y)\\n return self.transform_perspective(x, y)\",\n \"def recalculate_transform(self):\\n bias_mat = wireframe.translationMatrix(*self.bias)\\n rot_mat = wireframe.rotateMatrix(*self.view_angle)\\n scale_mat = wireframe.scaleMatrix(*self.scale)\\n\\n self.tf_wireframe.nodes = (self.wireframe.nodes - self._center_half) @ \\\\\\n rot_mat @ scale_mat @ bias_mat\",\n \"def testTranslateAffine(self):\\n affineClass = xyTransformRegistry[\\\"affine\\\"]\\n affineConfig = affineClass.ConfigClass()\\n affineConfig.translation = (1.2, -3.4)\\n with lsst.utils.tests.getTempFilePath(\\\".py\\\") as filePath:\\n self.checkConfig(affineClass, affineConfig, filePath)\\n affine = affineClass(affineConfig)\\n for fromPoint in self.fromIter():\\n toPoint = affine.forwardTransform(fromPoint)\\n predToPoint = fromPoint + Extent2D(*affineConfig.translation)\\n for i in range(2):\\n self.assertAlmostEqual(toPoint[i], predToPoint[i])\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n reshaped_inp = np.reshape(x,(int(x.shape[0]),int(np.prod(x.shape) / x.shape[0])))\\n out = reshaped_inp.dot(w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def get_affine_transform_2d(gps_coords, pdr_coords):\\n X = np.array(pdr_coords)\\n Xp = np.array(gps_coords)\\n\\n # Estimate 2d similarity to align to GPS\\n T = tf.affine_matrix_from_points(X.T[:2], Xp.T[:2], shear=False)\\n s = np.linalg.det(T[:2, :2]) ** 0.5\\n A = np.eye(3)\\n A[:2, :2] = T[:2, :2] / s\\n b = np.array([\\n T[0, 2],\\n T[1, 2],\\n Xp[:, 2].mean() - s * X[:, 2].mean() # vertical alignment\\n ])\\n\\n return s, A, b\",\n \"def affine_forward(x, w, b):\\n ############################################################################\\n # TODO: Implement the affine forward pass. Store the result in 'out'. You #\\n # will need to reshape the input into rows. #\\n ############################################################################\\n ############################################################################\\n # START OF YOUR CODE #\\n ############################################################################\\n N = len(x)\\n D,M = w.shape\\n # reshape get a new x\\n new_x = x.reshape(N,D)\\n # get the output\\n out = np.dot(new_x,w) + np.expand_dims(b,axis=0)\\n \\n ############################################################################\\n # END OF YOUR CODE #\\n ############################################################################\\n return out\",\n \"def affine_to_shift(affine_matrix, volshape, shift_center=True, indexing='ij'):\\n\\n if isinstance(volshape, (tf.compat.v1.Dimension, tf.TensorShape)):\\n volshape = volshape.as_list()\\n \\n if affine_matrix.dtype != 'float32':\\n affine_matrix = tf.cast(affine_matrix, 'float32')\\n\\n nb_dims = len(volshape)\\n\\n if len(affine_matrix.shape) == 1:\\n if len(affine_matrix) != (nb_dims * (nb_dims + 1)):\\n raise ValueError('transform is supposed a vector of len ndims * (ndims + 1).'\\n 'Got len %d' % len(affine_matrix))\\n\\n affine_matrix = tf.reshape(affine_matrix, [nb_dims, nb_dims + 1])\\n\\n if not (affine_matrix.shape[0] in [nb_dims, nb_dims + 1] and affine_matrix.shape[1] == (nb_dims + 1)):\\n shape1 = '(%d x %d)' % (nb_dims + 1, nb_dims + 1)\\n shape2 = '(%d x %s)' % (nb_dims, nb_dims + 1)\\n true_shape = str(affine_matrix.shape)\\n raise Exception('Affine shape should match %s or %s, but got: %s' % (shape1, shape2, true_shape))\\n\\n # list of volume ndgrid\\n # N-long list, each entry of shape volshape\\n mesh = ne.utils.volshape_to_meshgrid(volshape, indexing=indexing) \\n mesh = [tf.cast(f, 'float32') for f in mesh]\\n \\n if shift_center:\\n mesh = [mesh[f] - (volshape[f]-1)/2 for f in range(len(volshape))]\\n\\n # add an all-ones entry and transform into a large matrix\\n flat_mesh = [ne.utils.flatten(f) for f in mesh]\\n flat_mesh.append(tf.ones(flat_mesh[0].shape, dtype='float32'))\\n mesh_matrix = tf.transpose(tf.stack(flat_mesh, axis=1)) # 4 x nb_voxels\\n\\n # compute locations\\n loc_matrix = tf.matmul(affine_matrix, mesh_matrix) # N+1 x nb_voxels\\n loc_matrix = tf.transpose(loc_matrix[:nb_dims, :]) # nb_voxels x N\\n loc = tf.reshape(loc_matrix, list(volshape) + [nb_dims]) # *volshape x N\\n # loc = [loc[..., f] for f in range(nb_dims)] # N-long list, each entry of shape volshape\\n\\n # get shifts and return\\n return loc - tf.stack(mesh, axis=nb_dims)\",\n \"def _transform(\\n self, x: \\\"torch.Tensor\\\", y: Optional[\\\"torch.Tensor\\\"], **kwargs\\n ) -> Tuple[\\\"torch.Tensor\\\", Optional[\\\"torch.Tensor\\\"]]:\\n import torch\\n import torchvision.transforms.functional as F\\n\\n img_size = x.shape[:2]\\n\\n angle = float(\\n torch.empty(1)\\n .uniform_(float(self.degree_range[0]), float(self.degree_range[1]))\\n .item()\\n )\\n\\n max_dx = float(self.translate[0] * img_size[1])\\n max_dy = float(self.translate[1] * img_size[0])\\n tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))\\n ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))\\n translations = (tx, ty)\\n\\n scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())\\n\\n # x needs to have channel first\\n x = x.permute(2, 0, 1)\\n x = F.affine(\\n img=x, angle=angle, translate=translations, scale=scale, shear=(0.0, 0.0)\\n )\\n x = x.permute(1, 2, 0)\\n\\n return torch.clamp(x, min=self.clip_values[0], max=self.clip_values[1]), y\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6571072","0.641823","0.6383142","0.634022","0.62523025","0.62054753","0.62054753","0.61710006","0.6121375","0.6112866","0.6077915","0.6075933","0.60051006","0.5994395","0.59378403","0.59053254","0.58875793","0.58756226","0.5864575","0.5862957","0.58351576","0.58297646","0.5823089","0.5821312","0.58088326","0.5799198","0.5797699","0.57968426","0.5713533","0.5700425","0.5676096","0.56702274","0.56524724","0.56405985","0.5636582","0.5627914","0.5623479","0.5602209","0.5595536","0.5584489","0.5564299","0.55620265","0.5556791","0.5543062","0.5527707","0.5502577","0.5500383","0.5495089","0.54921234","0.5485243","0.5483164","0.54788023","0.54554194","0.5449177","0.54487103","0.5436091","0.542466","0.54193145","0.54158217","0.5393239","0.53863215","0.53496945","0.5348946","0.53484577","0.53484577","0.5344487","0.5330902","0.53304917","0.5326846","0.5322154","0.5313098","0.53121847","0.53067225","0.5298622","0.52968895","0.5293303","0.52847964","0.52831775","0.52805793","0.527052","0.52704424","0.5270087","0.5260689","0.5253801","0.52490956","0.5243087","0.52363807","0.5229008","0.52282906","0.5227788","0.5221249","0.5219656","0.52189106","0.52155983","0.5214619","0.5203658","0.52020913","0.51900464","0.5189775","0.5183079","0.5173995"],"string":"[\n \"0.6571072\",\n \"0.641823\",\n \"0.6383142\",\n \"0.634022\",\n \"0.62523025\",\n \"0.62054753\",\n \"0.62054753\",\n \"0.61710006\",\n \"0.6121375\",\n \"0.6112866\",\n \"0.6077915\",\n \"0.6075933\",\n \"0.60051006\",\n \"0.5994395\",\n \"0.59378403\",\n \"0.59053254\",\n \"0.58875793\",\n \"0.58756226\",\n \"0.5864575\",\n \"0.5862957\",\n \"0.58351576\",\n \"0.58297646\",\n \"0.5823089\",\n \"0.5821312\",\n \"0.58088326\",\n \"0.5799198\",\n \"0.5797699\",\n \"0.57968426\",\n \"0.5713533\",\n \"0.5700425\",\n \"0.5676096\",\n \"0.56702274\",\n \"0.56524724\",\n \"0.56405985\",\n \"0.5636582\",\n \"0.5627914\",\n \"0.5623479\",\n \"0.5602209\",\n \"0.5595536\",\n \"0.5584489\",\n \"0.5564299\",\n \"0.55620265\",\n \"0.5556791\",\n \"0.5543062\",\n \"0.5527707\",\n \"0.5502577\",\n \"0.5500383\",\n \"0.5495089\",\n \"0.54921234\",\n \"0.5485243\",\n \"0.5483164\",\n \"0.54788023\",\n \"0.54554194\",\n \"0.5449177\",\n \"0.54487103\",\n \"0.5436091\",\n \"0.542466\",\n \"0.54193145\",\n \"0.54158217\",\n \"0.5393239\",\n \"0.53863215\",\n \"0.53496945\",\n \"0.5348946\",\n \"0.53484577\",\n \"0.53484577\",\n \"0.5344487\",\n \"0.5330902\",\n \"0.53304917\",\n \"0.5326846\",\n \"0.5322154\",\n \"0.5313098\",\n \"0.53121847\",\n \"0.53067225\",\n \"0.5298622\",\n \"0.52968895\",\n \"0.5293303\",\n \"0.52847964\",\n \"0.52831775\",\n \"0.52805793\",\n \"0.527052\",\n \"0.52704424\",\n \"0.5270087\",\n \"0.5260689\",\n \"0.5253801\",\n \"0.52490956\",\n \"0.5243087\",\n \"0.52363807\",\n \"0.5229008\",\n \"0.52282906\",\n \"0.5227788\",\n \"0.5221249\",\n \"0.5219656\",\n \"0.52189106\",\n \"0.52155983\",\n \"0.5214619\",\n \"0.5203658\",\n \"0.52020913\",\n \"0.51900464\",\n \"0.5189775\",\n \"0.5183079\",\n \"0.5173995\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80386,"cells":{"query":{"kind":"string","value":"This is the identity block"},"document":{"kind":"string","value":"def identity_block(self,X,stage,block):\n conv_name_base='res'+str(stage)+block+'_branch'\n bn_name_base='bn'+str(stage)+block+'_branch'\n \n #retrieve the filters\n #F1,F2,F3=filters\n \n X_shortcut=X\n \n #first component of main path\n X=Conv3D(self.filter, (3,3,1),kernel_initializer=self.init,kernel_regularizer=self.regularizer,padding='same')(X)\n X=BatchNormalization(axis=3)(X)\n X=Activation('relu')(X)\n \n #second component of main path\n X=Conv3D(self.filter, (3,3,1),kernel_initializer=self.init,kernel_regularizer=self.regularizer,padding='same')(X)\n\n \n #final step, add the shortcut\n X=Add()([X,X_shortcut])\n X=Activation('relu')(X)\n \n \n return X"},"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 makeIdentity(self) -> None:\n ...","def setIdentity(self) -> None:\n ...","def test_create_identity(self):\n pass","def identity(cls):\n return super().identity(4, 4)","def confirm_identity(self):\n self.__not_implemented()","def next_identity(self) -> UserId:\n ...","def next_identity(self) -> PublicationId:\n ...","def ApplicationIdentity(self) -> _n_0_t_2:","def test_read_identity(self):\n pass","def block_one(self):","def test_patch_identity(self):\n pass","def begin():\n return BeginBlock()","def identity(self):\n return self.id","def _id(self):\n pass","def next_identity(self) -> OrganisationId:\n ...","def block_eid(self, block_id):\n ...","def identity():\n # We generate a name, an address, add them together and return that\n name = full_name()\n place_of_residence = address()\n new_identity = name + \", \" + place_of_residence\n return new_identity","def identifier(self):","def user_identity(self):\n self.raw['type'] = 'IDENTITY'\n return self","def __init__(self):\n super(uidGenerator,self).__init__()","def __init__(self):\n _hypre.HypreIdentity_swiginit(self, _hypre.new_HypreIdentity())","def identity(cls):\n return super().identity(3, 3)","def group_identity(self):\n self.raw['type'] = 'IDENTITY'\n return self","def identity( self, obj ):\n raise NotImplementedError(\"identity\")","def register(blk):\n pass","def block(self):\n pass","def test_replace_identity(self):\n pass","def getID():","def m_create_test_identities():\n\n # Get the ROOT account (it was created in the deployment of the Smart Contracts)\n ROOT_address, ROOT_key = wallet.account_from_name(\"ROOT\", \"ThePassword\")\n\n # Create the Alastria account for node \"ala\"\n print(f\"\\n==> Creating the Alastria account\")\n Alastria_account = wallet.new_account(\n \"Alastria\", \"ThePassword\")\n alakey = Alastria_account.key\n print(f\"Alastria key: {alakey}\")\n\n print(f\"Done\")\n\n # Set the subnode \"ala\"\n print(f\"\\n==> Creating the ala subnode in the Trust Framework\")\n success, _, _ = ens.setSubnodeOwner(\n node_name=\"root\",\n label=\"ala\",\n new_owner_address=Alastria_account.address,\n current_owner_key=ROOT_key\n )\n print(f\"ala subnode created\")\n\n # Assign the name for reverse resolution\n resolver.setName(\"ala\", \"ala\", Alastria_account.key)\n\n # And assign approval to the PublicResolver contract so it can call ENS methods on behalf of Alastria\n print(f\"Resolver address for ROOT: {resolver.address()}\")\n ens.setApprovalForAll(resolver.address(), True, Alastria_account.key)\n\n ################################\n # Heathrow airport\n print(f\"\\n==> Creating the Heathrow identity\")\n\n DID = \"did:elsi:VATGB-927365404\"\n domain_name = \"heathrow.ala\"\n website = \"www.heathrow.com\"\n commercial_name = \"Heathrow Airport Limited\"\n\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \"Alastria\", \"ThePassword\", False)\n if didDoc is not None:\n pprint(didDoc)\n\n ################################\n # AENA\n print(f\"\\n==> Creating the AENA identity\")\n\n DID = \"did:elsi:VATES-A86212420\"\n domain_name = \"aena.ala\"\n website = \"www.aena.es\"\n commercial_name = \"Aena\"\n\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \"Alastria\", \"ThePassword\", False)\n if didDoc is not None:\n pprint(didDoc)\n\n ################################\n # Lanzarote airport\n # The airport belongs to AENA and does not have independent entity (shares the same VAT, for example)\n # In production, the node should be created by AENA, as a subnode controlled by them.\n # In this PoC, the node is created automatically to facilitate the tests\n print(f\"\\n==> Creating the César Manrique airport identity\")\n\n DID = \"did:elsi:VATES-A86212420-1\"\n domain_name = \"ace.ala\"\n website = \"www.aena.es/es/aeropuerto-lanzarote\"\n commercial_name = \"Aeropuerto de Lanzarote-Cesar Manrique\"\n\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \"Alastria\", \"ThePassword\", False)\n if didDoc is not None:\n pprint(didDoc)\n\n ################################\n # Metrovacesa\n print(f\"\\n==> Creating the Metrovacesa identity\")\n\n DID = \"did:elsi:VATES-A87471264\"\n domain_name = \"metrovacesa.ala\"\n website = \"metrovacesa.com\"\n commercial_name = \"Metrovacesa\"\n\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \"Alastria\", \"ThePassword\", False)\n if didDoc is not None:\n pprint(didDoc)\n\n ################################\n # IN2\n print(f\"\\n==> Creating the IN2 identity\")\n\n DID = \"did:elsi:VATES-B60645900\"\n domain_name = \"in2.ala\"\n website = \"www.in2.es\"\n commercial_name = \"IN2 Innovating 2gether\"\n\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \"Alastria\", \"ThePassword\", False)\n if didDoc is not None:\n pprint(didDoc)\n\n ################################\n # Perfect Health\n print(f\"\\n==> Creating the Perfect Health identity\")\n\n DID = \"did:elsi:VATES-X12345678X\"\n domain_name = \"perfecthealth.ala\"\n website = \"www.perfecthealth.org\"\n commercial_name = \"Perfect Health plc\"\n\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \"Alastria\", \"ThePassword\", False)\n if didDoc is not None:\n pprint(didDoc)\n\n ################################\n # BME\n print(f\"\\n==> Creating the BME identity\")\n\n DID = \"did:elsi:VATES-A83246314\"\n domain_name = \"bme.ala\"\n website = \"www.bolsasymercados.es\"\n commercial_name = \"Bolsas y Mercados Españoles\"\n\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \"Alastria\", \"ThePassword\", False)\n if didDoc is not None:\n pprint(didDoc)","def getIdentity():\n return Sentience.__IDENTITY.lower()","def identity(self):\r\n self.piDD = {\"[1]\": None}\r\n self.top_node = \"[1]\"\r\n self.dim = 0","async def client_identity(self) -> Identity:\n raise NotImplementedError","def primary(self):\n ...","def identity_block(input_tensor, units):\n x = layers.Dense(units, kernel_regularizer=reg)(input_tensor)\n x = l()(x)\n x = layers.Activation('relu')(x)\n\n x = layers.Dense(units, kernel_regularizer=reg)(x)\n x = l()(x)\n x = layers.Activation('relu')(x)\n\n x = layers.Dense(units, kernel_regularizer=reg)(x)\n x = l()(x)\n x = layers.add([x, input_tensor])\n x = layers.Activation('relu')(x)\n\n return x","def test_identity_single(self, dev):\n dev = qml.device(dev, wires=1)\n\n @qml.qnode(dev)\n def circuit():\n qml.PauliX(0)\n return qml.expval(qml.Identity(0))\n\n assert circuit() == 1","async def server_identity(self) -> Identity:\n raise NotImplementedError","def identity_block(self, input_tensor, filters, activation, stage, block, start_with_batch_norm = True):\n\n # Set the naming convention\n conv_name_base = 'res' + str(stage) + block + '_branch'\n batch_norm_name_base = 'batch_norm' + str(stage) + block + '_branch'\n\n x_shortcut = input_tensor\n x = None\n\n if start_with_batch_norm:\n # Start with Batch Normalization\n x = BatchNormalization(axis = 3, name = batch_norm_name_base + '_1')(input_tensor)\n\n x = Activation(activation)(x)\n \n else:\n x = input_tensor\n \n # Main Path Block a\n x = self.main_path_block(\n x,\n filters,\n (3, 3),\n 'same',\n conv_name_base + '2a',\n batch_norm_name_base + '2a',\n activation = activation\n )\n\n # Main Path Block b\n x = self.main_path_block(\n x, filters,\n (3, 3), 'same',\n conv_name_base + '2b',\n batch_norm_name_base + '2b',\n batch_norm = False,\n activation = None\n )\n\n # Add skip connection\n x = Add()([x, x_shortcut])\n\n return x","def __init__(self):\n self.unconfirmed_transactions = [] \n self.chain = []\n self.create_genesis_block()","def test_identity(self):\n me = self.d.identity()\n self.assertEqual(me.data['consumer_name'], 'Test Client')\n self.assertEqual(me, self.d.user('example'))","def manually_get_block_identity_op(self, space):\n i = self.get_identity_op(space, OP_DOM, OP_DOM, OP_DUA)\n I = assembly.BlockedOperator(2, 2)\n I[0, 0] = i\n I[1, 1] = i\n return I","def begin(self):\n pass","def identity(self, *args, **kwargs):\n return {\n 'id': self.drone_id,\n }","def __init__(self, inst: ghidra.program.model.listing.Instruction, bl: ghidra.program.model.correlate.Block, ind: int):\n ...","def do_id (self, line) :\n\t\tprint \"\tuid=%s(%s)\tgid=%s\"\t% (self.__image['meta']['UID'], self.__image['meta']['user'], self.__image['meta']['GID'] )","def identity_block(self, input_tensor, middle_filter_size, filters, stage, block):\n\n # Set the naming convention\n conv_name_base = 'res' + str(stage) + block + '_branch'\n batch_norm_name_base = 'batch_norm' + str(stage) + block + '_branch'\n\n x_shortcut = input_tensor\n\n # First Block of main path\n x = self.main_path_block(\n input_tensor,\n filters[0],\n (1, 1),\n 'valid',\n conv_name_base + '2a',\n batch_norm_name_base + '2a',\n 'relu'\n )\n\n # Middle Block of main path\n x = self.main_path_block(\n x,\n filters[1],\n (\n middle_filter_size,\n middle_filter_size\n ),\n 'same',\n conv_name_base + '2b',\n batch_norm_name_base + '2b',\n 'relu'\n )\n\n # Last Block of main path\n x = self.main_path_block(\n x,\n filters[2],\n (1, 1),\n 'valid',\n conv_name_base + '2c',\n batch_norm_name_base + '2c',\n 'relu'\n )\n\n # Skip Connection\n x = Add()([x, x_shortcut])\n x = Activation('relu')(x)\n\n return x","def identify(self):\n self.__send_short(self.MGMSG_MOD_IDENTIFY, 0x00, 0x00)","def __init__(self):\n self.id = id(self)","def get_identity(self):\n return GetIdentity(*self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_IDENTITY, (), '', 33, '8s 8s c 3B 3B H'))","def testIdentity(self):\n identClass = xyTransformRegistry[\"identity\"]\n with lsst.utils.tests.getTempFilePath(\".py\") as filePath:\n self.checkConfig(identClass, identClass.ConfigClass(), filePath)\n ident = identClass(identClass.ConfigClass())\n self.assertEqual(type(ident), IdentityXYTransform)\n self.checkBasics(ident)\n for fromPoint in self.fromIter():\n toPoint = ident.forwardTransform(fromPoint)\n for i in range(2):\n self.assertAlmostEqual(fromPoint[i], toPoint[i])","def __init__(self):\n self.chain = [Block.genesis()]","def body(self):","def getIdent (self) :\n return self.id","def identity(self) -> Optional[pulumi.Input['IdentityArgs']]:\n return pulumi.get(self, \"identity\")","def __displayID(self): # displayID is a private method\n print(\"ID:\", self.ID)","def identityInfo(self) -> dict:\r\n\r\n return deepcopy(self.__identity_info)","def block_incidence_matrix(self, block_id, typestr, ctx): # -> tuple[Unknown, Unknown]:\n ...","def is_identity(node: onnx.NodeProto) -> bool:\n return node.op_type == 'Identity'","def __init__(self):\n self.id = uuid.uuid4().hex","def id(self):\n raise NotImplementedError()","def block(self):\n if self._block is None:\n bunchdt, block, data, commdct, idd_index, versiontuple = idfreader1(\n self.idfname, self.iddname, self, commdct=None, block=None\n )\n self._block = block\n self._idd_info = commdct\n self._idd_index = idd_index\n self._idfobjects = bunchdt\n self._model = data\n self._idd_version = versiontuple\n return self._block","def get_id(self): # pragma: no cover\n pass","def getId(self):","def identity(self) -> Optional[pulumi.Input['IdentityInfoArgs']]:\n return pulumi.get(self, \"identity\")","def blocks(self):\n pass","def identity_block(inputs, kernel_size, filters, stage, block,\n training):\n nb_filter1, nb_filter2, nb_filter3 = filters\n\n scope_name = 'conv'+str(stage)+block+'_branch'\n scope_name = 'bn'+str(stage)+block+'_branch'\n\n x = slim.conv2d(inputs,nb_filter1,[1,1],stride=[1,1],padding='VALID',activation_fn=None,\n trainable=training,scope=scope_name+'2a')\n x = slim.batch_norm(x,scope=scope_name+'2a',is_training=training)\n x = tf.nn.relu(x)\n\n\n x = slim.conv2d(x,nb_filter2,[kernel_size,kernel_size],stride=[1,1],padding='SAME',\n activation_fn=None,trainable=training,scope=scope_name+'2b')\n x = slim.batch_norm(x,scope=scope_name+'2b',is_training=training)\n x = tf.nn.relu(x)\n\n\n x = slim.conv2d(x,nb_filter3,[1,1],stride=[1,1],padding='VALID',\n activation_fn=None,trainable=training,scope=scope_name+'2c')\n x = slim.batch_norm(x,scope=scope_name+'2c',is_training=training)\n\n \n x = tf.add(x,inputs)\n x = tf.nn.relu(x,name='res'+str(stage)+block+\"_out\")\n return x","def identity_block(input_tensor, kernel_size, filters, stage, block):\n filters0, filters1, filters2 = filters\n conv_name_base = 'res' + str(stage) + block\n bn_name_base = 'bn' + str(stage) + block\n add_name = 'add' + str(stage) + \"_\" + block\n relu_name = 'relu' + str(stage) + \"_\" + block\n\n # Tensors\n input_tensor_chans = input_tensor.dims(\n 3) if input_tensor.shape.layout == sg.NHWC else input_tensor.dims(1)\n conv0_tensor = sg.Tensor(\n data_layout=sg.NHWC, tensor_data=generate_random_data(\n (filters0, 1, 1, input_tensor_chans)))\n bn0_mean_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters0)))\n bn0_var_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters0)))\n bn0_gamma_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters0)))\n bn0_beta_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters0)))\n conv1_tensor = sg.Tensor(\n data_layout=sg.NHWC, tensor_data=generate_random_data(\n (filters1, kernel_size, kernel_size, filters0)))\n bn1_mean_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters1)))\n bn1_var_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters1)))\n bn1_gamma_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters1)))\n bn1_beta_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters1)))\n conv2_tensor = sg.Tensor(\n data_layout=sg.NHWC, tensor_data=generate_random_data(\n (filters2, 1, 1, filters1)))\n bn2_mean_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters2)))\n bn2_var_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters2)))\n bn2_gamma_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters2)))\n bn2_beta_tensor = sg.Tensor(\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters2)))\n\n x = sg.nn.convolution(\n input_tensor, conv0_tensor, stride=[1, 1], padding=\"same\",\n name=conv_name_base + '_2a')\n x = sg.nn.batch_norm(\n x, bn0_mean_tensor, bn0_var_tensor, bn0_gamma_tensor, bn0_beta_tensor,\n activation=\"relu\", name=bn_name_base + '_2a')\n x = sg.nn.convolution(\n x, conv1_tensor, stride=[1, 1], padding=\"same\",\n name=conv_name_base + '_2b')\n x = sg.nn.batch_norm(\n x, bn1_mean_tensor, bn1_var_tensor, bn1_gamma_tensor, bn1_beta_tensor,\n activation=\"relu\", name=bn_name_base + '_2b')\n x = sg.nn.convolution(\n x, conv2_tensor, stride=[1, 1], padding=\"same\",\n name=conv_name_base + '_2c')\n x = sg.nn.batch_norm(\n x, bn2_mean_tensor, bn2_var_tensor, bn2_gamma_tensor, bn2_beta_tensor,\n name=bn_name_base + '_2c')\n x = sg.math.add(x, input_tensor, name=add_name)\n x = sg.nn.relu(x, name=relu_name)\n return x","def identity(self) -> pulumi.Output[Optional['outputs.IdentityResponse']]:\n return pulumi.get(self, \"identity\")","def __init__(self):\n self.block = 1000\n self._map = [] \n self.len = 0 \n self.incr()","async def begin_object(self):","def remember_identity(response, request, identity):\n raise NotImplementedError # pragma: nocoverage","def create_individual(self):\n pass","def identity(self) -> pulumi.Output[Optional['outputs.IdentityInfoResponse']]:\n return pulumi.get(self, \"identity\")","def name(self):\r\n return self.block_id","def spam(self):\n return id(self)","def __enter__(self):\n pass","def __enter__(self):\n pass","def degibber(self):","def identifier(self):\r\n return self.id","def id(self):\n return super().id()","def user(self):\n pass","def get_identity(self):\n return self.query_serial('*IDN?')","def user(self):","def identify(self):\r\n if \"signature\" in self or \"signatures\" in self:\r\n if len(self._multisignature):\r\n missings = \\\r\n self._multisignature[\"min\"] - \\\r\n len(self.get(\"signatures\", []))\r\n if missings:\r\n raise Exception(\"owner signature missing (%d)\" % missings)\r\n elif self._secondPublicKey:\r\n if \"signSignature\" not in self:\r\n raise Exception(\"second signature is missing\")\r\n dict.pop(self, \"id\", False)\r\n self[\"id\"] = dposlib.core.crypto.getIdFromBytes(\r\n serialize(self, exclude_multi_sig=False)\r\n )\r\n else:\r\n raise Exception(\"transaction not signed\")","def test_outgoing_missing_identity(self):\n community = DebugCommunity.create_community(self._dispersy, self._my_member)\n node = DebugNode(community)\n node.init_socket()\n node.init_my_member(candidate=False, identity=False)\n node.drop_packets()\n\n # NODE sends a message to SELF\n node.give_message(node.create_full_sync_text(\"Hello World\", 10))\n\n # SELF must not yet process the 'Hello World' message\n self.assertEqual(community.fetch_messages(u\"full-sync-text\"), [])\n\n # SELF must send a missing-identity to NODE\n responses = node.receive_messages()\n self.assertEqual(len(responses), 1)\n for _, response in responses:\n self.assertEqual(response.name, u\"dispersy-missing-identity\")\n self.assertEqual(response.payload.mid, node.my_member.mid)\n\n # NODE sends the identity to SELF\n node.give_message(node.create_dispersy_identity(2))\n\n # SELF must now process and store the 'Hello World' message\n messages = community.fetch_messages(u\"full-sync-text\")\n self.assertEqual(len(messages), 1)\n for message in messages:\n self.assertEqual(message.payload.text, \"Hello World\")","def __enter__(self):\r\n pass","def test_list_identity(self):\n pass","def identity(self) -> str:\n return self.requester.uuid","def _generate_genesis() -> None:\n logging.debug(\"Generating the genesis block\")\n new_recv_block(Block.genesis())","def add_anonymous_block(self, entity, layer=\"0\", typechar='U',\n basepoint=(0, 0), insert=(0, 0)):\n blockname = self.anonymous_blockname(typechar)\n block = DXFEngine.block(blockname, basepoint=basepoint,\n flags=const.BLK_ANONYMOUS)\n block.add(entity)\n self.blocks.add(block)\n insert = DXFEngine.insert(blockname, insert=insert, layer=layer)\n self.add(insert)\n return blockname","def _identity_block(self, input_tensor, kernel_size, filters, stage, block, dilation=1):\n filters1, filters2, filters3 = filters\n if backend.image_data_format() == 'channels_last':\n bn_axis = 3\n else:\n bn_axis = 1\n\n if block > 'z':\n block = chr(ord(block) - ord('z') + ord('A') - 1)\n\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n x = layers.Conv2D(filters1, (1, 1),\n kernel_initializer='he_normal',\n name=conv_name_base + '2a')(input_tensor)\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n x = layers.Activation('relu')(x)\n\n x = layers.Conv2D(filters2, kernel_size,\n padding='same',\n kernel_initializer='he_normal',\n name=conv_name_base + '2b',\n dilation_rate=dilation)(x)\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n x = layers.Activation('relu')(x)\n\n x = layers.Conv2D(filters3, (1, 1),\n kernel_initializer='he_normal',\n name=conv_name_base + '2c')(x)\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n x = layers.add([x, input_tensor])\n x = layers.Activation('relu')(x)\n return x","def unique_id(self):\n return f\"{self.wrapper.mac}-{self.block.index}\"","def test_delete_identity(self):\n pass","def onBlock(self, data) :\n pass","def identity_function(self, qubits):\n\n # Note: ProjectQ doesn't actually have an I gate, so this test does absolutely\n # nothing.","def get_identity(self):\n return GetIdentity(*self.ipcon.send_request(self, BrickletBarometerV2.FUNCTION_GET_IDENTITY, (), '', '8s 8s c 3B 3B H'))","def autoid(self) -> str:","def autoid(self) -> str:","def party_id(self):\n pass","def create_autoIT_block(self):\n current_counter = str(self.csh.counter.current())\n self.csh.add_task('KeePass_' + current_counter, self.create_autoit_function())","def generate(self):\n pass","def get_ident():\n return -1"],"string":"[\n \"def makeIdentity(self) -> None:\\n ...\",\n \"def setIdentity(self) -> None:\\n ...\",\n \"def test_create_identity(self):\\n pass\",\n \"def identity(cls):\\n return super().identity(4, 4)\",\n \"def confirm_identity(self):\\n self.__not_implemented()\",\n \"def next_identity(self) -> UserId:\\n ...\",\n \"def next_identity(self) -> PublicationId:\\n ...\",\n \"def ApplicationIdentity(self) -> _n_0_t_2:\",\n \"def test_read_identity(self):\\n pass\",\n \"def block_one(self):\",\n \"def test_patch_identity(self):\\n pass\",\n \"def begin():\\n return BeginBlock()\",\n \"def identity(self):\\n return self.id\",\n \"def _id(self):\\n pass\",\n \"def next_identity(self) -> OrganisationId:\\n ...\",\n \"def block_eid(self, block_id):\\n ...\",\n \"def identity():\\n # We generate a name, an address, add them together and return that\\n name = full_name()\\n place_of_residence = address()\\n new_identity = name + \\\", \\\" + place_of_residence\\n return new_identity\",\n \"def identifier(self):\",\n \"def user_identity(self):\\n self.raw['type'] = 'IDENTITY'\\n return self\",\n \"def __init__(self):\\n super(uidGenerator,self).__init__()\",\n \"def __init__(self):\\n _hypre.HypreIdentity_swiginit(self, _hypre.new_HypreIdentity())\",\n \"def identity(cls):\\n return super().identity(3, 3)\",\n \"def group_identity(self):\\n self.raw['type'] = 'IDENTITY'\\n return self\",\n \"def identity( self, obj ):\\n raise NotImplementedError(\\\"identity\\\")\",\n \"def register(blk):\\n pass\",\n \"def block(self):\\n pass\",\n \"def test_replace_identity(self):\\n pass\",\n \"def getID():\",\n \"def m_create_test_identities():\\n\\n # Get the ROOT account (it was created in the deployment of the Smart Contracts)\\n ROOT_address, ROOT_key = wallet.account_from_name(\\\"ROOT\\\", \\\"ThePassword\\\")\\n\\n # Create the Alastria account for node \\\"ala\\\"\\n print(f\\\"\\\\n==> Creating the Alastria account\\\")\\n Alastria_account = wallet.new_account(\\n \\\"Alastria\\\", \\\"ThePassword\\\")\\n alakey = Alastria_account.key\\n print(f\\\"Alastria key: {alakey}\\\")\\n\\n print(f\\\"Done\\\")\\n\\n # Set the subnode \\\"ala\\\"\\n print(f\\\"\\\\n==> Creating the ala subnode in the Trust Framework\\\")\\n success, _, _ = ens.setSubnodeOwner(\\n node_name=\\\"root\\\",\\n label=\\\"ala\\\",\\n new_owner_address=Alastria_account.address,\\n current_owner_key=ROOT_key\\n )\\n print(f\\\"ala subnode created\\\")\\n\\n # Assign the name for reverse resolution\\n resolver.setName(\\\"ala\\\", \\\"ala\\\", Alastria_account.key)\\n\\n # And assign approval to the PublicResolver contract so it can call ENS methods on behalf of Alastria\\n print(f\\\"Resolver address for ROOT: {resolver.address()}\\\")\\n ens.setApprovalForAll(resolver.address(), True, Alastria_account.key)\\n\\n ################################\\n # Heathrow airport\\n print(f\\\"\\\\n==> Creating the Heathrow identity\\\")\\n\\n DID = \\\"did:elsi:VATGB-927365404\\\"\\n domain_name = \\\"heathrow.ala\\\"\\n website = \\\"www.heathrow.com\\\"\\n commercial_name = \\\"Heathrow Airport Limited\\\"\\n\\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \\\"Alastria\\\", \\\"ThePassword\\\", False)\\n if didDoc is not None:\\n pprint(didDoc)\\n\\n ################################\\n # AENA\\n print(f\\\"\\\\n==> Creating the AENA identity\\\")\\n\\n DID = \\\"did:elsi:VATES-A86212420\\\"\\n domain_name = \\\"aena.ala\\\"\\n website = \\\"www.aena.es\\\"\\n commercial_name = \\\"Aena\\\"\\n\\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \\\"Alastria\\\", \\\"ThePassword\\\", False)\\n if didDoc is not None:\\n pprint(didDoc)\\n\\n ################################\\n # Lanzarote airport\\n # The airport belongs to AENA and does not have independent entity (shares the same VAT, for example)\\n # In production, the node should be created by AENA, as a subnode controlled by them.\\n # In this PoC, the node is created automatically to facilitate the tests\\n print(f\\\"\\\\n==> Creating the César Manrique airport identity\\\")\\n\\n DID = \\\"did:elsi:VATES-A86212420-1\\\"\\n domain_name = \\\"ace.ala\\\"\\n website = \\\"www.aena.es/es/aeropuerto-lanzarote\\\"\\n commercial_name = \\\"Aeropuerto de Lanzarote-Cesar Manrique\\\"\\n\\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \\\"Alastria\\\", \\\"ThePassword\\\", False)\\n if didDoc is not None:\\n pprint(didDoc)\\n\\n ################################\\n # Metrovacesa\\n print(f\\\"\\\\n==> Creating the Metrovacesa identity\\\")\\n\\n DID = \\\"did:elsi:VATES-A87471264\\\"\\n domain_name = \\\"metrovacesa.ala\\\"\\n website = \\\"metrovacesa.com\\\"\\n commercial_name = \\\"Metrovacesa\\\"\\n\\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \\\"Alastria\\\", \\\"ThePassword\\\", False)\\n if didDoc is not None:\\n pprint(didDoc)\\n\\n ################################\\n # IN2\\n print(f\\\"\\\\n==> Creating the IN2 identity\\\")\\n\\n DID = \\\"did:elsi:VATES-B60645900\\\"\\n domain_name = \\\"in2.ala\\\"\\n website = \\\"www.in2.es\\\"\\n commercial_name = \\\"IN2 Innovating 2gether\\\"\\n\\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \\\"Alastria\\\", \\\"ThePassword\\\", False)\\n if didDoc is not None:\\n pprint(didDoc)\\n\\n ################################\\n # Perfect Health\\n print(f\\\"\\\\n==> Creating the Perfect Health identity\\\")\\n\\n DID = \\\"did:elsi:VATES-X12345678X\\\"\\n domain_name = \\\"perfecthealth.ala\\\"\\n website = \\\"www.perfecthealth.org\\\"\\n commercial_name = \\\"Perfect Health plc\\\"\\n\\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \\\"Alastria\\\", \\\"ThePassword\\\", False)\\n if didDoc is not None:\\n pprint(didDoc)\\n\\n ################################\\n # BME\\n print(f\\\"\\\\n==> Creating the BME identity\\\")\\n\\n DID = \\\"did:elsi:VATES-A83246314\\\"\\n domain_name = \\\"bme.ala\\\"\\n website = \\\"www.bolsasymercados.es\\\"\\n commercial_name = \\\"Bolsas y Mercados Españoles\\\"\\n\\n error, didDoc = create_identity(DID, domain_name, website, commercial_name, \\\"Alastria\\\", \\\"ThePassword\\\", False)\\n if didDoc is not None:\\n pprint(didDoc)\",\n \"def getIdentity():\\n return Sentience.__IDENTITY.lower()\",\n \"def identity(self):\\r\\n self.piDD = {\\\"[1]\\\": None}\\r\\n self.top_node = \\\"[1]\\\"\\r\\n self.dim = 0\",\n \"async def client_identity(self) -> Identity:\\n raise NotImplementedError\",\n \"def primary(self):\\n ...\",\n \"def identity_block(input_tensor, units):\\n x = layers.Dense(units, kernel_regularizer=reg)(input_tensor)\\n x = l()(x)\\n x = layers.Activation('relu')(x)\\n\\n x = layers.Dense(units, kernel_regularizer=reg)(x)\\n x = l()(x)\\n x = layers.Activation('relu')(x)\\n\\n x = layers.Dense(units, kernel_regularizer=reg)(x)\\n x = l()(x)\\n x = layers.add([x, input_tensor])\\n x = layers.Activation('relu')(x)\\n\\n return x\",\n \"def test_identity_single(self, dev):\\n dev = qml.device(dev, wires=1)\\n\\n @qml.qnode(dev)\\n def circuit():\\n qml.PauliX(0)\\n return qml.expval(qml.Identity(0))\\n\\n assert circuit() == 1\",\n \"async def server_identity(self) -> Identity:\\n raise NotImplementedError\",\n \"def identity_block(self, input_tensor, filters, activation, stage, block, start_with_batch_norm = True):\\n\\n # Set the naming convention\\n conv_name_base = 'res' + str(stage) + block + '_branch'\\n batch_norm_name_base = 'batch_norm' + str(stage) + block + '_branch'\\n\\n x_shortcut = input_tensor\\n x = None\\n\\n if start_with_batch_norm:\\n # Start with Batch Normalization\\n x = BatchNormalization(axis = 3, name = batch_norm_name_base + '_1')(input_tensor)\\n\\n x = Activation(activation)(x)\\n \\n else:\\n x = input_tensor\\n \\n # Main Path Block a\\n x = self.main_path_block(\\n x,\\n filters,\\n (3, 3),\\n 'same',\\n conv_name_base + '2a',\\n batch_norm_name_base + '2a',\\n activation = activation\\n )\\n\\n # Main Path Block b\\n x = self.main_path_block(\\n x, filters,\\n (3, 3), 'same',\\n conv_name_base + '2b',\\n batch_norm_name_base + '2b',\\n batch_norm = False,\\n activation = None\\n )\\n\\n # Add skip connection\\n x = Add()([x, x_shortcut])\\n\\n return x\",\n \"def __init__(self):\\n self.unconfirmed_transactions = [] \\n self.chain = []\\n self.create_genesis_block()\",\n \"def test_identity(self):\\n me = self.d.identity()\\n self.assertEqual(me.data['consumer_name'], 'Test Client')\\n self.assertEqual(me, self.d.user('example'))\",\n \"def manually_get_block_identity_op(self, space):\\n i = self.get_identity_op(space, OP_DOM, OP_DOM, OP_DUA)\\n I = assembly.BlockedOperator(2, 2)\\n I[0, 0] = i\\n I[1, 1] = i\\n return I\",\n \"def begin(self):\\n pass\",\n \"def identity(self, *args, **kwargs):\\n return {\\n 'id': self.drone_id,\\n }\",\n \"def __init__(self, inst: ghidra.program.model.listing.Instruction, bl: ghidra.program.model.correlate.Block, ind: int):\\n ...\",\n \"def do_id (self, line) :\\n\\t\\tprint \\\"\\tuid=%s(%s)\\tgid=%s\\\"\\t% (self.__image['meta']['UID'], self.__image['meta']['user'], self.__image['meta']['GID'] )\",\n \"def identity_block(self, input_tensor, middle_filter_size, filters, stage, block):\\n\\n # Set the naming convention\\n conv_name_base = 'res' + str(stage) + block + '_branch'\\n batch_norm_name_base = 'batch_norm' + str(stage) + block + '_branch'\\n\\n x_shortcut = input_tensor\\n\\n # First Block of main path\\n x = self.main_path_block(\\n input_tensor,\\n filters[0],\\n (1, 1),\\n 'valid',\\n conv_name_base + '2a',\\n batch_norm_name_base + '2a',\\n 'relu'\\n )\\n\\n # Middle Block of main path\\n x = self.main_path_block(\\n x,\\n filters[1],\\n (\\n middle_filter_size,\\n middle_filter_size\\n ),\\n 'same',\\n conv_name_base + '2b',\\n batch_norm_name_base + '2b',\\n 'relu'\\n )\\n\\n # Last Block of main path\\n x = self.main_path_block(\\n x,\\n filters[2],\\n (1, 1),\\n 'valid',\\n conv_name_base + '2c',\\n batch_norm_name_base + '2c',\\n 'relu'\\n )\\n\\n # Skip Connection\\n x = Add()([x, x_shortcut])\\n x = Activation('relu')(x)\\n\\n return x\",\n \"def identify(self):\\n self.__send_short(self.MGMSG_MOD_IDENTIFY, 0x00, 0x00)\",\n \"def __init__(self):\\n self.id = id(self)\",\n \"def get_identity(self):\\n return GetIdentity(*self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_IDENTITY, (), '', 33, '8s 8s c 3B 3B H'))\",\n \"def testIdentity(self):\\n identClass = xyTransformRegistry[\\\"identity\\\"]\\n with lsst.utils.tests.getTempFilePath(\\\".py\\\") as filePath:\\n self.checkConfig(identClass, identClass.ConfigClass(), filePath)\\n ident = identClass(identClass.ConfigClass())\\n self.assertEqual(type(ident), IdentityXYTransform)\\n self.checkBasics(ident)\\n for fromPoint in self.fromIter():\\n toPoint = ident.forwardTransform(fromPoint)\\n for i in range(2):\\n self.assertAlmostEqual(fromPoint[i], toPoint[i])\",\n \"def __init__(self):\\n self.chain = [Block.genesis()]\",\n \"def body(self):\",\n \"def getIdent (self) :\\n return self.id\",\n \"def identity(self) -> Optional[pulumi.Input['IdentityArgs']]:\\n return pulumi.get(self, \\\"identity\\\")\",\n \"def __displayID(self): # displayID is a private method\\n print(\\\"ID:\\\", self.ID)\",\n \"def identityInfo(self) -> dict:\\r\\n\\r\\n return deepcopy(self.__identity_info)\",\n \"def block_incidence_matrix(self, block_id, typestr, ctx): # -> tuple[Unknown, Unknown]:\\n ...\",\n \"def is_identity(node: onnx.NodeProto) -> bool:\\n return node.op_type == 'Identity'\",\n \"def __init__(self):\\n self.id = uuid.uuid4().hex\",\n \"def id(self):\\n raise NotImplementedError()\",\n \"def block(self):\\n if self._block is None:\\n bunchdt, block, data, commdct, idd_index, versiontuple = idfreader1(\\n self.idfname, self.iddname, self, commdct=None, block=None\\n )\\n self._block = block\\n self._idd_info = commdct\\n self._idd_index = idd_index\\n self._idfobjects = bunchdt\\n self._model = data\\n self._idd_version = versiontuple\\n return self._block\",\n \"def get_id(self): # pragma: no cover\\n pass\",\n \"def getId(self):\",\n \"def identity(self) -> Optional[pulumi.Input['IdentityInfoArgs']]:\\n return pulumi.get(self, \\\"identity\\\")\",\n \"def blocks(self):\\n pass\",\n \"def identity_block(inputs, kernel_size, filters, stage, block,\\n training):\\n nb_filter1, nb_filter2, nb_filter3 = filters\\n\\n scope_name = 'conv'+str(stage)+block+'_branch'\\n scope_name = 'bn'+str(stage)+block+'_branch'\\n\\n x = slim.conv2d(inputs,nb_filter1,[1,1],stride=[1,1],padding='VALID',activation_fn=None,\\n trainable=training,scope=scope_name+'2a')\\n x = slim.batch_norm(x,scope=scope_name+'2a',is_training=training)\\n x = tf.nn.relu(x)\\n\\n\\n x = slim.conv2d(x,nb_filter2,[kernel_size,kernel_size],stride=[1,1],padding='SAME',\\n activation_fn=None,trainable=training,scope=scope_name+'2b')\\n x = slim.batch_norm(x,scope=scope_name+'2b',is_training=training)\\n x = tf.nn.relu(x)\\n\\n\\n x = slim.conv2d(x,nb_filter3,[1,1],stride=[1,1],padding='VALID',\\n activation_fn=None,trainable=training,scope=scope_name+'2c')\\n x = slim.batch_norm(x,scope=scope_name+'2c',is_training=training)\\n\\n \\n x = tf.add(x,inputs)\\n x = tf.nn.relu(x,name='res'+str(stage)+block+\\\"_out\\\")\\n return x\",\n \"def identity_block(input_tensor, kernel_size, filters, stage, block):\\n filters0, filters1, filters2 = filters\\n conv_name_base = 'res' + str(stage) + block\\n bn_name_base = 'bn' + str(stage) + block\\n add_name = 'add' + str(stage) + \\\"_\\\" + block\\n relu_name = 'relu' + str(stage) + \\\"_\\\" + block\\n\\n # Tensors\\n input_tensor_chans = input_tensor.dims(\\n 3) if input_tensor.shape.layout == sg.NHWC else input_tensor.dims(1)\\n conv0_tensor = sg.Tensor(\\n data_layout=sg.NHWC, tensor_data=generate_random_data(\\n (filters0, 1, 1, input_tensor_chans)))\\n bn0_mean_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters0)))\\n bn0_var_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters0)))\\n bn0_gamma_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters0)))\\n bn0_beta_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters0)))\\n conv1_tensor = sg.Tensor(\\n data_layout=sg.NHWC, tensor_data=generate_random_data(\\n (filters1, kernel_size, kernel_size, filters0)))\\n bn1_mean_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters1)))\\n bn1_var_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters1)))\\n bn1_gamma_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters1)))\\n bn1_beta_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters1)))\\n conv2_tensor = sg.Tensor(\\n data_layout=sg.NHWC, tensor_data=generate_random_data(\\n (filters2, 1, 1, filters1)))\\n bn2_mean_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters2)))\\n bn2_var_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters2)))\\n bn2_gamma_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters2)))\\n bn2_beta_tensor = sg.Tensor(\\n data_layout=sg.NC, tensor_data=generate_random_data((1, filters2)))\\n\\n x = sg.nn.convolution(\\n input_tensor, conv0_tensor, stride=[1, 1], padding=\\\"same\\\",\\n name=conv_name_base + '_2a')\\n x = sg.nn.batch_norm(\\n x, bn0_mean_tensor, bn0_var_tensor, bn0_gamma_tensor, bn0_beta_tensor,\\n activation=\\\"relu\\\", name=bn_name_base + '_2a')\\n x = sg.nn.convolution(\\n x, conv1_tensor, stride=[1, 1], padding=\\\"same\\\",\\n name=conv_name_base + '_2b')\\n x = sg.nn.batch_norm(\\n x, bn1_mean_tensor, bn1_var_tensor, bn1_gamma_tensor, bn1_beta_tensor,\\n activation=\\\"relu\\\", name=bn_name_base + '_2b')\\n x = sg.nn.convolution(\\n x, conv2_tensor, stride=[1, 1], padding=\\\"same\\\",\\n name=conv_name_base + '_2c')\\n x = sg.nn.batch_norm(\\n x, bn2_mean_tensor, bn2_var_tensor, bn2_gamma_tensor, bn2_beta_tensor,\\n name=bn_name_base + '_2c')\\n x = sg.math.add(x, input_tensor, name=add_name)\\n x = sg.nn.relu(x, name=relu_name)\\n return x\",\n \"def identity(self) -> pulumi.Output[Optional['outputs.IdentityResponse']]:\\n return pulumi.get(self, \\\"identity\\\")\",\n \"def __init__(self):\\n self.block = 1000\\n self._map = [] \\n self.len = 0 \\n self.incr()\",\n \"async def begin_object(self):\",\n \"def remember_identity(response, request, identity):\\n raise NotImplementedError # pragma: nocoverage\",\n \"def create_individual(self):\\n pass\",\n \"def identity(self) -> pulumi.Output[Optional['outputs.IdentityInfoResponse']]:\\n return pulumi.get(self, \\\"identity\\\")\",\n \"def name(self):\\r\\n return self.block_id\",\n \"def spam(self):\\n return id(self)\",\n \"def __enter__(self):\\n pass\",\n \"def __enter__(self):\\n pass\",\n \"def degibber(self):\",\n \"def identifier(self):\\r\\n return self.id\",\n \"def id(self):\\n return super().id()\",\n \"def user(self):\\n pass\",\n \"def get_identity(self):\\n return self.query_serial('*IDN?')\",\n \"def user(self):\",\n \"def identify(self):\\r\\n if \\\"signature\\\" in self or \\\"signatures\\\" in self:\\r\\n if len(self._multisignature):\\r\\n missings = \\\\\\r\\n self._multisignature[\\\"min\\\"] - \\\\\\r\\n len(self.get(\\\"signatures\\\", []))\\r\\n if missings:\\r\\n raise Exception(\\\"owner signature missing (%d)\\\" % missings)\\r\\n elif self._secondPublicKey:\\r\\n if \\\"signSignature\\\" not in self:\\r\\n raise Exception(\\\"second signature is missing\\\")\\r\\n dict.pop(self, \\\"id\\\", False)\\r\\n self[\\\"id\\\"] = dposlib.core.crypto.getIdFromBytes(\\r\\n serialize(self, exclude_multi_sig=False)\\r\\n )\\r\\n else:\\r\\n raise Exception(\\\"transaction not signed\\\")\",\n \"def test_outgoing_missing_identity(self):\\n community = DebugCommunity.create_community(self._dispersy, self._my_member)\\n node = DebugNode(community)\\n node.init_socket()\\n node.init_my_member(candidate=False, identity=False)\\n node.drop_packets()\\n\\n # NODE sends a message to SELF\\n node.give_message(node.create_full_sync_text(\\\"Hello World\\\", 10))\\n\\n # SELF must not yet process the 'Hello World' message\\n self.assertEqual(community.fetch_messages(u\\\"full-sync-text\\\"), [])\\n\\n # SELF must send a missing-identity to NODE\\n responses = node.receive_messages()\\n self.assertEqual(len(responses), 1)\\n for _, response in responses:\\n self.assertEqual(response.name, u\\\"dispersy-missing-identity\\\")\\n self.assertEqual(response.payload.mid, node.my_member.mid)\\n\\n # NODE sends the identity to SELF\\n node.give_message(node.create_dispersy_identity(2))\\n\\n # SELF must now process and store the 'Hello World' message\\n messages = community.fetch_messages(u\\\"full-sync-text\\\")\\n self.assertEqual(len(messages), 1)\\n for message in messages:\\n self.assertEqual(message.payload.text, \\\"Hello World\\\")\",\n \"def __enter__(self):\\r\\n pass\",\n \"def test_list_identity(self):\\n pass\",\n \"def identity(self) -> str:\\n return self.requester.uuid\",\n \"def _generate_genesis() -> None:\\n logging.debug(\\\"Generating the genesis block\\\")\\n new_recv_block(Block.genesis())\",\n \"def add_anonymous_block(self, entity, layer=\\\"0\\\", typechar='U',\\n basepoint=(0, 0), insert=(0, 0)):\\n blockname = self.anonymous_blockname(typechar)\\n block = DXFEngine.block(blockname, basepoint=basepoint,\\n flags=const.BLK_ANONYMOUS)\\n block.add(entity)\\n self.blocks.add(block)\\n insert = DXFEngine.insert(blockname, insert=insert, layer=layer)\\n self.add(insert)\\n return blockname\",\n \"def _identity_block(self, input_tensor, kernel_size, filters, stage, block, dilation=1):\\n filters1, filters2, filters3 = filters\\n if backend.image_data_format() == 'channels_last':\\n bn_axis = 3\\n else:\\n bn_axis = 1\\n\\n if block > 'z':\\n block = chr(ord(block) - ord('z') + ord('A') - 1)\\n\\n conv_name_base = 'res' + str(stage) + block + '_branch'\\n bn_name_base = 'bn' + str(stage) + block + '_branch'\\n\\n x = layers.Conv2D(filters1, (1, 1),\\n kernel_initializer='he_normal',\\n name=conv_name_base + '2a')(input_tensor)\\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\\n x = layers.Activation('relu')(x)\\n\\n x = layers.Conv2D(filters2, kernel_size,\\n padding='same',\\n kernel_initializer='he_normal',\\n name=conv_name_base + '2b',\\n dilation_rate=dilation)(x)\\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\\n x = layers.Activation('relu')(x)\\n\\n x = layers.Conv2D(filters3, (1, 1),\\n kernel_initializer='he_normal',\\n name=conv_name_base + '2c')(x)\\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\\n\\n x = layers.add([x, input_tensor])\\n x = layers.Activation('relu')(x)\\n return x\",\n \"def unique_id(self):\\n return f\\\"{self.wrapper.mac}-{self.block.index}\\\"\",\n \"def test_delete_identity(self):\\n pass\",\n \"def onBlock(self, data) :\\n pass\",\n \"def identity_function(self, qubits):\\n\\n # Note: ProjectQ doesn't actually have an I gate, so this test does absolutely\\n # nothing.\",\n \"def get_identity(self):\\n return GetIdentity(*self.ipcon.send_request(self, BrickletBarometerV2.FUNCTION_GET_IDENTITY, (), '', '8s 8s c 3B 3B H'))\",\n \"def autoid(self) -> str:\",\n \"def autoid(self) -> str:\",\n \"def party_id(self):\\n pass\",\n \"def create_autoIT_block(self):\\n current_counter = str(self.csh.counter.current())\\n self.csh.add_task('KeePass_' + current_counter, self.create_autoit_function())\",\n \"def generate(self):\\n pass\",\n \"def get_ident():\\n return -1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71393317","0.65090305","0.6266525","0.6132871","0.6096772","0.60785687","0.6077503","0.6056983","0.6046447","0.59905106","0.5987439","0.5978849","0.5946789","0.5917424","0.5910534","0.589202","0.58327717","0.5799545","0.57756495","0.5773348","0.57540077","0.57426196","0.5728346","0.5711338","0.5699427","0.56829643","0.5564756","0.5562783","0.5558736","0.55576533","0.55564857","0.55549586","0.55422014","0.5540238","0.55401576","0.5498208","0.54844546","0.5479229","0.54706097","0.54687905","0.54384536","0.54309046","0.54274005","0.54090285","0.53918463","0.5374507","0.5362546","0.53484714","0.5342866","0.5330077","0.5326558","0.5324572","0.53150433","0.5302083","0.5296673","0.52875876","0.5279317","0.5276109","0.52753246","0.5275312","0.52682567","0.5265325","0.52591634","0.52500236","0.5246571","0.5242145","0.5236873","0.5211785","0.52079445","0.52035743","0.5195203","0.5194131","0.51908565","0.51812816","0.5176082","0.5176082","0.5176043","0.5174815","0.5158021","0.5157652","0.5155299","0.5154195","0.51539683","0.51507187","0.5136403","0.5133841","0.5131115","0.5129045","0.5124897","0.5123857","0.5122898","0.51179534","0.511762","0.5115748","0.5113935","0.51102096","0.51102096","0.510092","0.5099767","0.5095715","0.50944513"],"string":"[\n \"0.71393317\",\n \"0.65090305\",\n \"0.6266525\",\n \"0.6132871\",\n \"0.6096772\",\n \"0.60785687\",\n \"0.6077503\",\n \"0.6056983\",\n \"0.6046447\",\n \"0.59905106\",\n \"0.5987439\",\n \"0.5978849\",\n \"0.5946789\",\n \"0.5917424\",\n \"0.5910534\",\n \"0.589202\",\n \"0.58327717\",\n \"0.5799545\",\n \"0.57756495\",\n \"0.5773348\",\n \"0.57540077\",\n \"0.57426196\",\n \"0.5728346\",\n \"0.5711338\",\n \"0.5699427\",\n \"0.56829643\",\n \"0.5564756\",\n \"0.5562783\",\n \"0.5558736\",\n \"0.55576533\",\n \"0.55564857\",\n \"0.55549586\",\n \"0.55422014\",\n \"0.5540238\",\n \"0.55401576\",\n \"0.5498208\",\n \"0.54844546\",\n \"0.5479229\",\n \"0.54706097\",\n \"0.54687905\",\n \"0.54384536\",\n \"0.54309046\",\n \"0.54274005\",\n \"0.54090285\",\n \"0.53918463\",\n \"0.5374507\",\n \"0.5362546\",\n \"0.53484714\",\n \"0.5342866\",\n \"0.5330077\",\n \"0.5326558\",\n \"0.5324572\",\n \"0.53150433\",\n \"0.5302083\",\n \"0.5296673\",\n \"0.52875876\",\n \"0.5279317\",\n \"0.5276109\",\n \"0.52753246\",\n \"0.5275312\",\n \"0.52682567\",\n \"0.5265325\",\n \"0.52591634\",\n \"0.52500236\",\n \"0.5246571\",\n \"0.5242145\",\n \"0.5236873\",\n \"0.5211785\",\n \"0.52079445\",\n \"0.52035743\",\n \"0.5195203\",\n \"0.5194131\",\n \"0.51908565\",\n \"0.51812816\",\n \"0.5176082\",\n \"0.5176082\",\n \"0.5176043\",\n \"0.5174815\",\n \"0.5158021\",\n \"0.5157652\",\n \"0.5155299\",\n \"0.5154195\",\n \"0.51539683\",\n \"0.51507187\",\n \"0.5136403\",\n \"0.5133841\",\n \"0.5131115\",\n \"0.5129045\",\n \"0.5124897\",\n \"0.5123857\",\n \"0.5122898\",\n \"0.51179534\",\n \"0.511762\",\n \"0.5115748\",\n \"0.5113935\",\n \"0.51102096\",\n \"0.51102096\",\n \"0.510092\",\n \"0.5099767\",\n \"0.5095715\",\n \"0.50944513\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80387,"cells":{"query":{"kind":"string","value":"Onehot encoding is applied to columns specified in a pandas DataFrame."},"document":{"kind":"string","value":"def encode_onehot(df, cols):\n vec = DictVectorizer()\n\n vec_data = pd.DataFrame(vec.fit_transform(df[cols].to_dict(orient='records')).toarray())\n vec_data.columns = vec.get_feature_names()\n vec_data.index = df.index\n\n df = df.drop(cols, axis=1)\n df = df.join(vec_data)\n return df"},"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 one_hot_encode(df, colnames):\r\n for col in colnames:\r\n oh_df = get_dummies(df[col], prefix=col)\r\n df = concat([oh_df, df], axis=1)\r\n df = df.drop([col], axis=1)\r\n return df","def one_hot(input_df, columns):\n df = input_df.copy()\n\n for col in columns:\n dummies = pd.get_dummies(df[col].str.lower())\n dummies.drop(dummies.columns[-1], axis=1, inplace=True)\n df = df.drop(col, axis=1).merge(dummies, left_index=True, right_index=True)\n \n return df","def one_hot_encoder(df, cols):\r\n\r\n for col in cols:\r\n if(\"admission\" in col):\r\n dummies = pd.get_dummies(df[col], drop_first=False)\r\n else:\r\n dummies = pd.get_dummies(df[col], prefix=col, drop_first=False)\r\n df = pd.concat([df, dummies], axis=1) \r\n df.drop([col],axis=1, inplace=True)\r\n return df","def one_hot_encode(df, ohe_cols):\n return pd.get_dummies(df, columns=ohe_cols)","def one_hot(df):\r\n # One-hot encode into \r\n cols = ['job', 'marital', 'education', 'month', 'day_of_week', 'poutcome']\r\n for each in cols:\r\n dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)\r\n df = pd.concat([df, dummies], axis=1)\r\n df = df.drop(cols,axis=1)\r\n return df","def one_hot(df, cols):\n for col in cols:\n dummies = pd.get_dummies(df[col], prefix=col, drop_first=False)\n df = pd.concat([df, dummies], axis=1)\n df = df.drop(col, axis=1)\n return df","def encode_one_hot(df, col_names) -> pd.DataFrame:\n for col_name in col_names:\n encoded = pd.get_dummies(df[col_name], prefix=col_name, drop_first=False)\n df = pd.concat([df, encoded], axis=1)\n df = df.drop([col_name], axis=1)\n return df","def one_hot_encode(df, col):\n return pd.get_dummies(df, columns=[col], drop_first=True)","def one_hot_enc(df, ohe_cols):\n for col in ohe_cols:\n df_ohe = pd.get_dummies(df[col], preffix=col)\n df = pd.concat([df, df_ohe], axis=1)\n df.drop(col, axis=1, inplace=True)\n return df","def encode_onehot(df, cols):\n vec = DictVectorizer()\n \n vec_data = pd.DataFrame(vec.fit_transform(df[cols].to_dict(outtype='records')).toarray())\n vec_data.columns = vec.get_feature_names()\n vec_data.index = df.index\n \n df = df.drop(cols, axis=1)\n df = df.join(vec_data)\n return df","def encoding_onehot(df, target=None):\n if not target:\n target = ['user_type', 'city']\n for col in target:\n # Following is exactily the df.join() but is inplace.\n one_hot = pandas.get_dummies(df[col])\n for item in one_hot:\n df[item] = one_hot[item]\n df.drop([col], axis=1, inplace=True)\n return None","def one_hot_encoder(df, nan_as_category=True):\n original_columns = list(df.columns)\n categorical_columns = [col for col in df.columns if\n df[col].dtype == 'object']\n df = pd.get_dummies(df, columns=categorical_columns,\n dummy_na=nan_as_category)\n new_columns = [c for c in df.columns if c not in original_columns]\n return df, new_columns","def __encode_one_hot_util(self):\n for col in self.cat_cols:\n if (\n col in self.train_df\n and col + str(\"Encoded\") not in self.ord_cols\n ):\n if self.test_df is not None:\n self.test_df = pd.concat(\n [\n self.test_df,\n pd.get_dummies(\n self.test_df[col], prefix=col\n ).astype(\"category\"),\n ],\n axis=1,\n )\n self.train_df = pd.concat(\n [\n self.train_df,\n pd.get_dummies(self.train_df[col], prefix=col).astype(\n \"category\"\n ),\n ],\n axis=1,\n )","def onehot_enc(df, categorical_columns, categories):\n noncategorical_cols = [col for col in df.columns if col not in categorical_columns]\n \n enc = OneHotEncoder(categories=categories,\n sparse=False,\n handle_unknown='ignore')\n y = enc.fit_transform(df[categorical_columns].fillna(\"None\"))\n \n ohe_cols = [\n f\"{col}_{c}\" for col, cats in zip(categorical_columns, categories) for c in cats]\n df1 = pd.DataFrame(y, columns=ohe_cols)\n \n output_df = pd.concat([df[noncategorical_cols], df1], axis=1)\n return output_df, ohe_cols","def pre_process_data(df):\n\n # one-hot encode categorical values\n df = pd.get_dummies(df)\n\n return df","def own_OneHotColumnCreator(df, columns):\n for col in cat_attribs:\n for value in df[col].unique():\n df[value] = (df[col] == value).astype(int)","def one_hot_encoding(data):\n\n data_encoded = pd.get_dummies(data)\n\n return data_encoded","def one_hot_encode(self, columns):\n if not columns:\n return None\n\n logging.info('one-hot-encoding columns: %s' % ','.join(columns))\n\n both_sets = pd.concat((self.train[columns], self.test[columns]))\n encoder = preprocessing.OneHotEncoder()\n encoded = encoder.fit_transform(both_sets).sorted_indices()\n\n # Split apart train and test set arrays after one-hot encoding.\n nd_train = self.train.shape[0]\n train_enc = encoded[:nd_train]\n test_enc = encoded[nd_train:]\n\n for col in columns:\n logging.debug('unique {}: {}'.format(col, both_sets[col].unique()))\n\n # Create a feature map for decoding one-hot encoding.\n counts = np.array([both_sets[col].unique().shape[0] for col in columns])\n fmap = []\n for i, column in enumerate(columns):\n unique_elements = np.sort(both_sets[column].unique())\n logging.debug('unique elements for col {}: {}'.format(\n column, unique_elements))\n fmap += ['%s-%d' % (column, idx) for idx in unique_elements]\n\n logging.info('after one-hot encoding, found # unique values:')\n for attr, n_values in zip(columns, counts):\n logging.info('%s: %d' % (attr, n_values))\n\n return train_enc, test_enc, fmap, encoder","def _one_hot_encoder(self):\n ohe = preprocessing.OneHotEncoder()\n ohe.fit(self.dataframe[self.cat_feats])\n return ohe.transform(self.dataframe_d_copy[self.cat_feats])","def one_hot_encode(self, column: str) -> None:\n # Create the encoder\n encoder = OneHotEncoder(sparse=False, drop=\"first\")\n\n # Fit the encoder and transform the data\n transformed_data = encoder.fit_transform(self.df[[column]])\n\n # Get feature names\n encoded_features = encoder.get_feature_names_out([column])\n\n # Convert the transformed data into a dataframe\n encoded_df = pd.DataFrame(transformed_data, columns=encoded_features)\n\n # Drop the original column from the dataframe\n self.df = self.df.drop(column, axis=1)\n\n # Concatenate the original dataframe with the encoded dataframe\n self.df = pd.concat([self.df, encoded_df], axis=1)\n\n # Update the features list to include the new one-hot encoded features and exclude the original categorical feature\n self.features = [\n feature for feature in self.features if feature != column\n ] + list(encoded_features)","def complete_one_hot_variables(df: pd.DataFrame, var_names: list) -> pd.DataFrame:\n\n df = df.copy()\n for var in var_names:\n # Create a column of 0s\n df[var] = 0\n return df","def onehot_encode(self, col_need, start_zero=True):\r\n self.onehot = OneHotEncoder(sparse=False)\r\n array_onehot = self.onehot.fit_transform(self.df.loc[:, col_need])\r\n\r\n col_onehot = []\r\n\r\n for col_index in range(len(col_need)):\r\n if start_zero:\r\n for hot_index in range(self.onehot.n_values_[col_index]):\r\n col_onehot.append(col_need[col_index] + str(hot_index))\r\n else:\r\n for hot_index in range(1, self.onehot.n_values_[col_index]):\r\n col_onehot.append(col_need[col_index] + str(hot_index))\r\n\r\n self.df.drop(col_need, axis=1, inplace=True)\r\n\r\n df_onehot = pd.DataFrame(array_onehot, columns=col_onehot, index=self.df.index)\r\n self.df = pd.concat([self.df, df_onehot], axis=1)","def one_hot_encoding(self):\n \n try: \n\n # Encode dependent variable\n le = LabelEncoder()\n le.fit(self.data[\"consumption\"])\n df_dependent_enc = pd.DataFrame(le.transform(self.data[\"consumption\"]))\n\n # Encode independent variable\n categorical_features = Config.FEATURE_DEFINITION[\"category_cols\"]\n categorical_df = self.data.loc[:, self.data.columns.isin(categorical_features)]\n oe = OrdinalEncoder()\n oe.fit(categorical_df)\n df_catindependent_enc = pd.DataFrame(oe.transform(categorical_df))\n df_catindependent_enc.columns = categorical_df.columns\n\n except KeyError: \n\n st.write(\"Cannot perform one-hot encoding for numerical variables. Please check if variables are properly defined.\")\n st.write(self.data.columns != \"consumption\")\n df_dependent_enc = []\n df_catindependent_enc = []\n\n else:\n \n return df_dependent_enc, df_catindependent_enc","def dummify_features(df):\n colnames = df.columns\n le_dict = {}\n for col in colnames:\n le_dict[col] = preprocessing.LabelEncoder()\n le_dict[col].fit(df[col])\n df.loc[:, col] = le_dict[col].transform(df[col])\n\n enc = preprocessing.OneHotEncoder()\n enc.fit(df)\n X = enc.transform(df)\n\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\n # for cv in colnames:\n # for modality in le_dict[cv].classes_:\n # dummy_colnames.append(cv + '_' + modality)\n\n return X, dummy_colnames, enc","def onehot_features(data):\n\n# Binary Features\n columns = ['Weekend', 'Revenue']\n for col in columns:\n data[col] = data[col].apply(lambda x: float(1) if x else float(0))\n\n columns = ['Month', 'OperatingSystems', 'Browser', 'Region', 'TrafficType',\n 'VisitorType']\n for col in columns:\n enc = OneHotEncoder()\n data_array = enc.fit_transform(data[[col]]).toarray()\n enc_data = pd.DataFrame(data_array)\n enc_data.columns = list(enc.get_feature_names([col]))\n data = data.join(enc_data)\n\n data = data.drop(columns={'Month', 'Month_May', 'OperatingSystems',\n 'OperatingSystems_2', 'Browser', 'Browser_2',\n 'Region', 'Region_1.0', 'TrafficType',\n 'TrafficType_2', 'VisitorType',\n 'VisitorType_Returning_Visitor'})\n return data","def encoding_df(df, cols):\n import pandas as pd\n df = df[cols]\n obj_df = df.select_dtypes(include=['object']).copy()\n num_var = df.select_dtypes(include=['int','float']).copy()\n cat_var = pd.get_dummies(obj_df, columns = obj_df.columns)\n encoded_df = pd.concat([num_var, cat_var], axis=1, sort=False)\n return encoded_df","def one_hot_encode(self, meta_field):\n one_hot = pd.get_dummies(self.sample_meta[meta_field]).values\n return one_hot","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 onehot_encode_labels(y):\n\treturn OneHotEncoder(categories=\"auto\", sparse=False).fit_transform(y.reshape(y.shape[0],1))","def one_hot(x, num_classes, dtype=jnp.float32):\n return jax.nn.one_hot(x, num_classes).astype(dtype)","def one_hot_encoding(data):\r\n encoder = LabelEncoder()\r\n y = encoder.fit_transform(data)\r\n return(y)","def process(self, data_set: DataSet):\n logging.info(f\"\\nOne hot encoding DataSet...\")\n if len(data_set.get_categorical_attribute_indexes()) == 0:\n logging.info(f\"No need to one hot encode. No categorical variables left.\")\n return\n\n for index in data_set.get_categorical_attribute_indexes():\n column = data_set.get_attribute_column(index)\n\n unique = np.unique(column)\n logging.info(f\"Attribute index '{index}' has categorical variables: {unique}\")\n unique_dict = {x: i for i, x in enumerate(unique)}\n new_columns = [[0. for i in range(len(data_set))] for j in unique]\n\n for i, value in enumerate(column):\n new_columns[unique_dict[value]][i] = 1.\n\n for col in new_columns:\n data_set.add_column(col)\n\n for index in sorted(data_set.get_categorical_attribute_indexes(), reverse=True):\n data_set.remove_column(index)\n\n logging.info('One hot encoded DataSet:')\n logging.info(data_set.summary())","def onehot(train_cat, test_cat):\n train_enc = []\n test_enc = []\n columns = []\n for c in cols(train_cat):\n train = train_cat.loc[:,c]\n test = test_cat.loc[:,c]\n labels = list(set(train.unique().tolist()) |\n set(test.unique().tolist()))\n\n l_encoder = LabelEncoder()\n l_encoder.fit(labels)\n oh_encoder = OneHotEncoder(sparse=False, n_values=len(labels))\n\n train_ftr = l_encoder.transform(train).reshape(len(train), 1)\n train_enc.append(oh_encoder.fit_transform(train_ftr))\n test_ftr = l_encoder.transform(test).reshape(len(test), 1)\n test_enc.append(oh_encoder.fit_transform(test_ftr))\n\n col_tmp = list(l_encoder.inverse_transform(range(len(labels))))\n columns = columns + [\"%s_%s\" % (c, i) for i in col_tmp]\n\n new_train_cat = pd.DataFrame(np.column_stack(train_enc), columns=columns,\n dtype=np.uint8)\n new_test_cat = pd.DataFrame(np.column_stack(test_enc), columns=columns,\n dtype=np.uint8)\n return new_train_cat, new_test_cat","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 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 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 dummify(df, colname):\n\n dummies = pd.get_dummies(df[colname])\n #dummies.drop(dummies.columns[0], axis=1, inplace=True) #remove arbitrarely the first column to avoid colinearity problem\n df.drop(colname, inplace=True, axis=1)\n return pd.concat([df, dummies], axis=1)","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 one_hot_encoding(data_dict):\n merged_seq = []\n merged_label = []\n for key in __MERGE_KEYS__:\n merged_seq += list(data_dict[key])\n merged_label += list([__MERGE_LABELS__[key]] * len(data_dict[key]))\n merged_seq_int = list(map(map_base_to_int, merged_seq))\n\n X = to_categorical(merged_seq_int)\n Y = to_categorical(merged_label)\n return X, Y","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 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 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_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(x, n_classes):\n return np.eye(n_classes)[x]","def _convert_to_dummies_pandas(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n print(\"Raw dataset shape = {}\".format(df.shape))\n print(\"Creating one hot encoded categories...\")\n for col in df.columns:\n if col in CATEGORICAL_TRANS and col != \"isFraud\" and not col.startswith(\"card\"):\n print(\"Handling category: {}\".format(col))\n # Convert to categorical type, may not be necessary.\n df[col] = pd.Categorical(df[col])\n one_hot_encoded = pd.get_dummies(df[col], prefix=col)\n df = df.drop(columns=[col]) # remove the original categorical column.\n # Add the one-hot-encoded column.\n df = pd.concat([df, one_hot_encoded], axis=1)\n print(\"One-hot-encoded dataset shape = {}\".format(df.shape))\n return df","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 dummify_all_categorical(df):\n\n df = pd.get_dummies(df)\n df = dummify(df, \"detailed industry recode\")\n df = dummify(df, \"detailed occupation recode\") ## add some variables that are encoded as int64 but that are in fact categorical\n return df","def create_dummies(df):","def get_binary_values(data_frame):\n all_columns = pandas.DataFrame( index = data_frame.index)\n for col in data_frame.columns:\n data = pandas.get_dummies(data_frame[col], prefix=col.encode('ascii', 'replace'))\n all_columns = pandas.concat([all_columns, data], axis=1)\n return all_columns","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 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 oneHotEncoding(X, list_indices):\n print('Onehotencoding..')\n X = X.astype(float)\n for i in list_indices:\n onehotencoder = OneHotEncoder(categorical_features=[i])\n X = onehotencoder.fit_transform(X)\n return X[1:]","def one_hot_encoding(l, keep_aggregate=False):\n df_enriched = pd.DataFrame({'cl': l})\n for i in l.unique():\n df_enriched[i] = list(map(int, df_enriched['cl'] == i))\n if not keep_aggregate:\n del df_enriched['cl']\n return df_enriched","def oneHot(df, y, clip=True, thresh=False):\n n = y.max() + 1\n m = df.shape[0]\n oh = np.zeros((m, n))\n for i in range(m):\n oh[i][y[i]] = 1 \n if clip is True:\n sum_ = oh.sum(axis=0)\n frac = sum_ / float(sum(sum_))\n if thresh is False:\n x = oh[:,frac > frac.mean()]\n else:\n x = oh[:,frac > thresh]\n else:\n x = oh\n return x","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(x, dtype=np.float32):\n return np.array(x[:, None] == np.arange(x.max()+1), dtype)","def _one_hot(self, key):\n train_df = self.train.copy(deep=False)\n one_hot = pd.get_dummies(train_df[key],drop_first=True)\n one_hot.columns=[key+'_'+col for col in one_hot.columns.values]\n train_df = pd.concat([train_df,one_hot],axis=1,sort=True)\n train_dfg = train_df.groupby('fullVisitorId')\n return train_dfg[one_hot.columns.values].sum()","def get_binary_values(data_frame):\n all_columns = pandas.DataFrame( index = data_frame.index)\n for col in data_frame.columns:\n data = pandas.get_dummies(data_frame[col], prefix=col.encode('ascii', 'replace'))\n all_columns = pandas.concat([all_columns, data], axis=1)\n return all_columns","def oneHot(index, n):\n x = np.zeros(n)\n x[index] = 1\n return x","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 get_dummies(df):\n col = df.select_dtypes('O').columns.tolist()\n nunique = df[col].nunique()\n col_binary = nunique[nunique==2].index.tolist()\n [col.remove(c) for c in col_binary]\n df = pd.get_dummies(df, columns=col)\n df = pd.get_dummies(df, columns=col_binary, drop_first=True)\n df.columns = [c.replace(' ', '-') for c in df.columns]\n return df","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 _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(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_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 process_categorical_data(data_df):\n return pd.get_dummies(data_df, columns=Columns.categorical)","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_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 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(a, actions, dtype=t.float32):\n assert 1 == a.shape[-1]\n return (a == t.arange(actions, device=a.device)).to(dtype=dtype)","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 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_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]","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]","def to_onehot(x, num_classes):\n return np.eye(num_classes, dtype='float32')[x]","def convert_categorical(df):\n print(\" --- Converting Categories into binary features.\")\n columns = df.columns\n categorical = [x for x in columns if x.startswith('c_')]\n for col in categorical:\n print(\" ---- Converting: {}\".format(col))\n category_binary = pd.get_dummies(df[col], prefix=col)\n df = pd.concat([df, category_binary], axis=1)\n df = df.drop(categorical, axis=1)\n print(\" --- Finished converting Categories into binary features.\")\n return df","def _convert_to_dummies_sklearn(df: pd.DataFrame) -> pd.DataFrame:\n raise NotImplementedError","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 _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 one_hot(y, num_classes):\n return np.eye(num_classes)[y]","def train(self, df):\n self.encoders = []\n self.feature_names = []\n for feature_name, column in df.iteritems():\n if column.dtype != object:\n continue\n self.feature_names.append(feature_name)\n column = column.to_numpy().reshape(-1, 1)\n ohe = sklearn.preprocessing.OneHotEncoder()\n ohe.fit(column)\n self.encoders.append(ohe)\n return self","def df2onehot(df, y_min=10, perc_min_num=0.8, dtypes='pandas', excl_background=None, verbose=3):\n from df2onehot import df2onehot\n\n # Convert dataframe to onehot by keeping only categorical variables.\n out = df2onehot(df, y_min=y_min, perc_min_num=perc_min_num, dtypes=dtypes, excl_background=excl_background, hot_only=True, verbose=verbose)\n # Numerical\n df_num = out['numeric'].iloc[:, out['dtypes']=='cat']\n df_num = df_num.astype(int)\n # One-hot\n df_hot = out['onehot']\n df_hot.columns = df_hot.columns.str.replace('_4.0', '_4')\n df_hot.columns = df_hot.columns.str.replace('_3.0', '_3')\n df_hot.columns = df_hot.columns.str.replace('_2.0', '_2')\n df_hot.columns = df_hot.columns.str.replace('_1.0', '_1')\n df_hot.columns = df_hot.columns.str.replace('_0.0', '_0')\n\n return df_hot, df_num","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 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, 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 generate_dummies(df, columns):\n\n dummy_df = pd.DataFrame()\n for col in columns:\n dummy_df = pd.concat([dummy_df, pd.get_dummies(df[col])], axis=1)\n\n return dummy_df","def To1hot(label,num_class):\n onehot = np.zeros(num_class)\n onehot[label] = 1\n return onehot","def df2onehot(df, y_min=10, perc_min_num=0.8, dtypes='pandas', excl_background=None, verbose=3):\n from df2onehot import df2onehot as df2hot\n\n # Convert dataframe to onehot by keeping only categorical variables.\n out = df2hot(df, y_min=y_min, perc_min_num=perc_min_num, dtypes=dtypes, excl_background=excl_background, hot_only=True, verbose=verbose)\n # Numerical\n df_num = out['numeric'].iloc[:, out['dtypes']=='cat']\n df_num = df_num.astype(int)\n # One-hot\n df_hot = out['onehot']\n df_hot.columns = df_hot.columns.str.replace('_4.0', '_4')\n df_hot.columns = df_hot.columns.str.replace('_3.0', '_3')\n df_hot.columns = df_hot.columns.str.replace('_2.0', '_2')\n df_hot.columns = df_hot.columns.str.replace('_1.0', '_1')\n df_hot.columns = df_hot.columns.str.replace('_0.0', '_0')\n\n return df_hot, df_num","def one_hot(index):\n\toutput = np.zeros(100)\n\toutput[index] = 1\n\treturn output","def one_hot_encode(Y, classes):\n if type(classes) is not int:\n return None\n if Y is None or type(Y) != np.ndarray:\n return None\n for c in Y:\n if c >= classes or c < 0:\n return None\n m = Y.shape[0]\n mtx = np.zeros((m, classes))\n\n for row, c_label in zip(mtx, Y):\n row[c_label] = 1\n\n return mtx.T","def transform_categorical_feature(\n df: pd.DataFrame, column_name: str, column_prefix: str = \"\"\n) -> pd.DataFrame:\n\n df1 = pd.get_dummies(df[column_name].astype(str))\n if column_prefix != \"\":\n df1.columns = [\"is_type_\" + col for col in df1.columns]\n\n new_df = pd.concat([df, df1], axis=1)\n\n # we don't need transformed column anymore\n new_df = new_df.drop(columns=[column_name])\n\n return new_df","def one_hot_encode_soy(soy_data, classes):\n\n print('[ INFO ]: One-hot-encoding soy data...')\n\n for col in soy_data.columns:\n if col not in ['index', 'attr_35']:\n\n # Find the min and max values in each column to account for all values\n col_min = min(soy_data[col])\n col_max = max(soy_data[col]) + 1\n for u in range(col_min, col_max):\n soy_data[col + '_' + str(u)] = (soy_data[col] == u).astype(int)\n soy_data = soy_data.drop(col, axis=1)\n if col == 'attr_35':\n\n # Create new columns for each unique class\n for u in soy_data[col].unique():\n soy_data['{}_class'.format(u)] = np.where(soy_data[col] == u, 1, 0)\n classes = np.char.replace(classes, u, u + '_class')\n soy_data = soy_data.drop(col, axis=1)\n\n return soy_data, classes","def one_hot(dataset, match):\n\n dataset['y'] = 0\n dataset.loc[dataset[match] == 'FAKE', 'y'] = 1\n return dataset","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 one_hot_encode(idx, vocab_size):\n # Initialize the encoded array\n one_hot = np.zeros(vocab_size)\n \n # Set the appropriate element to one\n one_hot[idx] = 1.0\n\n return one_hot","def OneHotEncoder(self):\n splits = [-float(\"inf\"), 500, 1200, 1700, float(\"inf\")]\n self.bucketizer = Bucketizer(splitsArray=[splits, splits, splits],\n inputCols=[\"CRSDepTime\",\n \"CRSArrTime\",\n \"DepTime\"],\n outputCols=[\"CatCRSDepTime\",\n \"CatCRSArrTime\",\n \"CatDepTime\"])\n\n self.varIdxer = StringIndexer(inputCol=\"OrigDest\",\n outputCol=\"IndOrigDest\").setHandleInvalid(\"skip\")\n\n self.oneHot = OneHotEncoder(inputCols=['Month',\n 'DayOfWeek',\n 'CatCRSDepTime',\n 'CatCRSArrTime',\n 'IndOrigDest',\n 'CatDepTime'],\n outputCols=['HotMonth',\n 'HotDayOfWeek',\n 'HotCRSCatDepTime',\n 'HotCRSCatArrTime',\n 'HotIndOrigDest',\n 'HotDepTime']).setHandleInvalid(\"keep\")","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 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 categorical(df):\n\n # variables which need to be transformed to categorical\n categorical = [\"prop_country_id\", \"visitor_location_country_id\"]\n\n for var in categorical:\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\n del df[var]\n\n return df","def one_hot_encode(self, y: np.ndarray) -> np.ndarray:\n return np.eye(self.output_size)[y]"],"string":"[\n \"def one_hot_encode(df, colnames):\\r\\n for col in colnames:\\r\\n oh_df = get_dummies(df[col], prefix=col)\\r\\n df = concat([oh_df, df], axis=1)\\r\\n df = df.drop([col], axis=1)\\r\\n return df\",\n \"def one_hot(input_df, columns):\\n df = input_df.copy()\\n\\n for col in columns:\\n dummies = pd.get_dummies(df[col].str.lower())\\n dummies.drop(dummies.columns[-1], axis=1, inplace=True)\\n df = df.drop(col, axis=1).merge(dummies, left_index=True, right_index=True)\\n \\n return df\",\n \"def one_hot_encoder(df, cols):\\r\\n\\r\\n for col in cols:\\r\\n if(\\\"admission\\\" in col):\\r\\n dummies = pd.get_dummies(df[col], drop_first=False)\\r\\n else:\\r\\n dummies = pd.get_dummies(df[col], prefix=col, drop_first=False)\\r\\n df = pd.concat([df, dummies], axis=1) \\r\\n df.drop([col],axis=1, inplace=True)\\r\\n return df\",\n \"def one_hot_encode(df, ohe_cols):\\n return pd.get_dummies(df, columns=ohe_cols)\",\n \"def one_hot(df):\\r\\n # One-hot encode into \\r\\n cols = ['job', 'marital', 'education', 'month', 'day_of_week', 'poutcome']\\r\\n for each in cols:\\r\\n dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)\\r\\n df = pd.concat([df, dummies], axis=1)\\r\\n df = df.drop(cols,axis=1)\\r\\n return df\",\n \"def one_hot(df, cols):\\n for col in cols:\\n dummies = pd.get_dummies(df[col], prefix=col, drop_first=False)\\n df = pd.concat([df, dummies], axis=1)\\n df = df.drop(col, axis=1)\\n return df\",\n \"def encode_one_hot(df, col_names) -> pd.DataFrame:\\n for col_name in col_names:\\n encoded = pd.get_dummies(df[col_name], prefix=col_name, drop_first=False)\\n df = pd.concat([df, encoded], axis=1)\\n df = df.drop([col_name], axis=1)\\n return df\",\n \"def one_hot_encode(df, col):\\n return pd.get_dummies(df, columns=[col], drop_first=True)\",\n \"def one_hot_enc(df, ohe_cols):\\n for col in ohe_cols:\\n df_ohe = pd.get_dummies(df[col], preffix=col)\\n df = pd.concat([df, df_ohe], axis=1)\\n df.drop(col, axis=1, inplace=True)\\n return df\",\n \"def encode_onehot(df, cols):\\n vec = DictVectorizer()\\n \\n vec_data = pd.DataFrame(vec.fit_transform(df[cols].to_dict(outtype='records')).toarray())\\n vec_data.columns = vec.get_feature_names()\\n vec_data.index = df.index\\n \\n df = df.drop(cols, axis=1)\\n df = df.join(vec_data)\\n return df\",\n \"def encoding_onehot(df, target=None):\\n if not target:\\n target = ['user_type', 'city']\\n for col in target:\\n # Following is exactily the df.join() but is inplace.\\n one_hot = pandas.get_dummies(df[col])\\n for item in one_hot:\\n df[item] = one_hot[item]\\n df.drop([col], axis=1, inplace=True)\\n return None\",\n \"def one_hot_encoder(df, nan_as_category=True):\\n original_columns = list(df.columns)\\n categorical_columns = [col for col in df.columns if\\n df[col].dtype == 'object']\\n df = pd.get_dummies(df, columns=categorical_columns,\\n dummy_na=nan_as_category)\\n new_columns = [c for c in df.columns if c not in original_columns]\\n return df, new_columns\",\n \"def __encode_one_hot_util(self):\\n for col in self.cat_cols:\\n if (\\n col in self.train_df\\n and col + str(\\\"Encoded\\\") not in self.ord_cols\\n ):\\n if self.test_df is not None:\\n self.test_df = pd.concat(\\n [\\n self.test_df,\\n pd.get_dummies(\\n self.test_df[col], prefix=col\\n ).astype(\\\"category\\\"),\\n ],\\n axis=1,\\n )\\n self.train_df = pd.concat(\\n [\\n self.train_df,\\n pd.get_dummies(self.train_df[col], prefix=col).astype(\\n \\\"category\\\"\\n ),\\n ],\\n axis=1,\\n )\",\n \"def onehot_enc(df, categorical_columns, categories):\\n noncategorical_cols = [col for col in df.columns if col not in categorical_columns]\\n \\n enc = OneHotEncoder(categories=categories,\\n sparse=False,\\n handle_unknown='ignore')\\n y = enc.fit_transform(df[categorical_columns].fillna(\\\"None\\\"))\\n \\n ohe_cols = [\\n f\\\"{col}_{c}\\\" for col, cats in zip(categorical_columns, categories) for c in cats]\\n df1 = pd.DataFrame(y, columns=ohe_cols)\\n \\n output_df = pd.concat([df[noncategorical_cols], df1], axis=1)\\n return output_df, ohe_cols\",\n \"def pre_process_data(df):\\n\\n # one-hot encode categorical values\\n df = pd.get_dummies(df)\\n\\n return df\",\n \"def own_OneHotColumnCreator(df, columns):\\n for col in cat_attribs:\\n for value in df[col].unique():\\n df[value] = (df[col] == value).astype(int)\",\n \"def one_hot_encoding(data):\\n\\n data_encoded = pd.get_dummies(data)\\n\\n return data_encoded\",\n \"def one_hot_encode(self, columns):\\n if not columns:\\n return None\\n\\n logging.info('one-hot-encoding columns: %s' % ','.join(columns))\\n\\n both_sets = pd.concat((self.train[columns], self.test[columns]))\\n encoder = preprocessing.OneHotEncoder()\\n encoded = encoder.fit_transform(both_sets).sorted_indices()\\n\\n # Split apart train and test set arrays after one-hot encoding.\\n nd_train = self.train.shape[0]\\n train_enc = encoded[:nd_train]\\n test_enc = encoded[nd_train:]\\n\\n for col in columns:\\n logging.debug('unique {}: {}'.format(col, both_sets[col].unique()))\\n\\n # Create a feature map for decoding one-hot encoding.\\n counts = np.array([both_sets[col].unique().shape[0] for col in columns])\\n fmap = []\\n for i, column in enumerate(columns):\\n unique_elements = np.sort(both_sets[column].unique())\\n logging.debug('unique elements for col {}: {}'.format(\\n column, unique_elements))\\n fmap += ['%s-%d' % (column, idx) for idx in unique_elements]\\n\\n logging.info('after one-hot encoding, found # unique values:')\\n for attr, n_values in zip(columns, counts):\\n logging.info('%s: %d' % (attr, n_values))\\n\\n return train_enc, test_enc, fmap, encoder\",\n \"def _one_hot_encoder(self):\\n ohe = preprocessing.OneHotEncoder()\\n ohe.fit(self.dataframe[self.cat_feats])\\n return ohe.transform(self.dataframe_d_copy[self.cat_feats])\",\n \"def one_hot_encode(self, column: str) -> None:\\n # Create the encoder\\n encoder = OneHotEncoder(sparse=False, drop=\\\"first\\\")\\n\\n # Fit the encoder and transform the data\\n transformed_data = encoder.fit_transform(self.df[[column]])\\n\\n # Get feature names\\n encoded_features = encoder.get_feature_names_out([column])\\n\\n # Convert the transformed data into a dataframe\\n encoded_df = pd.DataFrame(transformed_data, columns=encoded_features)\\n\\n # Drop the original column from the dataframe\\n self.df = self.df.drop(column, axis=1)\\n\\n # Concatenate the original dataframe with the encoded dataframe\\n self.df = pd.concat([self.df, encoded_df], axis=1)\\n\\n # Update the features list to include the new one-hot encoded features and exclude the original categorical feature\\n self.features = [\\n feature for feature in self.features if feature != column\\n ] + list(encoded_features)\",\n \"def complete_one_hot_variables(df: pd.DataFrame, var_names: list) -> pd.DataFrame:\\n\\n df = df.copy()\\n for var in var_names:\\n # Create a column of 0s\\n df[var] = 0\\n return df\",\n \"def onehot_encode(self, col_need, start_zero=True):\\r\\n self.onehot = OneHotEncoder(sparse=False)\\r\\n array_onehot = self.onehot.fit_transform(self.df.loc[:, col_need])\\r\\n\\r\\n col_onehot = []\\r\\n\\r\\n for col_index in range(len(col_need)):\\r\\n if start_zero:\\r\\n for hot_index in range(self.onehot.n_values_[col_index]):\\r\\n col_onehot.append(col_need[col_index] + str(hot_index))\\r\\n else:\\r\\n for hot_index in range(1, self.onehot.n_values_[col_index]):\\r\\n col_onehot.append(col_need[col_index] + str(hot_index))\\r\\n\\r\\n self.df.drop(col_need, axis=1, inplace=True)\\r\\n\\r\\n df_onehot = pd.DataFrame(array_onehot, columns=col_onehot, index=self.df.index)\\r\\n self.df = pd.concat([self.df, df_onehot], axis=1)\",\n \"def one_hot_encoding(self):\\n \\n try: \\n\\n # Encode dependent variable\\n le = LabelEncoder()\\n le.fit(self.data[\\\"consumption\\\"])\\n df_dependent_enc = pd.DataFrame(le.transform(self.data[\\\"consumption\\\"]))\\n\\n # Encode independent variable\\n categorical_features = Config.FEATURE_DEFINITION[\\\"category_cols\\\"]\\n categorical_df = self.data.loc[:, self.data.columns.isin(categorical_features)]\\n oe = OrdinalEncoder()\\n oe.fit(categorical_df)\\n df_catindependent_enc = pd.DataFrame(oe.transform(categorical_df))\\n df_catindependent_enc.columns = categorical_df.columns\\n\\n except KeyError: \\n\\n st.write(\\\"Cannot perform one-hot encoding for numerical variables. Please check if variables are properly defined.\\\")\\n st.write(self.data.columns != \\\"consumption\\\")\\n df_dependent_enc = []\\n df_catindependent_enc = []\\n\\n else:\\n \\n return df_dependent_enc, df_catindependent_enc\",\n \"def dummify_features(df):\\n colnames = df.columns\\n le_dict = {}\\n for col in colnames:\\n le_dict[col] = preprocessing.LabelEncoder()\\n le_dict[col].fit(df[col])\\n df.loc[:, col] = le_dict[col].transform(df[col])\\n\\n enc = preprocessing.OneHotEncoder()\\n enc.fit(df)\\n X = enc.transform(df)\\n\\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\\n # for cv in colnames:\\n # for modality in le_dict[cv].classes_:\\n # dummy_colnames.append(cv + '_' + modality)\\n\\n return X, dummy_colnames, enc\",\n \"def onehot_features(data):\\n\\n# Binary Features\\n columns = ['Weekend', 'Revenue']\\n for col in columns:\\n data[col] = data[col].apply(lambda x: float(1) if x else float(0))\\n\\n columns = ['Month', 'OperatingSystems', 'Browser', 'Region', 'TrafficType',\\n 'VisitorType']\\n for col in columns:\\n enc = OneHotEncoder()\\n data_array = enc.fit_transform(data[[col]]).toarray()\\n enc_data = pd.DataFrame(data_array)\\n enc_data.columns = list(enc.get_feature_names([col]))\\n data = data.join(enc_data)\\n\\n data = data.drop(columns={'Month', 'Month_May', 'OperatingSystems',\\n 'OperatingSystems_2', 'Browser', 'Browser_2',\\n 'Region', 'Region_1.0', 'TrafficType',\\n 'TrafficType_2', 'VisitorType',\\n 'VisitorType_Returning_Visitor'})\\n return data\",\n \"def encoding_df(df, cols):\\n import pandas as pd\\n df = df[cols]\\n obj_df = df.select_dtypes(include=['object']).copy()\\n num_var = df.select_dtypes(include=['int','float']).copy()\\n cat_var = pd.get_dummies(obj_df, columns = obj_df.columns)\\n encoded_df = pd.concat([num_var, cat_var], axis=1, sort=False)\\n return encoded_df\",\n \"def one_hot_encode(self, meta_field):\\n one_hot = pd.get_dummies(self.sample_meta[meta_field]).values\\n return one_hot\",\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 onehot_encode_labels(y):\\n\\treturn OneHotEncoder(categories=\\\"auto\\\", sparse=False).fit_transform(y.reshape(y.shape[0],1))\",\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_encoding(data):\\r\\n encoder = LabelEncoder()\\r\\n y = encoder.fit_transform(data)\\r\\n return(y)\",\n \"def process(self, data_set: DataSet):\\n logging.info(f\\\"\\\\nOne hot encoding DataSet...\\\")\\n if len(data_set.get_categorical_attribute_indexes()) == 0:\\n logging.info(f\\\"No need to one hot encode. No categorical variables left.\\\")\\n return\\n\\n for index in data_set.get_categorical_attribute_indexes():\\n column = data_set.get_attribute_column(index)\\n\\n unique = np.unique(column)\\n logging.info(f\\\"Attribute index '{index}' has categorical variables: {unique}\\\")\\n unique_dict = {x: i for i, x in enumerate(unique)}\\n new_columns = [[0. for i in range(len(data_set))] for j in unique]\\n\\n for i, value in enumerate(column):\\n new_columns[unique_dict[value]][i] = 1.\\n\\n for col in new_columns:\\n data_set.add_column(col)\\n\\n for index in sorted(data_set.get_categorical_attribute_indexes(), reverse=True):\\n data_set.remove_column(index)\\n\\n logging.info('One hot encoded DataSet:')\\n logging.info(data_set.summary())\",\n \"def onehot(train_cat, test_cat):\\n train_enc = []\\n test_enc = []\\n columns = []\\n for c in cols(train_cat):\\n train = train_cat.loc[:,c]\\n test = test_cat.loc[:,c]\\n labels = list(set(train.unique().tolist()) |\\n set(test.unique().tolist()))\\n\\n l_encoder = LabelEncoder()\\n l_encoder.fit(labels)\\n oh_encoder = OneHotEncoder(sparse=False, n_values=len(labels))\\n\\n train_ftr = l_encoder.transform(train).reshape(len(train), 1)\\n train_enc.append(oh_encoder.fit_transform(train_ftr))\\n test_ftr = l_encoder.transform(test).reshape(len(test), 1)\\n test_enc.append(oh_encoder.fit_transform(test_ftr))\\n\\n col_tmp = list(l_encoder.inverse_transform(range(len(labels))))\\n columns = columns + [\\\"%s_%s\\\" % (c, i) for i in col_tmp]\\n\\n new_train_cat = pd.DataFrame(np.column_stack(train_enc), columns=columns,\\n dtype=np.uint8)\\n new_test_cat = pd.DataFrame(np.column_stack(test_enc), columns=columns,\\n dtype=np.uint8)\\n return new_train_cat, new_test_cat\",\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 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 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 dummify(df, colname):\\n\\n dummies = pd.get_dummies(df[colname])\\n #dummies.drop(dummies.columns[0], axis=1, inplace=True) #remove arbitrarely the first column to avoid colinearity problem\\n df.drop(colname, inplace=True, axis=1)\\n return pd.concat([df, dummies], axis=1)\",\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 one_hot_encoding(data_dict):\\n merged_seq = []\\n merged_label = []\\n for key in __MERGE_KEYS__:\\n merged_seq += list(data_dict[key])\\n merged_label += list([__MERGE_LABELS__[key]] * len(data_dict[key]))\\n merged_seq_int = list(map(map_base_to_int, merged_seq))\\n\\n X = to_categorical(merged_seq_int)\\n Y = to_categorical(merged_label)\\n return X, Y\",\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 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 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_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(x, n_classes):\\n return np.eye(n_classes)[x]\",\n \"def _convert_to_dummies_pandas(df: pd.DataFrame) -> pd.DataFrame:\\n df = df.copy()\\n print(\\\"Raw dataset shape = {}\\\".format(df.shape))\\n print(\\\"Creating one hot encoded categories...\\\")\\n for col in df.columns:\\n if col in CATEGORICAL_TRANS and col != \\\"isFraud\\\" and not col.startswith(\\\"card\\\"):\\n print(\\\"Handling category: {}\\\".format(col))\\n # Convert to categorical type, may not be necessary.\\n df[col] = pd.Categorical(df[col])\\n one_hot_encoded = pd.get_dummies(df[col], prefix=col)\\n df = df.drop(columns=[col]) # remove the original categorical column.\\n # Add the one-hot-encoded column.\\n df = pd.concat([df, one_hot_encoded], axis=1)\\n print(\\\"One-hot-encoded dataset shape = {}\\\".format(df.shape))\\n return df\",\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 dummify_all_categorical(df):\\n\\n df = pd.get_dummies(df)\\n df = dummify(df, \\\"detailed industry recode\\\")\\n df = dummify(df, \\\"detailed occupation recode\\\") ## add some variables that are encoded as int64 but that are in fact categorical\\n return df\",\n \"def create_dummies(df):\",\n \"def get_binary_values(data_frame):\\n all_columns = pandas.DataFrame( index = data_frame.index)\\n for col in data_frame.columns:\\n data = pandas.get_dummies(data_frame[col], prefix=col.encode('ascii', 'replace'))\\n all_columns = pandas.concat([all_columns, data], axis=1)\\n return all_columns\",\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 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 oneHotEncoding(X, list_indices):\\n print('Onehotencoding..')\\n X = X.astype(float)\\n for i in list_indices:\\n onehotencoder = OneHotEncoder(categorical_features=[i])\\n X = onehotencoder.fit_transform(X)\\n return X[1:]\",\n \"def one_hot_encoding(l, keep_aggregate=False):\\n df_enriched = pd.DataFrame({'cl': l})\\n for i in l.unique():\\n df_enriched[i] = list(map(int, df_enriched['cl'] == i))\\n if not keep_aggregate:\\n del df_enriched['cl']\\n return df_enriched\",\n \"def oneHot(df, y, clip=True, thresh=False):\\n n = y.max() + 1\\n m = df.shape[0]\\n oh = np.zeros((m, n))\\n for i in range(m):\\n oh[i][y[i]] = 1 \\n if clip is True:\\n sum_ = oh.sum(axis=0)\\n frac = sum_ / float(sum(sum_))\\n if thresh is False:\\n x = oh[:,frac > frac.mean()]\\n else:\\n x = oh[:,frac > thresh]\\n else:\\n x = oh\\n return x\",\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(x, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(x.max()+1), dtype)\",\n \"def _one_hot(self, key):\\n train_df = self.train.copy(deep=False)\\n one_hot = pd.get_dummies(train_df[key],drop_first=True)\\n one_hot.columns=[key+'_'+col for col in one_hot.columns.values]\\n train_df = pd.concat([train_df,one_hot],axis=1,sort=True)\\n train_dfg = train_df.groupby('fullVisitorId')\\n return train_dfg[one_hot.columns.values].sum()\",\n \"def get_binary_values(data_frame):\\n all_columns = pandas.DataFrame( index = data_frame.index)\\n for col in data_frame.columns:\\n data = pandas.get_dummies(data_frame[col], prefix=col.encode('ascii', 'replace'))\\n all_columns = pandas.concat([all_columns, data], axis=1)\\n return all_columns\",\n \"def oneHot(index, n):\\n x = np.zeros(n)\\n x[index] = 1\\n return x\",\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 get_dummies(df):\\n col = df.select_dtypes('O').columns.tolist()\\n nunique = df[col].nunique()\\n col_binary = nunique[nunique==2].index.tolist()\\n [col.remove(c) for c in col_binary]\\n df = pd.get_dummies(df, columns=col)\\n df = pd.get_dummies(df, columns=col_binary, drop_first=True)\\n df.columns = [c.replace(' ', '-') for c in df.columns]\\n return df\",\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 _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(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_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 process_categorical_data(data_df):\\n return pd.get_dummies(data_df, columns=Columns.categorical)\",\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_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 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(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 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 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_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 \"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 \"def to_onehot(x, num_classes):\\n return np.eye(num_classes, dtype='float32')[x]\",\n \"def convert_categorical(df):\\n print(\\\" --- Converting Categories into binary features.\\\")\\n columns = df.columns\\n categorical = [x for x in columns if x.startswith('c_')]\\n for col in categorical:\\n print(\\\" ---- Converting: {}\\\".format(col))\\n category_binary = pd.get_dummies(df[col], prefix=col)\\n df = pd.concat([df, category_binary], axis=1)\\n df = df.drop(categorical, axis=1)\\n print(\\\" --- Finished converting Categories into binary features.\\\")\\n return df\",\n \"def _convert_to_dummies_sklearn(df: pd.DataFrame) -> pd.DataFrame:\\n raise NotImplementedError\",\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 _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 one_hot(y, num_classes):\\n return np.eye(num_classes)[y]\",\n \"def train(self, df):\\n self.encoders = []\\n self.feature_names = []\\n for feature_name, column in df.iteritems():\\n if column.dtype != object:\\n continue\\n self.feature_names.append(feature_name)\\n column = column.to_numpy().reshape(-1, 1)\\n ohe = sklearn.preprocessing.OneHotEncoder()\\n ohe.fit(column)\\n self.encoders.append(ohe)\\n return self\",\n \"def df2onehot(df, y_min=10, perc_min_num=0.8, dtypes='pandas', excl_background=None, verbose=3):\\n from df2onehot import df2onehot\\n\\n # Convert dataframe to onehot by keeping only categorical variables.\\n out = df2onehot(df, y_min=y_min, perc_min_num=perc_min_num, dtypes=dtypes, excl_background=excl_background, hot_only=True, verbose=verbose)\\n # Numerical\\n df_num = out['numeric'].iloc[:, out['dtypes']=='cat']\\n df_num = df_num.astype(int)\\n # One-hot\\n df_hot = out['onehot']\\n df_hot.columns = df_hot.columns.str.replace('_4.0', '_4')\\n df_hot.columns = df_hot.columns.str.replace('_3.0', '_3')\\n df_hot.columns = df_hot.columns.str.replace('_2.0', '_2')\\n df_hot.columns = df_hot.columns.str.replace('_1.0', '_1')\\n df_hot.columns = df_hot.columns.str.replace('_0.0', '_0')\\n\\n return df_hot, df_num\",\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 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, 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 generate_dummies(df, columns):\\n\\n dummy_df = pd.DataFrame()\\n for col in columns:\\n dummy_df = pd.concat([dummy_df, pd.get_dummies(df[col])], axis=1)\\n\\n return dummy_df\",\n \"def To1hot(label,num_class):\\n onehot = np.zeros(num_class)\\n onehot[label] = 1\\n return onehot\",\n \"def df2onehot(df, y_min=10, perc_min_num=0.8, dtypes='pandas', excl_background=None, verbose=3):\\n from df2onehot import df2onehot as df2hot\\n\\n # Convert dataframe to onehot by keeping only categorical variables.\\n out = df2hot(df, y_min=y_min, perc_min_num=perc_min_num, dtypes=dtypes, excl_background=excl_background, hot_only=True, verbose=verbose)\\n # Numerical\\n df_num = out['numeric'].iloc[:, out['dtypes']=='cat']\\n df_num = df_num.astype(int)\\n # One-hot\\n df_hot = out['onehot']\\n df_hot.columns = df_hot.columns.str.replace('_4.0', '_4')\\n df_hot.columns = df_hot.columns.str.replace('_3.0', '_3')\\n df_hot.columns = df_hot.columns.str.replace('_2.0', '_2')\\n df_hot.columns = df_hot.columns.str.replace('_1.0', '_1')\\n df_hot.columns = df_hot.columns.str.replace('_0.0', '_0')\\n\\n return df_hot, df_num\",\n \"def one_hot(index):\\n\\toutput = np.zeros(100)\\n\\toutput[index] = 1\\n\\treturn output\",\n \"def one_hot_encode(Y, classes):\\n if type(classes) is not int:\\n return None\\n if Y is None or type(Y) != np.ndarray:\\n return None\\n for c in Y:\\n if c >= classes or c < 0:\\n return None\\n m = Y.shape[0]\\n mtx = np.zeros((m, classes))\\n\\n for row, c_label in zip(mtx, Y):\\n row[c_label] = 1\\n\\n return mtx.T\",\n \"def transform_categorical_feature(\\n df: pd.DataFrame, column_name: str, column_prefix: str = \\\"\\\"\\n) -> pd.DataFrame:\\n\\n df1 = pd.get_dummies(df[column_name].astype(str))\\n if column_prefix != \\\"\\\":\\n df1.columns = [\\\"is_type_\\\" + col for col in df1.columns]\\n\\n new_df = pd.concat([df, df1], axis=1)\\n\\n # we don't need transformed column anymore\\n new_df = new_df.drop(columns=[column_name])\\n\\n return new_df\",\n \"def one_hot_encode_soy(soy_data, classes):\\n\\n print('[ INFO ]: One-hot-encoding soy data...')\\n\\n for col in soy_data.columns:\\n if col not in ['index', 'attr_35']:\\n\\n # Find the min and max values in each column to account for all values\\n col_min = min(soy_data[col])\\n col_max = max(soy_data[col]) + 1\\n for u in range(col_min, col_max):\\n soy_data[col + '_' + str(u)] = (soy_data[col] == u).astype(int)\\n soy_data = soy_data.drop(col, axis=1)\\n if col == 'attr_35':\\n\\n # Create new columns for each unique class\\n for u in soy_data[col].unique():\\n soy_data['{}_class'.format(u)] = np.where(soy_data[col] == u, 1, 0)\\n classes = np.char.replace(classes, u, u + '_class')\\n soy_data = soy_data.drop(col, axis=1)\\n\\n return soy_data, classes\",\n \"def one_hot(dataset, match):\\n\\n dataset['y'] = 0\\n dataset.loc[dataset[match] == 'FAKE', 'y'] = 1\\n return dataset\",\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 one_hot_encode(idx, vocab_size):\\n # Initialize the encoded array\\n one_hot = np.zeros(vocab_size)\\n \\n # Set the appropriate element to one\\n one_hot[idx] = 1.0\\n\\n return one_hot\",\n \"def OneHotEncoder(self):\\n splits = [-float(\\\"inf\\\"), 500, 1200, 1700, float(\\\"inf\\\")]\\n self.bucketizer = Bucketizer(splitsArray=[splits, splits, splits],\\n inputCols=[\\\"CRSDepTime\\\",\\n \\\"CRSArrTime\\\",\\n \\\"DepTime\\\"],\\n outputCols=[\\\"CatCRSDepTime\\\",\\n \\\"CatCRSArrTime\\\",\\n \\\"CatDepTime\\\"])\\n\\n self.varIdxer = StringIndexer(inputCol=\\\"OrigDest\\\",\\n outputCol=\\\"IndOrigDest\\\").setHandleInvalid(\\\"skip\\\")\\n\\n self.oneHot = OneHotEncoder(inputCols=['Month',\\n 'DayOfWeek',\\n 'CatCRSDepTime',\\n 'CatCRSArrTime',\\n 'IndOrigDest',\\n 'CatDepTime'],\\n outputCols=['HotMonth',\\n 'HotDayOfWeek',\\n 'HotCRSCatDepTime',\\n 'HotCRSCatArrTime',\\n 'HotIndOrigDest',\\n 'HotDepTime']).setHandleInvalid(\\\"keep\\\")\",\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 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 categorical(df):\\n\\n # variables which need to be transformed to categorical\\n categorical = [\\\"prop_country_id\\\", \\\"visitor_location_country_id\\\"]\\n\\n for var in categorical:\\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\\n del df[var]\\n\\n return df\",\n \"def one_hot_encode(self, y: np.ndarray) -> np.ndarray:\\n return np.eye(self.output_size)[y]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.83890945","0.83782166","0.8353686","0.8352055","0.83429927","0.83196855","0.8312187","0.8312064","0.82263744","0.8203636","0.78942597","0.7849938","0.78145325","0.7612664","0.7542009","0.7526136","0.75124633","0.7379414","0.7350976","0.72332495","0.7226009","0.72146106","0.7193515","0.7170493","0.7108039","0.7087632","0.7077889","0.7034674","0.7006075","0.6987344","0.69662803","0.69654495","0.69526684","0.692499","0.6921151","0.6878316","0.6872133","0.6869902","0.68639386","0.68482536","0.6845603","0.68357795","0.68190396","0.6818418","0.68163645","0.6807554","0.680259","0.6801019","0.67747486","0.6753819","0.67205244","0.67057437","0.67044204","0.6700452","0.6695154","0.66946965","0.66847557","0.66772455","0.66660994","0.66574115","0.6652114","0.6650125","0.66349345","0.66256595","0.6616765","0.6597638","0.65970665","0.65870506","0.65553176","0.6547226","0.65420794","0.6533984","0.65329057","0.65329057","0.65223074","0.6519128","0.6516083","0.6515357","0.65035003","0.6491568","0.64869297","0.6480859","0.6472091","0.64709014","0.6470651","0.64671713","0.6464572","0.6458574","0.64537144","0.6452257","0.6439254","0.6438393","0.64270765","0.64258003","0.6415281","0.6404083","0.6399024","0.6386846","0.6385054","0.63825995"],"string":"[\n \"0.83890945\",\n \"0.83782166\",\n \"0.8353686\",\n \"0.8352055\",\n \"0.83429927\",\n \"0.83196855\",\n \"0.8312187\",\n \"0.8312064\",\n \"0.82263744\",\n \"0.8203636\",\n \"0.78942597\",\n \"0.7849938\",\n \"0.78145325\",\n \"0.7612664\",\n \"0.7542009\",\n \"0.7526136\",\n \"0.75124633\",\n \"0.7379414\",\n \"0.7350976\",\n \"0.72332495\",\n \"0.7226009\",\n \"0.72146106\",\n \"0.7193515\",\n \"0.7170493\",\n \"0.7108039\",\n \"0.7087632\",\n \"0.7077889\",\n \"0.7034674\",\n \"0.7006075\",\n \"0.6987344\",\n \"0.69662803\",\n \"0.69654495\",\n \"0.69526684\",\n \"0.692499\",\n \"0.6921151\",\n \"0.6878316\",\n \"0.6872133\",\n \"0.6869902\",\n \"0.68639386\",\n \"0.68482536\",\n \"0.6845603\",\n \"0.68357795\",\n \"0.68190396\",\n \"0.6818418\",\n \"0.68163645\",\n \"0.6807554\",\n \"0.680259\",\n \"0.6801019\",\n \"0.67747486\",\n \"0.6753819\",\n \"0.67205244\",\n \"0.67057437\",\n \"0.67044204\",\n \"0.6700452\",\n \"0.6695154\",\n \"0.66946965\",\n \"0.66847557\",\n \"0.66772455\",\n \"0.66660994\",\n \"0.66574115\",\n \"0.6652114\",\n \"0.6650125\",\n \"0.66349345\",\n \"0.66256595\",\n \"0.6616765\",\n \"0.6597638\",\n \"0.65970665\",\n \"0.65870506\",\n \"0.65553176\",\n \"0.6547226\",\n \"0.65420794\",\n \"0.6533984\",\n \"0.65329057\",\n \"0.65329057\",\n \"0.65223074\",\n \"0.6519128\",\n \"0.6516083\",\n \"0.6515357\",\n \"0.65035003\",\n \"0.6491568\",\n \"0.64869297\",\n \"0.6480859\",\n \"0.6472091\",\n \"0.64709014\",\n \"0.6470651\",\n \"0.64671713\",\n \"0.6464572\",\n \"0.6458574\",\n \"0.64537144\",\n \"0.6452257\",\n \"0.6439254\",\n \"0.6438393\",\n \"0.64270765\",\n \"0.64258003\",\n \"0.6415281\",\n \"0.6404083\",\n \"0.6399024\",\n \"0.6386846\",\n \"0.6385054\",\n \"0.63825995\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8227697"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":80388,"cells":{"query":{"kind":"string","value":"execute all ci stages in one run"},"document":{"kind":"string","value":"def all_(tag, field_tests, img_passwd_file, registry, install_server_hostname,\n quiet, builder):\n manager = Manager(\n 'all', tag, registry_url=registry, field_tests=field_tests,\n img_passwd_file=img_passwd_file,\n install_server_hostname=install_server_hostname, verbose=not quiet,\n builder_hostname=builder)\n manager.run()"},"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 _run_ci_test():\n _run_install(False)\n _run_coverage_html(False)\n _run_typecheck_xml(False)\n _run_lint(True)","def run(self):\n self._build_docker_images()\n self._build_docker_image_test()\n self._run_workflow_in_matrix_of_envs()","def _run_ci_publish():\n _run_install(False)\n _run_tests(False)\n _run_publish(True)","def _run_ci_integration_test():\n _run_install(False)\n _run_integration_tests_on_github(False)","def main():\n\n if not os.environ.get('TRAVIS_PULL_REQUEST', 'false') == 'false':\n return\n\n git_config_setup()\n populate_source()\n build_and_deploy()","def main():\n gh = Github(os.environ['GH_TOKEN'])\n # gh_token = os.environ['GH_TOKEN']\n gc_token_path = os.environ['GC_STORAGE_KEY']\n\n vars_file_path = os.getenv('VARS_FILE', \"\")\n pipeline_id = os.getenv('CI_PIPELINE_ID', 0)\n\n repo = gh.get_repo(\"kubeinit/kubeinit\")\n branches = repo.get_branches()\n\n output = 0\n # Something linke:\n # url = \"https://gitlab.com/kubeinit/kubeinit-ci/pipelines/\"\n url = os.getenv('CI_PIPELINE_URL', \"\")\n print(\"The job results will be published in runtime at: \" + url)\n\n for branch in branches:\n for pr in repo.get_pulls(state='open', sort='created', base=branch.name):\n labels = [item.name for item in pr.labels]\n\n sha = pr.head.sha\n committer_email = repo.get_commit(sha=sha).commit.committer.email\n print(committer_email)\n\n execute = False\n # We assign the executed label to avoid executing this agains the same PR over and over\n # We mark the PR as e2e-executed\n\n for label in labels:\n if re.match(r\".*-.*-.*-.*-.*-.*-.*\", label):\n print('Matching a PR label')\n params = label.split(\"-\")\n distro = params[0]\n driver = params[1]\n master = params[2]\n worker = params[4]\n scenario = params[6]\n execute = True\n remove_label(label, pr, repo)\n break\n\n if execute:\n now = datetime.now()\n now.strftime(\"%m.%d.%Y.%H.%M.%S\")\n job_name = pipeline_id + \"-\" + distro + \"-\" + driver + \"-\" + master + \"-\" + worker + \"-\" + scenario + \"-\" + now.strftime(\"%Y.%m.%d.%H.%M.%S\")\n print(\"Let's run the e2e job, distro %s driver %s \" % (distro, driver))\n print(\"-------------\")\n print(\"-------------\")\n print(\"Running the e2e job for: \" + str(pr.number) + \" \" + pr.title)\n print(\"-------------\")\n print(\"-------------\")\n print(\"-------------\")\n\n # We update the status to show that we are executing the e2e test\n print(\"Current status\")\n print(repo.get_commit(sha=sha).get_statuses())\n repo.get_commit(sha=sha).create_status(state=\"pending\",\n target_url=url + str(pipeline_id),\n description=\"Running...\",\n context=\"%s-%s-%s-controller-%s-compute-%s\" % (distro,\n driver,\n master,\n worker,\n scenario))\n print(\"The pipeline ID is: \" + str(pipeline_id))\n print(\"The clouds.yml path is: \" + str(vars_file_path))\n # We trigger the e2e job\n start_time = time.time()\n try:\n print(\"We call the downstream job configuring its parameters\")\n subprocess.check_call(\"./ci/run_kubeinit.sh %s %s %s %s %s %s %s %s\" % (str(branch.name),\n str(pr.number),\n str(vars_file_path),\n str(distro),\n str(driver),\n str(master),\n str(worker),\n str(scenario)),\n shell=True)\n except Exception as e:\n print('An exception hapened executing Ansible')\n print(e)\n output = 1\n\n try:\n print(\"Render ara data\")\n subprocess.check_call(\"./ci/ara.sh %s\" % (str(job_name) + \"-\" + str(output)), shell=True)\n except Exception as e:\n print('An exception hapened rendering ara data')\n print(e)\n output = 1\n\n print(\"starting the uploader job\")\n # No matter if the job passed or failed we always use go as the suffix\n # upload_error = upload_logs_to_github(str(job_name) + \"-\" + str(output), gh_token)\n upload_error = upload_logs_to_google_cloud(str(job_name) + \"-\" + str(output), gc_token_path)\n render_index(gc_token_path)\n print(\"finishing the uploader job\")\n\n if output == 0:\n state = \"success\"\n else:\n state = \"failure\"\n\n desc = (\"Ended with %s in %s minutes\" % (state, round((time.time() - start_time) / 60, 2)))\n\n print(desc)\n print(state)\n\n if upload_error == 1:\n dest_url = url + str(pipeline_id)\n else:\n dest_url = 'https://storage.googleapis.com/kubeinit-ci/jobs/pr/' + str(job_name) + \"-\" + str(output) + '/index.html'\n\n print(\"The destination URL is: \" + dest_url)\n # We update the status with the job result\n repo.get_commit(sha=sha).create_status(state=state,\n target_url=dest_url,\n description=desc,\n context=\"%s-%s-%s-controller-%s-compute-%s\" % (distro,\n driver,\n master,\n worker,\n scenario))\n else:\n print(\"No need to do anything\")\n if execute:\n exit()","def RunStages(self):\n self._RunStage(build_stages.InitSDKStage)\n self.RunSetupBoard()\n self._RunStage(report_stages.RefreshPackageStatusStage)","def commands_all():\n lint()\n complexity()\n coverage()","def ci(session):\n session.install('-rrequirements-dev.txt')\n session.install('-e', '.')\n run_sphinx(session)\n run_yapf(session, True)\n run_all_linters(session)\n run_pytest_units(session)\n run_pytest_integrations(session)","def main(argv):\n parser = argparse.ArgumentParser(description=\"\"\"Bootstrap CI Scripts\"\"\")\n parser.add_argument(\"-d\", \"--directory\",\n type=str,\n required=True,\n help=(\"\"\"Directory to store language runtimes, \"\"\"\n \"\"\"scripts and other script details in\"\"\"))\n parser.add_argument(\"-s\", \"--script\",\n type=str,\n help=\"\"\"Script to pass control to\"\"\")\n parser.add_argument(\"-e\", \"--eval-output\",\n type=str,\n choices=[\n \"bash\",\n \"powershell\"\n ],\n help=\"\"\"Evaluate output in shell\"\"\")\n parser.add_argument(\"-p\", \"--print-to\",\n type=str,\n help=\"\"\"Where to print output script to\"\"\")\n parser.add_argument(\"-r\", \"--scripts-directory\",\n type=str,\n help=(\"\"\"Directory where scripts are already \"\"\"\n \"\"\"stored in\"\"\"))\n parser.add_argument(\"--keep-scripts\",\n action=\"store_true\",\n help=\"\"\"Don't remove stale scripts.\"\"\")\n args, remainder = parser.parse_known_args(argv)\n\n print_script_to, print_messages_to = _determine_outputs(args.print_to)\n\n with closing(print_script_to):\n parent_shell = construct_parent_shell(args.eval_output,\n print_script_to)\n container = ContainerDir(parent_shell,\n stale_check=_stale_check_url(args),\n **(vars(args)))\n util = container.fetch_and_import(\"util.py\")\n # suppress(unused-attribute)\n util.PRINT_MESSAGES_TO = print_messages_to\n bootstrap_script = container.script_path(\"bootstrap.py\").fs_path\n bootstrap_script_components = bootstrap_script.split(os.path.sep)\n scripts_path = os.path.sep.join(bootstrap_script_components[:-2])\n\n # Overwrite CONTAINER_DIR in the output script, but not\n # for our own invocation, we'll need the parent instance\n # if we're actually in a test\n parent_shell.overwrite_environment_variable(\"CONTAINER_DIR\",\n container.path())\n _set_ci_environment_variables(parent_shell)\n\n _define_script_command(\"polysquare_run\",\n parent_shell,\n bootstrap_script,\n container.path(),\n scripts_path,\n None)\n _define_script_command(\"polysquare_cleanup\",\n parent_shell,\n bootstrap_script,\n container.path(),\n scripts_path,\n \"clean.py\")\n\n # Done, pass control to the script we're to run\n container.fetch_and_import(args.script).run(container,\n util,\n parent_shell,\n argv=remainder)\n\n # Print a final new line so that active messages don't get\n # truncated.\n util.print_message(\"\\n\")\n\n if container.return_code() != 0:\n parent_shell.exit(container.return_code())\n\n return container.return_code()","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()","def runall():\n sclogic.runall()","def _run_workflow_in_matrix_of_envs(self):\n for name, status, sha in zip(\n self.env_names, self.docker_status, self.neurodocker_specs.keys()\n ):\n if status == \"docker ok\":\n image = \"repronim/testkraken:{}\".format(sha)\n self._run_pydra(image=image, soft_ver_str=name)\n\n # running tests separately\n for name, status, sha in zip(\n self.env_names, self.docker_status, self.neurodocker_specs.keys()\n ):\n # running tests only if docker container was created and if this is not\n # the reference environment\n if status == \"docker ok\" and name != self.ref_env_ind:\n self._run_tests(soft_ver_str=name)","def run_all_tests():\n remove_dbs()\n run_training_tests()\n run_custom_training_tests()\n run_training_save_tests()\n run_validation_tests()\n run_feature_extraction_tests()","def main():\r\n run_processes('tests.csv', 'labs.csv')","def execute(self):\n for test in self.tests:\n test.execute()\n self.logger.dump()\n print(\"Finished!\")","def before_all(context):\n def run_next_action():\n \"\"\"Run Next-action and return both stderr and stdout.\"\"\"\n os.environ[\"BROWSER\"] = 'echo %s'\n result = subprocess.run(context.arguments, stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding=\"utf-8\")\n return result.stdout + result.stderr\n\n context.next_action = run_next_action\n subprocess.run([\"coverage\", \"erase\"])","def run_test_suite():\n local('. fabric_factory/ve/bin/activate; fabric_factory/src/project/manage.py test')","def deploy():\n test()\n if not env.is_staging:\n backup()\n prepare()\n restart_api()","def _stage1(self):\n self.start_progress()\n tasks = list(self._chain_dict(self._model.adjust_tasks))\n if len(tasks) == 0:\n self._stage2(self._no_adjustments_case())\n else:\n task = lambda : self._run_adjust_tasks(tasks)\n locator.get(\"pool\").submit(task, self._stage2)","def _main(self):\n\n i = 0\n\n for i, step in enumerate(self._run_list):\n return_code = self._check_step_completed(i)\n\n if return_code == 0:\n logger.debug('Successful: \"{}\".'.format(list(step.keys())[0]))\n else:\n return_code = 1\n self._submit_step(i)\n\n # do not enter while loop for continuous monitoring\n if not self.monitor:\n break\n\n time.sleep(1)\n while return_code == 1 and self.monitor:\n time.sleep(5)\n return_code = self._check_step_completed(i)\n\n if return_code == 2:\n module, f_config = self._get_command_config(i)\n raise ExecutionError('Pipeline failed at step '\n '{} \"{}\" {}'\n .format(i, module, f_config))\n\n if i + 1 == len(self._run_list) and return_code == 0:\n logger.info('Pipeline job \"{}\" is complete.'\n .format(self._config.name))\n logger.debug('Output directory is: \"{}\"'\n .format(self._config.dirout))","def main():\n init()\n separator_len = 40\n for s in stage_instances:\n print('='*separator_len)\n print(s.name)\n print('-'*separator_len)\n\n s.add_tasks() # Add tasks from previous stage\n s.revive_or_archive() # Revive killed tasks or move them to failed\n s.schedule_jobs() # Schedule new jobs if needed\n s.print_status()\n print('='*separator_len + '\\n')\n render(stage_instances)","def before_all(context):\n context.config.setup_logging()\n context.start_system = _start_system\n context.teardown_system = _teardown_system\n context.restart_system = _restart_system\n context.run_command_in_service = _run_command_in_service\n context.exec_command_in_container = _exec_command_in_container\n context.is_running = _is_running\n context.is_jobs_debug_api_running = _is_jobs_debug_api_running\n context.is_component_search_service_running = _is_component_search_service_running\n context.is_master_tag_list_service_running = _is_master_tag_list_service_running\n context.wait_for_master_tag_list_service = _wait_for_master_tag_list_service\n context.is_get_untagged_component_service_running = _is_get_untagged_component_service_running\n context.wait_for_get_untagged_component_service = _wait_for_get_untagged_component_service\n context.send_json_file = _send_json_file\n context.wait_for_jobs_debug_api_service = _wait_for_jobs_debug_api_service\n context.wait_for_component_search_service = _wait_for_component_search_service\n context.is_3scale_staging_running = _is_3scale_staging_running\n context.is_3scale_preview_running = _is_3scale_preview_running\n context.is_backbone_api_running = _is_backbone_api_running\n context.is_gemini_api_running = _is_gemini_api_running\n\n # Configure container logging\n context.dump_logs = _read_boolean_setting(context, 'dump_logs')\n tail_logs = int(context.config.userdata.get('tail_logs', 0))\n dump_errors = _read_boolean_setting(context, 'dump_errors')\n if tail_logs:\n dump_errors = True\n else:\n tail_logs = 50\n context.dump_errors = dump_errors\n context.tail_logs = tail_logs\n\n # Configure system under test\n context.kubernetes_dir_path = context.config.userdata.get('kubernetes_dir', None)\n if context.kubernetes_dir_path is not None:\n context.docker_compose_path = None\n else:\n # If we're not running Kubernetes, use the local Docker Compose setup\n _set_default_compose_path(context)\n\n # for now, we just assume we know what compose file looks like (what services need what images)\n context.images = {}\n context.images['bayesian/bayesian-api'] = context.config.userdata.get(\n 'coreapi_server_image',\n 'registry.devshift.net/bayesian/bayesian-api')\n context.images['bayesian/cucos-worker'] = context.config.userdata.get(\n 'coreapi_worker_image',\n 'registry.devshift.net/bayesian/cucos-worker')\n core_v2_api_url = _read_url_from_env_var('F8A_API_V2_URL')\n coreapi_url = _read_url_from_env_var('F8A_API_URL')\n core_v2_api_url = _read_url_from_env_var('F8A_API_V2_URL')\n valid_synk_token = os.environ.get(\"SNYK_TOKEN\")\n uuid = os.environ.get(\"REGISTERED_USER_UUID\")\n jobs_api_url = _read_url_from_env_var('F8A_JOB_API_URL')\n gremlin_url = _read_url_from_env_var('F8A_GREMLIN_URL')\n threescale_url = _read_url_from_env_var('F8A_3SCALE_URL')\n threescale_preview_url = _read_url_from_env_var('F8A_THREE_SCALE_PREVIEW_URL')\n backbone_api_url = _read_url_from_env_var('F8A_BACKBONE_API_URL')\n service_id = _read_url_from_env_var('F8A_SERVICE_ID')\n gemini_api_url = _read_url_from_env_var('F8A_GEMINI_API_URL')\n license_service_url = _read_url_from_env_var('F8A_LICENSE_SERVICE_URL')\n\n context.running_locally = _running_locally(coreapi_url, jobs_api_url)\n check_test_environment(context, coreapi_url)\n\n context.coreapi_url = _get_url(context, coreapi_url, 'coreapi_url', _FABRIC8_ANALYTICS_SERVER)\n context.core_v2_api_url = core_v2_api_url\n context.jobs_api_url = _get_url(context, jobs_api_url, 'jobs_api_url', _FABRIC8_ANALYTICS_JOBS)\n context.gremlin_url = _get_url(context, gremlin_url, \"gremlin_url\", _FABRIC8_GREMLIN_SERVICE)\n context.license_service_url = _get_url(context, license_service_url, 'license_service_url',\n _FABRIC8_LICENSE_SERVICE)\n context.core_v2_api_url = core_v2_api_url\n context.threescale_url = threescale_url\n context.valid_synk_token = valid_synk_token\n context.uuid = uuid\n\n context.threescale_preview_url = threescale_preview_url\n\n context.backbone_api_url = backbone_api_url\n\n context.service_id = service_id\n\n context.gemini_api_url = gemini_api_url\n\n # we can retrieve access token by using refresh/offline token\n\n # informations needed to access S3 database from tests\n _check_env_var_presence_s3_db('AWS_ACCESS_KEY_ID')\n _check_env_var_presence_s3_db('AWS_SECRET_ACCESS_KEY')\n _check_env_var_presence_s3_db('S3_REGION_NAME')\n\n aws_access_key_id = os.environ.get('AWS_ACCESS_KEY_ID')\n aws_secret_access_key = os.environ.get('AWS_SECRET_ACCESS_KEY')\n s3_region_name = os.environ.get('S3_REGION_NAME')\n deployment_prefix = os.environ.get('DEPLOYMENT_PREFIX', 'STAGE')\n context.reports_bucket = os.environ.get('DEVELOPER_ANALYTICS_REPORTS_BUCKET')\n\n context.s3interface = S3Interface(aws_access_key_id, aws_secret_access_key,\n s3_region_name, deployment_prefix)\n\n context.client = None\n\n # timeout values can be overwritten by environment variables\n stack_analysis_timeout = _parse_int_env_var('F8A_STACK_ANALYSIS_TIMEOUT')\n component_analysis_timeout = _parse_int_env_var('F8A_COMPONENT_ANALYSIS_TIMEOUT')\n\n context.stack_analysis_timeout = stack_analysis_timeout or _DEFAULT_STACK_ANALYSIS_TIMEOUT\n\n context.component_analysis_timeout = component_analysis_timeout \\\n or _DEFAULT_COMPONENT_ANALYSIS_TIMEOUT\n\n if context.running_locally:\n context.client = docker.AutoVersionClient()\n\n for desired, actual in context.images.items():\n desired = 'registry.devshift.net/' + desired\n if desired != actual:\n context.client.tag(actual, desired, force=True)\n\n # Specify the analyses checked for when looking for \"complete\" results\n def _get_expected_component_analyses(ecosystem):\n common = context.EXPECTED_COMPONENT_ANALYSES\n specific = context.ECOSYSTEM_DEPENDENT_ANALYSES.get(ecosystem, set())\n return common | specific\n context.get_expected_component_analyses = _get_expected_component_analyses\n\n def _compare_analysis_sets(actual, expected):\n unreliable = context.UNRELIABLE_ANALYSES\n missing = expected - actual - unreliable\n unexpected = actual - expected - unreliable\n return missing, unexpected\n context.compare_analysis_sets = _compare_analysis_sets\n\n context.EXPECTED_COMPONENT_ANALYSES = {\n 'metadata', 'source_licenses', 'digests',\n 'dependency_snapshot', 'code_metrics'\n # The follower workers are currently disabled by default:\n # 'static_analysis', 'binary_data', 'languages', 'crypto_algorithms'\n }\n # Analyses that are only executed for particular language ecosystems\n context.ECOSYSTEM_DEPENDENT_ANALYSES = dict()\n # Results that use a nonstandard format, so we don't check for the\n # standard \"status\", \"summary\", and \"details\" keys\n context.NONSTANDARD_ANALYSIS_FORMATS = set()\n # Analyses that are just plain unreliable and so need to be excluded from\n # consideration when determining whether or not an analysis is complete\n context.UNRELIABLE_ANALYSES = {\n 'github_details', # if no github api token provided\n 'security_issues' # needs Snyk vulndb in S3\n }","def nrfconnect_project_ci_setup(ctx):\n with open(CI_MANIFEST_OVERRIDE_PATH, \"w\") as f:\n f.write(CI_MANIFEST_OVERRIDE_TXT)\n\n with ctx.cd(CI_MEMFAULT_SDK_REPO_ROOT):\n ctx.run(\"git init\")\n ctx.run('git config user.email \"hello@memfault.com\"')\n ctx.run('git config user.name \"Memfault Inc.\"')\n\n ctx.run(\"git add .\")\n ctx.run('git commit -m \"CI Test Changes\"')\n\n with ctx.cd(NRFCONNECT_ROOT):\n ctx.run(f\"west init -l {NRFCONNECT_DEMO_APP_ROOT}\")\n ctx.run(\"west update\")","def run(self):\n if self.all:\n cmd = self.apply_options(self.test_all_cmd)\n self.call_and_exit(cmd)\n else:\n cmds = (self.apply_options(self.unit_test_cmd, (\"coverage\",)),)\n if self.coverage:\n cmds += (self.apply_options(self.coverage_cmd),)\n self.call_in_sequence(cmds)","def run_stage_loop(cls, _opts, tests_results, put_next_stage):\n for _, result in tests_results:\n put_next_stage(result)","def test_3_full_pipeline(install_test_files, data_dir):\n with make_workdir() as workdir:\n cl = [\"bcbio_nextgen.py\",\n get_post_process_yaml(data_dir, workdir),\n os.path.join(data_dir, os.pardir, \"110106_FC70BUKAAXX\"),\n os.path.join(data_dir, \"run_info.yaml\")]\n subprocess.check_call(cl)","def test_all(test_fn):\n success_count = 0\n total_count = 0\n for env_name in sorted(ngym.all_envs()):\n total_count += 1\n print('Running env: {:s} Wrapped with SideBias'.format(env_name))\n try:\n test_fn(env_name)\n print('Success')\n success_count += 1\n except BaseException as e:\n print('Failure at running env: {:s}'.format(env_name))\n print(e)\n print('')\n\n print('Success {:d}/{:d} envs'.format(success_count, total_count))","def _with_deploy_env(commands=[]):\n with cd(env.directory):\n for command in commands:\n run(command)","def main():\n\n # Make sure we're running in the cellranger environment\n verify_environment()\n\n # Load all the available stages\n stages = load_stages()\n\n # Create an argument parser out of the stages\n parser = get_parser(stages)\n\n args = parser.parse_args()\n args.func(args)","def run(self):\n for tool in self.tools:\n tool.run()\n return","def run_models(\n self,\n normal=True,\n interrupt=True,\n run_start=None,\n state_builder=\"acis\",\n hrc=False,\n ):\n if hrc:\n loads = hrc_loads\n else:\n loads = test_loads\n if normal and \"normal\" in loads:\n for load in loads[\"normal\"]:\n self.run_model(\n load_week=load,\n run_start=run_start,\n state_builder=state_builder,\n )\n if interrupt and \"interrupt\" in loads:\n for load in loads[\"interrupt\"]:\n self.run_model(\n load_week=load,\n interrupt=True,\n run_start=run_start,\n state_builder=state_builder,\n )","def execute(self) -> None:\n env_vars['AWS_DEFAULT_REGION'] = self.task.parsed_payload['sagemaker_region']\n\n steps: tuple = (\n self._assume_service_account_role,\n self._retrieve_model_binary_stream_from_db, # Retrieve Model BLOB\n self._deserialize_artifact_stream, # Deserialize it to the Disk\n self._deploy_model_to_sagemaker, # Deploy model to SageMaker\n )\n\n for execute_step in steps:\n execute_step()","def start_all(provision_vm='False'):\n subprocess.check_call(['./run.py'], shell=True, cwd=orc8_docker_path)\n subprocess.check_call('docker-compose up -d', shell=True, cwd=feg_docker_integ_test_path)\n subprocess.check_call(\n 'fab start_magma:provision_vm=%s' % provision_vm,\n shell=True, cwd=agw_path,\n )","def run_all(self):\n failures, errors = [], []\n\n # Run each test case registered with us and agglomerate the results.\n for case_ in self.cases:\n case_.run()\n update_results(failures, errors, case_)\n\n # Display our results.\n print_errors(errors)\n print_failures(failures)\n print_overview(errors, failures)\n\n # Exit with 0 if all tests passed, >0 otherwise.\n sys.exit(len(failures) + len(errors))","def gen_stage_loop(cls, _opts, tests, put_next_stage, _put_result_stage):\n for test in tests:\n put_next_stage(test)","def project_run(\n project_dir: Path,\n subcommand: str,\n *,\n overrides: Dict[str, Any] = SimpleFrozenDict(),\n force: bool = False,\n dry: bool = False,\n capture: bool = False,\n skip_requirements_check: bool = False,\n) -> None:\n config = load_project_config(project_dir, overrides=overrides)\n commands = {cmd[\"name\"]: cmd for cmd in config.get(\"commands\", [])}\n workflows = config.get(\"workflows\", {})\n validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)\n\n req_path = project_dir / \"requirements.txt\"\n if not skip_requirements_check:\n if config.get(\"check_requirements\", True) and os.path.exists(req_path):\n with req_path.open() as requirements_file:\n _check_requirements([req.strip() for req in requirements_file])\n\n if subcommand in workflows:\n msg.info(f\"Running workflow '{subcommand}'\")\n for cmd in workflows[subcommand]:\n project_run(\n project_dir,\n cmd,\n overrides=overrides,\n force=force,\n dry=dry,\n capture=capture,\n skip_requirements_check=True,\n )\n else:\n cmd = commands[subcommand]\n for dep in cmd.get(\"deps\", []):\n if not (project_dir / dep).exists():\n err = f\"Missing dependency specified by command '{subcommand}': {dep}\"\n err_help = \"Maybe you forgot to run the 'project assets' command or a previous step?\"\n err_exits = 1 if not dry else None\n msg.fail(err, err_help, exits=err_exits)\n check_spacy_commit = check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION)\n with working_dir(project_dir) as current_dir:\n msg.divider(subcommand)\n rerun = check_rerun(current_dir, cmd, check_spacy_commit=check_spacy_commit)\n if not rerun and not force:\n msg.info(f\"Skipping '{cmd['name']}': nothing changed\")\n else:\n run_commands(cmd[\"script\"], dry=dry, capture=capture)\n if not dry:\n update_lockfile(current_dir, cmd)","def run_tests(event, context):\n try:\n jobId = event['CodePipeline.job']['id']\n user_parameters = json.loads(event['CodePipeline.job']['data']['actionConfiguration']['configuration']['UserParameters'])\n runscope_trigger_url = user_parameters['runscopeTriggerUrl']\n runscope_access_token = user_parameters['runscopeAccessToken']\n\n tests = start_tests(runscope_trigger_url)\n aggregate_status = wait_for_tests_to_complete(tests, runscope_access_token)\n if aggregate_status == \"pass\":\n code_pipeline.put_job_success_result(jobId=jobId)\n else:\n code_pipeline.put_job_failure_result(jobId=jobId, failureDetails={\n 'type': 'JobFailed',\n 'message': 'One or more tests failed'\n })\n except:\n code_pipeline.put_job_failure_result(jobId=jobId, failureDetails={\n 'type': 'JobFailed',\n 'message': 'Unhandled exception during Runscope tests execution'\n })","def run_tests(self):\n # Charm does not defer hooks so that test is not included.\n # Trigger a package change which requires a restart\n self.run_package_change_test(\n 'ovn-central',\n 'ovn-central')","def run_tests(self):\n # Trigger a config change which triggers a deferred hook.\n self.run_charm_change_hook_test('configure_ovs')\n\n # Trigger a package change which requires a restart\n self.run_package_change_test(\n 'openvswitch-switch',\n 'openvswitch-switch')","def run_tests(self):\n # Trigger a config change which triggers a deferred hook.\n self.run_charm_change_hook_test('configure_ovs')\n\n # Trigger a package change which requires a restart\n self.run_package_change_test(\n 'openvswitch-switch',\n 'openvswitch-switch')","def process_steps(steplist, build, buildslave, build_status, basedir):\n for step in steplist:\n step.setBuild(build)\n step.setBuildSlave(buildslave)\n step.setStepStatus(build_status.addStepWithName(step.name))\n step.setDefaultWorkdir(os.path.join(basedir, 'build'))\n step.workdir = os.path.join(basedir, 'build')","def run_training_save_tests():\n test_training_save()\n test_distributed_training_save()\n test_multimodel_training_save()\n test_distributed_multimodel_training_save()","def _execute_stage(self, index, stage, stop):\n if stop.is_set():\n _LOGGER.info(\"Stopped pipeline on group %s\", self._group)\n return\n _LOGGER.info(\" -> Running stage '%s' on group %s\", stage, self._group)\n if stage.name == 'on':\n self._group.on = True\n elif stage.name == 'off':\n self._group.on = False\n elif stage.name == 'hue':\n self._group.hue = stage.args[0]\n elif stage.name == 'saturation':\n self._group.saturation = stage.args[0]\n elif stage.name == 'color':\n self._group.color = Color(*stage.args)\n elif stage.name == 'brightness':\n self._group.brightness = stage.args[0]\n elif stage.name == 'temperature':\n self._group.temperature = stage.args[0]\n elif stage.name == 'transition':\n self._group.transition(*stage.args, **stage.kwargs)\n elif stage.name == 'white':\n self._group.white()\n elif stage.name == 'white_up':\n self._group.white_up()\n elif stage.name == 'white_down':\n self._group.white_down()\n elif stage.name == 'red_up':\n self._group.red_up()\n elif stage.name == 'red_down':\n self._group.red_down()\n elif stage.name == 'green_up':\n self._group.green_up()\n elif stage.name == 'green_down':\n self._group.green_down()\n elif stage.name == 'blue_up':\n self._group.blue_up()\n elif stage.name == 'blue_down':\n self._group.blue_down()\n elif stage.name == 'night_light':\n self._group.night_light()\n elif stage.name == 'link':\n self._group.link()\n elif stage.name == 'unlink':\n self._group.unlink()\n elif stage.name == 'flash':\n self._group.flash(**stage.kwargs)\n elif stage.name == 'repeat':\n self._repeat(index, stage, stop)\n elif stage.name == 'wait':\n time.sleep(*stage.args)\n elif stage.name == 'callback':\n stage.args[0](*stage.args[1:], **stage.kwargs)","def run_all_tasks(data_dir):\n print(\"Training and testing for all tasks ...\")\n for t in range(20):\n run_task(data_dir, task_id=t + 1)","def onPreFork(self):","def make_all():\n\n if not MASTER.exists():\n os.makedirs(MASTER)\n members = inspect.getmembers(sys.modules[__name__])\n members = [f for f in members if 'test_' in f[0]]\n for member in members:\n print('Running %s...' % member[0], end='')\n member[1](master=True)\n print('done!')","def make_all():\n\n if not MASTER.exists():\n os.makedirs(MASTER)\n members = inspect.getmembers(sys.modules[__name__])\n members = [f for f in members if 'test_' in f[0]]\n for member in members:\n print('Running %s...' % member[0], end='')\n member[1](master=True)\n print('done!')","def execute(self):\n for action in self.actions:\n self._logger.info('[~] Executing %s.', action)\n self._execute_action(action)","def main():\n run_test_all()","def test_python(width=10):\n\n stage_1 = [diamond(sleep=60, inputs=[0])]\n\n stage_2 = []\n for i in range(0, width):\n stage_2.extend([diamond(sleep=20, inputs=stage_1)])\n\n stage_3 = [diamond(sleep=30, inputs=stage_2)]\n\n if not stage_3[0].done():\n time.sleep(30)\n for sitename in dfk.executors:\n print(dfk.executors[sitename].status())","def test_run():\n # Only few steps for test\n timesteps = 128\n\n # Compute all sub testing conf\n envs = ['CartPole-v0']\n ml_platforms = ['torch', 'tf']\n agents = ['dqn', 'a2c']\n\n test_combinations = list(it.product(\n envs,\n ml_platforms,\n agents\n )\n )\n\n # Finally test them all\n for conf in test_combinations:\n env_str, ml_platform_str, agent_str = conf\n run(\n agent_str,\n ml_platform_str,\n env_str,\n 'dense',\n timesteps,\n './target/')","def runall(sources='sources'):\n init()\n upload_sources(sources)\n compile()\n run()","def execute(self):\n\n self._status = 'Running'\n\n try:\n self._init_staf_handle()\n self._setup_results()\n self._stage()\n self._install()\n self._get_remote_results()\n self._status = 'Pass'\n except CoreError as e:\n self._status = 'Fatal'\n self._message = e.msg\n finally:\n self._close_staf_handle()\n\n #Notify TestCase that a failure occurred.\n if self._status == 'Fatal': raise FatalError(self._message)","def run(self):\n list_test_scenarios = self.__get_list_scenarios_in_folder()\n\n if not list_test_scenarios:\n utils.print_error(\n \"\\n{}\\n\".format(constant.ERR_CANNOT_FIND_ANY_TEST_SCENARIOS))\n exit(1)\n\n (tests_pass, tests_fail) = self.__execute_tests(list_test_scenarios)\n\n complete_message = constant.INFO_TEST_PASS_FAIL.format(\n tests_pass, tests_fail)\n\n print(complete_message)\n\n self.__execute_reporter()","def main():\n with open('config.json') as config_file:\n configs = json.load(config_file)\n\n jar_list = utilities.upload_jars(configs)\n utilities.sign_jars(configs)\n\n artifact_folder = utilities.prepare_artifacts(configs, jar_list)\n\n repo_id = utilities.create_staging_repo(configs)\n utilities.deploy_to_staging_repo(configs, artifact_folder, repo_id)\n utilities.close_staging_repo(configs, repo_id)","def do_work():\n args = sys.argv\n args = args[1:] # First element of args is the file name\n\n if len(args) == 0:\n print('You have not passed any commands in!')\n else:\n for a in args:\n if a == '--help':\n print('AWS - EKS - Terraform : Demo Tool')\n print('Options:')\n print(' --help -> show this help menu.')\n print(' --tf -> execute terraform validate and apply.')\n print(' --passtest -> pass unit test - flask deployment')\n print(' --failtest -> fail unit test - flask deployment')\n print(' --prepare -> prepare tests')\n elif a == '--passtest':\n os.system(\"python python_test.py\")\n elif a == '--failtest':\n os.system(\"python python_test_fail.py\")\n elif a == '--tf':\n os.system(\"cd terraform && terraform validate && terraform apply -auto-approve && cd ..\")\n elif a == '--prepare':\n os.system(\"aws eks update-kubeconfig --name flugeldev-cluster && kubectl delete -f curl.yml && kubectl apply -f curl.yml\")\n else:\n print('Unrecognised argument.')","def run(self):\n self.archive_bash_inits()\n self.create_paths()\n self.copy_files()\n self.make_git_config()\n self.ensure_bash_history()","def full_deploy():\n refresh_cts()\n push_mockups()\n deploy()","def run_all_tests(self):\n for index in range(len(self.__test_set_list)):\n self.run_test(index)","def deploy(upgrade=False):\n print(\"Deploying project on {} !\".format(env.stage))\n execute('system.setup')\n execute('git.checkout')\n execute('virtualenv.setup')\n execute('django.setup')\n execute('cron.setup')\n execute('uwsgi.setup')\n execute('supervisor.setup')\n execute('nginx.setup')","def run_scenario(self):\n self.initialize_random_map()\n self.visualize_environment('initial')\n self.get_tower_target_coverages()\n self.solve_environment()\n self.visualize_environment('solved')","def run_all(operations=ops):\n for operation in operations:\n run(operation)","def do_stage(self, images):\n\n for i, image in enumerate(images):\n pass\n # logging_tags = logs.image_config_to_tags(image, self.group_by_keywords)","def RunStage(self):\n\n # Stage construction is usually done as late as possible because the tests\n # set up the build configuration and options used in constructing the stage.\n results_lib.Results.Clear()\n stage = self.ConstructStage()\n stage.Run()\n self.assertTrue(results_lib.Results.BuildSucceededSoFar())","def main() -> None:\n ## Setup logging\n # Format the logs as JSON for simplicity\n formatting = json.dumps(\n {\n \"timestamp\": \"%(asctime)s\",\n \"namespace\": \"%(name)s\",\n \"loglevel\": \"%(levelname)s\",\n \"message\": \"%(message)s\",\n }\n )\n # Default to a log level of WARNING until the config is parsed\n logging.basicConfig(level=\"WARNING\", format=formatting)\n log = logging.getLogger(__project_name__)\n\n # Get the effective config\n try:\n config = get_config()\n except ValueError:\n log.error(\"Unable to create a valid configuration\")\n sys.exit(1)\n\n # Update the log level to whatever was set in the config\n logging.getLogger().setLevel(config[\"loglevel\"])\n\n # Create the API objects and apply the config\n try:\n results_api = apply_config(\n api=ResultsAPI(app_id=config[\"apis\"][\"results\"][\"app_id\"]), config=config\n )\n upload_api = apply_config(\n api=UploadAPI(app_id=config[\"apis\"][\"upload\"][\"app_id\"]), config=config\n )\n if \"sandbox_name\" in config[\"apis\"][\"sandbox\"]:\n sandbox_api = apply_config(\n api=SandboxAPI(\n app_id=config[\"apis\"][\"sandbox\"][\"app_id\"],\n sandbox_name=config[\"apis\"][\"sandbox\"][\"sandbox_name\"],\n ),\n config=config,\n )\n else:\n sandbox_api = None\n except (TypeError, NameError):\n log.error(\"Unable to create valid API objects\")\n sys.exit(1)\n\n # Configure the environment\n for step in config[\"workflow\"]:\n if step == \"submit_artifacts\":\n configure_environment(\n api_key_id=config[\"api_key_id\"], api_key_secret=config[\"api_key_secret\"]\n )\n success = submit_artifacts(upload_api=upload_api, sandbox_api=sandbox_api)\n\n if success:\n log.info(\"Successfully submit build artifacts for scanning\")\n else:\n log.error(\"Failed to submit build artifacts for scanning\")\n sys.exit(1)\n elif step == \"check_compliance\":\n configure_environment(\n api_key_id=config[\"api_key_id\"], api_key_secret=config[\"api_key_secret\"]\n )\n if not check_compliance(results_api=results_api):\n sys.exit(1)","def run(self):\n self.scion_sh('run', 'nobuild')","def run_ci(project: str, branch: str, job: str, revision: Optional[str],\n tag: Optional[str]) -> None:\n config = get_config()\n\n if config.circleci_api_token is None:\n raise ValueError('circleci_api_token not configured. Run `sriracha '\n 'configure` to configure')\n\n resp = scci.trigger_job(api_token=config.circleci_api_token,\n project=project, branch=branch, job=job,\n revision=revision, tag=tag)\n\n click.echo_via_pager(json.dumps(resp, indent=4))","def task_test(argv):\n run_tests(\"python2\", argv)\n run_tests(\"python3\", argv)","def run_presub(stages):\n if is_presub(stages[0]):\n # Pop the first stage\n stage, stages = stages[0], stages[1:]\n # Execute it only if Python was invoked normally, not in a queue job\n if STAGE_ID is None:\n msg = \"Only the first stage can execute on the login node\"\n assert all([not is_presub(i) for i in stages]), msg\n alog.info(\"Pre-submit stage starting: %s\", stage)\n stage()\n alog.info(\"Pre-submit stage done: %s\", stage)\n return stages","def main():\n configuration = {'resource-folder': 'resources',\n 'build-folder': 'build',\n 'log-folder': 'logfiles',\n 'use-preloaded': False,\n 'addi-metrics': 'addi-metrics.json',\n 'jenkins': {'dependency-filename': 'dependencies.txt',\n 'server': 'http://is.dbc.dk',\n 'repository-project': 'opensearch-3rd-party-dependencies'},\n 'log-zip-file':'logs.zip'}\n configuration.update(cli())\n setup_logger(configuration['verbose'])\n run_performance_test(configuration)","def __post_bootstrap_exec(self):\n info = model_info(juju.get_current_model())\n # Set our provider type environment var so that it is\n # exposed in future processing tasks\n app.env['JUJU_PROVIDERTYPE'] = info['provider-type']\n\n _post_bootstrap_sh = path.join(app.config['spell-dir'],\n 'conjure/steps/00_post-bootstrap')\n app.log.debug(\n 'Checking for post bootstrap task: {}'.format(_post_bootstrap_sh))\n if path.isfile(_post_bootstrap_sh) \\\n and os.access(_post_bootstrap_sh, os.X_OK):\n app.ui.set_footer('Running post-bootstrap tasks...')\n utils.pollinate(app.session_id, 'J001')\n app.log.debug(\"post_bootstrap running: {}\".format(\n _post_bootstrap_sh\n ))\n try:\n future = async.submit(partial(check_output,\n _post_bootstrap_sh,\n shell=True,\n env=app.env),\n self.__handle_exception)\n future.add_done_callback(self.__post_bootstrap_done)\n except Exception as e:\n return self.__handle_exception(e)","def upload_all_completed_analyses(context: click.Context, pipeline: Pipeline = None):\n\n LOG.info(\"----------------- AUTO -----------------\")\n\n status_db: Store = context.obj.status_db\n\n exit_code = 0\n for analysis_obj in status_db.get_analyses_to_upload(pipeline=pipeline):\n if analysis_obj.family.analyses[0].uploaded_at is not None:\n LOG.warning(\n f\"Skipping upload for case {analysis_obj.family.internal_id}. \"\n f\"It has been already uploaded at {analysis_obj.family.analyses[0].uploaded_at}.\"\n )\n continue\n\n case_id = analysis_obj.family.internal_id\n LOG.info(\"Uploading analysis for case: %s\", case_id)\n try:\n context.invoke(upload, case_id=case_id)\n except Exception:\n LOG.error(f\"Case {case_id} upload failed\")\n LOG.error(traceback.format_exc())\n exit_code = 1\n\n sys.exit(exit_code)","def run_all_steps(self, *args, **kwargs):\n\n # the following configopts are common to all builds\n if self.toolchain.options['pic']:\n self.cfg.update('configopts', '--with-pic')\n\n if self.cfg['with_shared']:\n self.cfg.update('configopts', '--enable-shared')\n\n if self.cfg['with_generic_kernel']:\n self.cfg.update('configopts', '--enable-generic')\n\n if self.cfg['with_single']:\n self.cfg.update('configopts', '--enable-single-precision')\n\n for flag in ELPA_CPU_FEATURE_FLAGS:\n # many ELPA kernels are enabled by default, even when the\n # CPU does not support them, so we disable them all, except\n # when the appropriate CPU flag is found\n # sse kernels require sse4_2\n if flag == 'sse4_2':\n if getattr(self, flag):\n self.cfg.update('configopts','--enable-sse')\n self.cfg.update('configopts', '--enable-sse-assembly')\n else:\n self.cfg.update('configopts', '--disable-sse')\n self.cfg.update('configopts', '--disable-sse-assembly')\n elif flag == 'avx512f':\n if getattr(self, 'avx512f'):\n self.cfg.update('configopts', '--enable-avx512')\n else:\n self.cfg.update('configopts', '--disable-avx512')\n else:\n if getattr(self, flag):\n self.cfg.update('configopts', '--enable-%s' % flag)\n else:\n self.cfg.update('configopts', '--disable-%s' % flag)\n\n # By default ELPA tries to use MPI and configure fails if it's not available\n # so we turn off MPI support unless MPI support is requested via the usempi toolchain option.\n # We also set the LIBS environmet variable to detect the correct linalg library\n # depending on the MPI availability.\n if self.toolchain.options.get('usempi', None):\n self.cfg.update('configopts', '--with-mpi=yes')\n self.cfg.update('configopts', 'LIBS=\"$LIBSCALAPACK\"')\n else:\n self.cfg.update('configopts', '--with-mpi=no')\n self.cfg.update('configopts', 'LIBS=\"$LIBLAPACK\"')\n\n self.cfg.update('configopts', 'LDFLAGS=\"-L${EBROOTZLIB}/lib -L${EBROOTLIBXML2}/lib -lxml2 -lz\"')\n\n # make all builds verbose\n self.cfg.update('buildopts', 'V=1')\n\n # keep track of common configopts specified in easyconfig file,\n # so we can include them in each iteration later\n common_config_opts = self.cfg['configopts']\n\n self.cfg['configopts'] = []\n\n self.cfg.update('configopts', ['--disable-openmp ' + common_config_opts])\n if self.toolchain.options.get('openmp', False):\n self.cfg.update('configopts', ['--enable-openmp ' + common_config_opts])\n\n self.log.debug(\"List of configure options to iterate over: %s\", self.cfg['configopts'])\n\n return super(EB_ELPA, self).run_all_steps(*args, **kwargs)","def step_impl(context):\n for executor in context.executors_to_run:\n now = datetime.now() + timedelta(seconds=2)\n trigger = DateTrigger(run_date=now)\n context.manager.update_executor_trigger(executor.id, trigger)\n\n # give time to execute the jobs\n wait_for_job_executed(context, executor.id)\n\n context.last_executor_id = executor.id","def run(pars, #parameter files\n #directory of scenario files\n scen_dir = r'C:\\LS\\03_TOOLS\\_git\\COVID_01\\scenarios',\n \n #map to scenario files\n scen_d = {\n 'NoNPI':'NPI_Scenario1_None.R',\n 'BI1918':'NPI_Scenario2_Bootsma_1918Influenza.R',\n 'SouthKorea':'NPI_Scenario3_SouthKorea.R',\n 'Reduced':'NPI_Scenario4_ReducedGamma.R', \n }\n ):\n \n \n \n #===========================================================================\n # precheck \n #===========================================================================\n assert len(pars)==4, 'unexpected inputs count'\n print('pars: \\n%s'%pars)\n \n #check the R Environment variables\n assert 'R_USER' in os.environ\n assert 'R_HOME' in os.environ\n \n #print('R_USER=%s \\nR_HOME=%s'%(os.getenv('R_USER'), os.getenv('R_HOME')))\n\n \n \n \n \n #===========================================================================\n # setup\n #===========================================================================\n s = setup.Setup(setup_name = 'mid_utah_'+pars[2],\n spatial_setup = WestCoastSpatialSetup(),\n nsim = int(pars[1]),\n ti = datetime.date(2020, 3, 6),\n tf = datetime.date(2020, 10, 1),\n interactive = False,\n write_csv = True,\n dt = 1/4)\n \n #===========================================================================\n # set the scenario parmaters\n #===========================================================================\n\n \n \n assert pars[2] in scen_d, 'unrecognized scenario: %s'%pars[2]\n \n rfp = os.path.join(scen_dir, scen_d[pars[2]])\n assert os.path.exists(rfp)\n \n s.script_npi = rfp\n \n print('set script_npi=%s'%s.script_npi)\n\n #===========================================================================\n # execute\n #===========================================================================\n\n print()\n print()\n print(f\">>> Starting {s.nsim} model runs on {pars[3]} processes\")\n print(f\">>> Setup *** {s.setup_name} *** from {s.ti} to {s.tf} !\")\n print(f\">>> writing to folder : {s.datadir}{s.setup_name}\")\n print()\n print()\n \n tic = time.time()\n \n res_l = seir.run_parallel(s, int(pars[3]))\n print(f\">>> Runs done in {time.time()-tic} seconds...\")","def _run_actions(self):\n\n if \"install-bento\" in self.actions:\n self._do_action_bento_setup()\n\n if \"create-tables\" in self.actions:\n self._do_action_tables_create()\n\n if \"import-ratings\" in self.actions:\n self._do_action_import_ratings()\n\n if \"import-user-info\" in self.actions:\n self._do_action_import_user_info()\n\n if \"import-movie-info\" in self.actions:\n self._do_action_import_movie_info()\n\n if \"train-item-item-cf\" in self.actions:\n self._do_action_train()\n\n if \"register-freshener\" in self.actions:\n self._do_action_register_freshener()","def deploy():\n stage(branch='live', role='live')","def main():\n args = main_parse_args()\n\n log_dir = os.path.join(args.working_dir, args.logs_path)\n if not os.path.exists(log_dir):\n os.makedirs(log_dir)\n\n # if there are scheduled jobs, return\n job_status = get_status(args.chronos)\n if len(job_status['all']) > 0:\n print(\"Cannot begin build with pre-existing jobs on chronos!\")\n else:\n run_step(\"MYSQL\", False, args)\n run_step(\"REDIS\", False, args)\n wait_for_port(int(args.redis_port), args.redis_host)\n wait_for_port(int(args.mysql_port), args.mysql_host)\n run_step('SETUP', True, args)\n run_step('CHECK', True, args)\n run_step('IMPORT', True, args)\n run_step('EXPORT1', True, args)\n run_step('EXPORT2', True, args)\n print(\"KnowNet Pipeline Completed!\")","def run_test_procedures(self):\r\n print('\\nDeploying prediction model...\\n')\r\n self.final_df_output = pd.read_json(path.data_final)\r\n self.final_df_output.sort_index(inplace=True)\r\n self.fill_testing_dates()\r\n self.perform_backtests()\r\n self.create_full_predictions_dataframe()\r\n print('\\nDeployment complete!')","def runtests():\r\n\r\n app_abspath = os.path.dirname(os.path.dirname(__file__))\r\n models_abspath = os.path.join(app_abspath, 'models.py')\r\n models_exists = os.path.isfile(models_abspath)\r\n urls_abspath = os.path.join(app_abspath, 'urls.py')\r\n urls_exists = os.path.isfile(urls_abspath)\r\n views_abspath = os.path.join(app_abspath, 'views')\r\n views_exists = os.path.isdir(views_abspath)\r\n tpls_abspath = os.path.join(app_abspath, 'templates')\r\n tpls_exists = os.path.isdir(tpls_abspath)\r\n\r\n for f in [models_abspath, urls_abspath]:\r\n if os.path.isfile(f):\r\n subprocess.call('cp {} {}.orig'.format(f, f), shell=True)\r\n\r\n if views_exists:\r\n subprocess.call('cp -r {} {}.orig'.format(views_abspath, views_abspath), shell=True)\r\n\r\n if tpls_exists:\r\n subprocess.call('cp -r {} {}.orig'.format(tpls_abspath, tpls_abspath), shell=True)\r\n\r\n overwrite_project_language('ja')\r\n subprocess.call('python manage.py generatescaffold test_app I18nModel title:string', shell=True)\r\n time.sleep(1)\r\n overwrite_project_language('en-us')\r\n time.sleep(1)\r\n\r\n subprocess.call('python manage.py generatescaffold test_app GeneratedNoTimestampModel title:string description:text --no-timestamps', shell=True)\r\n time.sleep(2) # Give time for Django's AppCache to clear\r\n\r\n subprocess.call('python manage.py generatescaffold test_app GeneratedModel title:string description:text', shell=True)\r\n\r\n test_status = subprocess.call('python manage.py test --with-selenium --with-selenium-fixtures --with-cherrypyliveserver --noinput', shell=True)\r\n\r\n if models_exists:\r\n subprocess.call('mv {}.orig {}'.format(models_abspath, models_abspath), shell=True)\r\n else:\r\n subprocess.call('rm {}'.format(models_abspath), shell=True)\r\n\r\n if urls_exists:\r\n subprocess.call('mv {}.orig {}'.format(urls_abspath, urls_abspath), shell=True)\r\n else:\r\n subprocess.call('rm {}'.format(urls_abspath), shell=True)\r\n\r\n if views_exists:\r\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\r\n subprocess.call('mv {}.orig {}'.format(views_abspath, views_abspath), shell=True)\r\n else:\r\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\r\n\r\n if tpls_exists:\r\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\r\n subprocess.call('mv {}.orig {}'.format(tpls_abspath, tpls_abspath), shell=True)\r\n else:\r\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\r\n\r\n subprocess.call('rm {}/*.pyc'.format(app_abspath), shell=True)\r\n\r\n sys.exit(test_status)","def run_custom_training_tests():\n test_custom_training()\n test_custom_distributed_training()\n test_custom_multimodel_training()\n test_custom_distributed_multimodel_training()","def run(cont, util, shell, argv=list()):\n\n cache_dir = cont.named_cache_dir(\"travis_container_downloads\",\n ephemeral=False)\n cache_dir_key = \"_POLYSQUARE_TRAVIS_CONTAINER_TEST_CACHE_DIR\"\n shell.overwrite_environment_variable(cache_dir_key, cache_dir)\n\n cont.fetch_and_import(\"setup/python/setup.py\").run(cont, util, shell, argv)\n\n config_python = \"setup/project/configure_python.py\"\n py_ver = util.language_version(\"python3\")\n py_cont = cont.fetch_and_import(config_python).get(cont,\n util,\n shell,\n py_ver)\n\n with py_cont.activated(util):\n with util.Task(\"\"\"Downloading all distributions\"\"\"):\n os.environ[cache_dir_key] = cache_dir\n util.execute(cont,\n util.long_running_suppressed_output(),\n util.which(\"python\"),\n \"download-all-distros-to.py\")","def deploy():\n upload_static()\n compile_code()\n upload_code()\n upload_supervisor()\n start_server()","def execute(helper, config, args):\n env_config = parse_env_config(config, args.environment)\n cname_prefix = env_config.get('cname_prefix', None)\n env_name = args.environment\n\n # change version\n if args.version_label:\n helper.deploy_version(env_name, args.version_label)\n if not args.dont_wait:\n helper.wait_for_environments(env_name, status='Ready', version_label=args.version_label)\n\n # update it\n env = parse_env_config(config, env_name)\n option_settings = parse_option_settings(env.get('option_settings', {}))\n helper.update_environment(env_name,\n description=env.get('description', None),\n option_settings=option_settings,\n tier_type=env.get('tier_type'),\n tier_name=env.get('tier_name'),\n tier_version=env.get('tier_version'))\n\n # wait\n if not args.dont_wait:\n helper.wait_for_environments(env_name, health='Green', status='Ready', version_label=args.version_label)\n\n # delete unused\n helper.delete_unused_versions(versions_to_keep=int( get(config, 'app.versions_to_keep', 10) ))","def test_build_pipeline_one(self):\n args = \"Test_APP ONE TWO THREE\".split(\" \")\n task_list = build_pipeline(args, False)\n self.assertEqual(\"Task One\", task_list[0].execute())\n self.assertEqual(\"Task Two\", task_list[1].execute())\n self.assertEqual(\"Task Three\", task_list[2].execute())\n self.assertEqual(3, len(task_list))","def setup_for_compilation_testcase(self):\n os.chdir(self.tmp_work)\n\n for container in self.containers:\n self._setup_single_directory_for_compilation(container.directory)\n # Run any necessary pre_commands\n self._run_pre_commands(container.directory)","def execute(self):\n\n self._status = 'Running'\n\n self._checkout_resources()\n\n for test in self._tests:\n try:\n self._update_resource_timeouts(test.timeout)\n test.execute()\n except Failure as e:\n self._status = 'Fail'\n self._message = ('The \"{0}\" test in the test case \"{1}\" failed with the '\n 'message: \"{2}\"'.format(test.name, self.name, e.msg))\n except FatalError as e:\n self._checkin_resources()\n self._status = 'Fatal'\n self._message = ('The \"{0}\" test in the test case \"{1}\" encountered the fatal '\n 'error: \"{2}\"'.format(test.name, self.name, e.msg))\n raise FatalError(self._message)\n\n if self._status == 'Fail':\n self._checkin_resources()\n raise Failure(self._message)\n\n self._status = 'Pass'\n self._checkin_resources()","def setup():\n _confirm_branch()\n \n require('settings', provided_by=[production, staging])\n require('branch', provided_by=[stable, master, branch])\n \n setup_directories()\n setup_virtualenv()\n clone_repo()\n checkout_latest()\n install_requirements()\n install_apache_conf()\n deploy_to_s3()","def execute(self):\n\n self.__create_assets_directory()\n self.__create_datasets_directories()\n self.__create_structures_directories()\n\n self.logger.write_action_required(\"The repository specified in the readme file must be cloned in the path: \" +\n IMAGES_PATH)\n self.logger.write_action_required(\"Execute the command 'pip install -r requirements.txt' \")\n self.logger.write_info(\"Strategy executed successfully\")","def running_on_ci() -> bool:\r\n return os.environ.get(\"JANITOR_CI_MACHINE\") is not None","def execute(self):\n\n self._status = 'Running'\n\n for test_case in self._test_cases.values():\n try:\n test_case.execute()\n except Failure as e:\n self._status = 'Fail'\n self._message = ('The \"{0}\" test case in the test plan \"{1}\" failed with the '\n 'message: \"{2}\"'.format(test_case.name, self.name, e.msg))\n except FatalError as e:\n self._status = 'Fail'\n self._message = ('The \"{0}\" test case in the test plan \"{1}\" encountered the fatal '\n 'error: \"{2}\"'.format(test_case.name, self.name, e.msg))\n raise FatalError(self._message)\n\n if self._status == 'Fail':\n raise Failure(self._message)\n\n self._status = 'Pass'","def main(force):\n secret = get_secret()\n service = RestIO(secret)\n\n # Make sure we re-create our debug log for each run\n if DEBUG_LOG_FILE.exists():\n DEBUG_LOG_FILE.unlink()\n\n if force:\n # Regenerate all cassette data until we hit our rate limit\n click.secho(\"Regenerating all recorded cassettes\")\n result = run_tests(shlex.split(\"--record-mode=rewrite\"))\n num_failures = analyse_report(load_report())\n else:\n click.secho(\"Generating baseline...\")\n # Start by generating a baseline state without using any API calls\n run_tests(shlex.split(\"--record-mode=none --block-network\"))\n num_failures = analyse_report(load_report())\n if num_failures == 0:\n click.secho(\"All unit tests passed. Aborting rebuild.\", fg=\"yellow\")\n click.secho(\"To force a rebuild of all cassettes try --force\", fg=\"yellow\")\n return\n\n # The re-run any failed tests, forcing the cassettes to get regenerated\n # We append --lf to only rerun the tests that failed on the last pass\n click.secho(\"Rebuilding initial cassettes...\")\n result = run_tests(shlex.split(\"--record-mode=rewrite --lf\"))\n num_failures = analyse_report(load_report())\n\n iteration = 1\n while result == ExitCode.TESTS_FAILED and num_failures != 0:\n # check headers to see when the next token renewal is\n now = datetime.now(tz=tz.tzlocal())\n renewal = service.headers.time_to_refresh\n service.refresh_headers()\n wait_time = renewal - now\n minutes = math.ceil(wait_time.total_seconds() / 60)\n\n # if the rate limit has expired wait until the limit has been refreshed\n if minutes > 0:\n click.secho(\"Next renewal: {0}\".format(renewal.astimezone(tz.tzlocal())))\n click.secho(\"Sleeping for {0} minutes...\".format(minutes))\n # Give regular status updates to the user via a progress bar once every minute\n for _ in trange(minutes):\n sleep(60)\n\n # Give the API a few additional seconds before we try again to account for clock skew\n sleep(10)\n\n click.secho(\"Running test iteration \" + str(iteration))\n # We append --lf to only rerun the tests that failed on the previous run\n result = run_tests(shlex.split(\"--record-mode=rewrite --lf\"))\n\n # If the number of failing tests hasn't changed or has gotten worse, we are not making any progress\n # and thus we should exit to avoid a deadlock\n temp = analyse_report(load_report())\n if temp >= num_failures:\n raise Exception(\"Last unit test run had {0} failures and current run had {1}\".format(num_failures, temp))\n num_failures = temp\n\n # repeat until all tests pass\n iteration += 1\n\n # return the final test run result to the caller\n if result != ExitCode.OK:\n raise ClickException(\"Regeneration failed for unexpected reason: \" + str(result))","def bootstrap():\n\n require('environment', provided_by=env.environments)\n sudo('mkdir -p %(root)s' % env, user=env.deploy_user)\n clone_repo()\n setup_dirs()\n link_config_files()\n update_services()\n create_virtualenv()\n update_requirements()\n create_local_settings()","def _run_tests(self):\n for pyunit_testcase in self.cfg.testcases:\n yield self._run_testsuite(pyunit_testcase)","def run_sagemaker_tests(images):\n if not images:\n return\n pool_number = len(images)\n with Pool(pool_number) as p:\n p.map(run_sagemaker_pytest_cmd, images)","def main(args=sys.argv[1:]):\n\n # Parse arguments\n args = create_parser().parse_args(args)\n\n execution = API.create_execution(\n args.script, {'inputs': [['dataset-id', args.dataset]]})\n\n execution_id = execution['resource']\n e_id = re.split('execution/', execution_id)[1]\n execution_status = 0\n\n while execution_status != FINISHED:\n execution_resource = API.get_execution(execution_id)\n execution_status = execution_resource['object']['status']['code']\n number_of_models = API.list_models(\n \"execution_id=%s\" % e_id)['meta']['total_count']\n print \"models: %s\" % number_of_models\n return","def deploy():\n build()\n collect()\n commit()\n push()","def prepare_run(self, **kwargs):\n assert self.cloud\n LOGGER.debug('Validating run tests...')\n for test in kwargs.get('tests', self.stests):\n if test in self.stests:\n self.tests.append(test)\n else:\n raise Exception(f\"Test name '{test}' is invalid\")\n\n if not os.path.exists(self.task_dir):\n os.makedirs(self.task_dir)\n\n task = os.path.join(self.rally_dir, 'task.yaml')\n if not os.path.exists(task):\n LOGGER.error(\"Task file '%s' does not exist.\", task)\n raise Exception(f\"Task file '{task}' does not exist.\")\n self.task_file = os.path.join(self.task_dir, 'task.yaml')\n shutil.copyfile(task, self.task_file)\n\n task_macro = os.path.join(self.rally_dir, 'macro')\n if not os.path.exists(task_macro):\n LOGGER.error(\"Task macro dir '%s' does not exist.\", task_macro)\n raise Exception(f\"Task macro dir '{task_macro}' does not exist.\")\n macro_dir = os.path.join(self.task_dir, 'macro')\n if os.path.exists(macro_dir):\n shutil.rmtree(macro_dir)\n shutil.copytree(task_macro, macro_dir)\n\n self.update_keystone_default_role()\n self.compute_cnt = self.count_hypervisors()\n self.network_extensions = self.cloud.get_network_extensions()\n self.flavor_alt = self.create_flavor_alt()\n self.services = [service.name for service in\n functest_utils.list_services(self.cloud)]\n\n LOGGER.debug(\"flavor: %s\", self.flavor_alt)","def setup():\n\n with cd(env.homedir):\n\n # clone repository from github\n sudo('git clone https://github.com/collective/demo.plone.de.git', user=env.deploy_user) # noqa: E501\n\n with cd(env.directory):\n\n # requirements\n # sudo('python python-dev build-essential zlib1g-dev libssl-dev libxml2-dev libxslt1-dev wv poppler-utils libtiff5-dev libjpeg62-dev zlib1g-dev libfreetype6-dev liblcms1-dev libwebp-dev') # noqa: E501\n\n # prepare buildout\n if env.latest:\n if env.python3:\n sudo('ln -s local_demo_nightly_py3.cfg local.cfg', user=env.deploy_user) # noqa: E501\n else:\n sudo('ln -s local_demo_nightly_py2.cfg local.cfg', user=env.deploy_user) # noqa: E501\n else:\n sudo('ln -s local_production.cfg local.cfg', user=env.deploy_user)\n sudo('echo -e \"[buildout]\\nlogin = admin\\npassword = admin\" > secret.cfg', user=env.deploy_user) # noqa: E501\n\n # bootstrap and run bildout once\n if env.latest:\n sudo('./bin/pip install --no-cache-dir -r https://raw.githubusercontent.com/plone/buildout.coredev/5.2/requirements.txt', user=env.deploy_user) # noqa: E501\n else:\n sudo('./bin/pip install --no-cache-dir -r https://raw.githubusercontent.com/starzel/buildout/5.2/requirements.txt', user=env.deploy_user) # noqa: E501\n sudo('./bin/buildout', user=env.deploy_user)\n\n # start supervisor which starts plone instance also\n sudo('./bin/supervisord', user=env.deploy_user)","def test_run_exec(self):\n from multiprocessing import Process, Queue\n output = Queue()\n repodir = \"~/codes/ci/tests/repo\"\n processes = []\n for i in range(3):\n processes.append(Process(target=run_exec, args=(repodir, \"ls -la\", output, i)))\n processes[-1].start()\n \n #Wait for the unit tests to all finish.\n for p in processes:\n p.join()\n results = [output.get() for p in processes]\n ordered = {o[\"index\"]: o for o in results}\n\n #We consider the test successful if the output files were created and the end time\n #is not None. That means that the process ran correctly and python didn't lose\n #control of the subprocess.\n from os import path\n fullrepo = path.expanduser(repodir)\n for i in range(3):\n self.assertTrue(path.isfile(path.join(fullrepo, \"{}.cidat\".format(i))))\n self.assertIsNotNone(ordered[i][\"end\"])\n self.assertEqual(ordered[i][\"code\"], 0)"],"string":"[\n \"def _run_ci_test():\\n _run_install(False)\\n _run_coverage_html(False)\\n _run_typecheck_xml(False)\\n _run_lint(True)\",\n \"def run(self):\\n self._build_docker_images()\\n self._build_docker_image_test()\\n self._run_workflow_in_matrix_of_envs()\",\n \"def _run_ci_publish():\\n _run_install(False)\\n _run_tests(False)\\n _run_publish(True)\",\n \"def _run_ci_integration_test():\\n _run_install(False)\\n _run_integration_tests_on_github(False)\",\n \"def main():\\n\\n if not os.environ.get('TRAVIS_PULL_REQUEST', 'false') == 'false':\\n return\\n\\n git_config_setup()\\n populate_source()\\n build_and_deploy()\",\n \"def main():\\n gh = Github(os.environ['GH_TOKEN'])\\n # gh_token = os.environ['GH_TOKEN']\\n gc_token_path = os.environ['GC_STORAGE_KEY']\\n\\n vars_file_path = os.getenv('VARS_FILE', \\\"\\\")\\n pipeline_id = os.getenv('CI_PIPELINE_ID', 0)\\n\\n repo = gh.get_repo(\\\"kubeinit/kubeinit\\\")\\n branches = repo.get_branches()\\n\\n output = 0\\n # Something linke:\\n # url = \\\"https://gitlab.com/kubeinit/kubeinit-ci/pipelines/\\\"\\n url = os.getenv('CI_PIPELINE_URL', \\\"\\\")\\n print(\\\"The job results will be published in runtime at: \\\" + url)\\n\\n for branch in branches:\\n for pr in repo.get_pulls(state='open', sort='created', base=branch.name):\\n labels = [item.name for item in pr.labels]\\n\\n sha = pr.head.sha\\n committer_email = repo.get_commit(sha=sha).commit.committer.email\\n print(committer_email)\\n\\n execute = False\\n # We assign the executed label to avoid executing this agains the same PR over and over\\n # We mark the PR as e2e-executed\\n\\n for label in labels:\\n if re.match(r\\\".*-.*-.*-.*-.*-.*-.*\\\", label):\\n print('Matching a PR label')\\n params = label.split(\\\"-\\\")\\n distro = params[0]\\n driver = params[1]\\n master = params[2]\\n worker = params[4]\\n scenario = params[6]\\n execute = True\\n remove_label(label, pr, repo)\\n break\\n\\n if execute:\\n now = datetime.now()\\n now.strftime(\\\"%m.%d.%Y.%H.%M.%S\\\")\\n job_name = pipeline_id + \\\"-\\\" + distro + \\\"-\\\" + driver + \\\"-\\\" + master + \\\"-\\\" + worker + \\\"-\\\" + scenario + \\\"-\\\" + now.strftime(\\\"%Y.%m.%d.%H.%M.%S\\\")\\n print(\\\"Let's run the e2e job, distro %s driver %s \\\" % (distro, driver))\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n print(\\\"Running the e2e job for: \\\" + str(pr.number) + \\\" \\\" + pr.title)\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n\\n # We update the status to show that we are executing the e2e test\\n print(\\\"Current status\\\")\\n print(repo.get_commit(sha=sha).get_statuses())\\n repo.get_commit(sha=sha).create_status(state=\\\"pending\\\",\\n target_url=url + str(pipeline_id),\\n description=\\\"Running...\\\",\\n context=\\\"%s-%s-%s-controller-%s-compute-%s\\\" % (distro,\\n driver,\\n master,\\n worker,\\n scenario))\\n print(\\\"The pipeline ID is: \\\" + str(pipeline_id))\\n print(\\\"The clouds.yml path is: \\\" + str(vars_file_path))\\n # We trigger the e2e job\\n start_time = time.time()\\n try:\\n print(\\\"We call the downstream job configuring its parameters\\\")\\n subprocess.check_call(\\\"./ci/run_kubeinit.sh %s %s %s %s %s %s %s %s\\\" % (str(branch.name),\\n str(pr.number),\\n str(vars_file_path),\\n str(distro),\\n str(driver),\\n str(master),\\n str(worker),\\n str(scenario)),\\n shell=True)\\n except Exception as e:\\n print('An exception hapened executing Ansible')\\n print(e)\\n output = 1\\n\\n try:\\n print(\\\"Render ara data\\\")\\n subprocess.check_call(\\\"./ci/ara.sh %s\\\" % (str(job_name) + \\\"-\\\" + str(output)), shell=True)\\n except Exception as e:\\n print('An exception hapened rendering ara data')\\n print(e)\\n output = 1\\n\\n print(\\\"starting the uploader job\\\")\\n # No matter if the job passed or failed we always use go as the suffix\\n # upload_error = upload_logs_to_github(str(job_name) + \\\"-\\\" + str(output), gh_token)\\n upload_error = upload_logs_to_google_cloud(str(job_name) + \\\"-\\\" + str(output), gc_token_path)\\n render_index(gc_token_path)\\n print(\\\"finishing the uploader job\\\")\\n\\n if output == 0:\\n state = \\\"success\\\"\\n else:\\n state = \\\"failure\\\"\\n\\n desc = (\\\"Ended with %s in %s minutes\\\" % (state, round((time.time() - start_time) / 60, 2)))\\n\\n print(desc)\\n print(state)\\n\\n if upload_error == 1:\\n dest_url = url + str(pipeline_id)\\n else:\\n dest_url = 'https://storage.googleapis.com/kubeinit-ci/jobs/pr/' + str(job_name) + \\\"-\\\" + str(output) + '/index.html'\\n\\n print(\\\"The destination URL is: \\\" + dest_url)\\n # We update the status with the job result\\n repo.get_commit(sha=sha).create_status(state=state,\\n target_url=dest_url,\\n description=desc,\\n context=\\\"%s-%s-%s-controller-%s-compute-%s\\\" % (distro,\\n driver,\\n master,\\n worker,\\n scenario))\\n else:\\n print(\\\"No need to do anything\\\")\\n if execute:\\n exit()\",\n \"def RunStages(self):\\n self._RunStage(build_stages.InitSDKStage)\\n self.RunSetupBoard()\\n self._RunStage(report_stages.RefreshPackageStatusStage)\",\n \"def commands_all():\\n lint()\\n complexity()\\n coverage()\",\n \"def ci(session):\\n session.install('-rrequirements-dev.txt')\\n session.install('-e', '.')\\n run_sphinx(session)\\n run_yapf(session, True)\\n run_all_linters(session)\\n run_pytest_units(session)\\n run_pytest_integrations(session)\",\n \"def main(argv):\\n parser = argparse.ArgumentParser(description=\\\"\\\"\\\"Bootstrap CI Scripts\\\"\\\"\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--directory\\\",\\n type=str,\\n required=True,\\n help=(\\\"\\\"\\\"Directory to store language runtimes, \\\"\\\"\\\"\\n \\\"\\\"\\\"scripts and other script details in\\\"\\\"\\\"))\\n parser.add_argument(\\\"-s\\\", \\\"--script\\\",\\n type=str,\\n help=\\\"\\\"\\\"Script to pass control to\\\"\\\"\\\")\\n parser.add_argument(\\\"-e\\\", \\\"--eval-output\\\",\\n type=str,\\n choices=[\\n \\\"bash\\\",\\n \\\"powershell\\\"\\n ],\\n help=\\\"\\\"\\\"Evaluate output in shell\\\"\\\"\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--print-to\\\",\\n type=str,\\n help=\\\"\\\"\\\"Where to print output script to\\\"\\\"\\\")\\n parser.add_argument(\\\"-r\\\", \\\"--scripts-directory\\\",\\n type=str,\\n help=(\\\"\\\"\\\"Directory where scripts are already \\\"\\\"\\\"\\n \\\"\\\"\\\"stored in\\\"\\\"\\\"))\\n parser.add_argument(\\\"--keep-scripts\\\",\\n action=\\\"store_true\\\",\\n help=\\\"\\\"\\\"Don't remove stale scripts.\\\"\\\"\\\")\\n args, remainder = parser.parse_known_args(argv)\\n\\n print_script_to, print_messages_to = _determine_outputs(args.print_to)\\n\\n with closing(print_script_to):\\n parent_shell = construct_parent_shell(args.eval_output,\\n print_script_to)\\n container = ContainerDir(parent_shell,\\n stale_check=_stale_check_url(args),\\n **(vars(args)))\\n util = container.fetch_and_import(\\\"util.py\\\")\\n # suppress(unused-attribute)\\n util.PRINT_MESSAGES_TO = print_messages_to\\n bootstrap_script = container.script_path(\\\"bootstrap.py\\\").fs_path\\n bootstrap_script_components = bootstrap_script.split(os.path.sep)\\n scripts_path = os.path.sep.join(bootstrap_script_components[:-2])\\n\\n # Overwrite CONTAINER_DIR in the output script, but not\\n # for our own invocation, we'll need the parent instance\\n # if we're actually in a test\\n parent_shell.overwrite_environment_variable(\\\"CONTAINER_DIR\\\",\\n container.path())\\n _set_ci_environment_variables(parent_shell)\\n\\n _define_script_command(\\\"polysquare_run\\\",\\n parent_shell,\\n bootstrap_script,\\n container.path(),\\n scripts_path,\\n None)\\n _define_script_command(\\\"polysquare_cleanup\\\",\\n parent_shell,\\n bootstrap_script,\\n container.path(),\\n scripts_path,\\n \\\"clean.py\\\")\\n\\n # Done, pass control to the script we're to run\\n container.fetch_and_import(args.script).run(container,\\n util,\\n parent_shell,\\n argv=remainder)\\n\\n # Print a final new line so that active messages don't get\\n # truncated.\\n util.print_message(\\\"\\\\n\\\")\\n\\n if container.return_code() != 0:\\n parent_shell.exit(container.return_code())\\n\\n return container.return_code()\",\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 runall():\\n sclogic.runall()\",\n \"def _run_workflow_in_matrix_of_envs(self):\\n for name, status, sha in zip(\\n self.env_names, self.docker_status, self.neurodocker_specs.keys()\\n ):\\n if status == \\\"docker ok\\\":\\n image = \\\"repronim/testkraken:{}\\\".format(sha)\\n self._run_pydra(image=image, soft_ver_str=name)\\n\\n # running tests separately\\n for name, status, sha in zip(\\n self.env_names, self.docker_status, self.neurodocker_specs.keys()\\n ):\\n # running tests only if docker container was created and if this is not\\n # the reference environment\\n if status == \\\"docker ok\\\" and name != self.ref_env_ind:\\n self._run_tests(soft_ver_str=name)\",\n \"def run_all_tests():\\n remove_dbs()\\n run_training_tests()\\n run_custom_training_tests()\\n run_training_save_tests()\\n run_validation_tests()\\n run_feature_extraction_tests()\",\n \"def main():\\r\\n run_processes('tests.csv', 'labs.csv')\",\n \"def execute(self):\\n for test in self.tests:\\n test.execute()\\n self.logger.dump()\\n print(\\\"Finished!\\\")\",\n \"def before_all(context):\\n def run_next_action():\\n \\\"\\\"\\\"Run Next-action and return both stderr and stdout.\\\"\\\"\\\"\\n os.environ[\\\"BROWSER\\\"] = 'echo %s'\\n result = subprocess.run(context.arguments, stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding=\\\"utf-8\\\")\\n return result.stdout + result.stderr\\n\\n context.next_action = run_next_action\\n subprocess.run([\\\"coverage\\\", \\\"erase\\\"])\",\n \"def run_test_suite():\\n local('. fabric_factory/ve/bin/activate; fabric_factory/src/project/manage.py test')\",\n \"def deploy():\\n test()\\n if not env.is_staging:\\n backup()\\n prepare()\\n restart_api()\",\n \"def _stage1(self):\\n self.start_progress()\\n tasks = list(self._chain_dict(self._model.adjust_tasks))\\n if len(tasks) == 0:\\n self._stage2(self._no_adjustments_case())\\n else:\\n task = lambda : self._run_adjust_tasks(tasks)\\n locator.get(\\\"pool\\\").submit(task, self._stage2)\",\n \"def _main(self):\\n\\n i = 0\\n\\n for i, step in enumerate(self._run_list):\\n return_code = self._check_step_completed(i)\\n\\n if return_code == 0:\\n logger.debug('Successful: \\\"{}\\\".'.format(list(step.keys())[0]))\\n else:\\n return_code = 1\\n self._submit_step(i)\\n\\n # do not enter while loop for continuous monitoring\\n if not self.monitor:\\n break\\n\\n time.sleep(1)\\n while return_code == 1 and self.monitor:\\n time.sleep(5)\\n return_code = self._check_step_completed(i)\\n\\n if return_code == 2:\\n module, f_config = self._get_command_config(i)\\n raise ExecutionError('Pipeline failed at step '\\n '{} \\\"{}\\\" {}'\\n .format(i, module, f_config))\\n\\n if i + 1 == len(self._run_list) and return_code == 0:\\n logger.info('Pipeline job \\\"{}\\\" is complete.'\\n .format(self._config.name))\\n logger.debug('Output directory is: \\\"{}\\\"'\\n .format(self._config.dirout))\",\n \"def main():\\n init()\\n separator_len = 40\\n for s in stage_instances:\\n print('='*separator_len)\\n print(s.name)\\n print('-'*separator_len)\\n\\n s.add_tasks() # Add tasks from previous stage\\n s.revive_or_archive() # Revive killed tasks or move them to failed\\n s.schedule_jobs() # Schedule new jobs if needed\\n s.print_status()\\n print('='*separator_len + '\\\\n')\\n render(stage_instances)\",\n \"def before_all(context):\\n context.config.setup_logging()\\n context.start_system = _start_system\\n context.teardown_system = _teardown_system\\n context.restart_system = _restart_system\\n context.run_command_in_service = _run_command_in_service\\n context.exec_command_in_container = _exec_command_in_container\\n context.is_running = _is_running\\n context.is_jobs_debug_api_running = _is_jobs_debug_api_running\\n context.is_component_search_service_running = _is_component_search_service_running\\n context.is_master_tag_list_service_running = _is_master_tag_list_service_running\\n context.wait_for_master_tag_list_service = _wait_for_master_tag_list_service\\n context.is_get_untagged_component_service_running = _is_get_untagged_component_service_running\\n context.wait_for_get_untagged_component_service = _wait_for_get_untagged_component_service\\n context.send_json_file = _send_json_file\\n context.wait_for_jobs_debug_api_service = _wait_for_jobs_debug_api_service\\n context.wait_for_component_search_service = _wait_for_component_search_service\\n context.is_3scale_staging_running = _is_3scale_staging_running\\n context.is_3scale_preview_running = _is_3scale_preview_running\\n context.is_backbone_api_running = _is_backbone_api_running\\n context.is_gemini_api_running = _is_gemini_api_running\\n\\n # Configure container logging\\n context.dump_logs = _read_boolean_setting(context, 'dump_logs')\\n tail_logs = int(context.config.userdata.get('tail_logs', 0))\\n dump_errors = _read_boolean_setting(context, 'dump_errors')\\n if tail_logs:\\n dump_errors = True\\n else:\\n tail_logs = 50\\n context.dump_errors = dump_errors\\n context.tail_logs = tail_logs\\n\\n # Configure system under test\\n context.kubernetes_dir_path = context.config.userdata.get('kubernetes_dir', None)\\n if context.kubernetes_dir_path is not None:\\n context.docker_compose_path = None\\n else:\\n # If we're not running Kubernetes, use the local Docker Compose setup\\n _set_default_compose_path(context)\\n\\n # for now, we just assume we know what compose file looks like (what services need what images)\\n context.images = {}\\n context.images['bayesian/bayesian-api'] = context.config.userdata.get(\\n 'coreapi_server_image',\\n 'registry.devshift.net/bayesian/bayesian-api')\\n context.images['bayesian/cucos-worker'] = context.config.userdata.get(\\n 'coreapi_worker_image',\\n 'registry.devshift.net/bayesian/cucos-worker')\\n core_v2_api_url = _read_url_from_env_var('F8A_API_V2_URL')\\n coreapi_url = _read_url_from_env_var('F8A_API_URL')\\n core_v2_api_url = _read_url_from_env_var('F8A_API_V2_URL')\\n valid_synk_token = os.environ.get(\\\"SNYK_TOKEN\\\")\\n uuid = os.environ.get(\\\"REGISTERED_USER_UUID\\\")\\n jobs_api_url = _read_url_from_env_var('F8A_JOB_API_URL')\\n gremlin_url = _read_url_from_env_var('F8A_GREMLIN_URL')\\n threescale_url = _read_url_from_env_var('F8A_3SCALE_URL')\\n threescale_preview_url = _read_url_from_env_var('F8A_THREE_SCALE_PREVIEW_URL')\\n backbone_api_url = _read_url_from_env_var('F8A_BACKBONE_API_URL')\\n service_id = _read_url_from_env_var('F8A_SERVICE_ID')\\n gemini_api_url = _read_url_from_env_var('F8A_GEMINI_API_URL')\\n license_service_url = _read_url_from_env_var('F8A_LICENSE_SERVICE_URL')\\n\\n context.running_locally = _running_locally(coreapi_url, jobs_api_url)\\n check_test_environment(context, coreapi_url)\\n\\n context.coreapi_url = _get_url(context, coreapi_url, 'coreapi_url', _FABRIC8_ANALYTICS_SERVER)\\n context.core_v2_api_url = core_v2_api_url\\n context.jobs_api_url = _get_url(context, jobs_api_url, 'jobs_api_url', _FABRIC8_ANALYTICS_JOBS)\\n context.gremlin_url = _get_url(context, gremlin_url, \\\"gremlin_url\\\", _FABRIC8_GREMLIN_SERVICE)\\n context.license_service_url = _get_url(context, license_service_url, 'license_service_url',\\n _FABRIC8_LICENSE_SERVICE)\\n context.core_v2_api_url = core_v2_api_url\\n context.threescale_url = threescale_url\\n context.valid_synk_token = valid_synk_token\\n context.uuid = uuid\\n\\n context.threescale_preview_url = threescale_preview_url\\n\\n context.backbone_api_url = backbone_api_url\\n\\n context.service_id = service_id\\n\\n context.gemini_api_url = gemini_api_url\\n\\n # we can retrieve access token by using refresh/offline token\\n\\n # informations needed to access S3 database from tests\\n _check_env_var_presence_s3_db('AWS_ACCESS_KEY_ID')\\n _check_env_var_presence_s3_db('AWS_SECRET_ACCESS_KEY')\\n _check_env_var_presence_s3_db('S3_REGION_NAME')\\n\\n aws_access_key_id = os.environ.get('AWS_ACCESS_KEY_ID')\\n aws_secret_access_key = os.environ.get('AWS_SECRET_ACCESS_KEY')\\n s3_region_name = os.environ.get('S3_REGION_NAME')\\n deployment_prefix = os.environ.get('DEPLOYMENT_PREFIX', 'STAGE')\\n context.reports_bucket = os.environ.get('DEVELOPER_ANALYTICS_REPORTS_BUCKET')\\n\\n context.s3interface = S3Interface(aws_access_key_id, aws_secret_access_key,\\n s3_region_name, deployment_prefix)\\n\\n context.client = None\\n\\n # timeout values can be overwritten by environment variables\\n stack_analysis_timeout = _parse_int_env_var('F8A_STACK_ANALYSIS_TIMEOUT')\\n component_analysis_timeout = _parse_int_env_var('F8A_COMPONENT_ANALYSIS_TIMEOUT')\\n\\n context.stack_analysis_timeout = stack_analysis_timeout or _DEFAULT_STACK_ANALYSIS_TIMEOUT\\n\\n context.component_analysis_timeout = component_analysis_timeout \\\\\\n or _DEFAULT_COMPONENT_ANALYSIS_TIMEOUT\\n\\n if context.running_locally:\\n context.client = docker.AutoVersionClient()\\n\\n for desired, actual in context.images.items():\\n desired = 'registry.devshift.net/' + desired\\n if desired != actual:\\n context.client.tag(actual, desired, force=True)\\n\\n # Specify the analyses checked for when looking for \\\"complete\\\" results\\n def _get_expected_component_analyses(ecosystem):\\n common = context.EXPECTED_COMPONENT_ANALYSES\\n specific = context.ECOSYSTEM_DEPENDENT_ANALYSES.get(ecosystem, set())\\n return common | specific\\n context.get_expected_component_analyses = _get_expected_component_analyses\\n\\n def _compare_analysis_sets(actual, expected):\\n unreliable = context.UNRELIABLE_ANALYSES\\n missing = expected - actual - unreliable\\n unexpected = actual - expected - unreliable\\n return missing, unexpected\\n context.compare_analysis_sets = _compare_analysis_sets\\n\\n context.EXPECTED_COMPONENT_ANALYSES = {\\n 'metadata', 'source_licenses', 'digests',\\n 'dependency_snapshot', 'code_metrics'\\n # The follower workers are currently disabled by default:\\n # 'static_analysis', 'binary_data', 'languages', 'crypto_algorithms'\\n }\\n # Analyses that are only executed for particular language ecosystems\\n context.ECOSYSTEM_DEPENDENT_ANALYSES = dict()\\n # Results that use a nonstandard format, so we don't check for the\\n # standard \\\"status\\\", \\\"summary\\\", and \\\"details\\\" keys\\n context.NONSTANDARD_ANALYSIS_FORMATS = set()\\n # Analyses that are just plain unreliable and so need to be excluded from\\n # consideration when determining whether or not an analysis is complete\\n context.UNRELIABLE_ANALYSES = {\\n 'github_details', # if no github api token provided\\n 'security_issues' # needs Snyk vulndb in S3\\n }\",\n \"def nrfconnect_project_ci_setup(ctx):\\n with open(CI_MANIFEST_OVERRIDE_PATH, \\\"w\\\") as f:\\n f.write(CI_MANIFEST_OVERRIDE_TXT)\\n\\n with ctx.cd(CI_MEMFAULT_SDK_REPO_ROOT):\\n ctx.run(\\\"git init\\\")\\n ctx.run('git config user.email \\\"hello@memfault.com\\\"')\\n ctx.run('git config user.name \\\"Memfault Inc.\\\"')\\n\\n ctx.run(\\\"git add .\\\")\\n ctx.run('git commit -m \\\"CI Test Changes\\\"')\\n\\n with ctx.cd(NRFCONNECT_ROOT):\\n ctx.run(f\\\"west init -l {NRFCONNECT_DEMO_APP_ROOT}\\\")\\n ctx.run(\\\"west update\\\")\",\n \"def run(self):\\n if self.all:\\n cmd = self.apply_options(self.test_all_cmd)\\n self.call_and_exit(cmd)\\n else:\\n cmds = (self.apply_options(self.unit_test_cmd, (\\\"coverage\\\",)),)\\n if self.coverage:\\n cmds += (self.apply_options(self.coverage_cmd),)\\n self.call_in_sequence(cmds)\",\n \"def run_stage_loop(cls, _opts, tests_results, put_next_stage):\\n for _, result in tests_results:\\n put_next_stage(result)\",\n \"def test_3_full_pipeline(install_test_files, data_dir):\\n with make_workdir() as workdir:\\n cl = [\\\"bcbio_nextgen.py\\\",\\n get_post_process_yaml(data_dir, workdir),\\n os.path.join(data_dir, os.pardir, \\\"110106_FC70BUKAAXX\\\"),\\n os.path.join(data_dir, \\\"run_info.yaml\\\")]\\n subprocess.check_call(cl)\",\n \"def test_all(test_fn):\\n success_count = 0\\n total_count = 0\\n for env_name in sorted(ngym.all_envs()):\\n total_count += 1\\n print('Running env: {:s} Wrapped with SideBias'.format(env_name))\\n try:\\n test_fn(env_name)\\n print('Success')\\n success_count += 1\\n except BaseException as e:\\n print('Failure at running env: {:s}'.format(env_name))\\n print(e)\\n print('')\\n\\n print('Success {:d}/{:d} envs'.format(success_count, total_count))\",\n \"def _with_deploy_env(commands=[]):\\n with cd(env.directory):\\n for command in commands:\\n run(command)\",\n \"def main():\\n\\n # Make sure we're running in the cellranger environment\\n verify_environment()\\n\\n # Load all the available stages\\n stages = load_stages()\\n\\n # Create an argument parser out of the stages\\n parser = get_parser(stages)\\n\\n args = parser.parse_args()\\n args.func(args)\",\n \"def run(self):\\n for tool in self.tools:\\n tool.run()\\n return\",\n \"def run_models(\\n self,\\n normal=True,\\n interrupt=True,\\n run_start=None,\\n state_builder=\\\"acis\\\",\\n hrc=False,\\n ):\\n if hrc:\\n loads = hrc_loads\\n else:\\n loads = test_loads\\n if normal and \\\"normal\\\" in loads:\\n for load in loads[\\\"normal\\\"]:\\n self.run_model(\\n load_week=load,\\n run_start=run_start,\\n state_builder=state_builder,\\n )\\n if interrupt and \\\"interrupt\\\" in loads:\\n for load in loads[\\\"interrupt\\\"]:\\n self.run_model(\\n load_week=load,\\n interrupt=True,\\n run_start=run_start,\\n state_builder=state_builder,\\n )\",\n \"def execute(self) -> None:\\n env_vars['AWS_DEFAULT_REGION'] = self.task.parsed_payload['sagemaker_region']\\n\\n steps: tuple = (\\n self._assume_service_account_role,\\n self._retrieve_model_binary_stream_from_db, # Retrieve Model BLOB\\n self._deserialize_artifact_stream, # Deserialize it to the Disk\\n self._deploy_model_to_sagemaker, # Deploy model to SageMaker\\n )\\n\\n for execute_step in steps:\\n execute_step()\",\n \"def start_all(provision_vm='False'):\\n subprocess.check_call(['./run.py'], shell=True, cwd=orc8_docker_path)\\n subprocess.check_call('docker-compose up -d', shell=True, cwd=feg_docker_integ_test_path)\\n subprocess.check_call(\\n 'fab start_magma:provision_vm=%s' % provision_vm,\\n shell=True, cwd=agw_path,\\n )\",\n \"def run_all(self):\\n failures, errors = [], []\\n\\n # Run each test case registered with us and agglomerate the results.\\n for case_ in self.cases:\\n case_.run()\\n update_results(failures, errors, case_)\\n\\n # Display our results.\\n print_errors(errors)\\n print_failures(failures)\\n print_overview(errors, failures)\\n\\n # Exit with 0 if all tests passed, >0 otherwise.\\n sys.exit(len(failures) + len(errors))\",\n \"def gen_stage_loop(cls, _opts, tests, put_next_stage, _put_result_stage):\\n for test in tests:\\n put_next_stage(test)\",\n \"def project_run(\\n project_dir: Path,\\n subcommand: str,\\n *,\\n overrides: Dict[str, Any] = SimpleFrozenDict(),\\n force: bool = False,\\n dry: bool = False,\\n capture: bool = False,\\n skip_requirements_check: bool = False,\\n) -> None:\\n config = load_project_config(project_dir, overrides=overrides)\\n commands = {cmd[\\\"name\\\"]: cmd for cmd in config.get(\\\"commands\\\", [])}\\n workflows = config.get(\\\"workflows\\\", {})\\n validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)\\n\\n req_path = project_dir / \\\"requirements.txt\\\"\\n if not skip_requirements_check:\\n if config.get(\\\"check_requirements\\\", True) and os.path.exists(req_path):\\n with req_path.open() as requirements_file:\\n _check_requirements([req.strip() for req in requirements_file])\\n\\n if subcommand in workflows:\\n msg.info(f\\\"Running workflow '{subcommand}'\\\")\\n for cmd in workflows[subcommand]:\\n project_run(\\n project_dir,\\n cmd,\\n overrides=overrides,\\n force=force,\\n dry=dry,\\n capture=capture,\\n skip_requirements_check=True,\\n )\\n else:\\n cmd = commands[subcommand]\\n for dep in cmd.get(\\\"deps\\\", []):\\n if not (project_dir / dep).exists():\\n err = f\\\"Missing dependency specified by command '{subcommand}': {dep}\\\"\\n err_help = \\\"Maybe you forgot to run the 'project assets' command or a previous step?\\\"\\n err_exits = 1 if not dry else None\\n msg.fail(err, err_help, exits=err_exits)\\n check_spacy_commit = check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION)\\n with working_dir(project_dir) as current_dir:\\n msg.divider(subcommand)\\n rerun = check_rerun(current_dir, cmd, check_spacy_commit=check_spacy_commit)\\n if not rerun and not force:\\n msg.info(f\\\"Skipping '{cmd['name']}': nothing changed\\\")\\n else:\\n run_commands(cmd[\\\"script\\\"], dry=dry, capture=capture)\\n if not dry:\\n update_lockfile(current_dir, cmd)\",\n \"def run_tests(event, context):\\n try:\\n jobId = event['CodePipeline.job']['id']\\n user_parameters = json.loads(event['CodePipeline.job']['data']['actionConfiguration']['configuration']['UserParameters'])\\n runscope_trigger_url = user_parameters['runscopeTriggerUrl']\\n runscope_access_token = user_parameters['runscopeAccessToken']\\n\\n tests = start_tests(runscope_trigger_url)\\n aggregate_status = wait_for_tests_to_complete(tests, runscope_access_token)\\n if aggregate_status == \\\"pass\\\":\\n code_pipeline.put_job_success_result(jobId=jobId)\\n else:\\n code_pipeline.put_job_failure_result(jobId=jobId, failureDetails={\\n 'type': 'JobFailed',\\n 'message': 'One or more tests failed'\\n })\\n except:\\n code_pipeline.put_job_failure_result(jobId=jobId, failureDetails={\\n 'type': 'JobFailed',\\n 'message': 'Unhandled exception during Runscope tests execution'\\n })\",\n \"def run_tests(self):\\n # Charm does not defer hooks so that test is not included.\\n # Trigger a package change which requires a restart\\n self.run_package_change_test(\\n 'ovn-central',\\n 'ovn-central')\",\n \"def run_tests(self):\\n # Trigger a config change which triggers a deferred hook.\\n self.run_charm_change_hook_test('configure_ovs')\\n\\n # Trigger a package change which requires a restart\\n self.run_package_change_test(\\n 'openvswitch-switch',\\n 'openvswitch-switch')\",\n \"def run_tests(self):\\n # Trigger a config change which triggers a deferred hook.\\n self.run_charm_change_hook_test('configure_ovs')\\n\\n # Trigger a package change which requires a restart\\n self.run_package_change_test(\\n 'openvswitch-switch',\\n 'openvswitch-switch')\",\n \"def process_steps(steplist, build, buildslave, build_status, basedir):\\n for step in steplist:\\n step.setBuild(build)\\n step.setBuildSlave(buildslave)\\n step.setStepStatus(build_status.addStepWithName(step.name))\\n step.setDefaultWorkdir(os.path.join(basedir, 'build'))\\n step.workdir = os.path.join(basedir, 'build')\",\n \"def run_training_save_tests():\\n test_training_save()\\n test_distributed_training_save()\\n test_multimodel_training_save()\\n test_distributed_multimodel_training_save()\",\n \"def _execute_stage(self, index, stage, stop):\\n if stop.is_set():\\n _LOGGER.info(\\\"Stopped pipeline on group %s\\\", self._group)\\n return\\n _LOGGER.info(\\\" -> Running stage '%s' on group %s\\\", stage, self._group)\\n if stage.name == 'on':\\n self._group.on = True\\n elif stage.name == 'off':\\n self._group.on = False\\n elif stage.name == 'hue':\\n self._group.hue = stage.args[0]\\n elif stage.name == 'saturation':\\n self._group.saturation = stage.args[0]\\n elif stage.name == 'color':\\n self._group.color = Color(*stage.args)\\n elif stage.name == 'brightness':\\n self._group.brightness = stage.args[0]\\n elif stage.name == 'temperature':\\n self._group.temperature = stage.args[0]\\n elif stage.name == 'transition':\\n self._group.transition(*stage.args, **stage.kwargs)\\n elif stage.name == 'white':\\n self._group.white()\\n elif stage.name == 'white_up':\\n self._group.white_up()\\n elif stage.name == 'white_down':\\n self._group.white_down()\\n elif stage.name == 'red_up':\\n self._group.red_up()\\n elif stage.name == 'red_down':\\n self._group.red_down()\\n elif stage.name == 'green_up':\\n self._group.green_up()\\n elif stage.name == 'green_down':\\n self._group.green_down()\\n elif stage.name == 'blue_up':\\n self._group.blue_up()\\n elif stage.name == 'blue_down':\\n self._group.blue_down()\\n elif stage.name == 'night_light':\\n self._group.night_light()\\n elif stage.name == 'link':\\n self._group.link()\\n elif stage.name == 'unlink':\\n self._group.unlink()\\n elif stage.name == 'flash':\\n self._group.flash(**stage.kwargs)\\n elif stage.name == 'repeat':\\n self._repeat(index, stage, stop)\\n elif stage.name == 'wait':\\n time.sleep(*stage.args)\\n elif stage.name == 'callback':\\n stage.args[0](*stage.args[1:], **stage.kwargs)\",\n \"def run_all_tasks(data_dir):\\n print(\\\"Training and testing for all tasks ...\\\")\\n for t in range(20):\\n run_task(data_dir, task_id=t + 1)\",\n \"def onPreFork(self):\",\n \"def make_all():\\n\\n if not MASTER.exists():\\n os.makedirs(MASTER)\\n members = inspect.getmembers(sys.modules[__name__])\\n members = [f for f in members if 'test_' in f[0]]\\n for member in members:\\n print('Running %s...' % member[0], end='')\\n member[1](master=True)\\n print('done!')\",\n \"def make_all():\\n\\n if not MASTER.exists():\\n os.makedirs(MASTER)\\n members = inspect.getmembers(sys.modules[__name__])\\n members = [f for f in members if 'test_' in f[0]]\\n for member in members:\\n print('Running %s...' % member[0], end='')\\n member[1](master=True)\\n print('done!')\",\n \"def execute(self):\\n for action in self.actions:\\n self._logger.info('[~] Executing %s.', action)\\n self._execute_action(action)\",\n \"def main():\\n run_test_all()\",\n \"def test_python(width=10):\\n\\n stage_1 = [diamond(sleep=60, inputs=[0])]\\n\\n stage_2 = []\\n for i in range(0, width):\\n stage_2.extend([diamond(sleep=20, inputs=stage_1)])\\n\\n stage_3 = [diamond(sleep=30, inputs=stage_2)]\\n\\n if not stage_3[0].done():\\n time.sleep(30)\\n for sitename in dfk.executors:\\n print(dfk.executors[sitename].status())\",\n \"def test_run():\\n # Only few steps for test\\n timesteps = 128\\n\\n # Compute all sub testing conf\\n envs = ['CartPole-v0']\\n ml_platforms = ['torch', 'tf']\\n agents = ['dqn', 'a2c']\\n\\n test_combinations = list(it.product(\\n envs,\\n ml_platforms,\\n agents\\n )\\n )\\n\\n # Finally test them all\\n for conf in test_combinations:\\n env_str, ml_platform_str, agent_str = conf\\n run(\\n agent_str,\\n ml_platform_str,\\n env_str,\\n 'dense',\\n timesteps,\\n './target/')\",\n \"def runall(sources='sources'):\\n init()\\n upload_sources(sources)\\n compile()\\n run()\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n try:\\n self._init_staf_handle()\\n self._setup_results()\\n self._stage()\\n self._install()\\n self._get_remote_results()\\n self._status = 'Pass'\\n except CoreError as e:\\n self._status = 'Fatal'\\n self._message = e.msg\\n finally:\\n self._close_staf_handle()\\n\\n #Notify TestCase that a failure occurred.\\n if self._status == 'Fatal': raise FatalError(self._message)\",\n \"def run(self):\\n list_test_scenarios = self.__get_list_scenarios_in_folder()\\n\\n if not list_test_scenarios:\\n utils.print_error(\\n \\\"\\\\n{}\\\\n\\\".format(constant.ERR_CANNOT_FIND_ANY_TEST_SCENARIOS))\\n exit(1)\\n\\n (tests_pass, tests_fail) = self.__execute_tests(list_test_scenarios)\\n\\n complete_message = constant.INFO_TEST_PASS_FAIL.format(\\n tests_pass, tests_fail)\\n\\n print(complete_message)\\n\\n self.__execute_reporter()\",\n \"def main():\\n with open('config.json') as config_file:\\n configs = json.load(config_file)\\n\\n jar_list = utilities.upload_jars(configs)\\n utilities.sign_jars(configs)\\n\\n artifact_folder = utilities.prepare_artifacts(configs, jar_list)\\n\\n repo_id = utilities.create_staging_repo(configs)\\n utilities.deploy_to_staging_repo(configs, artifact_folder, repo_id)\\n utilities.close_staging_repo(configs, repo_id)\",\n \"def do_work():\\n args = sys.argv\\n args = args[1:] # First element of args is the file name\\n\\n if len(args) == 0:\\n print('You have not passed any commands in!')\\n else:\\n for a in args:\\n if a == '--help':\\n print('AWS - EKS - Terraform : Demo Tool')\\n print('Options:')\\n print(' --help -> show this help menu.')\\n print(' --tf -> execute terraform validate and apply.')\\n print(' --passtest -> pass unit test - flask deployment')\\n print(' --failtest -> fail unit test - flask deployment')\\n print(' --prepare -> prepare tests')\\n elif a == '--passtest':\\n os.system(\\\"python python_test.py\\\")\\n elif a == '--failtest':\\n os.system(\\\"python python_test_fail.py\\\")\\n elif a == '--tf':\\n os.system(\\\"cd terraform && terraform validate && terraform apply -auto-approve && cd ..\\\")\\n elif a == '--prepare':\\n os.system(\\\"aws eks update-kubeconfig --name flugeldev-cluster && kubectl delete -f curl.yml && kubectl apply -f curl.yml\\\")\\n else:\\n print('Unrecognised argument.')\",\n \"def run(self):\\n self.archive_bash_inits()\\n self.create_paths()\\n self.copy_files()\\n self.make_git_config()\\n self.ensure_bash_history()\",\n \"def full_deploy():\\n refresh_cts()\\n push_mockups()\\n deploy()\",\n \"def run_all_tests(self):\\n for index in range(len(self.__test_set_list)):\\n self.run_test(index)\",\n \"def deploy(upgrade=False):\\n print(\\\"Deploying project on {} !\\\".format(env.stage))\\n execute('system.setup')\\n execute('git.checkout')\\n execute('virtualenv.setup')\\n execute('django.setup')\\n execute('cron.setup')\\n execute('uwsgi.setup')\\n execute('supervisor.setup')\\n execute('nginx.setup')\",\n \"def run_scenario(self):\\n self.initialize_random_map()\\n self.visualize_environment('initial')\\n self.get_tower_target_coverages()\\n self.solve_environment()\\n self.visualize_environment('solved')\",\n \"def run_all(operations=ops):\\n for operation in operations:\\n run(operation)\",\n \"def do_stage(self, images):\\n\\n for i, image in enumerate(images):\\n pass\\n # logging_tags = logs.image_config_to_tags(image, self.group_by_keywords)\",\n \"def RunStage(self):\\n\\n # Stage construction is usually done as late as possible because the tests\\n # set up the build configuration and options used in constructing the stage.\\n results_lib.Results.Clear()\\n stage = self.ConstructStage()\\n stage.Run()\\n self.assertTrue(results_lib.Results.BuildSucceededSoFar())\",\n \"def main() -> None:\\n ## Setup logging\\n # Format the logs as JSON for simplicity\\n formatting = json.dumps(\\n {\\n \\\"timestamp\\\": \\\"%(asctime)s\\\",\\n \\\"namespace\\\": \\\"%(name)s\\\",\\n \\\"loglevel\\\": \\\"%(levelname)s\\\",\\n \\\"message\\\": \\\"%(message)s\\\",\\n }\\n )\\n # Default to a log level of WARNING until the config is parsed\\n logging.basicConfig(level=\\\"WARNING\\\", format=formatting)\\n log = logging.getLogger(__project_name__)\\n\\n # Get the effective config\\n try:\\n config = get_config()\\n except ValueError:\\n log.error(\\\"Unable to create a valid configuration\\\")\\n sys.exit(1)\\n\\n # Update the log level to whatever was set in the config\\n logging.getLogger().setLevel(config[\\\"loglevel\\\"])\\n\\n # Create the API objects and apply the config\\n try:\\n results_api = apply_config(\\n api=ResultsAPI(app_id=config[\\\"apis\\\"][\\\"results\\\"][\\\"app_id\\\"]), config=config\\n )\\n upload_api = apply_config(\\n api=UploadAPI(app_id=config[\\\"apis\\\"][\\\"upload\\\"][\\\"app_id\\\"]), config=config\\n )\\n if \\\"sandbox_name\\\" in config[\\\"apis\\\"][\\\"sandbox\\\"]:\\n sandbox_api = apply_config(\\n api=SandboxAPI(\\n app_id=config[\\\"apis\\\"][\\\"sandbox\\\"][\\\"app_id\\\"],\\n sandbox_name=config[\\\"apis\\\"][\\\"sandbox\\\"][\\\"sandbox_name\\\"],\\n ),\\n config=config,\\n )\\n else:\\n sandbox_api = None\\n except (TypeError, NameError):\\n log.error(\\\"Unable to create valid API objects\\\")\\n sys.exit(1)\\n\\n # Configure the environment\\n for step in config[\\\"workflow\\\"]:\\n if step == \\\"submit_artifacts\\\":\\n configure_environment(\\n api_key_id=config[\\\"api_key_id\\\"], api_key_secret=config[\\\"api_key_secret\\\"]\\n )\\n success = submit_artifacts(upload_api=upload_api, sandbox_api=sandbox_api)\\n\\n if success:\\n log.info(\\\"Successfully submit build artifacts for scanning\\\")\\n else:\\n log.error(\\\"Failed to submit build artifacts for scanning\\\")\\n sys.exit(1)\\n elif step == \\\"check_compliance\\\":\\n configure_environment(\\n api_key_id=config[\\\"api_key_id\\\"], api_key_secret=config[\\\"api_key_secret\\\"]\\n )\\n if not check_compliance(results_api=results_api):\\n sys.exit(1)\",\n \"def run(self):\\n self.scion_sh('run', 'nobuild')\",\n \"def run_ci(project: str, branch: str, job: str, revision: Optional[str],\\n tag: Optional[str]) -> None:\\n config = get_config()\\n\\n if config.circleci_api_token is None:\\n raise ValueError('circleci_api_token not configured. Run `sriracha '\\n 'configure` to configure')\\n\\n resp = scci.trigger_job(api_token=config.circleci_api_token,\\n project=project, branch=branch, job=job,\\n revision=revision, tag=tag)\\n\\n click.echo_via_pager(json.dumps(resp, indent=4))\",\n \"def task_test(argv):\\n run_tests(\\\"python2\\\", argv)\\n run_tests(\\\"python3\\\", argv)\",\n \"def run_presub(stages):\\n if is_presub(stages[0]):\\n # Pop the first stage\\n stage, stages = stages[0], stages[1:]\\n # Execute it only if Python was invoked normally, not in a queue job\\n if STAGE_ID is None:\\n msg = \\\"Only the first stage can execute on the login node\\\"\\n assert all([not is_presub(i) for i in stages]), msg\\n alog.info(\\\"Pre-submit stage starting: %s\\\", stage)\\n stage()\\n alog.info(\\\"Pre-submit stage done: %s\\\", stage)\\n return stages\",\n \"def main():\\n configuration = {'resource-folder': 'resources',\\n 'build-folder': 'build',\\n 'log-folder': 'logfiles',\\n 'use-preloaded': False,\\n 'addi-metrics': 'addi-metrics.json',\\n 'jenkins': {'dependency-filename': 'dependencies.txt',\\n 'server': 'http://is.dbc.dk',\\n 'repository-project': 'opensearch-3rd-party-dependencies'},\\n 'log-zip-file':'logs.zip'}\\n configuration.update(cli())\\n setup_logger(configuration['verbose'])\\n run_performance_test(configuration)\",\n \"def __post_bootstrap_exec(self):\\n info = model_info(juju.get_current_model())\\n # Set our provider type environment var so that it is\\n # exposed in future processing tasks\\n app.env['JUJU_PROVIDERTYPE'] = info['provider-type']\\n\\n _post_bootstrap_sh = path.join(app.config['spell-dir'],\\n 'conjure/steps/00_post-bootstrap')\\n app.log.debug(\\n 'Checking for post bootstrap task: {}'.format(_post_bootstrap_sh))\\n if path.isfile(_post_bootstrap_sh) \\\\\\n and os.access(_post_bootstrap_sh, os.X_OK):\\n app.ui.set_footer('Running post-bootstrap tasks...')\\n utils.pollinate(app.session_id, 'J001')\\n app.log.debug(\\\"post_bootstrap running: {}\\\".format(\\n _post_bootstrap_sh\\n ))\\n try:\\n future = async.submit(partial(check_output,\\n _post_bootstrap_sh,\\n shell=True,\\n env=app.env),\\n self.__handle_exception)\\n future.add_done_callback(self.__post_bootstrap_done)\\n except Exception as e:\\n return self.__handle_exception(e)\",\n \"def upload_all_completed_analyses(context: click.Context, pipeline: Pipeline = None):\\n\\n LOG.info(\\\"----------------- AUTO -----------------\\\")\\n\\n status_db: Store = context.obj.status_db\\n\\n exit_code = 0\\n for analysis_obj in status_db.get_analyses_to_upload(pipeline=pipeline):\\n if analysis_obj.family.analyses[0].uploaded_at is not None:\\n LOG.warning(\\n f\\\"Skipping upload for case {analysis_obj.family.internal_id}. \\\"\\n f\\\"It has been already uploaded at {analysis_obj.family.analyses[0].uploaded_at}.\\\"\\n )\\n continue\\n\\n case_id = analysis_obj.family.internal_id\\n LOG.info(\\\"Uploading analysis for case: %s\\\", case_id)\\n try:\\n context.invoke(upload, case_id=case_id)\\n except Exception:\\n LOG.error(f\\\"Case {case_id} upload failed\\\")\\n LOG.error(traceback.format_exc())\\n exit_code = 1\\n\\n sys.exit(exit_code)\",\n \"def run_all_steps(self, *args, **kwargs):\\n\\n # the following configopts are common to all builds\\n if self.toolchain.options['pic']:\\n self.cfg.update('configopts', '--with-pic')\\n\\n if self.cfg['with_shared']:\\n self.cfg.update('configopts', '--enable-shared')\\n\\n if self.cfg['with_generic_kernel']:\\n self.cfg.update('configopts', '--enable-generic')\\n\\n if self.cfg['with_single']:\\n self.cfg.update('configopts', '--enable-single-precision')\\n\\n for flag in ELPA_CPU_FEATURE_FLAGS:\\n # many ELPA kernels are enabled by default, even when the\\n # CPU does not support them, so we disable them all, except\\n # when the appropriate CPU flag is found\\n # sse kernels require sse4_2\\n if flag == 'sse4_2':\\n if getattr(self, flag):\\n self.cfg.update('configopts','--enable-sse')\\n self.cfg.update('configopts', '--enable-sse-assembly')\\n else:\\n self.cfg.update('configopts', '--disable-sse')\\n self.cfg.update('configopts', '--disable-sse-assembly')\\n elif flag == 'avx512f':\\n if getattr(self, 'avx512f'):\\n self.cfg.update('configopts', '--enable-avx512')\\n else:\\n self.cfg.update('configopts', '--disable-avx512')\\n else:\\n if getattr(self, flag):\\n self.cfg.update('configopts', '--enable-%s' % flag)\\n else:\\n self.cfg.update('configopts', '--disable-%s' % flag)\\n\\n # By default ELPA tries to use MPI and configure fails if it's not available\\n # so we turn off MPI support unless MPI support is requested via the usempi toolchain option.\\n # We also set the LIBS environmet variable to detect the correct linalg library\\n # depending on the MPI availability.\\n if self.toolchain.options.get('usempi', None):\\n self.cfg.update('configopts', '--with-mpi=yes')\\n self.cfg.update('configopts', 'LIBS=\\\"$LIBSCALAPACK\\\"')\\n else:\\n self.cfg.update('configopts', '--with-mpi=no')\\n self.cfg.update('configopts', 'LIBS=\\\"$LIBLAPACK\\\"')\\n\\n self.cfg.update('configopts', 'LDFLAGS=\\\"-L${EBROOTZLIB}/lib -L${EBROOTLIBXML2}/lib -lxml2 -lz\\\"')\\n\\n # make all builds verbose\\n self.cfg.update('buildopts', 'V=1')\\n\\n # keep track of common configopts specified in easyconfig file,\\n # so we can include them in each iteration later\\n common_config_opts = self.cfg['configopts']\\n\\n self.cfg['configopts'] = []\\n\\n self.cfg.update('configopts', ['--disable-openmp ' + common_config_opts])\\n if self.toolchain.options.get('openmp', False):\\n self.cfg.update('configopts', ['--enable-openmp ' + common_config_opts])\\n\\n self.log.debug(\\\"List of configure options to iterate over: %s\\\", self.cfg['configopts'])\\n\\n return super(EB_ELPA, self).run_all_steps(*args, **kwargs)\",\n \"def step_impl(context):\\n for executor in context.executors_to_run:\\n now = datetime.now() + timedelta(seconds=2)\\n trigger = DateTrigger(run_date=now)\\n context.manager.update_executor_trigger(executor.id, trigger)\\n\\n # give time to execute the jobs\\n wait_for_job_executed(context, executor.id)\\n\\n context.last_executor_id = executor.id\",\n \"def run(pars, #parameter files\\n #directory of scenario files\\n scen_dir = r'C:\\\\LS\\\\03_TOOLS\\\\_git\\\\COVID_01\\\\scenarios',\\n \\n #map to scenario files\\n scen_d = {\\n 'NoNPI':'NPI_Scenario1_None.R',\\n 'BI1918':'NPI_Scenario2_Bootsma_1918Influenza.R',\\n 'SouthKorea':'NPI_Scenario3_SouthKorea.R',\\n 'Reduced':'NPI_Scenario4_ReducedGamma.R', \\n }\\n ):\\n \\n \\n \\n #===========================================================================\\n # precheck \\n #===========================================================================\\n assert len(pars)==4, 'unexpected inputs count'\\n print('pars: \\\\n%s'%pars)\\n \\n #check the R Environment variables\\n assert 'R_USER' in os.environ\\n assert 'R_HOME' in os.environ\\n \\n #print('R_USER=%s \\\\nR_HOME=%s'%(os.getenv('R_USER'), os.getenv('R_HOME')))\\n\\n \\n \\n \\n \\n #===========================================================================\\n # setup\\n #===========================================================================\\n s = setup.Setup(setup_name = 'mid_utah_'+pars[2],\\n spatial_setup = WestCoastSpatialSetup(),\\n nsim = int(pars[1]),\\n ti = datetime.date(2020, 3, 6),\\n tf = datetime.date(2020, 10, 1),\\n interactive = False,\\n write_csv = True,\\n dt = 1/4)\\n \\n #===========================================================================\\n # set the scenario parmaters\\n #===========================================================================\\n\\n \\n \\n assert pars[2] in scen_d, 'unrecognized scenario: %s'%pars[2]\\n \\n rfp = os.path.join(scen_dir, scen_d[pars[2]])\\n assert os.path.exists(rfp)\\n \\n s.script_npi = rfp\\n \\n print('set script_npi=%s'%s.script_npi)\\n\\n #===========================================================================\\n # execute\\n #===========================================================================\\n\\n print()\\n print()\\n print(f\\\">>> Starting {s.nsim} model runs on {pars[3]} processes\\\")\\n print(f\\\">>> Setup *** {s.setup_name} *** from {s.ti} to {s.tf} !\\\")\\n print(f\\\">>> writing to folder : {s.datadir}{s.setup_name}\\\")\\n print()\\n print()\\n \\n tic = time.time()\\n \\n res_l = seir.run_parallel(s, int(pars[3]))\\n print(f\\\">>> Runs done in {time.time()-tic} seconds...\\\")\",\n \"def _run_actions(self):\\n\\n if \\\"install-bento\\\" in self.actions:\\n self._do_action_bento_setup()\\n\\n if \\\"create-tables\\\" in self.actions:\\n self._do_action_tables_create()\\n\\n if \\\"import-ratings\\\" in self.actions:\\n self._do_action_import_ratings()\\n\\n if \\\"import-user-info\\\" in self.actions:\\n self._do_action_import_user_info()\\n\\n if \\\"import-movie-info\\\" in self.actions:\\n self._do_action_import_movie_info()\\n\\n if \\\"train-item-item-cf\\\" in self.actions:\\n self._do_action_train()\\n\\n if \\\"register-freshener\\\" in self.actions:\\n self._do_action_register_freshener()\",\n \"def deploy():\\n stage(branch='live', role='live')\",\n \"def main():\\n args = main_parse_args()\\n\\n log_dir = os.path.join(args.working_dir, args.logs_path)\\n if not os.path.exists(log_dir):\\n os.makedirs(log_dir)\\n\\n # if there are scheduled jobs, return\\n job_status = get_status(args.chronos)\\n if len(job_status['all']) > 0:\\n print(\\\"Cannot begin build with pre-existing jobs on chronos!\\\")\\n else:\\n run_step(\\\"MYSQL\\\", False, args)\\n run_step(\\\"REDIS\\\", False, args)\\n wait_for_port(int(args.redis_port), args.redis_host)\\n wait_for_port(int(args.mysql_port), args.mysql_host)\\n run_step('SETUP', True, args)\\n run_step('CHECK', True, args)\\n run_step('IMPORT', True, args)\\n run_step('EXPORT1', True, args)\\n run_step('EXPORT2', True, args)\\n print(\\\"KnowNet Pipeline Completed!\\\")\",\n \"def run_test_procedures(self):\\r\\n print('\\\\nDeploying prediction model...\\\\n')\\r\\n self.final_df_output = pd.read_json(path.data_final)\\r\\n self.final_df_output.sort_index(inplace=True)\\r\\n self.fill_testing_dates()\\r\\n self.perform_backtests()\\r\\n self.create_full_predictions_dataframe()\\r\\n print('\\\\nDeployment complete!')\",\n \"def runtests():\\r\\n\\r\\n app_abspath = os.path.dirname(os.path.dirname(__file__))\\r\\n models_abspath = os.path.join(app_abspath, 'models.py')\\r\\n models_exists = os.path.isfile(models_abspath)\\r\\n urls_abspath = os.path.join(app_abspath, 'urls.py')\\r\\n urls_exists = os.path.isfile(urls_abspath)\\r\\n views_abspath = os.path.join(app_abspath, 'views')\\r\\n views_exists = os.path.isdir(views_abspath)\\r\\n tpls_abspath = os.path.join(app_abspath, 'templates')\\r\\n tpls_exists = os.path.isdir(tpls_abspath)\\r\\n\\r\\n for f in [models_abspath, urls_abspath]:\\r\\n if os.path.isfile(f):\\r\\n subprocess.call('cp {} {}.orig'.format(f, f), shell=True)\\r\\n\\r\\n if views_exists:\\r\\n subprocess.call('cp -r {} {}.orig'.format(views_abspath, views_abspath), shell=True)\\r\\n\\r\\n if tpls_exists:\\r\\n subprocess.call('cp -r {} {}.orig'.format(tpls_abspath, tpls_abspath), shell=True)\\r\\n\\r\\n overwrite_project_language('ja')\\r\\n subprocess.call('python manage.py generatescaffold test_app I18nModel title:string', shell=True)\\r\\n time.sleep(1)\\r\\n overwrite_project_language('en-us')\\r\\n time.sleep(1)\\r\\n\\r\\n subprocess.call('python manage.py generatescaffold test_app GeneratedNoTimestampModel title:string description:text --no-timestamps', shell=True)\\r\\n time.sleep(2) # Give time for Django's AppCache to clear\\r\\n\\r\\n subprocess.call('python manage.py generatescaffold test_app GeneratedModel title:string description:text', shell=True)\\r\\n\\r\\n test_status = subprocess.call('python manage.py test --with-selenium --with-selenium-fixtures --with-cherrypyliveserver --noinput', shell=True)\\r\\n\\r\\n if models_exists:\\r\\n subprocess.call('mv {}.orig {}'.format(models_abspath, models_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm {}'.format(models_abspath), shell=True)\\r\\n\\r\\n if urls_exists:\\r\\n subprocess.call('mv {}.orig {}'.format(urls_abspath, urls_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm {}'.format(urls_abspath), shell=True)\\r\\n\\r\\n if views_exists:\\r\\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\\r\\n subprocess.call('mv {}.orig {}'.format(views_abspath, views_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\\r\\n\\r\\n if tpls_exists:\\r\\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\\r\\n subprocess.call('mv {}.orig {}'.format(tpls_abspath, tpls_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\\r\\n\\r\\n subprocess.call('rm {}/*.pyc'.format(app_abspath), shell=True)\\r\\n\\r\\n sys.exit(test_status)\",\n \"def run_custom_training_tests():\\n test_custom_training()\\n test_custom_distributed_training()\\n test_custom_multimodel_training()\\n test_custom_distributed_multimodel_training()\",\n \"def run(cont, util, shell, argv=list()):\\n\\n cache_dir = cont.named_cache_dir(\\\"travis_container_downloads\\\",\\n ephemeral=False)\\n cache_dir_key = \\\"_POLYSQUARE_TRAVIS_CONTAINER_TEST_CACHE_DIR\\\"\\n shell.overwrite_environment_variable(cache_dir_key, cache_dir)\\n\\n cont.fetch_and_import(\\\"setup/python/setup.py\\\").run(cont, util, shell, argv)\\n\\n config_python = \\\"setup/project/configure_python.py\\\"\\n py_ver = util.language_version(\\\"python3\\\")\\n py_cont = cont.fetch_and_import(config_python).get(cont,\\n util,\\n shell,\\n py_ver)\\n\\n with py_cont.activated(util):\\n with util.Task(\\\"\\\"\\\"Downloading all distributions\\\"\\\"\\\"):\\n os.environ[cache_dir_key] = cache_dir\\n util.execute(cont,\\n util.long_running_suppressed_output(),\\n util.which(\\\"python\\\"),\\n \\\"download-all-distros-to.py\\\")\",\n \"def deploy():\\n upload_static()\\n compile_code()\\n upload_code()\\n upload_supervisor()\\n start_server()\",\n \"def execute(helper, config, args):\\n env_config = parse_env_config(config, args.environment)\\n cname_prefix = env_config.get('cname_prefix', None)\\n env_name = args.environment\\n\\n # change version\\n if args.version_label:\\n helper.deploy_version(env_name, args.version_label)\\n if not args.dont_wait:\\n helper.wait_for_environments(env_name, status='Ready', version_label=args.version_label)\\n\\n # update it\\n env = parse_env_config(config, env_name)\\n option_settings = parse_option_settings(env.get('option_settings', {}))\\n helper.update_environment(env_name,\\n description=env.get('description', None),\\n option_settings=option_settings,\\n tier_type=env.get('tier_type'),\\n tier_name=env.get('tier_name'),\\n tier_version=env.get('tier_version'))\\n\\n # wait\\n if not args.dont_wait:\\n helper.wait_for_environments(env_name, health='Green', status='Ready', version_label=args.version_label)\\n\\n # delete unused\\n helper.delete_unused_versions(versions_to_keep=int( get(config, 'app.versions_to_keep', 10) ))\",\n \"def test_build_pipeline_one(self):\\n args = \\\"Test_APP ONE TWO THREE\\\".split(\\\" \\\")\\n task_list = build_pipeline(args, False)\\n self.assertEqual(\\\"Task One\\\", task_list[0].execute())\\n self.assertEqual(\\\"Task Two\\\", task_list[1].execute())\\n self.assertEqual(\\\"Task Three\\\", task_list[2].execute())\\n self.assertEqual(3, len(task_list))\",\n \"def setup_for_compilation_testcase(self):\\n os.chdir(self.tmp_work)\\n\\n for container in self.containers:\\n self._setup_single_directory_for_compilation(container.directory)\\n # Run any necessary pre_commands\\n self._run_pre_commands(container.directory)\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n self._checkout_resources()\\n\\n for test in self._tests:\\n try:\\n self._update_resource_timeouts(test.timeout)\\n test.execute()\\n except Failure as e:\\n self._status = 'Fail'\\n self._message = ('The \\\"{0}\\\" test in the test case \\\"{1}\\\" failed with the '\\n 'message: \\\"{2}\\\"'.format(test.name, self.name, e.msg))\\n except FatalError as e:\\n self._checkin_resources()\\n self._status = 'Fatal'\\n self._message = ('The \\\"{0}\\\" test in the test case \\\"{1}\\\" encountered the fatal '\\n 'error: \\\"{2}\\\"'.format(test.name, self.name, e.msg))\\n raise FatalError(self._message)\\n\\n if self._status == 'Fail':\\n self._checkin_resources()\\n raise Failure(self._message)\\n\\n self._status = 'Pass'\\n self._checkin_resources()\",\n \"def setup():\\n _confirm_branch()\\n \\n require('settings', provided_by=[production, staging])\\n require('branch', provided_by=[stable, master, branch])\\n \\n setup_directories()\\n setup_virtualenv()\\n clone_repo()\\n checkout_latest()\\n install_requirements()\\n install_apache_conf()\\n deploy_to_s3()\",\n \"def execute(self):\\n\\n self.__create_assets_directory()\\n self.__create_datasets_directories()\\n self.__create_structures_directories()\\n\\n self.logger.write_action_required(\\\"The repository specified in the readme file must be cloned in the path: \\\" +\\n IMAGES_PATH)\\n self.logger.write_action_required(\\\"Execute the command 'pip install -r requirements.txt' \\\")\\n self.logger.write_info(\\\"Strategy executed successfully\\\")\",\n \"def running_on_ci() -> bool:\\r\\n return os.environ.get(\\\"JANITOR_CI_MACHINE\\\") is not None\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n for test_case in self._test_cases.values():\\n try:\\n test_case.execute()\\n except Failure as e:\\n self._status = 'Fail'\\n self._message = ('The \\\"{0}\\\" test case in the test plan \\\"{1}\\\" failed with the '\\n 'message: \\\"{2}\\\"'.format(test_case.name, self.name, e.msg))\\n except FatalError as e:\\n self._status = 'Fail'\\n self._message = ('The \\\"{0}\\\" test case in the test plan \\\"{1}\\\" encountered the fatal '\\n 'error: \\\"{2}\\\"'.format(test_case.name, self.name, e.msg))\\n raise FatalError(self._message)\\n\\n if self._status == 'Fail':\\n raise Failure(self._message)\\n\\n self._status = 'Pass'\",\n \"def main(force):\\n secret = get_secret()\\n service = RestIO(secret)\\n\\n # Make sure we re-create our debug log for each run\\n if DEBUG_LOG_FILE.exists():\\n DEBUG_LOG_FILE.unlink()\\n\\n if force:\\n # Regenerate all cassette data until we hit our rate limit\\n click.secho(\\\"Regenerating all recorded cassettes\\\")\\n result = run_tests(shlex.split(\\\"--record-mode=rewrite\\\"))\\n num_failures = analyse_report(load_report())\\n else:\\n click.secho(\\\"Generating baseline...\\\")\\n # Start by generating a baseline state without using any API calls\\n run_tests(shlex.split(\\\"--record-mode=none --block-network\\\"))\\n num_failures = analyse_report(load_report())\\n if num_failures == 0:\\n click.secho(\\\"All unit tests passed. Aborting rebuild.\\\", fg=\\\"yellow\\\")\\n click.secho(\\\"To force a rebuild of all cassettes try --force\\\", fg=\\\"yellow\\\")\\n return\\n\\n # The re-run any failed tests, forcing the cassettes to get regenerated\\n # We append --lf to only rerun the tests that failed on the last pass\\n click.secho(\\\"Rebuilding initial cassettes...\\\")\\n result = run_tests(shlex.split(\\\"--record-mode=rewrite --lf\\\"))\\n num_failures = analyse_report(load_report())\\n\\n iteration = 1\\n while result == ExitCode.TESTS_FAILED and num_failures != 0:\\n # check headers to see when the next token renewal is\\n now = datetime.now(tz=tz.tzlocal())\\n renewal = service.headers.time_to_refresh\\n service.refresh_headers()\\n wait_time = renewal - now\\n minutes = math.ceil(wait_time.total_seconds() / 60)\\n\\n # if the rate limit has expired wait until the limit has been refreshed\\n if minutes > 0:\\n click.secho(\\\"Next renewal: {0}\\\".format(renewal.astimezone(tz.tzlocal())))\\n click.secho(\\\"Sleeping for {0} minutes...\\\".format(minutes))\\n # Give regular status updates to the user via a progress bar once every minute\\n for _ in trange(minutes):\\n sleep(60)\\n\\n # Give the API a few additional seconds before we try again to account for clock skew\\n sleep(10)\\n\\n click.secho(\\\"Running test iteration \\\" + str(iteration))\\n # We append --lf to only rerun the tests that failed on the previous run\\n result = run_tests(shlex.split(\\\"--record-mode=rewrite --lf\\\"))\\n\\n # If the number of failing tests hasn't changed or has gotten worse, we are not making any progress\\n # and thus we should exit to avoid a deadlock\\n temp = analyse_report(load_report())\\n if temp >= num_failures:\\n raise Exception(\\\"Last unit test run had {0} failures and current run had {1}\\\".format(num_failures, temp))\\n num_failures = temp\\n\\n # repeat until all tests pass\\n iteration += 1\\n\\n # return the final test run result to the caller\\n if result != ExitCode.OK:\\n raise ClickException(\\\"Regeneration failed for unexpected reason: \\\" + str(result))\",\n \"def bootstrap():\\n\\n require('environment', provided_by=env.environments)\\n sudo('mkdir -p %(root)s' % env, user=env.deploy_user)\\n clone_repo()\\n setup_dirs()\\n link_config_files()\\n update_services()\\n create_virtualenv()\\n update_requirements()\\n create_local_settings()\",\n \"def _run_tests(self):\\n for pyunit_testcase in self.cfg.testcases:\\n yield self._run_testsuite(pyunit_testcase)\",\n \"def run_sagemaker_tests(images):\\n if not images:\\n return\\n pool_number = len(images)\\n with Pool(pool_number) as p:\\n p.map(run_sagemaker_pytest_cmd, images)\",\n \"def main(args=sys.argv[1:]):\\n\\n # Parse arguments\\n args = create_parser().parse_args(args)\\n\\n execution = API.create_execution(\\n args.script, {'inputs': [['dataset-id', args.dataset]]})\\n\\n execution_id = execution['resource']\\n e_id = re.split('execution/', execution_id)[1]\\n execution_status = 0\\n\\n while execution_status != FINISHED:\\n execution_resource = API.get_execution(execution_id)\\n execution_status = execution_resource['object']['status']['code']\\n number_of_models = API.list_models(\\n \\\"execution_id=%s\\\" % e_id)['meta']['total_count']\\n print \\\"models: %s\\\" % number_of_models\\n return\",\n \"def deploy():\\n build()\\n collect()\\n commit()\\n push()\",\n \"def prepare_run(self, **kwargs):\\n assert self.cloud\\n LOGGER.debug('Validating run tests...')\\n for test in kwargs.get('tests', self.stests):\\n if test in self.stests:\\n self.tests.append(test)\\n else:\\n raise Exception(f\\\"Test name '{test}' is invalid\\\")\\n\\n if not os.path.exists(self.task_dir):\\n os.makedirs(self.task_dir)\\n\\n task = os.path.join(self.rally_dir, 'task.yaml')\\n if not os.path.exists(task):\\n LOGGER.error(\\\"Task file '%s' does not exist.\\\", task)\\n raise Exception(f\\\"Task file '{task}' does not exist.\\\")\\n self.task_file = os.path.join(self.task_dir, 'task.yaml')\\n shutil.copyfile(task, self.task_file)\\n\\n task_macro = os.path.join(self.rally_dir, 'macro')\\n if not os.path.exists(task_macro):\\n LOGGER.error(\\\"Task macro dir '%s' does not exist.\\\", task_macro)\\n raise Exception(f\\\"Task macro dir '{task_macro}' does not exist.\\\")\\n macro_dir = os.path.join(self.task_dir, 'macro')\\n if os.path.exists(macro_dir):\\n shutil.rmtree(macro_dir)\\n shutil.copytree(task_macro, macro_dir)\\n\\n self.update_keystone_default_role()\\n self.compute_cnt = self.count_hypervisors()\\n self.network_extensions = self.cloud.get_network_extensions()\\n self.flavor_alt = self.create_flavor_alt()\\n self.services = [service.name for service in\\n functest_utils.list_services(self.cloud)]\\n\\n LOGGER.debug(\\\"flavor: %s\\\", self.flavor_alt)\",\n \"def setup():\\n\\n with cd(env.homedir):\\n\\n # clone repository from github\\n sudo('git clone https://github.com/collective/demo.plone.de.git', user=env.deploy_user) # noqa: E501\\n\\n with cd(env.directory):\\n\\n # requirements\\n # sudo('python python-dev build-essential zlib1g-dev libssl-dev libxml2-dev libxslt1-dev wv poppler-utils libtiff5-dev libjpeg62-dev zlib1g-dev libfreetype6-dev liblcms1-dev libwebp-dev') # noqa: E501\\n\\n # prepare buildout\\n if env.latest:\\n if env.python3:\\n sudo('ln -s local_demo_nightly_py3.cfg local.cfg', user=env.deploy_user) # noqa: E501\\n else:\\n sudo('ln -s local_demo_nightly_py2.cfg local.cfg', user=env.deploy_user) # noqa: E501\\n else:\\n sudo('ln -s local_production.cfg local.cfg', user=env.deploy_user)\\n sudo('echo -e \\\"[buildout]\\\\nlogin = admin\\\\npassword = admin\\\" > secret.cfg', user=env.deploy_user) # noqa: E501\\n\\n # bootstrap and run bildout once\\n if env.latest:\\n sudo('./bin/pip install --no-cache-dir -r https://raw.githubusercontent.com/plone/buildout.coredev/5.2/requirements.txt', user=env.deploy_user) # noqa: E501\\n else:\\n sudo('./bin/pip install --no-cache-dir -r https://raw.githubusercontent.com/starzel/buildout/5.2/requirements.txt', user=env.deploy_user) # noqa: E501\\n sudo('./bin/buildout', user=env.deploy_user)\\n\\n # start supervisor which starts plone instance also\\n sudo('./bin/supervisord', user=env.deploy_user)\",\n \"def test_run_exec(self):\\n from multiprocessing import Process, Queue\\n output = Queue()\\n repodir = \\\"~/codes/ci/tests/repo\\\"\\n processes = []\\n for i in range(3):\\n processes.append(Process(target=run_exec, args=(repodir, \\\"ls -la\\\", output, i)))\\n processes[-1].start()\\n \\n #Wait for the unit tests to all finish.\\n for p in processes:\\n p.join()\\n results = [output.get() for p in processes]\\n ordered = {o[\\\"index\\\"]: o for o in results}\\n\\n #We consider the test successful if the output files were created and the end time\\n #is not None. That means that the process ran correctly and python didn't lose\\n #control of the subprocess.\\n from os import path\\n fullrepo = path.expanduser(repodir)\\n for i in range(3):\\n self.assertTrue(path.isfile(path.join(fullrepo, \\\"{}.cidat\\\".format(i))))\\n self.assertIsNotNone(ordered[i][\\\"end\\\"])\\n self.assertEqual(ordered[i][\\\"code\\\"], 0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7215023","0.6739235","0.67389643","0.6676614","0.63964593","0.6364255","0.6358811","0.6307575","0.62078595","0.5984018","0.5947946","0.59442085","0.59306777","0.5906521","0.5804627","0.57899594","0.5749922","0.57092535","0.56613225","0.56181085","0.5615245","0.5607815","0.5600671","0.5597849","0.55911875","0.55900294","0.5588381","0.55576736","0.5555396","0.5519936","0.5500112","0.5497543","0.5494437","0.54897666","0.5432452","0.5408157","0.54048085","0.5399076","0.5392348","0.53711665","0.53711665","0.53643006","0.5361199","0.5342953","0.5337086","0.53345364","0.5333078","0.5333078","0.53302705","0.53139025","0.5313886","0.5313036","0.5309913","0.5307991","0.53028905","0.52905166","0.52879936","0.5285324","0.527925","0.52704936","0.52654433","0.52653706","0.52576023","0.52556443","0.5252234","0.5249111","0.52489436","0.5241361","0.52329975","0.52238536","0.5222821","0.5222299","0.52203536","0.52175915","0.5214105","0.52130467","0.52051246","0.52005166","0.5195916","0.5194002","0.5193223","0.5191262","0.51855916","0.518389","0.51734185","0.5172292","0.5168214","0.51589304","0.5153672","0.51503193","0.51500714","0.5147767","0.5143857","0.51295656","0.51219594","0.51149094","0.5110571","0.5108434","0.51070356","0.5102402","0.5092208"],"string":"[\n \"0.7215023\",\n \"0.6739235\",\n \"0.67389643\",\n \"0.6676614\",\n \"0.63964593\",\n \"0.6364255\",\n \"0.6358811\",\n \"0.6307575\",\n \"0.62078595\",\n \"0.5984018\",\n \"0.5947946\",\n \"0.59442085\",\n \"0.59306777\",\n \"0.5906521\",\n \"0.5804627\",\n \"0.57899594\",\n \"0.5749922\",\n \"0.57092535\",\n \"0.56613225\",\n \"0.56181085\",\n \"0.5615245\",\n \"0.5607815\",\n \"0.5600671\",\n \"0.5597849\",\n \"0.55911875\",\n \"0.55900294\",\n \"0.5588381\",\n \"0.55576736\",\n \"0.5555396\",\n \"0.5519936\",\n \"0.5500112\",\n \"0.5497543\",\n \"0.5494437\",\n \"0.54897666\",\n \"0.5432452\",\n \"0.5408157\",\n \"0.54048085\",\n \"0.5399076\",\n \"0.5392348\",\n \"0.53711665\",\n \"0.53711665\",\n \"0.53643006\",\n \"0.5361199\",\n \"0.5342953\",\n \"0.5337086\",\n \"0.53345364\",\n \"0.5333078\",\n \"0.5333078\",\n \"0.53302705\",\n \"0.53139025\",\n \"0.5313886\",\n \"0.5313036\",\n \"0.5309913\",\n \"0.5307991\",\n \"0.53028905\",\n \"0.52905166\",\n \"0.52879936\",\n \"0.5285324\",\n \"0.527925\",\n \"0.52704936\",\n \"0.52654433\",\n \"0.52653706\",\n \"0.52576023\",\n \"0.52556443\",\n \"0.5252234\",\n \"0.5249111\",\n \"0.52489436\",\n \"0.5241361\",\n \"0.52329975\",\n \"0.52238536\",\n \"0.5222821\",\n \"0.5222299\",\n \"0.52203536\",\n \"0.52175915\",\n \"0.5214105\",\n \"0.52130467\",\n \"0.52051246\",\n \"0.52005166\",\n \"0.5195916\",\n \"0.5194002\",\n \"0.5193223\",\n \"0.5191262\",\n \"0.51855916\",\n \"0.518389\",\n \"0.51734185\",\n \"0.5172292\",\n \"0.5168214\",\n \"0.51589304\",\n \"0.5153672\",\n \"0.51503193\",\n \"0.51500714\",\n \"0.5147767\",\n \"0.5143857\",\n \"0.51295656\",\n \"0.51219594\",\n \"0.51149094\",\n \"0.5110571\",\n \"0.5108434\",\n \"0.51070356\",\n \"0.5102402\",\n \"0.5092208\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80389,"cells":{"query":{"kind":"string","value":"build one or more images"},"document":{"kind":"string","value":"def build(images, tag, quiet):\n # images to build\n build_images = None\n\n # list of available images\n image_list = build_image_map().keys()\n\n # image specified: check if it exists\n if images:\n build_images = []\n for check_name in images:\n if check_name in image_list:\n build_images.append(check_name)\n continue\n raise click.ClickException(\n \"invalid image name '{}'\".format(check_name))\n click.echo('building images {}'.format(', '.join(build_images)))\n # no image specified: build all\n else:\n click.echo('building images {}'.format(', '.join(image_list)))\n\n manager = Manager('build', tag, images=build_images, verbose=not quiet)\n manager.run()"},"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 build_filler_images(self):","def _build_docker_images(self):\n print(f\"+ building {len(self.neurodocker_specs)} Docker images\")\n self.docker_status = []\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\n try:\n print(\"++ building image: {}\".format(neurodocker_dict))\n cg.docker_main(\n self.working_dir,\n neurodocker_dict,\n sha1,\n build_context=self.build_context,\n )\n self.docker_status.append(\"docker ok\")\n except Exception as e:\n self.docker_status.append(\n \"failed to build image with SHA1 {}: {}\".format(sha1, e)\n )","def build(image_name, path='.'):\n try:\n doxy.images.build(path=path, tag=image_name)\n message = '[*] Image {} built.'\n print message.format(image_name)\n except Exception as err:\n print err\n raise","def buildImages(files, targets, type):\n images = []\n for file in files:\n targets.append(file)\n with open(file, \"rb\") as f:\n if type == \"Byte\":\n images.append(bytePlot(list(f.read())))\n elif type == \"Markov\":\n images.append(markovPlot(list(f.read())))\n elif type == \"Hilbert\":\n images.append(hilbertPlot(list(f.read())))\n smp.imsave(\"{}.png\".format(file), images[-1])\n return images, targets","def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None, skip_build=False):\n value_modifications = {}\n for name, options in images.items():\n image_path = options.get('contextPath', os.path.join('images', name))\n image_tag = tag\n # include chartpress.yaml itself as it can contain build args and\n # similar that influence the image that would be built\n paths = list(options.get('paths', [])) + [image_path, 'chartpress.yaml']\n last_commit = last_modified_commit(*paths)\n if tag is None:\n if chart_version:\n image_tag = \"{}-{}\".format(chart_version, last_commit)\n else:\n image_tag = last_commit\n image_name = prefix + name\n image_spec = '{}:{}'.format(image_name, image_tag)\n\n value_modifications[options['valuesPath']] = {\n 'repository': image_name,\n 'tag': SingleQuotedScalarString(image_tag),\n }\n\n if skip_build:\n continue\n\n template_namespace = {\n 'LAST_COMMIT': last_commit,\n 'TAG': image_tag,\n }\n\n if tag or image_needs_building(image_spec):\n build_args = render_build_args(options, template_namespace)\n build_image(image_path, image_spec, build_args, options.get('dockerfilePath'))\n else:\n print(f\"Skipping build for {image_spec}, it already exists\")\n\n if push:\n if tag or image_needs_pushing(image_spec):\n check_call([\n 'docker', 'push', image_spec\n ])\n else:\n print(f\"Skipping push for {image_spec}, already on registry\")\n return value_modifications","def build_images(formulas, dir_images, quality=100, density=200, down_ratio=2,\n buckets=None, n_threads=4):\n init_dir(dir_images)\n existing_idx = sorted(set([int(file_name.split('.')[0]) for file_name in\n get_files(dir_images) if file_name.split('.')[-1] == \"png\"]))\n\n pool = Pool(n_threads)\n result = pool.map(build_image, [(idx, form, dir_images, quality, density,\n down_ratio, buckets) for idx, form in formulas.items()\n if idx not in existing_idx])\n pool.close()\n pool.join()\n\n result += [(str(idx) + \".png\", idx) for idx in existing_idx]\n\n return result","def _build_final_image(self, image):\n raise NotImplementedError","def build(path, tag):\n print (\"*** BUILDING IS INITIATED\")\n try:\n response = client.images.build(path = path, tag=tag, rm=True)\n except Exception as ex:\n template = \"An exception of type {0} occured. Arguments:\\n{1!r}\"\n message = template.format(type(ex).__name__, ex.args)\n click.echo(message)\n print(\"*** BUILD OUTPUT:\")\n for object in response[1]:\n click.echo(object)\n click.echo('*** BUILDING FINISHED')","def create_target_image(self, builder, target, base_image, parameters):","def build_all_images_for_lang(lang):\n if not args.git_checkout:\n if args.release != \"master\":\n print(\n \"Cannot use --release without also enabling --git_checkout.\\n\"\n )\n sys.exit(1)\n releases = [args.release]\n else:\n if args.release == \"all\":\n releases = client_matrix.get_release_tags(lang)\n else:\n # Build a particular release.\n if args.release not in [\"master\"] + client_matrix.get_release_tags(\n lang\n ):\n jobset.message(\n \"SKIPPED\",\n \"%s for %s is not defined\" % (args.release, lang),\n do_newline=True,\n )\n return []\n releases = [args.release]\n\n images = []\n for release in releases:\n images += build_all_images_for_release(lang, release)\n jobset.message(\n \"SUCCESS\",\n \"All docker images built for %s at %s.\" % (lang, releases),\n do_newline=True,\n )\n return images","def create_base_image(self, builder, template, parameters):","def build_img_combiner(self, function_builder, builder_args):\n \n self.combine_images = function_builder(*builder_args)","def build_impl(**kwargs: Any) -> None:\n try:\n config = configuration.create_transient_build_config(kwargs)\n except configuration.CLIArgumentError as e:\n print(e, file=sys.stderr)\n sys.exit(1)\n store = image.ImageStore(backend_dir=config.image_backend, frontend_dir=None)\n builder = build.ImageBuilder(config, store)\n builder.build()\n sys.exit(0)","def build_container(client):\n client.images.build(path=os.path.join(os.path.abspath(\"\"), \"docker\"), tag=\"scrape_light\")","def initImages(self):\n pass","def initImages(self):\n pass","def initImages(self):\n pass","def run_build_image(image_list):\n success_image_list = list()\n failed_image_list = list()\n for image in image_list:\n docker_build_success_flag = image.run_docker_build()\n if not docker_build_success_flag:\n failed_image_list.append(image)\n continue\n smoke_test_sccess_flag = image.run_smoke_test()\n if not smoke_test_sccess_flag:\n failed_image_list.append(image)\n continue\n success_image_list.append(image)\n\n logger.info(\"--- docker build summary ---\")\n logger.info(\"following image succeed:\")\n for image in success_image_list:\n logger.info(f\" {image.local_identifier}\")\n logger.info(\"following image failed:\")\n for image in failed_image_list:\n logger.info(f\" {image.local_identifier}\")\n return success_image_list, failed_image_list","def build():","def __call__(self, images, targets):\n pass","def main():\n me = SimpleImage(\"images/me.JPG\")\n dinosaur = SimpleImage(\"images/dinosaur.jpg\")\n\n dinosaur.make_as_big_as(me)\n combine = magic(me, dinosaur)\n combine.show()","def construct_image(imgs):\n\n # todo fill missing pieces and\n\n if len(imgs) == 0:\n return None\n # taking the first\n w, h = imgs[0][1].size\n img_array = order_2d(imgs)\n x_count = len(img_array[0])\n y_count = len(img_array)\n height = h * y_count\n width = w * x_count\n new_im = Image.new('RGB', (width, height))\n for y in range(y_count):\n for x in range(x_count):\n _, im = img_array[y][x]\n new_im.paste(im, (x * w, y * h))\n return new_im","def main():\n fg = SimpleImage('image_contest/me.jpg')\n bg = SimpleImage('image_contest/house.png')\n bg.make_as_big_as(fg)\n combined_img = combine(bg, fg)\n combined_img.show()","def make_images(self):\n self._images = [tree.to_image() for tree in self.reaction_trees]\n self._update_route_dict(self._images, \"image\")","def images(self, **kwargs):\n\n raise NotImplementedError","def create_png_images(self):\n if self.subject is None:\n print Console.WARNING + 'You need to specify a subject first' + Console.ENDC\n return\n\n check_dir_of = self.locations.check_dir_of\n check_dir_of(self.locations.HISTO_PNG_U)\n check_dir_of(self.locations.HISTO_PNG)\n check_dir_of(self.locations.SOURCE_PNG)\n\n\n\n fmap_img = ImageUtils.load_nifti_image(self.locations.HIST_FMAP) #loading subject nifti files\n volumes = []\n try:\n for s in self.locations.SOURCES:\n volumes.append(ImageUtils.load_nifti_image(s))\n except IOError as e:\n print Console.FAIL + 'There are errors loading nifi files for subject %s'%self.subject + Console.ENDC\n return False\n \n\n num_slices = volumes[0].shape[2] #use first volume to check expected number of slices\n\n self.locations.create_empty_dir(self.locations.IMAGES_DIR)\n\n print 'Creating input PNGs for %s'%self.subject\n for k, vol in enumerate(volumes):\n for i in range(num_slices):\n imslice = ImageUtils.data_to_bytescale_rgb(vol[:, :, i])\n im = Image.fromarray(imslice)\n im.save(self.locations.SOURCE_PNG % (self.locations.LABELS[k],i))\n\n \n print 'Creating histology PNGs for %s'%self.subject\n for i in range(num_slices):\n\n im_unscaled = ImageUtils.data_to_unscaled_rgb(fmap_img[:, :, i]); #keeps the original values\n im_unscaled = Image.fromarray(im_unscaled)\n im_unscaled = im_unscaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\n im_unscaled.save(self.locations.HISTO_PNG_U % i)\n\n im_scaled = ImageUtils.data_to_bytescale_rgb(fmap_img[:,:,i]); # bytescaled histology\n im_scaled = Image.fromarray(im_scaled)\n im_scaled = im_scaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\n im_scaled.save(self.locations.HISTO_PNG % i)\n\n print\n return True","def build_all_images_for_release(lang, release):\n docker_images = []\n build_jobs = []\n\n env = {}\n # If we not using current tree or the sibling for grpc stack, do checkout.\n stack_base = \"\"\n if args.git_checkout:\n stack_base = checkout_grpc_stack(lang, release)\n var = {\n \"go\": \"GRPC_GO_ROOT\",\n \"java\": \"GRPC_JAVA_ROOT\",\n \"node\": \"GRPC_NODE_ROOT\",\n }.get(lang, \"GRPC_ROOT\")\n env[var] = stack_base\n\n for runtime in client_matrix.get_runtimes_for_lang_release(lang, release):\n job = build_image_jobspec(runtime, env, release, stack_base)\n docker_images.append(job.tag)\n build_jobs.append(job)\n\n jobset.message(\"START\", \"Building interop docker images.\", do_newline=True)\n print(\"Jobs to run: \\n%s\\n\" % \"\\n\".join(str(j) for j in build_jobs))\n\n num_failures, _ = jobset.run(\n build_jobs, newline_on_success=True, maxjobs=multiprocessing.cpu_count()\n )\n if num_failures:\n jobset.message(\n \"FAILED\", \"Failed to build interop docker images.\", do_newline=True\n )\n docker_images_cleanup.extend(docker_images)\n sys.exit(1)\n\n jobset.message(\n \"SUCCESS\",\n \"All docker images built for %s at %s.\" % (lang, release),\n do_newline=True,\n )\n\n if release != \"master\":\n commit_log = os.path.join(stack_base, \"commit_log\")\n if os.path.exists(commit_log):\n for image in docker_images:\n add_files_to_image(image, [commit_log], \"release=%s\" % release)\n return docker_images","def build_container_image(self) -> None:\n print_version_of_tools()\n try:\n self.fs_watcher.start()\n runner = PluginsRunner(self,\n self.plugins_conf,\n self.plugin_files,\n self.keep_plugins_running,\n plugins_results=self.data.plugins_results)\n runner.run()\n finally:\n self.fs_watcher.finish()","def createAllImageFiles(poly, name) :\n \n for i in range(len(poly.getPaths())):\n fileName = name + \"_\" + str(i) + \".dot\"\n imgName = name + \"_\" + str(i) + \".jpg\"\n \n Command = \"neato -Tjpeg \" + fileName + \" -o \" + imgName\n run(Command, shell=True)","def build_image(self, df_path):\n cmd = \"{} build -f {} -t {} .\".format(\n self.binary, df_path, self.vars['image'])\n LOG.info(\"Running: {}\".format(cmd))\n res = subprocess.run(cmd, shell=True)\n if res.returncode != 0:\n sys.exit(2)\n return res","def __call__(self, images, targets):\n return images, targets","def main():\n parser = argparse.ArgumentParser(\n epilog=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\n )\n parser.add_argument(\n \"-d\", \"--dry-run\", action=\"store_true\", default=0, help=\"Dry run mode.\"\n )\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"count\",\n default=0,\n help=\"Verbosity. Default is WARNING level.\",\n )\n\n subparsers = parser.add_subparsers(help=\"Sub commands\", dest=\"subparser\")\n subparsers.required = True\n\n build_parser = subparsers.add_parser(\n \"build\",\n description=\"Build an image from Dockerfile, caching image hierarchy\",\n help=\"Build an image from a Dockerfile\",\n )\n build_parser.add_argument(\n \"path\", metavar=\"PATH\", help=\"The build context directory\"\n )\n build_parser.add_argument(\n \"-f\",\n \"--file\",\n help=\"Name of the Dockerfile. If not provided, \"\n \"will use config.DOCKERFILE_PATH_PATTERN to compute. \",\n )\n build_parser.add_argument(\n \"-v\",\n \"--git-sha\",\n required=True,\n help=\"The version of code to build against, \" \"will pass as GIT_SHA variable\",\n )\n build_parser.add_argument(\n \"-n\", \"--name\", required=True, help=\"The name of the image to build\"\n )\n build_parser.add_argument(\n \"--build-arg\",\n metavar=\"ARG=VALUE\",\n nargs=\"*\",\n default=[],\n help=\"Set extra build-time variables. GIT_SHA, TIMESTAMP will be passed by default.\",\n )\n build_parser.add_argument(\n \"-r\",\n \"--raw\",\n action=\"store_true\",\n help=\"Whether to use raw docker build command to build, skipping caching logic\",\n )\n build_parser.add_argument(\n \"--registry\",\n default=config.DOCKER_REGISTRY,\n help=\"Docker registry use to determine the image identity, \"\n \"can be set via IMAGE_BUILDER_DOCKER_REGISTRY environment variable, \"\n 'or set DOCKER_REGISTRY in config.py. Default is \"%(default)s\"',\n )\n build_parser.add_argument(\n \"-t\",\n \"--tag-pattern\",\n default=config.GIT_SHA_TAG_PATTERN,\n help=\"Tag pattern, can only include one `{git_sha}` placeholder, \"\n 'such as \"{git_sha}-new\". If the tag exists, we won\\'t rebuild it. '\n 'Default is \"%(default)s\"',\n )\n build_parser.add_argument(\n \"-e\",\n \"--extra-tag\",\n nargs=\"*\",\n default=[],\n help=\"Extra tags to tag to the final images\",\n )\n build_parser.add_argument(\n \"--extra-name\",\n nargs=\"*\",\n default=[],\n help=\"Extra name and optionally with a tag in the 'name:tag' format\",\n )\n build_parser.add_argument(\n \"-o\", \"--output-hash\", help=\"The output filename of the files hash log.\"\n )\n build_parser.set_defaults(func=build)\n\n args = parser.parse_args()\n if args.dry_run:\n # DRY_RUN env will be read in image_builder.libs.process\n os.environ[\"DRY_RUN\"] = \"1\"\n\n if args.func == build:\n args.path = expand_path(args.path)\n if args.output_hash:\n args.output_hash = expand_path(args.output_hash)\n\n args.file = args.file or locate_dockerfile(args.name)\n args.file = expand_path(args.file)\n # set environ for main dockerfile for possibly retrieving later\n os.environ[\n config.DOCKERFILE_ENV_PATTERN.format(image_name=args.name)\n ] = args.file\n\n # change CWD to PATH\n os.chdir(args.path)\n\n if not args.registry:\n parser.error(\n \"--registry should be provied \"\n \"or specified by IMAGE_BUILDER_DOCKER_REGISTRY environment variable or set DOCKER_REGISTRY in config.py\"\n )\n if not all(\"=\" in kv for kv in args.build_arg):\n parser.error(\"--build_arg must be in ARG=VALUE format\")\n\n # set git_sha_tag\n try:\n args.git_sha_tag = args.tag_pattern.format(git_sha=args.git_sha)\n except KeyError:\n parser.error(\n 'Wrong --tag-pattern provided. Can only include one `{git_sha}` placeholder, such as \"{git_sha}-new\"'\n )\n\n # setup logging\n level = logging.WARNING - args.verbose * 10\n logging.basicConfig(\n level=level, format=\"%(asctime)s %(name)s %(levelname)s %(message)s\"\n )\n\n if args.output_hash:\n h = logging.FileHandler(args.output_hash)\n h.setLevel(logging.DEBUG)\n h.setFormatter(logging.Formatter(\"%(message)s\"))\n hash_logger.addHandler(h)\n\n # Suppress warning when we don't verify ssl\n import urllib3\n\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n return args.func(args)","def _add_images(self, epoch, train_batch, train_outputs, valid_batch, valid_outputs):\n raise NotImplementedError","def build_image(image, build_args):\n\n subprocess.check_call(['docker', 'build', '--no-cache', '--force-rm', '-t',\n image] + build_args)","def _process_images(self, docname: pathlib.Path, images: List[nodes.image]) -> None:\n logger.debug(\"[nbtutorial]: Processing images for %s\", docname)\n\n if len(images) == 0:\n return\n\n img_dir = pathlib.Path(self.outdir, docname.parent, RESOURCE_DIR)\n\n if not img_dir.exists():\n img_dir.mkdir(parents=True)\n\n for img in images:\n fname = pathlib.Path(img[\"uri\"]).name\n\n source = pathlib.Path(self.app.confdir, img[\"uri\"])\n destination = pathlib.Path(img_dir, fname)\n\n shutil.copy(source, destination)","def build_docker(params) -> None:\n print(\"Building docker image...\")\n cmd = \"cd bg_changer && docker build --tag bg_changer . >/dev/null 2>&1\"\n if os.system(cmd) == 0:\n print(\" Success !\")\n else:\n print(\" Failure !\")","def build(_):","def gather_images():\n # Import an empty image\n null_img = Image.open('assests/null/null.png')\n null_img = ImageTk.PhotoImage(null_img.resize((100,100), Image.ANTIALIAS))\n\n # Import image and icon for X\n X_img = Image.open('assests/X_Assets/X.png')\n X_icon = ImageTk.PhotoImage(X_img.resize((15, 12), Image.ANTIALIAS))\n X_img = ImageTk.PhotoImage(X_img.resize((95, 80), Image.ANTIALIAS))\n\n # Import horizontally striked X\n X_hor = Image.open('assests/X_Assets/X_hor.png')\n X_hor = ImageTk.PhotoImage(X_hor.resize((95, 80), Image.ANTIALIAS))\n\n # Import vertically striked X\n X_vert = Image.open('assests/X_Assets/X_vert.png')\n X_vert = ImageTk.PhotoImage(X_vert.resize((95, 80), Image.ANTIALIAS))\n\n # Import diagonally strikedX\n X_diag = Image.open('assests/X_Assets/X_diag.png')\n X_diag = ImageTk.PhotoImage(X_diag.resize((95, 80), Image.ANTIALIAS))\n\n # Import another diagonally striked X\n X_diag2 = Image.open('assests/X_Assets/X_diag2.png')\n X_diag2 = ImageTk.PhotoImage(X_diag2.resize((95, 80), Image.ANTIALIAS))\n\n # Import image and icon for O\n O_img = Image.open('assests/O_Assets/O.png')\n O_icon = ImageTk.PhotoImage(O_img.resize((14, 14), Image.ANTIALIAS))\n O_img = ImageTk.PhotoImage(O_img.resize((90, 90), Image.ANTIALIAS))\n\n # Import horizontally striked O\n O_hor = Image.open('assests/O_Assets/O_hor2.png')\n O_hor = ImageTk.PhotoImage(O_hor.resize((90, 90), Image.ANTIALIAS))\n\n # Import vertically striked O\n O_vert = Image.open('assests/O_Assets/O_vert2.png')\n O_vert = ImageTk.PhotoImage(O_vert.resize((90, 90), Image.ANTIALIAS))\n\n # Import diagonally striked O\n O_diag = Image.open('assests/O_Assets/O_diag.png')\n O_diag = ImageTk.PhotoImage(O_diag.resize((90, 90), Image.ANTIALIAS))\n\n # Import another diagonally striked O\n O_diag2 = Image.open('assests/O_Assets/O_diag2.png')\n O_diag2 = ImageTk.PhotoImage(O_diag2.resize((90, 90), Image.ANTIALIAS))\n\n return (null_img, X_icon, X_img, X_hor, X_vert, X_diag, X_diag2, O_icon, O_img, O_hor, O_vert, O_diag, O_diag2)","def load_images():\n print(\"[+] UPDATE - Begin loading images\")\n\n colors = [\"w\", \"b\"]\n piece_types = [\"p\", \"R\", \"N\", \"B\", \"K\", \"Q\"]\n for color in colors:\n for type in piece_types:\n piece = color + type\n IMAGES[piece] = p.transform.scale(p.image.load(\"images/\" + piece + \".png\"), (SQ_SIZE, SQ_SIZE))\n\n print(\"[+] UPDATE - Images loaded\")","def appendpics(pathofimg, w_sub, h_sub, step):\n num = 0\n dirlist = []\n images = [] # images in each folder\n for root, dirs, fileswer in os.walk(pathofimg):\n if len(dirs)!= 0:\n for dir in dirs:\n dirlist.append(dir)\n for rooert, dirwerwes, files in os.walk(pathofimg+'/'+dir):\n for file in files:\n if(file.endswith('.png')):\n images.append(Image.open(pathofimg+'/'+dir+'/'+file))\n if(len(images)==81):\n break\n target = montage(images, w_sub, h_sub, step)\n target.save(pathofimg +'/'+ dir + '.png', quality=100)\n else:\n dir = 'Generated'\n for file in fileswer:\n if (file.endswith('.png')):\n images.append(Image.open(pathofimg +'/'+ file))\n target1 = montage(images, w_sub, h_sub, step)\n savepath = pathofimg +'/'+ 'generated'\n os.makedirs(savepath)\n target1.save(savepath +'/'+ dir + '.png', quality=100)","def builder_will_create_target_image(self, builder, target, image_id, template, parameters):","def test_build_image(self):\n labels = {\"apple\": \"red\", \"grape\": \"green\"}\n self.docker.images.build(\n path=\"test/python/docker/build_labels\",\n labels=labels,\n tag=\"labels\",\n isolation=\"default\",\n )\n image = self.docker.images.get(\"labels\")\n self.assertEqual(image.labels[\"apple\"], labels[\"apple\"])\n self.assertEqual(image.labels[\"grape\"], labels[\"grape\"])","def make_image(self):\n\n if self.type == 'passthrough':\n return\n render_template(\n os.path.dirname(self.main_module),\n os.path.basename(self.main_module_path),\n self.language,\n self.requirements,\n self.whitelist,\n self.type,\n into=self.code_dir)\n self.build()","def main():\n images = Images()\n #print images.create_image_urls()\n print images.get_image_random()\n print images.get_image(12)","def setup_images(self, images):\n if isinstance(images, str):\n images = [images]\n self._image_list.extend(images)","def build(self):\n # open json, len 161,260\n at_json = open_json(self.json_names[0])\n link_json = open_json(self.json_names[1])\n # if need preprocessing, do it\n if self.args.img_preprocessing:\n print(\"resize imgs\")\n for i in tqdm(range(len(link_json))):\n image_url = \"image/\" + link_json[i][\"image_url_4x\"].split('/')[-1]\n img = Image.open(image_url)\n img = img.resize((224, 224))\n img.save(image_url)\n\n # create dataset\n itemlen = 0\n previd = 0\n for i in tqdm(range(len(link_json))):\n image_url = link_json[i][\"image_url_4x\"].split('/')[-1]\n uid = image_url.split('-')[0]\n if previd != uid:\n self.label.append(list(at_json[i].values())[2:])\n if i != 0:\n self.itemlen.append(itemlen)\n itemlen = 0\n self.input.append(f\"{self.frontpath}dataset/image/\" + image_url)\n previd = uid\n itemlen += 1\n self.itemlen.append(itemlen)\n self.separate()\n self.dataset = {\n 'train': self.train,\n 'validation': self.val,\n 'test': self.test\n }\n\n print('finished dataset')","def generate_image(self):\n pass","def populate_image_lists():\r\n with os.scandir(os.path.join(dir_path, \"inputs\", \"type_a\")) as filepaths:\r\n for path in filepaths:\r\n extension = os.path.splitext(path)[1].lower()\r\n if extension == \".png\" or extension == \".jpg\":\r\n images_a.append(path.path)\r\n with os.scandir(os.path.join(dir_path, \"inputs\", \"type_b\")) as filepaths:\r\n for path in filepaths:\r\n extension = os.path.splitext(path)[1].lower()\r\n if extension == \".png\" or extension == \".jpg\":\r\n images_b.append(path.path)","def getimgs():","def build_command(self, args):\n self._validate_common(args)\n self._set_manifests(args)\n\n manifest = self._manager._recipes\n\n records = self._get_matching_records(args, manifest)\n\n if not len(records):\n sys.exit(\"No matching items found.\")\n\n for record in records:\n result = self._manager.build(record['_type'], **record)\n print('The requested asset has been built: {}'.format(result['_path']))\n\n if len(records) > 1:\n print('All files have been successfully built. Thank you.')","def get_images_to_build(fuzzers, benchmarks):\n images = {}\n templates = _get_image_type_templates()\n for fuzzer in fuzzers:\n for benchmark in benchmarks:\n for name_templ, obj_templ in templates.items():\n name, obj = _instantiate_image_obj(name_templ, obj_templ,\n fuzzer, benchmark)\n images[name] = obj\n return images","def load_images(self):\r\n self.standing_frame = [load_image(\"cat1.png\")]\r\n self.walk_frames_r = [load_image(\"cat2.png\"), load_image(\"cat3.png\"),\r\n load_image(\"cat4.png\")]","def describe_image_builders(Names=None, MaxResults=None, NextToken=None):\n pass","def new_robot_images(images): #py:new_robot_images\n RUR._new_robot_images_(images)","def build(obj):\n logger.info(ASCII_LOGO)\n logger.info(\"Started building SageMaker Docker image. It will take some minutes...\\n\")\n\n try:\n config_file_path = os.path.join('.sagify.json')\n if not os.path.isfile(config_file_path):\n raise ValueError()\n\n config = ConfigManager(config_file_path).get_config()\n api_build.build(\n source_dir=config.sagify_module_dir,\n requirements_dir=config.requirements_dir,\n docker_tag=obj['docker_tag'],\n image_name=config.image_name,\n python_version=config.python_version)\n\n logger.info(\"Docker image built successfully!\")\n except ValueError:\n logger.info(\"This is not a sagify directory: {}\".format(dir))\n sys.exit(-1)\n except subprocess.CalledProcessError as e:\n logger.debug(e.output)\n raise\n except Exception as e:\n logger.info(\"{}\".format(e))\n sys.exit(-1)","def get_build_images(self) -> List[Image]:\n images = []\n image_names = []\n conflicting_names = []\n for registry_name, registry in self.registries.items():\n # if the registry is not marked as source, skip it\n if not registry.source:\n continue\n\n images += self.get_images_from_registry(registry)\n\n if conflicting_names:\n raise RuntimeError(\"Images found in multiple 'source' repositories: %s\", conflicting_names)\n\n return images","def auto(cls, images):\n raise NotImplementedError","def build(self):\n self._remove_swarm_keys()\n self._remove_pod_keys()\n self._set_image()\n self._translate_docker_properties()","def build_all(self):\n self.android_build()\n self.generate_patch_build('')\n self.generate_specs_build()\n self.generate_interfaces()","def prepare():\n sh('docker build --rm -t {image} {dir}', image=IMAGE, dir=os.path.dirname(__file__))","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['image_dir']\n\n combo_df = pd.merge(cat_df, bbox_df, how='outer', on='image_name')\n combo_df['image_name'] = combo_df['image_name'].apply(\n lambda x: x[len('img'):-len('.jpg')])\n labels = Labels(combo_df, img_dir, n_images_loaded=-1)\n labels.set_data_target('raw_image', chunksize=3000)\n return labels","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'\n )\n\n for image_name in images:\n print(image_name, \"image:\")\n\n image = open_image(image_name)\n display_image(image, \"Original input \" + image_name)\n\n grayscale_v = transform_colors(image)\n display_image(grayscale_v[:,:,0], \"Grayscale \" + image_name)\n save_image(image_name + \"-grayscale\", grayscale_v[:,:,0])\n\n contours_v, contours = get_contours(grayscale_v)\n display_image(contours_v, \"Contours \" + image_name)\n save_image(image_name + \"-contours\", contours_v)\n\n labeled_img, areas = get_measures(image, contours[1:])\n display_image(labeled_img, \"Labeled \" + image_name)\n save_image(image_name + \"-labeled\", labeled_img)\n\n areas_histogram(areas, image_name)","def createImages(self, geneticInstances):\n genomes = []\n for geneticInstance in geneticInstances:\n genomes.append(geneticInstance.toGenomeRepresentation())\n generatePlantImages(genomes)\n # We now have the output pictures. We'll get to them using the database instances' filenames","def __call__(self, images: Union[str, List[str], \"Image\", List[\"Image\"]], **kwargs):\n return super().__call__(images, **kwargs)","def _build_image_names(self, num_sequence, image_count):\n for i in self._get_image_sequence_list(image_count):\n self._image_names.append(self._get_image_name(i))","def combine_images(images: list) -> Image:\n img_width = images[0][0].width\n img_height = images[0][0].height\n new_size = (img_width * len(images[0]), img_height * len(images))\n new_image = Image.new('RGB', new_size)\n\n # Add all the images from the grid to the new, blank image\n for rowindex, row in enumerate(images):\n for colindex, image in enumerate(row):\n location = (colindex * img_width, rowindex * img_height)\n new_image.paste(image, location)\n\n return new_image","def create_preset_images(self):\n for f in sorted(self.get_files_from_data()):\n photoInstances = {}\n for preset in self.generator.settings[\"GALLERY_PRESETS\"]:\n preset_dir = \"%s%s%s\" % (self.absolute_output_path,\n os.sep, \n preset[\"name\"])\n photoInstances[preset[\"name\"]] = Photo(self, f, preset_dir, preset)\n \n self.photos.append(photoInstances)","def buildImages(self, config, base, file_num, image_num, obj_num, ignore, logger):\n # This is basically the same as the base class version. Just a few extra things to\n # add to the ignore list.\n ignore += [ 'file_name', 'dir', 'nfiles', 'det_num',\n 'only_dets', 'readout', 'exptime', 'camera' ]\n\n opt = {\n 'cosmic_ray_rate': float,\n 'cosmic_ray_catalog': str,\n 'header': dict\n }\n params, safe = galsim.config.GetAllParams(config, base, opt=opt, ignore=ignore)\n\n image = galsim.config.BuildImage(base, image_num, obj_num, logger=logger)\n\n # Add cosmic rays.\n cosmic_ray_rate = params.get('cosmic_ray_rate', 0)\n if cosmic_ray_rate > 0:\n cosmic_ray_catalog = params.get('cosmic_ray_catalog', None)\n if cosmic_ray_catalog is None:\n cosmic_ray_catalog = os.path.join(data_dir, 'cosmic_rays_itl_2017.fits.gz')\n if not os.path.isfile(cosmic_ray_catalog):\n raise FileNotFoundError(f'{cosmic_ray_catalog} not found')\n\n logger.info('Adding cosmic rays with rate %f using %s.',\n cosmic_ray_rate, cosmic_ray_catalog)\n exptime = base['exptime']\n det_name = base['det_name']\n cosmic_rays = CosmicRays(cosmic_ray_rate, cosmic_ray_catalog)\n rng = galsim.config.GetRNG(config, base)\n cosmic_rays.paint(image.array, rng, exptime=exptime)\n\n # Add header keywords for various values written to the primary\n # header of the simulated raw output file, so that all the needed\n # information is in the eimage file.\n image.header = galsim.FitsHeader()\n exptime = base['exptime']\n image.header['EXPTIME'] = exptime\n image.header['DET_NAME'] = base['det_name']\n\n header_vals = copy.deepcopy(params.get('header', {}))\n opsim_data = get_opsim_data(config, base)\n\n # Helper function to parse a value with priority:\n # 1. from header_vals (popped from dict if present)\n # 2. from opsim_data\n # 3. specified default\n def parse(item, type, default):\n if item in header_vals:\n val = galsim.config.ParseValue(header_vals, item, base, type)[0]\n del header_vals[item]\n else:\n val = opsim_data.get(item, default)\n return val\n\n # Get a few items needed more than once first\n mjd = parse('mjd', float, 51444.0)\n mjd_obs = parse('observationStartMJD', float, mjd)\n mjd_end = mjd_obs + exptime/86400.0\n seqnum = parse('seqnum', int, 0)\n ratel = parse('fieldRA', float, 0.0)\n dectel = parse('fieldDec', float, 0.0)\n airmass = parse('airmass', float, 'N/A')\n\n # Now construct the image header\n image.header['MJD'] = mjd\n image.header['MJD-OBS'] = mjd_obs, 'Start of exposure'\n # NOTE: Should this day be the current day,\n # or the day at the time of the most recent noon?\n dayobs = astropy.time.Time(mjd_obs, format='mjd').strftime('%Y%m%d')\n image.header['DAYOBS'] = dayobs\n image.header['SEQNUM'] = seqnum\n image.header['CONTRLLR'] = 'P', 'simulated data'\n image.header['RUNNUM'] = parse('observationId', int, -999)\n image.header['OBSID'] = f\"IM_P_{dayobs}_{seqnum:06d}\"\n image.header['IMGTYPE'] = parse('image_type', str, 'SKYEXP')\n image.header['REASON'] = parse('reason', str, 'survey')\n image.header['RATEL'] = ratel\n image.header['DECTEL'] = dectel\n with warnings.catch_warnings():\n # Silence FITS warning about long header keyword\n warnings.simplefilter('ignore')\n image.header['ROTTELPOS'] = parse('rotTelPos', float, 0.0)\n image.header['FILTER'] = parse('band', str, 'N/A/')\n image.header['CAMERA'] = base['output']['camera']\n image.header['HASTART'] = opsim_data.getHourAngle(mjd_obs, ratel)\n image.header['HAEND'] = opsim_data.getHourAngle(mjd_end, ratel)\n image.header['AMSTART'] = airmass\n image.header['AMEND'] = airmass # wrong, does anyone care?\n\n # If there's anything left in header_vals, add it to the header.\n for k in header_vals:\n image.header[k] = galsim.config.ParseValue(\n header_vals, k, base, None\n )[0]\n\n return [ image ]","def _build_container(\n self, target_image, odcs, repo_type, repo_list, terminate_event,\n scratch, record):\n self.logger.info(\"Building image: %s\" % target_image)\n cmd_list = [\"rhpkg\", \"--path=%s\" % self.distgit_dir]\n\n if self.runtime.user is not None:\n cmd_list.append(\"--user=%s\" % self.runtime.user)\n\n cmd_list += (\n \"container-build\",\n \"--nowait\",\n )\n\n if odcs:\n if odcs == 'signed':\n odcs = 'release' # convenience option for those used to the old types\n cmd_list.append('--signing-intent')\n cmd_list.append(odcs)\n else:\n if repo_type:\n repo_list = list(repo_list) # In case we get a tuple\n repo_list.append(self.metadata.cgit_url(\".oit/\" + repo_type + \".repo\"))\n\n if repo_list:\n # rhpkg supports --repo-url [URL [URL ...]]\n cmd_list.append(\"--repo-url\")\n cmd_list.extend(repo_list)\n\n if scratch:\n cmd_list.append(\"--scratch\")\n\n # Run the build with --nowait so that we can immediately get information about the brew task\n rc, out, err = exectools.cmd_gather(cmd_list)\n\n if rc != 0:\n # Probably no point in continuing.. can't contact brew?\n self.logger.info(\"Unable to create brew task: out={} ; err={}\".format(out, err))\n return False\n\n # Otherwise, we should have a brew task we can monitor listed in the stdout.\n out_lines = out.splitlines()\n\n # Look for a line like: \"Created task: 13949050\" . Extract the identifier.\n task_id = next((created_line.split(\":\")[1]).strip() for created_line in out_lines if\n created_line.startswith(\"Created task:\"))\n\n record[\"task_id\"] = task_id\n\n # Look for a line like: \"Task info: https://brewweb.engineering.redhat.com/brew/taskinfo?taskID=13948942\"\n task_url = next((info_line.split(\":\", 1)[1]).strip() for info_line in out_lines if\n info_line.startswith(\"Task info:\"))\n\n self.logger.info(\"Build running: {}\".format(task_url))\n\n record[\"task_url\"] = task_url\n\n # Now that we have the basics about the task, wait for it to complete\n error = watch_task(self.logger.info, task_id, terminate_event)\n\n # Looking for something like the following to conclude the image has already been built:\n # BuildError: Build for openshift-enterprise-base-v3.7.0-0.117.0.0 already exists, id 588961\n if error is not None and \"already exists\" in error:\n self.logger.info(\"Image already built against this dist-git commit (or version-release tag): {}\".format(target_image))\n error = None\n\n # Gather brew-logs\n logs_dir = \"%s/%s\" % (self.runtime.brew_logs_dir, self.metadata.name)\n logs_rc, _, logs_err = exectools.cmd_gather([\"brew\", \"download-logs\", \"-d\", logs_dir, task_id])\n\n if logs_rc != 0:\n self.logger.info(\"Error downloading build logs from brew for task %s: %s\" % (task_id, logs_err))\n\n if error is not None:\n # An error occurred. We don't have a viable build.\n self.logger.info(\"Error building image: {}, {}\".format(task_url, error))\n return False\n\n self.logger.info(\"Successfully built image: {} ; {}\".format(target_image, task_url))\n return True","def process_images():\n create_dirs()\n for root, dirs, files in os.walk(IN):\n for name in files:\n if name[0] == '.':\n continue\n process_image(name)","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()","def build(self, images):\n\n shape = images.get_shape().as_list()\n images = tf.reshape(images,\n [shape[0] * shape[1], shape[2], shape[3], shape[4]])\n\n with slim.arg_scope(\n [slim.conv2d, slim.fully_connected],\n activation_fn=tf.nn.relu,\n weights_regularizer=slim.l2_regularizer(self._params.weight_decay_rate),\n biases_initializer=tf.zeros_initializer()):\n with slim.arg_scope([slim.conv2d], padding='SAME'):\n # convert the image to one hot if needed.\n if self._params.to_one_hot:\n net = tf.one_hot(\n tf.squeeze(tf.to_int32(images), axis=[-1]),\n self._params.one_hot_length)\n else:\n net = images\n\n p = self._params\n # Adding conv layers with the specified configurations.\n for conv_id, kernel_stride_channel in enumerate(\n zip(p.conv_sizes, p.conv_strides, p.conv_channels)):\n kernel_size, stride, channels = kernel_stride_channel\n net = slim.conv2d(\n net,\n channels, [kernel_size, kernel_size],\n stride,\n scope='conv_{}'.format(conv_id + 1))\n\n net = slim.flatten(net)\n net = slim.fully_connected(net, self._params.embedding_size, scope='fc')\n\n output = tf.reshape(net, [shape[0], shape[1], -1])\n return output","def _build(build_context,\n image_tag,\n image_name,\n nocache,\n credstore_env=None,\n registries=None):\n _logger.info('Starting build ...')\n\n # Build the image\n docker_builder = DockerBuilder(\n build_context=build_context,\n image_name=image_name,\n image_tag=image_tag,\n credstore_env=credstore_env,\n registries=registries,\n )\n docker_builder.login_private_registries()\n if docker_builder.check_image():\n # Image already built\n docker_builder.clean()\n return docker_builder\n if not docker_builder.build(nocache=nocache):\n docker_builder.clean()\n raise BuildException('The docker image could not be built.')\n return docker_builder","def all(tag=None, env=None):\n build_images(tag=tag)\n push_images(tag=tag)\n deploy(env=env, tag=tag)","def _image(self):\n print(\"imaging\")\n self.images.append(self.device_control.image())\n yield","def docker_build(c):\n cli_tasks.docker_build.run(c)","def start_image_builder(Name=None, AppstreamAgentVersion=None):\n pass","def OnBuildPNGs(self, e):\n if (not defaults.use_tex):\n msg = \"LaTeX is disabled in the defaults.py file. To use this functionality, change the\"\n msg += \" use_tex option to True and restart the GUI.\"\n ShowMessage(msg, kind='warn')\n return\n question = \"Quantity code formulas are displayed using PNG images, which need to be generated.\"\n question += \"\\n\\n\\nImages should only be generated if they do not already exist or\"\n question += \" the quantity codes have changed, e.g., more custom outputs have been added.\"\n question += \"\\n\\n\\nThis can take ~60 sec, do you want to proceed?\"\n proceed = AskYesNo(question, title='Generate LaTeX Formula Images?')\n if (not proceed): return\n\n question = \"Choose a path where the images will be saved. The default value from defaults.py is shown.\"\n path = AskText(question, default=defaults.quantity_code_image_path, title=\"Where to store images?\")\n if (path is None): return\n defaults.quantity_code_image_path = path # user overrode this quantity, remember for later\n\n question = \"If image files already exist, do you want to overwrite them?\"\n overwrite = AskYesNo(question, title='Overwrite Existing Files?')\n\n # call render routine and display a progress bar\n Nq = len(self.mainparent.nmlpanel.output_quantities.quantities)\n offsets = list(self.mainparent.nmlpanel.output_quantities.offsets.keys())\n\n P = ProgressBar(Nq)\n P(0)\n for i,Q in enumerate(self.mainparent.nmlpanel.output_quantities.quantities):\n if (Q.name in offsets): continue\n render_tex(Q.code, Q.tex, defaults.quantity_code_image_path, overwrite=overwrite)\n\n P(i+1) # update progress bar","def combine_pictures(images):\n widths, heights = zip(*(i.size for i in images))\n\n total_width = sum(widths)\n max_height = max(heights)\n\n new_im = Image.new('RGB', (total_width, max_height))\n\n x_offset = 0\n for im in images:\n new_im.paste(im, (x_offset, 0))\n x_offset += im.size[0]\n\n new_im.save('test.jpg')\n\n return True","def building_image(img_google):\n img = cv.imread(img_google)\n img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n\n ret, thresh1 = cv.threshold(img_gray, 234, 255, cv.THRESH_BINARY) # 234 # with residential buildings 236\n ret, thresh2 = cv.threshold(img_gray, 237, 255, cv.THRESH_BINARY) # 237 without residential buildings 237\n residentiel = thresh1 - thresh2 # residential buildings in white\n\n ret, thresh3 = cv.threshold(img_gray, 247, 255, cv.THRESH_BINARY) # with commercial buildings\n ret, thresh4 = cv.threshold(img_gray, 248, 255, cv.THRESH_BINARY) # without commercial buildings\n commercial = thresh3 - thresh4 # commercial buildings in white\n # TODO 3D cause nouveaux problèmes\n ret, thresh5 = cv.threshold(img_gray, 238, 255, cv.THRESH_BINARY) # with 3D buildings 239\n ret, thresh6 = cv.threshold(img_gray, 240, 255, cv.THRESH_BINARY) # without 3D buildings 240\n building_3d = thresh5 - thresh6 # 3D buildings in white\n\n # Erase white lines\n minThick = 15 # Define minimum thickness\n se = cv.getStructuringElement(cv.MORPH_ELLIPSE, (minThick, minThick)) # define a disk element\n img_bat = 255 * cv.morphologyEx(residentiel.astype('uint8'), cv.MORPH_OPEN, se)\n img_bat += 255 * cv.morphologyEx(commercial.astype('uint8'), cv.MORPH_OPEN, se)\n img_bat += 255 * cv.morphologyEx(building_3d.astype('uint8'), cv.MORPH_OPEN, se)\n return img_bat","def append_component_images(pldm_fw_up_pkg, image_files):\n for image in image_files:\n with open(image, \"rb\") as file:\n for line in file:\n pldm_fw_up_pkg.write(line)","def build_and_push_3p_images():\n\n log.info(\n \"Building Lambda Stack docker images (these are not on DockerHub). \"\n \"FMI see https://github.com/lambdal/lambda-stack-dockerfiles\")\n run_cmd(\"\"\"\n cd /tmp &&\n (rm -rf lambda-stack-dockerfiles || true) &&\n git clone https://github.com/lambdal/lambda-stack-dockerfiles &&\n cd lambda-stack-dockerfiles &&\n git checkout d762400d61636c074533416674426a84cc4d8992 &&\n docker build -t oarphpy/lambda-stack:22.04 -f Dockerfile.jammy . &&\n docker push oarphpy/lambda-stack:22.04\n \"\"\")","def make_image():\n click.echo(\"make_image\")","def setImage(*args):","def load_images(self, files, sub_dir):\n\n for f in files:\n self.images.append(Image(f, sub_dir))","def build(parser):\n parser.add_argument(\n '-i', '--identity-file',\n help=(\n 'A SSH private key file which may be used to pull down '\n 'repositories when building.'\n ),\n )\n parser.add_argument(\n '-e', '--env',\n action='append',\n default=[],\n help=(\n 'Add environ variables to the build. These may be accessed in '\n 'the build scripts. Each variable should be of the format '\n 'KEY=VALUE. This may be used to pass in credentials required '\n 'to access private repositories. May be specified more than once.'\n ),\n )\n parser.add_argument(\n '-b', '--build-dir',\n default=os.getcwd(),\n help=(\n 'This folder should be accessible from the docker instance.'\n ),\n )\n parser.add_argument(\n '--archive',\n help=(\n 'Archive the build files into a local tarball.'\n ),\n )\n parser.add_argument(\n '--archive-only',\n action='store_true',\n default=False,\n help=(\n 'Skip tagging and building the runner image.'\n ),\n )\n parser.add_argument(\n '-t', '--tag',\n help=(\n 'Tag to apply to the built image. '\n 'This will default to the current date/time.'\n ),\n )\n parser.add_argument(\n '--no-cache',\n dest='use_cache',\n action='store_false',\n default=True,\n help=(\n 'Do not mount a cache volume when compiling the app.'\n ),\n )\n parser.add_argument(\n '--cache',\n metavar='CONTAINER:PATH',\n help=(\n 'An optional volume or location for the cache. The format is '\n '\"<volume_id>:<path>\" where the \"volume_id\" must be the '\n 'name or hash of an existing volume. The \"path\" is an absolute '\n 'path to the cache folder/volume within the build container.'\n '\\n\\n'\n 'By default a container will be created by mangling the name of '\n 'the app by appending \"__buildcache\" (e.g. \"myapp__buildcache\").'\n '\\n\\n'\n 'This option is ignored if --no-cache is specified.'\n '\\n\\n'\n 'The \"volume_id\" may be an absolute path on the host filesystem.'\n '\\n\\n'\n 'The \"path\" may be dropped, in which case it will default to '\n '/tmp/cache inside the build container.'\n '\\n\\n'\n 'Examples:'\n '\\n\\n'\n ' # custom volume with default path\\n'\n ' --cache my_cache'\n '\\n\\n'\n ' # custom path inside of volume\\n'\n ' --cache my_cache:/tmp/cache'\n '\\n\\n'\n ' # host filesystem\\n'\n ' --cache /tmp/cache'\n ),\n )\n parser.add_argument(\n '--rebuild-cache',\n action='store_true',\n default=False,\n help=(\n 'Delete any cached artifacts prior to building.'\n ),\n )\n parser.add_argument(\n '--skip-cleanup',\n action='store_true',\n default=False,\n help=(\n 'Skip removal of images and containers.'\n ),\n )\n parser.add_argument(\n 'app',\n help=(\n 'Path to an application folder with a meta.yml file'\n ),\n )","def build_container(\n self, odcs, repo_type, repo, push_to_defaults, additional_registries, terminate_event,\n scratch=False, retries=3):\n if self.org_image_name is None or self.org_version is None:\n if not os.path.isfile(os.path.join(self.distgit_dir, 'Dockerfile')):\n self.logger.info('No Dockerfile found in {}'.format(self.distgit_dir))\n else:\n self.logger.info('Unknown error loading Dockerfile information')\n return False\n\n action = \"build\"\n release = self.org_release if self.org_release is not None else '?'\n record = {\n \"dir\": self.distgit_dir,\n \"dockerfile\": \"%s/Dockerfile\" % self.distgit_dir,\n \"distgit\": self.metadata.name,\n \"image\": self.org_image_name,\n \"version\": self.org_version,\n \"release\": release,\n \"message\": \"Unknown failure\",\n \"task_id\": \"n/a\",\n \"task_url\": \"n/a\",\n \"status\": -1,\n \"push_status\": -1,\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\n }\n\n target_tag = \"-\".join((self.org_version, release))\n target_image = \":\".join((self.org_image_name, target_tag))\n\n try:\n if not scratch and self.org_release is not None \\\n and self.metadata.tag_exists(target_tag):\n self.logger.info(\"Image already built for: {}\".format(target_image))\n else:\n # If this image is FROM another group member, we need to wait on that group member\n # Use .get('from',None) since from is a reserved word.\n image_from = Model(self.config.get('from', None))\n if image_from.member is not Missing:\n self._set_wait_for(image_from.member, terminate_event)\n for builder in image_from.get('builder', []):\n if 'member' in builder:\n self._set_wait_for(builder['member'], terminate_event)\n\n # Allow an image to wait on an arbitrary image in the group. This is presently\n # just a workaround for: https://projects.engineering.redhat.com/browse/OSBS-5592\n if self.config.wait_for is not Missing:\n self._set_wait_for(self.config.wait_for, terminate_event)\n\n def wait(n):\n self.logger.info(\"Async error in image build thread [attempt #{}]\".format(n + 1))\n # Brew does not handle an immediate retry correctly, wait\n # before trying another build, terminating if interrupted.\n if terminate_event.wait(timeout=5 * 60):\n raise KeyboardInterrupt()\n\n exectools.retry(\n retries=3, wait_f=wait,\n task_f=lambda: self._build_container(\n target_image, odcs, repo_type, repo, terminate_event,\n scratch, record))\n\n # Just in case someone else is building an image, go ahead and find what was just\n # built so that push_image will have a fixed point of reference and not detect any\n # subsequent builds.\n push_version, push_release = ('','')\n if not scratch:\n _, push_version, push_release = self.metadata.get_latest_build_info()\n record[\"message\"] = \"Success\"\n record[\"status\"] = 0\n self.build_status = True\n\n except (Exception, KeyboardInterrupt):\n tb = traceback.format_exc()\n record[\"message\"] = \"Exception occurred:\\n{}\".format(tb)\n self.logger.info(\"Exception occurred during build:\\n{}\".format(tb))\n # This is designed to fall through to finally. Since this method is designed to be\n # threaded, we should not throw an exception; instead return False.\n finally:\n # Regardless of success, allow other images depending on this one to progress or fail.\n self.build_lock.release()\n\n self.push_status = True # if if never pushes, the status is True\n if not scratch and self.build_status and (push_to_defaults or additional_registries):\n # If this is a scratch build, we aren't going to be pushing. We might be able to determine the\n # image name by parsing the build log, but not worth the effort until we need scratch builds.\n # The image name for a scratch build looks something like:\n # brew-pulp-docker01.web.prod.ext.phx2.redhat.com:8888/openshift3/ose-base:rhaos-3.7-rhel-7-docker-candidate-16066-20170829214444\n\n # To ensure we don't overwhelm the system building, pull & push synchronously\n with self.runtime.mutex:\n self.push_status = False\n try:\n self.push_image([], push_to_defaults, additional_registries, version_release_tuple=(push_version, push_release))\n self.push_status = True\n except Exception as push_e:\n self.logger.info(\"Error during push after successful build: %s\" % str(push_e))\n self.push_status = False\n\n record['push_status'] = '0' if self.push_status else '-1'\n\n self.runtime.add_record(action, **record)\n return self.build_status and self.push_status","def main():\n os.makedirs(\"./img/event_generated/\", exist_ok=True)\n for category in categories():\n render_icon(category + \".png\")","def do_stage(self, images):\n\n for i, image in enumerate(images):\n pass\n # logging_tags = logs.image_config_to_tags(image, self.group_by_keywords)","def build_docker(c):\n tag = c.run('git describe', hide=True)\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\n c.run(f'docker build -t {docker_img} .')","def build(config, version_tag):\n config_dict = get_config(config)\n image_name = config_dict['IMAGE'].split(':')[0]\n image = '{}:{}'.format(image_name, version_tag)\n base_image = config_dict['BASE_IMAGE']\n\n cmd = 'docker build -t {image} --build-arg base_image={base_image} .'.format(image=image,\n base_image=base_image)\n with cd(env.project_dir):\n run(cmd)\n return image","def process_images(self):\n source_images = self.get_build_images()\n self.log.info(\"Got %s images for publishing. Processing..\", len(source_images))\n\n for image in source_images:\n self.fetch_image(image)\n\n for target in image.push_registries:\n for tag in image.release_tags:\n repository = \"%s/%s\" % (target, image.repository.name)\n self.tag_image(image, repository, tag)\n retry_count = 1\n while retry_count <= self.retry_limit:\n self.log.info(\"Pushing %s:%s to %s (%d/%d)\", repository, tag, target, retry_count, self.retry_limit)\n try:\n self.publish_image(target, repository, tag)\n break\n except ImagePushError as e:\n self.log.error(\"%s\", e.message)\n retry_count = retry_count + 1\n else:\n return False\n return True","def do_command(self, args):\n imageops = dbops.Images()\n imageops.add(args)","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)","def load_images(self):\n for image in self.gltf.images:\n self.images.append(image.load(self.path.parent))","def __build__(self,data_index=0):\n \n super(Image,self).__build__()\n # -- How to read the image\n self._build_properties = dict(\n data_index = data_index,\n header_exptime = \"EXPTIME\",\n dataslice0=\"undefined\",\n dataslice1=\"undefined\",\n bkgdbox={\"bh\":100,\"bw\":100,\"fh\":3,\"fw\":3},\n )","def RemoteBuild(self, image):\n raise NotImplementedError()","def RegisterImages(self):\n images = [(autocomp.TYPE_FUNCTION, ed_glob.ID_FUNCT_TYPE),\n (autocomp.TYPE_METHOD, ed_glob.ID_METHOD_TYPE),\n (autocomp.TYPE_PROPERTY, ed_glob.ID_PROPERTY_TYPE),\n (autocomp.TYPE_ATTRIBUTE, ed_glob.ID_ATTR_TYPE),\n (autocomp.TYPE_CLASS, ed_glob.ID_CLASS_TYPE),\n (autocomp.TYPE_VARIABLE, ed_glob.ID_VARIABLE_TYPE),\n (autocomp.TYPE_ELEMENT, ed_glob.ID_ELEM_TYPE)]\n for idx, img in images:\n bmp = wx.ArtProvider.GetBitmap(str(img), wx.ART_MENU)\n if bmp.IsOk():\n self.RegisterImage(idx, bmp)","def build():\n os.makedirs(DIST_DIR, exist_ok=True)\n\n if \"WHEEL\" in os.environ:\n whl = build_wheel()\n else:\n click.echo(\"Not building wheels.\")\n\n if \"WHEEL\" in os.environ and \"DOCKER\" in os.environ:\n # Docker image requires wheels\n build_docker_image(whl)\n else:\n click.echo(\"Not building Docker image.\")\n\n if \"PYINSTALLER\" in os.environ:\n build_pyinstaller()\n else:\n click.echo(\"Not building PyInstaller packages.\")","def _Build(self, image):\n image = _ContainerImage(image)\n build_start = time.time()\n if not FLAGS.local_container_build:\n try:\n # Build the image remotely using an image building service.\n self.RemoteBuild(image)\n self.remote_build_times[image.name] = time.time() - build_start\n return\n except NotImplementedError:\n pass\n\n self.PrePush(image)\n # Build the image locally using docker.\n build_start = time.time()\n self.LocalBuildAndPush(image)\n self.local_build_times[image.name] = time.time() - build_start"],"string":"[\n \"def build_filler_images(self):\",\n \"def _build_docker_images(self):\\n print(f\\\"+ building {len(self.neurodocker_specs)} Docker images\\\")\\n self.docker_status = []\\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\\n try:\\n print(\\\"++ building image: {}\\\".format(neurodocker_dict))\\n cg.docker_main(\\n self.working_dir,\\n neurodocker_dict,\\n sha1,\\n build_context=self.build_context,\\n )\\n self.docker_status.append(\\\"docker ok\\\")\\n except Exception as e:\\n self.docker_status.append(\\n \\\"failed to build image with SHA1 {}: {}\\\".format(sha1, e)\\n )\",\n \"def build(image_name, path='.'):\\n try:\\n doxy.images.build(path=path, tag=image_name)\\n message = '[*] Image {} built.'\\n print message.format(image_name)\\n except Exception as err:\\n print err\\n raise\",\n \"def buildImages(files, targets, type):\\n images = []\\n for file in files:\\n targets.append(file)\\n with open(file, \\\"rb\\\") as f:\\n if type == \\\"Byte\\\":\\n images.append(bytePlot(list(f.read())))\\n elif type == \\\"Markov\\\":\\n images.append(markovPlot(list(f.read())))\\n elif type == \\\"Hilbert\\\":\\n images.append(hilbertPlot(list(f.read())))\\n smp.imsave(\\\"{}.png\\\".format(file), images[-1])\\n return images, targets\",\n \"def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None, skip_build=False):\\n value_modifications = {}\\n for name, options in images.items():\\n image_path = options.get('contextPath', os.path.join('images', name))\\n image_tag = tag\\n # include chartpress.yaml itself as it can contain build args and\\n # similar that influence the image that would be built\\n paths = list(options.get('paths', [])) + [image_path, 'chartpress.yaml']\\n last_commit = last_modified_commit(*paths)\\n if tag is None:\\n if chart_version:\\n image_tag = \\\"{}-{}\\\".format(chart_version, last_commit)\\n else:\\n image_tag = last_commit\\n image_name = prefix + name\\n image_spec = '{}:{}'.format(image_name, image_tag)\\n\\n value_modifications[options['valuesPath']] = {\\n 'repository': image_name,\\n 'tag': SingleQuotedScalarString(image_tag),\\n }\\n\\n if skip_build:\\n continue\\n\\n template_namespace = {\\n 'LAST_COMMIT': last_commit,\\n 'TAG': image_tag,\\n }\\n\\n if tag or image_needs_building(image_spec):\\n build_args = render_build_args(options, template_namespace)\\n build_image(image_path, image_spec, build_args, options.get('dockerfilePath'))\\n else:\\n print(f\\\"Skipping build for {image_spec}, it already exists\\\")\\n\\n if push:\\n if tag or image_needs_pushing(image_spec):\\n check_call([\\n 'docker', 'push', image_spec\\n ])\\n else:\\n print(f\\\"Skipping push for {image_spec}, already on registry\\\")\\n return value_modifications\",\n \"def build_images(formulas, dir_images, quality=100, density=200, down_ratio=2,\\n buckets=None, n_threads=4):\\n init_dir(dir_images)\\n existing_idx = sorted(set([int(file_name.split('.')[0]) for file_name in\\n get_files(dir_images) if file_name.split('.')[-1] == \\\"png\\\"]))\\n\\n pool = Pool(n_threads)\\n result = pool.map(build_image, [(idx, form, dir_images, quality, density,\\n down_ratio, buckets) for idx, form in formulas.items()\\n if idx not in existing_idx])\\n pool.close()\\n pool.join()\\n\\n result += [(str(idx) + \\\".png\\\", idx) for idx in existing_idx]\\n\\n return result\",\n \"def _build_final_image(self, image):\\n raise NotImplementedError\",\n \"def build(path, tag):\\n print (\\\"*** BUILDING IS INITIATED\\\")\\n try:\\n response = client.images.build(path = path, tag=tag, rm=True)\\n except Exception as ex:\\n template = \\\"An exception of type {0} occured. Arguments:\\\\n{1!r}\\\"\\n message = template.format(type(ex).__name__, ex.args)\\n click.echo(message)\\n print(\\\"*** BUILD OUTPUT:\\\")\\n for object in response[1]:\\n click.echo(object)\\n click.echo('*** BUILDING FINISHED')\",\n \"def create_target_image(self, builder, target, base_image, parameters):\",\n \"def build_all_images_for_lang(lang):\\n if not args.git_checkout:\\n if args.release != \\\"master\\\":\\n print(\\n \\\"Cannot use --release without also enabling --git_checkout.\\\\n\\\"\\n )\\n sys.exit(1)\\n releases = [args.release]\\n else:\\n if args.release == \\\"all\\\":\\n releases = client_matrix.get_release_tags(lang)\\n else:\\n # Build a particular release.\\n if args.release not in [\\\"master\\\"] + client_matrix.get_release_tags(\\n lang\\n ):\\n jobset.message(\\n \\\"SKIPPED\\\",\\n \\\"%s for %s is not defined\\\" % (args.release, lang),\\n do_newline=True,\\n )\\n return []\\n releases = [args.release]\\n\\n images = []\\n for release in releases:\\n images += build_all_images_for_release(lang, release)\\n jobset.message(\\n \\\"SUCCESS\\\",\\n \\\"All docker images built for %s at %s.\\\" % (lang, releases),\\n do_newline=True,\\n )\\n return images\",\n \"def create_base_image(self, builder, template, parameters):\",\n \"def build_img_combiner(self, function_builder, builder_args):\\n \\n self.combine_images = function_builder(*builder_args)\",\n \"def build_impl(**kwargs: Any) -> None:\\n try:\\n config = configuration.create_transient_build_config(kwargs)\\n except configuration.CLIArgumentError as e:\\n print(e, file=sys.stderr)\\n sys.exit(1)\\n store = image.ImageStore(backend_dir=config.image_backend, frontend_dir=None)\\n builder = build.ImageBuilder(config, store)\\n builder.build()\\n sys.exit(0)\",\n \"def build_container(client):\\n client.images.build(path=os.path.join(os.path.abspath(\\\"\\\"), \\\"docker\\\"), tag=\\\"scrape_light\\\")\",\n \"def initImages(self):\\n pass\",\n \"def initImages(self):\\n pass\",\n \"def initImages(self):\\n pass\",\n \"def run_build_image(image_list):\\n success_image_list = list()\\n failed_image_list = list()\\n for image in image_list:\\n docker_build_success_flag = image.run_docker_build()\\n if not docker_build_success_flag:\\n failed_image_list.append(image)\\n continue\\n smoke_test_sccess_flag = image.run_smoke_test()\\n if not smoke_test_sccess_flag:\\n failed_image_list.append(image)\\n continue\\n success_image_list.append(image)\\n\\n logger.info(\\\"--- docker build summary ---\\\")\\n logger.info(\\\"following image succeed:\\\")\\n for image in success_image_list:\\n logger.info(f\\\" {image.local_identifier}\\\")\\n logger.info(\\\"following image failed:\\\")\\n for image in failed_image_list:\\n logger.info(f\\\" {image.local_identifier}\\\")\\n return success_image_list, failed_image_list\",\n \"def build():\",\n \"def __call__(self, images, targets):\\n pass\",\n \"def main():\\n me = SimpleImage(\\\"images/me.JPG\\\")\\n dinosaur = SimpleImage(\\\"images/dinosaur.jpg\\\")\\n\\n dinosaur.make_as_big_as(me)\\n combine = magic(me, dinosaur)\\n combine.show()\",\n \"def construct_image(imgs):\\n\\n # todo fill missing pieces and\\n\\n if len(imgs) == 0:\\n return None\\n # taking the first\\n w, h = imgs[0][1].size\\n img_array = order_2d(imgs)\\n x_count = len(img_array[0])\\n y_count = len(img_array)\\n height = h * y_count\\n width = w * x_count\\n new_im = Image.new('RGB', (width, height))\\n for y in range(y_count):\\n for x in range(x_count):\\n _, im = img_array[y][x]\\n new_im.paste(im, (x * w, y * h))\\n return new_im\",\n \"def main():\\n fg = SimpleImage('image_contest/me.jpg')\\n bg = SimpleImage('image_contest/house.png')\\n bg.make_as_big_as(fg)\\n combined_img = combine(bg, fg)\\n combined_img.show()\",\n \"def make_images(self):\\n self._images = [tree.to_image() for tree in self.reaction_trees]\\n self._update_route_dict(self._images, \\\"image\\\")\",\n \"def images(self, **kwargs):\\n\\n raise NotImplementedError\",\n \"def create_png_images(self):\\n if self.subject is None:\\n print Console.WARNING + 'You need to specify a subject first' + Console.ENDC\\n return\\n\\n check_dir_of = self.locations.check_dir_of\\n check_dir_of(self.locations.HISTO_PNG_U)\\n check_dir_of(self.locations.HISTO_PNG)\\n check_dir_of(self.locations.SOURCE_PNG)\\n\\n\\n\\n fmap_img = ImageUtils.load_nifti_image(self.locations.HIST_FMAP) #loading subject nifti files\\n volumes = []\\n try:\\n for s in self.locations.SOURCES:\\n volumes.append(ImageUtils.load_nifti_image(s))\\n except IOError as e:\\n print Console.FAIL + 'There are errors loading nifi files for subject %s'%self.subject + Console.ENDC\\n return False\\n \\n\\n num_slices = volumes[0].shape[2] #use first volume to check expected number of slices\\n\\n self.locations.create_empty_dir(self.locations.IMAGES_DIR)\\n\\n print 'Creating input PNGs for %s'%self.subject\\n for k, vol in enumerate(volumes):\\n for i in range(num_slices):\\n imslice = ImageUtils.data_to_bytescale_rgb(vol[:, :, i])\\n im = Image.fromarray(imslice)\\n im.save(self.locations.SOURCE_PNG % (self.locations.LABELS[k],i))\\n\\n \\n print 'Creating histology PNGs for %s'%self.subject\\n for i in range(num_slices):\\n\\n im_unscaled = ImageUtils.data_to_unscaled_rgb(fmap_img[:, :, i]); #keeps the original values\\n im_unscaled = Image.fromarray(im_unscaled)\\n im_unscaled = im_unscaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\\n im_unscaled.save(self.locations.HISTO_PNG_U % i)\\n\\n im_scaled = ImageUtils.data_to_bytescale_rgb(fmap_img[:,:,i]); # bytescaled histology\\n im_scaled = Image.fromarray(im_scaled)\\n im_scaled = im_scaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\\n im_scaled.save(self.locations.HISTO_PNG % i)\\n\\n print\\n return True\",\n \"def build_all_images_for_release(lang, release):\\n docker_images = []\\n build_jobs = []\\n\\n env = {}\\n # If we not using current tree or the sibling for grpc stack, do checkout.\\n stack_base = \\\"\\\"\\n if args.git_checkout:\\n stack_base = checkout_grpc_stack(lang, release)\\n var = {\\n \\\"go\\\": \\\"GRPC_GO_ROOT\\\",\\n \\\"java\\\": \\\"GRPC_JAVA_ROOT\\\",\\n \\\"node\\\": \\\"GRPC_NODE_ROOT\\\",\\n }.get(lang, \\\"GRPC_ROOT\\\")\\n env[var] = stack_base\\n\\n for runtime in client_matrix.get_runtimes_for_lang_release(lang, release):\\n job = build_image_jobspec(runtime, env, release, stack_base)\\n docker_images.append(job.tag)\\n build_jobs.append(job)\\n\\n jobset.message(\\\"START\\\", \\\"Building interop docker images.\\\", do_newline=True)\\n print(\\\"Jobs to run: \\\\n%s\\\\n\\\" % \\\"\\\\n\\\".join(str(j) for j in build_jobs))\\n\\n num_failures, _ = jobset.run(\\n build_jobs, newline_on_success=True, maxjobs=multiprocessing.cpu_count()\\n )\\n if num_failures:\\n jobset.message(\\n \\\"FAILED\\\", \\\"Failed to build interop docker images.\\\", do_newline=True\\n )\\n docker_images_cleanup.extend(docker_images)\\n sys.exit(1)\\n\\n jobset.message(\\n \\\"SUCCESS\\\",\\n \\\"All docker images built for %s at %s.\\\" % (lang, release),\\n do_newline=True,\\n )\\n\\n if release != \\\"master\\\":\\n commit_log = os.path.join(stack_base, \\\"commit_log\\\")\\n if os.path.exists(commit_log):\\n for image in docker_images:\\n add_files_to_image(image, [commit_log], \\\"release=%s\\\" % release)\\n return docker_images\",\n \"def build_container_image(self) -> None:\\n print_version_of_tools()\\n try:\\n self.fs_watcher.start()\\n runner = PluginsRunner(self,\\n self.plugins_conf,\\n self.plugin_files,\\n self.keep_plugins_running,\\n plugins_results=self.data.plugins_results)\\n runner.run()\\n finally:\\n self.fs_watcher.finish()\",\n \"def createAllImageFiles(poly, name) :\\n \\n for i in range(len(poly.getPaths())):\\n fileName = name + \\\"_\\\" + str(i) + \\\".dot\\\"\\n imgName = name + \\\"_\\\" + str(i) + \\\".jpg\\\"\\n \\n Command = \\\"neato -Tjpeg \\\" + fileName + \\\" -o \\\" + imgName\\n run(Command, shell=True)\",\n \"def build_image(self, df_path):\\n cmd = \\\"{} build -f {} -t {} .\\\".format(\\n self.binary, df_path, self.vars['image'])\\n LOG.info(\\\"Running: {}\\\".format(cmd))\\n res = subprocess.run(cmd, shell=True)\\n if res.returncode != 0:\\n sys.exit(2)\\n return res\",\n \"def __call__(self, images, targets):\\n return images, targets\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n epilog=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\\n )\\n parser.add_argument(\\n \\\"-d\\\", \\\"--dry-run\\\", action=\\\"store_true\\\", default=0, help=\\\"Dry run mode.\\\"\\n )\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--verbose\\\",\\n action=\\\"count\\\",\\n default=0,\\n help=\\\"Verbosity. Default is WARNING level.\\\",\\n )\\n\\n subparsers = parser.add_subparsers(help=\\\"Sub commands\\\", dest=\\\"subparser\\\")\\n subparsers.required = True\\n\\n build_parser = subparsers.add_parser(\\n \\\"build\\\",\\n description=\\\"Build an image from Dockerfile, caching image hierarchy\\\",\\n help=\\\"Build an image from a Dockerfile\\\",\\n )\\n build_parser.add_argument(\\n \\\"path\\\", metavar=\\\"PATH\\\", help=\\\"The build context directory\\\"\\n )\\n build_parser.add_argument(\\n \\\"-f\\\",\\n \\\"--file\\\",\\n help=\\\"Name of the Dockerfile. If not provided, \\\"\\n \\\"will use config.DOCKERFILE_PATH_PATTERN to compute. \\\",\\n )\\n build_parser.add_argument(\\n \\\"-v\\\",\\n \\\"--git-sha\\\",\\n required=True,\\n help=\\\"The version of code to build against, \\\" \\\"will pass as GIT_SHA variable\\\",\\n )\\n build_parser.add_argument(\\n \\\"-n\\\", \\\"--name\\\", required=True, help=\\\"The name of the image to build\\\"\\n )\\n build_parser.add_argument(\\n \\\"--build-arg\\\",\\n metavar=\\\"ARG=VALUE\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Set extra build-time variables. GIT_SHA, TIMESTAMP will be passed by default.\\\",\\n )\\n build_parser.add_argument(\\n \\\"-r\\\",\\n \\\"--raw\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Whether to use raw docker build command to build, skipping caching logic\\\",\\n )\\n build_parser.add_argument(\\n \\\"--registry\\\",\\n default=config.DOCKER_REGISTRY,\\n help=\\\"Docker registry use to determine the image identity, \\\"\\n \\\"can be set via IMAGE_BUILDER_DOCKER_REGISTRY environment variable, \\\"\\n 'or set DOCKER_REGISTRY in config.py. Default is \\\"%(default)s\\\"',\\n )\\n build_parser.add_argument(\\n \\\"-t\\\",\\n \\\"--tag-pattern\\\",\\n default=config.GIT_SHA_TAG_PATTERN,\\n help=\\\"Tag pattern, can only include one `{git_sha}` placeholder, \\\"\\n 'such as \\\"{git_sha}-new\\\". If the tag exists, we won\\\\'t rebuild it. '\\n 'Default is \\\"%(default)s\\\"',\\n )\\n build_parser.add_argument(\\n \\\"-e\\\",\\n \\\"--extra-tag\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Extra tags to tag to the final images\\\",\\n )\\n build_parser.add_argument(\\n \\\"--extra-name\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Extra name and optionally with a tag in the 'name:tag' format\\\",\\n )\\n build_parser.add_argument(\\n \\\"-o\\\", \\\"--output-hash\\\", help=\\\"The output filename of the files hash log.\\\"\\n )\\n build_parser.set_defaults(func=build)\\n\\n args = parser.parse_args()\\n if args.dry_run:\\n # DRY_RUN env will be read in image_builder.libs.process\\n os.environ[\\\"DRY_RUN\\\"] = \\\"1\\\"\\n\\n if args.func == build:\\n args.path = expand_path(args.path)\\n if args.output_hash:\\n args.output_hash = expand_path(args.output_hash)\\n\\n args.file = args.file or locate_dockerfile(args.name)\\n args.file = expand_path(args.file)\\n # set environ for main dockerfile for possibly retrieving later\\n os.environ[\\n config.DOCKERFILE_ENV_PATTERN.format(image_name=args.name)\\n ] = args.file\\n\\n # change CWD to PATH\\n os.chdir(args.path)\\n\\n if not args.registry:\\n parser.error(\\n \\\"--registry should be provied \\\"\\n \\\"or specified by IMAGE_BUILDER_DOCKER_REGISTRY environment variable or set DOCKER_REGISTRY in config.py\\\"\\n )\\n if not all(\\\"=\\\" in kv for kv in args.build_arg):\\n parser.error(\\\"--build_arg must be in ARG=VALUE format\\\")\\n\\n # set git_sha_tag\\n try:\\n args.git_sha_tag = args.tag_pattern.format(git_sha=args.git_sha)\\n except KeyError:\\n parser.error(\\n 'Wrong --tag-pattern provided. Can only include one `{git_sha}` placeholder, such as \\\"{git_sha}-new\\\"'\\n )\\n\\n # setup logging\\n level = logging.WARNING - args.verbose * 10\\n logging.basicConfig(\\n level=level, format=\\\"%(asctime)s %(name)s %(levelname)s %(message)s\\\"\\n )\\n\\n if args.output_hash:\\n h = logging.FileHandler(args.output_hash)\\n h.setLevel(logging.DEBUG)\\n h.setFormatter(logging.Formatter(\\\"%(message)s\\\"))\\n hash_logger.addHandler(h)\\n\\n # Suppress warning when we don't verify ssl\\n import urllib3\\n\\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\\n\\n return args.func(args)\",\n \"def _add_images(self, epoch, train_batch, train_outputs, valid_batch, valid_outputs):\\n raise NotImplementedError\",\n \"def build_image(image, build_args):\\n\\n subprocess.check_call(['docker', 'build', '--no-cache', '--force-rm', '-t',\\n image] + build_args)\",\n \"def _process_images(self, docname: pathlib.Path, images: List[nodes.image]) -> None:\\n logger.debug(\\\"[nbtutorial]: Processing images for %s\\\", docname)\\n\\n if len(images) == 0:\\n return\\n\\n img_dir = pathlib.Path(self.outdir, docname.parent, RESOURCE_DIR)\\n\\n if not img_dir.exists():\\n img_dir.mkdir(parents=True)\\n\\n for img in images:\\n fname = pathlib.Path(img[\\\"uri\\\"]).name\\n\\n source = pathlib.Path(self.app.confdir, img[\\\"uri\\\"])\\n destination = pathlib.Path(img_dir, fname)\\n\\n shutil.copy(source, destination)\",\n \"def build_docker(params) -> None:\\n print(\\\"Building docker image...\\\")\\n cmd = \\\"cd bg_changer && docker build --tag bg_changer . >/dev/null 2>&1\\\"\\n if os.system(cmd) == 0:\\n print(\\\" Success !\\\")\\n else:\\n print(\\\" Failure !\\\")\",\n \"def build(_):\",\n \"def gather_images():\\n # Import an empty image\\n null_img = Image.open('assests/null/null.png')\\n null_img = ImageTk.PhotoImage(null_img.resize((100,100), Image.ANTIALIAS))\\n\\n # Import image and icon for X\\n X_img = Image.open('assests/X_Assets/X.png')\\n X_icon = ImageTk.PhotoImage(X_img.resize((15, 12), Image.ANTIALIAS))\\n X_img = ImageTk.PhotoImage(X_img.resize((95, 80), Image.ANTIALIAS))\\n\\n # Import horizontally striked X\\n X_hor = Image.open('assests/X_Assets/X_hor.png')\\n X_hor = ImageTk.PhotoImage(X_hor.resize((95, 80), Image.ANTIALIAS))\\n\\n # Import vertically striked X\\n X_vert = Image.open('assests/X_Assets/X_vert.png')\\n X_vert = ImageTk.PhotoImage(X_vert.resize((95, 80), Image.ANTIALIAS))\\n\\n # Import diagonally strikedX\\n X_diag = Image.open('assests/X_Assets/X_diag.png')\\n X_diag = ImageTk.PhotoImage(X_diag.resize((95, 80), Image.ANTIALIAS))\\n\\n # Import another diagonally striked X\\n X_diag2 = Image.open('assests/X_Assets/X_diag2.png')\\n X_diag2 = ImageTk.PhotoImage(X_diag2.resize((95, 80), Image.ANTIALIAS))\\n\\n # Import image and icon for O\\n O_img = Image.open('assests/O_Assets/O.png')\\n O_icon = ImageTk.PhotoImage(O_img.resize((14, 14), Image.ANTIALIAS))\\n O_img = ImageTk.PhotoImage(O_img.resize((90, 90), Image.ANTIALIAS))\\n\\n # Import horizontally striked O\\n O_hor = Image.open('assests/O_Assets/O_hor2.png')\\n O_hor = ImageTk.PhotoImage(O_hor.resize((90, 90), Image.ANTIALIAS))\\n\\n # Import vertically striked O\\n O_vert = Image.open('assests/O_Assets/O_vert2.png')\\n O_vert = ImageTk.PhotoImage(O_vert.resize((90, 90), Image.ANTIALIAS))\\n\\n # Import diagonally striked O\\n O_diag = Image.open('assests/O_Assets/O_diag.png')\\n O_diag = ImageTk.PhotoImage(O_diag.resize((90, 90), Image.ANTIALIAS))\\n\\n # Import another diagonally striked O\\n O_diag2 = Image.open('assests/O_Assets/O_diag2.png')\\n O_diag2 = ImageTk.PhotoImage(O_diag2.resize((90, 90), Image.ANTIALIAS))\\n\\n return (null_img, X_icon, X_img, X_hor, X_vert, X_diag, X_diag2, O_icon, O_img, O_hor, O_vert, O_diag, O_diag2)\",\n \"def load_images():\\n print(\\\"[+] UPDATE - Begin loading images\\\")\\n\\n colors = [\\\"w\\\", \\\"b\\\"]\\n piece_types = [\\\"p\\\", \\\"R\\\", \\\"N\\\", \\\"B\\\", \\\"K\\\", \\\"Q\\\"]\\n for color in colors:\\n for type in piece_types:\\n piece = color + type\\n IMAGES[piece] = p.transform.scale(p.image.load(\\\"images/\\\" + piece + \\\".png\\\"), (SQ_SIZE, SQ_SIZE))\\n\\n print(\\\"[+] UPDATE - Images loaded\\\")\",\n \"def appendpics(pathofimg, w_sub, h_sub, step):\\n num = 0\\n dirlist = []\\n images = [] # images in each folder\\n for root, dirs, fileswer in os.walk(pathofimg):\\n if len(dirs)!= 0:\\n for dir in dirs:\\n dirlist.append(dir)\\n for rooert, dirwerwes, files in os.walk(pathofimg+'/'+dir):\\n for file in files:\\n if(file.endswith('.png')):\\n images.append(Image.open(pathofimg+'/'+dir+'/'+file))\\n if(len(images)==81):\\n break\\n target = montage(images, w_sub, h_sub, step)\\n target.save(pathofimg +'/'+ dir + '.png', quality=100)\\n else:\\n dir = 'Generated'\\n for file in fileswer:\\n if (file.endswith('.png')):\\n images.append(Image.open(pathofimg +'/'+ file))\\n target1 = montage(images, w_sub, h_sub, step)\\n savepath = pathofimg +'/'+ 'generated'\\n os.makedirs(savepath)\\n target1.save(savepath +'/'+ dir + '.png', quality=100)\",\n \"def builder_will_create_target_image(self, builder, target, image_id, template, parameters):\",\n \"def test_build_image(self):\\n labels = {\\\"apple\\\": \\\"red\\\", \\\"grape\\\": \\\"green\\\"}\\n self.docker.images.build(\\n path=\\\"test/python/docker/build_labels\\\",\\n labels=labels,\\n tag=\\\"labels\\\",\\n isolation=\\\"default\\\",\\n )\\n image = self.docker.images.get(\\\"labels\\\")\\n self.assertEqual(image.labels[\\\"apple\\\"], labels[\\\"apple\\\"])\\n self.assertEqual(image.labels[\\\"grape\\\"], labels[\\\"grape\\\"])\",\n \"def make_image(self):\\n\\n if self.type == 'passthrough':\\n return\\n render_template(\\n os.path.dirname(self.main_module),\\n os.path.basename(self.main_module_path),\\n self.language,\\n self.requirements,\\n self.whitelist,\\n self.type,\\n into=self.code_dir)\\n self.build()\",\n \"def main():\\n images = Images()\\n #print images.create_image_urls()\\n print images.get_image_random()\\n print images.get_image(12)\",\n \"def setup_images(self, images):\\n if isinstance(images, str):\\n images = [images]\\n self._image_list.extend(images)\",\n \"def build(self):\\n # open json, len 161,260\\n at_json = open_json(self.json_names[0])\\n link_json = open_json(self.json_names[1])\\n # if need preprocessing, do it\\n if self.args.img_preprocessing:\\n print(\\\"resize imgs\\\")\\n for i in tqdm(range(len(link_json))):\\n image_url = \\\"image/\\\" + link_json[i][\\\"image_url_4x\\\"].split('/')[-1]\\n img = Image.open(image_url)\\n img = img.resize((224, 224))\\n img.save(image_url)\\n\\n # create dataset\\n itemlen = 0\\n previd = 0\\n for i in tqdm(range(len(link_json))):\\n image_url = link_json[i][\\\"image_url_4x\\\"].split('/')[-1]\\n uid = image_url.split('-')[0]\\n if previd != uid:\\n self.label.append(list(at_json[i].values())[2:])\\n if i != 0:\\n self.itemlen.append(itemlen)\\n itemlen = 0\\n self.input.append(f\\\"{self.frontpath}dataset/image/\\\" + image_url)\\n previd = uid\\n itemlen += 1\\n self.itemlen.append(itemlen)\\n self.separate()\\n self.dataset = {\\n 'train': self.train,\\n 'validation': self.val,\\n 'test': self.test\\n }\\n\\n print('finished dataset')\",\n \"def generate_image(self):\\n pass\",\n \"def populate_image_lists():\\r\\n with os.scandir(os.path.join(dir_path, \\\"inputs\\\", \\\"type_a\\\")) as filepaths:\\r\\n for path in filepaths:\\r\\n extension = os.path.splitext(path)[1].lower()\\r\\n if extension == \\\".png\\\" or extension == \\\".jpg\\\":\\r\\n images_a.append(path.path)\\r\\n with os.scandir(os.path.join(dir_path, \\\"inputs\\\", \\\"type_b\\\")) as filepaths:\\r\\n for path in filepaths:\\r\\n extension = os.path.splitext(path)[1].lower()\\r\\n if extension == \\\".png\\\" or extension == \\\".jpg\\\":\\r\\n images_b.append(path.path)\",\n \"def getimgs():\",\n \"def build_command(self, args):\\n self._validate_common(args)\\n self._set_manifests(args)\\n\\n manifest = self._manager._recipes\\n\\n records = self._get_matching_records(args, manifest)\\n\\n if not len(records):\\n sys.exit(\\\"No matching items found.\\\")\\n\\n for record in records:\\n result = self._manager.build(record['_type'], **record)\\n print('The requested asset has been built: {}'.format(result['_path']))\\n\\n if len(records) > 1:\\n print('All files have been successfully built. Thank you.')\",\n \"def get_images_to_build(fuzzers, benchmarks):\\n images = {}\\n templates = _get_image_type_templates()\\n for fuzzer in fuzzers:\\n for benchmark in benchmarks:\\n for name_templ, obj_templ in templates.items():\\n name, obj = _instantiate_image_obj(name_templ, obj_templ,\\n fuzzer, benchmark)\\n images[name] = obj\\n return images\",\n \"def load_images(self):\\r\\n self.standing_frame = [load_image(\\\"cat1.png\\\")]\\r\\n self.walk_frames_r = [load_image(\\\"cat2.png\\\"), load_image(\\\"cat3.png\\\"),\\r\\n load_image(\\\"cat4.png\\\")]\",\n \"def describe_image_builders(Names=None, MaxResults=None, NextToken=None):\\n pass\",\n \"def new_robot_images(images): #py:new_robot_images\\n RUR._new_robot_images_(images)\",\n \"def build(obj):\\n logger.info(ASCII_LOGO)\\n logger.info(\\\"Started building SageMaker Docker image. It will take some minutes...\\\\n\\\")\\n\\n try:\\n config_file_path = os.path.join('.sagify.json')\\n if not os.path.isfile(config_file_path):\\n raise ValueError()\\n\\n config = ConfigManager(config_file_path).get_config()\\n api_build.build(\\n source_dir=config.sagify_module_dir,\\n requirements_dir=config.requirements_dir,\\n docker_tag=obj['docker_tag'],\\n image_name=config.image_name,\\n python_version=config.python_version)\\n\\n logger.info(\\\"Docker image built successfully!\\\")\\n except ValueError:\\n logger.info(\\\"This is not a sagify directory: {}\\\".format(dir))\\n sys.exit(-1)\\n except subprocess.CalledProcessError as e:\\n logger.debug(e.output)\\n raise\\n except Exception as e:\\n logger.info(\\\"{}\\\".format(e))\\n sys.exit(-1)\",\n \"def get_build_images(self) -> List[Image]:\\n images = []\\n image_names = []\\n conflicting_names = []\\n for registry_name, registry in self.registries.items():\\n # if the registry is not marked as source, skip it\\n if not registry.source:\\n continue\\n\\n images += self.get_images_from_registry(registry)\\n\\n if conflicting_names:\\n raise RuntimeError(\\\"Images found in multiple 'source' repositories: %s\\\", conflicting_names)\\n\\n return images\",\n \"def auto(cls, images):\\n raise NotImplementedError\",\n \"def build(self):\\n self._remove_swarm_keys()\\n self._remove_pod_keys()\\n self._set_image()\\n self._translate_docker_properties()\",\n \"def build_all(self):\\n self.android_build()\\n self.generate_patch_build('')\\n self.generate_specs_build()\\n self.generate_interfaces()\",\n \"def prepare():\\n sh('docker build --rm -t {image} {dir}', image=IMAGE, dir=os.path.dirname(__file__))\",\n \"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['image_dir']\\n\\n combo_df = pd.merge(cat_df, bbox_df, how='outer', on='image_name')\\n combo_df['image_name'] = combo_df['image_name'].apply(\\n lambda x: x[len('img'):-len('.jpg')])\\n labels = Labels(combo_df, img_dir, n_images_loaded=-1)\\n labels.set_data_target('raw_image', chunksize=3000)\\n return labels\",\n \"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'\\n )\\n\\n for image_name in images:\\n print(image_name, \\\"image:\\\")\\n\\n image = open_image(image_name)\\n display_image(image, \\\"Original input \\\" + image_name)\\n\\n grayscale_v = transform_colors(image)\\n display_image(grayscale_v[:,:,0], \\\"Grayscale \\\" + image_name)\\n save_image(image_name + \\\"-grayscale\\\", grayscale_v[:,:,0])\\n\\n contours_v, contours = get_contours(grayscale_v)\\n display_image(contours_v, \\\"Contours \\\" + image_name)\\n save_image(image_name + \\\"-contours\\\", contours_v)\\n\\n labeled_img, areas = get_measures(image, contours[1:])\\n display_image(labeled_img, \\\"Labeled \\\" + image_name)\\n save_image(image_name + \\\"-labeled\\\", labeled_img)\\n\\n areas_histogram(areas, image_name)\",\n \"def createImages(self, geneticInstances):\\n genomes = []\\n for geneticInstance in geneticInstances:\\n genomes.append(geneticInstance.toGenomeRepresentation())\\n generatePlantImages(genomes)\\n # We now have the output pictures. We'll get to them using the database instances' filenames\",\n \"def __call__(self, images: Union[str, List[str], \\\"Image\\\", List[\\\"Image\\\"]], **kwargs):\\n return super().__call__(images, **kwargs)\",\n \"def _build_image_names(self, num_sequence, image_count):\\n for i in self._get_image_sequence_list(image_count):\\n self._image_names.append(self._get_image_name(i))\",\n \"def combine_images(images: list) -> Image:\\n img_width = images[0][0].width\\n img_height = images[0][0].height\\n new_size = (img_width * len(images[0]), img_height * len(images))\\n new_image = Image.new('RGB', new_size)\\n\\n # Add all the images from the grid to the new, blank image\\n for rowindex, row in enumerate(images):\\n for colindex, image in enumerate(row):\\n location = (colindex * img_width, rowindex * img_height)\\n new_image.paste(image, location)\\n\\n return new_image\",\n \"def create_preset_images(self):\\n for f in sorted(self.get_files_from_data()):\\n photoInstances = {}\\n for preset in self.generator.settings[\\\"GALLERY_PRESETS\\\"]:\\n preset_dir = \\\"%s%s%s\\\" % (self.absolute_output_path,\\n os.sep, \\n preset[\\\"name\\\"])\\n photoInstances[preset[\\\"name\\\"]] = Photo(self, f, preset_dir, preset)\\n \\n self.photos.append(photoInstances)\",\n \"def buildImages(self, config, base, file_num, image_num, obj_num, ignore, logger):\\n # This is basically the same as the base class version. Just a few extra things to\\n # add to the ignore list.\\n ignore += [ 'file_name', 'dir', 'nfiles', 'det_num',\\n 'only_dets', 'readout', 'exptime', 'camera' ]\\n\\n opt = {\\n 'cosmic_ray_rate': float,\\n 'cosmic_ray_catalog': str,\\n 'header': dict\\n }\\n params, safe = galsim.config.GetAllParams(config, base, opt=opt, ignore=ignore)\\n\\n image = galsim.config.BuildImage(base, image_num, obj_num, logger=logger)\\n\\n # Add cosmic rays.\\n cosmic_ray_rate = params.get('cosmic_ray_rate', 0)\\n if cosmic_ray_rate > 0:\\n cosmic_ray_catalog = params.get('cosmic_ray_catalog', None)\\n if cosmic_ray_catalog is None:\\n cosmic_ray_catalog = os.path.join(data_dir, 'cosmic_rays_itl_2017.fits.gz')\\n if not os.path.isfile(cosmic_ray_catalog):\\n raise FileNotFoundError(f'{cosmic_ray_catalog} not found')\\n\\n logger.info('Adding cosmic rays with rate %f using %s.',\\n cosmic_ray_rate, cosmic_ray_catalog)\\n exptime = base['exptime']\\n det_name = base['det_name']\\n cosmic_rays = CosmicRays(cosmic_ray_rate, cosmic_ray_catalog)\\n rng = galsim.config.GetRNG(config, base)\\n cosmic_rays.paint(image.array, rng, exptime=exptime)\\n\\n # Add header keywords for various values written to the primary\\n # header of the simulated raw output file, so that all the needed\\n # information is in the eimage file.\\n image.header = galsim.FitsHeader()\\n exptime = base['exptime']\\n image.header['EXPTIME'] = exptime\\n image.header['DET_NAME'] = base['det_name']\\n\\n header_vals = copy.deepcopy(params.get('header', {}))\\n opsim_data = get_opsim_data(config, base)\\n\\n # Helper function to parse a value with priority:\\n # 1. from header_vals (popped from dict if present)\\n # 2. from opsim_data\\n # 3. specified default\\n def parse(item, type, default):\\n if item in header_vals:\\n val = galsim.config.ParseValue(header_vals, item, base, type)[0]\\n del header_vals[item]\\n else:\\n val = opsim_data.get(item, default)\\n return val\\n\\n # Get a few items needed more than once first\\n mjd = parse('mjd', float, 51444.0)\\n mjd_obs = parse('observationStartMJD', float, mjd)\\n mjd_end = mjd_obs + exptime/86400.0\\n seqnum = parse('seqnum', int, 0)\\n ratel = parse('fieldRA', float, 0.0)\\n dectel = parse('fieldDec', float, 0.0)\\n airmass = parse('airmass', float, 'N/A')\\n\\n # Now construct the image header\\n image.header['MJD'] = mjd\\n image.header['MJD-OBS'] = mjd_obs, 'Start of exposure'\\n # NOTE: Should this day be the current day,\\n # or the day at the time of the most recent noon?\\n dayobs = astropy.time.Time(mjd_obs, format='mjd').strftime('%Y%m%d')\\n image.header['DAYOBS'] = dayobs\\n image.header['SEQNUM'] = seqnum\\n image.header['CONTRLLR'] = 'P', 'simulated data'\\n image.header['RUNNUM'] = parse('observationId', int, -999)\\n image.header['OBSID'] = f\\\"IM_P_{dayobs}_{seqnum:06d}\\\"\\n image.header['IMGTYPE'] = parse('image_type', str, 'SKYEXP')\\n image.header['REASON'] = parse('reason', str, 'survey')\\n image.header['RATEL'] = ratel\\n image.header['DECTEL'] = dectel\\n with warnings.catch_warnings():\\n # Silence FITS warning about long header keyword\\n warnings.simplefilter('ignore')\\n image.header['ROTTELPOS'] = parse('rotTelPos', float, 0.0)\\n image.header['FILTER'] = parse('band', str, 'N/A/')\\n image.header['CAMERA'] = base['output']['camera']\\n image.header['HASTART'] = opsim_data.getHourAngle(mjd_obs, ratel)\\n image.header['HAEND'] = opsim_data.getHourAngle(mjd_end, ratel)\\n image.header['AMSTART'] = airmass\\n image.header['AMEND'] = airmass # wrong, does anyone care?\\n\\n # If there's anything left in header_vals, add it to the header.\\n for k in header_vals:\\n image.header[k] = galsim.config.ParseValue(\\n header_vals, k, base, None\\n )[0]\\n\\n return [ image ]\",\n \"def _build_container(\\n self, target_image, odcs, repo_type, repo_list, terminate_event,\\n scratch, record):\\n self.logger.info(\\\"Building image: %s\\\" % target_image)\\n cmd_list = [\\\"rhpkg\\\", \\\"--path=%s\\\" % self.distgit_dir]\\n\\n if self.runtime.user is not None:\\n cmd_list.append(\\\"--user=%s\\\" % self.runtime.user)\\n\\n cmd_list += (\\n \\\"container-build\\\",\\n \\\"--nowait\\\",\\n )\\n\\n if odcs:\\n if odcs == 'signed':\\n odcs = 'release' # convenience option for those used to the old types\\n cmd_list.append('--signing-intent')\\n cmd_list.append(odcs)\\n else:\\n if repo_type:\\n repo_list = list(repo_list) # In case we get a tuple\\n repo_list.append(self.metadata.cgit_url(\\\".oit/\\\" + repo_type + \\\".repo\\\"))\\n\\n if repo_list:\\n # rhpkg supports --repo-url [URL [URL ...]]\\n cmd_list.append(\\\"--repo-url\\\")\\n cmd_list.extend(repo_list)\\n\\n if scratch:\\n cmd_list.append(\\\"--scratch\\\")\\n\\n # Run the build with --nowait so that we can immediately get information about the brew task\\n rc, out, err = exectools.cmd_gather(cmd_list)\\n\\n if rc != 0:\\n # Probably no point in continuing.. can't contact brew?\\n self.logger.info(\\\"Unable to create brew task: out={} ; err={}\\\".format(out, err))\\n return False\\n\\n # Otherwise, we should have a brew task we can monitor listed in the stdout.\\n out_lines = out.splitlines()\\n\\n # Look for a line like: \\\"Created task: 13949050\\\" . Extract the identifier.\\n task_id = next((created_line.split(\\\":\\\")[1]).strip() for created_line in out_lines if\\n created_line.startswith(\\\"Created task:\\\"))\\n\\n record[\\\"task_id\\\"] = task_id\\n\\n # Look for a line like: \\\"Task info: https://brewweb.engineering.redhat.com/brew/taskinfo?taskID=13948942\\\"\\n task_url = next((info_line.split(\\\":\\\", 1)[1]).strip() for info_line in out_lines if\\n info_line.startswith(\\\"Task info:\\\"))\\n\\n self.logger.info(\\\"Build running: {}\\\".format(task_url))\\n\\n record[\\\"task_url\\\"] = task_url\\n\\n # Now that we have the basics about the task, wait for it to complete\\n error = watch_task(self.logger.info, task_id, terminate_event)\\n\\n # Looking for something like the following to conclude the image has already been built:\\n # BuildError: Build for openshift-enterprise-base-v3.7.0-0.117.0.0 already exists, id 588961\\n if error is not None and \\\"already exists\\\" in error:\\n self.logger.info(\\\"Image already built against this dist-git commit (or version-release tag): {}\\\".format(target_image))\\n error = None\\n\\n # Gather brew-logs\\n logs_dir = \\\"%s/%s\\\" % (self.runtime.brew_logs_dir, self.metadata.name)\\n logs_rc, _, logs_err = exectools.cmd_gather([\\\"brew\\\", \\\"download-logs\\\", \\\"-d\\\", logs_dir, task_id])\\n\\n if logs_rc != 0:\\n self.logger.info(\\\"Error downloading build logs from brew for task %s: %s\\\" % (task_id, logs_err))\\n\\n if error is not None:\\n # An error occurred. We don't have a viable build.\\n self.logger.info(\\\"Error building image: {}, {}\\\".format(task_url, error))\\n return False\\n\\n self.logger.info(\\\"Successfully built image: {} ; {}\\\".format(target_image, task_url))\\n return True\",\n \"def process_images():\\n create_dirs()\\n for root, dirs, files in os.walk(IN):\\n for name in files:\\n if name[0] == '.':\\n continue\\n process_image(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 build(self, images):\\n\\n shape = images.get_shape().as_list()\\n images = tf.reshape(images,\\n [shape[0] * shape[1], shape[2], shape[3], shape[4]])\\n\\n with slim.arg_scope(\\n [slim.conv2d, slim.fully_connected],\\n activation_fn=tf.nn.relu,\\n weights_regularizer=slim.l2_regularizer(self._params.weight_decay_rate),\\n biases_initializer=tf.zeros_initializer()):\\n with slim.arg_scope([slim.conv2d], padding='SAME'):\\n # convert the image to one hot if needed.\\n if self._params.to_one_hot:\\n net = tf.one_hot(\\n tf.squeeze(tf.to_int32(images), axis=[-1]),\\n self._params.one_hot_length)\\n else:\\n net = images\\n\\n p = self._params\\n # Adding conv layers with the specified configurations.\\n for conv_id, kernel_stride_channel in enumerate(\\n zip(p.conv_sizes, p.conv_strides, p.conv_channels)):\\n kernel_size, stride, channels = kernel_stride_channel\\n net = slim.conv2d(\\n net,\\n channels, [kernel_size, kernel_size],\\n stride,\\n scope='conv_{}'.format(conv_id + 1))\\n\\n net = slim.flatten(net)\\n net = slim.fully_connected(net, self._params.embedding_size, scope='fc')\\n\\n output = tf.reshape(net, [shape[0], shape[1], -1])\\n return output\",\n \"def _build(build_context,\\n image_tag,\\n image_name,\\n nocache,\\n credstore_env=None,\\n registries=None):\\n _logger.info('Starting build ...')\\n\\n # Build the image\\n docker_builder = DockerBuilder(\\n build_context=build_context,\\n image_name=image_name,\\n image_tag=image_tag,\\n credstore_env=credstore_env,\\n registries=registries,\\n )\\n docker_builder.login_private_registries()\\n if docker_builder.check_image():\\n # Image already built\\n docker_builder.clean()\\n return docker_builder\\n if not docker_builder.build(nocache=nocache):\\n docker_builder.clean()\\n raise BuildException('The docker image could not be built.')\\n return docker_builder\",\n \"def all(tag=None, env=None):\\n build_images(tag=tag)\\n push_images(tag=tag)\\n deploy(env=env, tag=tag)\",\n \"def _image(self):\\n print(\\\"imaging\\\")\\n self.images.append(self.device_control.image())\\n yield\",\n \"def docker_build(c):\\n cli_tasks.docker_build.run(c)\",\n \"def start_image_builder(Name=None, AppstreamAgentVersion=None):\\n pass\",\n \"def OnBuildPNGs(self, e):\\n if (not defaults.use_tex):\\n msg = \\\"LaTeX is disabled in the defaults.py file. To use this functionality, change the\\\"\\n msg += \\\" use_tex option to True and restart the GUI.\\\"\\n ShowMessage(msg, kind='warn')\\n return\\n question = \\\"Quantity code formulas are displayed using PNG images, which need to be generated.\\\"\\n question += \\\"\\\\n\\\\n\\\\nImages should only be generated if they do not already exist or\\\"\\n question += \\\" the quantity codes have changed, e.g., more custom outputs have been added.\\\"\\n question += \\\"\\\\n\\\\n\\\\nThis can take ~60 sec, do you want to proceed?\\\"\\n proceed = AskYesNo(question, title='Generate LaTeX Formula Images?')\\n if (not proceed): return\\n\\n question = \\\"Choose a path where the images will be saved. The default value from defaults.py is shown.\\\"\\n path = AskText(question, default=defaults.quantity_code_image_path, title=\\\"Where to store images?\\\")\\n if (path is None): return\\n defaults.quantity_code_image_path = path # user overrode this quantity, remember for later\\n\\n question = \\\"If image files already exist, do you want to overwrite them?\\\"\\n overwrite = AskYesNo(question, title='Overwrite Existing Files?')\\n\\n # call render routine and display a progress bar\\n Nq = len(self.mainparent.nmlpanel.output_quantities.quantities)\\n offsets = list(self.mainparent.nmlpanel.output_quantities.offsets.keys())\\n\\n P = ProgressBar(Nq)\\n P(0)\\n for i,Q in enumerate(self.mainparent.nmlpanel.output_quantities.quantities):\\n if (Q.name in offsets): continue\\n render_tex(Q.code, Q.tex, defaults.quantity_code_image_path, overwrite=overwrite)\\n\\n P(i+1) # update progress bar\",\n \"def combine_pictures(images):\\n widths, heights = zip(*(i.size for i in images))\\n\\n total_width = sum(widths)\\n max_height = max(heights)\\n\\n new_im = Image.new('RGB', (total_width, max_height))\\n\\n x_offset = 0\\n for im in images:\\n new_im.paste(im, (x_offset, 0))\\n x_offset += im.size[0]\\n\\n new_im.save('test.jpg')\\n\\n return True\",\n \"def building_image(img_google):\\n img = cv.imread(img_google)\\n img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\\n\\n ret, thresh1 = cv.threshold(img_gray, 234, 255, cv.THRESH_BINARY) # 234 # with residential buildings 236\\n ret, thresh2 = cv.threshold(img_gray, 237, 255, cv.THRESH_BINARY) # 237 without residential buildings 237\\n residentiel = thresh1 - thresh2 # residential buildings in white\\n\\n ret, thresh3 = cv.threshold(img_gray, 247, 255, cv.THRESH_BINARY) # with commercial buildings\\n ret, thresh4 = cv.threshold(img_gray, 248, 255, cv.THRESH_BINARY) # without commercial buildings\\n commercial = thresh3 - thresh4 # commercial buildings in white\\n # TODO 3D cause nouveaux problèmes\\n ret, thresh5 = cv.threshold(img_gray, 238, 255, cv.THRESH_BINARY) # with 3D buildings 239\\n ret, thresh6 = cv.threshold(img_gray, 240, 255, cv.THRESH_BINARY) # without 3D buildings 240\\n building_3d = thresh5 - thresh6 # 3D buildings in white\\n\\n # Erase white lines\\n minThick = 15 # Define minimum thickness\\n se = cv.getStructuringElement(cv.MORPH_ELLIPSE, (minThick, minThick)) # define a disk element\\n img_bat = 255 * cv.morphologyEx(residentiel.astype('uint8'), cv.MORPH_OPEN, se)\\n img_bat += 255 * cv.morphologyEx(commercial.astype('uint8'), cv.MORPH_OPEN, se)\\n img_bat += 255 * cv.morphologyEx(building_3d.astype('uint8'), cv.MORPH_OPEN, se)\\n return img_bat\",\n \"def append_component_images(pldm_fw_up_pkg, image_files):\\n for image in image_files:\\n with open(image, \\\"rb\\\") as file:\\n for line in file:\\n pldm_fw_up_pkg.write(line)\",\n \"def build_and_push_3p_images():\\n\\n log.info(\\n \\\"Building Lambda Stack docker images (these are not on DockerHub). \\\"\\n \\\"FMI see https://github.com/lambdal/lambda-stack-dockerfiles\\\")\\n run_cmd(\\\"\\\"\\\"\\n cd /tmp &&\\n (rm -rf lambda-stack-dockerfiles || true) &&\\n git clone https://github.com/lambdal/lambda-stack-dockerfiles &&\\n cd lambda-stack-dockerfiles &&\\n git checkout d762400d61636c074533416674426a84cc4d8992 &&\\n docker build -t oarphpy/lambda-stack:22.04 -f Dockerfile.jammy . &&\\n docker push oarphpy/lambda-stack:22.04\\n \\\"\\\"\\\")\",\n \"def make_image():\\n click.echo(\\\"make_image\\\")\",\n \"def setImage(*args):\",\n \"def load_images(self, files, sub_dir):\\n\\n for f in files:\\n self.images.append(Image(f, sub_dir))\",\n \"def build(parser):\\n parser.add_argument(\\n '-i', '--identity-file',\\n help=(\\n 'A SSH private key file which may be used to pull down '\\n 'repositories when building.'\\n ),\\n )\\n parser.add_argument(\\n '-e', '--env',\\n action='append',\\n default=[],\\n help=(\\n 'Add environ variables to the build. These may be accessed in '\\n 'the build scripts. Each variable should be of the format '\\n 'KEY=VALUE. This may be used to pass in credentials required '\\n 'to access private repositories. May be specified more than once.'\\n ),\\n )\\n parser.add_argument(\\n '-b', '--build-dir',\\n default=os.getcwd(),\\n help=(\\n 'This folder should be accessible from the docker instance.'\\n ),\\n )\\n parser.add_argument(\\n '--archive',\\n help=(\\n 'Archive the build files into a local tarball.'\\n ),\\n )\\n parser.add_argument(\\n '--archive-only',\\n action='store_true',\\n default=False,\\n help=(\\n 'Skip tagging and building the runner image.'\\n ),\\n )\\n parser.add_argument(\\n '-t', '--tag',\\n help=(\\n 'Tag to apply to the built image. '\\n 'This will default to the current date/time.'\\n ),\\n )\\n parser.add_argument(\\n '--no-cache',\\n dest='use_cache',\\n action='store_false',\\n default=True,\\n help=(\\n 'Do not mount a cache volume when compiling the app.'\\n ),\\n )\\n parser.add_argument(\\n '--cache',\\n metavar='CONTAINER:PATH',\\n help=(\\n 'An optional volume or location for the cache. The format is '\\n '\\\"<volume_id>:<path>\\\" where the \\\"volume_id\\\" must be the '\\n 'name or hash of an existing volume. The \\\"path\\\" is an absolute '\\n 'path to the cache folder/volume within the build container.'\\n '\\\\n\\\\n'\\n 'By default a container will be created by mangling the name of '\\n 'the app by appending \\\"__buildcache\\\" (e.g. \\\"myapp__buildcache\\\").'\\n '\\\\n\\\\n'\\n 'This option is ignored if --no-cache is specified.'\\n '\\\\n\\\\n'\\n 'The \\\"volume_id\\\" may be an absolute path on the host filesystem.'\\n '\\\\n\\\\n'\\n 'The \\\"path\\\" may be dropped, in which case it will default to '\\n '/tmp/cache inside the build container.'\\n '\\\\n\\\\n'\\n 'Examples:'\\n '\\\\n\\\\n'\\n ' # custom volume with default path\\\\n'\\n ' --cache my_cache'\\n '\\\\n\\\\n'\\n ' # custom path inside of volume\\\\n'\\n ' --cache my_cache:/tmp/cache'\\n '\\\\n\\\\n'\\n ' # host filesystem\\\\n'\\n ' --cache /tmp/cache'\\n ),\\n )\\n parser.add_argument(\\n '--rebuild-cache',\\n action='store_true',\\n default=False,\\n help=(\\n 'Delete any cached artifacts prior to building.'\\n ),\\n )\\n parser.add_argument(\\n '--skip-cleanup',\\n action='store_true',\\n default=False,\\n help=(\\n 'Skip removal of images and containers.'\\n ),\\n )\\n parser.add_argument(\\n 'app',\\n help=(\\n 'Path to an application folder with a meta.yml file'\\n ),\\n )\",\n \"def build_container(\\n self, odcs, repo_type, repo, push_to_defaults, additional_registries, terminate_event,\\n scratch=False, retries=3):\\n if self.org_image_name is None or self.org_version is None:\\n if not os.path.isfile(os.path.join(self.distgit_dir, 'Dockerfile')):\\n self.logger.info('No Dockerfile found in {}'.format(self.distgit_dir))\\n else:\\n self.logger.info('Unknown error loading Dockerfile information')\\n return False\\n\\n action = \\\"build\\\"\\n release = self.org_release if self.org_release is not None else '?'\\n record = {\\n \\\"dir\\\": self.distgit_dir,\\n \\\"dockerfile\\\": \\\"%s/Dockerfile\\\" % self.distgit_dir,\\n \\\"distgit\\\": self.metadata.name,\\n \\\"image\\\": self.org_image_name,\\n \\\"version\\\": self.org_version,\\n \\\"release\\\": release,\\n \\\"message\\\": \\\"Unknown failure\\\",\\n \\\"task_id\\\": \\\"n/a\\\",\\n \\\"task_url\\\": \\\"n/a\\\",\\n \\\"status\\\": -1,\\n \\\"push_status\\\": -1,\\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\\n }\\n\\n target_tag = \\\"-\\\".join((self.org_version, release))\\n target_image = \\\":\\\".join((self.org_image_name, target_tag))\\n\\n try:\\n if not scratch and self.org_release is not None \\\\\\n and self.metadata.tag_exists(target_tag):\\n self.logger.info(\\\"Image already built for: {}\\\".format(target_image))\\n else:\\n # If this image is FROM another group member, we need to wait on that group member\\n # Use .get('from',None) since from is a reserved word.\\n image_from = Model(self.config.get('from', None))\\n if image_from.member is not Missing:\\n self._set_wait_for(image_from.member, terminate_event)\\n for builder in image_from.get('builder', []):\\n if 'member' in builder:\\n self._set_wait_for(builder['member'], terminate_event)\\n\\n # Allow an image to wait on an arbitrary image in the group. This is presently\\n # just a workaround for: https://projects.engineering.redhat.com/browse/OSBS-5592\\n if self.config.wait_for is not Missing:\\n self._set_wait_for(self.config.wait_for, terminate_event)\\n\\n def wait(n):\\n self.logger.info(\\\"Async error in image build thread [attempt #{}]\\\".format(n + 1))\\n # Brew does not handle an immediate retry correctly, wait\\n # before trying another build, terminating if interrupted.\\n if terminate_event.wait(timeout=5 * 60):\\n raise KeyboardInterrupt()\\n\\n exectools.retry(\\n retries=3, wait_f=wait,\\n task_f=lambda: self._build_container(\\n target_image, odcs, repo_type, repo, terminate_event,\\n scratch, record))\\n\\n # Just in case someone else is building an image, go ahead and find what was just\\n # built so that push_image will have a fixed point of reference and not detect any\\n # subsequent builds.\\n push_version, push_release = ('','')\\n if not scratch:\\n _, push_version, push_release = self.metadata.get_latest_build_info()\\n record[\\\"message\\\"] = \\\"Success\\\"\\n record[\\\"status\\\"] = 0\\n self.build_status = True\\n\\n except (Exception, KeyboardInterrupt):\\n tb = traceback.format_exc()\\n record[\\\"message\\\"] = \\\"Exception occurred:\\\\n{}\\\".format(tb)\\n self.logger.info(\\\"Exception occurred during build:\\\\n{}\\\".format(tb))\\n # This is designed to fall through to finally. Since this method is designed to be\\n # threaded, we should not throw an exception; instead return False.\\n finally:\\n # Regardless of success, allow other images depending on this one to progress or fail.\\n self.build_lock.release()\\n\\n self.push_status = True # if if never pushes, the status is True\\n if not scratch and self.build_status and (push_to_defaults or additional_registries):\\n # If this is a scratch build, we aren't going to be pushing. We might be able to determine the\\n # image name by parsing the build log, but not worth the effort until we need scratch builds.\\n # The image name for a scratch build looks something like:\\n # brew-pulp-docker01.web.prod.ext.phx2.redhat.com:8888/openshift3/ose-base:rhaos-3.7-rhel-7-docker-candidate-16066-20170829214444\\n\\n # To ensure we don't overwhelm the system building, pull & push synchronously\\n with self.runtime.mutex:\\n self.push_status = False\\n try:\\n self.push_image([], push_to_defaults, additional_registries, version_release_tuple=(push_version, push_release))\\n self.push_status = True\\n except Exception as push_e:\\n self.logger.info(\\\"Error during push after successful build: %s\\\" % str(push_e))\\n self.push_status = False\\n\\n record['push_status'] = '0' if self.push_status else '-1'\\n\\n self.runtime.add_record(action, **record)\\n return self.build_status and self.push_status\",\n \"def main():\\n os.makedirs(\\\"./img/event_generated/\\\", exist_ok=True)\\n for category in categories():\\n render_icon(category + \\\".png\\\")\",\n \"def do_stage(self, images):\\n\\n for i, image in enumerate(images):\\n pass\\n # logging_tags = logs.image_config_to_tags(image, self.group_by_keywords)\",\n \"def build_docker(c):\\n tag = c.run('git describe', hide=True)\\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\\n c.run(f'docker build -t {docker_img} .')\",\n \"def build(config, version_tag):\\n config_dict = get_config(config)\\n image_name = config_dict['IMAGE'].split(':')[0]\\n image = '{}:{}'.format(image_name, version_tag)\\n base_image = config_dict['BASE_IMAGE']\\n\\n cmd = 'docker build -t {image} --build-arg base_image={base_image} .'.format(image=image,\\n base_image=base_image)\\n with cd(env.project_dir):\\n run(cmd)\\n return image\",\n \"def process_images(self):\\n source_images = self.get_build_images()\\n self.log.info(\\\"Got %s images for publishing. Processing..\\\", len(source_images))\\n\\n for image in source_images:\\n self.fetch_image(image)\\n\\n for target in image.push_registries:\\n for tag in image.release_tags:\\n repository = \\\"%s/%s\\\" % (target, image.repository.name)\\n self.tag_image(image, repository, tag)\\n retry_count = 1\\n while retry_count <= self.retry_limit:\\n self.log.info(\\\"Pushing %s:%s to %s (%d/%d)\\\", repository, tag, target, retry_count, self.retry_limit)\\n try:\\n self.publish_image(target, repository, tag)\\n break\\n except ImagePushError as e:\\n self.log.error(\\\"%s\\\", e.message)\\n retry_count = retry_count + 1\\n else:\\n return False\\n return True\",\n \"def do_command(self, args):\\n imageops = dbops.Images()\\n imageops.add(args)\",\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 load_images(self):\\n for image in self.gltf.images:\\n self.images.append(image.load(self.path.parent))\",\n \"def __build__(self,data_index=0):\\n \\n super(Image,self).__build__()\\n # -- How to read the image\\n self._build_properties = dict(\\n data_index = data_index,\\n header_exptime = \\\"EXPTIME\\\",\\n dataslice0=\\\"undefined\\\",\\n dataslice1=\\\"undefined\\\",\\n bkgdbox={\\\"bh\\\":100,\\\"bw\\\":100,\\\"fh\\\":3,\\\"fw\\\":3},\\n )\",\n \"def RemoteBuild(self, image):\\n raise NotImplementedError()\",\n \"def RegisterImages(self):\\n images = [(autocomp.TYPE_FUNCTION, ed_glob.ID_FUNCT_TYPE),\\n (autocomp.TYPE_METHOD, ed_glob.ID_METHOD_TYPE),\\n (autocomp.TYPE_PROPERTY, ed_glob.ID_PROPERTY_TYPE),\\n (autocomp.TYPE_ATTRIBUTE, ed_glob.ID_ATTR_TYPE),\\n (autocomp.TYPE_CLASS, ed_glob.ID_CLASS_TYPE),\\n (autocomp.TYPE_VARIABLE, ed_glob.ID_VARIABLE_TYPE),\\n (autocomp.TYPE_ELEMENT, ed_glob.ID_ELEM_TYPE)]\\n for idx, img in images:\\n bmp = wx.ArtProvider.GetBitmap(str(img), wx.ART_MENU)\\n if bmp.IsOk():\\n self.RegisterImage(idx, bmp)\",\n \"def build():\\n os.makedirs(DIST_DIR, exist_ok=True)\\n\\n if \\\"WHEEL\\\" in os.environ:\\n whl = build_wheel()\\n else:\\n click.echo(\\\"Not building wheels.\\\")\\n\\n if \\\"WHEEL\\\" in os.environ and \\\"DOCKER\\\" in os.environ:\\n # Docker image requires wheels\\n build_docker_image(whl)\\n else:\\n click.echo(\\\"Not building Docker image.\\\")\\n\\n if \\\"PYINSTALLER\\\" in os.environ:\\n build_pyinstaller()\\n else:\\n click.echo(\\\"Not building PyInstaller packages.\\\")\",\n \"def _Build(self, image):\\n image = _ContainerImage(image)\\n build_start = time.time()\\n if not FLAGS.local_container_build:\\n try:\\n # Build the image remotely using an image building service.\\n self.RemoteBuild(image)\\n self.remote_build_times[image.name] = time.time() - build_start\\n return\\n except NotImplementedError:\\n pass\\n\\n self.PrePush(image)\\n # Build the image locally using docker.\\n build_start = time.time()\\n self.LocalBuildAndPush(image)\\n self.local_build_times[image.name] = time.time() - build_start\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7279872","0.7093156","0.7073595","0.6907964","0.67056745","0.66842973","0.66243804","0.66157806","0.6573795","0.651644","0.64935124","0.64864373","0.6419387","0.63917524","0.63883257","0.63883257","0.63883257","0.6385622","0.637723","0.63701296","0.6363489","0.6333686","0.62991834","0.6245393","0.6227533","0.61987853","0.61734074","0.6140724","0.61337304","0.61331666","0.6132477","0.6077943","0.60681075","0.6061988","0.6060921","0.6034017","0.6021998","0.60207933","0.6016777","0.59952927","0.5989262","0.5975513","0.59733444","0.5970079","0.59618753","0.59523726","0.5950296","0.5948791","0.5948226","0.5924448","0.5919748","0.5919666","0.59182507","0.5912336","0.5898837","0.5832139","0.5831287","0.58209485","0.58197474","0.58088195","0.58028334","0.57910484","0.5783557","0.5780843","0.5774229","0.5772977","0.5771029","0.5770644","0.5759813","0.57591003","0.5747662","0.5741603","0.5741558","0.5737665","0.5732146","0.57320035","0.5730953","0.57240677","0.57173896","0.57171595","0.5716572","0.57102525","0.56991416","0.5695643","0.56868255","0.5683276","0.5682162","0.56782436","0.5672556","0.5668354","0.5663098","0.566115","0.56600535","0.5655683","0.5655293","0.565462","0.56498915","0.5635912","0.5634421","0.56338626"],"string":"[\n \"0.7279872\",\n \"0.7093156\",\n \"0.7073595\",\n \"0.6907964\",\n \"0.67056745\",\n \"0.66842973\",\n \"0.66243804\",\n \"0.66157806\",\n \"0.6573795\",\n \"0.651644\",\n \"0.64935124\",\n \"0.64864373\",\n \"0.6419387\",\n \"0.63917524\",\n \"0.63883257\",\n \"0.63883257\",\n \"0.63883257\",\n \"0.6385622\",\n \"0.637723\",\n \"0.63701296\",\n \"0.6363489\",\n \"0.6333686\",\n \"0.62991834\",\n \"0.6245393\",\n \"0.6227533\",\n \"0.61987853\",\n \"0.61734074\",\n \"0.6140724\",\n \"0.61337304\",\n \"0.61331666\",\n \"0.6132477\",\n \"0.6077943\",\n \"0.60681075\",\n \"0.6061988\",\n \"0.6060921\",\n \"0.6034017\",\n \"0.6021998\",\n \"0.60207933\",\n \"0.6016777\",\n \"0.59952927\",\n \"0.5989262\",\n \"0.5975513\",\n \"0.59733444\",\n \"0.5970079\",\n \"0.59618753\",\n \"0.59523726\",\n \"0.5950296\",\n \"0.5948791\",\n \"0.5948226\",\n \"0.5924448\",\n \"0.5919748\",\n \"0.5919666\",\n \"0.59182507\",\n \"0.5912336\",\n \"0.5898837\",\n \"0.5832139\",\n \"0.5831287\",\n \"0.58209485\",\n \"0.58197474\",\n \"0.58088195\",\n \"0.58028334\",\n \"0.57910484\",\n \"0.5783557\",\n \"0.5780843\",\n \"0.5774229\",\n \"0.5772977\",\n \"0.5771029\",\n \"0.5770644\",\n \"0.5759813\",\n \"0.57591003\",\n \"0.5747662\",\n \"0.5741603\",\n \"0.5741558\",\n \"0.5737665\",\n \"0.5732146\",\n \"0.57320035\",\n \"0.5730953\",\n \"0.57240677\",\n \"0.57173896\",\n \"0.57171595\",\n \"0.5716572\",\n \"0.57102525\",\n \"0.56991416\",\n \"0.5695643\",\n \"0.56868255\",\n \"0.5683276\",\n \"0.5682162\",\n \"0.56782436\",\n \"0.5672556\",\n \"0.5668354\",\n \"0.5663098\",\n \"0.566115\",\n \"0.56600535\",\n \"0.5655683\",\n \"0.5655293\",\n \"0.565462\",\n \"0.56498915\",\n \"0.5635912\",\n \"0.5634421\",\n \"0.56338626\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7616135"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":80390,"cells":{"query":{"kind":"string","value":"execute the cleanup stage"},"document":{"kind":"string","value":"def cleanup(tag, keepimg, builder):\n manager = Manager('cleanup', tag, keepimg=keepimg,\n builder_hostname=builder)\n manager.run()"},"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 cleanup():","def cleanup(self):\r\n logging.info(\"entered the cleanup\")","def cleanup(self):","def cleanup(self):","def cleanup(self):","def cleanup(self):\n\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup (self):\n pass","def cleanup(self):\r\n pass","def cleanup(self):\r\n pass","def cleanup(self):\r\n pass","def cleanup(self, *args, **kwargs):","def _final_cleanup(self):\n # Clean up and remove the temporary gisdbase\n self._cleanup()\n # Remove resource directories\n if \"error\" in self.run_state or \"terminated\" in self.run_state:\n self.storage_interface.remove_resources()","def post_cleanup(self):\n pass","def clean_up(self) -> None:\n print('Doing some clean-up work...')","def cleanup(ctx):\n pass","def cleanup(self):\r\n print(\"Cleanup not implemented\")","def cleanup():\n management.call_command('cleanup')","def cleanup(self):\n raise NotImplementedError","def _cleanup(self):\n pass","def finalize(self):\n print('Cleaning up...')","def postRunCleanup(self):\n self.logDesc(\"Post Run Cleanup\")","def cleanup(self):\n raise NotImplementedError()","def cleanup(self):\n raise NotImplementedError()","def final_cleanup(self):\n raise NotImplementedError()","def cleanup(self):\n self._tmp_obj.cleanup()","def cleanup(self,result):\n pass","def cleanUp():\n pass","async def cleanup(self):\n pass","def cleanup(self):\n\n print \"Cleaning up...\",\n sys.stdout.flush()\n\n builddir = os.path.join(self.build)\n\n comm = 'rm -rf '+builddir\n #+' '+libdir+' '+logdir\n (output, error, retz) = runShellCommand(comm)\n\n print \"done.\"","def cleanup(self) -> None:\n raise NotImplementedError()","def cleanup(self):\n self._faucet_collector.cleanup()","def agent_cleanup():\n\t# clean up\n\n\treturn","def cleanup(self):\n superClass.cleanup(self)\n # TODO Release resources and cleanup stuff here","def cleanup(self):\n\t\tself.pb.cleanup()\n\t\tsys.exit()","def cleanup(self):\n log.info(\"== Stage 7: Cleanup ==\")\n # Close current connection to free up all the temporary resource\n # and locks\n cleanup_start_time = time.time()\n try:\n self.rename_back()\n self.start_slave_sql()\n if self.is_myrocks_table and self.is_myrocks_ttl_table:\n self.enable_ttl_for_myrocks()\n self.release_osc_lock()\n self.close_conn()\n except Exception:\n log.exception(\n \"Ignore following exception, because we want to try our \"\n \"best to cleanup, and free disk space:\"\n )\n self._cleanup_payload.mysql_user = self.mysql_user\n self._cleanup_payload.mysql_pass = self.mysql_pass\n self._cleanup_payload.socket = self.socket\n self._cleanup_payload.get_conn_func = self.get_conn_func\n self._cleanup_payload.cleanup(self._current_db)\n self.stats[\"time_in_cleanup\"] = time.time() - cleanup_start_time","def cleanup(self):\n if self.cleanup_function:\n self.cleanup_function(self.data)","def agent_cleanup():\n\treturn","def on_cleanup(self):\n raise NotImplementedError","def cleanup(self):\n self.GP.cleanup()","def CleanUp(self):\n self.cmd.CleanUp()","async def clean_up(self) -> None:","def postRunCleanup(self):\n self.logDesc(\"Post Run Cleanup\")\n self.logout()","def postRunCleanup(self):\n self.logDesc(\"Post Run Cleanup\")\n self.logout()","def cleanup(self):\n self.log.debug('upm - in upm cleanup()')\n # Add resource setup code here","def postRunCleanup(self):\n self.logDesc(\"Post Run Cleanup\")\n #logout of application\n self.logout()","def cleanup_processor(self):\n pass","def agent_cleanup():\n # clean up\n return","def agent_cleanup():\n # clean up\n return","def agent_cleanup(self):\n pass","def cleanUp(self):\r\n pass","def cleanup(self):\n logger.debug('Beginning cleanup ...')\n self.stop()\n\n #Clear subscriptions\n for sub, dev in self.subs.items():\n dev.clear_sub(sub)\n\n #Clear databases\n self.subs.clear()\n self.cmds.clear()\n\n #Remove pcaspy internals\n self._thread = None\n self.driver = None\n self.server = None\n logger.info('Cleanup finished')","def cleanup():\n dist.destroy_process_group()","def cleanup(self):\n return True;","def cleanup(self):\n self._write_transaction(tx.cleanup)","def clean_up(self):\n dist.destroy_process_group()","def cleanup(self):\r\n for action in self._actions:\r\n action.cleanup()","def cleanup(self):\n raise NotImplementedError(\"Cleanup not supported for servers\")","def cleanup(self):\n super(Test200SmartSanityDownload004, self).cleanup()","def cleanup(self):\n self.all_wav_to_mp3()\n self.past_songs_db.close()\n self.move_tracks_to_music_folder( )\n self.delete_leftovers()\n print \"Cleanup finished\"","def cleanup(self):\n logging.debug(\"TrackerPlugin.cleanup\")","def __del__(self):\r\n self.cleanup()","def cleanup(self):\n #\n # TODO: Implement this if needed, be symmetrical with prepare(...)\n #\n client.cleanup(self)","def tearDown(self):\n self.tmp.cleanup()","def _clean_up(self):","def do_cleanup_cruft(self): # pragma: no cover\n\n for cruft in self.get_cruft():\n cruft.cleanup()\n self.post_cleanup()","def cleanUp(self):\n self.dirMonitor.stop()\n self.filesList.cleanUp()","def cleanup(self):\n with hide(\"output\", \"warnings\", \"running\"):\n self.stop_all()\n self._execute_standard(\"rm -rf {model_repo}\".format(model_repo=MODEL_REPO))\n self._execute_root(\"docker rmi --force $(docker images -q)\", warn_only=True)\n self._execute_root(\"docker network rm clipper_nw\", warn_only=True)","def cleanup(self):\n logging.debug(\"cleanup called\")\n self.delete_networks()\n self.delete_machines()","def __del__(self):\n self._cleanup()","def cleanup(self, action):\n pass","def __exit__(self, exc_type, exc_value, traceback):\n\t\tself.delete_extracted()\n\t\tself.delete()","def cleanupResources():\n None","def postShutdown(self):\r\n self._network.cleanUp()\r\n self._balancer.cleanUp()\r\n self._distributor.cleanUp()","def _cleanup(self):\n # delete stdout/stderr\n if os.path.isfile(self.stdout):\n os.unlink(self.stdout)","def cleanup(self):\n self.log.debug('RFSwitch - in RFSwitch cleanup()')\n # Add resource setup code here\n print(\"Calling RFSwitch:cleanup\")","def cleanup():\n logger.critical(\"Program termination cleanup routine executing.\")\n # Using os._exit() to fix a bug in subprocess.popen that causes the\n # interpreter to hang after on regular sys.exit, exit, or quit call.\n os._exit(0)","def clean_up(self):\n\t\tpass","def finalizer():\n for resource_type in pods, pvcs, storageclasses, secrets:\n for resource in resource_type:\n resource.delete()\n resource.ocp.wait_for_delete(resource.name)\n if pools:\n # Delete only the RBD pool\n pools[0].delete()\n if projects:\n for project in projects:\n project.delete(resource_name=project.namespace)\n project.wait_for_delete(project.namespace)","def clean_up(self):\n # TODO: Implement if needed\n pass","def clean_up(self):\n pass","def clean_up(self):\n pass","def __exit__(self, exc_type, exc_val, exc_tb):\n printy(\"Cleaning after myself...\")\n self.key.delete()\n if self.instance:\n self.instance.terminate()\n # wait for the machine to terminate\n self.wait_for_status(48)\n\n self.sec_grp.delete()\n os.remove(self.key.name + \".pem\")\n printy(\"Builder teardown complete\")","def cleanup(self):\n process_handler.terminate_root_and_child_processes(self._proc.pid)\n self._read_thread.join()\n if self._data_dir:\n shutil.rmtree(self._data_dir, ignore_errors=True)","def _cleanup(self):\n os.system(\"rm -r %s/*\" %(self._snippet_index_dir))\n os.system(\"rm %s/*\" %(self._para_dir))\n os.system(\"rm %s/*\" %(self._temp_dir))\n os.system(\"rm %s/*\" %(self._snippet_result_dir))","def cleanup(self):\n if self.cleanup_allowed:\n shutil.rmtree(self.out_dir)\n self.train_df, self.valid_df, self.test_df = None, None, None","def __exit__(self, type, value, tb):\n self._with_count -= 1\n if self._with_count > 0:\n return\n\n logging.info('Cleaning up Amber root: ' + self._amber_root)\n shutil.rmtree(self._amber_root)\n self._amber_root = None\n\n self._UnregisterAmberRepository()\n self._pm_serve_task.kill()\n self._pm_serve_task = None","def __exit__(self, exc_type, exc_value, traceback):\n self._cleanup()","def after_scenario(context, _):\n context.backup_root_raw.cleanup()","def finalize():","def finalize():"],"string":"[\n \"def cleanup():\",\n \"def cleanup(self):\\r\\n logging.info(\\\"entered the cleanup\\\")\",\n \"def cleanup(self):\",\n \"def cleanup(self):\",\n \"def cleanup(self):\",\n \"def cleanup(self):\\n\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup (self):\\n pass\",\n \"def cleanup(self):\\r\\n pass\",\n \"def cleanup(self):\\r\\n pass\",\n \"def cleanup(self):\\r\\n pass\",\n \"def cleanup(self, *args, **kwargs):\",\n \"def _final_cleanup(self):\\n # Clean up and remove the temporary gisdbase\\n self._cleanup()\\n # Remove resource directories\\n if \\\"error\\\" in self.run_state or \\\"terminated\\\" in self.run_state:\\n self.storage_interface.remove_resources()\",\n \"def post_cleanup(self):\\n pass\",\n \"def clean_up(self) -> None:\\n print('Doing some clean-up work...')\",\n \"def cleanup(ctx):\\n pass\",\n \"def cleanup(self):\\r\\n print(\\\"Cleanup not implemented\\\")\",\n \"def cleanup():\\n management.call_command('cleanup')\",\n \"def cleanup(self):\\n raise NotImplementedError\",\n \"def _cleanup(self):\\n pass\",\n \"def finalize(self):\\n print('Cleaning up...')\",\n \"def postRunCleanup(self):\\n self.logDesc(\\\"Post Run Cleanup\\\")\",\n \"def cleanup(self):\\n raise NotImplementedError()\",\n \"def cleanup(self):\\n raise NotImplementedError()\",\n \"def final_cleanup(self):\\n raise NotImplementedError()\",\n \"def cleanup(self):\\n self._tmp_obj.cleanup()\",\n \"def cleanup(self,result):\\n pass\",\n \"def cleanUp():\\n pass\",\n \"async def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n\\n print \\\"Cleaning up...\\\",\\n sys.stdout.flush()\\n\\n builddir = os.path.join(self.build)\\n\\n comm = 'rm -rf '+builddir\\n #+' '+libdir+' '+logdir\\n (output, error, retz) = runShellCommand(comm)\\n\\n print \\\"done.\\\"\",\n \"def cleanup(self) -> None:\\n raise NotImplementedError()\",\n \"def cleanup(self):\\n self._faucet_collector.cleanup()\",\n \"def agent_cleanup():\\n\\t# clean up\\n\\n\\treturn\",\n \"def cleanup(self):\\n superClass.cleanup(self)\\n # TODO Release resources and cleanup stuff here\",\n \"def cleanup(self):\\n\\t\\tself.pb.cleanup()\\n\\t\\tsys.exit()\",\n \"def cleanup(self):\\n log.info(\\\"== Stage 7: Cleanup ==\\\")\\n # Close current connection to free up all the temporary resource\\n # and locks\\n cleanup_start_time = time.time()\\n try:\\n self.rename_back()\\n self.start_slave_sql()\\n if self.is_myrocks_table and self.is_myrocks_ttl_table:\\n self.enable_ttl_for_myrocks()\\n self.release_osc_lock()\\n self.close_conn()\\n except Exception:\\n log.exception(\\n \\\"Ignore following exception, because we want to try our \\\"\\n \\\"best to cleanup, and free disk space:\\\"\\n )\\n self._cleanup_payload.mysql_user = self.mysql_user\\n self._cleanup_payload.mysql_pass = self.mysql_pass\\n self._cleanup_payload.socket = self.socket\\n self._cleanup_payload.get_conn_func = self.get_conn_func\\n self._cleanup_payload.cleanup(self._current_db)\\n self.stats[\\\"time_in_cleanup\\\"] = time.time() - cleanup_start_time\",\n \"def cleanup(self):\\n if self.cleanup_function:\\n self.cleanup_function(self.data)\",\n \"def agent_cleanup():\\n\\treturn\",\n \"def on_cleanup(self):\\n raise NotImplementedError\",\n \"def cleanup(self):\\n self.GP.cleanup()\",\n \"def CleanUp(self):\\n self.cmd.CleanUp()\",\n \"async def clean_up(self) -> None:\",\n \"def postRunCleanup(self):\\n self.logDesc(\\\"Post Run Cleanup\\\")\\n self.logout()\",\n \"def postRunCleanup(self):\\n self.logDesc(\\\"Post Run Cleanup\\\")\\n self.logout()\",\n \"def cleanup(self):\\n self.log.debug('upm - in upm cleanup()')\\n # Add resource setup code here\",\n \"def postRunCleanup(self):\\n self.logDesc(\\\"Post Run Cleanup\\\")\\n #logout of application\\n self.logout()\",\n \"def cleanup_processor(self):\\n pass\",\n \"def agent_cleanup():\\n # clean up\\n return\",\n \"def agent_cleanup():\\n # clean up\\n return\",\n \"def agent_cleanup(self):\\n pass\",\n \"def cleanUp(self):\\r\\n pass\",\n \"def cleanup(self):\\n logger.debug('Beginning cleanup ...')\\n self.stop()\\n\\n #Clear subscriptions\\n for sub, dev in self.subs.items():\\n dev.clear_sub(sub)\\n\\n #Clear databases\\n self.subs.clear()\\n self.cmds.clear()\\n\\n #Remove pcaspy internals\\n self._thread = None\\n self.driver = None\\n self.server = None\\n logger.info('Cleanup finished')\",\n \"def cleanup():\\n dist.destroy_process_group()\",\n \"def cleanup(self):\\n return True;\",\n \"def cleanup(self):\\n self._write_transaction(tx.cleanup)\",\n \"def clean_up(self):\\n dist.destroy_process_group()\",\n \"def cleanup(self):\\r\\n for action in self._actions:\\r\\n action.cleanup()\",\n \"def cleanup(self):\\n raise NotImplementedError(\\\"Cleanup not supported for servers\\\")\",\n \"def cleanup(self):\\n super(Test200SmartSanityDownload004, self).cleanup()\",\n \"def cleanup(self):\\n self.all_wav_to_mp3()\\n self.past_songs_db.close()\\n self.move_tracks_to_music_folder( )\\n self.delete_leftovers()\\n print \\\"Cleanup finished\\\"\",\n \"def cleanup(self):\\n logging.debug(\\\"TrackerPlugin.cleanup\\\")\",\n \"def __del__(self):\\r\\n self.cleanup()\",\n \"def cleanup(self):\\n #\\n # TODO: Implement this if needed, be symmetrical with prepare(...)\\n #\\n client.cleanup(self)\",\n \"def tearDown(self):\\n self.tmp.cleanup()\",\n \"def _clean_up(self):\",\n \"def do_cleanup_cruft(self): # pragma: no cover\\n\\n for cruft in self.get_cruft():\\n cruft.cleanup()\\n self.post_cleanup()\",\n \"def cleanUp(self):\\n self.dirMonitor.stop()\\n self.filesList.cleanUp()\",\n \"def cleanup(self):\\n with hide(\\\"output\\\", \\\"warnings\\\", \\\"running\\\"):\\n self.stop_all()\\n self._execute_standard(\\\"rm -rf {model_repo}\\\".format(model_repo=MODEL_REPO))\\n self._execute_root(\\\"docker rmi --force $(docker images -q)\\\", warn_only=True)\\n self._execute_root(\\\"docker network rm clipper_nw\\\", warn_only=True)\",\n \"def cleanup(self):\\n logging.debug(\\\"cleanup called\\\")\\n self.delete_networks()\\n self.delete_machines()\",\n \"def __del__(self):\\n self._cleanup()\",\n \"def cleanup(self, action):\\n pass\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n\\t\\tself.delete_extracted()\\n\\t\\tself.delete()\",\n \"def cleanupResources():\\n None\",\n \"def postShutdown(self):\\r\\n self._network.cleanUp()\\r\\n self._balancer.cleanUp()\\r\\n self._distributor.cleanUp()\",\n \"def _cleanup(self):\\n # delete stdout/stderr\\n if os.path.isfile(self.stdout):\\n os.unlink(self.stdout)\",\n \"def cleanup(self):\\n self.log.debug('RFSwitch - in RFSwitch cleanup()')\\n # Add resource setup code here\\n print(\\\"Calling RFSwitch:cleanup\\\")\",\n \"def cleanup():\\n logger.critical(\\\"Program termination cleanup routine executing.\\\")\\n # Using os._exit() to fix a bug in subprocess.popen that causes the\\n # interpreter to hang after on regular sys.exit, exit, or quit call.\\n os._exit(0)\",\n \"def clean_up(self):\\n\\t\\tpass\",\n \"def finalizer():\\n for resource_type in pods, pvcs, storageclasses, secrets:\\n for resource in resource_type:\\n resource.delete()\\n resource.ocp.wait_for_delete(resource.name)\\n if pools:\\n # Delete only the RBD pool\\n pools[0].delete()\\n if projects:\\n for project in projects:\\n project.delete(resource_name=project.namespace)\\n project.wait_for_delete(project.namespace)\",\n \"def clean_up(self):\\n # TODO: Implement if needed\\n pass\",\n \"def clean_up(self):\\n pass\",\n \"def clean_up(self):\\n pass\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n printy(\\\"Cleaning after myself...\\\")\\n self.key.delete()\\n if self.instance:\\n self.instance.terminate()\\n # wait for the machine to terminate\\n self.wait_for_status(48)\\n\\n self.sec_grp.delete()\\n os.remove(self.key.name + \\\".pem\\\")\\n printy(\\\"Builder teardown complete\\\")\",\n \"def cleanup(self):\\n process_handler.terminate_root_and_child_processes(self._proc.pid)\\n self._read_thread.join()\\n if self._data_dir:\\n shutil.rmtree(self._data_dir, ignore_errors=True)\",\n \"def _cleanup(self):\\n os.system(\\\"rm -r %s/*\\\" %(self._snippet_index_dir))\\n os.system(\\\"rm %s/*\\\" %(self._para_dir))\\n os.system(\\\"rm %s/*\\\" %(self._temp_dir))\\n os.system(\\\"rm %s/*\\\" %(self._snippet_result_dir))\",\n \"def cleanup(self):\\n if self.cleanup_allowed:\\n shutil.rmtree(self.out_dir)\\n self.train_df, self.valid_df, self.test_df = None, None, None\",\n \"def __exit__(self, type, value, tb):\\n self._with_count -= 1\\n if self._with_count > 0:\\n return\\n\\n logging.info('Cleaning up Amber root: ' + self._amber_root)\\n shutil.rmtree(self._amber_root)\\n self._amber_root = None\\n\\n self._UnregisterAmberRepository()\\n self._pm_serve_task.kill()\\n self._pm_serve_task = None\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n self._cleanup()\",\n \"def after_scenario(context, _):\\n context.backup_root_raw.cleanup()\",\n \"def finalize():\",\n \"def finalize():\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.8597375\",\n \"0.8467043\",\n \"0.8215665\",\n \"0.8215665\",\n \"0.8215665\",\n \"0.81636655\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81482863\",\n \"0.81438214\",\n \"0.8142126\",\n \"0.8112586\",\n \"0.8112586\",\n \"0.81116855\",\n \"0.8010137\",\n \"0.7996655\",\n \"0.7991651\",\n \"0.79868305\",\n \"0.792883\",\n \"0.79226005\",\n \"0.78968173\",\n \"0.78014815\",\n \"0.7792027\",\n \"0.7687791\",\n \"0.7662168\",\n \"0.7662168\",\n \"0.76613843\",\n \"0.76497114\",\n \"0.7634346\",\n \"0.76277244\",\n \"0.7616557\",\n \"0.7612914\",\n \"0.7598626\",\n \"0.7578038\",\n \"0.75667363\",\n \"0.75160813\",\n \"0.7511302\",\n \"0.7505586\",\n \"0.7495846\",\n \"0.7478461\",\n \"0.74720407\",\n \"0.7469952\",\n \"0.7458981\",\n \"0.7449654\",\n \"0.7441484\",\n \"0.7441484\",\n \"0.7431872\",\n \"0.74105275\",\n \"0.740537\",\n \"0.73960227\",\n \"0.73960227\",\n \"0.73956025\",\n \"0.73952776\",\n \"0.7393985\",\n \"0.7382075\",\n \"0.737019\",\n \"0.7319694\",\n \"0.7319206\",\n \"0.7299099\",\n \"0.7294295\",\n \"0.72895414\",\n \"0.72629285\",\n \"0.7259509\",\n \"0.7257605\",\n \"0.7247278\",\n \"0.72248715\",\n \"0.7224705\",\n \"0.72031975\",\n \"0.7193735\",\n \"0.71878326\",\n \"0.7167157\",\n \"0.71402353\",\n \"0.7136355\",\n \"0.7125559\",\n \"0.71247154\",\n \"0.71240366\",\n \"0.7120109\",\n \"0.7096707\",\n \"0.70804816\",\n \"0.707009\",\n \"0.70652735\",\n \"0.70559514\",\n \"0.7019935\",\n \"0.7019935\",\n \"0.70112514\",\n \"0.7008216\",\n \"0.6991779\",\n \"0.69764984\",\n \"0.69676197\",\n \"0.69611996\",\n \"0.69405705\",\n \"0.6937805\",\n \"0.6937805\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80391,"cells":{"query":{"kind":"string","value":"execute the client integration tests"},"document":{"kind":"string","value":"def clitest(tag, field_tests, img_passwd_file, install_server_hostname,\n custom_cli_subnet, custom_db_subnet, builder):\n manager = Manager(\n 'clitest', tag, field_tests=field_tests,\n img_passwd_file=img_passwd_file,\n install_server_hostname=install_server_hostname,\n custom_cli_subnet=custom_cli_subnet, custom_db_subnet=custom_db_subnet,\n builder_hostname=builder)\n manager.run()"},"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_for_client():","def runTests(self):\n \n pass","def runtest(self):","def test_basic_execution(self):","def test_integration1(self):\n self._test_integration(1)","def run_integration_tests():\n run_pytest('integration')\n return redirect(url_for('main.test_reports', report='integration'))","def main() -> None:\n commands: Dict[str, Callable] = {\n 'swis-alert-list': swis_alert_list_command,\n 'swis-event-list': swis_event_list_command,\n 'swis-query': swis_query_command\n }\n command = demisto.command()\n demisto.debug(f'Command being called is {command}')\n try:\n params = demisto.params()\n args = demisto.args()\n server = params['server']\n credentials = params.get('credentials', {})\n\n verify_certificate = not params.get('insecure', False)\n proxy = params.get('proxy', False)\n\n client = Client(\n server=server,\n credentials=credentials,\n verify=verify_certificate,\n proxy=proxy)\n\n for key, value in args.items():\n if isinstance(value, str):\n args[key] = value.strip()\n\n remove_nulls_from_dictionary(args)\n\n if command == 'test-module':\n # This is the call made when pressing the integration Test button.\n result = test_module(client, params)\n return_results(result)\n\n elif command in commands:\n return_results(commands[command](client, args))\n\n elif command == 'fetch-incidents':\n last_run = demisto.getLastRun()\n next_run, incidents = fetch_incidents(client, last_run, params)\n demisto.incidents(incidents)\n demisto.setLastRun(next_run)\n\n # Log exceptions and return errors\n except Exception as e:\n return_error(f'Failed to execute {demisto.command()} command.\\nError:\\n{str(e)}')","def run(args):\n\n logging.info('Starting integration test')\n\n # read configuration from our args\n verbose = args.verbose\n device_token = args.device_token\n policystore_url = args.policystore\n encoder_url = args.encoder\n datastore_url = args.datastore\n\n # create a policy\n policy_credentials = create_policy(policystore_url,\n create_policy_request(), verbose)\n\n # read the policies back from the policystore\n policies = list_policies(policystore_url, verbose)\n\n # search for our created policy in the returned list\n for p in policies['policies']:\n if policy_credentials['policy_id'] == p['policy_id']:\n policy = p\n break\n else:\n # clean up the previously created policy\n delete_policy(policystore_url, policy_credentials, verbose)\n sys.exit(\n \"Unable to find created policy in list read from policy store\")\n\n # create a stream\n stream_credentials = create_stream(\n encoder_url, create_stream_request(device_token, policy), verbose)\n\n # read some data from the datastore for the policy\n read_request = create_read_request(policy)\n success = read_data(datastore_url, read_request, verbose)\n\n # delete the created stream\n delete_stream(encoder_url, stream_credentials, verbose)\n\n # delete the created policy\n delete_policy(policystore_url, policy_credentials, verbose)\n\n if success:\n logging.info('SUCCESS: All tests succeeded')\n else:\n sys.exit('FAILED: Failed to read data')","def test(base_url='http://localhost:8000/'):\n with env.cd(settings.PROJECT_PATH):\n # env.run('python rnacentral/apiv1/tests.py --base_url=%s' % base_url)\n env.run('python rnacentral/portal/tests/selenium_tests.py --base_url %s --driver=phantomjs' % base_url) # pylint: disable=C0301\n env.run('python rnacentral/apiv1/search/sequence/tests.py --base_url %s' % base_url) # pylint: disable=C0301","def _run_ci_integration_test():\n _run_install(False)\n _run_integration_tests_on_github(False)","def runTestCase(self):\n \n #Login\n self.login() \n \n #Performing Configure Resources \n ResultCR, statusCR = self.test_configureResourec()\n \n if statusCR:\n self.succeed(\"Configure Resources Step Successfully Completed %s\"%ResultCR)\n \n else:\n self.failure(\"Failed to Configure Resources Step %s\"%ResultCR)\n \n time.sleep(120)","def tests():\n api.local('nosetests')","def test_module():\n try:\n bigquery_client = start_and_return_bigquery_client(demisto.params()['google_service_creds'])\n query_job = bigquery_client.query(TEST_QUERY)\n query_results = query_job.result()\n results_rows_iterator = iter(query_results)\n next(results_rows_iterator)\n demisto.results(\"ok\")\n except Exception as ex:\n return_error(\"Authentication error: credentials JSON provided is invalid.\\n Exception recieved:\"\n \"{}\".format(ex))","def test_module():\n try:\n bigquery_client = start_and_return_bigquery_client(demisto.params()['google_service_creds'])\n query_job = bigquery_client.query(TEST_QUERY)\n query_results = query_job.result()\n results_rows_iterator = iter(query_results)\n next(results_rows_iterator)\n demisto.results(\"ok\")\n except Exception as ex:\n return_error(\"Authentication error: credentials JSON provided is invalid.\\n Exception recieved:\"\n \"{}\".format(ex))","def _run_online_test(*args, **kwargs):\n import responses # noqa: F401","def test_integration_tests(mocker, testclient):\n if not PARAMS.get(\"token\"):\n # Pass if no token for acceptance tests\n return\n\n test_data = {}\n test_data[\"list_schedules\"] = list_schedule_tester(testclient)\n test_data[\"get_schedules\"] = get_schedule_tester(testclient, test_data[\"list_schedules\"][\"data\"][0][\"id\"])\n test_data[\"on_call\"] = get_on_call_tester(testclient, test_data[\"list_schedules\"][\"data\"][0][\"id\"])\n\n # Create alert\n alert_raw_response = create_alerts_tester(testclient)\n test_data[\"create_alert\"] = alert_raw_response\n alert_id = alert_raw_response.get(\"alertId\")\n # List alerts\n test_data[\"list_alerts\"] = list_alerts_tester(testclient)\n # Get the alert we just created\n test_data[\"get_alert\"] = get_alert_tester(testclient, alert_id)\n # Ack the same alert\n test_data[\"ack_alert\"] = ack_alert_tester(testclient, alert_id)\n # Close the same alert\n test_data[\"close_alert\"] = close_alert_tester(testclient, alert_id)\n # Delete the alert we just created\n test_data[\"delete_alert\"] = delete_alert_tester(testclient, alert_id)\n\n if os.getenv(\"GEN_TEST_DATA\"):\n # If set, test JSON added to test_data\n for k, v in test_data.items():\n with open(f\"test_data/{k}.json\", \"w\") as fh:\n json.dump(v, fh, indent=4, sort_keys=True)","def client():\n from csuibot import app\n app.config['TESTING'] = True\n return app.test_client()","def main() -> None: # pragma: no cover\n params = demisto.params()\n command = demisto.command()\n args = demisto.args()\n\n server = params.get('server', '')\n rest_port = params.get('rest_port', '')\n api_key = params.get('api_key', {}).get('password', '')\n user = params.get('credentials', {}).get('identifier', '')\n password = params.get('credentials', {}).get('password', '')\n verify_server_cert = False\n handle_proxy(proxy_param_name='use_proxy')\n\n demisto.debug(f\"Command being called is {command}\")\n try:\n acceptable_status_codes: Tuple[int, ...] = tuple(\n int(code) for code in requests.status_codes.codes if IS_2XX(code)\n )\n client = CyberTriageClient(server, rest_port, api_key, user, password,\n verify_server_cert, acceptable_status_codes)\n # This is the call made when running the ct-triage-endpoint command.\n if command == 'ct-triage-endpoint':\n return_results(triage_endpoint_command(client, args))\n\n # This is the call made when pressing the integration test button.\n elif command == 'test-module':\n return_results(test_connection_command(client))\n else:\n raise NotImplementedError(f'command={command} not implemented in this integration')\n except Exception as e:\n return_error(\n f\"Failed to execute {command} command.\\nError: {str(e)}\"\n )","def runTest(self):\n try:\n print('TestCase runTest===>')\n self.test_runner.run_test(self.testcase_dict)\n\n finally:\n self.meta_data = getattr(self.test_runner.http_client_session, \"meta_data\", {})","def tests():","def setUp(self):\n self.c = Client(host=\"localhost\")","def execute_test(self):\n try:\n self._execute()\n except Exception as ex:\n self._fail(exception=ex)\n raise","def run(self, test, env):\n\n raise NotImplementedError","def main():\n run_test_all()","def test_get_client(self):\n pass","def setUp(self):\n self.client = Client()","def setUp(self):\n self.client = Client()","def setUp(self):\n self.client = Client()","def setUp(self):\n self.client = Client()","def test_example_runs(self):\n run_example(\n verbose=False,\n testapp=self.testapp,\n )","def test_integration3(self):\n self._test_integration(3)","def test_create_client(self):\n pass","def setUp(self):\n self.client = DummyClient()","def main(*tests):\n parser = create_client_options_parser()\n arguments = parser.parse_args()\n\n core_log.debug('Using configuration file %r', arguments.config_path)\n config = AttrDict(chain(\n six.iteritems(default_config),\n six.iteritems(parse_config_file(arguments.config_path,\n DEFAULT_SCHEMA_PATH)),\n filter_valid_values(vars(arguments)),\n ))\n\n # In case we're called via 'python test.py ...'\n if not sys.argv[0].endswith(\"rotest\"):\n main_module = inspect.getfile(__import__(\"__main__\"))\n config.paths = (main_module,)\n\n if len(tests) == 0:\n tests = list(discover_tests_under_paths(config.paths))\n\n if config.filter is not None:\n tests = [test for test in tests\n if match_tags(get_tags_by_class(test), config.filter)]\n\n for tag in reversed(config.order):\n tests.sort(\n reverse=True,\n key=lambda test_class: match_tags(get_tags_by_class(test_class),\n tag))\n\n for entry_point in \\\n pkg_resources.iter_entry_points(\"rotest.cli_client_actions\"):\n core_log.debug(\"Applying entry point %s\", entry_point.name)\n extension_action = entry_point.load()\n extension_action(tests, config)\n\n if len(tests) == 0:\n print(\"No test was found\")\n sys.exit(1)\n\n class AlmightySuite(TestSuite):\n components = tests\n\n run_tests(test=AlmightySuite, config=config)","def startTestRun(self):","def testApi(self):","def client(test_app):\n yield test_app.test_client()","def test_script(self) -> None:\n main()","def test_runner(self):\n run(HelloWorldApi, [\"/bin/test_api.py app_root/bin/libs\", \"app_root/bin/libs\"], sys.stdin.buffer,\n sys.__stdout__.buffer)","def test_orchestrator_http_simple(self):\n pass","def run(self):\r\n self.dut.create_device_and_transport(self.config_module.config_data)\r\n self.logger.info(\"Run the Test Case\")","def run_tests(self):\n raise NotImplementedError","def invoke():\n # CONFIG: Read configuration information\n config = conf.get_yaml_field(gl.configFile)\n dd_enable = config['ENABLE_DDING']\n dd_token = config['DD_TOKEN']\n dd_url = config['DING_URL']\n email_enable = config['EMAIL_ENABLE']\n # END CONFIG.\n\n # Test report file name.\n time_str = time.strftime('%Y%m%d_%H%M%S', time.localtime())\n path = RunTestCase.create_report_file()\n\n # Start test the send pin message.\n if dd_enable:\n scripts.send_msg_dding(\n '{}:★开始API接口自动化测试★'.format(time_str),\n dd_token,\n dd_url\n )\n\n # Execute the test and send the test report.\n RunTestCase.run(path)\n if dd_enable:\n # Template message.\n dir_list = path.split('\\\\')\n low_path = dir_list[len(dir_list) - 2]\n msg = RunTestCase.tmpl_msg(low_path)\n print(msg)\n scripts.send_msg_dding(msg, dd_token, dd_url)\n\n if email_enable:\n # Send test report to EMAIL.\n email = EmailClass()\n email.send(path)","def test_client():\n testing_client = app.app.test_client()\n testing_client.testing = True\n\n yield testing_client","def main():\n demisto.info('Command being called is ' + demisto.command())\n\n \"\"\"\n PARSE AND VALIDATE INTEGRATION PARAMS\n \"\"\"\n\n rest_client = RestClient(\n base_url=BASE_URL,\n verify=VERIFY_CERT,\n )\n\n try:\n if demisto.command() == 'test-module':\n test_module(rest_client)\n demisto.results('ok')\n\n elif demisto.command() == 'fetch-incidents':\n # get all tenant ids\n next_run, incidents = fetch_incidents(rest_client, demisto.getLastRun())\n demisto.setLastRun(next_run)\n demisto.incidents(incidents)\n\n elif demisto.command() == 'mad-close-incident':\n return_outputs(close_incident_command(rest_client, demisto.args()))\n\n elif demisto.command() == 'mad-assign-user':\n return_outputs(assign_user_command(rest_client, demisto.args()))\n\n elif demisto.command() == 'mad-remove-user':\n return_outputs(remove_user_command(rest_client, demisto.args()))\n\n elif demisto.command() == 'mad-get-incident':\n return_results(get_incident_command(rest_client, demisto.args()))\n\n elif demisto.command() == 'update-remote-system':\n return_results(update_remote_system_command(rest_client, demisto.args()))\n\n elif demisto.command() == 'get-mapping-fields':\n return_results(get_mapping_fields_command())\n\n elif demisto.command() == 'get-remote-data':\n return_results(get_remote_data_command(rest_client, demisto.args()))\n\n elif demisto.command() == 'mad-get-escalations':\n return_results(get_escalations_command(rest_client, demisto.args()))\n\n else:\n raise NotImplementedError('Command not implemented')\n\n except NotImplementedError:\n raise\n except Exception as err:\n demisto.error(traceback.format_exc()) # print the traceback\n return_error(f'Failed to execute {demisto.command()} command.\\nError:\\n{str(err)}')","def setUp(self):\n\t\tself.conn = Client([\"127.0.0.1:11211\"], debug = 1)","def test_all():\n test_get_to()\n test_error_type()\n test_exchange()\n print(\"All tests passed.\")","def test_main_succeeds(app_tester: ApplicationTester) -> None:\n assert app_tester.execute(\"\") == 0","def test_output_many_clients(self):\n self.test_case = 'one_client'\n self._run_test_case()\n self._run_test_case()","def test_execution(self):\n self.assertTrue(True)","def test_run_started(self):","def setUp(self):\n self.client = api.Client(config.get_config(), api.json_handler)","def main() -> None:\n\n # INTEGRATION DEVELOPER TIP\n # You can use functions such as ``demisto.debug()``, ``demisto.info()``,\n # etc. to print information in the XSOAR server log. You can set the log\n # level on the server configuration\n # See: https://xsoar.pan.dev/docs/integrations/code-conventions#logging\n\n command = demisto.command()\n args = demisto.args()\n demisto.info(f\"Command running: {demisto.command()}\")\n\n try:\n client = create_client()\n xm = XM(client)\n\n # commands dict\n # key - command key\n # value - command execution function that get two params:\n # 1) XM object\n # 2) args dict\n # return value - CommandResults\n commandsDict = {\n \"test-module\": test_module_command_internal, # This is the call made when pressing the integration Test button.\n \"xmcyber-f-incidents\": fetch_incidents_command, # for debugging of fetch incidents\n # XM Cyber Command list\n # xmcyber-command-name: function_command\n \"xmcyber-version-get\": get_version_command,\n \"xmcyber-version-supported\": is_xm_version_supported_command,\n \"xmcyber-affected-critical-assets-list\": affected_critical_assets_list_command,\n \"xmcyber-affected-entities-list\": affected_entities_list_command,\n # Common commands\n \"xmcyber-enrich-from-ip\": enrich_from_ip,\n \"xmcyber-enrich-from-hostname\": enrich_from_hostname,\n \"xmcyber-enrich-from-entityId\": enrich_from_entity_id,\n \"xmcyber-enrich-from-fields\": enrich_entity_from_fields,\n }\n\n if command == \"fetch-incidents\":\n xm.is_fetch_incidents = True\n fetch_incidents_command(xm, args)\n elif command in commandsDict:\n return_results(commandsDict[command](xm, args))\n else:\n raise Exception(\"Unsupported command: \" + command)\n\n # Log exceptions and return errors\n except Exception as e:\n demisto.error(traceback.format_exc()) # print the traceback\n return_error(\n f\"Failed to execute {demisto.command()} command.\\nError:\\n{str(e)}\\n\"\n f\"Traceback:\\n{traceback.format_exc()}\"\n )","def test_get_run(self):\n pass","def testExecute(self):\n client = ICSClientFactory(self.config, 'slave', 'master') \n\n #Test reading and writing coils\n reply = client.execute(cst.READ_COILS, 10, 2)\n assert reply[0] == False \n assert reply[1] == False\n reply = client.execute(cst.WRITE_SINGLE_COIL, 10, 1, output_value=1)\n reply = client.execute(cst.READ_COILS, 10, 2)\n assert reply[0] == True\n assert reply[1] == False\n reply = client.execute(cst.WRITE_MULTIPLE_COILS, 10, 2,\n output_value=[0,1])\n reply = client.execute(cst.READ_COILS, 10, 2)\n assert reply[1] == True\n assert reply[0] == False\n \n #Test reading and writing input regs\n #reply = client.execute(cst.READ_INPUT_REGISTERS, 30002, 1)\n #print \"BRDEBUG: Reply: \", reply\n #assert reply[0] == 17\n\n #Test reading and setting holding regs\n reply = client.execute(cst.READ_HOLDING_REGISTERS, 40003, 1)\n assert reply[0] == 15.0\n reply = client.execute(cst.WRITE_SINGLE_REGISTER, 40003, 1,\n output_value = 10)\n reply = client.execute(cst.READ_HOLDING_REGISTERS, 40003, 1)\n assert reply[0] == 10.0","def test_integration2(self):\n self._test_integration(2)","def test_get_single_question(self):\n with self.client:\n \n \"\"\"first insert a question\"\"\"\n response = self.add_question(\"1\",\"hello\",\"hello world\",\"java\",\"kenneth\")\n self.assertEqual(response.status_code, 201)\n \"\"\"then get a specific question\"\"\"\n response = self.get_one_question(1)\n self.assertEqual(response.status_code, 200)","def test_module(client: Client) -> str:\n # This should validate all the inputs given in the integration configuration panel,\n # either manually or by using an API that uses them.\n if client.client_credentials:\n raise DemistoException(\"When using a self-deployed configuration, run the !microsoft-365-defender-auth-test \"\n \"command in order to test the connection\")\n\n test_connection(client)\n\n return \"ok\"","def runTest(self):\n self.setUp()\n self.test_Analytics1()","def test_06_test_via_endpoint(self):\n\n # set up all the bits we need\n dataset = []\n for i in range(10):\n data = ArticleFixtureFactory.make_incoming_api_article(doi=\"10.123/test/\" + str(i),\n fulltext=\"http://example.com/\" + str(i))\n dataset.append(data)\n\n # create the main account we're going to work as\n article_owner = models.Account()\n article_owner.set_id(\"test\")\n article_owner.set_name(\"Tester\")\n article_owner.set_email(\"test@test.com\")\n article_owner.generate_api_key()\n article_owner.add_role('publisher')\n article_owner.add_role('api')\n article_owner.save(blocking=True)\n\n # Add another user who doesn't own these articles\n somebody_else = models.Account()\n somebody_else.set_id(\"somebody_else\")\n somebody_else.set_name(\"Somebody Else\")\n somebody_else.set_email(\"somebodyelse@test.com\")\n somebody_else.generate_api_key()\n somebody_else.add_role('publisher')\n somebody_else.add_role('api')\n somebody_else.save(blocking=True)\n\n assert article_owner.api_key != somebody_else.api_key\n\n # add a journal to the article owner account to create that link between account and articles\n journal = models.Journal(**JournalFixtureFactory.make_journal_source(in_doaj=True))\n journal.set_owner(article_owner.id)\n journal.save(blocking=True)\n\n with self.app_test.test_request_context():\n with self.app_test.test_client() as t_client:\n\n # Bulk create\n # The wrong owner can't create articles\n resp = t_client.post(url_for('api_v3.bulk_article_create', api_key=somebody_else.api_key),\n data=json.dumps(dataset))\n assert resp.status_code == 400, resp.status_code\n\n # Bulk create\n # redirected from v1\n # resp = t_client.post(url_for('api_v1.bulk_article_create', api_key=somebody_else.api_key),\n # data=json.dumps(dataset))\n # assert resp.status_code == 301, resp.status_code\n\n # But the correct owner can create articles\n resp = t_client.post(url_for('api_v3.bulk_article_create', api_key=article_owner.api_key),\n data=json.dumps(dataset))\n assert resp.status_code == 201\n reply = json.loads(resp.data.decode(\"utf-8\"))\n assert len(reply) == len(dataset)\n first_art = reply.pop()\n assert first_art['status'] == 'created'\n # Check we actually created new records\n time.sleep(1)\n assert len(models.Article.all()) == len(dataset)\n\n # Bulk delete\n all_but_one = [new_art['id'] for new_art in reply]\n resp = t_client.delete(url_for('api_v3.bulk_article_delete', api_key=article_owner.api_key),\n data=json.dumps(all_but_one))\n assert resp.status_code == 204\n time.sleep(1)\n # we should have deleted all but one of the articles.\n assert len(models.Article.all()) == 1\n # And our other user isn't allowed to delete the remaining one.\n resp = t_client.delete(url_for('api_v3.bulk_article_delete', api_key=somebody_else.api_key),\n data=json.dumps([first_art['id']]))\n assert resp.status_code == 400","def test():\n\n tests = unittest.TestLoader().discover('api/tests/', pattern='*/test_*.py')\n unittest.TextTestRunner(verbosity=2).run(tests)","def _run_local_tests(self, *args, **kwargs):\n pass","def test_client_retrieve(self):\n pass","def test_functionality(self):\n \n self.browserObject = globalVars.browserObject\n\n self.preRunSetup()\n \n self.runTestCase()\n \n self.postRunCleanup()\n \n self.logout()","def test_module(client: Client, *args) -> Tuple[str, dict, dict]:\n\n client.run_parameters_validations()\n\n for service in client.services:\n # if there are risk rules, select the first one for test\n risk_rule = client.risk_rule[0] if client.risk_rule else None\n client.build_iterator(service, client.indicator_type, risk_rule)\n client.get_batches_from_file(limit=1)\n return 'ok', {}, {}","def _handler_test_run_tests(self, *args, **kwargs):\n next_state = None\n result = None\n\n tc_pass = False\n tt_pass = False\n tp_pass = False\n tc_result = None\n tt_result = None\n tp_result = None\n\n test_result = {}\n\n try:\n tc_pass, tc_result = self._do_cmd_resp('tc', timeout=200)\n tt_pass, tt_result = self._do_cmd_resp('tt', timeout=200)\n tp_pass, tp_result = self._do_cmd_resp('tp', timeout=200)\n \n except Exception as e:\n test_result['exception'] = e\n test_result['message'] = 'Error running instrument tests.'\n \n finally:\n test_result['cond_test'] = 'Passed' if tc_pass else 'Failed'\n test_result['cond_data'] = tc_result\n test_result['temp_test'] = 'Passed' if tt_pass else 'Failed'\n test_result['temp_data'] = tt_result\n test_result['pres_test'] = 'Passed' if tp_pass else 'Failed'\n test_result['pres_data'] = tp_result\n test_result['success'] = 'Passed' if (tc_pass and tt_pass and tp_pass) else 'Failed'\n \n self._driver_event(DriverAsyncEvent.TEST_RESULT, test_result)\n next_state = SBE37ProtocolState.COMMAND\n \n return (next_state, result)","def run_test(self):\n raise NotImplementedError","def test_integrations(session):\n session.install('-rrequirements-dev.txt')\n session.install('-e', '.')\n run_pytest_integrations(session)","def run_main(): # pragma: no cover\n RunTestsCLI.run()","def testExecute(self):\n client = ICSClientFactory(self.config, 'slave', 'master') \n\n #Test reading and writing coils\n reply = client.execute(cst.READ_COILS, 10, 2)\n assert reply[0] == False \n assert reply[1] == False\n reply = client.execute(cst.WRITE_SINGLE_COIL, 10, 1, output_value=1)\n reply = client.execute(cst.READ_COILS, 10, 2)\n assert reply[0] == True\n assert reply[1] == False\n reply = client.execute(cst.WRITE_MULTIPLE_COILS, 10, 2,\n output_value=[0,1])\n reply = client.execute(cst.READ_COILS, 10, 2)\n assert reply[1] == True\n assert reply[0] == False\n \n #Test reading and writing input regs\n #reply = client.execute(cst.READ_INPUT_REGISTERS, 30002, 1)\n #print \"BRDEBUG: Reply: \", reply\n #assert reply[0] == 17\n\n #Test reading and setting holding regs\n reply = client.execute(cst.READ_HOLDING_REGISTERS, 40003, 2)\n #print \"BRDEBUG: Reply\", reply\n assert reply[0] == 16752 \n assert reply[1] == 0 \n reply = client.execute(cst.WRITE_SINGLE_REGISTER, 40003, 1,\n output_value = 10)\n reply = client.execute(cst.READ_HOLDING_REGISTERS, 40003, 1)\n #print \"BRDEBUG: Reply\", reply\n assert reply[0] == 10","def test_call(self):\n\n with Client('username', 'password') as client:\n self.setSessionResponse(content='{\"ok\": true}')\n data = client.call(**self.build_parameters)\n self.assertEqual(data, '{\"ok\": true}')","def run_tests(self):\n # Charm does not defer hooks so that test is not included.\n # Trigger a package change which requires a restart\n self.run_package_change_test(\n 'ovn-central',\n 'ovn-central')","def runTest(self):\n self.setUp()\n self.test_TractQuerier()","def test_execute_deployment(self):\n pass","def setUp(self):\n\n self.client = server.app.test_client()\n server.app.config['TESTING'] = True\n server.app.config['SECRET_KEY'] = \"123\"\n\n # Connect to test database\n model.connect_to_db(server.app, \"postgresql:///testdb\")\n\n # Create tables and add sample data\n model.db.create_all()\n # example_data()\n\n with self.client as c:\n with c.session_transaction() as session:\n session['user_id'] = 1\n session['username'] = 'j'\n session['name'] = 'l'\n session['cal_id'] = 1","def run(args):\n write_request(args.num_tests)\n\n if args.request == 'check':\n script_files = ['modules/check_vault.py']\n elif args.request == 'create':\n script_files = ['modules/create_vault.py']\n else:\n raise AssertionError\n\n if args.version is None:\n python_bins = [python2_exec, python3_exec]\n elif args.version == 2:\n python_bins = [python2_exec]\n elif args.version == 3:\n python_bins = [python3_exec]\n else:\n raise AssertionError\n\n for script_file in script_files:\n for python_bin in python_bins:\n cmd = [python_bin, script_file]\n subprocess.check_call(cmd)\n\n os.unlink('request.txt')\n\n # This allows to call this test from another script, that runs other\n # tests as well.\n if not args.is_background:\n send_notification('regression')","def client(app):\n yield app.test_client()","def client(app):\n yield app.test_client()","def setUp(self):\r\n super(CLITestNameorID, self).setUp()\r\n self.mox = mox.Mox()\r\n self.endurl = test_cli20.ENDURL\r\n self.client = client.Client(token=test_cli20.TOKEN,\r\n endpoint_url=self.endurl)\r\n self.addCleanup(self.mox.VerifyAll)\r\n self.addCleanup(self.mox.UnsetStubs)","def runTest(self):\r\n self.setUp()","def test_update_client(self):\n pass","def test():\n import unittest\n\n tests = unittest.TestLoader().discover(\"tests\")\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_answer_question(self):\n with self.client:\n \n \"\"\"first insert a question\"\"\"\n response = self.add_question(\"1\",\"hello\",\"hello world\",\"java\",\"kenneth\")\n self.assertEqual(response.status_code, 201)\n \"\"\"then get a specific question to answer\"\"\"\n response = self.post_answer(1,\"1\",\"try removing errors\")\n self.assertEqual(response.status_code, 201)","def execute(self):\n for test in self.tests:\n test.execute()\n self.logger.dump()\n print(\"Finished!\")","def test_integration(self):\n self.assertTrue(return_true())","def setUp(self):\n\n self.client = server.app.test_client()\n server.app.config['TESTING'] = True\n server.app.config['SECRET_KEY'] = \"123\"\n\n # Connect to test database\n model.connect_to_db(server.app, \"postgresql:///testdb\")\n\n # Create tables and add sample data\n model.db.create_all()\n # example_data()\n\n with self.client as c:\n with c.session_transaction() as session:\n session['user_id'] = 33\n session['username'] = 'balloonicorn'\n session['name'] = 'balloonicorn'","async def run_tests():\n start = time.time()\n logging.basicConfig(level=logging.DEBUG)\n\n # Read the settings.json file\n path_index = __file__.rfind(\"/\")\n if path_index == -1:\n filepath = \"settings.json\"\n else:\n top_path = __file__[0:path_index]\n filepath = f\"{top_path}/settings.json\"\n with open(filepath) as json_file:\n data = json.load(json_file)\n host = data[\"connection\"][\"host\"]\n port = data[\"connection\"][\"port\"]\n username = data[\"connection\"][\"username\"]\n password = data[\"connection\"][\"password\"]\n use_ssl = data[\"connection\"][\"use_ssl\"]\n\n session = ClientSession()\n secspy = SecuritySpyServer(host, port, username, password, use_ssl, session)\n\n cameras = await camera_list(secspy, True)\n # await get_server_info(secspy)\n # await snapshots(secspy, cameras)\n # await recording_mode(secspy, 2, RECORDING_MODE_MOTION)\n # await get_recording_mode(secspy, 2)\n\n #Close the session\n await session.close()\n\n end = time.time()\n _LOGGER.info(\"Execution time: %s seconds\", end - start)","def setUp(self):\r\n\r\n app.config['TESTING'] = True\r\n self.client = app.test_client()","def testclient():\n base_url = PARAMS.get(\"url\") + \"/v2\"\n client = Client(\n base_url=base_url,\n headers={\n \"Authorization\": f\"GenieKey {PARAMS.get('token')}\",\n }\n )\n return client","def setUp(self):\n _, instance_path, shared_inputs = sys.argv\n app = lnt.server.ui.app.App.create_standalone(instance_path)\n app.testing = True\n self.client = app.test_client()\n self.shared_inputs = shared_inputs","def test_client_list(self):\n pass","def test(self):\n return self._test(result_count=1, failure_amount=1)","def test(self):\n return self._test(result_count=1, failure_amount=1)","def run_integration(session):\n set_environment_variables(PYBAMM_ENV, session=session)\n session.run_always(\"pip\", \"install\", \"-e\", \".[all]\")\n if sys.platform == \"linux\":\n session.run_always(\"pip\", \"install\", \"-e\", \".[odes]\")\n session.run(\"python\", \"run-tests.py\", \"--integration\")","def client():\n flask_app = create_app(\"testing\")\n test_client = flask_app.test_client()\n\n # create app context\n ctx = flask_app.app_context()\n ctx.push()\n\n yield test_client\n \"\"\"\n TESTS RUN HERE\n \"\"\"\n\n ctx.pop()","def setUp(self):\n self.app = create_app('testing')\n self.client = self.app.test_client()","def client():\n\n gcomics_scrape.APP.config['TESTING'] = True\n test_client = gcomics_scrape.APP.test_client()\n\n yield test_client","def setUp(self):\n #app['TESTING'] = True\n self.test_app = app.test_client()","def runTest(self):\n\t\tself.setUp()\n\t\tself.test_postopProgramming1()","def runtests():\r\n\r\n app_abspath = os.path.dirname(os.path.dirname(__file__))\r\n models_abspath = os.path.join(app_abspath, 'models.py')\r\n models_exists = os.path.isfile(models_abspath)\r\n urls_abspath = os.path.join(app_abspath, 'urls.py')\r\n urls_exists = os.path.isfile(urls_abspath)\r\n views_abspath = os.path.join(app_abspath, 'views')\r\n views_exists = os.path.isdir(views_abspath)\r\n tpls_abspath = os.path.join(app_abspath, 'templates')\r\n tpls_exists = os.path.isdir(tpls_abspath)\r\n\r\n for f in [models_abspath, urls_abspath]:\r\n if os.path.isfile(f):\r\n subprocess.call('cp {} {}.orig'.format(f, f), shell=True)\r\n\r\n if views_exists:\r\n subprocess.call('cp -r {} {}.orig'.format(views_abspath, views_abspath), shell=True)\r\n\r\n if tpls_exists:\r\n subprocess.call('cp -r {} {}.orig'.format(tpls_abspath, tpls_abspath), shell=True)\r\n\r\n overwrite_project_language('ja')\r\n subprocess.call('python manage.py generatescaffold test_app I18nModel title:string', shell=True)\r\n time.sleep(1)\r\n overwrite_project_language('en-us')\r\n time.sleep(1)\r\n\r\n subprocess.call('python manage.py generatescaffold test_app GeneratedNoTimestampModel title:string description:text --no-timestamps', shell=True)\r\n time.sleep(2) # Give time for Django's AppCache to clear\r\n\r\n subprocess.call('python manage.py generatescaffold test_app GeneratedModel title:string description:text', shell=True)\r\n\r\n test_status = subprocess.call('python manage.py test --with-selenium --with-selenium-fixtures --with-cherrypyliveserver --noinput', shell=True)\r\n\r\n if models_exists:\r\n subprocess.call('mv {}.orig {}'.format(models_abspath, models_abspath), shell=True)\r\n else:\r\n subprocess.call('rm {}'.format(models_abspath), shell=True)\r\n\r\n if urls_exists:\r\n subprocess.call('mv {}.orig {}'.format(urls_abspath, urls_abspath), shell=True)\r\n else:\r\n subprocess.call('rm {}'.format(urls_abspath), shell=True)\r\n\r\n if views_exists:\r\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\r\n subprocess.call('mv {}.orig {}'.format(views_abspath, views_abspath), shell=True)\r\n else:\r\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\r\n\r\n if tpls_exists:\r\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\r\n subprocess.call('mv {}.orig {}'.format(tpls_abspath, tpls_abspath), shell=True)\r\n else:\r\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\r\n\r\n subprocess.call('rm {}/*.pyc'.format(app_abspath), shell=True)\r\n\r\n sys.exit(test_status)","def client():\n yield tests.example_server.app.test_client()"],"string":"[\n \"def test_for_client():\",\n \"def runTests(self):\\n \\n pass\",\n \"def runtest(self):\",\n \"def test_basic_execution(self):\",\n \"def test_integration1(self):\\n self._test_integration(1)\",\n \"def run_integration_tests():\\n run_pytest('integration')\\n return redirect(url_for('main.test_reports', report='integration'))\",\n \"def main() -> None:\\n commands: Dict[str, Callable] = {\\n 'swis-alert-list': swis_alert_list_command,\\n 'swis-event-list': swis_event_list_command,\\n 'swis-query': swis_query_command\\n }\\n command = demisto.command()\\n demisto.debug(f'Command being called is {command}')\\n try:\\n params = demisto.params()\\n args = demisto.args()\\n server = params['server']\\n credentials = params.get('credentials', {})\\n\\n verify_certificate = not params.get('insecure', False)\\n proxy = params.get('proxy', False)\\n\\n client = Client(\\n server=server,\\n credentials=credentials,\\n verify=verify_certificate,\\n proxy=proxy)\\n\\n for key, value in args.items():\\n if isinstance(value, str):\\n args[key] = value.strip()\\n\\n remove_nulls_from_dictionary(args)\\n\\n if command == 'test-module':\\n # This is the call made when pressing the integration Test button.\\n result = test_module(client, params)\\n return_results(result)\\n\\n elif command in commands:\\n return_results(commands[command](client, args))\\n\\n elif command == 'fetch-incidents':\\n last_run = demisto.getLastRun()\\n next_run, incidents = fetch_incidents(client, last_run, params)\\n demisto.incidents(incidents)\\n demisto.setLastRun(next_run)\\n\\n # Log exceptions and return errors\\n except Exception as e:\\n return_error(f'Failed to execute {demisto.command()} command.\\\\nError:\\\\n{str(e)}')\",\n \"def run(args):\\n\\n logging.info('Starting integration test')\\n\\n # read configuration from our args\\n verbose = args.verbose\\n device_token = args.device_token\\n policystore_url = args.policystore\\n encoder_url = args.encoder\\n datastore_url = args.datastore\\n\\n # create a policy\\n policy_credentials = create_policy(policystore_url,\\n create_policy_request(), verbose)\\n\\n # read the policies back from the policystore\\n policies = list_policies(policystore_url, verbose)\\n\\n # search for our created policy in the returned list\\n for p in policies['policies']:\\n if policy_credentials['policy_id'] == p['policy_id']:\\n policy = p\\n break\\n else:\\n # clean up the previously created policy\\n delete_policy(policystore_url, policy_credentials, verbose)\\n sys.exit(\\n \\\"Unable to find created policy in list read from policy store\\\")\\n\\n # create a stream\\n stream_credentials = create_stream(\\n encoder_url, create_stream_request(device_token, policy), verbose)\\n\\n # read some data from the datastore for the policy\\n read_request = create_read_request(policy)\\n success = read_data(datastore_url, read_request, verbose)\\n\\n # delete the created stream\\n delete_stream(encoder_url, stream_credentials, verbose)\\n\\n # delete the created policy\\n delete_policy(policystore_url, policy_credentials, verbose)\\n\\n if success:\\n logging.info('SUCCESS: All tests succeeded')\\n else:\\n sys.exit('FAILED: Failed to read data')\",\n \"def test(base_url='http://localhost:8000/'):\\n with env.cd(settings.PROJECT_PATH):\\n # env.run('python rnacentral/apiv1/tests.py --base_url=%s' % base_url)\\n env.run('python rnacentral/portal/tests/selenium_tests.py --base_url %s --driver=phantomjs' % base_url) # pylint: disable=C0301\\n env.run('python rnacentral/apiv1/search/sequence/tests.py --base_url %s' % base_url) # pylint: disable=C0301\",\n \"def _run_ci_integration_test():\\n _run_install(False)\\n _run_integration_tests_on_github(False)\",\n \"def runTestCase(self):\\n \\n #Login\\n self.login() \\n \\n #Performing Configure Resources \\n ResultCR, statusCR = self.test_configureResourec()\\n \\n if statusCR:\\n self.succeed(\\\"Configure Resources Step Successfully Completed %s\\\"%ResultCR)\\n \\n else:\\n self.failure(\\\"Failed to Configure Resources Step %s\\\"%ResultCR)\\n \\n time.sleep(120)\",\n \"def tests():\\n api.local('nosetests')\",\n \"def test_module():\\n try:\\n bigquery_client = start_and_return_bigquery_client(demisto.params()['google_service_creds'])\\n query_job = bigquery_client.query(TEST_QUERY)\\n query_results = query_job.result()\\n results_rows_iterator = iter(query_results)\\n next(results_rows_iterator)\\n demisto.results(\\\"ok\\\")\\n except Exception as ex:\\n return_error(\\\"Authentication error: credentials JSON provided is invalid.\\\\n Exception recieved:\\\"\\n \\\"{}\\\".format(ex))\",\n \"def test_module():\\n try:\\n bigquery_client = start_and_return_bigquery_client(demisto.params()['google_service_creds'])\\n query_job = bigquery_client.query(TEST_QUERY)\\n query_results = query_job.result()\\n results_rows_iterator = iter(query_results)\\n next(results_rows_iterator)\\n demisto.results(\\\"ok\\\")\\n except Exception as ex:\\n return_error(\\\"Authentication error: credentials JSON provided is invalid.\\\\n Exception recieved:\\\"\\n \\\"{}\\\".format(ex))\",\n \"def _run_online_test(*args, **kwargs):\\n import responses # noqa: F401\",\n \"def test_integration_tests(mocker, testclient):\\n if not PARAMS.get(\\\"token\\\"):\\n # Pass if no token for acceptance tests\\n return\\n\\n test_data = {}\\n test_data[\\\"list_schedules\\\"] = list_schedule_tester(testclient)\\n test_data[\\\"get_schedules\\\"] = get_schedule_tester(testclient, test_data[\\\"list_schedules\\\"][\\\"data\\\"][0][\\\"id\\\"])\\n test_data[\\\"on_call\\\"] = get_on_call_tester(testclient, test_data[\\\"list_schedules\\\"][\\\"data\\\"][0][\\\"id\\\"])\\n\\n # Create alert\\n alert_raw_response = create_alerts_tester(testclient)\\n test_data[\\\"create_alert\\\"] = alert_raw_response\\n alert_id = alert_raw_response.get(\\\"alertId\\\")\\n # List alerts\\n test_data[\\\"list_alerts\\\"] = list_alerts_tester(testclient)\\n # Get the alert we just created\\n test_data[\\\"get_alert\\\"] = get_alert_tester(testclient, alert_id)\\n # Ack the same alert\\n test_data[\\\"ack_alert\\\"] = ack_alert_tester(testclient, alert_id)\\n # Close the same alert\\n test_data[\\\"close_alert\\\"] = close_alert_tester(testclient, alert_id)\\n # Delete the alert we just created\\n test_data[\\\"delete_alert\\\"] = delete_alert_tester(testclient, alert_id)\\n\\n if os.getenv(\\\"GEN_TEST_DATA\\\"):\\n # If set, test JSON added to test_data\\n for k, v in test_data.items():\\n with open(f\\\"test_data/{k}.json\\\", \\\"w\\\") as fh:\\n json.dump(v, fh, indent=4, sort_keys=True)\",\n \"def client():\\n from csuibot import app\\n app.config['TESTING'] = True\\n return app.test_client()\",\n \"def main() -> None: # pragma: no cover\\n params = demisto.params()\\n command = demisto.command()\\n args = demisto.args()\\n\\n server = params.get('server', '')\\n rest_port = params.get('rest_port', '')\\n api_key = params.get('api_key', {}).get('password', '')\\n user = params.get('credentials', {}).get('identifier', '')\\n password = params.get('credentials', {}).get('password', '')\\n verify_server_cert = False\\n handle_proxy(proxy_param_name='use_proxy')\\n\\n demisto.debug(f\\\"Command being called is {command}\\\")\\n try:\\n acceptable_status_codes: Tuple[int, ...] = tuple(\\n int(code) for code in requests.status_codes.codes if IS_2XX(code)\\n )\\n client = CyberTriageClient(server, rest_port, api_key, user, password,\\n verify_server_cert, acceptable_status_codes)\\n # This is the call made when running the ct-triage-endpoint command.\\n if command == 'ct-triage-endpoint':\\n return_results(triage_endpoint_command(client, args))\\n\\n # This is the call made when pressing the integration test button.\\n elif command == 'test-module':\\n return_results(test_connection_command(client))\\n else:\\n raise NotImplementedError(f'command={command} not implemented in this integration')\\n except Exception as e:\\n return_error(\\n f\\\"Failed to execute {command} command.\\\\nError: {str(e)}\\\"\\n )\",\n \"def runTest(self):\\n try:\\n print('TestCase runTest===>')\\n self.test_runner.run_test(self.testcase_dict)\\n\\n finally:\\n self.meta_data = getattr(self.test_runner.http_client_session, \\\"meta_data\\\", {})\",\n \"def tests():\",\n \"def setUp(self):\\n self.c = Client(host=\\\"localhost\\\")\",\n \"def execute_test(self):\\n try:\\n self._execute()\\n except Exception as ex:\\n self._fail(exception=ex)\\n raise\",\n \"def run(self, test, env):\\n\\n raise NotImplementedError\",\n \"def main():\\n run_test_all()\",\n \"def test_get_client(self):\\n pass\",\n \"def setUp(self):\\n self.client = Client()\",\n \"def setUp(self):\\n self.client = Client()\",\n \"def setUp(self):\\n self.client = Client()\",\n \"def setUp(self):\\n self.client = Client()\",\n \"def test_example_runs(self):\\n run_example(\\n verbose=False,\\n testapp=self.testapp,\\n )\",\n \"def test_integration3(self):\\n self._test_integration(3)\",\n \"def test_create_client(self):\\n pass\",\n \"def setUp(self):\\n self.client = DummyClient()\",\n \"def main(*tests):\\n parser = create_client_options_parser()\\n arguments = parser.parse_args()\\n\\n core_log.debug('Using configuration file %r', arguments.config_path)\\n config = AttrDict(chain(\\n six.iteritems(default_config),\\n six.iteritems(parse_config_file(arguments.config_path,\\n DEFAULT_SCHEMA_PATH)),\\n filter_valid_values(vars(arguments)),\\n ))\\n\\n # In case we're called via 'python test.py ...'\\n if not sys.argv[0].endswith(\\\"rotest\\\"):\\n main_module = inspect.getfile(__import__(\\\"__main__\\\"))\\n config.paths = (main_module,)\\n\\n if len(tests) == 0:\\n tests = list(discover_tests_under_paths(config.paths))\\n\\n if config.filter is not None:\\n tests = [test for test in tests\\n if match_tags(get_tags_by_class(test), config.filter)]\\n\\n for tag in reversed(config.order):\\n tests.sort(\\n reverse=True,\\n key=lambda test_class: match_tags(get_tags_by_class(test_class),\\n tag))\\n\\n for entry_point in \\\\\\n pkg_resources.iter_entry_points(\\\"rotest.cli_client_actions\\\"):\\n core_log.debug(\\\"Applying entry point %s\\\", entry_point.name)\\n extension_action = entry_point.load()\\n extension_action(tests, config)\\n\\n if len(tests) == 0:\\n print(\\\"No test was found\\\")\\n sys.exit(1)\\n\\n class AlmightySuite(TestSuite):\\n components = tests\\n\\n run_tests(test=AlmightySuite, config=config)\",\n \"def startTestRun(self):\",\n \"def testApi(self):\",\n \"def client(test_app):\\n yield test_app.test_client()\",\n \"def test_script(self) -> None:\\n main()\",\n \"def test_runner(self):\\n run(HelloWorldApi, [\\\"/bin/test_api.py app_root/bin/libs\\\", \\\"app_root/bin/libs\\\"], sys.stdin.buffer,\\n sys.__stdout__.buffer)\",\n \"def test_orchestrator_http_simple(self):\\n pass\",\n \"def run(self):\\r\\n self.dut.create_device_and_transport(self.config_module.config_data)\\r\\n self.logger.info(\\\"Run the Test Case\\\")\",\n \"def run_tests(self):\\n raise NotImplementedError\",\n \"def invoke():\\n # CONFIG: Read configuration information\\n config = conf.get_yaml_field(gl.configFile)\\n dd_enable = config['ENABLE_DDING']\\n dd_token = config['DD_TOKEN']\\n dd_url = config['DING_URL']\\n email_enable = config['EMAIL_ENABLE']\\n # END CONFIG.\\n\\n # Test report file name.\\n time_str = time.strftime('%Y%m%d_%H%M%S', time.localtime())\\n path = RunTestCase.create_report_file()\\n\\n # Start test the send pin message.\\n if dd_enable:\\n scripts.send_msg_dding(\\n '{}:★开始API接口自动化测试★'.format(time_str),\\n dd_token,\\n dd_url\\n )\\n\\n # Execute the test and send the test report.\\n RunTestCase.run(path)\\n if dd_enable:\\n # Template message.\\n dir_list = path.split('\\\\\\\\')\\n low_path = dir_list[len(dir_list) - 2]\\n msg = RunTestCase.tmpl_msg(low_path)\\n print(msg)\\n scripts.send_msg_dding(msg, dd_token, dd_url)\\n\\n if email_enable:\\n # Send test report to EMAIL.\\n email = EmailClass()\\n email.send(path)\",\n \"def test_client():\\n testing_client = app.app.test_client()\\n testing_client.testing = True\\n\\n yield testing_client\",\n \"def main():\\n demisto.info('Command being called is ' + demisto.command())\\n\\n \\\"\\\"\\\"\\n PARSE AND VALIDATE INTEGRATION PARAMS\\n \\\"\\\"\\\"\\n\\n rest_client = RestClient(\\n base_url=BASE_URL,\\n verify=VERIFY_CERT,\\n )\\n\\n try:\\n if demisto.command() == 'test-module':\\n test_module(rest_client)\\n demisto.results('ok')\\n\\n elif demisto.command() == 'fetch-incidents':\\n # get all tenant ids\\n next_run, incidents = fetch_incidents(rest_client, demisto.getLastRun())\\n demisto.setLastRun(next_run)\\n demisto.incidents(incidents)\\n\\n elif demisto.command() == 'mad-close-incident':\\n return_outputs(close_incident_command(rest_client, demisto.args()))\\n\\n elif demisto.command() == 'mad-assign-user':\\n return_outputs(assign_user_command(rest_client, demisto.args()))\\n\\n elif demisto.command() == 'mad-remove-user':\\n return_outputs(remove_user_command(rest_client, demisto.args()))\\n\\n elif demisto.command() == 'mad-get-incident':\\n return_results(get_incident_command(rest_client, demisto.args()))\\n\\n elif demisto.command() == 'update-remote-system':\\n return_results(update_remote_system_command(rest_client, demisto.args()))\\n\\n elif demisto.command() == 'get-mapping-fields':\\n return_results(get_mapping_fields_command())\\n\\n elif demisto.command() == 'get-remote-data':\\n return_results(get_remote_data_command(rest_client, demisto.args()))\\n\\n elif demisto.command() == 'mad-get-escalations':\\n return_results(get_escalations_command(rest_client, demisto.args()))\\n\\n else:\\n raise NotImplementedError('Command not implemented')\\n\\n except NotImplementedError:\\n raise\\n except Exception as err:\\n demisto.error(traceback.format_exc()) # print the traceback\\n return_error(f'Failed to execute {demisto.command()} command.\\\\nError:\\\\n{str(err)}')\",\n \"def setUp(self):\\n\\t\\tself.conn = Client([\\\"127.0.0.1:11211\\\"], debug = 1)\",\n \"def test_all():\\n test_get_to()\\n test_error_type()\\n test_exchange()\\n print(\\\"All tests passed.\\\")\",\n \"def test_main_succeeds(app_tester: ApplicationTester) -> None:\\n assert app_tester.execute(\\\"\\\") == 0\",\n \"def test_output_many_clients(self):\\n self.test_case = 'one_client'\\n self._run_test_case()\\n self._run_test_case()\",\n \"def test_execution(self):\\n self.assertTrue(True)\",\n \"def test_run_started(self):\",\n \"def setUp(self):\\n self.client = api.Client(config.get_config(), api.json_handler)\",\n \"def main() -> None:\\n\\n # INTEGRATION DEVELOPER TIP\\n # You can use functions such as ``demisto.debug()``, ``demisto.info()``,\\n # etc. to print information in the XSOAR server log. You can set the log\\n # level on the server configuration\\n # See: https://xsoar.pan.dev/docs/integrations/code-conventions#logging\\n\\n command = demisto.command()\\n args = demisto.args()\\n demisto.info(f\\\"Command running: {demisto.command()}\\\")\\n\\n try:\\n client = create_client()\\n xm = XM(client)\\n\\n # commands dict\\n # key - command key\\n # value - command execution function that get two params:\\n # 1) XM object\\n # 2) args dict\\n # return value - CommandResults\\n commandsDict = {\\n \\\"test-module\\\": test_module_command_internal, # This is the call made when pressing the integration Test button.\\n \\\"xmcyber-f-incidents\\\": fetch_incidents_command, # for debugging of fetch incidents\\n # XM Cyber Command list\\n # xmcyber-command-name: function_command\\n \\\"xmcyber-version-get\\\": get_version_command,\\n \\\"xmcyber-version-supported\\\": is_xm_version_supported_command,\\n \\\"xmcyber-affected-critical-assets-list\\\": affected_critical_assets_list_command,\\n \\\"xmcyber-affected-entities-list\\\": affected_entities_list_command,\\n # Common commands\\n \\\"xmcyber-enrich-from-ip\\\": enrich_from_ip,\\n \\\"xmcyber-enrich-from-hostname\\\": enrich_from_hostname,\\n \\\"xmcyber-enrich-from-entityId\\\": enrich_from_entity_id,\\n \\\"xmcyber-enrich-from-fields\\\": enrich_entity_from_fields,\\n }\\n\\n if command == \\\"fetch-incidents\\\":\\n xm.is_fetch_incidents = True\\n fetch_incidents_command(xm, args)\\n elif command in commandsDict:\\n return_results(commandsDict[command](xm, args))\\n else:\\n raise Exception(\\\"Unsupported command: \\\" + command)\\n\\n # Log exceptions and return errors\\n except Exception as e:\\n demisto.error(traceback.format_exc()) # print the traceback\\n return_error(\\n f\\\"Failed to execute {demisto.command()} command.\\\\nError:\\\\n{str(e)}\\\\n\\\"\\n f\\\"Traceback:\\\\n{traceback.format_exc()}\\\"\\n )\",\n \"def test_get_run(self):\\n pass\",\n \"def testExecute(self):\\n client = ICSClientFactory(self.config, 'slave', 'master') \\n\\n #Test reading and writing coils\\n reply = client.execute(cst.READ_COILS, 10, 2)\\n assert reply[0] == False \\n assert reply[1] == False\\n reply = client.execute(cst.WRITE_SINGLE_COIL, 10, 1, output_value=1)\\n reply = client.execute(cst.READ_COILS, 10, 2)\\n assert reply[0] == True\\n assert reply[1] == False\\n reply = client.execute(cst.WRITE_MULTIPLE_COILS, 10, 2,\\n output_value=[0,1])\\n reply = client.execute(cst.READ_COILS, 10, 2)\\n assert reply[1] == True\\n assert reply[0] == False\\n \\n #Test reading and writing input regs\\n #reply = client.execute(cst.READ_INPUT_REGISTERS, 30002, 1)\\n #print \\\"BRDEBUG: Reply: \\\", reply\\n #assert reply[0] == 17\\n\\n #Test reading and setting holding regs\\n reply = client.execute(cst.READ_HOLDING_REGISTERS, 40003, 1)\\n assert reply[0] == 15.0\\n reply = client.execute(cst.WRITE_SINGLE_REGISTER, 40003, 1,\\n output_value = 10)\\n reply = client.execute(cst.READ_HOLDING_REGISTERS, 40003, 1)\\n assert reply[0] == 10.0\",\n \"def test_integration2(self):\\n self._test_integration(2)\",\n \"def test_get_single_question(self):\\n with self.client:\\n \\n \\\"\\\"\\\"first insert a question\\\"\\\"\\\"\\n response = self.add_question(\\\"1\\\",\\\"hello\\\",\\\"hello world\\\",\\\"java\\\",\\\"kenneth\\\")\\n self.assertEqual(response.status_code, 201)\\n \\\"\\\"\\\"then get a specific question\\\"\\\"\\\"\\n response = self.get_one_question(1)\\n self.assertEqual(response.status_code, 200)\",\n \"def test_module(client: Client) -> str:\\n # This should validate all the inputs given in the integration configuration panel,\\n # either manually or by using an API that uses them.\\n if client.client_credentials:\\n raise DemistoException(\\\"When using a self-deployed configuration, run the !microsoft-365-defender-auth-test \\\"\\n \\\"command in order to test the connection\\\")\\n\\n test_connection(client)\\n\\n return \\\"ok\\\"\",\n \"def runTest(self):\\n self.setUp()\\n self.test_Analytics1()\",\n \"def test_06_test_via_endpoint(self):\\n\\n # set up all the bits we need\\n dataset = []\\n for i in range(10):\\n data = ArticleFixtureFactory.make_incoming_api_article(doi=\\\"10.123/test/\\\" + str(i),\\n fulltext=\\\"http://example.com/\\\" + str(i))\\n dataset.append(data)\\n\\n # create the main account we're going to work as\\n article_owner = models.Account()\\n article_owner.set_id(\\\"test\\\")\\n article_owner.set_name(\\\"Tester\\\")\\n article_owner.set_email(\\\"test@test.com\\\")\\n article_owner.generate_api_key()\\n article_owner.add_role('publisher')\\n article_owner.add_role('api')\\n article_owner.save(blocking=True)\\n\\n # Add another user who doesn't own these articles\\n somebody_else = models.Account()\\n somebody_else.set_id(\\\"somebody_else\\\")\\n somebody_else.set_name(\\\"Somebody Else\\\")\\n somebody_else.set_email(\\\"somebodyelse@test.com\\\")\\n somebody_else.generate_api_key()\\n somebody_else.add_role('publisher')\\n somebody_else.add_role('api')\\n somebody_else.save(blocking=True)\\n\\n assert article_owner.api_key != somebody_else.api_key\\n\\n # add a journal to the article owner account to create that link between account and articles\\n journal = models.Journal(**JournalFixtureFactory.make_journal_source(in_doaj=True))\\n journal.set_owner(article_owner.id)\\n journal.save(blocking=True)\\n\\n with self.app_test.test_request_context():\\n with self.app_test.test_client() as t_client:\\n\\n # Bulk create\\n # The wrong owner can't create articles\\n resp = t_client.post(url_for('api_v3.bulk_article_create', api_key=somebody_else.api_key),\\n data=json.dumps(dataset))\\n assert resp.status_code == 400, resp.status_code\\n\\n # Bulk create\\n # redirected from v1\\n # resp = t_client.post(url_for('api_v1.bulk_article_create', api_key=somebody_else.api_key),\\n # data=json.dumps(dataset))\\n # assert resp.status_code == 301, resp.status_code\\n\\n # But the correct owner can create articles\\n resp = t_client.post(url_for('api_v3.bulk_article_create', api_key=article_owner.api_key),\\n data=json.dumps(dataset))\\n assert resp.status_code == 201\\n reply = json.loads(resp.data.decode(\\\"utf-8\\\"))\\n assert len(reply) == len(dataset)\\n first_art = reply.pop()\\n assert first_art['status'] == 'created'\\n # Check we actually created new records\\n time.sleep(1)\\n assert len(models.Article.all()) == len(dataset)\\n\\n # Bulk delete\\n all_but_one = [new_art['id'] for new_art in reply]\\n resp = t_client.delete(url_for('api_v3.bulk_article_delete', api_key=article_owner.api_key),\\n data=json.dumps(all_but_one))\\n assert resp.status_code == 204\\n time.sleep(1)\\n # we should have deleted all but one of the articles.\\n assert len(models.Article.all()) == 1\\n # And our other user isn't allowed to delete the remaining one.\\n resp = t_client.delete(url_for('api_v3.bulk_article_delete', api_key=somebody_else.api_key),\\n data=json.dumps([first_art['id']]))\\n assert resp.status_code == 400\",\n \"def test():\\n\\n tests = unittest.TestLoader().discover('api/tests/', pattern='*/test_*.py')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def _run_local_tests(self, *args, **kwargs):\\n pass\",\n \"def test_client_retrieve(self):\\n pass\",\n \"def test_functionality(self):\\n \\n self.browserObject = globalVars.browserObject\\n\\n self.preRunSetup()\\n \\n self.runTestCase()\\n \\n self.postRunCleanup()\\n \\n self.logout()\",\n \"def test_module(client: Client, *args) -> Tuple[str, dict, dict]:\\n\\n client.run_parameters_validations()\\n\\n for service in client.services:\\n # if there are risk rules, select the first one for test\\n risk_rule = client.risk_rule[0] if client.risk_rule else None\\n client.build_iterator(service, client.indicator_type, risk_rule)\\n client.get_batches_from_file(limit=1)\\n return 'ok', {}, {}\",\n \"def _handler_test_run_tests(self, *args, **kwargs):\\n next_state = None\\n result = None\\n\\n tc_pass = False\\n tt_pass = False\\n tp_pass = False\\n tc_result = None\\n tt_result = None\\n tp_result = None\\n\\n test_result = {}\\n\\n try:\\n tc_pass, tc_result = self._do_cmd_resp('tc', timeout=200)\\n tt_pass, tt_result = self._do_cmd_resp('tt', timeout=200)\\n tp_pass, tp_result = self._do_cmd_resp('tp', timeout=200)\\n \\n except Exception as e:\\n test_result['exception'] = e\\n test_result['message'] = 'Error running instrument tests.'\\n \\n finally:\\n test_result['cond_test'] = 'Passed' if tc_pass else 'Failed'\\n test_result['cond_data'] = tc_result\\n test_result['temp_test'] = 'Passed' if tt_pass else 'Failed'\\n test_result['temp_data'] = tt_result\\n test_result['pres_test'] = 'Passed' if tp_pass else 'Failed'\\n test_result['pres_data'] = tp_result\\n test_result['success'] = 'Passed' if (tc_pass and tt_pass and tp_pass) else 'Failed'\\n \\n self._driver_event(DriverAsyncEvent.TEST_RESULT, test_result)\\n next_state = SBE37ProtocolState.COMMAND\\n \\n return (next_state, result)\",\n \"def run_test(self):\\n raise NotImplementedError\",\n \"def test_integrations(session):\\n session.install('-rrequirements-dev.txt')\\n session.install('-e', '.')\\n run_pytest_integrations(session)\",\n \"def run_main(): # pragma: no cover\\n RunTestsCLI.run()\",\n \"def testExecute(self):\\n client = ICSClientFactory(self.config, 'slave', 'master') \\n\\n #Test reading and writing coils\\n reply = client.execute(cst.READ_COILS, 10, 2)\\n assert reply[0] == False \\n assert reply[1] == False\\n reply = client.execute(cst.WRITE_SINGLE_COIL, 10, 1, output_value=1)\\n reply = client.execute(cst.READ_COILS, 10, 2)\\n assert reply[0] == True\\n assert reply[1] == False\\n reply = client.execute(cst.WRITE_MULTIPLE_COILS, 10, 2,\\n output_value=[0,1])\\n reply = client.execute(cst.READ_COILS, 10, 2)\\n assert reply[1] == True\\n assert reply[0] == False\\n \\n #Test reading and writing input regs\\n #reply = client.execute(cst.READ_INPUT_REGISTERS, 30002, 1)\\n #print \\\"BRDEBUG: Reply: \\\", reply\\n #assert reply[0] == 17\\n\\n #Test reading and setting holding regs\\n reply = client.execute(cst.READ_HOLDING_REGISTERS, 40003, 2)\\n #print \\\"BRDEBUG: Reply\\\", reply\\n assert reply[0] == 16752 \\n assert reply[1] == 0 \\n reply = client.execute(cst.WRITE_SINGLE_REGISTER, 40003, 1,\\n output_value = 10)\\n reply = client.execute(cst.READ_HOLDING_REGISTERS, 40003, 1)\\n #print \\\"BRDEBUG: Reply\\\", reply\\n assert reply[0] == 10\",\n \"def test_call(self):\\n\\n with Client('username', 'password') as client:\\n self.setSessionResponse(content='{\\\"ok\\\": true}')\\n data = client.call(**self.build_parameters)\\n self.assertEqual(data, '{\\\"ok\\\": true}')\",\n \"def run_tests(self):\\n # Charm does not defer hooks so that test is not included.\\n # Trigger a package change which requires a restart\\n self.run_package_change_test(\\n 'ovn-central',\\n 'ovn-central')\",\n \"def runTest(self):\\n self.setUp()\\n self.test_TractQuerier()\",\n \"def test_execute_deployment(self):\\n pass\",\n \"def setUp(self):\\n\\n self.client = server.app.test_client()\\n server.app.config['TESTING'] = True\\n server.app.config['SECRET_KEY'] = \\\"123\\\"\\n\\n # Connect to test database\\n model.connect_to_db(server.app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables and add sample data\\n model.db.create_all()\\n # example_data()\\n\\n with self.client as c:\\n with c.session_transaction() as session:\\n session['user_id'] = 1\\n session['username'] = 'j'\\n session['name'] = 'l'\\n session['cal_id'] = 1\",\n \"def run(args):\\n write_request(args.num_tests)\\n\\n if args.request == 'check':\\n script_files = ['modules/check_vault.py']\\n elif args.request == 'create':\\n script_files = ['modules/create_vault.py']\\n else:\\n raise AssertionError\\n\\n if args.version is None:\\n python_bins = [python2_exec, python3_exec]\\n elif args.version == 2:\\n python_bins = [python2_exec]\\n elif args.version == 3:\\n python_bins = [python3_exec]\\n else:\\n raise AssertionError\\n\\n for script_file in script_files:\\n for python_bin in python_bins:\\n cmd = [python_bin, script_file]\\n subprocess.check_call(cmd)\\n\\n os.unlink('request.txt')\\n\\n # This allows to call this test from another script, that runs other\\n # tests as well.\\n if not args.is_background:\\n send_notification('regression')\",\n \"def client(app):\\n yield app.test_client()\",\n \"def client(app):\\n yield app.test_client()\",\n \"def setUp(self):\\r\\n super(CLITestNameorID, self).setUp()\\r\\n self.mox = mox.Mox()\\r\\n self.endurl = test_cli20.ENDURL\\r\\n self.client = client.Client(token=test_cli20.TOKEN,\\r\\n endpoint_url=self.endurl)\\r\\n self.addCleanup(self.mox.VerifyAll)\\r\\n self.addCleanup(self.mox.UnsetStubs)\",\n \"def runTest(self):\\r\\n self.setUp()\",\n \"def test_update_client(self):\\n pass\",\n \"def test():\\n import unittest\\n\\n tests = unittest.TestLoader().discover(\\\"tests\\\")\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_answer_question(self):\\n with self.client:\\n \\n \\\"\\\"\\\"first insert a question\\\"\\\"\\\"\\n response = self.add_question(\\\"1\\\",\\\"hello\\\",\\\"hello world\\\",\\\"java\\\",\\\"kenneth\\\")\\n self.assertEqual(response.status_code, 201)\\n \\\"\\\"\\\"then get a specific question to answer\\\"\\\"\\\"\\n response = self.post_answer(1,\\\"1\\\",\\\"try removing errors\\\")\\n self.assertEqual(response.status_code, 201)\",\n \"def execute(self):\\n for test in self.tests:\\n test.execute()\\n self.logger.dump()\\n print(\\\"Finished!\\\")\",\n \"def test_integration(self):\\n self.assertTrue(return_true())\",\n \"def setUp(self):\\n\\n self.client = server.app.test_client()\\n server.app.config['TESTING'] = True\\n server.app.config['SECRET_KEY'] = \\\"123\\\"\\n\\n # Connect to test database\\n model.connect_to_db(server.app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables and add sample data\\n model.db.create_all()\\n # example_data()\\n\\n with self.client as c:\\n with c.session_transaction() as session:\\n session['user_id'] = 33\\n session['username'] = 'balloonicorn'\\n session['name'] = 'balloonicorn'\",\n \"async def run_tests():\\n start = time.time()\\n logging.basicConfig(level=logging.DEBUG)\\n\\n # Read the settings.json file\\n path_index = __file__.rfind(\\\"/\\\")\\n if path_index == -1:\\n filepath = \\\"settings.json\\\"\\n else:\\n top_path = __file__[0:path_index]\\n filepath = f\\\"{top_path}/settings.json\\\"\\n with open(filepath) as json_file:\\n data = json.load(json_file)\\n host = data[\\\"connection\\\"][\\\"host\\\"]\\n port = data[\\\"connection\\\"][\\\"port\\\"]\\n username = data[\\\"connection\\\"][\\\"username\\\"]\\n password = data[\\\"connection\\\"][\\\"password\\\"]\\n use_ssl = data[\\\"connection\\\"][\\\"use_ssl\\\"]\\n\\n session = ClientSession()\\n secspy = SecuritySpyServer(host, port, username, password, use_ssl, session)\\n\\n cameras = await camera_list(secspy, True)\\n # await get_server_info(secspy)\\n # await snapshots(secspy, cameras)\\n # await recording_mode(secspy, 2, RECORDING_MODE_MOTION)\\n # await get_recording_mode(secspy, 2)\\n\\n #Close the session\\n await session.close()\\n\\n end = time.time()\\n _LOGGER.info(\\\"Execution time: %s seconds\\\", end - start)\",\n \"def setUp(self):\\r\\n\\r\\n app.config['TESTING'] = True\\r\\n self.client = app.test_client()\",\n \"def testclient():\\n base_url = PARAMS.get(\\\"url\\\") + \\\"/v2\\\"\\n client = Client(\\n base_url=base_url,\\n headers={\\n \\\"Authorization\\\": f\\\"GenieKey {PARAMS.get('token')}\\\",\\n }\\n )\\n return client\",\n \"def setUp(self):\\n _, instance_path, shared_inputs = sys.argv\\n app = lnt.server.ui.app.App.create_standalone(instance_path)\\n app.testing = True\\n self.client = app.test_client()\\n self.shared_inputs = shared_inputs\",\n \"def test_client_list(self):\\n pass\",\n \"def test(self):\\n return self._test(result_count=1, failure_amount=1)\",\n \"def test(self):\\n return self._test(result_count=1, failure_amount=1)\",\n \"def run_integration(session):\\n set_environment_variables(PYBAMM_ENV, session=session)\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[all]\\\")\\n if sys.platform == \\\"linux\\\":\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[odes]\\\")\\n session.run(\\\"python\\\", \\\"run-tests.py\\\", \\\"--integration\\\")\",\n \"def client():\\n flask_app = create_app(\\\"testing\\\")\\n test_client = flask_app.test_client()\\n\\n # create app context\\n ctx = flask_app.app_context()\\n ctx.push()\\n\\n yield test_client\\n \\\"\\\"\\\"\\n TESTS RUN HERE\\n \\\"\\\"\\\"\\n\\n ctx.pop()\",\n \"def setUp(self):\\n self.app = create_app('testing')\\n self.client = self.app.test_client()\",\n \"def client():\\n\\n gcomics_scrape.APP.config['TESTING'] = True\\n test_client = gcomics_scrape.APP.test_client()\\n\\n yield test_client\",\n \"def setUp(self):\\n #app['TESTING'] = True\\n self.test_app = app.test_client()\",\n \"def runTest(self):\\n\\t\\tself.setUp()\\n\\t\\tself.test_postopProgramming1()\",\n \"def runtests():\\r\\n\\r\\n app_abspath = os.path.dirname(os.path.dirname(__file__))\\r\\n models_abspath = os.path.join(app_abspath, 'models.py')\\r\\n models_exists = os.path.isfile(models_abspath)\\r\\n urls_abspath = os.path.join(app_abspath, 'urls.py')\\r\\n urls_exists = os.path.isfile(urls_abspath)\\r\\n views_abspath = os.path.join(app_abspath, 'views')\\r\\n views_exists = os.path.isdir(views_abspath)\\r\\n tpls_abspath = os.path.join(app_abspath, 'templates')\\r\\n tpls_exists = os.path.isdir(tpls_abspath)\\r\\n\\r\\n for f in [models_abspath, urls_abspath]:\\r\\n if os.path.isfile(f):\\r\\n subprocess.call('cp {} {}.orig'.format(f, f), shell=True)\\r\\n\\r\\n if views_exists:\\r\\n subprocess.call('cp -r {} {}.orig'.format(views_abspath, views_abspath), shell=True)\\r\\n\\r\\n if tpls_exists:\\r\\n subprocess.call('cp -r {} {}.orig'.format(tpls_abspath, tpls_abspath), shell=True)\\r\\n\\r\\n overwrite_project_language('ja')\\r\\n subprocess.call('python manage.py generatescaffold test_app I18nModel title:string', shell=True)\\r\\n time.sleep(1)\\r\\n overwrite_project_language('en-us')\\r\\n time.sleep(1)\\r\\n\\r\\n subprocess.call('python manage.py generatescaffold test_app GeneratedNoTimestampModel title:string description:text --no-timestamps', shell=True)\\r\\n time.sleep(2) # Give time for Django's AppCache to clear\\r\\n\\r\\n subprocess.call('python manage.py generatescaffold test_app GeneratedModel title:string description:text', shell=True)\\r\\n\\r\\n test_status = subprocess.call('python manage.py test --with-selenium --with-selenium-fixtures --with-cherrypyliveserver --noinput', shell=True)\\r\\n\\r\\n if models_exists:\\r\\n subprocess.call('mv {}.orig {}'.format(models_abspath, models_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm {}'.format(models_abspath), shell=True)\\r\\n\\r\\n if urls_exists:\\r\\n subprocess.call('mv {}.orig {}'.format(urls_abspath, urls_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm {}'.format(urls_abspath), shell=True)\\r\\n\\r\\n if views_exists:\\r\\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\\r\\n subprocess.call('mv {}.orig {}'.format(views_abspath, views_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\\r\\n\\r\\n if tpls_exists:\\r\\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\\r\\n subprocess.call('mv {}.orig {}'.format(tpls_abspath, tpls_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\\r\\n\\r\\n subprocess.call('rm {}/*.pyc'.format(app_abspath), shell=True)\\r\\n\\r\\n sys.exit(test_status)\",\n \"def client():\\n yield tests.example_server.app.test_client()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7719503","0.7161856","0.7011778","0.69300866","0.6815633","0.67563385","0.67350364","0.6734357","0.6718839","0.6702629","0.6696539","0.6628397","0.65992844","0.65992844","0.6576469","0.65701425","0.65472716","0.65330935","0.6529378","0.6528179","0.65236264","0.65185237","0.65092653","0.6508919","0.6500684","0.64757746","0.64757746","0.64757746","0.64757746","0.64686996","0.6464241","0.6461123","0.6439687","0.6429957","0.64293325","0.641545","0.639558","0.6389907","0.63885206","0.6377802","0.6372604","0.63719314","0.6361801","0.6357071","0.6356283","0.6344431","0.63359416","0.63309014","0.6325925","0.6324534","0.6317951","0.6317075","0.63081455","0.6301115","0.63002443","0.62982804","0.629274","0.6286182","0.62794375","0.62763774","0.6275714","0.62729365","0.62711716","0.6269902","0.62666935","0.6264459","0.6261143","0.626003","0.6246929","0.62436527","0.6236897","0.62304217","0.6228919","0.6216849","0.62132657","0.62112224","0.6208683","0.6208683","0.619284","0.6192464","0.6190269","0.61896914","0.61883324","0.61874557","0.61830306","0.6164188","0.61638874","0.6162628","0.6161753","0.61597043","0.6158659","0.61578923","0.61578923","0.61550236","0.6154931","0.6154153","0.61479986","0.61473775","0.6147058","0.6141074","0.6140632"],"string":"[\n \"0.7719503\",\n \"0.7161856\",\n \"0.7011778\",\n \"0.69300866\",\n \"0.6815633\",\n \"0.67563385\",\n \"0.67350364\",\n \"0.6734357\",\n \"0.6718839\",\n \"0.6702629\",\n \"0.6696539\",\n \"0.6628397\",\n \"0.65992844\",\n \"0.65992844\",\n \"0.6576469\",\n \"0.65701425\",\n \"0.65472716\",\n \"0.65330935\",\n \"0.6529378\",\n \"0.6528179\",\n \"0.65236264\",\n \"0.65185237\",\n \"0.65092653\",\n \"0.6508919\",\n \"0.6500684\",\n \"0.64757746\",\n \"0.64757746\",\n \"0.64757746\",\n \"0.64757746\",\n \"0.64686996\",\n \"0.6464241\",\n \"0.6461123\",\n \"0.6439687\",\n \"0.6429957\",\n \"0.64293325\",\n \"0.641545\",\n \"0.639558\",\n \"0.6389907\",\n \"0.63885206\",\n \"0.6377802\",\n \"0.6372604\",\n \"0.63719314\",\n \"0.6361801\",\n \"0.6357071\",\n \"0.6356283\",\n \"0.6344431\",\n \"0.63359416\",\n \"0.63309014\",\n \"0.6325925\",\n \"0.6324534\",\n \"0.6317951\",\n \"0.6317075\",\n \"0.63081455\",\n \"0.6301115\",\n \"0.63002443\",\n \"0.62982804\",\n \"0.629274\",\n \"0.6286182\",\n \"0.62794375\",\n \"0.62763774\",\n \"0.6275714\",\n \"0.62729365\",\n \"0.62711716\",\n \"0.6269902\",\n \"0.62666935\",\n \"0.6264459\",\n \"0.6261143\",\n \"0.626003\",\n \"0.6246929\",\n \"0.62436527\",\n \"0.6236897\",\n \"0.62304217\",\n \"0.6228919\",\n \"0.6216849\",\n \"0.62132657\",\n \"0.62112224\",\n \"0.6208683\",\n \"0.6208683\",\n \"0.619284\",\n \"0.6192464\",\n \"0.6190269\",\n \"0.61896914\",\n \"0.61883324\",\n \"0.61874557\",\n \"0.61830306\",\n \"0.6164188\",\n \"0.61638874\",\n \"0.6162628\",\n \"0.6161753\",\n \"0.61597043\",\n \"0.6158659\",\n \"0.61578923\",\n \"0.61578923\",\n \"0.61550236\",\n \"0.6154931\",\n \"0.6154153\",\n \"0.61479986\",\n \"0.61473775\",\n \"0.6147058\",\n \"0.6141074\",\n \"0.6140632\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80392,"cells":{"query":{"kind":"string","value":"start and keep services running until manually stopped"},"document":{"kind":"string","value":"def run(tag, devmode, img_passwd_file, install_server_hostname,\n custom_cli_subnet, custom_db_subnet, clitests, builder):\n manager = Manager(\n 'run', tag, devmode=devmode, img_passwd_file=img_passwd_file,\n install_server_hostname=install_server_hostname,\n custom_cli_subnet=custom_cli_subnet, custom_db_subnet=custom_db_subnet,\n clitests=clitests, builder_hostname=builder)\n manager.run()"},"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 ensure_running_service_daemon(context):\n svc = Management()\n if svc._check_if_its_up(usutil.UBERSERVER):\n context.temp_service_daemon = None\n else:\n svc.start()\n context.temp_service_daemon = svc","def startServices():\n # dnsmasq\n out_dnsmasq = subprocess.run([\"systemctl\", \"restart\", \"dnsmasq\"], stdout=subprocess.PIPE)\n if out_dnsmasq.returncode == 0:\n logging.info(\"dnsmasq service started/restarted successfully\")\n else:\n logging.error(\"dnsmasq service start restart error\")\n # 3proxy\n out_3proxy = subprocess.run([\"systemctl\", \"restart\", \"3proxy\"], stdout=subprocess.PIPE)\n if out_3proxy.returncode == 0:\n logging.info(\"3proxy service started/restarted successfully\")\n else:\n logging.error(\"3proxy service start restart error\")","def start( self ):\n\n self.service()","def start_services(self):\n logger.info(\"Starting services: %s\", self.services)\n for service in self.services:\n with hide(*fab_quiet):\n sudo('service %s start' % service)","async def start(self):\n self.log.debug(\"Starting\")\n self.running = True\n self.log.debug(\"Starting service tasks...\")\n await self._start_service_tasks()\n try:\n self.log.debug(\"Starting nested services...\")\n await self._start_nested_services()\n except Exception:\n self.log.exception(\"Failed to start nested service\")\n self.running = False\n self.should_stop = True\n await self._stop_service_tasks()\n raise\n self._monitoring_task = self.loop.create_task(self.monitoring_task(),\n name=f\"{self.name}.monitoring_task\")\n await self.start_bus_reader()\n self.log.debug(\"Service was started\")","def __stop(self):\n\n # send commands\n poller = Poller()\n for (pipe, svc) in self.__services.items():\n pipe.send_string('STOP')\n self.logger.debug('sent STOP command to %s service' % svc)\n poller.register(pipe, POLLIN)\n\n # give services a few seconds to cleanup and exit before checking responses\n sleep(1)\n\n max_attempts = len(self.__services)\n attempts = 0\n\n while self.__some_alive() and attempts < max_attempts:\n attempts += 1\n\n # poll for any replies\n items = dict(poller.poll(60000)) # wait for messages\n\n # mark responding services as stopped\n alive = dict(self.__services) # make copy\n for (pipe, svc) in alive.items():\n if pipe in items:\n reply = pipe.recv_string()\n if 'STOPPED' == reply:\n self.logger.debug('received STOPPED control reply from %s service' % svc)\n svc.join(timeout=5) # STOPPED response should be sent right before svc exit\n if svc.is_alive():\n self.logger.error('%s service is still alive; not waiting' % svc)\n else:\n self.logger.debug('%s service thread stopped' % svc)\n poller.unregister(pipe)\n pipe.close()\n del (self.__services[pipe])\n else:\n self.logger.debug('unknown control reply: %s' % reply)\n\n # log some useful info\n if len(self.__services) > 0:\n msg = '%s services still alive after %d cycles; ' % (\n [str(s) for s in self.__services.values()], attempts)\n if attempts < max_attempts:\n msg += 'waiting'\n else:\n msg += 'giving up'\n self.logger.debug(msg)","def run_services():\n for service in (\"minvd\", \"httpd\", \"ntpd\"):\n sudo(\"service %s start\" % service)\n sudo(\"chkconfig %s on\" % service)","async def start_all(self):\n try:\n for service in self.services:\n try:\n await service.start()\n await service.healthcheck()\n except Exception as e:\n log.exception(\"Exception while starting %s service\", service)\n raise ServiceStartupException from e\n self.started_services.append(service)\n except ServiceStartupException:\n log.error(\"Stopping services on startup failure\")\n await self.stop_all()\n raise","def start(name):\n __salt__[\"file.remove\"](\"{}/down\".format(_service_path(name)))\n cmd = \"svc -u {}\".format(_service_path(name))\n return not __salt__[\"cmd.retcode\"](cmd, python_shell=False)","async def schedule_services_run(self):\n self._running_svcs = [svc.run() for svc in self.services]\n await self.add_task_fn(self._running_svcs)","def _start_stop(self):\n if not self.running:\n #get configuration from gui\n self._start_session()\n else:\n self._stop_session()","def _keep_alive(self):\n yield WaitDBusNameChange('org.freesmartphone.ogsmd')\n logger.error(\"org.freesmartphone.ogsmd crashed\")\n logger.info(\"Attempt to re-init the service\")\n yield self.init()","def services(status):\n\n run(\"sudo systemctl %s xprof.service\" % status)","def run():\r\n autostartup()","def start_services(self):\n ircd_result = True\n synapse_result = self.start_synapse()\n if self.charm_config.get(\"enable-ircd\"):\n ircd_result = self.start_ircd()\n return synapse_result and ircd_result","def run():\n server = current_server()\n server._auto_stop = True\n return start()","def run_forever(self):\n self.factory.manager.run_forever()","def service( self ):\n\n self.alive = time.time()","def reset_services():\n service_init = ServiceContainerInit()\n service_init.notify_listeners()","def stopService(self):\n self.stopping = True\n self.deferreds = {}\n for name in self.processes:\n self.deferreds[name] = Deferred()\n super(DelayedStartupProcessMonitor, self).stopService()\n\n # Cancel any outstanding restarts\n for name, delayedCall in self.restart.items():\n if delayedCall.active():\n delayedCall.cancel()\n\n # Stop processes in the reverse order from which they were added and\n # started\n for name in reversed(self.processes):\n self.stopProcess(name)\n return gatherResults(self.deferreds.values())","def startService(self):\n super(SpawnerService, self).startService()\n for spawn in self.pendingSpawns:\n self.spawn(*spawn)\n self.pendingSpawns = []","def on_start(self):\n self.run_in_background(self.__run_client)","def start_docker_compose_services():\n try:\n run_docker_compose_command('up')\n except KeyboardInterrupt:\n remove_docker_compose_file()","def run(self):\n while not self.stop_event.is_set():\n self.manage_cache_tasks()","def start(self):\n if self.isAlive == False:\n try:\n time.sleep(1)\n os.remove(os.path.join(self.inbox, 'stop_service.txt'))\n except:\n pass\n try:\n time.sleep(1)\n os.remove(os.path.join(self.inbox, 'ReadDirectoryChangesW.txt'))\n except:\n pass\n return\n \n serviceconfig.logger.debug('*** \"%s\": Starting the worker thread' % self.inbox)\n self.queue = Queue()\n t = Thread(target=self.worker)\n t.start()\n \n \"\"\"\n If files were dropped during the recovering process,\n we need to handle those files\n \"\"\"\n timer = Timer(1, self.triggerChangeEvent, kwargs={})\n timer.start()\n \n while self.isAlive:\n self.queue.put(win32file.ReadDirectoryChangesW (\n self.hDir,\n 1024,\n True,\n win32con.FILE_NOTIFY_CHANGE_FILE_NAME |\n win32con.FILE_NOTIFY_CHANGE_DIR_NAME |\n win32con.FILE_NOTIFY_CHANGE_ATTRIBUTES |\n win32con.FILE_NOTIFY_CHANGE_SIZE |\n win32con.FILE_NOTIFY_CHANGE_LAST_WRITE |\n win32con.FILE_NOTIFY_CHANGE_SECURITY,\n None,\n None\n ))\n self.queue.join()\n timer.join()\n \n \"\"\"\n Delete the stop_service.txt file generated by stopping the service\n \"\"\"\n try:\n os.remove(os.path.join(self.inbox, 'stop_service.txt'))\n except:\n pass","def start_service(self, service):\n host.service(\"start\", service)\n host.service(\"enable\", service)\n return host.service_running(service)","def stop(name):\n __salt__[\"file.touch\"](\"{}/down\".format(_service_path(name)))\n cmd = \"svc -d {}\".format(_service_path(name))\n return not __salt__[\"cmd.retcode\"](cmd, python_shell=False)","def stop(self):\n for service_id in self.keys():\n self[service_id].stop()\n del self[service_id]\n\n self._stopped = True","def continue_or_kill_server(self):\n try:\n will_shut_down = self.service.shutdown_service_flag\n except AttributeError:\n print('did not define service shutdown flag in your service. Server cannot be closed with this flag')\n else:\n if will_shut_down:\n self.active = False\n self.close()","def start():\n if env.latest:\n if env.python3:\n sudo('/bin/systemctl start demo-latest-py3', shell=False)\n else:\n sudo('/bin/systemctl start demo-latest.service', shell=False)\n else:\n with cd(env.directory):\n sudo('./bin/supervisorctl start zeoserver', user=env.deploy_user)\n sudo(\"sleep 2\")\n sudo('./bin/supervisorctl start zeoclient1', user=env.deploy_user)\n sudo(\"sleep 2\")\n sudo('./bin/supervisorctl start zeoclient2', user=env.deploy_user)","def run(self):\n while self.container.process(): pass","def daemonControlStop (self):\n self.stop()","async def monitoring_task(self):\n while not self.should_stop:\n try:\n await self.healthcheck()\n except UnhealthyException:\n self.log.exception(\"Healthcheck failed with exception\")\n break\n except Exception: # noqa\n self.log.exception(\"Service healthcheck failed with unexpected exception\")\n break\n await asyncio.sleep(self._monitoring_interval)\n # terminate service on exit\n if not self.should_stop:\n await self.stop()","def stop_services(self):\n logger.info(\"Stopping services: %s\", self.services)\n for service in self.services:\n with hide(*fab_quiet):\n sudo('service %s stop' % service)","async def listen(self):\n self.failed_attempts = 0\n while self.state != STATE_STOPPED:\n await self.running()","def run_forever(self):\n self.ws.run_forever()","def start(self):\n try:\n self.getEverything()\n self._watchFolder()\n except Unauthorized, e:\n self.authorize()\n self.start()\n \n #TODO: make this work\n #self._setPeriodicSync()\n \n print 'stopped'","def startService(self):\n self.world.start()","def stop_running(self):\n self.running = False","async def stop(self):\n self.should_stop = True\n self.running = False\n await self.stop_bus_reader()\n if self._monitoring_task:\n self._monitoring_task.cancel()\n self.log.debug(\"Stopping nested services...\")\n await self._stop_nested_services()\n self.log.debug(\"Stopping service tasks...\")\n await self._stop_service_tasks()\n self.log.debug(\"Service was stopped\")","def restart():\n stop()\n start()","def start_service(service_name):\n subprocess.run([SUPERVISOR_CMD, \"start\", service_name])","def run(self):\n if not self.running:\n self.loop.run_forever()","def start(self) -> None:\n self.stopping.clear()\n self.thread = threading.Thread(target=self._run, daemon=True, name=self.thread_name)\n self.thread.start()","def service(self):\n\n # pylint: disable=W0201\n thread = threading.Thread(target=self.run)\n thread.start()\n try:\n while not self.started:\n time.sleep(1e-3)\n yield\n\n finally:\n self.should_exit = True\n thread.join()","def restart():\n log('reiniciando servicos', yellow)\n nginx_stop()\n nginx_start()\n nginx_restart()\n nginx_reload()\n supervisor_stop()\n supervisor_start()","def _keep_running():\n return True","def run_service(self):\n self.alerts_queue = Queue(maxsize=SERVICE_ALERT_QUEUE_SIZE)\n self.thread_server = Thread(target=self._on_server_start)\n # self.thread_server.daemon = True\n self.thread_server.start()\n\n try:\n while self.thread_server.is_alive():\n try:\n new_alert = self.alerts_queue.get(timeout=1)\n self.emit(**new_alert)\n except Empty:\n continue\n except KeyboardInterrupt:\n self.logger.debug(\"Caught KeyboardInterrupt, shutting service down gracefully\")\n raise\n except Exception as exc:\n self.logger.exception(exc)\n finally:\n self._on_server_shutdown()","def terminate_services(self, services):\n services = self._filter_cid(services)\n for service in services:\n ctr = self.check_service_running(service,\n raise_on=['terminated'])\n logger.info(\"Stopping and \"\n \"removing docker instance : %s\" % service)\n self.driver.stop_container(ctr['Id'], remove=True)\n if service not in self._dirty_service:\n self._dirty_service[service] = {\"ctr\": ctr,\n \"terminated\": True}\n else:\n self._dirty_service[service][\"terminated\"] = True\n return services","def startRhevmDbRelatedServices():\n (output, rc) = etlService.conditionalStart()\n if rc != 0:\n logging.warn(\"Failed to start rhevm-etl\")\n controller.MESSAGES.append(output_messages.ERR_FAILED_START_SERVICE % \"rhevm-etl\")\n\n (output, rc) = notificationService.conditionalStart()\n if rc != 0:\n logging.warn(\"Failed to start rhevm-notifierd\")\n controller.MESSAGES.append(output_messages.ERR_FAILED_START_SERVICE % \"rhevm-notifierd\")","def kill(self):\n # Prevent a weird behavior: when STOPPED and kill() is called, app crashes (FIXME)\n if self.__state is not ServiceState.STOPPED:\n os.kill(int(self.__properties['MainPID']), signal.SIGKILL)\n # Not nice but simple and currently working (FIXME)\n # TODO: Change time.sleep to wait until process of same service but different PID is up and running\n time.sleep(0.5)","def stop_services(self, services):\n services = self._filter_cid(services)\n for service in services:\n ctr = self.check_service_running(service, raise_on=['terminated'])\n logger.info(\"Stopping docker instance : %s\" % service)\n self.driver.stop_container(ctr['Id'])\n if service not in self._dirty_service:\n self._dirty_service[service] = {\"ctr\": ctr,\n \"terminated\": False}\n\n # self.store.update_service_map()\n return services","def stop_all():\n\twhile _running:\n\t\t_running[0].stop(noerror=True)","def reload_svc(self, svc_names: List[str]):\n self._send_signals(svc_names, \"SIGHUP\")","def _periodic(self, name, action, args=()):\n if self._services[name].stop_flag.is_set():\n self.log.debug('Service {name} done'.format(**locals()))\n self._services[name].running = False\n self._services[name].thread = None\n self._services[name].stop_flag = None\n self._services[name].scheduler_event = None\n return\n\n service = self._services[name].service\n self._services[name].scheduler_event = \\\n self._scheduler.enter(service.interval, service.priority, self._periodic, (name, service.step, args))\n self.log.debug('Running service step {name}'.format(**locals()))\n action(*args)","def stop(self):\n self.stopped = True\n self.broadcast('host down')","def run(self): \n #\n\n \n # forever loop\n while True: \n \n for app in self.app_list:\n self.check(app) \n #print(\"check\")\n \n gevent.sleep(SLEEP_SECONDS)","def update_services(self, new_services_list):\n to_stop = [service for service in self if service not in new_services_list]\n for service_id in to_stop:\n self[service_id].stop()\n del self[service_id]\n\n for service_id in new_services_list:\n if service_id not in self:\n self[service_id] = ServiceManager(self.zk_client, self.project_id,\n service_id, self.callback)","def kill_background_processes_and_run_apt_get_update(ec2_conn):\n apt_daily_services_list = [\"apt-daily.service\", \"apt-daily-upgrade.service\", \"unattended-upgrades.service\"]\n apt_daily_services = \" \".join(apt_daily_services_list)\n ec2_conn.run(f\"sudo systemctl stop {apt_daily_services}\")\n ec2_conn.run(f\"sudo systemctl kill --kill-who=all {apt_daily_services}\")\n num_stopped_services = 0\n # The `systemctl kill` command is expected to take about 1 second. The 60 second loop here exists to force\n # the execution to wait (if needed) for a longer amount of time than it would normally take to kill the services.\n for _ in range(60):\n sleep(1)\n # List the apt-daily services, get the number of dead services\n num_stopped_services = int(ec2_conn.run(\n f\"systemctl list-units --all {apt_daily_services} | egrep '(dead|failed)' | wc -l\"\n ).stdout.strip())\n # Exit condition for the loop is when all apt daily services are dead.\n if num_stopped_services == len(apt_daily_services_list):\n break\n if num_stopped_services != len(apt_daily_services_list):\n raise RuntimeError(\n \"Failed to kill background services to allow apt installs on SM Local EC2 instance. \"\n f\"{len(apt_daily_services) - num_stopped_services} still remaining.\"\n )\n ec2_conn.run(\"sudo rm -rf /var/lib/dpkg/lock*;\")\n ec2_conn.run(\"sudo dpkg --configure -a;\")\n ec2_conn.run(\"sudo apt-get update\")\n return","def run(self):\n while True:\n self.sm.run()\n time.sleep(0.05)","def monitor(self):\n if self.startup():\n time.sleep(0.250)\n self.run()","def stop_service(service_name):\n subprocess.run([SUPERVISOR_CMD, \"stop\", service_name])","def stopEngines():\n pass","def on_stop(self):\n pass","def on_stop(self):\n pass","def on_stop(self):\n pass","def on_stop(self):\n pass","def on_stop(self):\n pass","def on_stop(self):\n pass","def on_stop(self):\n pass","def Restart(self, udp=False):\n self.Stop()\n self.Start(udp)","def gracefully_terminate(self):\n self.running = False","async def _start_service_monitor(cls):\n cls.service_monitor = Monitor()\n await cls.service_monitor.start()","def run(self):\n self._app.processEvents()\n try:\n while not self._stop:\n # GRobot._app.processEvents()\n while self._app.hasPendingEvents():\n self._app.processEvents()\n gevent.sleep(0.01)\n except Exception, e:\n logger.error(e)\n logger.debug('Goodbye GRobot')","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def setup_service(path):\n\n pidfile = join(path, 'run.pid')\n if exists(pidfile):\n try:\n handle = open(pidfile, 'r')\n pid = int(handle.read())\n handle.close()\n os.kill(pid, signal.SIGHUP)\n # Wait until the proces has quit -- otherwise it will remove\n # our pid file and conflict with our bind calls.\n for i in range(20): # 4 seconds\n try:\n os.kill(pid, 0)\n time.sleep(0.2)\n except OSError:\n break;\n except Exception as e:\n print('Error killing old wmbinder instance: %s' % e)\n\n\n try:\n handle = open(pidfile, 'w')\n handle.write(str(os.getpid()))\n handle.close()\n atexit.register(_remove_pid_file, pidfile)\n except Exception as e:\n print('Error writing pid file: %s' % e)","def run(self):\n try:\n while self._running:\n time.sleep(1)\n finally:\n self._exit()","def loop_forever(self):\n while self.running:\n time.sleep(0.01)","def _start(self):\n\n super(PySwitchLibApiDaemonRunner, self)._start()","def start_services(torconfig):\n for service in config.custom['services']:\n # load service\n try:\n service_mod = imp.load_module(\n service, *imp.find_module(service, [config.services_dir]))\n except ImportError:\n return log.err('Cannot import service %s' % service)\n except Exception as e:\n traceback.print_tb(sys.exc_info()[2])\n return log.err('Error loading service %s -\\n %s' % (service, e))\n\n service = getattr(service_mod, 'ServiceDescriptor', None)\n if not service:\n log.err('Unable to find class Service in ', repr(service_mod))\n continue\n\n # create hidden service\n add_service(torconfig, service())","def services():\n return list(set(chain(*restart_map().values())))","def reset_and_stop(self):\n self.enabled = False\n self.start_time = None","def start_as_service(self):\n from ..program_manager import ProgramManager\n send_action(ProgramManager.NAME, 'start', self.name)","def startService(self):\n super(MasterService, self).startService()\n self.dispatcher.startDispatching()","def stop(self):\r\n self.running = False","def stop(self):\r\n self.running = False","def restart(self):\n self.stop()\n self.start(init=False)","def stop(self):\n\n log_info(\"Stopping LiteServ ...\")\n\n self.logfile.flush()\n self.logfile.close()\n self.process.kill()\n self.process.wait()\n\n self._verify_not_running()","def run(self):\n self._keep_running()","def test_stopServiceCleanupScheduledRestarts(self):\r\n self.pm.threshold = 5\r\n self.pm.minRestartDelay = 5\r\n # Start service and add a process (started immediately)\r\n self.pm.startService()\r\n self.pm.addProcess(\"foo\", [\"foo\"])\r\n # Stop the process after 1s\r\n self.reactor.advance(1)\r\n self.pm.stopProcess(\"foo\")\r\n # Wait 1s for it to exit it will be scheduled to restart 5s later\r\n self.reactor.advance(1)\r\n # Meanwhile stop the service\r\n self.pm.stopService()\r\n # Advance to beyond the process restart time\r\n self.reactor.advance(6)\r\n # The process shouldn't have restarted because stopService has cancelled\r\n # all pending process restarts.\r\n self.assertEqual(self.pm.protocols, {})","def restart(service):\n # TODO: replace this with your relevant restart logic\n assert service.isalpha()\n run(\"service\", service, \"restart\")","async def start(self):\n while not self.should_stop:\n self.executor.submit(self._submit_unpack_kwargs, self.params)\n if self.oneshot:\n break\n await asyncio.sleep(self.interval)","def stop(self):\r\n self.stopped = True\r\n time.sleep(1)","def restart():\n run_commands('python manage.py supervisor restart all')","def start(self):\n gevent.spawn_later(self._period, self._run)","def start(self):\n try:\n self._unsafe_run()\n except Exception:\n _logger.exception(\"Failure while managing state machine.\")\n for shutdown_flag in self._shutdown_flags:\n shutdown_flag.set_value()","def stop():\n # Wyczyszczenie schedulera spowoduje czyste zakonczenie watku\n with lock:\n global running, s\n\n for event in s.queue:\n s.cancel(event)\n\n running = False\n s = sched.scheduler()\n statuses.clear()"],"string":"[\n \"def ensure_running_service_daemon(context):\\n svc = Management()\\n if svc._check_if_its_up(usutil.UBERSERVER):\\n context.temp_service_daemon = None\\n else:\\n svc.start()\\n context.temp_service_daemon = svc\",\n \"def startServices():\\n # dnsmasq\\n out_dnsmasq = subprocess.run([\\\"systemctl\\\", \\\"restart\\\", \\\"dnsmasq\\\"], stdout=subprocess.PIPE)\\n if out_dnsmasq.returncode == 0:\\n logging.info(\\\"dnsmasq service started/restarted successfully\\\")\\n else:\\n logging.error(\\\"dnsmasq service start restart error\\\")\\n # 3proxy\\n out_3proxy = subprocess.run([\\\"systemctl\\\", \\\"restart\\\", \\\"3proxy\\\"], stdout=subprocess.PIPE)\\n if out_3proxy.returncode == 0:\\n logging.info(\\\"3proxy service started/restarted successfully\\\")\\n else:\\n logging.error(\\\"3proxy service start restart error\\\")\",\n \"def start( self ):\\n\\n self.service()\",\n \"def start_services(self):\\n logger.info(\\\"Starting services: %s\\\", self.services)\\n for service in self.services:\\n with hide(*fab_quiet):\\n sudo('service %s start' % service)\",\n \"async def start(self):\\n self.log.debug(\\\"Starting\\\")\\n self.running = True\\n self.log.debug(\\\"Starting service tasks...\\\")\\n await self._start_service_tasks()\\n try:\\n self.log.debug(\\\"Starting nested services...\\\")\\n await self._start_nested_services()\\n except Exception:\\n self.log.exception(\\\"Failed to start nested service\\\")\\n self.running = False\\n self.should_stop = True\\n await self._stop_service_tasks()\\n raise\\n self._monitoring_task = self.loop.create_task(self.monitoring_task(),\\n name=f\\\"{self.name}.monitoring_task\\\")\\n await self.start_bus_reader()\\n self.log.debug(\\\"Service was started\\\")\",\n \"def __stop(self):\\n\\n # send commands\\n poller = Poller()\\n for (pipe, svc) in self.__services.items():\\n pipe.send_string('STOP')\\n self.logger.debug('sent STOP command to %s service' % svc)\\n poller.register(pipe, POLLIN)\\n\\n # give services a few seconds to cleanup and exit before checking responses\\n sleep(1)\\n\\n max_attempts = len(self.__services)\\n attempts = 0\\n\\n while self.__some_alive() and attempts < max_attempts:\\n attempts += 1\\n\\n # poll for any replies\\n items = dict(poller.poll(60000)) # wait for messages\\n\\n # mark responding services as stopped\\n alive = dict(self.__services) # make copy\\n for (pipe, svc) in alive.items():\\n if pipe in items:\\n reply = pipe.recv_string()\\n if 'STOPPED' == reply:\\n self.logger.debug('received STOPPED control reply from %s service' % svc)\\n svc.join(timeout=5) # STOPPED response should be sent right before svc exit\\n if svc.is_alive():\\n self.logger.error('%s service is still alive; not waiting' % svc)\\n else:\\n self.logger.debug('%s service thread stopped' % svc)\\n poller.unregister(pipe)\\n pipe.close()\\n del (self.__services[pipe])\\n else:\\n self.logger.debug('unknown control reply: %s' % reply)\\n\\n # log some useful info\\n if len(self.__services) > 0:\\n msg = '%s services still alive after %d cycles; ' % (\\n [str(s) for s in self.__services.values()], attempts)\\n if attempts < max_attempts:\\n msg += 'waiting'\\n else:\\n msg += 'giving up'\\n self.logger.debug(msg)\",\n \"def run_services():\\n for service in (\\\"minvd\\\", \\\"httpd\\\", \\\"ntpd\\\"):\\n sudo(\\\"service %s start\\\" % service)\\n sudo(\\\"chkconfig %s on\\\" % service)\",\n \"async def start_all(self):\\n try:\\n for service in self.services:\\n try:\\n await service.start()\\n await service.healthcheck()\\n except Exception as e:\\n log.exception(\\\"Exception while starting %s service\\\", service)\\n raise ServiceStartupException from e\\n self.started_services.append(service)\\n except ServiceStartupException:\\n log.error(\\\"Stopping services on startup failure\\\")\\n await self.stop_all()\\n raise\",\n \"def start(name):\\n __salt__[\\\"file.remove\\\"](\\\"{}/down\\\".format(_service_path(name)))\\n cmd = \\\"svc -u {}\\\".format(_service_path(name))\\n return not __salt__[\\\"cmd.retcode\\\"](cmd, python_shell=False)\",\n \"async def schedule_services_run(self):\\n self._running_svcs = [svc.run() for svc in self.services]\\n await self.add_task_fn(self._running_svcs)\",\n \"def _start_stop(self):\\n if not self.running:\\n #get configuration from gui\\n self._start_session()\\n else:\\n self._stop_session()\",\n \"def _keep_alive(self):\\n yield WaitDBusNameChange('org.freesmartphone.ogsmd')\\n logger.error(\\\"org.freesmartphone.ogsmd crashed\\\")\\n logger.info(\\\"Attempt to re-init the service\\\")\\n yield self.init()\",\n \"def services(status):\\n\\n run(\\\"sudo systemctl %s xprof.service\\\" % status)\",\n \"def run():\\r\\n autostartup()\",\n \"def start_services(self):\\n ircd_result = True\\n synapse_result = self.start_synapse()\\n if self.charm_config.get(\\\"enable-ircd\\\"):\\n ircd_result = self.start_ircd()\\n return synapse_result and ircd_result\",\n \"def run():\\n server = current_server()\\n server._auto_stop = True\\n return start()\",\n \"def run_forever(self):\\n self.factory.manager.run_forever()\",\n \"def service( self ):\\n\\n self.alive = time.time()\",\n \"def reset_services():\\n service_init = ServiceContainerInit()\\n service_init.notify_listeners()\",\n \"def stopService(self):\\n self.stopping = True\\n self.deferreds = {}\\n for name in self.processes:\\n self.deferreds[name] = Deferred()\\n super(DelayedStartupProcessMonitor, self).stopService()\\n\\n # Cancel any outstanding restarts\\n for name, delayedCall in self.restart.items():\\n if delayedCall.active():\\n delayedCall.cancel()\\n\\n # Stop processes in the reverse order from which they were added and\\n # started\\n for name in reversed(self.processes):\\n self.stopProcess(name)\\n return gatherResults(self.deferreds.values())\",\n \"def startService(self):\\n super(SpawnerService, self).startService()\\n for spawn in self.pendingSpawns:\\n self.spawn(*spawn)\\n self.pendingSpawns = []\",\n \"def on_start(self):\\n self.run_in_background(self.__run_client)\",\n \"def start_docker_compose_services():\\n try:\\n run_docker_compose_command('up')\\n except KeyboardInterrupt:\\n remove_docker_compose_file()\",\n \"def run(self):\\n while not self.stop_event.is_set():\\n self.manage_cache_tasks()\",\n \"def start(self):\\n if self.isAlive == False:\\n try:\\n time.sleep(1)\\n os.remove(os.path.join(self.inbox, 'stop_service.txt'))\\n except:\\n pass\\n try:\\n time.sleep(1)\\n os.remove(os.path.join(self.inbox, 'ReadDirectoryChangesW.txt'))\\n except:\\n pass\\n return\\n \\n serviceconfig.logger.debug('*** \\\"%s\\\": Starting the worker thread' % self.inbox)\\n self.queue = Queue()\\n t = Thread(target=self.worker)\\n t.start()\\n \\n \\\"\\\"\\\"\\n If files were dropped during the recovering process,\\n we need to handle those files\\n \\\"\\\"\\\"\\n timer = Timer(1, self.triggerChangeEvent, kwargs={})\\n timer.start()\\n \\n while self.isAlive:\\n self.queue.put(win32file.ReadDirectoryChangesW (\\n self.hDir,\\n 1024,\\n True,\\n win32con.FILE_NOTIFY_CHANGE_FILE_NAME |\\n win32con.FILE_NOTIFY_CHANGE_DIR_NAME |\\n win32con.FILE_NOTIFY_CHANGE_ATTRIBUTES |\\n win32con.FILE_NOTIFY_CHANGE_SIZE |\\n win32con.FILE_NOTIFY_CHANGE_LAST_WRITE |\\n win32con.FILE_NOTIFY_CHANGE_SECURITY,\\n None,\\n None\\n ))\\n self.queue.join()\\n timer.join()\\n \\n \\\"\\\"\\\"\\n Delete the stop_service.txt file generated by stopping the service\\n \\\"\\\"\\\"\\n try:\\n os.remove(os.path.join(self.inbox, 'stop_service.txt'))\\n except:\\n pass\",\n \"def start_service(self, service):\\n host.service(\\\"start\\\", service)\\n host.service(\\\"enable\\\", service)\\n return host.service_running(service)\",\n \"def stop(name):\\n __salt__[\\\"file.touch\\\"](\\\"{}/down\\\".format(_service_path(name)))\\n cmd = \\\"svc -d {}\\\".format(_service_path(name))\\n return not __salt__[\\\"cmd.retcode\\\"](cmd, python_shell=False)\",\n \"def stop(self):\\n for service_id in self.keys():\\n self[service_id].stop()\\n del self[service_id]\\n\\n self._stopped = True\",\n \"def continue_or_kill_server(self):\\n try:\\n will_shut_down = self.service.shutdown_service_flag\\n except AttributeError:\\n print('did not define service shutdown flag in your service. Server cannot be closed with this flag')\\n else:\\n if will_shut_down:\\n self.active = False\\n self.close()\",\n \"def start():\\n if env.latest:\\n if env.python3:\\n sudo('/bin/systemctl start demo-latest-py3', shell=False)\\n else:\\n sudo('/bin/systemctl start demo-latest.service', shell=False)\\n else:\\n with cd(env.directory):\\n sudo('./bin/supervisorctl start zeoserver', user=env.deploy_user)\\n sudo(\\\"sleep 2\\\")\\n sudo('./bin/supervisorctl start zeoclient1', user=env.deploy_user)\\n sudo(\\\"sleep 2\\\")\\n sudo('./bin/supervisorctl start zeoclient2', user=env.deploy_user)\",\n \"def run(self):\\n while self.container.process(): pass\",\n \"def daemonControlStop (self):\\n self.stop()\",\n \"async def monitoring_task(self):\\n while not self.should_stop:\\n try:\\n await self.healthcheck()\\n except UnhealthyException:\\n self.log.exception(\\\"Healthcheck failed with exception\\\")\\n break\\n except Exception: # noqa\\n self.log.exception(\\\"Service healthcheck failed with unexpected exception\\\")\\n break\\n await asyncio.sleep(self._monitoring_interval)\\n # terminate service on exit\\n if not self.should_stop:\\n await self.stop()\",\n \"def stop_services(self):\\n logger.info(\\\"Stopping services: %s\\\", self.services)\\n for service in self.services:\\n with hide(*fab_quiet):\\n sudo('service %s stop' % service)\",\n \"async def listen(self):\\n self.failed_attempts = 0\\n while self.state != STATE_STOPPED:\\n await self.running()\",\n \"def run_forever(self):\\n self.ws.run_forever()\",\n \"def start(self):\\n try:\\n self.getEverything()\\n self._watchFolder()\\n except Unauthorized, e:\\n self.authorize()\\n self.start()\\n \\n #TODO: make this work\\n #self._setPeriodicSync()\\n \\n print 'stopped'\",\n \"def startService(self):\\n self.world.start()\",\n \"def stop_running(self):\\n self.running = False\",\n \"async def stop(self):\\n self.should_stop = True\\n self.running = False\\n await self.stop_bus_reader()\\n if self._monitoring_task:\\n self._monitoring_task.cancel()\\n self.log.debug(\\\"Stopping nested services...\\\")\\n await self._stop_nested_services()\\n self.log.debug(\\\"Stopping service tasks...\\\")\\n await self._stop_service_tasks()\\n self.log.debug(\\\"Service was stopped\\\")\",\n \"def restart():\\n stop()\\n start()\",\n \"def start_service(service_name):\\n subprocess.run([SUPERVISOR_CMD, \\\"start\\\", service_name])\",\n \"def run(self):\\n if not self.running:\\n self.loop.run_forever()\",\n \"def start(self) -> None:\\n self.stopping.clear()\\n self.thread = threading.Thread(target=self._run, daemon=True, name=self.thread_name)\\n self.thread.start()\",\n \"def service(self):\\n\\n # pylint: disable=W0201\\n thread = threading.Thread(target=self.run)\\n thread.start()\\n try:\\n while not self.started:\\n time.sleep(1e-3)\\n yield\\n\\n finally:\\n self.should_exit = True\\n thread.join()\",\n \"def restart():\\n log('reiniciando servicos', yellow)\\n nginx_stop()\\n nginx_start()\\n nginx_restart()\\n nginx_reload()\\n supervisor_stop()\\n supervisor_start()\",\n \"def _keep_running():\\n return True\",\n \"def run_service(self):\\n self.alerts_queue = Queue(maxsize=SERVICE_ALERT_QUEUE_SIZE)\\n self.thread_server = Thread(target=self._on_server_start)\\n # self.thread_server.daemon = True\\n self.thread_server.start()\\n\\n try:\\n while self.thread_server.is_alive():\\n try:\\n new_alert = self.alerts_queue.get(timeout=1)\\n self.emit(**new_alert)\\n except Empty:\\n continue\\n except KeyboardInterrupt:\\n self.logger.debug(\\\"Caught KeyboardInterrupt, shutting service down gracefully\\\")\\n raise\\n except Exception as exc:\\n self.logger.exception(exc)\\n finally:\\n self._on_server_shutdown()\",\n \"def terminate_services(self, services):\\n services = self._filter_cid(services)\\n for service in services:\\n ctr = self.check_service_running(service,\\n raise_on=['terminated'])\\n logger.info(\\\"Stopping and \\\"\\n \\\"removing docker instance : %s\\\" % service)\\n self.driver.stop_container(ctr['Id'], remove=True)\\n if service not in self._dirty_service:\\n self._dirty_service[service] = {\\\"ctr\\\": ctr,\\n \\\"terminated\\\": True}\\n else:\\n self._dirty_service[service][\\\"terminated\\\"] = True\\n return services\",\n \"def startRhevmDbRelatedServices():\\n (output, rc) = etlService.conditionalStart()\\n if rc != 0:\\n logging.warn(\\\"Failed to start rhevm-etl\\\")\\n controller.MESSAGES.append(output_messages.ERR_FAILED_START_SERVICE % \\\"rhevm-etl\\\")\\n\\n (output, rc) = notificationService.conditionalStart()\\n if rc != 0:\\n logging.warn(\\\"Failed to start rhevm-notifierd\\\")\\n controller.MESSAGES.append(output_messages.ERR_FAILED_START_SERVICE % \\\"rhevm-notifierd\\\")\",\n \"def kill(self):\\n # Prevent a weird behavior: when STOPPED and kill() is called, app crashes (FIXME)\\n if self.__state is not ServiceState.STOPPED:\\n os.kill(int(self.__properties['MainPID']), signal.SIGKILL)\\n # Not nice but simple and currently working (FIXME)\\n # TODO: Change time.sleep to wait until process of same service but different PID is up and running\\n time.sleep(0.5)\",\n \"def stop_services(self, services):\\n services = self._filter_cid(services)\\n for service in services:\\n ctr = self.check_service_running(service, raise_on=['terminated'])\\n logger.info(\\\"Stopping docker instance : %s\\\" % service)\\n self.driver.stop_container(ctr['Id'])\\n if service not in self._dirty_service:\\n self._dirty_service[service] = {\\\"ctr\\\": ctr,\\n \\\"terminated\\\": False}\\n\\n # self.store.update_service_map()\\n return services\",\n \"def stop_all():\\n\\twhile _running:\\n\\t\\t_running[0].stop(noerror=True)\",\n \"def reload_svc(self, svc_names: List[str]):\\n self._send_signals(svc_names, \\\"SIGHUP\\\")\",\n \"def _periodic(self, name, action, args=()):\\n if self._services[name].stop_flag.is_set():\\n self.log.debug('Service {name} done'.format(**locals()))\\n self._services[name].running = False\\n self._services[name].thread = None\\n self._services[name].stop_flag = None\\n self._services[name].scheduler_event = None\\n return\\n\\n service = self._services[name].service\\n self._services[name].scheduler_event = \\\\\\n self._scheduler.enter(service.interval, service.priority, self._periodic, (name, service.step, args))\\n self.log.debug('Running service step {name}'.format(**locals()))\\n action(*args)\",\n \"def stop(self):\\n self.stopped = True\\n self.broadcast('host down')\",\n \"def run(self): \\n #\\n\\n \\n # forever loop\\n while True: \\n \\n for app in self.app_list:\\n self.check(app) \\n #print(\\\"check\\\")\\n \\n gevent.sleep(SLEEP_SECONDS)\",\n \"def update_services(self, new_services_list):\\n to_stop = [service for service in self if service not in new_services_list]\\n for service_id in to_stop:\\n self[service_id].stop()\\n del self[service_id]\\n\\n for service_id in new_services_list:\\n if service_id not in self:\\n self[service_id] = ServiceManager(self.zk_client, self.project_id,\\n service_id, self.callback)\",\n \"def kill_background_processes_and_run_apt_get_update(ec2_conn):\\n apt_daily_services_list = [\\\"apt-daily.service\\\", \\\"apt-daily-upgrade.service\\\", \\\"unattended-upgrades.service\\\"]\\n apt_daily_services = \\\" \\\".join(apt_daily_services_list)\\n ec2_conn.run(f\\\"sudo systemctl stop {apt_daily_services}\\\")\\n ec2_conn.run(f\\\"sudo systemctl kill --kill-who=all {apt_daily_services}\\\")\\n num_stopped_services = 0\\n # The `systemctl kill` command is expected to take about 1 second. The 60 second loop here exists to force\\n # the execution to wait (if needed) for a longer amount of time than it would normally take to kill the services.\\n for _ in range(60):\\n sleep(1)\\n # List the apt-daily services, get the number of dead services\\n num_stopped_services = int(ec2_conn.run(\\n f\\\"systemctl list-units --all {apt_daily_services} | egrep '(dead|failed)' | wc -l\\\"\\n ).stdout.strip())\\n # Exit condition for the loop is when all apt daily services are dead.\\n if num_stopped_services == len(apt_daily_services_list):\\n break\\n if num_stopped_services != len(apt_daily_services_list):\\n raise RuntimeError(\\n \\\"Failed to kill background services to allow apt installs on SM Local EC2 instance. \\\"\\n f\\\"{len(apt_daily_services) - num_stopped_services} still remaining.\\\"\\n )\\n ec2_conn.run(\\\"sudo rm -rf /var/lib/dpkg/lock*;\\\")\\n ec2_conn.run(\\\"sudo dpkg --configure -a;\\\")\\n ec2_conn.run(\\\"sudo apt-get update\\\")\\n return\",\n \"def run(self):\\n while True:\\n self.sm.run()\\n time.sleep(0.05)\",\n \"def monitor(self):\\n if self.startup():\\n time.sleep(0.250)\\n self.run()\",\n \"def stop_service(service_name):\\n subprocess.run([SUPERVISOR_CMD, \\\"stop\\\", service_name])\",\n \"def stopEngines():\\n pass\",\n \"def on_stop(self):\\n pass\",\n \"def on_stop(self):\\n pass\",\n \"def on_stop(self):\\n pass\",\n \"def on_stop(self):\\n pass\",\n \"def on_stop(self):\\n pass\",\n \"def on_stop(self):\\n pass\",\n \"def on_stop(self):\\n pass\",\n \"def Restart(self, udp=False):\\n self.Stop()\\n self.Start(udp)\",\n \"def gracefully_terminate(self):\\n self.running = False\",\n \"async def _start_service_monitor(cls):\\n cls.service_monitor = Monitor()\\n await cls.service_monitor.start()\",\n \"def run(self):\\n self._app.processEvents()\\n try:\\n while not self._stop:\\n # GRobot._app.processEvents()\\n while self._app.hasPendingEvents():\\n self._app.processEvents()\\n gevent.sleep(0.01)\\n except Exception, e:\\n logger.error(e)\\n logger.debug('Goodbye GRobot')\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def setup_service(path):\\n\\n pidfile = join(path, 'run.pid')\\n if exists(pidfile):\\n try:\\n handle = open(pidfile, 'r')\\n pid = int(handle.read())\\n handle.close()\\n os.kill(pid, signal.SIGHUP)\\n # Wait until the proces has quit -- otherwise it will remove\\n # our pid file and conflict with our bind calls.\\n for i in range(20): # 4 seconds\\n try:\\n os.kill(pid, 0)\\n time.sleep(0.2)\\n except OSError:\\n break;\\n except Exception as e:\\n print('Error killing old wmbinder instance: %s' % e)\\n\\n\\n try:\\n handle = open(pidfile, 'w')\\n handle.write(str(os.getpid()))\\n handle.close()\\n atexit.register(_remove_pid_file, pidfile)\\n except Exception as e:\\n print('Error writing pid file: %s' % e)\",\n \"def run(self):\\n try:\\n while self._running:\\n time.sleep(1)\\n finally:\\n self._exit()\",\n \"def loop_forever(self):\\n while self.running:\\n time.sleep(0.01)\",\n \"def _start(self):\\n\\n super(PySwitchLibApiDaemonRunner, self)._start()\",\n \"def start_services(torconfig):\\n for service in config.custom['services']:\\n # load service\\n try:\\n service_mod = imp.load_module(\\n service, *imp.find_module(service, [config.services_dir]))\\n except ImportError:\\n return log.err('Cannot import service %s' % service)\\n except Exception as e:\\n traceback.print_tb(sys.exc_info()[2])\\n return log.err('Error loading service %s -\\\\n %s' % (service, e))\\n\\n service = getattr(service_mod, 'ServiceDescriptor', None)\\n if not service:\\n log.err('Unable to find class Service in ', repr(service_mod))\\n continue\\n\\n # create hidden service\\n add_service(torconfig, service())\",\n \"def services():\\n return list(set(chain(*restart_map().values())))\",\n \"def reset_and_stop(self):\\n self.enabled = False\\n self.start_time = None\",\n \"def start_as_service(self):\\n from ..program_manager import ProgramManager\\n send_action(ProgramManager.NAME, 'start', self.name)\",\n \"def startService(self):\\n super(MasterService, self).startService()\\n self.dispatcher.startDispatching()\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def restart(self):\\n self.stop()\\n self.start(init=False)\",\n \"def stop(self):\\n\\n log_info(\\\"Stopping LiteServ ...\\\")\\n\\n self.logfile.flush()\\n self.logfile.close()\\n self.process.kill()\\n self.process.wait()\\n\\n self._verify_not_running()\",\n \"def run(self):\\n self._keep_running()\",\n \"def test_stopServiceCleanupScheduledRestarts(self):\\r\\n self.pm.threshold = 5\\r\\n self.pm.minRestartDelay = 5\\r\\n # Start service and add a process (started immediately)\\r\\n self.pm.startService()\\r\\n self.pm.addProcess(\\\"foo\\\", [\\\"foo\\\"])\\r\\n # Stop the process after 1s\\r\\n self.reactor.advance(1)\\r\\n self.pm.stopProcess(\\\"foo\\\")\\r\\n # Wait 1s for it to exit it will be scheduled to restart 5s later\\r\\n self.reactor.advance(1)\\r\\n # Meanwhile stop the service\\r\\n self.pm.stopService()\\r\\n # Advance to beyond the process restart time\\r\\n self.reactor.advance(6)\\r\\n # The process shouldn't have restarted because stopService has cancelled\\r\\n # all pending process restarts.\\r\\n self.assertEqual(self.pm.protocols, {})\",\n \"def restart(service):\\n # TODO: replace this with your relevant restart logic\\n assert service.isalpha()\\n run(\\\"service\\\", service, \\\"restart\\\")\",\n \"async def start(self):\\n while not self.should_stop:\\n self.executor.submit(self._submit_unpack_kwargs, self.params)\\n if self.oneshot:\\n break\\n await asyncio.sleep(self.interval)\",\n \"def stop(self):\\r\\n self.stopped = True\\r\\n time.sleep(1)\",\n \"def restart():\\n run_commands('python manage.py supervisor restart all')\",\n \"def start(self):\\n gevent.spawn_later(self._period, self._run)\",\n \"def start(self):\\n try:\\n self._unsafe_run()\\n except Exception:\\n _logger.exception(\\\"Failure while managing state machine.\\\")\\n for shutdown_flag in self._shutdown_flags:\\n shutdown_flag.set_value()\",\n \"def stop():\\n # Wyczyszczenie schedulera spowoduje czyste zakonczenie watku\\n with lock:\\n global running, s\\n\\n for event in s.queue:\\n s.cancel(event)\\n\\n running = False\\n s = sched.scheduler()\\n statuses.clear()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7066118","0.6894916","0.6701344","0.66644454","0.6636169","0.6594103","0.65772367","0.6504517","0.6455384","0.64464915","0.6408886","0.6372164","0.63390625","0.63344496","0.6302941","0.6277452","0.62744546","0.62490195","0.6241172","0.62357146","0.62187606","0.6201864","0.61886895","0.61756605","0.6173071","0.61505866","0.6139951","0.6128637","0.61144096","0.61127025","0.60911167","0.6083856","0.60509264","0.60302716","0.60206646","0.60192627","0.6016223","0.5997826","0.5997101","0.59822047","0.5971405","0.5969158","0.5967297","0.5955142","0.5955005","0.5929722","0.59220886","0.59140617","0.58989745","0.58881634","0.5878092","0.5871136","0.58508354","0.5849122","0.58475596","0.5844626","0.58431304","0.58397466","0.58324504","0.58269227","0.58244866","0.5823611","0.5818689","0.581465","0.581465","0.581465","0.581465","0.581465","0.581465","0.581465","0.5807359","0.5805842","0.5792149","0.57775027","0.5776288","0.5776288","0.5776288","0.5776288","0.5776288","0.5770898","0.5768945","0.57675606","0.57670766","0.5766816","0.5763809","0.57563573","0.57525855","0.57518387","0.5745467","0.5745467","0.574037","0.57386374","0.5733396","0.5726716","0.57236755","0.5719075","0.57179224","0.57160974","0.57151264","0.571203","0.57025135"],"string":"[\n \"0.7066118\",\n \"0.6894916\",\n \"0.6701344\",\n \"0.66644454\",\n \"0.6636169\",\n \"0.6594103\",\n \"0.65772367\",\n \"0.6504517\",\n \"0.6455384\",\n \"0.64464915\",\n \"0.6408886\",\n \"0.6372164\",\n \"0.63390625\",\n \"0.63344496\",\n \"0.6302941\",\n \"0.6277452\",\n \"0.62744546\",\n \"0.62490195\",\n \"0.6241172\",\n \"0.62357146\",\n \"0.62187606\",\n \"0.6201864\",\n \"0.61886895\",\n \"0.61756605\",\n \"0.6173071\",\n \"0.61505866\",\n \"0.6139951\",\n \"0.6128637\",\n \"0.61144096\",\n \"0.61127025\",\n \"0.60911167\",\n \"0.6083856\",\n \"0.60509264\",\n \"0.60302716\",\n \"0.60206646\",\n \"0.60192627\",\n \"0.6016223\",\n \"0.5997826\",\n \"0.5997101\",\n \"0.59822047\",\n \"0.5971405\",\n \"0.5969158\",\n \"0.5967297\",\n \"0.5955142\",\n \"0.5955005\",\n \"0.5929722\",\n \"0.59220886\",\n \"0.59140617\",\n \"0.58989745\",\n \"0.58881634\",\n \"0.5878092\",\n \"0.5871136\",\n \"0.58508354\",\n \"0.5849122\",\n \"0.58475596\",\n \"0.5844626\",\n \"0.58431304\",\n \"0.58397466\",\n \"0.58324504\",\n \"0.58269227\",\n \"0.58244866\",\n \"0.5823611\",\n \"0.5818689\",\n \"0.581465\",\n \"0.581465\",\n \"0.581465\",\n \"0.581465\",\n \"0.581465\",\n \"0.581465\",\n \"0.581465\",\n \"0.5807359\",\n \"0.5805842\",\n \"0.5792149\",\n \"0.57775027\",\n \"0.5776288\",\n \"0.5776288\",\n \"0.5776288\",\n \"0.5776288\",\n \"0.5776288\",\n \"0.5770898\",\n \"0.5768945\",\n \"0.57675606\",\n \"0.57670766\",\n \"0.5766816\",\n \"0.5763809\",\n \"0.57563573\",\n \"0.57525855\",\n \"0.57518387\",\n \"0.5745467\",\n \"0.5745467\",\n \"0.574037\",\n \"0.57386374\",\n \"0.5733396\",\n \"0.5726716\",\n \"0.57236755\",\n \"0.5719075\",\n \"0.57179224\",\n \"0.57160974\",\n \"0.57151264\",\n \"0.571203\",\n \"0.57025135\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80393,"cells":{"query":{"kind":"string","value":"list the docker images available for build"},"document":{"kind":"string","value":"def list_images():\n image_map = build_image_map()\n click.echo('')\n click.echo('List of available images (Name - Description)')\n click.echo('')\n for name in image_map:\n click.echo('{} -> {}'.format(name, image_map[name]))"},"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 list_docker_images():\n raw_result = subprocess.getstatusoutput('docker images')\n return result_handler(raw_result)","def _build_docker_images(self):\n print(f\"+ building {len(self.neurodocker_specs)} Docker images\")\n self.docker_status = []\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\n try:\n print(\"++ building image: {}\".format(neurodocker_dict))\n cg.docker_main(\n self.working_dir,\n neurodocker_dict,\n sha1,\n build_context=self.build_context,\n )\n self.docker_status.append(\"docker ok\")\n except Exception as e:\n self.docker_status.append(\n \"failed to build image with SHA1 {}: {}\".format(sha1, e)\n )","def build_all_images_for_lang(lang):\n if not args.git_checkout:\n if args.release != \"master\":\n print(\n \"Cannot use --release without also enabling --git_checkout.\\n\"\n )\n sys.exit(1)\n releases = [args.release]\n else:\n if args.release == \"all\":\n releases = client_matrix.get_release_tags(lang)\n else:\n # Build a particular release.\n if args.release not in [\"master\"] + client_matrix.get_release_tags(\n lang\n ):\n jobset.message(\n \"SKIPPED\",\n \"%s for %s is not defined\" % (args.release, lang),\n do_newline=True,\n )\n return []\n releases = [args.release]\n\n images = []\n for release in releases:\n images += build_all_images_for_release(lang, release)\n jobset.message(\n \"SUCCESS\",\n \"All docker images built for %s at %s.\" % (lang, releases),\n do_newline=True,\n )\n return images","def images_list(self, kwargs=None):\n\n try:\n scode, images = Rest.get('Image')\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n return\n\n if len(images) == 0:\n Console.info(\"No images exist\")\n return\n\n n = 1\n e = {}\n for image in images:\n d = {}\n d['Ip'] = image['Ip']\n d['Id'] = image['Id']\n if image['RepoTags'] == None:\n d['Repository'] = image['RepoDigests'][0]\n else:\n d['Repository'] = image['RepoTags'][0]\n # d['Size'] = image['Size']\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2) # Converting the size to GB\n e[n] = d\n n = n + 1\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))","def get_build_images(self) -> List[Image]:\n images = []\n image_names = []\n conflicting_names = []\n for registry_name, registry in self.registries.items():\n # if the registry is not marked as source, skip it\n if not registry.source:\n continue\n\n images += self.get_images_from_registry(registry)\n\n if conflicting_names:\n raise RuntimeError(\"Images found in multiple 'source' repositories: %s\", conflicting_names)\n\n return images","def get_images(name):\n url = \"/\".join([REGISTRY_BASE, name, \"/tags/list\"])\n response = req(url)\n image_list = []\n if response is not None:\n headers = {\"Accept\": \"application/vnd.docker.distribution.manifest.v2+json\"}\n tags = response[\"tags\"]\n for tag in tags:\n url = \"/\".join([REGISTRY_BASE, name, \"/manifests\", tag])\n response = req(url, headers)\n if response is not None:\n image = {}\n image[\"size\"] = response[\"config\"][\"size\"]\n for i in response[\"layers\"]:\n image[\"size\"] += i[\"size\"]\n image[\"size\"] = round(float(image[\"size\"]) / 1024 / 1024, 2)\n image[\"id\"] = response[\"config\"][\"digest\"][7:19]\n image[\"tag\"] = tag\n image[\"cmd\"] = \"docker pull uk8s.com/\" + name + \":\" + tag\n image_list.append(image)\n return sorted(image_list, reverse=True)","def run_build_image(image_list):\n success_image_list = list()\n failed_image_list = list()\n for image in image_list:\n docker_build_success_flag = image.run_docker_build()\n if not docker_build_success_flag:\n failed_image_list.append(image)\n continue\n smoke_test_sccess_flag = image.run_smoke_test()\n if not smoke_test_sccess_flag:\n failed_image_list.append(image)\n continue\n success_image_list.append(image)\n\n logger.info(\"--- docker build summary ---\")\n logger.info(\"following image succeed:\")\n for image in success_image_list:\n logger.info(f\" {image.local_identifier}\")\n logger.info(\"following image failed:\")\n for image in failed_image_list:\n logger.info(f\" {image.local_identifier}\")\n return success_image_list, failed_image_list","def pullall():\n\tprint(red('\\t\\tStarting download of QNIBTerminal images\\t\\t'))\n\t# pull all the needed images\n\tdocker_images={'fd20','terminal','helixdns','elk','slurm','compute'}\n\tfor image in docker_images:\n\t\tprint 'docker pull qnib/'+image\n\t\t# run('docker pull qnib/'+image)","def build_all_images_for_release(lang, release):\n docker_images = []\n build_jobs = []\n\n env = {}\n # If we not using current tree or the sibling for grpc stack, do checkout.\n stack_base = \"\"\n if args.git_checkout:\n stack_base = checkout_grpc_stack(lang, release)\n var = {\n \"go\": \"GRPC_GO_ROOT\",\n \"java\": \"GRPC_JAVA_ROOT\",\n \"node\": \"GRPC_NODE_ROOT\",\n }.get(lang, \"GRPC_ROOT\")\n env[var] = stack_base\n\n for runtime in client_matrix.get_runtimes_for_lang_release(lang, release):\n job = build_image_jobspec(runtime, env, release, stack_base)\n docker_images.append(job.tag)\n build_jobs.append(job)\n\n jobset.message(\"START\", \"Building interop docker images.\", do_newline=True)\n print(\"Jobs to run: \\n%s\\n\" % \"\\n\".join(str(j) for j in build_jobs))\n\n num_failures, _ = jobset.run(\n build_jobs, newline_on_success=True, maxjobs=multiprocessing.cpu_count()\n )\n if num_failures:\n jobset.message(\n \"FAILED\", \"Failed to build interop docker images.\", do_newline=True\n )\n docker_images_cleanup.extend(docker_images)\n sys.exit(1)\n\n jobset.message(\n \"SUCCESS\",\n \"All docker images built for %s at %s.\" % (lang, release),\n do_newline=True,\n )\n\n if release != \"master\":\n commit_log = os.path.join(stack_base, \"commit_log\")\n if os.path.exists(commit_log):\n for image in docker_images:\n add_files_to_image(image, [commit_log], \"release=%s\" % release)\n return docker_images","def pull_dlc_images(images):\n for image in images:\n run(f\"docker pull {image}\", hide=\"out\")","def list_images(self):\n raise NotImplementedError()","def pull_image(self):\n status = []\n for key, container in self.containers.items():\n result = container.pull()\n status.append(result)\n return status","def handle_api_list_images(self, http_context):\n\n command = self.docker + ['images', '--format', '\\'{{json .}}\\'', '--no-trunc', '-a']\n images = []\n for line in subprocess.check_output(command).decode().splitlines():\n image = json.loads(line)\n image['hash'] = image['ID'].split(':')[1][:12]\n images.append(image)\n return images","def _get_docker_images_for_lint(\n self,\n script_obj: Dict,\n script_id: str,\n docker_image_flag: str,\n docker_image_target: Optional[str],\n ) -> List[str]:\n log_prompt = f\"{self._pack_name} - Get All Docker Images For Lint\"\n logger.info(\n f\"{log_prompt} - Requested docker image flag is: '{docker_image_flag}'\"\n )\n imgs = []\n\n if (\n docker_image_flag == DockerImageFlagOption.FROM_YML.value\n ): # the default option\n # Desirable docker images are the docker images from the yml file (alt-dockerimages included)\n logger.info(f\"{self._pack_name} - Get Docker Image from YML - Started\")\n if imgs := get_docker_images_from_yml(script_obj):\n logger.info(\n f\"{log_prompt} - Docker images to run on are: {', '.join(imgs)}\"\n )\n return imgs\n\n di_from_yml = script_obj.get(\"dockerimage\")\n # If the 'dockerimage' key does not exist in yml - run on native image checks will be skipped\n native_image_config = (\n NativeImageConfig()\n ) # parsed docker_native_image_config.json file (a singleton obj)\n supported_native_images_obj = ScriptIntegrationSupportedNativeImages(\n _id=script_id,\n native_image_config=native_image_config,\n docker_image=di_from_yml,\n )\n supported_native_images = set(\n supported_native_images_obj.get_supported_native_image_versions(\n only_production_tags=False\n )\n )\n\n if docker_image_flag.startswith(DockerImageFlagOption.NATIVE.value):\n # Desirable docker image to run on is a native image\n\n self._check_native_image_flag(docker_image_flag)\n\n image_support = docker_image_flag\n if docker_image_flag == DockerImageFlagOption.NATIVE_TARGET.value:\n image_support = DockerImageFlagOption.NATIVE_DEV.value\n\n if native_image := self._get_native_image_name_from_config_file(\n image_support\n ):\n\n if self._is_native_image_support_script(\n native_image, supported_native_images, script_id\n ): # Integration/Script is supported by the requested native image\n native_image_ref: Optional[str] = \"\"\n\n if (\n docker_image_flag == DockerImageFlagOption.NATIVE_TARGET.value\n and docker_image_target\n ):\n # Desirable docker image to run is the target image only on native supported content.\n native_image_ref = docker_image_target\n\n elif docker_image_flag == DockerImageFlagOption.NATIVE_DEV.value:\n # Desirable docker image to run on is the dev native image - get the latest tag from Docker Hub\n native_image_ref = self._get_dev_native_image(script_id)\n\n else:\n # Desirable docker image to run on is a versioned native image - get the docker ref from the\n # docker_native_image_config.json\n native_image_ref = self._get_versioned_native_image(\n native_image\n )\n\n if native_image_ref:\n imgs.append(native_image_ref)\n logger.info(\n f\"{log_prompt} - Native image to run on is: {native_image_ref}\"\n )\n\n elif docker_image_flag == DockerImageFlagOption.ALL_IMAGES.value:\n # Desirable docker images are the docker images from the yml file, the supported versioned native images\n # and the dev native image\n if imgs := self._get_all_docker_images(\n script_obj, script_id, supported_native_images\n ):\n logger.info(\n f\"{log_prompt} - Docker images to run on are: {', '.join(imgs)}\"\n )\n\n else:\n # The flag is a specific docker image (from Docker Hub) or an invalid input -\n # In both cases we will try to run on the given input, if it does not exist in docker hub the run of lint\n # will fail later on.\n imgs.append(docker_image_flag)\n logger.info(\n f\"{log_prompt} - Docker image to run on is: {docker_image_flag}\"\n )\n\n return imgs","def list_images():\n return json_response(list_manifests())","def docker():\n try:\n client = docker_from_env(\n version=os.environ.get('DOCKER_API_VERSION', '1.24'))\n\n containers = []\n\n for container in client.containers.list():\n include_container = False\n if INTERESTING_CONTAINERS.search(container.name):\n include_container = True\n else:\n for tag in container.image.attrs.get('RepoTags', []):\n if INTERESTING_TAGS.match(tag):\n include_container = True\n break\n\n if not include_container:\n continue\n\n docker_metrics = {\n \"stats_type\": \"docker\",\n \"docker\": {\n \"id\": container.short_id,\n \"name\": container.name,\n \"status\": container.status,\n \"labels\": [\"%s=%s\" % (k, v)\n for k, v in container.labels.items()],\n \"tags\": container.image.attrs['RepoTags'],\n 'created': container.image.attrs['Created'],\n }\n }\n if 'version' in container.labels:\n docker_metrics['docker']['image_version'] = \\\n container.labels['version']\n containers.append(docker_metrics)\n\n except Exception as exc:\n logging.debug(\"Error gathering Docker info: %s\", exc)\n return []\n\n return containers","def get_available_images():\n return AVAILABLE_IMAGES","def local_images(self, images):\n registries = self.known_docker_registries()\n found_images = []\n for image in images:\n # docker could have the image name as-is or prefixed with any registry\n imglist = [image] + [reg + \"/\" + image for reg in registries]\n if self.is_image_local(imglist):\n found_images.append(image)\n return found_images","def build(images, tag, quiet):\n # images to build\n build_images = None\n\n # list of available images\n image_list = build_image_map().keys()\n\n # image specified: check if it exists\n if images:\n build_images = []\n for check_name in images:\n if check_name in image_list:\n build_images.append(check_name)\n continue\n raise click.ClickException(\n \"invalid image name '{}'\".format(check_name))\n click.echo('building images {}'.format(', '.join(build_images)))\n # no image specified: build all\n else:\n click.echo('building images {}'.format(', '.join(image_list)))\n\n manager = Manager('build', tag, images=build_images, verbose=not quiet)\n manager.run()","def images_refresh(self, kwargs=None):\n scode, hosts = Rest.get('Host')\n filter = {}\n n = 1\n e = {}\n data = []\n for host in hosts:\n os.environ[\"DOCKER_HOST\"] = host['Ip'] + \":\" + str(host['Port'])\n filter['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n self.client = docker.from_env()\n try:\n images = self.client.images.list(**kwargs)\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n return\n\n if len(images) == 0:\n Console.info(\"No images exist\")\n continue\n\n for imagem in images:\n image = imagem.__dict__['attrs']\n image['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n data.append(image)\n d = {}\n d['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n d['Id'] = image['Id']\n if image['RepoTags'] == None:\n d['Repository'] = image['RepoDigests'][0]\n else:\n d['Repository'] = image['RepoTags'][0]\n # d['Size'] = image['Size']\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2)\n e[n] = d\n n = n + 1\n Rest.delete('Image', filter)\n Rest.post('Image', data)\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))","def list_image_impl(**kwargs: Any) -> None:\n try:\n config = configuration.create_transient_list_image_config(kwargs)\n except configuration.CLIArgumentError as e:\n print(e, file=sys.stderr)\n sys.exit(1)\n\n store = image.ImageStore(\n backend_dir=config.image_backend, frontend_dir=config.image_frontend\n )\n images = _find_requested_images(store, config)\n\n if len(images) == 0:\n print(\"No images match selection\", file=sys.stderr)\n sys.exit(1)\n\n frontend, backend = image.format_image_table(images)\n if len(frontend) > 0:\n print(\"Frontend Images:\")\n print(frontend)\n if len(backend) > 0:\n print(\"\\nBackend Images:\")\n print(backend)\n sys.exit(0)","def build_container(client):\n client.images.build(path=os.path.join(os.path.abspath(\"\"), \"docker\"), tag=\"scrape_light\")","def avail_images(call=None):\n if call == \"action\":\n raise SaltCloudSystemExit(\n \"The avail_images function must be called with \"\n \"-f or --function, or with the --list-images option\"\n )\n\n ret = {}\n conn = get_conn()\n\n for item in conn.list_images()[\"items\"]:\n image = {\"id\": item[\"id\"]}\n image.update(item[\"properties\"])\n ret[image[\"name\"]] = image\n\n return ret","def get_image_list(source_dir):\n\n dir_list = os.path.os.listdir(source_dir)\n# print(dir_list)\n image_list = []\n os.chdir(source_dir)\n for file in dir_list:\n print(\"Inspecting.... : {}\".format(file))\n\n try:\n if Image.open(file).format:\n image_list.append(file)\n print(\"{} : is an image\".format(file))\n except Exception as e:\n print(\"{} : failed the imageness test.i \\n {}\".format(file, e))\n continue\n\n# print(image_list)\n return image_list","def containers_from_image(image_name):\n jobs = Queue(maxsize=0)\n containers = list(\n filter(\n lambda x: image_name in x.attrs['Config']['Image'],\n doxy.containers.list()\n )\n )\n for container in containers:\n jobs.put(container)\n return jobs","def do_command(self, args):\n imageops = dbops.Images()\n listing = imageops.list(args)\n ordering = ['image_name', 'image_format', 'vendor_name', 'os_type_name',\n 'is_64bit', 'is_bigmem', 'is_smp', 'is_enabled']\n do_list(listing, ordering)","def images(self, **kwargs):\n return self.get_list(self.cloudman.compute.images(),\n kind=\"image\")","def pull_base_images(ctx, dockerfile, signed_pull=True):\n images = set()\n stages = set()\n\n with open(dockerfile, \"r\") as f:\n for line in f:\n words = line.split()\n # Get source images\n if len(words) < 2 or words[0].lower() != \"from\":\n continue\n images.add(words[1])\n # Get stage names to remove them from pull\n if len(words) < 4 or words[2].lower() != \"as\":\n continue\n stages.add(words[3])\n\n if stages:\n print(\"Ignoring intermediate stage names: {}\".format(\", \".join(stages)))\n images -= stages\n\n print(\"Pulling following base images: {}\".format(\", \".join(images)))\n\n pull_env = {}\n if signed_pull:\n pull_env[\"DOCKER_CONTENT_TRUST\"] = \"1\"\n\n for i in images:\n ctx.run(\"docker pull {}\".format(i), env=pull_env)","def _docker_search(term):\n http_conn = httplib.HTTPConnection('localhost', 4243)\n http_conn.request('GET',\n '/images/search?term={0}'.format(urllib.quote(term)))\n resp = http_conn.getresponse()\n data = resp.read()\n if resp.status != 200:\n return []\n return [repos['Name'] for repos in json.loads(data)]","def ls():\n # TODO: listing all availabe containers form sequence\n return","def list(self):\n r = self.target.ttbd_iface_call(\"images\", \"list\", method = \"GET\")\n return r['result']","def avail_images(call=None):\n if call == \"action\":\n raise SaltCloudSystemExit(\n \"This function must be called with -f, --function argument.\"\n )\n return template_list()","def build_docker(c):\n tag = c.run('git describe', hide=True)\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\n c.run(f'docker build -t {docker_img} .')","def get_installed_images(self):\n raise NotImplementedError","def all(tag=None, env=None):\n build_images(tag=tag)\n push_images(tag=tag)\n deploy(env=env, tag=tag)","def list_runtimes(self, docker_image_name='all'):\n logger.debug('Listing runtimes')\n logger.debug('Note that k8s job backend does not manage runtimes')\n return []","def list_images():\n resource_route = \"/static/img/\"\n file_request_path = request.base_url[:request.base_url.rfind('/')] + resource_route\n path_to_current_file = os.path.dirname(os.path.abspath(__file__))\n images_path = os.path.join(path_to_current_file, 'static', 'img')\n directory_list = os.listdir(images_path)\n image_files = [f for f in directory_list if os.path.isfile(os.path.join(images_path, f))]\n image_files.sort()\n if '.gitignore' in image_files:\n image_files.remove('.gitignore')\n full_image_paths = [file_request_path + f for f in image_files]\n response_code = 200\n return make_response(jsonify({'files': full_image_paths}), response_code)","async def fetch_all_images(sess: Session = Depends(get_db)):\n image_list = utils_com.get_com_image_list(sess)\n return image_list","def get_images(self):\n \n return self.img_lst","def list_images(self, repository_name, registry_id=None):\n repository = None\n found = False\n if repository_name in self.repositories:\n repository = self.repositories[repository_name]\n if registry_id:\n if repository.registry_id == registry_id:\n found = True\n else:\n found = True\n\n if not found:\n raise RepositoryNotFoundException(\n repository_name, registry_id or DEFAULT_REGISTRY_ID\n )\n\n images = []\n for image in repository.images:\n images.append(image)\n return images","def all_images():\n\n total = set()\n jobs = [nomad.parse(get_job(job.template)) for job in config.enabled_jobs]\n for spec in jobs:\n for image in nomad.get_images(spec):\n if image is not None and image != 'None':\n total |= set([image])\n return total","def do_list(cs, args):\n opts = {}\n opts['all_projects'] = args.all_projects\n opts['marker'] = args.marker\n opts['limit'] = args.limit\n opts['sort_key'] = args.sort_key\n opts['sort_dir'] = args.sort_dir\n opts['image'] = args.image\n opts['name'] = args.name\n opts['project_id'] = args.project_id\n opts['user_id'] = args.user_id\n opts['host'] = args.host\n opts['task_state'] = args.task_state\n opts['memory'] = args.memory\n opts['auto_remove'] = args.auto_remove\n opts['status'] = args.status\n opts = zun_utils.remove_null_parms(**opts)\n containers = cs.containers.list(**opts)\n zun_utils.list_containers(containers)","def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None, skip_build=False):\n value_modifications = {}\n for name, options in images.items():\n image_path = options.get('contextPath', os.path.join('images', name))\n image_tag = tag\n # include chartpress.yaml itself as it can contain build args and\n # similar that influence the image that would be built\n paths = list(options.get('paths', [])) + [image_path, 'chartpress.yaml']\n last_commit = last_modified_commit(*paths)\n if tag is None:\n if chart_version:\n image_tag = \"{}-{}\".format(chart_version, last_commit)\n else:\n image_tag = last_commit\n image_name = prefix + name\n image_spec = '{}:{}'.format(image_name, image_tag)\n\n value_modifications[options['valuesPath']] = {\n 'repository': image_name,\n 'tag': SingleQuotedScalarString(image_tag),\n }\n\n if skip_build:\n continue\n\n template_namespace = {\n 'LAST_COMMIT': last_commit,\n 'TAG': image_tag,\n }\n\n if tag or image_needs_building(image_spec):\n build_args = render_build_args(options, template_namespace)\n build_image(image_path, image_spec, build_args, options.get('dockerfilePath'))\n else:\n print(f\"Skipping build for {image_spec}, it already exists\")\n\n if push:\n if tag or image_needs_pushing(image_spec):\n check_call([\n 'docker', 'push', image_spec\n ])\n else:\n print(f\"Skipping push for {image_spec}, already on registry\")\n return value_modifications","def get_docker_info(dut):\n command = 'show version'\n output = st.show(dut, command)\n include_keys = ['repository', 'tag', 'image_id', 'size']\n rv = [{each_key: each_line[each_key] for each_key in each_line if each_key in include_keys} for each_line in\n output[1:]]\n return rv","def available_images(self, images, registries, task_vars):\n return [\n image for image in images\n if any(self.is_available_skopeo_image(image, registry, task_vars) for registry in registries)\n ]","def _get_pinned_docker_images() -> Mapping[str, Mapping[str, str]]:\n\n pinned_docker_images_file = resources_dir / \"pinned_docker_images.cfg\"\n all_pinned_docker_images = ConfigParser()\n all_pinned_docker_images.read(pinned_docker_images_file)\n return all_pinned_docker_images","def docker_response():\n return {\n \"results\": [\n {\"name\": \"dev\"},\n {\"name\": \"9.99.9\"},\n {\"name\": \"latest\"},\n {\"name\": \"rc\"},\n {\"name\": \"beta\"},\n {\"name\": \"9.99.9b0\"},\n {\"name\": \"9.98.9\"},\n {\"name\": \"9.98.9b0\"},\n {\"name\": \"9.97.9\"},\n {\"name\": \"9.97.9b0\"},\n ]\n }","def required_images(self):\n required = set()\n deployment_type = self.get_var(\"openshift_deployment_type\")\n host_groups = self.get_var(\"group_names\")\n # containerized etcd may not have openshift_image_tag, see bz 1466622\n image_tag = self.get_var(\"openshift_image_tag\", default=\"latest\")\n image_info = DEPLOYMENT_IMAGE_INFO[deployment_type]\n\n # template for images that run on top of OpenShift\n image_url = \"{}/{}-{}:{}\".format(image_info[\"namespace\"], image_info[\"name\"], \"${component}\", \"${version}\")\n image_url = self.get_var(\"oreg_url\", default=\"\") or image_url\n if 'nodes' in host_groups:\n for suffix in NODE_IMAGE_SUFFIXES:\n required.add(image_url.replace(\"${component}\", suffix).replace(\"${version}\", image_tag))\n # The registry-console is for some reason not prefixed with ose- like the other components.\n # Nor is it versioned the same, so just look for latest.\n # Also a completely different name is used for Origin.\n required.add(image_info[\"registry_console_image\"])\n\n # images for containerized components\n if self.get_var(\"openshift\", \"common\", \"is_containerized\"):\n components = set()\n if 'nodes' in host_groups:\n components.update([\"node\", \"openvswitch\"])\n if 'masters' in host_groups: # name is \"origin\" or \"ose\"\n components.add(image_info[\"name\"])\n for component in components:\n required.add(\"{}/{}:{}\".format(image_info[\"namespace\"], component, image_tag))\n if 'etcd' in host_groups: # special case, note it is the same for origin/enterprise\n required.add(\"registry.access.redhat.com/rhel7/etcd\") # and no image tag\n\n return required","def get_image_list(self, account):\n images = self.driver(account).list_images()\n return [image.name for image in images]","def list_images(ec2): # pragma: no coverage\n response = ec2.describe_images(Filters=[{'Name': 'is-public',\n 'Values': ['false']}])\n response.pop('ResponseMetadata')\n printy(\"{:12}\\t{:20}\\t\\tCreationDate:\".format(\"ImageId\", \"Name\"))\n\n for image in response['Images']:\n if len(image[\"Name\"]) > 20:\n image['Name'] = image['Name'][:20] + \"...\"\n print(\"{ImageId}\\t{Name:20}\\t\\t{CreationDate}\".format(**image))","def fetch_images(client, images):\n return [fetch_image(client, image) for image in images]","def get_images_online(self, img_names):\n images = []\n errs = []\n for img_name in img_names:\n try:\n path = IMAGES_FOLDER + img_name\n # print(path)\n img = get_image(path, self.img_shape)\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n images.append(x)\n except Exception as e:\n # print(path,str(e))\n errs.append(img_name)\n\n return np.vstack(images), errs","def run_docker_push(image_list, docker_client):\n logger.info(\"--- push image to registry ---\")\n if runtime == Runtime.local:\n logger.info(\"Detected local runtime, stop here.\")\n return\n\n if not len(success_image_list):\n logger.info(\"No success image to push, stop here.\")\n return\n\n if GIT_BRANCH != \"master\":\n logger.info(\"Not master branch, stop here\")\n return\n\n docker_client.login(username=DOCKER_HUB_USERNAME, password=DOCKER_HUB_PASSWORD)\n for image in image_list:\n image.run_docker_push(docker_client)\n print(\"Finished.\")","def pullDockerImage(client, names):\n imgNotExistList = []\n for name in names:\n try:\n client.images.pull(name)\n # some invalid image names will not be pulled but the pull method\n # will not throw an exception so the only way to confirm if a pull\n # succeeded is to attempt a docker inspect on the image\n client.images.get(name)\n except Exception:\n imgNotExistList.append(name)\n if len(imgNotExistList) != 0:\n raise DockerImageNotFoundError('Could not find multiple images ',\n image_name=imgNotExistList)","def get_images(stage=0):\n return get_files(stage)[0]","def list_images(self):\n \n logging.debug(\"list_images entered for %s\" % self.machine_name) \n snapshots = cs.list_snapshots()\n res = []\n server_id = self.cloudserver.id\n # find the one for this server\n for snapshot in snapshots:\n img = snapshot.metadata.get(\"instance_uuid\", None)\n # print img\n\n if img == server_id:\n print \"Server %s has snapshot %s\" % (server_id, img)\n res.append(img)\n\n return res","def describe_image_builders(Names=None, MaxResults=None, NextToken=None):\n pass","def containers():\n # TODO: can there be multiple names?\n cmd = [ 'docker', 'ps', '--format', '{{.Names}}' ]\n with popen_text(cmd) as docker:\n for ln in docker.stdout:\n yield ln[:-1]","def get_images(self):\n return self._get_brains(\"Image\")","def get_images(self, file_path: str) -> Iterable[Image]:\n return []","def getBuilds():","def docker_build(c):\n cli_tasks.docker_build.run(c)","def find_docker_files():\n docker_dirs = []\n if os.path.exists(TMP_DIR):\n docker_dirs = [os.path.join(TMP_DIR, d) for d in os.listdir(TMP_DIR)\n if os.path.isdir(os.path.join(TMP_DIR, d)) and\n not d.endswith('_working')]\n docker_dirs.sort(key=lambda x: os.path.getmtime(x))\n\n return docker_dirs","def container_list(ctx, token, all):\n try:\n out = ctx.obj.container_list(token, all)\n headers = ['CONTAINER ID', 'IMAGE', 'COMMAND',\n 'CREATED', 'STATUS', 'PORTS', 'NAMES']\n print_table(headers, out)\n except BaseException as e:\n print_error(e.message)","def get_images(fish):\n fish_dir = TRAIN_DIR+'{}'.format(fish)\n images = [fish+'/'+im for im in os.listdir(fish_dir)]\n return images","def get_images_list(self):\n return self.image_filenames_list","def get_pip_list(docker_image):\n # First try bash crawl (won't work with alpine)\n cmd = ['sudo', 'docker', 'run', '--env', \"LD_LIBRARY_PATH=''\", '--rm', '-ti',\n '--entrypoint=/bin/bash', '-v', '{}/commands:/tmp/my_commands'.format(pwd),\n docker_image, '/tmp/my_commands/bash_crawl.sh', 'pip*']\n\n print(' '.join(cmd))\n r = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE, universal_newlines=True)\n r.wait()\n output = str(r.stdout.read())\n print('output:')\n print(output)\n\n output = output.split('\\n')\n\n exp = \".*pip([0-9]?\\.?[0-9]?[0-9]?\\.?[0-9]?[0-9]?)$\"\n pip_list = []\n for result in output:\n m = None\n m = re.match(exp, result)\n if not m == None:\n new_pip = result.rstrip()\n if not new_pip in pip_list:\n pip_list.append(new_pip)\n\n if pip_list == []:\n pip_list = ['pip', 'pip2', 'pip3']\n\n pip_dir_list = []\n pip_ver_list = []\n\n pip_vers_list = []\n for pip in pip_list:\n cmd = ['sudo', 'docker', 'run', '--env', \"LD_LIBRARY_PATH=''\", '--rm', '-ti',\n '--entrypoint={}'.format(pip), docker_image, '--version']\n\n print(' '.join(cmd))\n r = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE, universal_newlines=True)\n r.wait()\n output = str(r.stdout.read())\n try:\n pip_vers = output.split()[-1][:-1]\n pip_dir = os.path.dirname(pip)\n if pip_dir in pip_dir_list and pip_vers in pip_ver_list:\n continue\n pip_vers_list.append((pip, pip_vers))\n\n pip_dir_list.append(pip_dir)\n pip_ver_list.append(pip_vers)\n\n except Exception as e:\n print('no pip version in {}'.format(output))\n print(e)\n\n # pprint.pprint(pip_vers_list)\n\n py2pip, py_list = match_pip_to_py(pip_vers_list, docker_image)\n\n return (py2pip, py_list, pip_vers_list)","def container_list(self, kwargs=None):\n try:\n scode, containers = Rest.get('Container')\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n return\n if len(containers) == 0:\n print(\"No containers exist\")\n return\n\n n = 1\n e = {}\n for container in containers:\n d = {}\n d['Ip'] = container['Ip']\n d['Id'] = container['Id']\n d['Name'] = container['Name']\n d['Image'] = container['Config']['Image']\n d['Status'] = container['State']['Status']\n d['StartedAt'] = container['State']['StartedAt']\n e[n] = d\n n = n + 1\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Name', 'Image', 'Status', 'StartedAt'])))","def default_image_list(self):\n for version in self.database.versions:\n release = self.database.latest_release(\n default.platform, version=version, architecture=default.architecture\n )\n\n if not release:\n continue\n\n image = self.database.get(\n version=version,\n platform=default.platform,\n release=release,\n architecture=default.architecture,\n )\n\n if not image:\n continue\n\n tags = [\n tag\n for tag in self.database.tags(image)\n if not any(\n (len(tag.version) == 4, tag.platform, tag.release, tag.architecture)\n )\n ]\n\n yield _format_image(image, tags)","def _get_images(image_path):\n logger.debug(\"Getting images: '%s'\", image_path)\n if not os.path.isdir(image_path):\n logger.debug(\"Folder does not exist\")\n return None\n files = [os.path.join(image_path, f)\n for f in os.listdir(image_path) if f.lower().endswith((\".png\", \".jpg\"))]\n logger.debug(\"Image files: %s\", files)\n return files","def getImages(self,Project=\"\"):\n #images = [\"image1.jpg\",\"image2.jpg\",\"image3.jpg\"]\n \n os.chdir(self.dataDir)\n images = glob.glob(\"*.png\")\n \n return images","def list_namespaced_image(self, **kwargs):\n\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method list_namespaced_image\" % key\n )\n params[key] = val\n del params['kwargs']\n\n\n resource_path = '/oapi/v1/images'.replace('{format}', 'json')\n path_params = {}\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n if 'label_selector' in params:\n query_params['labelSelector'] = params['label_selector']\n if 'field_selector' in params:\n query_params['fieldSelector'] = params['field_selector']\n if 'watch' in params:\n query_params['watch'] = params['watch']\n if 'resource_version' in params:\n query_params['resourceVersion'] = params['resource_version']\n if 'timeout_seconds' in params:\n query_params['timeoutSeconds'] = params['timeout_seconds']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='V1ImageList',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def populateImagesList(self):\n \n self._gui_server.getImagesList(self._populateImagesList)","def image_list(self):\n return self._image_list","def get_images():\n return _IMAGES","def getimgs():","def build_env_wheels() -> Iterable[Path]:\n return []","def resolve_running_docker_containers():\n container_ids = terminal.docker_ps(ps_filter='name={}'.format(CONDUCTR_NAME_PREFIX))\n container_names = [terminal.docker_inspect(container_id, '{{.Name}}')[1:] for container_id in container_ids]\n return sorted(container_names)","def get_images(directory=None): #import from mask.py\n \n if directory == None:\n directory = os.getcwd() # Use working directory if unspecified\n \n image_list = [] # Initialize aggregaotrs\n file_list = []\n \n directory_list = os.listdir(directory) # Get list of files\n for entry in directory_list:\n absolute_filename = os.path.join(directory, entry)\n try:\n image = PIL.Image.open(absolute_filename)\n file_list += [entry]\n image_list += [image]\n except IOError:\n pass # do nothing with errors tying to open non-images\n return image_list, file_list","async def imageList(self, ctx: Context, imageType=\"icons\"):\n imageSingular = self.getSingularImageType(imageType)\n allImages = await self.config.guild(ctx.guild).get_attr(imageType)()\n if not allImages:\n await ctx.send(f\"There are no {imageType}, please add some first!\")\n return\n\n async with self.config.guild(ctx.guild).get_attr(f\"{imageType}Dates\")() as imageDates:\n imageDates = dict(sorted(imageDates.items()))\n msg = \"\"\n for changeDate, name in imageDates.items():\n # YYYY-MM-DD\n theDate = date.fromisoformat(f\"2020-{changeDate}\").strftime(\"%B %d\")\n msg += f\"{theDate}: {name}\\n\"\n notAssigned = set(allImages) - set(imageDates.values())\n if notAssigned:\n msg += f\"Unassigned: \"\n msg += \", \".join(notAssigned)\n pageList = []\n pages = list(pagify(msg, page_length=500))\n totalPages = len(pages)\n async for pageNumber, page in AsyncIter(pages).enumerate(start=1):\n embed = discord.Embed(\n title=f\"Server {imageSingular} changes for {ctx.guild.name}\", description=page\n )\n embed.set_footer(text=f\"Page {pageNumber}/{totalPages}\")\n pageList.append(embed)\n await menu(ctx, pageList, DEFAULT_CONTROLS)","def test_list_cached_images_empty(self, mock_images):\n self._run_command(['list-cached'], cache_manage.SUCCESS)","def cli(ctx, image_file):\n if not image_file:\n return\n for pull_image in image_file.readline():\n pull_image = pull_image.rstrip('\\n')\n if len(pull_image) == 0:\n continue\n docker.pull(pull_image)\n push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,\n DEFAULR_NAMESPACE,\n pull_image.split('/')[-1])\n docker.tag(pull_image, push_image)\n docker.push(push_image)","def get_images(owner, tagvalue):\n try:\n images = ec2(credentials).describe_images(Owners=[owner],Filters=[{'Name':'tag-value', 'Values':[tagvalue]}])\n return images\n except Exception as e:\n print(\"Error: cannot get the list of images. %s\" % e)","def imageList(self):\n return self.__imageList","def _locate_images(self):\r\n extensions = '|'.join(self.valid_extensions)\r\n extension_re = re.compile('.+\\.(%s)$' % extensions, re.IGNORECASE)\r\n files = sorted(os.listdir(self.path))\r\n\r\n images = []\r\n for root, dirs, files in os.walk(self.path, followlinks=self.config['follow_links']):\r\n for filename in sorted(files):\r\n if not filename.startswith('.') and extension_re.match(filename):\r\n images.append(Image(path=os.path.join(root, filename), config=self.config))\r\n if not self.config['recursive']:\r\n break\r\n\r\n if not images:\r\n raise SourceImagesNotFoundError(self.path)\r\n\r\n images = sorted(images, reverse=self.config['algorithm_ordering'][0] != '-')\r\n\r\n return images","def listImages(name_list, path, as_grey=True):\n\timage_list = []\n\n\tfor name in name_list:\n\t\timage = io.imread(path + \"/\" + name + \".jpg\", as_grey=as_grey)\n\t\tif as_grey is True:\n\t\t\timage = resize(image, (200, 200))\n\t\timage_list.append(image)\n\n\treturn image_list","def getDockerfiles(fileList) -> dict:\n paths = {}\n for word in fileList:\n if \"/Dockerfile\" in word:\n try:\n path = word.split('/')\n image = path[0]\n tag = path[1]\n paths[len(paths)] = (image, tag)\n except IndexError: \n print(\"Image name and tag are required in path as \\'image/tag/Dockerfile\\'\")\n print(paths)\n return paths","def list_images(project):\n data = {constants.PROJECT_PARAMETER: project}\n res = requests.post(_url + \"list_images/\", data=data,\n auth=(_username, _password))\n click.echo(\"username:\")\n click.echo(_username)\n click.echo(_password)\n click.echo(_url)\n click.echo(data) \n if res.status_code == 200:\n images = json.loads(res.content)\n table = PrettyTable(field_names=[\"Image\"])\n for image in images:\n table.add_row([image])\n click.echo(table.get_string())\n else:\n click.echo(res.content)","def getimagelist(folder):\n imagefolder = Path(folder) \n imagelist = imagefolder.glob(\"**/*.png\") \n return list(imagelist)","def do_stage(self, images):\n\n for i, image in enumerate(images):\n pass\n # logging_tags = logs.image_config_to_tags(image, self.group_by_keywords)","def build_docker(params) -> None:\n print(\"Building docker image...\")\n cmd = \"cd bg_changer && docker build --tag bg_changer . >/dev/null 2>&1\"\n if os.system(cmd) == 0:\n print(\" Success !\")\n else:\n print(\" Failure !\")","def build_container_image(self) -> None:\n print_version_of_tools()\n try:\n self.fs_watcher.start()\n runner = PluginsRunner(self,\n self.plugins_conf,\n self.plugin_files,\n self.keep_plugins_running,\n plugins_results=self.data.plugins_results)\n runner.run()\n finally:\n self.fs_watcher.finish()","def images(self) -> Iterable[dto.Image]:\n raise errors.UnsupportedOperationError(\n \"Operation not supported for provider '{}'\".format(self.provider_name)\n )","def extract_images(\n self,\n graph_connector: DiGraph | None = None,\n definitions: _Definitions | None = None,\n definitions_raw: dict[str, list[tuple[int, str]]] | None = None\n ) -> list[Image]:\n\n pass","def pull_image(image):\n\n subprocess.check_call(['docker', 'pull', image])","def main():\n parser = argparse.ArgumentParser(\n epilog=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\n )\n parser.add_argument(\n \"-d\", \"--dry-run\", action=\"store_true\", default=0, help=\"Dry run mode.\"\n )\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"count\",\n default=0,\n help=\"Verbosity. Default is WARNING level.\",\n )\n\n subparsers = parser.add_subparsers(help=\"Sub commands\", dest=\"subparser\")\n subparsers.required = True\n\n build_parser = subparsers.add_parser(\n \"build\",\n description=\"Build an image from Dockerfile, caching image hierarchy\",\n help=\"Build an image from a Dockerfile\",\n )\n build_parser.add_argument(\n \"path\", metavar=\"PATH\", help=\"The build context directory\"\n )\n build_parser.add_argument(\n \"-f\",\n \"--file\",\n help=\"Name of the Dockerfile. If not provided, \"\n \"will use config.DOCKERFILE_PATH_PATTERN to compute. \",\n )\n build_parser.add_argument(\n \"-v\",\n \"--git-sha\",\n required=True,\n help=\"The version of code to build against, \" \"will pass as GIT_SHA variable\",\n )\n build_parser.add_argument(\n \"-n\", \"--name\", required=True, help=\"The name of the image to build\"\n )\n build_parser.add_argument(\n \"--build-arg\",\n metavar=\"ARG=VALUE\",\n nargs=\"*\",\n default=[],\n help=\"Set extra build-time variables. GIT_SHA, TIMESTAMP will be passed by default.\",\n )\n build_parser.add_argument(\n \"-r\",\n \"--raw\",\n action=\"store_true\",\n help=\"Whether to use raw docker build command to build, skipping caching logic\",\n )\n build_parser.add_argument(\n \"--registry\",\n default=config.DOCKER_REGISTRY,\n help=\"Docker registry use to determine the image identity, \"\n \"can be set via IMAGE_BUILDER_DOCKER_REGISTRY environment variable, \"\n 'or set DOCKER_REGISTRY in config.py. Default is \"%(default)s\"',\n )\n build_parser.add_argument(\n \"-t\",\n \"--tag-pattern\",\n default=config.GIT_SHA_TAG_PATTERN,\n help=\"Tag pattern, can only include one `{git_sha}` placeholder, \"\n 'such as \"{git_sha}-new\". If the tag exists, we won\\'t rebuild it. '\n 'Default is \"%(default)s\"',\n )\n build_parser.add_argument(\n \"-e\",\n \"--extra-tag\",\n nargs=\"*\",\n default=[],\n help=\"Extra tags to tag to the final images\",\n )\n build_parser.add_argument(\n \"--extra-name\",\n nargs=\"*\",\n default=[],\n help=\"Extra name and optionally with a tag in the 'name:tag' format\",\n )\n build_parser.add_argument(\n \"-o\", \"--output-hash\", help=\"The output filename of the files hash log.\"\n )\n build_parser.set_defaults(func=build)\n\n args = parser.parse_args()\n if args.dry_run:\n # DRY_RUN env will be read in image_builder.libs.process\n os.environ[\"DRY_RUN\"] = \"1\"\n\n if args.func == build:\n args.path = expand_path(args.path)\n if args.output_hash:\n args.output_hash = expand_path(args.output_hash)\n\n args.file = args.file or locate_dockerfile(args.name)\n args.file = expand_path(args.file)\n # set environ for main dockerfile for possibly retrieving later\n os.environ[\n config.DOCKERFILE_ENV_PATTERN.format(image_name=args.name)\n ] = args.file\n\n # change CWD to PATH\n os.chdir(args.path)\n\n if not args.registry:\n parser.error(\n \"--registry should be provied \"\n \"or specified by IMAGE_BUILDER_DOCKER_REGISTRY environment variable or set DOCKER_REGISTRY in config.py\"\n )\n if not all(\"=\" in kv for kv in args.build_arg):\n parser.error(\"--build_arg must be in ARG=VALUE format\")\n\n # set git_sha_tag\n try:\n args.git_sha_tag = args.tag_pattern.format(git_sha=args.git_sha)\n except KeyError:\n parser.error(\n 'Wrong --tag-pattern provided. Can only include one `{git_sha}` placeholder, such as \"{git_sha}-new\"'\n )\n\n # setup logging\n level = logging.WARNING - args.verbose * 10\n logging.basicConfig(\n level=level, format=\"%(asctime)s %(name)s %(levelname)s %(message)s\"\n )\n\n if args.output_hash:\n h = logging.FileHandler(args.output_hash)\n h.setLevel(logging.DEBUG)\n h.setFormatter(logging.Formatter(\"%(message)s\"))\n hash_logger.addHandler(h)\n\n # Suppress warning when we don't verify ssl\n import urllib3\n\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n return args.func(args)","def imageutil(self) -> imageutil.ImageUtil:\n return imageutil.ImageUtil(self.data.dockerfile_images, self.conf)","def images(self):\n return self._images","def images(self):\n return self._images","def list_images(basePath, validExts=(\".jpg\", \".jpeg\", \".png\", \".bmp\", \".tif\", \".tiff\"), contains=None):\n return _list_files(basePath, validExts, contains=contains)"],"string":"[\n \"def list_docker_images():\\n raw_result = subprocess.getstatusoutput('docker images')\\n return result_handler(raw_result)\",\n \"def _build_docker_images(self):\\n print(f\\\"+ building {len(self.neurodocker_specs)} Docker images\\\")\\n self.docker_status = []\\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\\n try:\\n print(\\\"++ building image: {}\\\".format(neurodocker_dict))\\n cg.docker_main(\\n self.working_dir,\\n neurodocker_dict,\\n sha1,\\n build_context=self.build_context,\\n )\\n self.docker_status.append(\\\"docker ok\\\")\\n except Exception as e:\\n self.docker_status.append(\\n \\\"failed to build image with SHA1 {}: {}\\\".format(sha1, e)\\n )\",\n \"def build_all_images_for_lang(lang):\\n if not args.git_checkout:\\n if args.release != \\\"master\\\":\\n print(\\n \\\"Cannot use --release without also enabling --git_checkout.\\\\n\\\"\\n )\\n sys.exit(1)\\n releases = [args.release]\\n else:\\n if args.release == \\\"all\\\":\\n releases = client_matrix.get_release_tags(lang)\\n else:\\n # Build a particular release.\\n if args.release not in [\\\"master\\\"] + client_matrix.get_release_tags(\\n lang\\n ):\\n jobset.message(\\n \\\"SKIPPED\\\",\\n \\\"%s for %s is not defined\\\" % (args.release, lang),\\n do_newline=True,\\n )\\n return []\\n releases = [args.release]\\n\\n images = []\\n for release in releases:\\n images += build_all_images_for_release(lang, release)\\n jobset.message(\\n \\\"SUCCESS\\\",\\n \\\"All docker images built for %s at %s.\\\" % (lang, releases),\\n do_newline=True,\\n )\\n return images\",\n \"def images_list(self, kwargs=None):\\n\\n try:\\n scode, images = Rest.get('Image')\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n return\\n\\n if len(images) == 0:\\n Console.info(\\\"No images exist\\\")\\n return\\n\\n n = 1\\n e = {}\\n for image in images:\\n d = {}\\n d['Ip'] = image['Ip']\\n d['Id'] = image['Id']\\n if image['RepoTags'] == None:\\n d['Repository'] = image['RepoDigests'][0]\\n else:\\n d['Repository'] = image['RepoTags'][0]\\n # d['Size'] = image['Size']\\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2) # Converting the size to GB\\n e[n] = d\\n n = n + 1\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))\",\n \"def get_build_images(self) -> List[Image]:\\n images = []\\n image_names = []\\n conflicting_names = []\\n for registry_name, registry in self.registries.items():\\n # if the registry is not marked as source, skip it\\n if not registry.source:\\n continue\\n\\n images += self.get_images_from_registry(registry)\\n\\n if conflicting_names:\\n raise RuntimeError(\\\"Images found in multiple 'source' repositories: %s\\\", conflicting_names)\\n\\n return images\",\n \"def get_images(name):\\n url = \\\"/\\\".join([REGISTRY_BASE, name, \\\"/tags/list\\\"])\\n response = req(url)\\n image_list = []\\n if response is not None:\\n headers = {\\\"Accept\\\": \\\"application/vnd.docker.distribution.manifest.v2+json\\\"}\\n tags = response[\\\"tags\\\"]\\n for tag in tags:\\n url = \\\"/\\\".join([REGISTRY_BASE, name, \\\"/manifests\\\", tag])\\n response = req(url, headers)\\n if response is not None:\\n image = {}\\n image[\\\"size\\\"] = response[\\\"config\\\"][\\\"size\\\"]\\n for i in response[\\\"layers\\\"]:\\n image[\\\"size\\\"] += i[\\\"size\\\"]\\n image[\\\"size\\\"] = round(float(image[\\\"size\\\"]) / 1024 / 1024, 2)\\n image[\\\"id\\\"] = response[\\\"config\\\"][\\\"digest\\\"][7:19]\\n image[\\\"tag\\\"] = tag\\n image[\\\"cmd\\\"] = \\\"docker pull uk8s.com/\\\" + name + \\\":\\\" + tag\\n image_list.append(image)\\n return sorted(image_list, reverse=True)\",\n \"def run_build_image(image_list):\\n success_image_list = list()\\n failed_image_list = list()\\n for image in image_list:\\n docker_build_success_flag = image.run_docker_build()\\n if not docker_build_success_flag:\\n failed_image_list.append(image)\\n continue\\n smoke_test_sccess_flag = image.run_smoke_test()\\n if not smoke_test_sccess_flag:\\n failed_image_list.append(image)\\n continue\\n success_image_list.append(image)\\n\\n logger.info(\\\"--- docker build summary ---\\\")\\n logger.info(\\\"following image succeed:\\\")\\n for image in success_image_list:\\n logger.info(f\\\" {image.local_identifier}\\\")\\n logger.info(\\\"following image failed:\\\")\\n for image in failed_image_list:\\n logger.info(f\\\" {image.local_identifier}\\\")\\n return success_image_list, failed_image_list\",\n \"def pullall():\\n\\tprint(red('\\\\t\\\\tStarting download of QNIBTerminal images\\\\t\\\\t'))\\n\\t# pull all the needed images\\n\\tdocker_images={'fd20','terminal','helixdns','elk','slurm','compute'}\\n\\tfor image in docker_images:\\n\\t\\tprint 'docker pull qnib/'+image\\n\\t\\t# run('docker pull qnib/'+image)\",\n \"def build_all_images_for_release(lang, release):\\n docker_images = []\\n build_jobs = []\\n\\n env = {}\\n # If we not using current tree or the sibling for grpc stack, do checkout.\\n stack_base = \\\"\\\"\\n if args.git_checkout:\\n stack_base = checkout_grpc_stack(lang, release)\\n var = {\\n \\\"go\\\": \\\"GRPC_GO_ROOT\\\",\\n \\\"java\\\": \\\"GRPC_JAVA_ROOT\\\",\\n \\\"node\\\": \\\"GRPC_NODE_ROOT\\\",\\n }.get(lang, \\\"GRPC_ROOT\\\")\\n env[var] = stack_base\\n\\n for runtime in client_matrix.get_runtimes_for_lang_release(lang, release):\\n job = build_image_jobspec(runtime, env, release, stack_base)\\n docker_images.append(job.tag)\\n build_jobs.append(job)\\n\\n jobset.message(\\\"START\\\", \\\"Building interop docker images.\\\", do_newline=True)\\n print(\\\"Jobs to run: \\\\n%s\\\\n\\\" % \\\"\\\\n\\\".join(str(j) for j in build_jobs))\\n\\n num_failures, _ = jobset.run(\\n build_jobs, newline_on_success=True, maxjobs=multiprocessing.cpu_count()\\n )\\n if num_failures:\\n jobset.message(\\n \\\"FAILED\\\", \\\"Failed to build interop docker images.\\\", do_newline=True\\n )\\n docker_images_cleanup.extend(docker_images)\\n sys.exit(1)\\n\\n jobset.message(\\n \\\"SUCCESS\\\",\\n \\\"All docker images built for %s at %s.\\\" % (lang, release),\\n do_newline=True,\\n )\\n\\n if release != \\\"master\\\":\\n commit_log = os.path.join(stack_base, \\\"commit_log\\\")\\n if os.path.exists(commit_log):\\n for image in docker_images:\\n add_files_to_image(image, [commit_log], \\\"release=%s\\\" % release)\\n return docker_images\",\n \"def pull_dlc_images(images):\\n for image in images:\\n run(f\\\"docker pull {image}\\\", hide=\\\"out\\\")\",\n \"def list_images(self):\\n raise NotImplementedError()\",\n \"def pull_image(self):\\n status = []\\n for key, container in self.containers.items():\\n result = container.pull()\\n status.append(result)\\n return status\",\n \"def handle_api_list_images(self, http_context):\\n\\n command = self.docker + ['images', '--format', '\\\\'{{json .}}\\\\'', '--no-trunc', '-a']\\n images = []\\n for line in subprocess.check_output(command).decode().splitlines():\\n image = json.loads(line)\\n image['hash'] = image['ID'].split(':')[1][:12]\\n images.append(image)\\n return images\",\n \"def _get_docker_images_for_lint(\\n self,\\n script_obj: Dict,\\n script_id: str,\\n docker_image_flag: str,\\n docker_image_target: Optional[str],\\n ) -> List[str]:\\n log_prompt = f\\\"{self._pack_name} - Get All Docker Images For Lint\\\"\\n logger.info(\\n f\\\"{log_prompt} - Requested docker image flag is: '{docker_image_flag}'\\\"\\n )\\n imgs = []\\n\\n if (\\n docker_image_flag == DockerImageFlagOption.FROM_YML.value\\n ): # the default option\\n # Desirable docker images are the docker images from the yml file (alt-dockerimages included)\\n logger.info(f\\\"{self._pack_name} - Get Docker Image from YML - Started\\\")\\n if imgs := get_docker_images_from_yml(script_obj):\\n logger.info(\\n f\\\"{log_prompt} - Docker images to run on are: {', '.join(imgs)}\\\"\\n )\\n return imgs\\n\\n di_from_yml = script_obj.get(\\\"dockerimage\\\")\\n # If the 'dockerimage' key does not exist in yml - run on native image checks will be skipped\\n native_image_config = (\\n NativeImageConfig()\\n ) # parsed docker_native_image_config.json file (a singleton obj)\\n supported_native_images_obj = ScriptIntegrationSupportedNativeImages(\\n _id=script_id,\\n native_image_config=native_image_config,\\n docker_image=di_from_yml,\\n )\\n supported_native_images = set(\\n supported_native_images_obj.get_supported_native_image_versions(\\n only_production_tags=False\\n )\\n )\\n\\n if docker_image_flag.startswith(DockerImageFlagOption.NATIVE.value):\\n # Desirable docker image to run on is a native image\\n\\n self._check_native_image_flag(docker_image_flag)\\n\\n image_support = docker_image_flag\\n if docker_image_flag == DockerImageFlagOption.NATIVE_TARGET.value:\\n image_support = DockerImageFlagOption.NATIVE_DEV.value\\n\\n if native_image := self._get_native_image_name_from_config_file(\\n image_support\\n ):\\n\\n if self._is_native_image_support_script(\\n native_image, supported_native_images, script_id\\n ): # Integration/Script is supported by the requested native image\\n native_image_ref: Optional[str] = \\\"\\\"\\n\\n if (\\n docker_image_flag == DockerImageFlagOption.NATIVE_TARGET.value\\n and docker_image_target\\n ):\\n # Desirable docker image to run is the target image only on native supported content.\\n native_image_ref = docker_image_target\\n\\n elif docker_image_flag == DockerImageFlagOption.NATIVE_DEV.value:\\n # Desirable docker image to run on is the dev native image - get the latest tag from Docker Hub\\n native_image_ref = self._get_dev_native_image(script_id)\\n\\n else:\\n # Desirable docker image to run on is a versioned native image - get the docker ref from the\\n # docker_native_image_config.json\\n native_image_ref = self._get_versioned_native_image(\\n native_image\\n )\\n\\n if native_image_ref:\\n imgs.append(native_image_ref)\\n logger.info(\\n f\\\"{log_prompt} - Native image to run on is: {native_image_ref}\\\"\\n )\\n\\n elif docker_image_flag == DockerImageFlagOption.ALL_IMAGES.value:\\n # Desirable docker images are the docker images from the yml file, the supported versioned native images\\n # and the dev native image\\n if imgs := self._get_all_docker_images(\\n script_obj, script_id, supported_native_images\\n ):\\n logger.info(\\n f\\\"{log_prompt} - Docker images to run on are: {', '.join(imgs)}\\\"\\n )\\n\\n else:\\n # The flag is a specific docker image (from Docker Hub) or an invalid input -\\n # In both cases we will try to run on the given input, if it does not exist in docker hub the run of lint\\n # will fail later on.\\n imgs.append(docker_image_flag)\\n logger.info(\\n f\\\"{log_prompt} - Docker image to run on is: {docker_image_flag}\\\"\\n )\\n\\n return imgs\",\n \"def list_images():\\n return json_response(list_manifests())\",\n \"def docker():\\n try:\\n client = docker_from_env(\\n version=os.environ.get('DOCKER_API_VERSION', '1.24'))\\n\\n containers = []\\n\\n for container in client.containers.list():\\n include_container = False\\n if INTERESTING_CONTAINERS.search(container.name):\\n include_container = True\\n else:\\n for tag in container.image.attrs.get('RepoTags', []):\\n if INTERESTING_TAGS.match(tag):\\n include_container = True\\n break\\n\\n if not include_container:\\n continue\\n\\n docker_metrics = {\\n \\\"stats_type\\\": \\\"docker\\\",\\n \\\"docker\\\": {\\n \\\"id\\\": container.short_id,\\n \\\"name\\\": container.name,\\n \\\"status\\\": container.status,\\n \\\"labels\\\": [\\\"%s=%s\\\" % (k, v)\\n for k, v in container.labels.items()],\\n \\\"tags\\\": container.image.attrs['RepoTags'],\\n 'created': container.image.attrs['Created'],\\n }\\n }\\n if 'version' in container.labels:\\n docker_metrics['docker']['image_version'] = \\\\\\n container.labels['version']\\n containers.append(docker_metrics)\\n\\n except Exception as exc:\\n logging.debug(\\\"Error gathering Docker info: %s\\\", exc)\\n return []\\n\\n return containers\",\n \"def get_available_images():\\n return AVAILABLE_IMAGES\",\n \"def local_images(self, images):\\n registries = self.known_docker_registries()\\n found_images = []\\n for image in images:\\n # docker could have the image name as-is or prefixed with any registry\\n imglist = [image] + [reg + \\\"/\\\" + image for reg in registries]\\n if self.is_image_local(imglist):\\n found_images.append(image)\\n return found_images\",\n \"def build(images, tag, quiet):\\n # images to build\\n build_images = None\\n\\n # list of available images\\n image_list = build_image_map().keys()\\n\\n # image specified: check if it exists\\n if images:\\n build_images = []\\n for check_name in images:\\n if check_name in image_list:\\n build_images.append(check_name)\\n continue\\n raise click.ClickException(\\n \\\"invalid image name '{}'\\\".format(check_name))\\n click.echo('building images {}'.format(', '.join(build_images)))\\n # no image specified: build all\\n else:\\n click.echo('building images {}'.format(', '.join(image_list)))\\n\\n manager = Manager('build', tag, images=build_images, verbose=not quiet)\\n manager.run()\",\n \"def images_refresh(self, kwargs=None):\\n scode, hosts = Rest.get('Host')\\n filter = {}\\n n = 1\\n e = {}\\n data = []\\n for host in hosts:\\n os.environ[\\\"DOCKER_HOST\\\"] = host['Ip'] + \\\":\\\" + str(host['Port'])\\n filter['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n self.client = docker.from_env()\\n try:\\n images = self.client.images.list(**kwargs)\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n return\\n\\n if len(images) == 0:\\n Console.info(\\\"No images exist\\\")\\n continue\\n\\n for imagem in images:\\n image = imagem.__dict__['attrs']\\n image['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n data.append(image)\\n d = {}\\n d['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n d['Id'] = image['Id']\\n if image['RepoTags'] == None:\\n d['Repository'] = image['RepoDigests'][0]\\n else:\\n d['Repository'] = image['RepoTags'][0]\\n # d['Size'] = image['Size']\\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2)\\n e[n] = d\\n n = n + 1\\n Rest.delete('Image', filter)\\n Rest.post('Image', data)\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))\",\n \"def list_image_impl(**kwargs: Any) -> None:\\n try:\\n config = configuration.create_transient_list_image_config(kwargs)\\n except configuration.CLIArgumentError as e:\\n print(e, file=sys.stderr)\\n sys.exit(1)\\n\\n store = image.ImageStore(\\n backend_dir=config.image_backend, frontend_dir=config.image_frontend\\n )\\n images = _find_requested_images(store, config)\\n\\n if len(images) == 0:\\n print(\\\"No images match selection\\\", file=sys.stderr)\\n sys.exit(1)\\n\\n frontend, backend = image.format_image_table(images)\\n if len(frontend) > 0:\\n print(\\\"Frontend Images:\\\")\\n print(frontend)\\n if len(backend) > 0:\\n print(\\\"\\\\nBackend Images:\\\")\\n print(backend)\\n sys.exit(0)\",\n \"def build_container(client):\\n client.images.build(path=os.path.join(os.path.abspath(\\\"\\\"), \\\"docker\\\"), tag=\\\"scrape_light\\\")\",\n \"def avail_images(call=None):\\n if call == \\\"action\\\":\\n raise SaltCloudSystemExit(\\n \\\"The avail_images function must be called with \\\"\\n \\\"-f or --function, or with the --list-images option\\\"\\n )\\n\\n ret = {}\\n conn = get_conn()\\n\\n for item in conn.list_images()[\\\"items\\\"]:\\n image = {\\\"id\\\": item[\\\"id\\\"]}\\n image.update(item[\\\"properties\\\"])\\n ret[image[\\\"name\\\"]] = image\\n\\n return ret\",\n \"def get_image_list(source_dir):\\n\\n dir_list = os.path.os.listdir(source_dir)\\n# print(dir_list)\\n image_list = []\\n os.chdir(source_dir)\\n for file in dir_list:\\n print(\\\"Inspecting.... : {}\\\".format(file))\\n\\n try:\\n if Image.open(file).format:\\n image_list.append(file)\\n print(\\\"{} : is an image\\\".format(file))\\n except Exception as e:\\n print(\\\"{} : failed the imageness test.i \\\\n {}\\\".format(file, e))\\n continue\\n\\n# print(image_list)\\n return image_list\",\n \"def containers_from_image(image_name):\\n jobs = Queue(maxsize=0)\\n containers = list(\\n filter(\\n lambda x: image_name in x.attrs['Config']['Image'],\\n doxy.containers.list()\\n )\\n )\\n for container in containers:\\n jobs.put(container)\\n return jobs\",\n \"def do_command(self, args):\\n imageops = dbops.Images()\\n listing = imageops.list(args)\\n ordering = ['image_name', 'image_format', 'vendor_name', 'os_type_name',\\n 'is_64bit', 'is_bigmem', 'is_smp', 'is_enabled']\\n do_list(listing, ordering)\",\n \"def images(self, **kwargs):\\n return self.get_list(self.cloudman.compute.images(),\\n kind=\\\"image\\\")\",\n \"def pull_base_images(ctx, dockerfile, signed_pull=True):\\n images = set()\\n stages = set()\\n\\n with open(dockerfile, \\\"r\\\") as f:\\n for line in f:\\n words = line.split()\\n # Get source images\\n if len(words) < 2 or words[0].lower() != \\\"from\\\":\\n continue\\n images.add(words[1])\\n # Get stage names to remove them from pull\\n if len(words) < 4 or words[2].lower() != \\\"as\\\":\\n continue\\n stages.add(words[3])\\n\\n if stages:\\n print(\\\"Ignoring intermediate stage names: {}\\\".format(\\\", \\\".join(stages)))\\n images -= stages\\n\\n print(\\\"Pulling following base images: {}\\\".format(\\\", \\\".join(images)))\\n\\n pull_env = {}\\n if signed_pull:\\n pull_env[\\\"DOCKER_CONTENT_TRUST\\\"] = \\\"1\\\"\\n\\n for i in images:\\n ctx.run(\\\"docker pull {}\\\".format(i), env=pull_env)\",\n \"def _docker_search(term):\\n http_conn = httplib.HTTPConnection('localhost', 4243)\\n http_conn.request('GET',\\n '/images/search?term={0}'.format(urllib.quote(term)))\\n resp = http_conn.getresponse()\\n data = resp.read()\\n if resp.status != 200:\\n return []\\n return [repos['Name'] for repos in json.loads(data)]\",\n \"def ls():\\n # TODO: listing all availabe containers form sequence\\n return\",\n \"def list(self):\\n r = self.target.ttbd_iface_call(\\\"images\\\", \\\"list\\\", method = \\\"GET\\\")\\n return r['result']\",\n \"def avail_images(call=None):\\n if call == \\\"action\\\":\\n raise SaltCloudSystemExit(\\n \\\"This function must be called with -f, --function argument.\\\"\\n )\\n return template_list()\",\n \"def build_docker(c):\\n tag = c.run('git describe', hide=True)\\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\\n c.run(f'docker build -t {docker_img} .')\",\n \"def get_installed_images(self):\\n raise NotImplementedError\",\n \"def all(tag=None, env=None):\\n build_images(tag=tag)\\n push_images(tag=tag)\\n deploy(env=env, tag=tag)\",\n \"def list_runtimes(self, docker_image_name='all'):\\n logger.debug('Listing runtimes')\\n logger.debug('Note that k8s job backend does not manage runtimes')\\n return []\",\n \"def list_images():\\n resource_route = \\\"/static/img/\\\"\\n file_request_path = request.base_url[:request.base_url.rfind('/')] + resource_route\\n path_to_current_file = os.path.dirname(os.path.abspath(__file__))\\n images_path = os.path.join(path_to_current_file, 'static', 'img')\\n directory_list = os.listdir(images_path)\\n image_files = [f for f in directory_list if os.path.isfile(os.path.join(images_path, f))]\\n image_files.sort()\\n if '.gitignore' in image_files:\\n image_files.remove('.gitignore')\\n full_image_paths = [file_request_path + f for f in image_files]\\n response_code = 200\\n return make_response(jsonify({'files': full_image_paths}), response_code)\",\n \"async def fetch_all_images(sess: Session = Depends(get_db)):\\n image_list = utils_com.get_com_image_list(sess)\\n return image_list\",\n \"def get_images(self):\\n \\n return self.img_lst\",\n \"def list_images(self, repository_name, registry_id=None):\\n repository = None\\n found = False\\n if repository_name in self.repositories:\\n repository = self.repositories[repository_name]\\n if registry_id:\\n if repository.registry_id == registry_id:\\n found = True\\n else:\\n found = True\\n\\n if not found:\\n raise RepositoryNotFoundException(\\n repository_name, registry_id or DEFAULT_REGISTRY_ID\\n )\\n\\n images = []\\n for image in repository.images:\\n images.append(image)\\n return images\",\n \"def all_images():\\n\\n total = set()\\n jobs = [nomad.parse(get_job(job.template)) for job in config.enabled_jobs]\\n for spec in jobs:\\n for image in nomad.get_images(spec):\\n if image is not None and image != 'None':\\n total |= set([image])\\n return total\",\n \"def do_list(cs, args):\\n opts = {}\\n opts['all_projects'] = args.all_projects\\n opts['marker'] = args.marker\\n opts['limit'] = args.limit\\n opts['sort_key'] = args.sort_key\\n opts['sort_dir'] = args.sort_dir\\n opts['image'] = args.image\\n opts['name'] = args.name\\n opts['project_id'] = args.project_id\\n opts['user_id'] = args.user_id\\n opts['host'] = args.host\\n opts['task_state'] = args.task_state\\n opts['memory'] = args.memory\\n opts['auto_remove'] = args.auto_remove\\n opts['status'] = args.status\\n opts = zun_utils.remove_null_parms(**opts)\\n containers = cs.containers.list(**opts)\\n zun_utils.list_containers(containers)\",\n \"def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None, skip_build=False):\\n value_modifications = {}\\n for name, options in images.items():\\n image_path = options.get('contextPath', os.path.join('images', name))\\n image_tag = tag\\n # include chartpress.yaml itself as it can contain build args and\\n # similar that influence the image that would be built\\n paths = list(options.get('paths', [])) + [image_path, 'chartpress.yaml']\\n last_commit = last_modified_commit(*paths)\\n if tag is None:\\n if chart_version:\\n image_tag = \\\"{}-{}\\\".format(chart_version, last_commit)\\n else:\\n image_tag = last_commit\\n image_name = prefix + name\\n image_spec = '{}:{}'.format(image_name, image_tag)\\n\\n value_modifications[options['valuesPath']] = {\\n 'repository': image_name,\\n 'tag': SingleQuotedScalarString(image_tag),\\n }\\n\\n if skip_build:\\n continue\\n\\n template_namespace = {\\n 'LAST_COMMIT': last_commit,\\n 'TAG': image_tag,\\n }\\n\\n if tag or image_needs_building(image_spec):\\n build_args = render_build_args(options, template_namespace)\\n build_image(image_path, image_spec, build_args, options.get('dockerfilePath'))\\n else:\\n print(f\\\"Skipping build for {image_spec}, it already exists\\\")\\n\\n if push:\\n if tag or image_needs_pushing(image_spec):\\n check_call([\\n 'docker', 'push', image_spec\\n ])\\n else:\\n print(f\\\"Skipping push for {image_spec}, already on registry\\\")\\n return value_modifications\",\n \"def get_docker_info(dut):\\n command = 'show version'\\n output = st.show(dut, command)\\n include_keys = ['repository', 'tag', 'image_id', 'size']\\n rv = [{each_key: each_line[each_key] for each_key in each_line if each_key in include_keys} for each_line in\\n output[1:]]\\n return rv\",\n \"def available_images(self, images, registries, task_vars):\\n return [\\n image for image in images\\n if any(self.is_available_skopeo_image(image, registry, task_vars) for registry in registries)\\n ]\",\n \"def _get_pinned_docker_images() -> Mapping[str, Mapping[str, str]]:\\n\\n pinned_docker_images_file = resources_dir / \\\"pinned_docker_images.cfg\\\"\\n all_pinned_docker_images = ConfigParser()\\n all_pinned_docker_images.read(pinned_docker_images_file)\\n return all_pinned_docker_images\",\n \"def docker_response():\\n return {\\n \\\"results\\\": [\\n {\\\"name\\\": \\\"dev\\\"},\\n {\\\"name\\\": \\\"9.99.9\\\"},\\n {\\\"name\\\": \\\"latest\\\"},\\n {\\\"name\\\": \\\"rc\\\"},\\n {\\\"name\\\": \\\"beta\\\"},\\n {\\\"name\\\": \\\"9.99.9b0\\\"},\\n {\\\"name\\\": \\\"9.98.9\\\"},\\n {\\\"name\\\": \\\"9.98.9b0\\\"},\\n {\\\"name\\\": \\\"9.97.9\\\"},\\n {\\\"name\\\": \\\"9.97.9b0\\\"},\\n ]\\n }\",\n \"def required_images(self):\\n required = set()\\n deployment_type = self.get_var(\\\"openshift_deployment_type\\\")\\n host_groups = self.get_var(\\\"group_names\\\")\\n # containerized etcd may not have openshift_image_tag, see bz 1466622\\n image_tag = self.get_var(\\\"openshift_image_tag\\\", default=\\\"latest\\\")\\n image_info = DEPLOYMENT_IMAGE_INFO[deployment_type]\\n\\n # template for images that run on top of OpenShift\\n image_url = \\\"{}/{}-{}:{}\\\".format(image_info[\\\"namespace\\\"], image_info[\\\"name\\\"], \\\"${component}\\\", \\\"${version}\\\")\\n image_url = self.get_var(\\\"oreg_url\\\", default=\\\"\\\") or image_url\\n if 'nodes' in host_groups:\\n for suffix in NODE_IMAGE_SUFFIXES:\\n required.add(image_url.replace(\\\"${component}\\\", suffix).replace(\\\"${version}\\\", image_tag))\\n # The registry-console is for some reason not prefixed with ose- like the other components.\\n # Nor is it versioned the same, so just look for latest.\\n # Also a completely different name is used for Origin.\\n required.add(image_info[\\\"registry_console_image\\\"])\\n\\n # images for containerized components\\n if self.get_var(\\\"openshift\\\", \\\"common\\\", \\\"is_containerized\\\"):\\n components = set()\\n if 'nodes' in host_groups:\\n components.update([\\\"node\\\", \\\"openvswitch\\\"])\\n if 'masters' in host_groups: # name is \\\"origin\\\" or \\\"ose\\\"\\n components.add(image_info[\\\"name\\\"])\\n for component in components:\\n required.add(\\\"{}/{}:{}\\\".format(image_info[\\\"namespace\\\"], component, image_tag))\\n if 'etcd' in host_groups: # special case, note it is the same for origin/enterprise\\n required.add(\\\"registry.access.redhat.com/rhel7/etcd\\\") # and no image tag\\n\\n return required\",\n \"def get_image_list(self, account):\\n images = self.driver(account).list_images()\\n return [image.name for image in images]\",\n \"def list_images(ec2): # pragma: no coverage\\n response = ec2.describe_images(Filters=[{'Name': 'is-public',\\n 'Values': ['false']}])\\n response.pop('ResponseMetadata')\\n printy(\\\"{:12}\\\\t{:20}\\\\t\\\\tCreationDate:\\\".format(\\\"ImageId\\\", \\\"Name\\\"))\\n\\n for image in response['Images']:\\n if len(image[\\\"Name\\\"]) > 20:\\n image['Name'] = image['Name'][:20] + \\\"...\\\"\\n print(\\\"{ImageId}\\\\t{Name:20}\\\\t\\\\t{CreationDate}\\\".format(**image))\",\n \"def fetch_images(client, images):\\n return [fetch_image(client, image) for image in images]\",\n \"def get_images_online(self, img_names):\\n images = []\\n errs = []\\n for img_name in img_names:\\n try:\\n path = IMAGES_FOLDER + img_name\\n # print(path)\\n img = get_image(path, self.img_shape)\\n x = image.img_to_array(img)\\n x = np.expand_dims(x, axis=0)\\n images.append(x)\\n except Exception as e:\\n # print(path,str(e))\\n errs.append(img_name)\\n\\n return np.vstack(images), errs\",\n \"def run_docker_push(image_list, docker_client):\\n logger.info(\\\"--- push image to registry ---\\\")\\n if runtime == Runtime.local:\\n logger.info(\\\"Detected local runtime, stop here.\\\")\\n return\\n\\n if not len(success_image_list):\\n logger.info(\\\"No success image to push, stop here.\\\")\\n return\\n\\n if GIT_BRANCH != \\\"master\\\":\\n logger.info(\\\"Not master branch, stop here\\\")\\n return\\n\\n docker_client.login(username=DOCKER_HUB_USERNAME, password=DOCKER_HUB_PASSWORD)\\n for image in image_list:\\n image.run_docker_push(docker_client)\\n print(\\\"Finished.\\\")\",\n \"def pullDockerImage(client, names):\\n imgNotExistList = []\\n for name in names:\\n try:\\n client.images.pull(name)\\n # some invalid image names will not be pulled but the pull method\\n # will not throw an exception so the only way to confirm if a pull\\n # succeeded is to attempt a docker inspect on the image\\n client.images.get(name)\\n except Exception:\\n imgNotExistList.append(name)\\n if len(imgNotExistList) != 0:\\n raise DockerImageNotFoundError('Could not find multiple images ',\\n image_name=imgNotExistList)\",\n \"def get_images(stage=0):\\n return get_files(stage)[0]\",\n \"def list_images(self):\\n \\n logging.debug(\\\"list_images entered for %s\\\" % self.machine_name) \\n snapshots = cs.list_snapshots()\\n res = []\\n server_id = self.cloudserver.id\\n # find the one for this server\\n for snapshot in snapshots:\\n img = snapshot.metadata.get(\\\"instance_uuid\\\", None)\\n # print img\\n\\n if img == server_id:\\n print \\\"Server %s has snapshot %s\\\" % (server_id, img)\\n res.append(img)\\n\\n return res\",\n \"def describe_image_builders(Names=None, MaxResults=None, NextToken=None):\\n pass\",\n \"def containers():\\n # TODO: can there be multiple names?\\n cmd = [ 'docker', 'ps', '--format', '{{.Names}}' ]\\n with popen_text(cmd) as docker:\\n for ln in docker.stdout:\\n yield ln[:-1]\",\n \"def get_images(self):\\n return self._get_brains(\\\"Image\\\")\",\n \"def get_images(self, file_path: str) -> Iterable[Image]:\\n return []\",\n \"def getBuilds():\",\n \"def docker_build(c):\\n cli_tasks.docker_build.run(c)\",\n \"def find_docker_files():\\n docker_dirs = []\\n if os.path.exists(TMP_DIR):\\n docker_dirs = [os.path.join(TMP_DIR, d) for d in os.listdir(TMP_DIR)\\n if os.path.isdir(os.path.join(TMP_DIR, d)) and\\n not d.endswith('_working')]\\n docker_dirs.sort(key=lambda x: os.path.getmtime(x))\\n\\n return docker_dirs\",\n \"def container_list(ctx, token, all):\\n try:\\n out = ctx.obj.container_list(token, all)\\n headers = ['CONTAINER ID', 'IMAGE', 'COMMAND',\\n 'CREATED', 'STATUS', 'PORTS', 'NAMES']\\n print_table(headers, out)\\n except BaseException as e:\\n print_error(e.message)\",\n \"def get_images(fish):\\n fish_dir = TRAIN_DIR+'{}'.format(fish)\\n images = [fish+'/'+im for im in os.listdir(fish_dir)]\\n return images\",\n \"def get_images_list(self):\\n return self.image_filenames_list\",\n \"def get_pip_list(docker_image):\\n # First try bash crawl (won't work with alpine)\\n cmd = ['sudo', 'docker', 'run', '--env', \\\"LD_LIBRARY_PATH=''\\\", '--rm', '-ti',\\n '--entrypoint=/bin/bash', '-v', '{}/commands:/tmp/my_commands'.format(pwd),\\n docker_image, '/tmp/my_commands/bash_crawl.sh', 'pip*']\\n\\n print(' '.join(cmd))\\n r = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE, universal_newlines=True)\\n r.wait()\\n output = str(r.stdout.read())\\n print('output:')\\n print(output)\\n\\n output = output.split('\\\\n')\\n\\n exp = \\\".*pip([0-9]?\\\\.?[0-9]?[0-9]?\\\\.?[0-9]?[0-9]?)$\\\"\\n pip_list = []\\n for result in output:\\n m = None\\n m = re.match(exp, result)\\n if not m == None:\\n new_pip = result.rstrip()\\n if not new_pip in pip_list:\\n pip_list.append(new_pip)\\n\\n if pip_list == []:\\n pip_list = ['pip', 'pip2', 'pip3']\\n\\n pip_dir_list = []\\n pip_ver_list = []\\n\\n pip_vers_list = []\\n for pip in pip_list:\\n cmd = ['sudo', 'docker', 'run', '--env', \\\"LD_LIBRARY_PATH=''\\\", '--rm', '-ti',\\n '--entrypoint={}'.format(pip), docker_image, '--version']\\n\\n print(' '.join(cmd))\\n r = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE, universal_newlines=True)\\n r.wait()\\n output = str(r.stdout.read())\\n try:\\n pip_vers = output.split()[-1][:-1]\\n pip_dir = os.path.dirname(pip)\\n if pip_dir in pip_dir_list and pip_vers in pip_ver_list:\\n continue\\n pip_vers_list.append((pip, pip_vers))\\n\\n pip_dir_list.append(pip_dir)\\n pip_ver_list.append(pip_vers)\\n\\n except Exception as e:\\n print('no pip version in {}'.format(output))\\n print(e)\\n\\n # pprint.pprint(pip_vers_list)\\n\\n py2pip, py_list = match_pip_to_py(pip_vers_list, docker_image)\\n\\n return (py2pip, py_list, pip_vers_list)\",\n \"def container_list(self, kwargs=None):\\n try:\\n scode, containers = Rest.get('Container')\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n return\\n if len(containers) == 0:\\n print(\\\"No containers exist\\\")\\n return\\n\\n n = 1\\n e = {}\\n for container in containers:\\n d = {}\\n d['Ip'] = container['Ip']\\n d['Id'] = container['Id']\\n d['Name'] = container['Name']\\n d['Image'] = container['Config']['Image']\\n d['Status'] = container['State']['Status']\\n d['StartedAt'] = container['State']['StartedAt']\\n e[n] = d\\n n = n + 1\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Name', 'Image', 'Status', 'StartedAt'])))\",\n \"def default_image_list(self):\\n for version in self.database.versions:\\n release = self.database.latest_release(\\n default.platform, version=version, architecture=default.architecture\\n )\\n\\n if not release:\\n continue\\n\\n image = self.database.get(\\n version=version,\\n platform=default.platform,\\n release=release,\\n architecture=default.architecture,\\n )\\n\\n if not image:\\n continue\\n\\n tags = [\\n tag\\n for tag in self.database.tags(image)\\n if not any(\\n (len(tag.version) == 4, tag.platform, tag.release, tag.architecture)\\n )\\n ]\\n\\n yield _format_image(image, tags)\",\n \"def _get_images(image_path):\\n logger.debug(\\\"Getting images: '%s'\\\", image_path)\\n if not os.path.isdir(image_path):\\n logger.debug(\\\"Folder does not exist\\\")\\n return None\\n files = [os.path.join(image_path, f)\\n for f in os.listdir(image_path) if f.lower().endswith((\\\".png\\\", \\\".jpg\\\"))]\\n logger.debug(\\\"Image files: %s\\\", files)\\n return files\",\n \"def getImages(self,Project=\\\"\\\"):\\n #images = [\\\"image1.jpg\\\",\\\"image2.jpg\\\",\\\"image3.jpg\\\"]\\n \\n os.chdir(self.dataDir)\\n images = glob.glob(\\\"*.png\\\")\\n \\n return images\",\n \"def list_namespaced_image(self, **kwargs):\\n\\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method list_namespaced_image\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n\\n resource_path = '/oapi/v1/images'.replace('{format}', 'json')\\n path_params = {}\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n if 'label_selector' in params:\\n query_params['labelSelector'] = params['label_selector']\\n if 'field_selector' in params:\\n query_params['fieldSelector'] = params['field_selector']\\n if 'watch' in params:\\n query_params['watch'] = params['watch']\\n if 'resource_version' in params:\\n query_params['resourceVersion'] = params['resource_version']\\n if 'timeout_seconds' in params:\\n query_params['timeoutSeconds'] = params['timeout_seconds']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'GET',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='V1ImageList',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def populateImagesList(self):\\n \\n self._gui_server.getImagesList(self._populateImagesList)\",\n \"def image_list(self):\\n return self._image_list\",\n \"def get_images():\\n return _IMAGES\",\n \"def getimgs():\",\n \"def build_env_wheels() -> Iterable[Path]:\\n return []\",\n \"def resolve_running_docker_containers():\\n container_ids = terminal.docker_ps(ps_filter='name={}'.format(CONDUCTR_NAME_PREFIX))\\n container_names = [terminal.docker_inspect(container_id, '{{.Name}}')[1:] for container_id in container_ids]\\n return sorted(container_names)\",\n \"def get_images(directory=None): #import from mask.py\\n \\n if directory == None:\\n directory = os.getcwd() # Use working directory if unspecified\\n \\n image_list = [] # Initialize aggregaotrs\\n file_list = []\\n \\n directory_list = os.listdir(directory) # Get list of files\\n for entry in directory_list:\\n absolute_filename = os.path.join(directory, entry)\\n try:\\n image = PIL.Image.open(absolute_filename)\\n file_list += [entry]\\n image_list += [image]\\n except IOError:\\n pass # do nothing with errors tying to open non-images\\n return image_list, file_list\",\n \"async def imageList(self, ctx: Context, imageType=\\\"icons\\\"):\\n imageSingular = self.getSingularImageType(imageType)\\n allImages = await self.config.guild(ctx.guild).get_attr(imageType)()\\n if not allImages:\\n await ctx.send(f\\\"There are no {imageType}, please add some first!\\\")\\n return\\n\\n async with self.config.guild(ctx.guild).get_attr(f\\\"{imageType}Dates\\\")() as imageDates:\\n imageDates = dict(sorted(imageDates.items()))\\n msg = \\\"\\\"\\n for changeDate, name in imageDates.items():\\n # YYYY-MM-DD\\n theDate = date.fromisoformat(f\\\"2020-{changeDate}\\\").strftime(\\\"%B %d\\\")\\n msg += f\\\"{theDate}: {name}\\\\n\\\"\\n notAssigned = set(allImages) - set(imageDates.values())\\n if notAssigned:\\n msg += f\\\"Unassigned: \\\"\\n msg += \\\", \\\".join(notAssigned)\\n pageList = []\\n pages = list(pagify(msg, page_length=500))\\n totalPages = len(pages)\\n async for pageNumber, page in AsyncIter(pages).enumerate(start=1):\\n embed = discord.Embed(\\n title=f\\\"Server {imageSingular} changes for {ctx.guild.name}\\\", description=page\\n )\\n embed.set_footer(text=f\\\"Page {pageNumber}/{totalPages}\\\")\\n pageList.append(embed)\\n await menu(ctx, pageList, DEFAULT_CONTROLS)\",\n \"def test_list_cached_images_empty(self, mock_images):\\n self._run_command(['list-cached'], cache_manage.SUCCESS)\",\n \"def cli(ctx, image_file):\\n if not image_file:\\n return\\n for pull_image in image_file.readline():\\n pull_image = pull_image.rstrip('\\\\n')\\n if len(pull_image) == 0:\\n continue\\n docker.pull(pull_image)\\n push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,\\n DEFAULR_NAMESPACE,\\n pull_image.split('/')[-1])\\n docker.tag(pull_image, push_image)\\n docker.push(push_image)\",\n \"def get_images(owner, tagvalue):\\n try:\\n images = ec2(credentials).describe_images(Owners=[owner],Filters=[{'Name':'tag-value', 'Values':[tagvalue]}])\\n return images\\n except Exception as e:\\n print(\\\"Error: cannot get the list of images. %s\\\" % e)\",\n \"def imageList(self):\\n return self.__imageList\",\n \"def _locate_images(self):\\r\\n extensions = '|'.join(self.valid_extensions)\\r\\n extension_re = re.compile('.+\\\\.(%s)$' % extensions, re.IGNORECASE)\\r\\n files = sorted(os.listdir(self.path))\\r\\n\\r\\n images = []\\r\\n for root, dirs, files in os.walk(self.path, followlinks=self.config['follow_links']):\\r\\n for filename in sorted(files):\\r\\n if not filename.startswith('.') and extension_re.match(filename):\\r\\n images.append(Image(path=os.path.join(root, filename), config=self.config))\\r\\n if not self.config['recursive']:\\r\\n break\\r\\n\\r\\n if not images:\\r\\n raise SourceImagesNotFoundError(self.path)\\r\\n\\r\\n images = sorted(images, reverse=self.config['algorithm_ordering'][0] != '-')\\r\\n\\r\\n return images\",\n \"def listImages(name_list, path, as_grey=True):\\n\\timage_list = []\\n\\n\\tfor name in name_list:\\n\\t\\timage = io.imread(path + \\\"/\\\" + name + \\\".jpg\\\", as_grey=as_grey)\\n\\t\\tif as_grey is True:\\n\\t\\t\\timage = resize(image, (200, 200))\\n\\t\\timage_list.append(image)\\n\\n\\treturn image_list\",\n \"def getDockerfiles(fileList) -> dict:\\n paths = {}\\n for word in fileList:\\n if \\\"/Dockerfile\\\" in word:\\n try:\\n path = word.split('/')\\n image = path[0]\\n tag = path[1]\\n paths[len(paths)] = (image, tag)\\n except IndexError: \\n print(\\\"Image name and tag are required in path as \\\\'image/tag/Dockerfile\\\\'\\\")\\n print(paths)\\n return paths\",\n \"def list_images(project):\\n data = {constants.PROJECT_PARAMETER: project}\\n res = requests.post(_url + \\\"list_images/\\\", data=data,\\n auth=(_username, _password))\\n click.echo(\\\"username:\\\")\\n click.echo(_username)\\n click.echo(_password)\\n click.echo(_url)\\n click.echo(data) \\n if res.status_code == 200:\\n images = json.loads(res.content)\\n table = PrettyTable(field_names=[\\\"Image\\\"])\\n for image in images:\\n table.add_row([image])\\n click.echo(table.get_string())\\n else:\\n click.echo(res.content)\",\n \"def getimagelist(folder):\\n imagefolder = Path(folder) \\n imagelist = imagefolder.glob(\\\"**/*.png\\\") \\n return list(imagelist)\",\n \"def do_stage(self, images):\\n\\n for i, image in enumerate(images):\\n pass\\n # logging_tags = logs.image_config_to_tags(image, self.group_by_keywords)\",\n \"def build_docker(params) -> None:\\n print(\\\"Building docker image...\\\")\\n cmd = \\\"cd bg_changer && docker build --tag bg_changer . >/dev/null 2>&1\\\"\\n if os.system(cmd) == 0:\\n print(\\\" Success !\\\")\\n else:\\n print(\\\" Failure !\\\")\",\n \"def build_container_image(self) -> None:\\n print_version_of_tools()\\n try:\\n self.fs_watcher.start()\\n runner = PluginsRunner(self,\\n self.plugins_conf,\\n self.plugin_files,\\n self.keep_plugins_running,\\n plugins_results=self.data.plugins_results)\\n runner.run()\\n finally:\\n self.fs_watcher.finish()\",\n \"def images(self) -> Iterable[dto.Image]:\\n raise errors.UnsupportedOperationError(\\n \\\"Operation not supported for provider '{}'\\\".format(self.provider_name)\\n )\",\n \"def extract_images(\\n self,\\n graph_connector: DiGraph | None = None,\\n definitions: _Definitions | None = None,\\n definitions_raw: dict[str, list[tuple[int, str]]] | None = None\\n ) -> list[Image]:\\n\\n pass\",\n \"def pull_image(image):\\n\\n subprocess.check_call(['docker', 'pull', image])\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n epilog=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\\n )\\n parser.add_argument(\\n \\\"-d\\\", \\\"--dry-run\\\", action=\\\"store_true\\\", default=0, help=\\\"Dry run mode.\\\"\\n )\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--verbose\\\",\\n action=\\\"count\\\",\\n default=0,\\n help=\\\"Verbosity. Default is WARNING level.\\\",\\n )\\n\\n subparsers = parser.add_subparsers(help=\\\"Sub commands\\\", dest=\\\"subparser\\\")\\n subparsers.required = True\\n\\n build_parser = subparsers.add_parser(\\n \\\"build\\\",\\n description=\\\"Build an image from Dockerfile, caching image hierarchy\\\",\\n help=\\\"Build an image from a Dockerfile\\\",\\n )\\n build_parser.add_argument(\\n \\\"path\\\", metavar=\\\"PATH\\\", help=\\\"The build context directory\\\"\\n )\\n build_parser.add_argument(\\n \\\"-f\\\",\\n \\\"--file\\\",\\n help=\\\"Name of the Dockerfile. If not provided, \\\"\\n \\\"will use config.DOCKERFILE_PATH_PATTERN to compute. \\\",\\n )\\n build_parser.add_argument(\\n \\\"-v\\\",\\n \\\"--git-sha\\\",\\n required=True,\\n help=\\\"The version of code to build against, \\\" \\\"will pass as GIT_SHA variable\\\",\\n )\\n build_parser.add_argument(\\n \\\"-n\\\", \\\"--name\\\", required=True, help=\\\"The name of the image to build\\\"\\n )\\n build_parser.add_argument(\\n \\\"--build-arg\\\",\\n metavar=\\\"ARG=VALUE\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Set extra build-time variables. GIT_SHA, TIMESTAMP will be passed by default.\\\",\\n )\\n build_parser.add_argument(\\n \\\"-r\\\",\\n \\\"--raw\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Whether to use raw docker build command to build, skipping caching logic\\\",\\n )\\n build_parser.add_argument(\\n \\\"--registry\\\",\\n default=config.DOCKER_REGISTRY,\\n help=\\\"Docker registry use to determine the image identity, \\\"\\n \\\"can be set via IMAGE_BUILDER_DOCKER_REGISTRY environment variable, \\\"\\n 'or set DOCKER_REGISTRY in config.py. Default is \\\"%(default)s\\\"',\\n )\\n build_parser.add_argument(\\n \\\"-t\\\",\\n \\\"--tag-pattern\\\",\\n default=config.GIT_SHA_TAG_PATTERN,\\n help=\\\"Tag pattern, can only include one `{git_sha}` placeholder, \\\"\\n 'such as \\\"{git_sha}-new\\\". If the tag exists, we won\\\\'t rebuild it. '\\n 'Default is \\\"%(default)s\\\"',\\n )\\n build_parser.add_argument(\\n \\\"-e\\\",\\n \\\"--extra-tag\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Extra tags to tag to the final images\\\",\\n )\\n build_parser.add_argument(\\n \\\"--extra-name\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Extra name and optionally with a tag in the 'name:tag' format\\\",\\n )\\n build_parser.add_argument(\\n \\\"-o\\\", \\\"--output-hash\\\", help=\\\"The output filename of the files hash log.\\\"\\n )\\n build_parser.set_defaults(func=build)\\n\\n args = parser.parse_args()\\n if args.dry_run:\\n # DRY_RUN env will be read in image_builder.libs.process\\n os.environ[\\\"DRY_RUN\\\"] = \\\"1\\\"\\n\\n if args.func == build:\\n args.path = expand_path(args.path)\\n if args.output_hash:\\n args.output_hash = expand_path(args.output_hash)\\n\\n args.file = args.file or locate_dockerfile(args.name)\\n args.file = expand_path(args.file)\\n # set environ for main dockerfile for possibly retrieving later\\n os.environ[\\n config.DOCKERFILE_ENV_PATTERN.format(image_name=args.name)\\n ] = args.file\\n\\n # change CWD to PATH\\n os.chdir(args.path)\\n\\n if not args.registry:\\n parser.error(\\n \\\"--registry should be provied \\\"\\n \\\"or specified by IMAGE_BUILDER_DOCKER_REGISTRY environment variable or set DOCKER_REGISTRY in config.py\\\"\\n )\\n if not all(\\\"=\\\" in kv for kv in args.build_arg):\\n parser.error(\\\"--build_arg must be in ARG=VALUE format\\\")\\n\\n # set git_sha_tag\\n try:\\n args.git_sha_tag = args.tag_pattern.format(git_sha=args.git_sha)\\n except KeyError:\\n parser.error(\\n 'Wrong --tag-pattern provided. Can only include one `{git_sha}` placeholder, such as \\\"{git_sha}-new\\\"'\\n )\\n\\n # setup logging\\n level = logging.WARNING - args.verbose * 10\\n logging.basicConfig(\\n level=level, format=\\\"%(asctime)s %(name)s %(levelname)s %(message)s\\\"\\n )\\n\\n if args.output_hash:\\n h = logging.FileHandler(args.output_hash)\\n h.setLevel(logging.DEBUG)\\n h.setFormatter(logging.Formatter(\\\"%(message)s\\\"))\\n hash_logger.addHandler(h)\\n\\n # Suppress warning when we don't verify ssl\\n import urllib3\\n\\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\\n\\n return args.func(args)\",\n \"def imageutil(self) -> imageutil.ImageUtil:\\n return imageutil.ImageUtil(self.data.dockerfile_images, self.conf)\",\n \"def images(self):\\n return self._images\",\n \"def images(self):\\n return self._images\",\n \"def list_images(basePath, validExts=(\\\".jpg\\\", \\\".jpeg\\\", \\\".png\\\", \\\".bmp\\\", \\\".tif\\\", \\\".tiff\\\"), contains=None):\\n return _list_files(basePath, validExts, contains=contains)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79035133","0.75807214","0.7104596","0.6980693","0.69144505","0.6904989","0.6883383","0.6788377","0.6781191","0.67198014","0.6546041","0.653512","0.6484546","0.6475429","0.642328","0.6303564","0.63027793","0.62536365","0.6235247","0.6217119","0.6206234","0.6204033","0.61892796","0.61594063","0.6137325","0.6119341","0.6112936","0.610643","0.6094533","0.60544354","0.60455686","0.60180074","0.6012547","0.60073775","0.59885794","0.5945832","0.5935869","0.59222","0.59037983","0.5898656","0.5896112","0.5882814","0.58812755","0.5850128","0.5847226","0.5845116","0.5832865","0.58144754","0.5800367","0.57989764","0.578957","0.5776594","0.57754743","0.57679105","0.57533175","0.57481635","0.57382977","0.5728096","0.57172215","0.5700238","0.5699983","0.5681555","0.56556344","0.5616129","0.5615335","0.5612921","0.5611041","0.56056184","0.557786","0.55674523","0.55585694","0.5553006","0.554814","0.5527993","0.55146617","0.54961175","0.5489825","0.54895955","0.54893446","0.5478604","0.54685795","0.54654807","0.5464465","0.5463131","0.546254","0.5451797","0.5439955","0.5439209","0.54217005","0.5420341","0.5419357","0.5417949","0.5417394","0.54161817","0.54148215","0.5413904","0.5407443","0.54040545","0.54040545","0.540324"],"string":"[\n \"0.79035133\",\n \"0.75807214\",\n \"0.7104596\",\n \"0.6980693\",\n \"0.69144505\",\n \"0.6904989\",\n \"0.6883383\",\n \"0.6788377\",\n \"0.6781191\",\n \"0.67198014\",\n \"0.6546041\",\n \"0.653512\",\n \"0.6484546\",\n \"0.6475429\",\n \"0.642328\",\n \"0.6303564\",\n \"0.63027793\",\n \"0.62536365\",\n \"0.6235247\",\n \"0.6217119\",\n \"0.6206234\",\n \"0.6204033\",\n \"0.61892796\",\n \"0.61594063\",\n \"0.6137325\",\n \"0.6119341\",\n \"0.6112936\",\n \"0.610643\",\n \"0.6094533\",\n \"0.60544354\",\n \"0.60455686\",\n \"0.60180074\",\n \"0.6012547\",\n \"0.60073775\",\n \"0.59885794\",\n \"0.5945832\",\n \"0.5935869\",\n \"0.59222\",\n \"0.59037983\",\n \"0.5898656\",\n \"0.5896112\",\n \"0.5882814\",\n \"0.58812755\",\n \"0.5850128\",\n \"0.5847226\",\n \"0.5845116\",\n \"0.5832865\",\n \"0.58144754\",\n \"0.5800367\",\n \"0.57989764\",\n \"0.578957\",\n \"0.5776594\",\n \"0.57754743\",\n \"0.57679105\",\n \"0.57533175\",\n \"0.57481635\",\n \"0.57382977\",\n \"0.5728096\",\n \"0.57172215\",\n \"0.5700238\",\n \"0.5699983\",\n \"0.5681555\",\n \"0.56556344\",\n \"0.5616129\",\n \"0.5615335\",\n \"0.5612921\",\n \"0.5611041\",\n \"0.56056184\",\n \"0.557786\",\n \"0.55674523\",\n \"0.55585694\",\n \"0.5553006\",\n \"0.554814\",\n \"0.5527993\",\n \"0.55146617\",\n \"0.54961175\",\n \"0.5489825\",\n \"0.54895955\",\n \"0.54893446\",\n \"0.5478604\",\n \"0.54685795\",\n \"0.54654807\",\n \"0.5464465\",\n \"0.5463131\",\n \"0.546254\",\n \"0.5451797\",\n \"0.5439955\",\n \"0.5439209\",\n \"0.54217005\",\n \"0.5420341\",\n \"0.5419357\",\n \"0.5417949\",\n \"0.5417394\",\n \"0.54161817\",\n \"0.54148215\",\n \"0.5413904\",\n \"0.5407443\",\n \"0.54040545\",\n \"0.54040545\",\n \"0.540324\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.691039"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":80394,"cells":{"query":{"kind":"string","value":"list the available ci stages for execution"},"document":{"kind":"string","value":"def list_stages():\n for name in Manager.STAGES:\n click.echo('{}'.format(name))"},"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 stages() -> List[Tuple[str, str]]:\n return [TestStatus.preparation, TestStatus.testing, TestStatus.completed]","def stage_list(args):\n\n for stage in args.stages:\n print stage","def stages(self):\n return self._stages","def stages(self):\n return self._stages","def process_stages(self) -> List[str]:\n return self._process_stages","def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\n return pulumi.get(self, \"job_stages\")","def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\n return pulumi.get(self, \"job_stages\")","def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\n return pulumi.get(self, \"job_stages\")","def sweep_stages(self) -> List[str]:\n return self._sweep_stages","def stages(self):\r\n return pipelines.Stages(self)","def stage_states(self) -> pulumi.Output[Sequence['outputs.ExecutionStageStateResponse']]:\n return pulumi.get(self, \"stage_states\")","def N_stages(self):\n return 5","def stage_status(self) -> str:\n return pulumi.get(self, \"stage_status\")","def stage_states(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ExecutionStageStateArgs']]]]:\n return pulumi.get(self, \"stage_states\")","def stages(self, stages):\n if stages is None:\n self._stages = None\n else:\n self._stages = stages if isinstance(stages, list) else [stages] * len(self.pidevice.allaxes)\n debug('ControllerStartup.stages = %s', itemstostr(self._stages))","def reset_stages(self) -> List[str]:\n return self._reset_stages","def _collect_stages(self) -> Set[str]:\n # Get the stage name associated with the previous deployment and update stage\n # Stage needs to be flushed so that new changes will be visible immediately\n api_resource = get_resource_by_id(self._stacks, ResourceIdentifier(self._api_identifier))\n stage_resources = get_resource_ids_by_type(self._stacks, AWS_APIGATEWAY_STAGE)\n deployment_resources = get_resource_ids_by_type(self._stacks, AWS_APIGATEWAY_DEPLOYMENT)\n\n stages = set()\n # If it is a SAM resource, get the StageName property\n if api_resource:\n if api_resource.get(\"Type\") == AWS_SERVERLESS_API:\n # The customer defined stage name\n stage_name = api_resource.get(\"Properties\", {}).get(\"StageName\")\n if stage_name:\n stages.add(cast(str, stage_name))\n\n # The stage called \"Stage\"\n if stage_name != \"Stage\":\n response_sta = cast(Dict, self._api_client.get_stages(restApiId=self._api_physical_id))\n for item in response_sta.get(\"item\"): # type: ignore\n if item.get(\"stageName\") == \"Stage\":\n stages.add(\"Stage\")\n\n # For both SAM and ApiGateway resource, check if any refs from stage resources\n for stage_resource in stage_resources:\n # RestApiId is a required field in stage\n stage_dict = get_resource_by_id(self._stacks, stage_resource)\n if not stage_dict:\n continue\n rest_api_id = stage_dict.get(\"Properties\", {}).get(\"RestApiId\")\n dep_id = stage_dict.get(\"Properties\", {}).get(\"DeploymentId\")\n # If the stage doesn't have a deployment associated then no need to update\n if dep_id is None:\n continue\n # If the stage's deployment ID is not static and the rest API ID matchs, then update\n for deployment_resource in deployment_resources:\n if deployment_resource.resource_iac_id == dep_id and rest_api_id == self._api_identifier:\n stages.add(cast(str, stage_dict.get(\"Properties\", {}).get(\"StageName\")))\n break\n\n return stages","def list(self):\n print \"\\nAvailable Test Cases\"\n print \"====================\"\n for case in self.cases:\n print case.__name__","def main():\n init()\n separator_len = 40\n for s in stage_instances:\n print('='*separator_len)\n print(s.name)\n print('-'*separator_len)\n\n s.add_tasks() # Add tasks from previous stage\n s.revive_or_archive() # Revive killed tasks or move them to failed\n s.schedule_jobs() # Schedule new jobs if needed\n s.print_status()\n print('='*separator_len + '\\n')\n render(stage_instances)","def get_fill_stages(self) -> Tuple[str]:\n return self._fill_stages","def RunStages(self):\n self._RunStage(build_stages.InitSDKStage)\n self.RunSetupBoard()\n self._RunStage(report_stages.RefreshPackageStatusStage)","def envs(self) -> Optional[Sequence['outputs.CSIUnitySpecDriverControllerEnvs']]:\n return pulumi.get(self, \"envs\")","def getConfigStages(cls):\n newlist = list([\"PRE_DELETE\", \"DELETE\", \"PRE_PATCH\", \"POST\",\n \"POST_PATCH\"])\n return newlist","def envs(self) -> Optional[Sequence['outputs.CSIIsilonSpecDriverControllerEnvs']]:\n return pulumi.get(self, \"envs\")","def envs(self) -> Optional[Sequence['outputs.CSIVXFlexOSSpecDriverControllerEnvs']]:\n return pulumi.get(self, \"envs\")","def testGetStageNamesSmoke(self):\n stage = self.ConstructStage()\n self.assertEqual(stage.GetStageNames(), ['Builder'])","def stage_describe(args):\n\n stage = args.stages[args.stage_name]\n\n print \"Stage:\", stage.name, \"\\n\"\n print \"Inputs:\"\n for input_ in stage.inputs:\n print \"\\t\", input_.name, input_.type, input_.help or \"\"\n\n print \"\\n\"\n print \"Outputs:\"\n for output in stage.outputs:\n print \"\\t\", output.name, output.type, output.help or \"\"\n\n print \"\\n\"\n print \"Splits:\"\n for split in stage.splits:\n print \"\\t\", split.name, split.type, split.help or \"\"\n\n print \"\\n\"\n print \"Source directory:\"\n print \"\\t\", stage.source","def load_stages():\n\n def load_stages_from_dir(mro_dir):\n \"\"\"Iterate over MRO file in a directory, parse them, and accumulate\n their stages.\n \"\"\"\n stages = {}\n for file_name in os.listdir(mro_dir):\n if file_name.endswith(\".mro\"):\n stages.update(mro_parser.get_stages(\n os.path.join(mro_dir, file_name)))\n return stages\n\n stages = {}\n for mro_dir in os.environ[\"MROPATH\"].split(':'):\n stages.update(load_stages_from_dir(mro_dir))\n return stages","def envs(self) -> Optional[Sequence['outputs.CSIPowerMaxSpecDriverControllerEnvs']]:\n return pulumi.get(self, \"envs\")","def load_stages(filename):\r\n data = np.load(filename)\r\n return data['stages']","def load_stages(filename):\r\n data = np.load(filename)\r\n return data['stages']","def get_stages(self):\n current_stage = self.wf['root']\n\n while current_stage:\n yield current_stage\n next_stage = set()\n for n in current_stage:\n next_stage.update(self.wf['action'][n].get('next', set()))\n current_stage = next_stage","def setstages(self):\n if not self._stages or self.prop['skipcst']:\n return\n debug('ControllerStartup.setstages()')\n allaxes = self.pidevice.qSAI_ALL()\n oldstages = self.pidevice.qCST()\n for i, newstage in enumerate(self._stages):\n if not newstage:\n continue\n axis = allaxes[i]\n oldstage = oldstages.get(axis, 'NOSTAGE')\n if oldstage != newstage or self.prop['forcecst']:\n warnmsg = applyconfig(self.pidevice, axis, newstage)\n self._databuf['cstdone'].append(axis)\n if self.prop['showlog'] and warnmsg:\n warning(warnmsg)\n elif self.prop['showlog']:\n info('stage %r on axis %r is already configured', oldstage, axis)","def envs(self) -> Optional[Sequence['outputs.CSIUnitySpecDriverCommonEnvs']]:\n return pulumi.get(self, \"envs\")","def _stage(self):\n\n pass","def all_statuses(cls):\n return list(cls.pipeline.keys())","def all_statuses(cls):\n return list(cls.pipeline.keys())","def getAllOperation(cls):\n # pylint: disable=W0108\n return list(map(lambda x: cls.getConfigOperation(x),\n cls.getConfigStages()))","def test_python(width=10):\n\n stage_1 = [diamond(sleep=60, inputs=[0])]\n\n stage_2 = []\n for i in range(0, width):\n stage_2.extend([diamond(sleep=20, inputs=stage_1)])\n\n stage_3 = [diamond(sleep=30, inputs=stage_2)]\n\n if not stage_3[0].done():\n time.sleep(30)\n for sitename in dfk.executors:\n print(dfk.executors[sitename].status())","def stages(self, pipeline_name):\n\n pipeline_name = self._require_param('pipeline_name', locals())\n\n response = self._session.get(\n path=self._session.urljoin(self.PIPELINES_RESOURCE_PATH, pipeline_name, 'stages.xml').format(\n base_api=self.base_api\n ),\n headers={'Accept': 'application/xml'},\n )\n\n return response.text","def envs(self) -> Optional[Sequence['outputs.CSIVXFlexOSSpecDriverCommonEnvs']]:\n return pulumi.get(self, \"envs\")","def envs(self) -> Optional[Sequence['outputs.CSIUnitySpecDriverNodeEnvs']]:\n return pulumi.get(self, \"envs\")","def envs(self) -> Optional[Sequence['outputs.CSIPowerMaxSpecDriverCommonEnvs']]:\n return pulumi.get(self, \"envs\")","def envs(self) -> Optional[Sequence['outputs.CSIVXFlexOSSpecDriverNodeEnvs']]:\n return pulumi.get(self, \"envs\")","def envs(self) -> Optional[Sequence['outputs.CSIIsilonSpecDriverCommonEnvs']]:\n return pulumi.get(self, \"envs\")","def show_git_versions(ctx):\n\n ws = get_workspace(config)\n\n exp = Experiment(ws, config[\"experiment_name\"])\n\n versions = [\n (run.id, run.get_properties()[\"azureml.git.commit\"]) for run in exp.get_runs()\n ]\n\n print(tabulate(versions, headers=[\"Run ID\", \"Git Version\"]))","def all_envs():\n return all_tasks.keys()","def git_status():\n\tl = []\n\tdebug(\"Not implemented\",1)\n\n\treturn l","def list_test_instances():\n run('ls -1 %s' % env.site_root)","def envs(self) -> Optional[Sequence['outputs.CSIPowerMaxSpecDriverNodeEnvs']]:\n return pulumi.get(self, \"envs\")","def num_stages(tree, order):\n p = len(order)\n stages = cstree_to_stages(tree, order)\n return sum([len(stages[i]) for i in range(1,p)])","def stage(self, stage: osbuild.Stage):","def stage(self):\n return self._stage","def stage(self):\n return self._stage","def stage(self):\n return self._stage","def envs(self) -> Optional[Sequence['outputs.CSIIsilonSpecDriverNodeEnvs']]:\n return pulumi.get(self, \"envs\")","def main():\n gh = Github(os.environ['GH_TOKEN'])\n # gh_token = os.environ['GH_TOKEN']\n gc_token_path = os.environ['GC_STORAGE_KEY']\n\n vars_file_path = os.getenv('VARS_FILE', \"\")\n pipeline_id = os.getenv('CI_PIPELINE_ID', 0)\n\n repo = gh.get_repo(\"kubeinit/kubeinit\")\n branches = repo.get_branches()\n\n output = 0\n # Something linke:\n # url = \"https://gitlab.com/kubeinit/kubeinit-ci/pipelines/\"\n url = os.getenv('CI_PIPELINE_URL', \"\")\n print(\"The job results will be published in runtime at: \" + url)\n\n for branch in branches:\n for pr in repo.get_pulls(state='open', sort='created', base=branch.name):\n labels = [item.name for item in pr.labels]\n\n sha = pr.head.sha\n committer_email = repo.get_commit(sha=sha).commit.committer.email\n print(committer_email)\n\n execute = False\n # We assign the executed label to avoid executing this agains the same PR over and over\n # We mark the PR as e2e-executed\n\n for label in labels:\n if re.match(r\".*-.*-.*-.*-.*-.*-.*\", label):\n print('Matching a PR label')\n params = label.split(\"-\")\n distro = params[0]\n driver = params[1]\n master = params[2]\n worker = params[4]\n scenario = params[6]\n execute = True\n remove_label(label, pr, repo)\n break\n\n if execute:\n now = datetime.now()\n now.strftime(\"%m.%d.%Y.%H.%M.%S\")\n job_name = pipeline_id + \"-\" + distro + \"-\" + driver + \"-\" + master + \"-\" + worker + \"-\" + scenario + \"-\" + now.strftime(\"%Y.%m.%d.%H.%M.%S\")\n print(\"Let's run the e2e job, distro %s driver %s \" % (distro, driver))\n print(\"-------------\")\n print(\"-------------\")\n print(\"Running the e2e job for: \" + str(pr.number) + \" \" + pr.title)\n print(\"-------------\")\n print(\"-------------\")\n print(\"-------------\")\n\n # We update the status to show that we are executing the e2e test\n print(\"Current status\")\n print(repo.get_commit(sha=sha).get_statuses())\n repo.get_commit(sha=sha).create_status(state=\"pending\",\n target_url=url + str(pipeline_id),\n description=\"Running...\",\n context=\"%s-%s-%s-controller-%s-compute-%s\" % (distro,\n driver,\n master,\n worker,\n scenario))\n print(\"The pipeline ID is: \" + str(pipeline_id))\n print(\"The clouds.yml path is: \" + str(vars_file_path))\n # We trigger the e2e job\n start_time = time.time()\n try:\n print(\"We call the downstream job configuring its parameters\")\n subprocess.check_call(\"./ci/run_kubeinit.sh %s %s %s %s %s %s %s %s\" % (str(branch.name),\n str(pr.number),\n str(vars_file_path),\n str(distro),\n str(driver),\n str(master),\n str(worker),\n str(scenario)),\n shell=True)\n except Exception as e:\n print('An exception hapened executing Ansible')\n print(e)\n output = 1\n\n try:\n print(\"Render ara data\")\n subprocess.check_call(\"./ci/ara.sh %s\" % (str(job_name) + \"-\" + str(output)), shell=True)\n except Exception as e:\n print('An exception hapened rendering ara data')\n print(e)\n output = 1\n\n print(\"starting the uploader job\")\n # No matter if the job passed or failed we always use go as the suffix\n # upload_error = upload_logs_to_github(str(job_name) + \"-\" + str(output), gh_token)\n upload_error = upload_logs_to_google_cloud(str(job_name) + \"-\" + str(output), gc_token_path)\n render_index(gc_token_path)\n print(\"finishing the uploader job\")\n\n if output == 0:\n state = \"success\"\n else:\n state = \"failure\"\n\n desc = (\"Ended with %s in %s minutes\" % (state, round((time.time() - start_time) / 60, 2)))\n\n print(desc)\n print(state)\n\n if upload_error == 1:\n dest_url = url + str(pipeline_id)\n else:\n dest_url = 'https://storage.googleapis.com/kubeinit-ci/jobs/pr/' + str(job_name) + \"-\" + str(output) + '/index.html'\n\n print(\"The destination URL is: \" + dest_url)\n # We update the status with the job result\n repo.get_commit(sha=sha).create_status(state=state,\n target_url=dest_url,\n description=desc,\n context=\"%s-%s-%s-controller-%s-compute-%s\" % (distro,\n driver,\n master,\n worker,\n scenario))\n else:\n print(\"No need to do anything\")\n if execute:\n exit()","def pipeline_results(self):\n repo = self.main_gitlab_repo()\n ret = []\n for branch in [\"master\", \"main\", \"devel\"]:\n build = repo.cibuild_set.filter(branch__name__endswith=branch).first()\n if build is not None:\n link = repo.url + f\"/-/pipelines/{build.build_id}\"\n sym = \"✓\" if build.passed else \"✘\"\n ret.append(f'<a href=\"{link}\">{branch}: {sym}</a>')\n\n return mark_safe(\"<br>\".join(ret))","def envs(self) -> Optional[Sequence['outputs.CSIPowerStoreSpecDriverControllerEnvs']]:\n return pulumi.get(self, \"envs\")","def separate_stages(self):\n if not wait_until(self.emulator.is_ui_element_on_screen, ui_element=self.mode_label_ui):\n return logger.error(f\"Can't find mission label: {self.mode_label_ui.name}.\")\n if wait_until(self.emulator.is_ui_element_on_screen, ui_element=ui.EQ_RECOMMENDED_LVL):\n stage_1 = self.emulator.get_screen_text(self.stage_1_ui)\n stage_2 = self.emulator.get_screen_text(self.stage_2_ui)\n stage_1_current, _ = self.game.get_current_and_max_values_from_text(stage_1)\n stage_2_current, _ = self.game.get_current_and_max_values_from_text(stage_2)\n return stage_1_current, stage_2_current\n logger.error(f\"Can't find {ui.EQ_RECOMMENDED_LVL} in mission lobby.\")","def stage(self):\n pass","def getBuilds():","def envs(self) -> Optional[Sequence['outputs.CSIIsilonSpecDriverSideCarsEnvs']]:\n return pulumi.get(self, \"envs\")","def envs(self) -> Optional[Sequence['outputs.CSIUnitySpecDriverSideCarsEnvs']]:\n return pulumi.get(self, \"envs\")","def stage_name(stages_dir):\r\n\r\n print(\"\\nLEVELS AVAILABLE:\"\r\n \"\\n\")\r\n stages_dir = os.path.expanduser(stages_dir)\r\n os.chdir(stages_dir)\r\n stage_lst = next(os.walk('.'))[1]\r\n os.chdir(config_writer.tool_path)\r\n\r\n for name in stage_lst:\r\n print(name)\r\n while True:\r\n stg_nm = input(\"\\nEnter stage name: \")\r\n if stg_nm not in stage_lst:\r\n print(\"\\nStage name not available! Try again.\")\r\n else:\r\n break\r\n\r\n return stg_nm","def deploy():\n stage(branch='live', role='live')","def initConfigstages(cls):\n mystageoperation.update(dict({'DELETE': str(\"DELETE\")}))\n mystageoperation.update(dict({'POST': str(\"POST\")}))\n mystageoperation.update(dict({'PRE_DELETE': str(\"PATCH\")}))\n mystageoperation.update(dict({'PRE_PATCH': str(\"PATCH\")}))\n mystageoperation.update(dict({'POST_PATCH': str(\"PATCH\")}))\n mystageoperation.update(dict({'PUT': str(\"PUT\")}))\n mystageoperation.update(dict({'GET': str(\"GET\")}))\n cls.log(1, \"Config stages Init\")","def pipeline_stage(self) -> aws_cdk.aws_codepipeline.IStage:\n return self._values.get(\"pipeline_stage\")","def get_stages(plan, problem_dic, problem_file, predicates_list):\r\n\r\n # Initial stage\r\n stages = problem_dic[0]['init'].copy()\r\n\r\n with open(problem_file) as file:\r\n text = file.read()\r\n objects = re.findall(r'\\b\\S+\\b', text[text.index(\"objects\")\r\n + len(\"objects\"):text.index(\"init\")])\r\n\r\n # Getting the list of actions from results returned from planning.domain api\r\n try:\r\n actionlist = plan['result']['plan']\r\n except KeyError:\r\n sys.exit(\"No plan have been returned\")\r\n cleanactionlist = remove_unused_char(actionlist)\r\n\r\n content = {\"stages\": [], \"objects\": objects}\r\n content['stages'].append({\"items\": stages.copy()})\r\n # 1. Go through the steps\r\n for counter in range(0, len(actionlist)):\r\n checklist = []\r\n init_object_list = server.PddLparser.visualiserFile.\\\r\n parser.problem_parser.\\\r\n get_object_list(predicates_list, cleanactionlist[counter])\r\n checklist = (init_object_list)\r\n\r\n # 2. Find the difference between 2 steps\r\n addactionlistarr = []\r\n removeactionlistarr = []\r\n for var in checklist:\r\n if var in stages:\r\n removeactionlistarr.append(var)\r\n else:\r\n addactionlistarr.append(var)\r\n\r\n # Append the list to get the final result\r\n for addvar in addactionlistarr:\r\n stages.append(addvar)\r\n for rmvar in removeactionlistarr:\r\n stages.remove(rmvar)\r\n\r\n # 3.\r\n # Append everything to get the final output - content\r\n result = {\"items\": stages.copy(),\r\n \"add\": addactionlistarr,\r\n \"remove\": removeactionlistarr}\r\n content['stages'].append(result)\r\n return content","def envs(self) -> Optional[Sequence['outputs.CSIPowerMaxSpecDriverSideCarsEnvs']]:\n return pulumi.get(self, \"envs\")","def envs(self) -> Optional[Sequence['outputs.CSIVXFlexOSSpecDriverSideCarsEnvs']]:\n return pulumi.get(self, \"envs\")","def active_config_names(self):\n return list(self.stage.active_configs.keys())","def list_cmd(repo):\n click.echo('Packages and scripts installed through pipsi:')\n for venv, scripts in repo.list_everything():\n if not scripts:\n continue\n click.echo(' Package \"%s\":' % venv)\n for script in scripts:\n click.echo(' ' + script)","def get_execution_envs(self):\n return self.execution_envs","def getPendingBuildsets():\n # TODO: this is not implemented anywhere","def step_list(test, checks=None):\n if checks is None:\n checks = []\n test.cmd(\n \"az networkcloud virtualmachine console list \"\n \"--resource-group {resourceGroup} --virtual-machine-name {virtualMachineName}\",\n checks=checks,\n )","def execute(self):\n findings = []\n for stack in cloudformation_client.stacks:\n if not stack.is_nested_stack:\n report = Check_Report_AWS(self.metadata())\n report.region = stack.region\n report.resource_id = stack.name\n report.resource_arn = stack.arn\n report.resource_tags = stack.tags\n\n if stack.enable_termination_protection:\n report.status = \"PASS\"\n report.status_extended = f\"CloudFormation {stack.name} has termination protection enabled\"\n else:\n report.status = \"FAIL\"\n report.status_extended = f\"CloudFormation {stack.name} has termination protection disabled\"\n findings.append(report)\n\n return findings","def test_iter_statuses(self):\n cassette_name = self.cassette_name('statuses')\n with self.recorder.use_cassette(cassette_name):\n repository = self.gh.repository('sigmavirus24', 'github3.py')\n assert repository is not None\n deployment = find(lambda d: d.id == 801,\n repository.iter_deployments())\n assert deployment is not None\n statuses = list(deployment.iter_statuses(5))\n\n for status in statuses:\n assert isinstance(status,\n github3.repos.deployment.DeploymentStatus)","def scenario(self):\n scenario = []\n for action in self.application_tree['scenario']:\n scenario.append(action)\n return scenario","def list_test_cases(program):\n\n return list(INFO[program].test_cases)","def n_delay_stages(self):\n return self._n_delay_stages","def n_delay_stages(self):\n return self._n_delay_stages","def available_statuses(self):\n return self.pipeline.get(self.status, ())","def available_statuses(self):\n return self.pipeline.get(self.status, ())","def running_on_ci() -> bool:\r\n return os.environ.get(\"JANITOR_CI_MACHINE\") is not None","def steps(self):\n\n if not os.path.exists(self.build_path):\n raise exceptions.ProjectNotBuildError()\n\n steps = []\n for filename in os.listdir(self.build_path):\n match = re.match(r'(\\d{4})_(.*)\\.json', filename)\n if not match:\n continue\n\n with open(os.path.join(self.build_path, filename), 'r') as f:\n template = json.loads(f.read())\n\n template_type = 'custom' if '_type' in template else 'cloudformation'\n steps.append((int(match.groups()[0]), match.groups()[1], filename, template_type))\n steps = sorted(steps, key=lambda x: x[0])\n\n return steps","def status():\n\n # General health of the server.\n run('cat /proc/loadavg')\n run('uptime')\n run('free')\n run('df -h')\n\n # Get an overview of the packages\n print '================================== Buildout'\n _with_deploy_env(['./bin/develop status',\n 'git status',\n 'git log -1'])\n\n git_packages = ['oerpub.rhaptoslabs.cnxml2htmlpreview',\n 'oerpub.rhaptoslabs.sword1cnx',\n 'oerpub.rhaptoslabs.sword2cnx',\n 'remix',\n 'rhaptos.cnxmlutils',\n ]\n\n for package in git_packages:\n print '================================== %s' % package\n with cd('%s/src/%s' % (env.directory, package)):\n run('git status')\n run('git log -1')","def stage_name(self) -> str:\n return pulumi.get(self, \"stage_name\")","def stage_name(self) -> str:\n return pulumi.get(self, \"stage_name\")","def _read_group_stage_ids(self, stages, domain, order):\n stage_ids = self.env['salon.stage'].search([])\n return stage_ids","def progress_step(inst) -> Any:\n try:\n return TestStatus.stages().index(inst)\n except ValueError:\n return -1","def test_basic_api_with_tags(self):\n self.create_and_verify_stack(\"single/basic_api_with_tags\")\n\n stages = self.get_api_stack_stages()\n self.assertEqual(len(stages), 2)\n\n stage = next(s for s in stages if s[\"stageName\"] == \"my-new-stage-name\")\n self.assertIsNotNone(stage)\n self.assertEqual(stage[\"tags\"][\"TagKey1\"], \"TagValue1\")\n self.assertEqual(stage[\"tags\"][\"TagKey2\"], \"\")","def get_labels(rf_pipeline):\n return rf_pipeline.stages[0].labels","def nonsingleton_stages(tree, nodes):\n existing_context_nodes = list(filter\n (lambda node: True if tree.nodes[node].get(\"context\",None) is not None else False, nodes))\n existing_contexts = set(tree.nodes[node][\"context\"] for node in existing_context_nodes)\n return existing_contexts","def envs(self) -> Optional[Sequence['outputs.CSIPowerStoreSpecDriverCommonEnvs']]:\n return pulumi.get(self, \"envs\")","def ls(_):\n client = utils.s3_client()\n\n projects = []\n\n config = utils.get_config()\n bucket = config[\"release\"][\"s3_bucket\"]\n deploys = config[\"deploy\"]\n\n resp = client.list_objects_v2(Bucket=bucket)\n for data in resp.get(\"Contents\", []):\n name = data[\"Key\"]\n\n projects.append(name)\n\n projects = sorted(projects)\n\n _projects = []\n\n for name in projects:\n try:\n release = get_release(client, bucket, name)\n except InvalidRelease:\n continue\n\n data = {\n \"Name\": name,\n \"Latest Release\": f\"v{release.version} {release.timestamp} ({release.commit})\",\n }\n\n for env_name, cfg in deploys.items():\n env_version, env_commit, env_timestamp = get_deployed_version(\n client, cfg[\"s3_bucket\"], name\n )\n\n data[env_name.title()] = f\"v{env_version} {env_timestamp} ({env_commit})\"\n\n _projects.append(data)\n\n projects = _projects\n\n utils.printfmt(projects)","def get_all_environments():\n return ENVIRONMENTS","def getCompileStatus():\n logger.debug(\"[FLASKWEB] Retrieving current active compilation status\")\n\n jobs = compileService.getActiveState()\n title = \"Active Compiling Tasks\" if jobs else \"NO Active Compiling Jobs\"\n\n if request.headers['Accept'] == 'application/json':\n return jsonify(jobs), 200\n else:\n return render_template(\"keyvalue.html\", title=title, store=jobs)","def describe_instances():\n # Instantiate the service resource object\n ec2_resource = session.resource('ec2', region_name=region)\n # Describe instances\n instances = ec2_resource.instances.all()\n for instance in instances:\n print('State of the instance \"' + instance.id + '\" is: \"' + instance.state['Name'] + '\"')\n return","def get_stages(self, ApiId: str, MaxResults: str = None, NextToken: str = None) -> Dict:\n pass"],"string":"[\n \"def stages() -> List[Tuple[str, str]]:\\n return [TestStatus.preparation, TestStatus.testing, TestStatus.completed]\",\n \"def stage_list(args):\\n\\n for stage in args.stages:\\n print stage\",\n \"def stages(self):\\n return self._stages\",\n \"def stages(self):\\n return self._stages\",\n \"def process_stages(self) -> List[str]:\\n return self._process_stages\",\n \"def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\\n return pulumi.get(self, \\\"job_stages\\\")\",\n \"def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\\n return pulumi.get(self, \\\"job_stages\\\")\",\n \"def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\\n return pulumi.get(self, \\\"job_stages\\\")\",\n \"def sweep_stages(self) -> List[str]:\\n return self._sweep_stages\",\n \"def stages(self):\\r\\n return pipelines.Stages(self)\",\n \"def stage_states(self) -> pulumi.Output[Sequence['outputs.ExecutionStageStateResponse']]:\\n return pulumi.get(self, \\\"stage_states\\\")\",\n \"def N_stages(self):\\n return 5\",\n \"def stage_status(self) -> str:\\n return pulumi.get(self, \\\"stage_status\\\")\",\n \"def stage_states(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ExecutionStageStateArgs']]]]:\\n return pulumi.get(self, \\\"stage_states\\\")\",\n \"def stages(self, stages):\\n if stages is None:\\n self._stages = None\\n else:\\n self._stages = stages if isinstance(stages, list) else [stages] * len(self.pidevice.allaxes)\\n debug('ControllerStartup.stages = %s', itemstostr(self._stages))\",\n \"def reset_stages(self) -> List[str]:\\n return self._reset_stages\",\n \"def _collect_stages(self) -> Set[str]:\\n # Get the stage name associated with the previous deployment and update stage\\n # Stage needs to be flushed so that new changes will be visible immediately\\n api_resource = get_resource_by_id(self._stacks, ResourceIdentifier(self._api_identifier))\\n stage_resources = get_resource_ids_by_type(self._stacks, AWS_APIGATEWAY_STAGE)\\n deployment_resources = get_resource_ids_by_type(self._stacks, AWS_APIGATEWAY_DEPLOYMENT)\\n\\n stages = set()\\n # If it is a SAM resource, get the StageName property\\n if api_resource:\\n if api_resource.get(\\\"Type\\\") == AWS_SERVERLESS_API:\\n # The customer defined stage name\\n stage_name = api_resource.get(\\\"Properties\\\", {}).get(\\\"StageName\\\")\\n if stage_name:\\n stages.add(cast(str, stage_name))\\n\\n # The stage called \\\"Stage\\\"\\n if stage_name != \\\"Stage\\\":\\n response_sta = cast(Dict, self._api_client.get_stages(restApiId=self._api_physical_id))\\n for item in response_sta.get(\\\"item\\\"): # type: ignore\\n if item.get(\\\"stageName\\\") == \\\"Stage\\\":\\n stages.add(\\\"Stage\\\")\\n\\n # For both SAM and ApiGateway resource, check if any refs from stage resources\\n for stage_resource in stage_resources:\\n # RestApiId is a required field in stage\\n stage_dict = get_resource_by_id(self._stacks, stage_resource)\\n if not stage_dict:\\n continue\\n rest_api_id = stage_dict.get(\\\"Properties\\\", {}).get(\\\"RestApiId\\\")\\n dep_id = stage_dict.get(\\\"Properties\\\", {}).get(\\\"DeploymentId\\\")\\n # If the stage doesn't have a deployment associated then no need to update\\n if dep_id is None:\\n continue\\n # If the stage's deployment ID is not static and the rest API ID matchs, then update\\n for deployment_resource in deployment_resources:\\n if deployment_resource.resource_iac_id == dep_id and rest_api_id == self._api_identifier:\\n stages.add(cast(str, stage_dict.get(\\\"Properties\\\", {}).get(\\\"StageName\\\")))\\n break\\n\\n return stages\",\n \"def list(self):\\n print \\\"\\\\nAvailable Test Cases\\\"\\n print \\\"====================\\\"\\n for case in self.cases:\\n print case.__name__\",\n \"def main():\\n init()\\n separator_len = 40\\n for s in stage_instances:\\n print('='*separator_len)\\n print(s.name)\\n print('-'*separator_len)\\n\\n s.add_tasks() # Add tasks from previous stage\\n s.revive_or_archive() # Revive killed tasks or move them to failed\\n s.schedule_jobs() # Schedule new jobs if needed\\n s.print_status()\\n print('='*separator_len + '\\\\n')\\n render(stage_instances)\",\n \"def get_fill_stages(self) -> Tuple[str]:\\n return self._fill_stages\",\n \"def RunStages(self):\\n self._RunStage(build_stages.InitSDKStage)\\n self.RunSetupBoard()\\n self._RunStage(report_stages.RefreshPackageStatusStage)\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIUnitySpecDriverControllerEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def getConfigStages(cls):\\n newlist = list([\\\"PRE_DELETE\\\", \\\"DELETE\\\", \\\"PRE_PATCH\\\", \\\"POST\\\",\\n \\\"POST_PATCH\\\"])\\n return newlist\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIIsilonSpecDriverControllerEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIVXFlexOSSpecDriverControllerEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def testGetStageNamesSmoke(self):\\n stage = self.ConstructStage()\\n self.assertEqual(stage.GetStageNames(), ['Builder'])\",\n \"def stage_describe(args):\\n\\n stage = args.stages[args.stage_name]\\n\\n print \\\"Stage:\\\", stage.name, \\\"\\\\n\\\"\\n print \\\"Inputs:\\\"\\n for input_ in stage.inputs:\\n print \\\"\\\\t\\\", input_.name, input_.type, input_.help or \\\"\\\"\\n\\n print \\\"\\\\n\\\"\\n print \\\"Outputs:\\\"\\n for output in stage.outputs:\\n print \\\"\\\\t\\\", output.name, output.type, output.help or \\\"\\\"\\n\\n print \\\"\\\\n\\\"\\n print \\\"Splits:\\\"\\n for split in stage.splits:\\n print \\\"\\\\t\\\", split.name, split.type, split.help or \\\"\\\"\\n\\n print \\\"\\\\n\\\"\\n print \\\"Source directory:\\\"\\n print \\\"\\\\t\\\", stage.source\",\n \"def load_stages():\\n\\n def load_stages_from_dir(mro_dir):\\n \\\"\\\"\\\"Iterate over MRO file in a directory, parse them, and accumulate\\n their stages.\\n \\\"\\\"\\\"\\n stages = {}\\n for file_name in os.listdir(mro_dir):\\n if file_name.endswith(\\\".mro\\\"):\\n stages.update(mro_parser.get_stages(\\n os.path.join(mro_dir, file_name)))\\n return stages\\n\\n stages = {}\\n for mro_dir in os.environ[\\\"MROPATH\\\"].split(':'):\\n stages.update(load_stages_from_dir(mro_dir))\\n return stages\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIPowerMaxSpecDriverControllerEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def load_stages(filename):\\r\\n data = np.load(filename)\\r\\n return data['stages']\",\n \"def load_stages(filename):\\r\\n data = np.load(filename)\\r\\n return data['stages']\",\n \"def get_stages(self):\\n current_stage = self.wf['root']\\n\\n while current_stage:\\n yield current_stage\\n next_stage = set()\\n for n in current_stage:\\n next_stage.update(self.wf['action'][n].get('next', set()))\\n current_stage = next_stage\",\n \"def setstages(self):\\n if not self._stages or self.prop['skipcst']:\\n return\\n debug('ControllerStartup.setstages()')\\n allaxes = self.pidevice.qSAI_ALL()\\n oldstages = self.pidevice.qCST()\\n for i, newstage in enumerate(self._stages):\\n if not newstage:\\n continue\\n axis = allaxes[i]\\n oldstage = oldstages.get(axis, 'NOSTAGE')\\n if oldstage != newstage or self.prop['forcecst']:\\n warnmsg = applyconfig(self.pidevice, axis, newstage)\\n self._databuf['cstdone'].append(axis)\\n if self.prop['showlog'] and warnmsg:\\n warning(warnmsg)\\n elif self.prop['showlog']:\\n info('stage %r on axis %r is already configured', oldstage, axis)\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIUnitySpecDriverCommonEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def _stage(self):\\n\\n pass\",\n \"def all_statuses(cls):\\n return list(cls.pipeline.keys())\",\n \"def all_statuses(cls):\\n return list(cls.pipeline.keys())\",\n \"def getAllOperation(cls):\\n # pylint: disable=W0108\\n return list(map(lambda x: cls.getConfigOperation(x),\\n cls.getConfigStages()))\",\n \"def test_python(width=10):\\n\\n stage_1 = [diamond(sleep=60, inputs=[0])]\\n\\n stage_2 = []\\n for i in range(0, width):\\n stage_2.extend([diamond(sleep=20, inputs=stage_1)])\\n\\n stage_3 = [diamond(sleep=30, inputs=stage_2)]\\n\\n if not stage_3[0].done():\\n time.sleep(30)\\n for sitename in dfk.executors:\\n print(dfk.executors[sitename].status())\",\n \"def stages(self, pipeline_name):\\n\\n pipeline_name = self._require_param('pipeline_name', locals())\\n\\n response = self._session.get(\\n path=self._session.urljoin(self.PIPELINES_RESOURCE_PATH, pipeline_name, 'stages.xml').format(\\n base_api=self.base_api\\n ),\\n headers={'Accept': 'application/xml'},\\n )\\n\\n return response.text\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIVXFlexOSSpecDriverCommonEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIUnitySpecDriverNodeEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIPowerMaxSpecDriverCommonEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIVXFlexOSSpecDriverNodeEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIIsilonSpecDriverCommonEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def show_git_versions(ctx):\\n\\n ws = get_workspace(config)\\n\\n exp = Experiment(ws, config[\\\"experiment_name\\\"])\\n\\n versions = [\\n (run.id, run.get_properties()[\\\"azureml.git.commit\\\"]) for run in exp.get_runs()\\n ]\\n\\n print(tabulate(versions, headers=[\\\"Run ID\\\", \\\"Git Version\\\"]))\",\n \"def all_envs():\\n return all_tasks.keys()\",\n \"def git_status():\\n\\tl = []\\n\\tdebug(\\\"Not implemented\\\",1)\\n\\n\\treturn l\",\n \"def list_test_instances():\\n run('ls -1 %s' % env.site_root)\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIPowerMaxSpecDriverNodeEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def num_stages(tree, order):\\n p = len(order)\\n stages = cstree_to_stages(tree, order)\\n return sum([len(stages[i]) for i in range(1,p)])\",\n \"def stage(self, stage: osbuild.Stage):\",\n \"def stage(self):\\n return self._stage\",\n \"def stage(self):\\n return self._stage\",\n \"def stage(self):\\n return self._stage\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIIsilonSpecDriverNodeEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def main():\\n gh = Github(os.environ['GH_TOKEN'])\\n # gh_token = os.environ['GH_TOKEN']\\n gc_token_path = os.environ['GC_STORAGE_KEY']\\n\\n vars_file_path = os.getenv('VARS_FILE', \\\"\\\")\\n pipeline_id = os.getenv('CI_PIPELINE_ID', 0)\\n\\n repo = gh.get_repo(\\\"kubeinit/kubeinit\\\")\\n branches = repo.get_branches()\\n\\n output = 0\\n # Something linke:\\n # url = \\\"https://gitlab.com/kubeinit/kubeinit-ci/pipelines/\\\"\\n url = os.getenv('CI_PIPELINE_URL', \\\"\\\")\\n print(\\\"The job results will be published in runtime at: \\\" + url)\\n\\n for branch in branches:\\n for pr in repo.get_pulls(state='open', sort='created', base=branch.name):\\n labels = [item.name for item in pr.labels]\\n\\n sha = pr.head.sha\\n committer_email = repo.get_commit(sha=sha).commit.committer.email\\n print(committer_email)\\n\\n execute = False\\n # We assign the executed label to avoid executing this agains the same PR over and over\\n # We mark the PR as e2e-executed\\n\\n for label in labels:\\n if re.match(r\\\".*-.*-.*-.*-.*-.*-.*\\\", label):\\n print('Matching a PR label')\\n params = label.split(\\\"-\\\")\\n distro = params[0]\\n driver = params[1]\\n master = params[2]\\n worker = params[4]\\n scenario = params[6]\\n execute = True\\n remove_label(label, pr, repo)\\n break\\n\\n if execute:\\n now = datetime.now()\\n now.strftime(\\\"%m.%d.%Y.%H.%M.%S\\\")\\n job_name = pipeline_id + \\\"-\\\" + distro + \\\"-\\\" + driver + \\\"-\\\" + master + \\\"-\\\" + worker + \\\"-\\\" + scenario + \\\"-\\\" + now.strftime(\\\"%Y.%m.%d.%H.%M.%S\\\")\\n print(\\\"Let's run the e2e job, distro %s driver %s \\\" % (distro, driver))\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n print(\\\"Running the e2e job for: \\\" + str(pr.number) + \\\" \\\" + pr.title)\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n\\n # We update the status to show that we are executing the e2e test\\n print(\\\"Current status\\\")\\n print(repo.get_commit(sha=sha).get_statuses())\\n repo.get_commit(sha=sha).create_status(state=\\\"pending\\\",\\n target_url=url + str(pipeline_id),\\n description=\\\"Running...\\\",\\n context=\\\"%s-%s-%s-controller-%s-compute-%s\\\" % (distro,\\n driver,\\n master,\\n worker,\\n scenario))\\n print(\\\"The pipeline ID is: \\\" + str(pipeline_id))\\n print(\\\"The clouds.yml path is: \\\" + str(vars_file_path))\\n # We trigger the e2e job\\n start_time = time.time()\\n try:\\n print(\\\"We call the downstream job configuring its parameters\\\")\\n subprocess.check_call(\\\"./ci/run_kubeinit.sh %s %s %s %s %s %s %s %s\\\" % (str(branch.name),\\n str(pr.number),\\n str(vars_file_path),\\n str(distro),\\n str(driver),\\n str(master),\\n str(worker),\\n str(scenario)),\\n shell=True)\\n except Exception as e:\\n print('An exception hapened executing Ansible')\\n print(e)\\n output = 1\\n\\n try:\\n print(\\\"Render ara data\\\")\\n subprocess.check_call(\\\"./ci/ara.sh %s\\\" % (str(job_name) + \\\"-\\\" + str(output)), shell=True)\\n except Exception as e:\\n print('An exception hapened rendering ara data')\\n print(e)\\n output = 1\\n\\n print(\\\"starting the uploader job\\\")\\n # No matter if the job passed or failed we always use go as the suffix\\n # upload_error = upload_logs_to_github(str(job_name) + \\\"-\\\" + str(output), gh_token)\\n upload_error = upload_logs_to_google_cloud(str(job_name) + \\\"-\\\" + str(output), gc_token_path)\\n render_index(gc_token_path)\\n print(\\\"finishing the uploader job\\\")\\n\\n if output == 0:\\n state = \\\"success\\\"\\n else:\\n state = \\\"failure\\\"\\n\\n desc = (\\\"Ended with %s in %s minutes\\\" % (state, round((time.time() - start_time) / 60, 2)))\\n\\n print(desc)\\n print(state)\\n\\n if upload_error == 1:\\n dest_url = url + str(pipeline_id)\\n else:\\n dest_url = 'https://storage.googleapis.com/kubeinit-ci/jobs/pr/' + str(job_name) + \\\"-\\\" + str(output) + '/index.html'\\n\\n print(\\\"The destination URL is: \\\" + dest_url)\\n # We update the status with the job result\\n repo.get_commit(sha=sha).create_status(state=state,\\n target_url=dest_url,\\n description=desc,\\n context=\\\"%s-%s-%s-controller-%s-compute-%s\\\" % (distro,\\n driver,\\n master,\\n worker,\\n scenario))\\n else:\\n print(\\\"No need to do anything\\\")\\n if execute:\\n exit()\",\n \"def pipeline_results(self):\\n repo = self.main_gitlab_repo()\\n ret = []\\n for branch in [\\\"master\\\", \\\"main\\\", \\\"devel\\\"]:\\n build = repo.cibuild_set.filter(branch__name__endswith=branch).first()\\n if build is not None:\\n link = repo.url + f\\\"/-/pipelines/{build.build_id}\\\"\\n sym = \\\"✓\\\" if build.passed else \\\"✘\\\"\\n ret.append(f'<a href=\\\"{link}\\\">{branch}: {sym}</a>')\\n\\n return mark_safe(\\\"<br>\\\".join(ret))\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIPowerStoreSpecDriverControllerEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def separate_stages(self):\\n if not wait_until(self.emulator.is_ui_element_on_screen, ui_element=self.mode_label_ui):\\n return logger.error(f\\\"Can't find mission label: {self.mode_label_ui.name}.\\\")\\n if wait_until(self.emulator.is_ui_element_on_screen, ui_element=ui.EQ_RECOMMENDED_LVL):\\n stage_1 = self.emulator.get_screen_text(self.stage_1_ui)\\n stage_2 = self.emulator.get_screen_text(self.stage_2_ui)\\n stage_1_current, _ = self.game.get_current_and_max_values_from_text(stage_1)\\n stage_2_current, _ = self.game.get_current_and_max_values_from_text(stage_2)\\n return stage_1_current, stage_2_current\\n logger.error(f\\\"Can't find {ui.EQ_RECOMMENDED_LVL} in mission lobby.\\\")\",\n \"def stage(self):\\n pass\",\n \"def getBuilds():\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIIsilonSpecDriverSideCarsEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIUnitySpecDriverSideCarsEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def stage_name(stages_dir):\\r\\n\\r\\n print(\\\"\\\\nLEVELS AVAILABLE:\\\"\\r\\n \\\"\\\\n\\\")\\r\\n stages_dir = os.path.expanduser(stages_dir)\\r\\n os.chdir(stages_dir)\\r\\n stage_lst = next(os.walk('.'))[1]\\r\\n os.chdir(config_writer.tool_path)\\r\\n\\r\\n for name in stage_lst:\\r\\n print(name)\\r\\n while True:\\r\\n stg_nm = input(\\\"\\\\nEnter stage name: \\\")\\r\\n if stg_nm not in stage_lst:\\r\\n print(\\\"\\\\nStage name not available! Try again.\\\")\\r\\n else:\\r\\n break\\r\\n\\r\\n return stg_nm\",\n \"def deploy():\\n stage(branch='live', role='live')\",\n \"def initConfigstages(cls):\\n mystageoperation.update(dict({'DELETE': str(\\\"DELETE\\\")}))\\n mystageoperation.update(dict({'POST': str(\\\"POST\\\")}))\\n mystageoperation.update(dict({'PRE_DELETE': str(\\\"PATCH\\\")}))\\n mystageoperation.update(dict({'PRE_PATCH': str(\\\"PATCH\\\")}))\\n mystageoperation.update(dict({'POST_PATCH': str(\\\"PATCH\\\")}))\\n mystageoperation.update(dict({'PUT': str(\\\"PUT\\\")}))\\n mystageoperation.update(dict({'GET': str(\\\"GET\\\")}))\\n cls.log(1, \\\"Config stages Init\\\")\",\n \"def pipeline_stage(self) -> aws_cdk.aws_codepipeline.IStage:\\n return self._values.get(\\\"pipeline_stage\\\")\",\n \"def get_stages(plan, problem_dic, problem_file, predicates_list):\\r\\n\\r\\n # Initial stage\\r\\n stages = problem_dic[0]['init'].copy()\\r\\n\\r\\n with open(problem_file) as file:\\r\\n text = file.read()\\r\\n objects = re.findall(r'\\\\b\\\\S+\\\\b', text[text.index(\\\"objects\\\")\\r\\n + len(\\\"objects\\\"):text.index(\\\"init\\\")])\\r\\n\\r\\n # Getting the list of actions from results returned from planning.domain api\\r\\n try:\\r\\n actionlist = plan['result']['plan']\\r\\n except KeyError:\\r\\n sys.exit(\\\"No plan have been returned\\\")\\r\\n cleanactionlist = remove_unused_char(actionlist)\\r\\n\\r\\n content = {\\\"stages\\\": [], \\\"objects\\\": objects}\\r\\n content['stages'].append({\\\"items\\\": stages.copy()})\\r\\n # 1. Go through the steps\\r\\n for counter in range(0, len(actionlist)):\\r\\n checklist = []\\r\\n init_object_list = server.PddLparser.visualiserFile.\\\\\\r\\n parser.problem_parser.\\\\\\r\\n get_object_list(predicates_list, cleanactionlist[counter])\\r\\n checklist = (init_object_list)\\r\\n\\r\\n # 2. Find the difference between 2 steps\\r\\n addactionlistarr = []\\r\\n removeactionlistarr = []\\r\\n for var in checklist:\\r\\n if var in stages:\\r\\n removeactionlistarr.append(var)\\r\\n else:\\r\\n addactionlistarr.append(var)\\r\\n\\r\\n # Append the list to get the final result\\r\\n for addvar in addactionlistarr:\\r\\n stages.append(addvar)\\r\\n for rmvar in removeactionlistarr:\\r\\n stages.remove(rmvar)\\r\\n\\r\\n # 3.\\r\\n # Append everything to get the final output - content\\r\\n result = {\\\"items\\\": stages.copy(),\\r\\n \\\"add\\\": addactionlistarr,\\r\\n \\\"remove\\\": removeactionlistarr}\\r\\n content['stages'].append(result)\\r\\n return content\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIPowerMaxSpecDriverSideCarsEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIVXFlexOSSpecDriverSideCarsEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def active_config_names(self):\\n return list(self.stage.active_configs.keys())\",\n \"def list_cmd(repo):\\n click.echo('Packages and scripts installed through pipsi:')\\n for venv, scripts in repo.list_everything():\\n if not scripts:\\n continue\\n click.echo(' Package \\\"%s\\\":' % venv)\\n for script in scripts:\\n click.echo(' ' + script)\",\n \"def get_execution_envs(self):\\n return self.execution_envs\",\n \"def getPendingBuildsets():\\n # TODO: this is not implemented anywhere\",\n \"def step_list(test, checks=None):\\n if checks is None:\\n checks = []\\n test.cmd(\\n \\\"az networkcloud virtualmachine console list \\\"\\n \\\"--resource-group {resourceGroup} --virtual-machine-name {virtualMachineName}\\\",\\n checks=checks,\\n )\",\n \"def execute(self):\\n findings = []\\n for stack in cloudformation_client.stacks:\\n if not stack.is_nested_stack:\\n report = Check_Report_AWS(self.metadata())\\n report.region = stack.region\\n report.resource_id = stack.name\\n report.resource_arn = stack.arn\\n report.resource_tags = stack.tags\\n\\n if stack.enable_termination_protection:\\n report.status = \\\"PASS\\\"\\n report.status_extended = f\\\"CloudFormation {stack.name} has termination protection enabled\\\"\\n else:\\n report.status = \\\"FAIL\\\"\\n report.status_extended = f\\\"CloudFormation {stack.name} has termination protection disabled\\\"\\n findings.append(report)\\n\\n return findings\",\n \"def test_iter_statuses(self):\\n cassette_name = self.cassette_name('statuses')\\n with self.recorder.use_cassette(cassette_name):\\n repository = self.gh.repository('sigmavirus24', 'github3.py')\\n assert repository is not None\\n deployment = find(lambda d: d.id == 801,\\n repository.iter_deployments())\\n assert deployment is not None\\n statuses = list(deployment.iter_statuses(5))\\n\\n for status in statuses:\\n assert isinstance(status,\\n github3.repos.deployment.DeploymentStatus)\",\n \"def scenario(self):\\n scenario = []\\n for action in self.application_tree['scenario']:\\n scenario.append(action)\\n return scenario\",\n \"def list_test_cases(program):\\n\\n return list(INFO[program].test_cases)\",\n \"def n_delay_stages(self):\\n return self._n_delay_stages\",\n \"def n_delay_stages(self):\\n return self._n_delay_stages\",\n \"def available_statuses(self):\\n return self.pipeline.get(self.status, ())\",\n \"def available_statuses(self):\\n return self.pipeline.get(self.status, ())\",\n \"def running_on_ci() -> bool:\\r\\n return os.environ.get(\\\"JANITOR_CI_MACHINE\\\") is not None\",\n \"def steps(self):\\n\\n if not os.path.exists(self.build_path):\\n raise exceptions.ProjectNotBuildError()\\n\\n steps = []\\n for filename in os.listdir(self.build_path):\\n match = re.match(r'(\\\\d{4})_(.*)\\\\.json', filename)\\n if not match:\\n continue\\n\\n with open(os.path.join(self.build_path, filename), 'r') as f:\\n template = json.loads(f.read())\\n\\n template_type = 'custom' if '_type' in template else 'cloudformation'\\n steps.append((int(match.groups()[0]), match.groups()[1], filename, template_type))\\n steps = sorted(steps, key=lambda x: x[0])\\n\\n return steps\",\n \"def status():\\n\\n # General health of the server.\\n run('cat /proc/loadavg')\\n run('uptime')\\n run('free')\\n run('df -h')\\n\\n # Get an overview of the packages\\n print '================================== Buildout'\\n _with_deploy_env(['./bin/develop status',\\n 'git status',\\n 'git log -1'])\\n\\n git_packages = ['oerpub.rhaptoslabs.cnxml2htmlpreview',\\n 'oerpub.rhaptoslabs.sword1cnx',\\n 'oerpub.rhaptoslabs.sword2cnx',\\n 'remix',\\n 'rhaptos.cnxmlutils',\\n ]\\n\\n for package in git_packages:\\n print '================================== %s' % package\\n with cd('%s/src/%s' % (env.directory, package)):\\n run('git status')\\n run('git log -1')\",\n \"def stage_name(self) -> str:\\n return pulumi.get(self, \\\"stage_name\\\")\",\n \"def stage_name(self) -> str:\\n return pulumi.get(self, \\\"stage_name\\\")\",\n \"def _read_group_stage_ids(self, stages, domain, order):\\n stage_ids = self.env['salon.stage'].search([])\\n return stage_ids\",\n \"def progress_step(inst) -> Any:\\n try:\\n return TestStatus.stages().index(inst)\\n except ValueError:\\n return -1\",\n \"def test_basic_api_with_tags(self):\\n self.create_and_verify_stack(\\\"single/basic_api_with_tags\\\")\\n\\n stages = self.get_api_stack_stages()\\n self.assertEqual(len(stages), 2)\\n\\n stage = next(s for s in stages if s[\\\"stageName\\\"] == \\\"my-new-stage-name\\\")\\n self.assertIsNotNone(stage)\\n self.assertEqual(stage[\\\"tags\\\"][\\\"TagKey1\\\"], \\\"TagValue1\\\")\\n self.assertEqual(stage[\\\"tags\\\"][\\\"TagKey2\\\"], \\\"\\\")\",\n \"def get_labels(rf_pipeline):\\n return rf_pipeline.stages[0].labels\",\n \"def nonsingleton_stages(tree, nodes):\\n existing_context_nodes = list(filter\\n (lambda node: True if tree.nodes[node].get(\\\"context\\\",None) is not None else False, nodes))\\n existing_contexts = set(tree.nodes[node][\\\"context\\\"] for node in existing_context_nodes)\\n return existing_contexts\",\n \"def envs(self) -> Optional[Sequence['outputs.CSIPowerStoreSpecDriverCommonEnvs']]:\\n return pulumi.get(self, \\\"envs\\\")\",\n \"def ls(_):\\n client = utils.s3_client()\\n\\n projects = []\\n\\n config = utils.get_config()\\n bucket = config[\\\"release\\\"][\\\"s3_bucket\\\"]\\n deploys = config[\\\"deploy\\\"]\\n\\n resp = client.list_objects_v2(Bucket=bucket)\\n for data in resp.get(\\\"Contents\\\", []):\\n name = data[\\\"Key\\\"]\\n\\n projects.append(name)\\n\\n projects = sorted(projects)\\n\\n _projects = []\\n\\n for name in projects:\\n try:\\n release = get_release(client, bucket, name)\\n except InvalidRelease:\\n continue\\n\\n data = {\\n \\\"Name\\\": name,\\n \\\"Latest Release\\\": f\\\"v{release.version} {release.timestamp} ({release.commit})\\\",\\n }\\n\\n for env_name, cfg in deploys.items():\\n env_version, env_commit, env_timestamp = get_deployed_version(\\n client, cfg[\\\"s3_bucket\\\"], name\\n )\\n\\n data[env_name.title()] = f\\\"v{env_version} {env_timestamp} ({env_commit})\\\"\\n\\n _projects.append(data)\\n\\n projects = _projects\\n\\n utils.printfmt(projects)\",\n \"def get_all_environments():\\n return ENVIRONMENTS\",\n \"def getCompileStatus():\\n logger.debug(\\\"[FLASKWEB] Retrieving current active compilation status\\\")\\n\\n jobs = compileService.getActiveState()\\n title = \\\"Active Compiling Tasks\\\" if jobs else \\\"NO Active Compiling Jobs\\\"\\n\\n if request.headers['Accept'] == 'application/json':\\n return jsonify(jobs), 200\\n else:\\n return render_template(\\\"keyvalue.html\\\", title=title, store=jobs)\",\n \"def describe_instances():\\n # Instantiate the service resource object\\n ec2_resource = session.resource('ec2', region_name=region)\\n # Describe instances\\n instances = ec2_resource.instances.all()\\n for instance in instances:\\n print('State of the instance \\\"' + instance.id + '\\\" is: \\\"' + instance.state['Name'] + '\\\"')\\n return\",\n \"def get_stages(self, ApiId: str, MaxResults: str = None, NextToken: str = None) -> Dict:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71553665","0.6959344","0.67542017","0.67542017","0.6622961","0.6178401","0.6178401","0.6178401","0.6153931","0.6117432","0.60700995","0.59009725","0.5881856","0.5803326","0.57920223","0.5780718","0.57623285","0.5628671","0.56034106","0.5523403","0.5511611","0.5492291","0.5470838","0.54687417","0.5463532","0.54622555","0.54274285","0.5426253","0.5423688","0.53549254","0.53549254","0.5337506","0.5310842","0.5277169","0.5271273","0.5269567","0.5269567","0.5242694","0.5224441","0.5217746","0.521566","0.5215651","0.52131253","0.5210373","0.5207126","0.51996166","0.5190897","0.5176079","0.5171603","0.516432","0.51550275","0.51547676","0.5146727","0.5146727","0.5146727","0.5128498","0.5122612","0.51182556","0.51177025","0.51071554","0.5095228","0.5091694","0.5083717","0.5069155","0.50610745","0.5057542","0.5053078","0.50443304","0.5030856","0.5029864","0.5014717","0.5012757","0.5008032","0.49884665","0.4983144","0.4982766","0.49805522","0.4979477","0.4976684","0.49725312","0.49696153","0.49696153","0.49612743","0.49612743","0.4957361","0.49524024","0.49486497","0.49406612","0.49406612","0.49363574","0.49313113","0.4930861","0.49257958","0.49145544","0.49046823","0.49018368","0.4893951","0.48847228","0.4876804","0.48666385"],"string":"[\n \"0.71553665\",\n \"0.6959344\",\n \"0.67542017\",\n \"0.67542017\",\n \"0.6622961\",\n \"0.6178401\",\n \"0.6178401\",\n \"0.6178401\",\n \"0.6153931\",\n \"0.6117432\",\n \"0.60700995\",\n \"0.59009725\",\n \"0.5881856\",\n \"0.5803326\",\n \"0.57920223\",\n \"0.5780718\",\n \"0.57623285\",\n \"0.5628671\",\n \"0.56034106\",\n \"0.5523403\",\n \"0.5511611\",\n \"0.5492291\",\n \"0.5470838\",\n \"0.54687417\",\n \"0.5463532\",\n \"0.54622555\",\n \"0.54274285\",\n \"0.5426253\",\n \"0.5423688\",\n \"0.53549254\",\n \"0.53549254\",\n \"0.5337506\",\n \"0.5310842\",\n \"0.5277169\",\n \"0.5271273\",\n \"0.5269567\",\n \"0.5269567\",\n \"0.5242694\",\n \"0.5224441\",\n \"0.5217746\",\n \"0.521566\",\n \"0.5215651\",\n \"0.52131253\",\n \"0.5210373\",\n \"0.5207126\",\n \"0.51996166\",\n \"0.5190897\",\n \"0.5176079\",\n \"0.5171603\",\n \"0.516432\",\n \"0.51550275\",\n \"0.51547676\",\n \"0.5146727\",\n \"0.5146727\",\n \"0.5146727\",\n \"0.5128498\",\n \"0.5122612\",\n \"0.51182556\",\n \"0.51177025\",\n \"0.51071554\",\n \"0.5095228\",\n \"0.5091694\",\n \"0.5083717\",\n \"0.5069155\",\n \"0.50610745\",\n \"0.5057542\",\n \"0.5053078\",\n \"0.50443304\",\n \"0.5030856\",\n \"0.5029864\",\n \"0.5014717\",\n \"0.5012757\",\n \"0.5008032\",\n \"0.49884665\",\n \"0.4983144\",\n \"0.4982766\",\n \"0.49805522\",\n \"0.4979477\",\n \"0.4976684\",\n \"0.49725312\",\n \"0.49696153\",\n \"0.49696153\",\n \"0.49612743\",\n \"0.49612743\",\n \"0.4957361\",\n \"0.49524024\",\n \"0.49486497\",\n \"0.49406612\",\n \"0.49406612\",\n \"0.49363574\",\n \"0.49313113\",\n \"0.4930861\",\n \"0.49257958\",\n \"0.49145544\",\n \"0.49046823\",\n \"0.49018368\",\n \"0.4893951\",\n \"0.48847228\",\n \"0.4876804\",\n \"0.48666385\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75736964"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":80395,"cells":{"query":{"kind":"string","value":"push images to a docker registry"},"document":{"kind":"string","value":"def push(images, tag, registry):\n manager = Manager('push', tag, images=images, registry_url=registry)\n manager.run()"},"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 push_image(image):\n\n subprocess.check_call(['docker', 'push', image])","def run_docker_push(image_list, docker_client):\n logger.info(\"--- push image to registry ---\")\n if runtime == Runtime.local:\n logger.info(\"Detected local runtime, stop here.\")\n return\n\n if not len(success_image_list):\n logger.info(\"No success image to push, stop here.\")\n return\n\n if GIT_BRANCH != \"master\":\n logger.info(\"Not master branch, stop here\")\n return\n\n docker_client.login(username=DOCKER_HUB_USERNAME, password=DOCKER_HUB_PASSWORD)\n for image in image_list:\n image.run_docker_push(docker_client)\n print(\"Finished.\")","def Push(self, docker_client):\n docker_client.tag(self.id, self.repo_tag, force=True)\n log.info('Pushing image to Google Container Registry...\\n')\n def InnerPush():\n for line in docker_client.push(self.repo_tag, stream=True):\n # Check for errors.\n _ProcessStreamingOutput(line)\n\n _Retry(InnerPush)\n metrics.CustomTimedEvent(metric_names.DOCKER_PUSH)","def push(self, repo, tag):\n logger.info(\"Pushing Docker image {}:{}\".format(repo, tag))\n stream = self.client.push(repo, tag=tag, stream=True, insecure_registry=True)\n log_output(stream)","def docker_push(c):\n cli_tasks.docker_push.run(c)","def push_base():\n docker('login')\n docker('push %s' % env.base_image_name)","def cli(ctx, image_file):\n if not image_file:\n return\n for pull_image in image_file.readline():\n pull_image = pull_image.rstrip('\\n')\n if len(pull_image) == 0:\n continue\n docker.pull(pull_image)\n push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,\n DEFAULR_NAMESPACE,\n pull_image.split('/')[-1])\n docker.tag(pull_image, push_image)\n docker.push(push_image)","def push(args, image_name_tag):\n if args.push is True:\n cmd_push = f\"docker push {image_name_tag}\"\n print(f\"COMMAND: {cmd_push}\")\n print(\"\", flush=True)\n return_code = subprocess.call(cmd_push, shell=True)\n if return_code != 0:\n exit(f\"Error with {cmd_push}\")\n return 0","def setup_image(self, image_name, image_name_in_repo):\n with self.docker_session():\n self._push_to_registry(image_name, image_name_in_repo)\n self._upload_docker_creds()","def push(config, version_tag):\n config_dict = get_config(config)\n image_name = config_dict['IMAGE'].split(':')[0]\n image = '{}:{}'.format(image_name, version_tag)\n\n build(config, version_tag)\n with cd(env.project_dir):\n run('gcloud docker -- push %s' % image)","def build_and_push_3p_images():\n\n log.info(\n \"Building Lambda Stack docker images (these are not on DockerHub). \"\n \"FMI see https://github.com/lambdal/lambda-stack-dockerfiles\")\n run_cmd(\"\"\"\n cd /tmp &&\n (rm -rf lambda-stack-dockerfiles || true) &&\n git clone https://github.com/lambdal/lambda-stack-dockerfiles &&\n cd lambda-stack-dockerfiles &&\n git checkout d762400d61636c074533416674426a84cc4d8992 &&\n docker build -t oarphpy/lambda-stack:22.04 -f Dockerfile.jammy . &&\n docker push oarphpy/lambda-stack:22.04\n \"\"\")","def all(tag=None, env=None):\n build_images(tag=tag)\n push_images(tag=tag)\n deploy(env=env, tag=tag)","def publish(digest, pid, api_key, tag):\n url = \"https://connect.redhat.com/api/v2/projects/{}/containers/{}/tags/{}/publish\".format(pid, digest, tag)\n headers = {\"accept\": \"*/*\",\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer {}\".format(api_key)}\n\n response = requests.post(url, headers=headers)\n\n if response.status_code != 201:\n print(\"Unable to publish, invalid status code: {}.\".format(response.status_code))\n print(response)\n print(response.content)\n sys.exit(1)\n else:\n print(\"Docker image '{}' successfully scheduled for publishing.\".format(digest))","def push_image(self, tag_list, push_to_defaults, additional_registries=[], version_release_tuple=None,\n push_late=False, dry_run=False):\n\n # Late pushes allow certain images to be the last of a group to be\n # pushed to mirrors. CI/CD systems may initiate operations based on the\n # update a given image and all other images need to be in place\n # when that special image is updated. The special images are there\n # pushed \"late\"\n # Actions that need to push all images need to push all images\n # need to make two passes/invocations of this method: one\n # with push_late=False and one with push_late=True.\n\n is_late_push = False\n if self.config.push.late is not Missing:\n is_late_push = self.config.push.late\n\n if push_late != is_late_push:\n return True\n\n push_names = []\n\n if push_to_defaults:\n push_names.extend(self.metadata.get_default_push_names())\n\n push_names.extend(self.metadata.get_additional_push_names(additional_registries))\n\n # Nothing to push to? We are done.\n if not push_names:\n return True\n\n with Dir(self.distgit_dir):\n\n if version_release_tuple:\n version = version_release_tuple[0]\n release = version_release_tuple[1]\n else:\n\n # History\n # We used to rely on the \"release\" label being set in the Dockerfile, but this is problematic for several reasons.\n # (1) If 'release' is not set, OSBS will determine one automatically that does not conflict\n # with a pre-existing image build. This is extremely helpful since we don't have to\n # worry about bumping the release during refresh images. This means we generally DON'T\n # want the release label in the file and can't, therefore, rely on it being there.\n # (2) People have logged into distgit before in order to bump the release field. This happening\n # at the wrong time breaks the build.\n\n # If the version & release information was not specified,\n # try to detect latest build from brew.\n # Read in version information from the Distgit dockerfile\n _, version, release = self.metadata.get_latest_build_info()\n\n try:\n record = {\n \"distgit_key\": self.metadata.distgit_key,\n \"distgit\": '{}/{}'.format(self.metadata.namespace, self.metadata.name),\n \"image\": self.config.name,\n \"version\": version,\n \"release\": release,\n \"message\": \"Unknown failure\",\n \"status\": -1,\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\n }\n\n # pull just the main image name first\n image_name_and_version = \"%s:%s-%s\" % (self.config.name, version, release)\n brew_image_url = \"/\".join((constants.BREW_IMAGE_HOST, image_name_and_version))\n pull_image(brew_image_url)\n record['message'] = \"Successfully pulled image\"\n record['status'] = 0\n except Exception as err:\n record[\"message\"] = \"Exception occurred: %s\" % str(err)\n self.logger.info(\"Error pulling %s: %s\" % (self.metadata.name, err))\n raise\n finally:\n self.runtime.add_record('pull', **record)\n\n push_tags = list(tag_list)\n\n # If no tags were specified, build defaults\n if not push_tags:\n push_tags = self.metadata.get_default_push_tags(version, release)\n\n for image_name in push_names:\n try:\n\n repo = image_name.split('/', 1)\n\n action = \"push\"\n record = {\n \"distgit_key\": self.metadata.distgit_key,\n \"distgit\": '{}/{}'.format(self.metadata.namespace, self.metadata.name),\n \"repo\": repo, # ns/repo\n \"name\": image_name, # full registry/ns/repo\n \"version\": version,\n \"release\": release,\n \"message\": \"Unknown failure\",\n \"tags\": \", \".join(push_tags),\n \"status\": -1,\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\n }\n\n for push_tag in push_tags:\n push_url = '{}:{}'.format(image_name, push_tag)\n\n if dry_run:\n rc = 0\n self.logger.info('Would have tagged {} as {}'.format(brew_image_url, push_url))\n self.logger.info('Would have pushed {}'.format(push_url))\n else:\n rc, out, err = exectools.cmd_gather([\"docker\", \"tag\", brew_image_url, push_url])\n\n if rc != 0:\n # Unable to tag the image\n raise IOError(\"Error tagging image as: %s\" % push_url)\n\n for r in range(10):\n self.logger.info(\"Pushing image to mirror [retry=%d]: %s\" % (r, push_url))\n rc, out, err = exectools.cmd_gather([\"docker\", \"push\", push_url])\n if rc == 0:\n break\n self.logger.info(\"Error pushing image -- retrying in 60 seconds\")\n time.sleep(60)\n\n if rc != 0:\n # Unable to push to registry\n raise IOError(\"Error pushing image: %s\" % push_url)\n\n record[\"message\"] = \"Successfully pushed all tags\"\n record[\"status\"] = 0\n\n except Exception as err:\n record[\"message\"] = \"Exception occurred: %s\" % str(err)\n self.logger.info(\"Error pushing %s: %s\" % (self.metadata.name, err))\n raise\n\n finally:\n self.runtime.add_record(action, **record)\n\n return True","def push_to_ecs(aws_session, docker_client, image_name=None, tag=None):\n\n if image_name is None:\n image_name = get_service_name()\n print('image_name: ' + image_name)\n\n if tag is None:\n tag = 'latest'\n\n repo = __get_docker_repository_name(aws_session, image_name)\n full_tag = image_name + ':' + tag\n image = __get_docker_image(docker_client, full_tag)\n\n # Push to the ecs repo\n print('Pushing the image \"' + full_tag + '\" up to \"' + repo + '\"...')\n try:\n docker_client.tag(repository=repo, image=image, tag=tag)\n except BaseException as e:\n print(e)\n raise Exception('An error occurred when tagging the image \"' + image + '\" with the tag \"' + full_tag + '\".')\n try:\n print_docker_output(docker_client.push(repository=repo, stream=True, tag=tag))\n except BaseException as e:\n print(e)\n raise Exception('An error occurred when pushing \"' + full_tag + '\" to ECR.')\n print('The image \"' + full_tag + '\" has now been pushed up to \"' + repo + '\".')","def push_dockerhub(c, app, version, latest=False):\n if app.lower() == 'core':\n c.run('sudo docker push kinecosystem/stellar-core:{version}'.format(version=version))\n if latest:\n c.run('sudo docker push kinecosystem/stellar-core:latest')\n elif app.lower() == 'horizon':\n c.run('sudo docker push kinecosystem/horizon:{version}'.format(version=version))\n if latest:\n c.run('sudo docker push kinecosystem/horizon:latest')\n elif app.lower() == 'friendbot':\n c.run('sudo docker push kinecosystem/friendbot:{version}'.format(version=version))\n if latest:\n c.run('sudo docker push kinecosystem/friendbot:latest')\n else:\n Exit('Unknown application {}'.format(app))","def image_needs_pushing(image):\n d = docker_client()\n try:\n d.images.get_registry_data(image)\n except docker.errors.APIError:\n # image not found on registry, needs pushing\n return True\n else:\n return False","def push_image_to_provider(self, builder, provider, credentials, target, target_image, parameters):","def LocalBuildAndPush(self, image):\n full_tag = self.GetFullRegistryTag(image.name)\n # Multiarch images require buildx create\n # https://github.com/docker/build-push-action/issues/302\n vm_util.IssueCommand(['docker', 'buildx', 'create', '--use'])\n cmd = ['docker', 'buildx', 'build']\n if _CONTAINER_CLUSTER_ARCHITECTURE.value:\n cmd += ['--platform', ','.join(_CONTAINER_CLUSTER_ARCHITECTURE.value)]\n cmd += ['--no-cache', '--push', '-t', full_tag, image.directory]\n vm_util.IssueCommand(cmd, timeout=None)\n vm_util.IssueCommand(['docker', 'buildx', 'stop'])","def build_and_push(build_context,\n image_tag,\n image_name,\n nocache,\n credstore_env=None,\n registries=None,\n max_retries=3,\n sleep_interval=1):\n _logger.info('Starting build ...')\n\n # Build the image\n docker_builder = build(build_context=build_context,\n image_tag=image_tag,\n image_name=image_name,\n nocache=nocache,\n credstore_env=credstore_env,\n registries=registries,\n max_retries=max_retries,\n sleep_interval=sleep_interval)\n push(docker_builder, max_retries=max_retries, sleep_interval=sleep_interval)","def images_refresh(self, kwargs=None):\n scode, hosts = Rest.get('Host')\n filter = {}\n n = 1\n e = {}\n data = []\n for host in hosts:\n os.environ[\"DOCKER_HOST\"] = host['Ip'] + \":\" + str(host['Port'])\n filter['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n self.client = docker.from_env()\n try:\n images = self.client.images.list(**kwargs)\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n return\n\n if len(images) == 0:\n Console.info(\"No images exist\")\n continue\n\n for imagem in images:\n image = imagem.__dict__['attrs']\n image['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n data.append(image)\n d = {}\n d['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n d['Id'] = image['Id']\n if image['RepoTags'] == None:\n d['Repository'] = image['RepoDigests'][0]\n else:\n d['Repository'] = image['RepoTags'][0]\n # d['Size'] = image['Size']\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2)\n e[n] = d\n n = n + 1\n Rest.delete('Image', filter)\n Rest.post('Image', data)\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))","def test_docker_push_target_ref(tmpdir, docker_client, registry):\n tmp = tmpdir.join('shipwright-localhost-sample')\n path = str(tmp)\n source = pkg_resources.resource_filename(\n __name__,\n 'examples/shipwright-localhost-sample',\n )\n repo = create_repo(path, source)\n tag = repo.head.ref.commit.hexsha[:12]\n\n tmp.join('service1/base.txt').write('Hi mum')\n commit_untracked(repo)\n new_tag = repo.head.ref.commit.hexsha[:12]\n\n client_cfg = docker_utils.kwargs_from_env()\n cli = docker_client\n\n defaults = get_defaults()\n defaults['push'] = True\n try:\n shipw_cli.run(\n path=path,\n client_cfg=client_cfg,\n arguments=defaults,\n environ={},\n )\n\n # Remove the build images:\n old_images = (\n cli.images(name='localhost:5000/service1', quiet=True) +\n cli.images(name='localhost:5000/shared', quiet=True) +\n cli.images(name='localhost:5000/base', quiet=True)\n )\n for image in old_images:\n cli.remove_image(image, force=True)\n\n images_after_delete = (\n cli.images(name='localhost:5000/service1') +\n cli.images(name='localhost:5000/shared') +\n cli.images(name='localhost:5000/base')\n )\n assert images_after_delete == []\n\n # Pull them again:\n for t in ['master', 'latest', new_tag, tag]:\n for repo in ['service1', 'shared', 'base']:\n cli.pull('localhost:5000/' + repo, t)\n\n service1, shared, base = (\n cli.images(name='localhost:5000/service1') +\n cli.images(name='localhost:5000/shared') +\n cli.images(name='localhost:5000/base')\n )\n\n assert set(service1['RepoTags']) == {\n 'localhost:5000/service1:master',\n 'localhost:5000/service1:latest',\n 'localhost:5000/service1:' + new_tag,\n }\n\n assert set(shared['RepoTags']) == {\n 'localhost:5000/shared:master',\n 'localhost:5000/shared:latest',\n 'localhost:5000/shared:' + new_tag,\n 'localhost:5000/shared:' + tag,\n }\n\n assert set(base['RepoTags']) == {\n 'localhost:5000/base:master',\n 'localhost:5000/base:latest',\n 'localhost:5000/base:' + new_tag,\n 'localhost:5000/base:' + tag,\n }\n finally:\n old_images = (\n cli.images(name='localhost:5000/service1', quiet=True) +\n cli.images(name='localhost:5000/shared', quiet=True) +\n cli.images(name='localhost:5000/base', quiet=True)\n )\n for image in old_images:\n cli.remove_image(image, force=True)","def _register_to_mongodb(logger, summary: Dict = None):\n logger.info('registering image to Jina Hub database...')\n\n with resource_stream('jina', '/'.join(('resources', 'hubapi.yml'))) as fp:\n hubapi_yml = yaml.load(fp)\n\n hubapi_url = hubapi_yml['hubapi']['url'] + hubapi_yml['hubapi']['push']\n\n if not credentials_file().is_file():\n logger.error(f'user hasnot logged in. please login using command: {colored(\"jina hub login\", attrs=[\"bold\"])}')\n return\n\n with open(credentials_file(), 'r') as cf:\n cred_yml = yaml.load(cf)\n access_token = cred_yml['access_token']\n\n if not access_token:\n logger.error(f'user has not logged in. please login using command: {colored(\"jina hub login\", attrs=[\"bold\"])}')\n return\n\n headers = {\n 'Accept': 'application/json',\n 'authorizationToken': access_token\n }\n try:\n import requests\n response = requests.post(url=f'{hubapi_url}',\n headers=headers,\n data=json.dumps(summary))\n if response.status_code == requests.codes.ok:\n logger.info(response.text)\n elif response.status_code == requests.codes.unauthorized:\n logger.error(f'user is unauthorized to perform push operation. '\n f'please login using command: {colored(\"jina hub login\", attrs=[\"bold\"])}')\n elif response.status_code == requests.codes.internal_server_error:\n if 'auth' in response.text.lower():\n logger.error(f'authentication issues!'\n f'please login using command: {colored(\"jina hub login\", attrs=[\"bold\"])}')\n logger.error(f'got an error from the API: {response.text}')\n except Exception as exp:\n logger.error(f'got an exception while invoking hubapi for push {repr(exp)}')","def push_image_version(self, app, image_name, image_tag, parent_task):\n\n assert isinstance(image_name, str)\n assert isinstance(image_tag, str)\n\n # The string \"local\" has a special meaning which means the most recent\n # local image of that name, so we skip the remote call/check.\n if image_tag == \"local\":\n raise ValueError(\"You cannot push the 'local' version\")\n\n # See if the registry is willing to give us a URL (it's logged in)\n registry = self.get_registry(app)\n if registry:\n registry_url = registry.url(self.host)\n else:\n registry_url = None\n if registry_url is None:\n raise RuntimeError(\"No registry configured\")\n\n task = Task(\n \"Pushing image {}:{}\".format(image_name, image_tag),\n parent=parent_task,\n progress_formatter=lambda x: \"{} MB\".format(x // (1024 ** 2)),\n )\n\n # Work out the name it needs to be and tag the image as that\n remote_name = \"{registry_url}/{image_name}\".format(\n registry_url=registry_url,\n image_name=image_name,\n )\n self.host.client.tag(\n image_name + \":\" + \"latest\",\n remote_name,\n tag=image_tag,\n force=True\n )\n\n # Push it up\n stream = self.host.client.push(remote_name, tag=image_tag, stream=True)\n layer_status = {}\n current = None\n total = None\n for line in stream:\n if isinstance(line, bytes):\n line = line.decode(\"ascii\")\n data = json.loads(line)\n if 'error' in data:\n task.finish(status=\"Failed\", status_flavor=Task.FLAVOR_WARNING)\n raise RuntimeError(\"Push error: %r\" % data['error'])\n elif 'id' in data:\n if data['status'].lower() == \"pushing\":\n layer_status[data['id']] = data['progressDetail']\n elif \"complete\" in data['status'].lower() and data['id'] in layer_status:\n layer_status[data['id']]['current'] = layer_status[data['id']]['total']\n if layer_status:\n statuses = [x for x in layer_status.values()\n if \"current\" in x and \"total\" in x]\n current = sum(x['current'] for x in statuses)\n total = sum(x['total'] for x in statuses)\n if total is not None:\n task.update(progress=(current, total))\n task.finish(status=\"Done\", status_flavor=Task.FLAVOR_GOOD)","def add_remote_image(local_image_path, filename):\n for host in APP.config['REMOTE_HOSTS']:\n user = APP.config['REMOTE_HOST_USERNAME']\n directory = APP.config['REMOTE_IMAGE_DIRECTORY']\n ssh_command = f'''ssh -oStrictHostKeyChecking=accept-new -i /root/.ssh/id_rsa \\\n {user}@{host} mkdir -p {directory}'''\n subprocess.run(ssh_command, check=True, shell=True)\n scp_command = f\"scp {local_image_path}/{filename} {user}@{host}:{directory}/{filename}\"\n subprocess.run(scp_command, check=True, shell=True)","def process_images(self):\n source_images = self.get_build_images()\n self.log.info(\"Got %s images for publishing. Processing..\", len(source_images))\n\n for image in source_images:\n self.fetch_image(image)\n\n for target in image.push_registries:\n for tag in image.release_tags:\n repository = \"%s/%s\" % (target, image.repository.name)\n self.tag_image(image, repository, tag)\n retry_count = 1\n while retry_count <= self.retry_limit:\n self.log.info(\"Pushing %s:%s to %s (%d/%d)\", repository, tag, target, retry_count, self.retry_limit)\n try:\n self.publish_image(target, repository, tag)\n break\n except ImagePushError as e:\n self.log.error(\"%s\", e.message)\n retry_count = retry_count + 1\n else:\n return False\n return True","def pull_dlc_images(images):\n for image in images:\n run(f\"docker pull {image}\", hide=\"out\")","def RemoteBuild(self, image):\n raise NotImplementedError()","def get_images(name):\n url = \"/\".join([REGISTRY_BASE, name, \"/tags/list\"])\n response = req(url)\n image_list = []\n if response is not None:\n headers = {\"Accept\": \"application/vnd.docker.distribution.manifest.v2+json\"}\n tags = response[\"tags\"]\n for tag in tags:\n url = \"/\".join([REGISTRY_BASE, name, \"/manifests\", tag])\n response = req(url, headers)\n if response is not None:\n image = {}\n image[\"size\"] = response[\"config\"][\"size\"]\n for i in response[\"layers\"]:\n image[\"size\"] += i[\"size\"]\n image[\"size\"] = round(float(image[\"size\"]) / 1024 / 1024, 2)\n image[\"id\"] = response[\"config\"][\"digest\"][7:19]\n image[\"tag\"] = tag\n image[\"cmd\"] = \"docker pull uk8s.com/\" + name + \":\" + tag\n image_list.append(image)\n return sorted(image_list, reverse=True)","def _build_docker_images(self):\n print(f\"+ building {len(self.neurodocker_specs)} Docker images\")\n self.docker_status = []\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\n try:\n print(\"++ building image: {}\".format(neurodocker_dict))\n cg.docker_main(\n self.working_dir,\n neurodocker_dict,\n sha1,\n build_context=self.build_context,\n )\n self.docker_status.append(\"docker ok\")\n except Exception as e:\n self.docker_status.append(\n \"failed to build image with SHA1 {}: {}\".format(sha1, e)\n )","def main():\n\n # get AWS credentials\n aws_credentials = read_aws_credentials()\n access_key_id = aws_credentials['access_key_id']\n secret_access_key = aws_credentials['secret_access_key']\n aws_region = aws_credentials['region']\n\n # build Docker image\n docker_client = docker.from_env()\n image, build_log = docker_client.images.build(\n path='.', tag=LOCAL_REPOSITORY, rm=True)\n\n # get AWS ECR login token\n ecr_client = boto3.client(\n 'ecr', aws_access_key_id=access_key_id, \n aws_secret_access_key=secret_access_key, region_name=aws_region)\n\n ecr_credentials = (\n ecr_client\n .get_authorization_token()\n ['authorizationData'][0])\n\n ecr_username = 'AWS'\n\n ecr_password = (\n base64.b64decode(ecr_credentials['authorizationToken'])\n .replace(b'AWS:', b'')\n .decode('utf-8'))\n\n ecr_url = ecr_credentials['proxyEndpoint']\n\n # get Docker to login/authenticate with ECR\n docker_client.login(\n username=ecr_username, password=ecr_password, registry=ecr_url)\n\n # tag image for AWS ECR\n ecr_repo_name = '{}/{}'.format(\n ecr_url.replace('https://', ''), LOCAL_REPOSITORY)\n\n image.tag(ecr_repo_name, tag='latest')\n\n # push image to AWS ECR\n push_log = docker_client.images.push(ecr_repo_name, tag='latest')\n\n # force new deployment of ECS service\n ecs_client = boto3.client(\n 'ecs', aws_access_key_id=access_key_id,\n aws_secret_access_key=secret_access_key, region_name=aws_region)\n\n ecs_client.update_service(\n cluster=ECS_CLUSTER, service=ECS_SERVICE, forceNewDeployment=True)\n\n return None","def pull_image(image):\n\n subprocess.check_call(['docker', 'pull', image])","def push(self) -> None:\n\n with ImportExtensions(required=True):\n import requests\n\n pkg_path = Path(self.args.path)\n if not pkg_path.exists():\n self.logger.critical(f'`{self.args.path}` is not a valid path!')\n exit(1)\n\n request_headers = self._get_request_header()\n\n try:\n # archive the executor package\n with TimeContext(f'Packaging {self.args.path}', self.logger):\n md5_hash = hashlib.md5()\n bytesio = archive_package(pkg_path)\n content = bytesio.getvalue()\n md5_hash.update(content)\n\n md5_digest = md5_hash.hexdigest()\n\n # upload the archived package\n form_data = {\n 'public': self.args.public if hasattr(self.args, 'public') else False,\n 'private': self.args.private\n if hasattr(self.args, 'private')\n else False,\n 'md5sum': md5_digest,\n 'force': self.args.force,\n 'secret': self.args.secret,\n }\n\n method = 'put' if self.args.force else 'post'\n\n hubble_url = get_hubble_url()\n # upload the archived executor to Jina Hub\n with TimeContext(\n f'Pushing to {hubble_url} ({method.upper()})',\n self.logger,\n ):\n resp = getattr(requests, method)(\n hubble_url,\n files={'file': content},\n data=form_data,\n headers=request_headers,\n )\n\n if 200 <= resp.status_code < 300:\n # TODO: only support single executor now\n image = resp.json()['executors'][0]\n\n uuid8 = image['id']\n secret = image['secret']\n visibility = image['visibility']\n\n info_table = [\n f'\\t🔑 ID:\\t\\t' + colored(f'{uuid8}', 'cyan'),\n f'\\t🔒 Secret:\\t'\n + colored(\n f'{secret}',\n 'cyan',\n )\n + colored(\n ' (👈 Please store this secret carefully, it wont show up again)',\n 'red',\n ),\n f'\\t👀 Visibility:\\t' + colored(f'{visibility}', 'cyan'),\n ]\n\n if 'alias' in image:\n info_table.append(f'\\t📛 Alias:\\t' + colored(image['alias'], 'cyan'))\n\n self.logger.success(f'🎉 Executor `{pkg_path}` is pushed successfully!')\n self.logger.info('\\n' + '\\n'.join(info_table))\n\n usage = (\n f'jinahub://{uuid8}'\n if visibility == 'public'\n else f'jinahub://{uuid8}:{secret}'\n )\n\n self.logger.info(f'You can use it via `uses={usage}` in the Flow/CLI.')\n elif resp.text:\n # NOTE: sometimes resp.text returns empty\n raise Exception(resp.text)\n else:\n resp.raise_for_status()\n except Exception as e: # IO related errors\n self.logger.error(\n f'Error while pushing `{self.args.path}` with session_id={request_headers[\"jinameta-session-id\"]}: '\n f'\\n{e!r}'\n )","def push(self, repository, stream=False, raise_on_error=False, **kwargs):\n response = super(DockerClientWrapper, self).push(repository, stream=stream, **kwargs)\n if stream:\n result = self._docker_status_stream(response, raise_on_error)\n else:\n result = self._docker_status_stream(response.split('\\r\\n') if response else (), raise_on_error)\n return result and not result.get('error')","def docker_worker():","def build_docker(c):\n tag = c.run('git describe', hide=True)\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\n c.run(f'docker build -t {docker_img} .')","def publish_release(self, source, config, target, deis_registry):\n # get the source repository name and tag\n src_name, src_tag = docker.utils.parse_repository_tag(source)\n # get the target repository name and tag\n name, tag = docker.utils.parse_repository_tag(target)\n # strip any \"http://host.domain:port\" prefix from the target repository name,\n # since we always publish to the Deis registry\n name = strip_prefix(name)\n\n # pull the source image from the registry\n # NOTE: this relies on an implementation detail of deis-builder, that\n # the image has been uploaded already to deis-registry\n if deis_registry:\n repo = \"{}/{}\".format(self.registry, src_name)\n else:\n repo = src_name\n self.pull(repo, src_tag)\n\n # tag the image locally without the repository URL\n image = \"{}:{}\".format(repo, src_tag)\n self.tag(image, src_name, tag=src_tag)\n\n # build a Docker image that adds a \"last-mile\" layer of environment\n config.update({'DEIS_APP': name, 'DEIS_RELEASE': tag})\n self.build(source, config, name, tag)\n\n # push the image to deis-registry\n self.push(\"{}/{}\".format(self.registry, name), tag)","def build_container(\n self, odcs, repo_type, repo, push_to_defaults, additional_registries, terminate_event,\n scratch=False, retries=3):\n if self.org_image_name is None or self.org_version is None:\n if not os.path.isfile(os.path.join(self.distgit_dir, 'Dockerfile')):\n self.logger.info('No Dockerfile found in {}'.format(self.distgit_dir))\n else:\n self.logger.info('Unknown error loading Dockerfile information')\n return False\n\n action = \"build\"\n release = self.org_release if self.org_release is not None else '?'\n record = {\n \"dir\": self.distgit_dir,\n \"dockerfile\": \"%s/Dockerfile\" % self.distgit_dir,\n \"distgit\": self.metadata.name,\n \"image\": self.org_image_name,\n \"version\": self.org_version,\n \"release\": release,\n \"message\": \"Unknown failure\",\n \"task_id\": \"n/a\",\n \"task_url\": \"n/a\",\n \"status\": -1,\n \"push_status\": -1,\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\n }\n\n target_tag = \"-\".join((self.org_version, release))\n target_image = \":\".join((self.org_image_name, target_tag))\n\n try:\n if not scratch and self.org_release is not None \\\n and self.metadata.tag_exists(target_tag):\n self.logger.info(\"Image already built for: {}\".format(target_image))\n else:\n # If this image is FROM another group member, we need to wait on that group member\n # Use .get('from',None) since from is a reserved word.\n image_from = Model(self.config.get('from', None))\n if image_from.member is not Missing:\n self._set_wait_for(image_from.member, terminate_event)\n for builder in image_from.get('builder', []):\n if 'member' in builder:\n self._set_wait_for(builder['member'], terminate_event)\n\n # Allow an image to wait on an arbitrary image in the group. This is presently\n # just a workaround for: https://projects.engineering.redhat.com/browse/OSBS-5592\n if self.config.wait_for is not Missing:\n self._set_wait_for(self.config.wait_for, terminate_event)\n\n def wait(n):\n self.logger.info(\"Async error in image build thread [attempt #{}]\".format(n + 1))\n # Brew does not handle an immediate retry correctly, wait\n # before trying another build, terminating if interrupted.\n if terminate_event.wait(timeout=5 * 60):\n raise KeyboardInterrupt()\n\n exectools.retry(\n retries=3, wait_f=wait,\n task_f=lambda: self._build_container(\n target_image, odcs, repo_type, repo, terminate_event,\n scratch, record))\n\n # Just in case someone else is building an image, go ahead and find what was just\n # built so that push_image will have a fixed point of reference and not detect any\n # subsequent builds.\n push_version, push_release = ('','')\n if not scratch:\n _, push_version, push_release = self.metadata.get_latest_build_info()\n record[\"message\"] = \"Success\"\n record[\"status\"] = 0\n self.build_status = True\n\n except (Exception, KeyboardInterrupt):\n tb = traceback.format_exc()\n record[\"message\"] = \"Exception occurred:\\n{}\".format(tb)\n self.logger.info(\"Exception occurred during build:\\n{}\".format(tb))\n # This is designed to fall through to finally. Since this method is designed to be\n # threaded, we should not throw an exception; instead return False.\n finally:\n # Regardless of success, allow other images depending on this one to progress or fail.\n self.build_lock.release()\n\n self.push_status = True # if if never pushes, the status is True\n if not scratch and self.build_status and (push_to_defaults or additional_registries):\n # If this is a scratch build, we aren't going to be pushing. We might be able to determine the\n # image name by parsing the build log, but not worth the effort until we need scratch builds.\n # The image name for a scratch build looks something like:\n # brew-pulp-docker01.web.prod.ext.phx2.redhat.com:8888/openshift3/ose-base:rhaos-3.7-rhel-7-docker-candidate-16066-20170829214444\n\n # To ensure we don't overwhelm the system building, pull & push synchronously\n with self.runtime.mutex:\n self.push_status = False\n try:\n self.push_image([], push_to_defaults, additional_registries, version_release_tuple=(push_version, push_release))\n self.push_status = True\n except Exception as push_e:\n self.logger.info(\"Error during push after successful build: %s\" % str(push_e))\n self.push_status = False\n\n record['push_status'] = '0' if self.push_status else '-1'\n\n self.runtime.add_record(action, **record)\n return self.build_status and self.push_status","def test_force_with_local_and_registry_image(self, mock_docker_environment):\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def build_all_images_for_release(lang, release):\n docker_images = []\n build_jobs = []\n\n env = {}\n # If we not using current tree or the sibling for grpc stack, do checkout.\n stack_base = \"\"\n if args.git_checkout:\n stack_base = checkout_grpc_stack(lang, release)\n var = {\n \"go\": \"GRPC_GO_ROOT\",\n \"java\": \"GRPC_JAVA_ROOT\",\n \"node\": \"GRPC_NODE_ROOT\",\n }.get(lang, \"GRPC_ROOT\")\n env[var] = stack_base\n\n for runtime in client_matrix.get_runtimes_for_lang_release(lang, release):\n job = build_image_jobspec(runtime, env, release, stack_base)\n docker_images.append(job.tag)\n build_jobs.append(job)\n\n jobset.message(\"START\", \"Building interop docker images.\", do_newline=True)\n print(\"Jobs to run: \\n%s\\n\" % \"\\n\".join(str(j) for j in build_jobs))\n\n num_failures, _ = jobset.run(\n build_jobs, newline_on_success=True, maxjobs=multiprocessing.cpu_count()\n )\n if num_failures:\n jobset.message(\n \"FAILED\", \"Failed to build interop docker images.\", do_newline=True\n )\n docker_images_cleanup.extend(docker_images)\n sys.exit(1)\n\n jobset.message(\n \"SUCCESS\",\n \"All docker images built for %s at %s.\" % (lang, release),\n do_newline=True,\n )\n\n if release != \"master\":\n commit_log = os.path.join(stack_base, \"commit_log\")\n if os.path.exists(commit_log):\n for image in docker_images:\n add_files_to_image(image, [commit_log], \"release=%s\" % release)\n return docker_images","def publish_manifest(ctx, name, tag, image, signed_push=False):\n manifest_spec = {\"image\": \"{}:{}\".format(name, tag)}\n src_images = []\n\n for img in image:\n img_splitted = img.replace(' ', '').split(',')\n if len(img_splitted) != 2:\n print(\"Impossible to parse source format for: '{}'\".format(img))\n raise Exit(code=1)\n\n platform_splitted = img_splitted[1].split('/')\n if len(platform_splitted) != 2:\n print(\"Impossible to parse platform format for: '{}'\".format(img))\n raise Exit(code=1)\n\n src_images.append(\n {\"image\": img_splitted[0], \"platform\": {\"architecture\": platform_splitted[1], \"os\": platform_splitted[0]}}\n )\n manifest_spec[\"manifests\"] = src_images\n\n with tempfile.NamedTemporaryFile(mode='w', delete=False) as f:\n temp_file_path = f.name\n yaml.dump(manifest_spec, f, default_flow_style=False)\n\n print(\"Using temp file: {}\".format(temp_file_path))\n ctx.run(\"cat {}\".format(temp_file_path))\n\n try:\n result = retry_run(ctx, \"manifest-tool push from-spec {}\".format(temp_file_path))\n if result.stdout:\n out = result.stdout.split('\\n')[0]\n fields = out.split(\" \")\n\n if len(fields) != 3:\n print(\"Unexpected output when invoking manifest-tool\")\n raise Exit(code=1)\n\n digest_fields = fields[1].split(\":\")\n\n if len(digest_fields) != 2 or digest_fields[0] != \"sha256\":\n print(\"Unexpected digest format in manifest-tool output\")\n raise Exit(code=1)\n\n digest = digest_fields[1]\n length = fields[2]\n\n if signed_push:\n cmd = \"\"\"\n notary -s https://notary.docker.io -d {home}/.docker/trust addhash \\\n -p docker.io/{name} {tag} {length} --sha256 {sha256} \\\n -r targets/releases\n \"\"\"\n retry_run(ctx, cmd.format(home=os.path.expanduser(\"~\"), name=name, tag=tag, length=length, sha256=digest))\n finally:\n os.remove(temp_file_path)","def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None, skip_build=False):\n value_modifications = {}\n for name, options in images.items():\n image_path = options.get('contextPath', os.path.join('images', name))\n image_tag = tag\n # include chartpress.yaml itself as it can contain build args and\n # similar that influence the image that would be built\n paths = list(options.get('paths', [])) + [image_path, 'chartpress.yaml']\n last_commit = last_modified_commit(*paths)\n if tag is None:\n if chart_version:\n image_tag = \"{}-{}\".format(chart_version, last_commit)\n else:\n image_tag = last_commit\n image_name = prefix + name\n image_spec = '{}:{}'.format(image_name, image_tag)\n\n value_modifications[options['valuesPath']] = {\n 'repository': image_name,\n 'tag': SingleQuotedScalarString(image_tag),\n }\n\n if skip_build:\n continue\n\n template_namespace = {\n 'LAST_COMMIT': last_commit,\n 'TAG': image_tag,\n }\n\n if tag or image_needs_building(image_spec):\n build_args = render_build_args(options, template_namespace)\n build_image(image_path, image_spec, build_args, options.get('dockerfilePath'))\n else:\n print(f\"Skipping build for {image_spec}, it already exists\")\n\n if push:\n if tag or image_needs_pushing(image_spec):\n check_call([\n 'docker', 'push', image_spec\n ])\n else:\n print(f\"Skipping push for {image_spec}, already on registry\")\n return value_modifications","def test_image_exists_registry(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n build_image_if_needed(TEST_IMAGE_NAME)\n mock_docker_environment.api.pull.assert_called_with(\n TEST_IMAGE_NAME, \"latest\", decode=True, stream=True\n )\n mock_docker_environment.images.build.assert_not_called()","def test_force_with_registry_image(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def build_container(client):\n client.images.build(path=os.path.join(os.path.abspath(\"\"), \"docker\"), tag=\"scrape_light\")","def pullall():\n\tprint(red('\\t\\tStarting download of QNIBTerminal images\\t\\t'))\n\t# pull all the needed images\n\tdocker_images={'fd20','terminal','helixdns','elk','slurm','compute'}\n\tfor image in docker_images:\n\t\tprint 'docker pull qnib/'+image\n\t\t# run('docker pull qnib/'+image)","def test_unknown_registry(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n build_image_if_needed(\"unknown.registry.com/foo/bar\")\n mock_docker_environment.images.build.assert_called_with(\n dockerfile=\"Dockerfile\",\n tag=\"unknown.registry.com/foo/bar:latest\",\n path=\"/opt/ixian_docker\",\n )","def upload():\n sh('python setup.py register sdist upload')","def test_image_exists_registry_no_pull(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n build_image_if_needed(TEST_IMAGE_NAME, pull=False)\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def pull_base_images(ctx, dockerfile, signed_pull=True):\n images = set()\n stages = set()\n\n with open(dockerfile, \"r\") as f:\n for line in f:\n words = line.split()\n # Get source images\n if len(words) < 2 or words[0].lower() != \"from\":\n continue\n images.add(words[1])\n # Get stage names to remove them from pull\n if len(words) < 4 or words[2].lower() != \"as\":\n continue\n stages.add(words[3])\n\n if stages:\n print(\"Ignoring intermediate stage names: {}\".format(\", \".join(stages)))\n images -= stages\n\n print(\"Pulling following base images: {}\".format(\", \".join(images)))\n\n pull_env = {}\n if signed_pull:\n pull_env[\"DOCKER_CONTENT_TRUST\"] = \"1\"\n\n for i in images:\n ctx.run(\"docker pull {}\".format(i), env=pull_env)","def run_in_docker(image, commands): # pragma: no cover\n repo_dir = os.path.abspath(os.path.join(__file__, '../../'))\n mount_option = '{}:/mnt:ro'.format(repo_dir)\n\n cmd = ('docker', 'run', '-v', mount_option, '-i', image, 'sh')\n proc = subprocess.Popen(\n cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,\n )\n\n lines = '\\n'.join(commands)\n return proc.communicate(lines.encode('utf-8'))[0].decode('utf-8')","def push(args):\n if args.type == 'ssh':\n cache = set(args.remote_cache).union(set(args.cache))\n for path in sorted(cache):\n if os.path.exists(os.path.join(args.base, path)) and not remote_exists(args.sftp, os.path.join(args.remote_base, path)):\n print('push: {}'.format(path))\n ensure_remote(args.sftp, os.path.dirname(os.path.join(args.remote_base, path)))\n args.sftp.put(\n os.path.join(args.base, path),\n os.path.join(args.remote_base, path)\n )\n args.remote_cache.append(path)\n args.remote_update = True\n elif args.type == 's3':\n raise NotImplementedError('s3:// remote type not yet supported!')\n elif args.type == 'gs':\n raise NotImplementedError('gs:// remote type not yet supported!')\n return","def assets_push(ctx, metadata, dir, brizo, price, service_endpoint, timeout):\n try:\n files = [f for f in os.listdir(dir) if os.path.isfile(dir+'/'+f)]\n except NotADirectoryError:\n files = [dir]\n\n response = []\n metadata = json.load(open(metadata, 'r'))\n\n for f in files:\n metadata['base']['files'][0]['url'] = f\n response += [ctx.invoke(assets_publish,\n metadata=metadata,\n brizo=brizo,\n price=price,\n service_endpoint=service_endpoint,\n timeout=timeout)]","def test_push(self, mock_docker_environment, snapshot, capsys):\n push_image(TEST_IMAGE_NAME)\n out, err = capsys.readouterr()\n snapshot.assert_match(out)","def cli(ctx, paths, **kwds):\n\n def upload(realized_repository):\n return shed.upload_repository(ctx, realized_repository, **kwds)\n\n exit_code = shed.for_each_repository(ctx, upload, paths, **kwds)\n sys.exit(exit_code)","def build_jar_and_push(project, branch):\n Print.GN('Building JAR and pushing image.')\n current_env = os.environ.copy()\n current_env['BRANCH'] = branch\n subprocess.check_call([\n os.path.join(SCRIPT_DIR, '../../docker/build.sh'),\n 'jar',\n '-p', project,\n '-d', 'push',\n '-r', 'gcr',\n ], env=current_env)","def upload():\n\n # Our credentials are only available from within the main repository and not forks.\n # We need to prevent uploads from all BUT the branches in the main repository.\n # Pull requests and master-branches of forks are not allowed to upload.\n is_pull_request = (\n (\"TRAVIS_PULL_REQUEST\" in os.environ and os.environ[\"TRAVIS_PULL_REQUEST\"] != \"false\") or\n \"APPVEYOR_PULL_REQUEST_NUMBER\" in os.environ\n )\n if is_pull_request:\n click.echo(\"Refusing to upload artifacts from a pull request!\")\n return\n\n if \"AWS_ACCESS_KEY_ID\" in os.environ:\n subprocess.check_call([\n \"aws\", \"s3\", \"cp\",\n \"--acl\", \"public-read\",\n DIST_DIR + \"/\",\n \"s3://snapshots.mitmproxy.org/{}/\".format(UPLOAD_DIR),\n \"--recursive\",\n ])\n\n upload_pypi = (\n TAG and\n \"WHEEL\" in os.environ and\n \"TWINE_USERNAME\" in os.environ and\n \"TWINE_PASSWORD\" in os.environ\n )\n if upload_pypi:\n whl = glob.glob(join(DIST_DIR, 'mitmproxy-*-py3-none-any.whl'))[0]\n click.echo(\"Uploading {} to PyPi...\".format(whl))\n subprocess.check_call([\n \"twine\",\n \"upload\",\n whl\n ])\n\n upload_docker = (\n (TAG or BRANCH == \"master\") and\n \"DOCKER\" in os.environ and\n \"DOCKER_USERNAME\" in os.environ and\n \"DOCKER_PASSWORD\" in os.environ\n )\n if upload_docker:\n docker_tag = \"dev\" if BRANCH == \"master\" else VERSION\n\n click.echo(\"Uploading Docker image to tag={}...\".format(docker_tag))\n subprocess.check_call([\n \"docker\",\n \"login\",\n \"-u\", os.environ[\"DOCKER_USERNAME\"],\n \"-p\", os.environ[\"DOCKER_PASSWORD\"],\n ])\n subprocess.check_call([\n \"docker\",\n \"push\",\n \"mitmproxy/mitmproxy:{}\".format(docker_tag),\n ])","def push(self):\n out, err, code = self.command( [\"git\", \"push\"], self.directory )","def release_docker_image(\n name: str, version: str, docker_image_prefix: str, exit_on_error: bool = True\n) -> subprocess.CompletedProcess:\n # Check if docker exists on the system\n build_utils.command_exists(\"docker\", exit_on_error=exit_on_error)\n\n if not docker_image_prefix:\n build_utils.log(\n \"The flag --docker-image-prefix cannot be blank when pushing a Docker image.\"\n )\n build_utils.exit_process(build_utils.EXIT_CODE_GENERAL)\n\n versioned_tag = get_image_name(name=name, tag=version)\n remote_versioned_tag = get_image_name(\n name=name, tag=version, image_prefix=docker_image_prefix\n )\n build_utils.run(\n \"docker tag \" + versioned_tag + \" \" + remote_versioned_tag,\n exit_on_error=exit_on_error,\n )\n completed_process = build_utils.run(\n \"docker push \" + remote_versioned_tag, exit_on_error=exit_on_error\n )\n\n if completed_process.returncode > 0:\n build_utils.log(f\"Failed to release Docker image {name}:{version}\")\n\n # Only push version with latest tag if no suffix is added (pre-release)\n if \"-\" not in version:\n remote_latest_tag = get_image_name(\n name=name, tag=\"latest\", image_prefix=docker_image_prefix\n )\n\n build_utils.log(\n \"Release Docker image with latest tag as well: \" + remote_latest_tag\n )\n\n build_utils.run(\n \"docker tag \" + versioned_tag + \" \" + remote_latest_tag,\n exit_on_error=exit_on_error,\n )\n build_utils.run(\"docker push \" + remote_latest_tag, exit_on_error=exit_on_error)\n\n return completed_process","def deploy():\n build()\n collect()\n commit()\n push()","def test_k8s_docker_registry_local_and_mirror(\n self, os_conn, env, configure_docker, environment, murano, session,\n cluster, pod, package, role, murano_k8s_image_username):\n docker_cfg_f = '/etc/default/docker'\n vm, vm_floating_ip, keypair = configure_docker\n\n # Add DockerHTTPd configuration\n environment = murano.get_environment(environment)\n murano.create_service(environment, session, murano.httpd(pod))\n\n # Get updated information about services for deploy\n all_services = murano.murano.services.list(environment.id, session.id)\n all_services_dict = [i.to_dict() for i in all_services]\n\n # Delete from murano env info about services to update them\n for service in all_services_dict:\n murano.delete_service(environment, session, service)\n\n # Set \"Custom Docker registry URL\" and \"Docker registry mirror URL\" in\n # \"Kubernetes Cluster\" config.\n local_reg_path = '{0}:{1}'.format(\n vm_floating_ip, self.docker_registry_port)\n local_mirr_path = 'http://{0}:{1}'.format(\n vm_floating_ip, self.docker_mirror_port)\n\n if role == 'custom_registry':\n all_services_dict[0]['dockerRegistry'] = local_reg_path\n elif role == 'registry_mirr':\n all_services_dict[0]['dockerMirror'] = local_mirr_path\n else:\n # both\n all_services_dict[0]['dockerRegistry'] = local_reg_path\n all_services_dict[0]['dockerMirror'] = local_mirr_path\n\n # Add back updated info about KubernetesCluster to murano env\n for service in all_services_dict:\n murano.create_service(environment, session, service)\n\n if role in ('custom_registry', 'both'):\n # Add 'Docker Container' service with local docker image\n docker_app = murano.docker_app(\n host=pod,\n image_location='{0}/local_redmine'.format(vm_floating_ip),\n port=3000,\n app_name='DockerImageFromLocalRepo')\n murano.create_service(environment, session, docker_app)\n\n # Deploy environment\n murano.deploy_environment(environment, session)\n\n # Get ssh to nodes\n vms = os_conn.nova.servers.list()\n minion_vm = [x for x in vms if 'minion' in x.to_dict()['name']][0]\n minion_vm_ssh = os_conn.ssh_to_instance(\n env, minion_vm, vm_keypair=keypair,\n username=murano_k8s_image_username)\n vm_ssh = os_conn.ssh_to_instance(\n env, vm, vm_keypair=keypair, username='ubuntu')\n\n # Read docker cfg file from minion node\n with minion_vm_ssh as remote:\n remote.check_call('sudo chmod 777 %s' % docker_cfg_f)\n docker_cfg_out = remote.check_call('cat %s' % docker_cfg_f)\n docker_cfg_out = docker_cfg_out.stdout_string\n\n # Check content of docker cfg file\n if role in ('custom_registry', 'both'):\n\n assert ('--insecure-registry {0}'.format(local_reg_path)\n in docker_cfg_out), (\n \"Docker local registry link does not present in Docker cfg \"\n \"file on Minion node.\\n{0}\".format(docker_cfg_out))\n\n if role in ('registry_mirr', 'both'):\n\n assert ('--registry-mirror={0}'.format(local_mirr_path)\n in docker_cfg_out), (\n \"Docker local mirror link does not present on Docker cfg file\"\n \"on Minion node.\\n{0}\".format(docker_cfg_out))\n\n # Check local registry and local mirror\n with minion_vm_ssh as remote:\n if role in ('registry_mirr', 'both'):\n remote.check_call('sudo docker pull wordpress')\n\n if role in ('custom_registry', 'both'):\n remote.check_call('sudo docker pull {0}/local_ubuntu'.format(\n local_reg_path))\n\n # Check logs of a docker registry mirror.\n # Time compare to pull from internet/mirror is not working as the\n # difference is very low.\n with vm_ssh as remote:\n mirr_logs = remote.check_call('docker logs mirror')\n mirr_logs = mirr_logs.stdout_string\n\n if role in ('registry_mirr', 'both'):\n assert 'wordpress' in mirr_logs, (\n \"Remote image does not present inside Docker Mirror logs\")\n\n if role in ('custom_registry', 'both'):\n assert 'local_ubuntu' not in mirr_logs, (\n \"Local image presents inside Docker Mirror logs\")","def upload():\n run('mkdir -p /srv/images/'+env.project_name+'/')\n rsync_project(\n env.project_dir, './',\n exclude=(\n '.git', '.gitignore', '__pycache__', '*.pyc', '.DS_Store', 'environment.yml',\n 'fabfile.py', 'Makefile', '.idea', 'bower_components', 'node_modules',\n '.env.example', 'README.md', 'var'\n ), delete=True)","def test_image_exists_registry_pull_not_found(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n mock_docker_environment.api.pull.side_effect = DockerNotFound(\"testing\")\n\n build_image_if_needed(TEST_IMAGE_NAME)\n mock_docker_environment.api.pull.assert_called_with(\n TEST_IMAGE_NAME, \"latest\", decode=True, stream=True\n )\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def test_image_exists_local_and_registry(self, mock_docker_environment):\n build_image_if_needed(TEST_IMAGE_NAME)\n mock_docker_environment.images.build.assert_not_called()","def _Build(self, image):\n image = _ContainerImage(image)\n build_start = time.time()\n if not FLAGS.local_container_build:\n try:\n # Build the image remotely using an image building service.\n self.RemoteBuild(image)\n self.remote_build_times[image.name] = time.time() - build_start\n return\n except NotImplementedError:\n pass\n\n self.PrePush(image)\n # Build the image locally using docker.\n build_start = time.time()\n self.LocalBuildAndPush(image)\n self.local_build_times[image.name] = time.time() - build_start","def docker_build(c):\n cli_tasks.docker_build.run(c)","def add_docker():\n pass\n # can i query docker for the location of the docker for the location of the\n # socket?\n # `docker info` will return \"Docker Root Dir: /var/lib/docker\"\n # check CoreOs project for detail on nvidia-docker socket","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.\")","def addDocker( self, name, **params ):\n defaults={'dimage': self.dimage, 'hostExchangeFolder': self.hostExchangeFolder}\n defaults.update(params)\n return self.addHost( name, cls=Docker, **defaults )","def post_image(img_file):\n img = np.array(Image.open(img_file))\n data={\"images\":img.tolist(),\"batch_size\":1}\n json_data=json.dumps(data)\n img=np.array(json.loads(json_data)[\"images\"])\n headers={}\n URL=\"http://127.0.0.1:8080/invocations\"\n response = requests.post(URL, data=json_data, headers=headers)\n return response","def init_actions():\n os.environ['DOCKER_HOST'] = settings.DOCKER_HOST\n for url, details in settings.DOCKER_REGISTRIES.items():\n login_registry(url, details)","def _build(registry, image_name, git_sha):\n\n image = f\"{registry}/{image_name}\"\n\n # If this image is not in our registry, nothing we can do.\n if registry != config.DOCKER_REGISTRY:\n return \"\"\n\n # If image:{git_sha} already exists, then return.\n logger.info(\n f\"Pulling git_sha tag {image}:{git_sha} to check if it already exists\"\n )\n if (\n not args.dry_run\n and docker_silent.pull(f\"{image}:{git_sha}\").returncode == 0\n ):\n # better to find the hash-{hash} of this image, and return hash\n # but currently, it is not easy to find all tags of the same image digest through registry API.\n # so we return image digest instead.\n digest = get_image_digest(f\"{image}:{git_sha}\")\n logger.info(\n f\"git_sha tag {image}:{git_sha} already exists, digest: %s\", digest\n )\n if not digest:\n raise Exception(\"Failed to get digest for existing image\")\n _tag_to_extra_tags(args, image, git_sha)\n return digest\n\n # Enter build context directory if it is specified\n build_context = enter_build_context(image_name)\n\n # Parse .dockerignore in build context\n dockerignore_files_set = parse_dockerignore(build_context)\n\n # Check if the dockerfile exists\n dockerfile_path = locate_dockerfile(image_name)\n if not os.path.isfile(dockerfile_path):\n logger.error(\n \"%s not exists or is not a file, so %s cannot get build\",\n dockerfile_path,\n image_name,\n )\n raise Exception(\"Building cannot continue\")\n\n dockerfile = Dockerfile(dockerfile_path, build_arg=build_arg)\n\n hasher = sha256()\n\n # Build parents, and calc parents hash\n for parent_image in dockerfile.parent_images:\n (\n parent_image_registry,\n parent_image_name,\n parent_image_tag,\n ) = parse_docker_image_identity(parent_image)\n parent_digest = _build(\n parent_image_registry, parent_image_name, parent_image_tag\n )\n if parent_digest is None:\n raise Exception(f\"Failed to get parent_digest for {image}\")\n hasher.update(parent_digest.encode())\n hash_logger.info(\n \"parent: %s, digest: (%s, %s), hash: %s\",\n parent_image,\n parent_digest,\n parent_digest.encode(),\n hasher.hexdigest(),\n )\n\n # Calc current image files hash\n\n def update_file_hash(f):\n if not os.path.isfile(f):\n return\n if f in dockerignore_files_set:\n hash_logger.debug(\"ignore: %s\", f)\n return\n with open(f, \"rb\") as open_file:\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\n hash_logger.info(\"update: %s, hash: %s\", f, hasher.hexdigest())\n\n srcs = [dockerfile_path] + dockerfile.copied_srcs + dockerfile.added_srcs\n # TODO: if the src is a url, download it and hash it (even crane didn't do that)\n for src in srcs:\n for f in sorted(glob.glob(src)):\n # We match every file in a directory recursively\n if os.path.isdir(f):\n for sub_f in sorted(glob.glob(f\"{f}/**\", recursive=True)):\n update_file_hash(sub_f)\n else:\n update_file_hash(f)\n\n files_hash = hasher.hexdigest()\n hash_logger.info(\"image: %s, hash: %s\", image, files_hash)\n\n hash_tag = config.FILES_HASH_TAG_PATTERN.format(files_hash=files_hash)\n # FIXME(harry): hack, remove this\n old_hash_image = f\"docker-registry.example.com:5000/{image_name}:{hash_tag}\"\n\n logger.info(\n f\"Pulling files_hash tag {image}:{hash_tag} to check if it already exists\"\n )\n # If image:hash-{hash} already exists,\n # then content didn't change, return.\n # We just need to tag it to latest code version.\n if (\n not args.dry_run\n and docker_silent.pull(f\"{image}:{hash_tag}\").returncode == 0\n ):\n logger.info(\n f\"files_hash tag {image}:{hash_tag} already exists, \"\n \"it means content didn't change, we can just tag the old image to new git_sha version tag\"\n )\n # FIXME(harry): hack, remove this\n elif not args.dry_run and docker_silent.pull(old_hash_image).returncode == 0:\n logger.info(f\"NOTE: files_hash tag {old_hash_image} already exists!\")\n # tag and push this hash image\n if docker.tag(old_hash_image, f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(\"Failed to tag old hash image\")\n return\n if docker.push(f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(\"Failed to push hash_tag image\")\n return\n # If image:hash-{hash} not exists, then build it from Dockerfile.\n else:\n logger.info(\n f\"files_hash tag {image}:{hash_tag} dosen't exists, \"\n \"it means content may changed, gonna build it from Dockerfile\"\n )\n if build_with_raw_command(args, image, dockerfile_path, hash_tag) != 0:\n logger.error(f\"Failed to build {image}:{hash_tag}\")\n return\n if docker.push(f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(f\"Failed to push image\")\n return\n logger.info(f\"image files_hash tag {image}:{hash_tag} is pushed\")\n\n # tag and push this final image\n if docker.tag(f\"{image}:{hash_tag}\", f\"{image}:{git_sha}\").returncode != 0:\n logger.error(\"Failed to tag image\")\n return\n _tag_to_extra_tags(args, image, git_sha)\n if docker.push(f\"{image}:{git_sha}\").returncode != 0:\n logger.error(\"Failed to push image\")\n return\n digest = get_image_digest(f\"{image}:{git_sha}\")\n if not digest:\n logger.error(\"Failed to get digest for image\")\n return\n logger.info(f\"image {image}:{git_sha} is pushed, digest: {digest}\")\n\n return digest","def local_images(self, images):\n registries = self.known_docker_registries()\n found_images = []\n for image in images:\n # docker could have the image name as-is or prefixed with any registry\n imglist = [image] + [reg + \"/\" + image for reg in registries]\n if self.is_image_local(imglist):\n found_images.append(image)\n return found_images","def push(targets, **namespace):","def create_image(name: str, distribution: str, apt_repo: str, release_track: str, release_label: str, flavour: str,\n organization: str, docker_registry: str, rosdistro_path: pathlib.Path, timestamp:str,\n publish: bool = False):\n\n # Read configuration files\n common_config = yaml.safe_load((rosdistro_path / 'config/recipes.yaml').open())['common']\n recipe = yaml.safe_load((rosdistro_path / 'config/images.yaml').open())['images']\n distro = recipe[name]['distro']\n build_type = recipe[name]['build_type']\n env = source_file(f'{os.environ[\"BUNDLE_ROOT\"]}/{distro}/setup.bash')\n today = timestamp\n extra_vars: List[Any] = []\n\n try:\n package = recipe[name]['package']\n provision_file = recipe[name]['provision_file']\n except KeyError:\n package = '/tailor-image'\n provision_file = f'{build_type}.yaml'\n\n env['ANSIBLE_CONFIG'] = find_package(package, 'ansible.cfg', env)\n template_path = f'/tailor-image/environment/image_recipes/{build_type}/{build_type}.json'\n provision_file_path = find_package(package, 'playbooks/' + provision_file, env)\n\n optional_vars = []\n optional_var_names = ['username', 'password', 'extra_arguments_ansible',\n 'ansible_command', 'description', 'disk_size', 'group']\n\n for var in optional_var_names:\n if var in recipe[name]:\n optional_vars.extend(['-var', f'{var}={recipe[name][var]}'])\n\n if build_type == 'docker':\n image_name = f'tailor-image-{name}-{distribution}-{release_label}'\n docker_registry_data = docker_registry.replace('https://', '').split('/')\n ecr_server = docker_registry_data[0]\n ecr_repository = docker_registry_data[1]\n extra_vars = [\n '-var', f'type={build_type}',\n '-var', f'bundle_flavour={flavour}',\n '-var', f'image_name={image_name}',\n '-var', f'ecr_server={ecr_server}',\n '-var', f'os_version={distribution}',\n '-var', f'ecr_repository={ecr_repository}',\n '-var', f'aws_access_key={os.environ[\"AWS_ACCESS_KEY_ID\"]}',\n '-var', f'aws_secret_key={os.environ[\"AWS_SECRET_ACCESS_KEY\"]}'\n ]\n\n if not publish:\n extra_vars += ['-except', 'publish']\n\n # Make sure we remove old containers before creting new ones\n run_command(['docker', 'rm', '-f', 'default'], check=False)\n\n elif build_type in ['bare_metal', 'lxd'] and publish:\n # Get information about base image\n base_image = recipe[name]['base_image'].replace('$distribution', distribution)\n\n # Get disk size to use\n disk_size = recipe[name].get('disk_size', 9) # In GB\n\n # Get base image\n base_image_local_path = '/tmp/' + base_image\n base_image_key = release_label + '/images/' + base_image\n click.echo(f'Downloading image from {base_image_key}')\n try:\n boto3.resource('s3').Bucket(apt_repo).download_file(base_image_key, base_image_local_path)\n except botocore.exceptions.ClientError:\n click.echo(f'Unable to download base image from {base_image_key}, creating a new one')\n run_command(['bash',\n '/tailor-image/environment/create_base_image.bash',\n f'{base_image_local_path}',\n f'{distribution}'])\n boto3.resource('s3').Bucket(apt_repo).upload_file(base_image_local_path, base_image_key)\n\n # Enable nbd kernel module, necesary for qemu's packer chroot builder\n run_command(['modprobe', 'nbd'])\n\n # Resize image\n run_command(['qemu-img', 'resize', base_image_local_path, '30G'])\n\n # Copy image\n tmp_image = base_image_local_path.replace('disk1', 'disk1-resized')\n run_command(['cp', base_image_local_path, tmp_image])\n\n # Resize partition inside qcow image\n run_command(['virt-resize', '--expand', '/dev/sda1', base_image_local_path, tmp_image])\n run_command(['mv', tmp_image, base_image_local_path])\n\n # Generate image name\n image_name = f'{organization}_{name}_{distribution}_{release_label}_{today}'\n\n extra_vars = [\n '-var', f'image_name={image_name}',\n '-var', f's3_bucket={apt_repo}',\n '-var', f'iso_image={base_image_local_path}',\n '-var', f'distribution={distribution}',\n '-var', f'disk_size={disk_size}'\n ]\n\n # Make sure to clean old image builds\n run_command(['rm', '-rf', '/tmp/images'])\n\n elif build_type == 'ami':\n image_name = f'{organization}_{name}_{distribution}_ami_{release_label}'\n # Get ami-id for base image\n source_ami_id = recipe[name]['source_ami'].get(distribution)\n\n if not source_ami_id:\n click.echo(f'You need to specify a bas AMI for the desired distribution {distribution}')\n sys.exit(1)\n\n # Increase fow how long we wait for image to be ready. Default is 30 minutes, sometime it might take longer\n env['AWS_MAX_ATTEMPTS'] = '90' # minutes\n env['AWS_POLL_DELAY_SECONDS'] = '60' # Poll for status every minute\n\n extra_vars = [\n '-var', f'build_date={today}',\n '-var', f'image_name={image_name}',\n '-var', f'name={name}',\n '-var', f'source_ami_id={source_ami_id}',\n '-var', f'distribution={distribution}',\n '-var', f'release_label={release_label}',\n '-var', f'aws_access_key={os.environ[\"AWS_ACCESS_KEY_ID\"]}',\n '-var', f'aws_secret_key={os.environ[\"AWS_SECRET_ACCESS_KEY\"]}'\n ]\n else:\n return 0\n\n extra_vars.extend(optional_vars)\n\n click.echo(f'Building {build_type} image with: {provision_file}', err=True)\n\n command = ['packer', 'build',\n '-var', f'playbook_file={provision_file_path}',\n '-var', f'organization={organization}',\n '-var', f'bundle_track={release_track}',\n '-var', f'bundle_version={release_label}'] + extra_vars + ['-timestamp-ui', template_path]\n\n run_command(command, env=env, cwd='/tmp')\n\n if build_type in ['bare_metal', 'lxd'] and publish:\n update_image_index(release_label, apt_repo, common_config, image_name)","def commit(self, repository, tag=None, push=False, **kwargs):\n logger.debug('Committing `%s` with container id %s ...', self.fqdn, self.container.short_id)\n image = self.container.commit(repository=repository, tag=tag, **kwargs)\n logger.debug('%s repo tags committed with image id as %s', image.tags, image.short_id)\n if push:\n logger.debug('Pushing image of `%s` to repository %s ...', self.fqdn, repository)\n for line in client.api.push(repository, tag, stream=True, decode=True):\n line.pop('progressDetail', None) # take out too much detail\n logger.debug(line)\n logger.debug('%s repo tags pushed for `%s`, whose image id is %s',\n image.tags, self.fqdn, image.short_id)","def pullDockerImage(client, names):\n imgNotExistList = []\n for name in names:\n try:\n client.images.pull(name)\n # some invalid image names will not be pulled but the pull method\n # will not throw an exception so the only way to confirm if a pull\n # succeeded is to attempt a docker inspect on the image\n client.images.get(name)\n except Exception:\n imgNotExistList.append(name)\n if len(imgNotExistList) != 0:\n raise DockerImageNotFoundError('Could not find multiple images ',\n image_name=imgNotExistList)","def repository_create_hosted_docker(ctx: click.Context, **kwargs):\n _create_repository(ctx, 'hosted', **kwargs)","def push(self):\n self.runtime.logger.info('Pushing config...')\n with Dir(self.runtime.metadata_dir):\n exectools.cmd_assert([\"git\", \"push\"])","def build_docker(params) -> None:\n print(\"Building docker image...\")\n cmd = \"cd bg_changer && docker build --tag bg_changer . >/dev/null 2>&1\"\n if os.system(cmd) == 0:\n print(\" Success !\")\n else:\n print(\" Failure !\")","def start_upload(self, group_name=None):\n rmt = BossRemote(cfg_file_or_dict=self.args.config)\n\n type_to_dtype = {'image': 'uint16', 'annotation': 'uint64'}\n\n img = tf.imread(os.path.expanduser(self.args.tif_stack))\n if self.args.type == 'annotation' and img.dtype != 'uint64':\n img = np.asarray(img, dtype='uint64')\n\n coll_name = self.args.collection\n exp_name = self.args.experiment\n chan_name = self.args.channel\n source_chan = []\n\n if self.args.source_channel != None:\n source_chan = [self.args.source_channel]\n\n # upload image back to boss\n channel_rsc = self._get_channel_resource(rmt, chan_name, coll_name, exp_name, type=self.args.type, sources=source_chan, datatype=type_to_dtype[self.args.type], new_channel=self.args.new_channel)\n\n if img.dtype != 'uint64' or img.dtype != 'uint16':\n if self.args.type == 'image':\n img = img.astype('uint16')\n else:\n img = img.astype('uint64')\n\n if not self.args.chunk:\n self._upload_to_boss(rmt, img, channel_rsc)\n else:\n self._upload_chunk_to_boss(rmt, img, channel_rsc, x_range=self.args.x_range, y_range=self.args.y_range, z_range=self.args.z_range)\n\n url = 'https://ndwebtools.neurodata.io/ndviz_url/{}/{}/'.format(coll_name, exp_name)\n\n if group_name:\n self._change_permissions(group_name)\n\n return url","def build(obj):\n logger.info(ASCII_LOGO)\n logger.info(\"Started building SageMaker Docker image. It will take some minutes...\\n\")\n\n try:\n config_file_path = os.path.join('.sagify.json')\n if not os.path.isfile(config_file_path):\n raise ValueError()\n\n config = ConfigManager(config_file_path).get_config()\n api_build.build(\n source_dir=config.sagify_module_dir,\n requirements_dir=config.requirements_dir,\n docker_tag=obj['docker_tag'],\n image_name=config.image_name,\n python_version=config.python_version)\n\n logger.info(\"Docker image built successfully!\")\n except ValueError:\n logger.info(\"This is not a sagify directory: {}\".format(dir))\n sys.exit(-1)\n except subprocess.CalledProcessError as e:\n logger.debug(e.output)\n raise\n except Exception as e:\n logger.info(\"{}\".format(e))\n sys.exit(-1)","def test_push_tag(self, mock_docker_environment, snapshot, capsys):\n mock_docker_environment.api.push = mock.Mock(\n return_value=event_streams.PUSH_SUCCESSFUL_CUSTOM_TAG\n )\n push_image(TEST_IMAGE_NAME, \"custom_tag\")\n out, err = capsys.readouterr()\n snapshot.assert_match(out)","def setup(self):\n\n exists = [i for i in self.client.images() if self.image in i['RepoTags']]\n\n # Only pull the image if we don't have it\n if not exists or self.pull:\n self.client.pull(self.image)\n self.logger.debug(\"Pulled {}\".format(self.image))\n\n self.container = self.client.create_container(\n image=self.image,\n host_config=self.host_config,\n name=self.name,\n command=self.command,\n environment=self.environment\n )\n self.logger.debug(\"Created container {}\".format(self.container['Id']))","def _ensure_image(testkit_path, branch_name, artifacts_path):\n # Construct Docker image name from branch name\n image_name = \"runner:%s\" % branch_name\n image_path = os.path.join(testkit_path, \"runner_image\")\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\n\n return image_name","def add_files_to_image(image, with_files, label=None):\n tag_idx = image.find(\":\")\n if tag_idx == -1:\n jobset.message(\n \"FAILED\", \"invalid docker image %s\" % image, do_newline=True\n )\n sys.exit(1)\n orig_tag = \"%s_\" % image\n subprocess.check_output([\"docker\", \"tag\", image, orig_tag])\n\n lines = [\"FROM \" + orig_tag]\n if label:\n lines.append(\"LABEL %s\" % label)\n\n temp_dir = tempfile.mkdtemp()\n atexit.register(lambda: subprocess.call([\"rm\", \"-rf\", temp_dir]))\n\n # Copy with_files inside the tmp directory, which will be the docker build\n # context.\n for f in with_files:\n shutil.copy(f, temp_dir)\n lines.append(\"COPY %s %s/\" % (os.path.basename(f), _BUILD_INFO))\n\n # Create a Dockerfile.\n with open(os.path.join(temp_dir, \"Dockerfile\"), \"w\") as f:\n f.write(\"\\n\".join(lines))\n\n jobset.message(\"START\", \"Repackaging %s\" % image, do_newline=True)\n build_cmd = [\"docker\", \"build\", \"--rm\", \"--tag\", image, temp_dir]\n subprocess.check_output(build_cmd)\n dockerjob.remove_image(orig_tag, skip_nonexistent=True)","def PrePush(self, image):\n pass","def push_sources():\n ensure_src_dir()\n push_rev = getattr(env, 'push_rev', None)\n if push_rev is None:\n push_rev = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n local(\"git tag -a {0} -m \\\"Tagged for release\\\"\".format(push_rev))\n local(\"git push origin master --tags\")\n\n with cd(SRC_DIR):\n run(\"git pull origin master\")\n run(\"git fetch -t\")\n run(\"git checkout {0}\".format(push_rev))","def pull_image(self):\n status = []\n for key, container in self.containers.items():\n result = container.pull()\n status.append(result)\n return status","def build_all_images_for_lang(lang):\n if not args.git_checkout:\n if args.release != \"master\":\n print(\n \"Cannot use --release without also enabling --git_checkout.\\n\"\n )\n sys.exit(1)\n releases = [args.release]\n else:\n if args.release == \"all\":\n releases = client_matrix.get_release_tags(lang)\n else:\n # Build a particular release.\n if args.release not in [\"master\"] + client_matrix.get_release_tags(\n lang\n ):\n jobset.message(\n \"SKIPPED\",\n \"%s for %s is not defined\" % (args.release, lang),\n do_newline=True,\n )\n return []\n releases = [args.release]\n\n images = []\n for release in releases:\n images += build_all_images_for_release(lang, release)\n jobset.message(\n \"SUCCESS\",\n \"All docker images built for %s at %s.\" % (lang, releases),\n do_newline=True,\n )\n return images","def pull(self, repo, tag):\n check_blacklist(repo)\n logger.info(\"Pulling Docker image {}:{}\".format(repo, tag))\n with SimpleFlock(self.FLOCKFILE, timeout=1200):\n stream = self.client.pull(repo, tag=tag, stream=True, insecure_registry=True)\n log_output(stream)","def run(args):\n docker(' '.join(args))","def pull(release):\n image = f\"breqwatr/rsyslog:{release}\"\n ecr.pull(image)","def build(path, tag):\n print (\"*** BUILDING IS INITIATED\")\n try:\n response = client.images.build(path = path, tag=tag, rm=True)\n except Exception as ex:\n template = \"An exception of type {0} occured. Arguments:\\n{1!r}\"\n message = template.format(type(ex).__name__, ex.args)\n click.echo(message)\n print(\"*** BUILD OUTPUT:\")\n for object in response[1]:\n click.echo(object)\n click.echo('*** BUILDING FINISHED')","def store_image(self, image_name):\n self.images.append(image_name)","def get_image_registry_url(self, image_name):\n c = self._oc_command([\"get\", \"is\", image_name,\n \"--output=jsonpath=\\'{ .status.dockerImageRepository }\\'\"])\n try:\n internal_registry_name = run_cmd(c, return_output=True)\n except subprocess.CalledProcessError as ex:\n raise ConuException(\"oc get is failed: %s\" % ex)\n\n logger.info(\"Image registry url: %s\", internal_registry_name)\n\n return internal_registry_name.replace(\"'\", \"\").replace('\"', '')","def test_docker_image_hint(mock_tools):\n # Mock the return values for Docker verification\n mock_tools.subprocess.check_output.side_effect = [\n VALID_DOCKER_VERSION,\n VALID_DOCKER_INFO,\n VALID_BUILDX_VERSION,\n VALID_USER_MAPPING_IMAGE_CACHE,\n ]\n\n Docker.verify(mock_tools, image_tag=\"myimage:tagtorulethemall\")\n\n mock_tools.subprocess.run.assert_has_calls(\n [\n call(\n [\n \"docker\",\n \"run\",\n \"--rm\",\n \"--volume\",\n f\"{Path.cwd() / 'build'}:/host_write_test:z\",\n \"myimage:tagtorulethemall\",\n \"touch\",\n PurePosixPath(\"/host_write_test/container_write_test\"),\n ],\n check=True,\n ),\n call(\n [\n \"docker\",\n \"run\",\n \"--rm\",\n \"--volume\",\n f\"{Path.cwd() / 'build'}:/host_write_test:z\",\n \"myimage:tagtorulethemall\",\n \"rm\",\n \"-f\",\n PurePosixPath(\"/host_write_test/container_write_test\"),\n ],\n check=True,\n ),\n ]\n )","def snapshot_image_on_provider(self, builder, provider, credentials, target, template, parameters):","def prepare():\n sh('docker build --rm -t {image} {dir}', image=IMAGE, dir=os.path.dirname(__file__))","def push(self):\n origin = self.git_repo.remotes.origin\n origin.push()"],"string":"[\n \"def push_image(image):\\n\\n subprocess.check_call(['docker', 'push', image])\",\n \"def run_docker_push(image_list, docker_client):\\n logger.info(\\\"--- push image to registry ---\\\")\\n if runtime == Runtime.local:\\n logger.info(\\\"Detected local runtime, stop here.\\\")\\n return\\n\\n if not len(success_image_list):\\n logger.info(\\\"No success image to push, stop here.\\\")\\n return\\n\\n if GIT_BRANCH != \\\"master\\\":\\n logger.info(\\\"Not master branch, stop here\\\")\\n return\\n\\n docker_client.login(username=DOCKER_HUB_USERNAME, password=DOCKER_HUB_PASSWORD)\\n for image in image_list:\\n image.run_docker_push(docker_client)\\n print(\\\"Finished.\\\")\",\n \"def Push(self, docker_client):\\n docker_client.tag(self.id, self.repo_tag, force=True)\\n log.info('Pushing image to Google Container Registry...\\\\n')\\n def InnerPush():\\n for line in docker_client.push(self.repo_tag, stream=True):\\n # Check for errors.\\n _ProcessStreamingOutput(line)\\n\\n _Retry(InnerPush)\\n metrics.CustomTimedEvent(metric_names.DOCKER_PUSH)\",\n \"def push(self, repo, tag):\\n logger.info(\\\"Pushing Docker image {}:{}\\\".format(repo, tag))\\n stream = self.client.push(repo, tag=tag, stream=True, insecure_registry=True)\\n log_output(stream)\",\n \"def docker_push(c):\\n cli_tasks.docker_push.run(c)\",\n \"def push_base():\\n docker('login')\\n docker('push %s' % env.base_image_name)\",\n \"def cli(ctx, image_file):\\n if not image_file:\\n return\\n for pull_image in image_file.readline():\\n pull_image = pull_image.rstrip('\\\\n')\\n if len(pull_image) == 0:\\n continue\\n docker.pull(pull_image)\\n push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,\\n DEFAULR_NAMESPACE,\\n pull_image.split('/')[-1])\\n docker.tag(pull_image, push_image)\\n docker.push(push_image)\",\n \"def push(args, image_name_tag):\\n if args.push is True:\\n cmd_push = f\\\"docker push {image_name_tag}\\\"\\n print(f\\\"COMMAND: {cmd_push}\\\")\\n print(\\\"\\\", flush=True)\\n return_code = subprocess.call(cmd_push, shell=True)\\n if return_code != 0:\\n exit(f\\\"Error with {cmd_push}\\\")\\n return 0\",\n \"def setup_image(self, image_name, image_name_in_repo):\\n with self.docker_session():\\n self._push_to_registry(image_name, image_name_in_repo)\\n self._upload_docker_creds()\",\n \"def push(config, version_tag):\\n config_dict = get_config(config)\\n image_name = config_dict['IMAGE'].split(':')[0]\\n image = '{}:{}'.format(image_name, version_tag)\\n\\n build(config, version_tag)\\n with cd(env.project_dir):\\n run('gcloud docker -- push %s' % image)\",\n \"def build_and_push_3p_images():\\n\\n log.info(\\n \\\"Building Lambda Stack docker images (these are not on DockerHub). \\\"\\n \\\"FMI see https://github.com/lambdal/lambda-stack-dockerfiles\\\")\\n run_cmd(\\\"\\\"\\\"\\n cd /tmp &&\\n (rm -rf lambda-stack-dockerfiles || true) &&\\n git clone https://github.com/lambdal/lambda-stack-dockerfiles &&\\n cd lambda-stack-dockerfiles &&\\n git checkout d762400d61636c074533416674426a84cc4d8992 &&\\n docker build -t oarphpy/lambda-stack:22.04 -f Dockerfile.jammy . &&\\n docker push oarphpy/lambda-stack:22.04\\n \\\"\\\"\\\")\",\n \"def all(tag=None, env=None):\\n build_images(tag=tag)\\n push_images(tag=tag)\\n deploy(env=env, tag=tag)\",\n \"def publish(digest, pid, api_key, tag):\\n url = \\\"https://connect.redhat.com/api/v2/projects/{}/containers/{}/tags/{}/publish\\\".format(pid, digest, tag)\\n headers = {\\\"accept\\\": \\\"*/*\\\",\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n \\\"Authorization\\\": \\\"Bearer {}\\\".format(api_key)}\\n\\n response = requests.post(url, headers=headers)\\n\\n if response.status_code != 201:\\n print(\\\"Unable to publish, invalid status code: {}.\\\".format(response.status_code))\\n print(response)\\n print(response.content)\\n sys.exit(1)\\n else:\\n print(\\\"Docker image '{}' successfully scheduled for publishing.\\\".format(digest))\",\n \"def push_image(self, tag_list, push_to_defaults, additional_registries=[], version_release_tuple=None,\\n push_late=False, dry_run=False):\\n\\n # Late pushes allow certain images to be the last of a group to be\\n # pushed to mirrors. CI/CD systems may initiate operations based on the\\n # update a given image and all other images need to be in place\\n # when that special image is updated. The special images are there\\n # pushed \\\"late\\\"\\n # Actions that need to push all images need to push all images\\n # need to make two passes/invocations of this method: one\\n # with push_late=False and one with push_late=True.\\n\\n is_late_push = False\\n if self.config.push.late is not Missing:\\n is_late_push = self.config.push.late\\n\\n if push_late != is_late_push:\\n return True\\n\\n push_names = []\\n\\n if push_to_defaults:\\n push_names.extend(self.metadata.get_default_push_names())\\n\\n push_names.extend(self.metadata.get_additional_push_names(additional_registries))\\n\\n # Nothing to push to? We are done.\\n if not push_names:\\n return True\\n\\n with Dir(self.distgit_dir):\\n\\n if version_release_tuple:\\n version = version_release_tuple[0]\\n release = version_release_tuple[1]\\n else:\\n\\n # History\\n # We used to rely on the \\\"release\\\" label being set in the Dockerfile, but this is problematic for several reasons.\\n # (1) If 'release' is not set, OSBS will determine one automatically that does not conflict\\n # with a pre-existing image build. This is extremely helpful since we don't have to\\n # worry about bumping the release during refresh images. This means we generally DON'T\\n # want the release label in the file and can't, therefore, rely on it being there.\\n # (2) People have logged into distgit before in order to bump the release field. This happening\\n # at the wrong time breaks the build.\\n\\n # If the version & release information was not specified,\\n # try to detect latest build from brew.\\n # Read in version information from the Distgit dockerfile\\n _, version, release = self.metadata.get_latest_build_info()\\n\\n try:\\n record = {\\n \\\"distgit_key\\\": self.metadata.distgit_key,\\n \\\"distgit\\\": '{}/{}'.format(self.metadata.namespace, self.metadata.name),\\n \\\"image\\\": self.config.name,\\n \\\"version\\\": version,\\n \\\"release\\\": release,\\n \\\"message\\\": \\\"Unknown failure\\\",\\n \\\"status\\\": -1,\\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\\n }\\n\\n # pull just the main image name first\\n image_name_and_version = \\\"%s:%s-%s\\\" % (self.config.name, version, release)\\n brew_image_url = \\\"/\\\".join((constants.BREW_IMAGE_HOST, image_name_and_version))\\n pull_image(brew_image_url)\\n record['message'] = \\\"Successfully pulled image\\\"\\n record['status'] = 0\\n except Exception as err:\\n record[\\\"message\\\"] = \\\"Exception occurred: %s\\\" % str(err)\\n self.logger.info(\\\"Error pulling %s: %s\\\" % (self.metadata.name, err))\\n raise\\n finally:\\n self.runtime.add_record('pull', **record)\\n\\n push_tags = list(tag_list)\\n\\n # If no tags were specified, build defaults\\n if not push_tags:\\n push_tags = self.metadata.get_default_push_tags(version, release)\\n\\n for image_name in push_names:\\n try:\\n\\n repo = image_name.split('/', 1)\\n\\n action = \\\"push\\\"\\n record = {\\n \\\"distgit_key\\\": self.metadata.distgit_key,\\n \\\"distgit\\\": '{}/{}'.format(self.metadata.namespace, self.metadata.name),\\n \\\"repo\\\": repo, # ns/repo\\n \\\"name\\\": image_name, # full registry/ns/repo\\n \\\"version\\\": version,\\n \\\"release\\\": release,\\n \\\"message\\\": \\\"Unknown failure\\\",\\n \\\"tags\\\": \\\", \\\".join(push_tags),\\n \\\"status\\\": -1,\\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\\n }\\n\\n for push_tag in push_tags:\\n push_url = '{}:{}'.format(image_name, push_tag)\\n\\n if dry_run:\\n rc = 0\\n self.logger.info('Would have tagged {} as {}'.format(brew_image_url, push_url))\\n self.logger.info('Would have pushed {}'.format(push_url))\\n else:\\n rc, out, err = exectools.cmd_gather([\\\"docker\\\", \\\"tag\\\", brew_image_url, push_url])\\n\\n if rc != 0:\\n # Unable to tag the image\\n raise IOError(\\\"Error tagging image as: %s\\\" % push_url)\\n\\n for r in range(10):\\n self.logger.info(\\\"Pushing image to mirror [retry=%d]: %s\\\" % (r, push_url))\\n rc, out, err = exectools.cmd_gather([\\\"docker\\\", \\\"push\\\", push_url])\\n if rc == 0:\\n break\\n self.logger.info(\\\"Error pushing image -- retrying in 60 seconds\\\")\\n time.sleep(60)\\n\\n if rc != 0:\\n # Unable to push to registry\\n raise IOError(\\\"Error pushing image: %s\\\" % push_url)\\n\\n record[\\\"message\\\"] = \\\"Successfully pushed all tags\\\"\\n record[\\\"status\\\"] = 0\\n\\n except Exception as err:\\n record[\\\"message\\\"] = \\\"Exception occurred: %s\\\" % str(err)\\n self.logger.info(\\\"Error pushing %s: %s\\\" % (self.metadata.name, err))\\n raise\\n\\n finally:\\n self.runtime.add_record(action, **record)\\n\\n return True\",\n \"def push_to_ecs(aws_session, docker_client, image_name=None, tag=None):\\n\\n if image_name is None:\\n image_name = get_service_name()\\n print('image_name: ' + image_name)\\n\\n if tag is None:\\n tag = 'latest'\\n\\n repo = __get_docker_repository_name(aws_session, image_name)\\n full_tag = image_name + ':' + tag\\n image = __get_docker_image(docker_client, full_tag)\\n\\n # Push to the ecs repo\\n print('Pushing the image \\\"' + full_tag + '\\\" up to \\\"' + repo + '\\\"...')\\n try:\\n docker_client.tag(repository=repo, image=image, tag=tag)\\n except BaseException as e:\\n print(e)\\n raise Exception('An error occurred when tagging the image \\\"' + image + '\\\" with the tag \\\"' + full_tag + '\\\".')\\n try:\\n print_docker_output(docker_client.push(repository=repo, stream=True, tag=tag))\\n except BaseException as e:\\n print(e)\\n raise Exception('An error occurred when pushing \\\"' + full_tag + '\\\" to ECR.')\\n print('The image \\\"' + full_tag + '\\\" has now been pushed up to \\\"' + repo + '\\\".')\",\n \"def push_dockerhub(c, app, version, latest=False):\\n if app.lower() == 'core':\\n c.run('sudo docker push kinecosystem/stellar-core:{version}'.format(version=version))\\n if latest:\\n c.run('sudo docker push kinecosystem/stellar-core:latest')\\n elif app.lower() == 'horizon':\\n c.run('sudo docker push kinecosystem/horizon:{version}'.format(version=version))\\n if latest:\\n c.run('sudo docker push kinecosystem/horizon:latest')\\n elif app.lower() == 'friendbot':\\n c.run('sudo docker push kinecosystem/friendbot:{version}'.format(version=version))\\n if latest:\\n c.run('sudo docker push kinecosystem/friendbot:latest')\\n else:\\n Exit('Unknown application {}'.format(app))\",\n \"def image_needs_pushing(image):\\n d = docker_client()\\n try:\\n d.images.get_registry_data(image)\\n except docker.errors.APIError:\\n # image not found on registry, needs pushing\\n return True\\n else:\\n return False\",\n \"def push_image_to_provider(self, builder, provider, credentials, target, target_image, parameters):\",\n \"def LocalBuildAndPush(self, image):\\n full_tag = self.GetFullRegistryTag(image.name)\\n # Multiarch images require buildx create\\n # https://github.com/docker/build-push-action/issues/302\\n vm_util.IssueCommand(['docker', 'buildx', 'create', '--use'])\\n cmd = ['docker', 'buildx', 'build']\\n if _CONTAINER_CLUSTER_ARCHITECTURE.value:\\n cmd += ['--platform', ','.join(_CONTAINER_CLUSTER_ARCHITECTURE.value)]\\n cmd += ['--no-cache', '--push', '-t', full_tag, image.directory]\\n vm_util.IssueCommand(cmd, timeout=None)\\n vm_util.IssueCommand(['docker', 'buildx', 'stop'])\",\n \"def build_and_push(build_context,\\n image_tag,\\n image_name,\\n nocache,\\n credstore_env=None,\\n registries=None,\\n max_retries=3,\\n sleep_interval=1):\\n _logger.info('Starting build ...')\\n\\n # Build the image\\n docker_builder = build(build_context=build_context,\\n image_tag=image_tag,\\n image_name=image_name,\\n nocache=nocache,\\n credstore_env=credstore_env,\\n registries=registries,\\n max_retries=max_retries,\\n sleep_interval=sleep_interval)\\n push(docker_builder, max_retries=max_retries, sleep_interval=sleep_interval)\",\n \"def images_refresh(self, kwargs=None):\\n scode, hosts = Rest.get('Host')\\n filter = {}\\n n = 1\\n e = {}\\n data = []\\n for host in hosts:\\n os.environ[\\\"DOCKER_HOST\\\"] = host['Ip'] + \\\":\\\" + str(host['Port'])\\n filter['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n self.client = docker.from_env()\\n try:\\n images = self.client.images.list(**kwargs)\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n return\\n\\n if len(images) == 0:\\n Console.info(\\\"No images exist\\\")\\n continue\\n\\n for imagem in images:\\n image = imagem.__dict__['attrs']\\n image['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n data.append(image)\\n d = {}\\n d['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n d['Id'] = image['Id']\\n if image['RepoTags'] == None:\\n d['Repository'] = image['RepoDigests'][0]\\n else:\\n d['Repository'] = image['RepoTags'][0]\\n # d['Size'] = image['Size']\\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2)\\n e[n] = d\\n n = n + 1\\n Rest.delete('Image', filter)\\n Rest.post('Image', data)\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))\",\n \"def test_docker_push_target_ref(tmpdir, docker_client, registry):\\n tmp = tmpdir.join('shipwright-localhost-sample')\\n path = str(tmp)\\n source = pkg_resources.resource_filename(\\n __name__,\\n 'examples/shipwright-localhost-sample',\\n )\\n repo = create_repo(path, source)\\n tag = repo.head.ref.commit.hexsha[:12]\\n\\n tmp.join('service1/base.txt').write('Hi mum')\\n commit_untracked(repo)\\n new_tag = repo.head.ref.commit.hexsha[:12]\\n\\n client_cfg = docker_utils.kwargs_from_env()\\n cli = docker_client\\n\\n defaults = get_defaults()\\n defaults['push'] = True\\n try:\\n shipw_cli.run(\\n path=path,\\n client_cfg=client_cfg,\\n arguments=defaults,\\n environ={},\\n )\\n\\n # Remove the build images:\\n old_images = (\\n cli.images(name='localhost:5000/service1', quiet=True) +\\n cli.images(name='localhost:5000/shared', quiet=True) +\\n cli.images(name='localhost:5000/base', quiet=True)\\n )\\n for image in old_images:\\n cli.remove_image(image, force=True)\\n\\n images_after_delete = (\\n cli.images(name='localhost:5000/service1') +\\n cli.images(name='localhost:5000/shared') +\\n cli.images(name='localhost:5000/base')\\n )\\n assert images_after_delete == []\\n\\n # Pull them again:\\n for t in ['master', 'latest', new_tag, tag]:\\n for repo in ['service1', 'shared', 'base']:\\n cli.pull('localhost:5000/' + repo, t)\\n\\n service1, shared, base = (\\n cli.images(name='localhost:5000/service1') +\\n cli.images(name='localhost:5000/shared') +\\n cli.images(name='localhost:5000/base')\\n )\\n\\n assert set(service1['RepoTags']) == {\\n 'localhost:5000/service1:master',\\n 'localhost:5000/service1:latest',\\n 'localhost:5000/service1:' + new_tag,\\n }\\n\\n assert set(shared['RepoTags']) == {\\n 'localhost:5000/shared:master',\\n 'localhost:5000/shared:latest',\\n 'localhost:5000/shared:' + new_tag,\\n 'localhost:5000/shared:' + tag,\\n }\\n\\n assert set(base['RepoTags']) == {\\n 'localhost:5000/base:master',\\n 'localhost:5000/base:latest',\\n 'localhost:5000/base:' + new_tag,\\n 'localhost:5000/base:' + tag,\\n }\\n finally:\\n old_images = (\\n cli.images(name='localhost:5000/service1', quiet=True) +\\n cli.images(name='localhost:5000/shared', quiet=True) +\\n cli.images(name='localhost:5000/base', quiet=True)\\n )\\n for image in old_images:\\n cli.remove_image(image, force=True)\",\n \"def _register_to_mongodb(logger, summary: Dict = None):\\n logger.info('registering image to Jina Hub database...')\\n\\n with resource_stream('jina', '/'.join(('resources', 'hubapi.yml'))) as fp:\\n hubapi_yml = yaml.load(fp)\\n\\n hubapi_url = hubapi_yml['hubapi']['url'] + hubapi_yml['hubapi']['push']\\n\\n if not credentials_file().is_file():\\n logger.error(f'user hasnot logged in. please login using command: {colored(\\\"jina hub login\\\", attrs=[\\\"bold\\\"])}')\\n return\\n\\n with open(credentials_file(), 'r') as cf:\\n cred_yml = yaml.load(cf)\\n access_token = cred_yml['access_token']\\n\\n if not access_token:\\n logger.error(f'user has not logged in. please login using command: {colored(\\\"jina hub login\\\", attrs=[\\\"bold\\\"])}')\\n return\\n\\n headers = {\\n 'Accept': 'application/json',\\n 'authorizationToken': access_token\\n }\\n try:\\n import requests\\n response = requests.post(url=f'{hubapi_url}',\\n headers=headers,\\n data=json.dumps(summary))\\n if response.status_code == requests.codes.ok:\\n logger.info(response.text)\\n elif response.status_code == requests.codes.unauthorized:\\n logger.error(f'user is unauthorized to perform push operation. '\\n f'please login using command: {colored(\\\"jina hub login\\\", attrs=[\\\"bold\\\"])}')\\n elif response.status_code == requests.codes.internal_server_error:\\n if 'auth' in response.text.lower():\\n logger.error(f'authentication issues!'\\n f'please login using command: {colored(\\\"jina hub login\\\", attrs=[\\\"bold\\\"])}')\\n logger.error(f'got an error from the API: {response.text}')\\n except Exception as exp:\\n logger.error(f'got an exception while invoking hubapi for push {repr(exp)}')\",\n \"def push_image_version(self, app, image_name, image_tag, parent_task):\\n\\n assert isinstance(image_name, str)\\n assert isinstance(image_tag, str)\\n\\n # The string \\\"local\\\" has a special meaning which means the most recent\\n # local image of that name, so we skip the remote call/check.\\n if image_tag == \\\"local\\\":\\n raise ValueError(\\\"You cannot push the 'local' version\\\")\\n\\n # See if the registry is willing to give us a URL (it's logged in)\\n registry = self.get_registry(app)\\n if registry:\\n registry_url = registry.url(self.host)\\n else:\\n registry_url = None\\n if registry_url is None:\\n raise RuntimeError(\\\"No registry configured\\\")\\n\\n task = Task(\\n \\\"Pushing image {}:{}\\\".format(image_name, image_tag),\\n parent=parent_task,\\n progress_formatter=lambda x: \\\"{} MB\\\".format(x // (1024 ** 2)),\\n )\\n\\n # Work out the name it needs to be and tag the image as that\\n remote_name = \\\"{registry_url}/{image_name}\\\".format(\\n registry_url=registry_url,\\n image_name=image_name,\\n )\\n self.host.client.tag(\\n image_name + \\\":\\\" + \\\"latest\\\",\\n remote_name,\\n tag=image_tag,\\n force=True\\n )\\n\\n # Push it up\\n stream = self.host.client.push(remote_name, tag=image_tag, stream=True)\\n layer_status = {}\\n current = None\\n total = None\\n for line in stream:\\n if isinstance(line, bytes):\\n line = line.decode(\\\"ascii\\\")\\n data = json.loads(line)\\n if 'error' in data:\\n task.finish(status=\\\"Failed\\\", status_flavor=Task.FLAVOR_WARNING)\\n raise RuntimeError(\\\"Push error: %r\\\" % data['error'])\\n elif 'id' in data:\\n if data['status'].lower() == \\\"pushing\\\":\\n layer_status[data['id']] = data['progressDetail']\\n elif \\\"complete\\\" in data['status'].lower() and data['id'] in layer_status:\\n layer_status[data['id']]['current'] = layer_status[data['id']]['total']\\n if layer_status:\\n statuses = [x for x in layer_status.values()\\n if \\\"current\\\" in x and \\\"total\\\" in x]\\n current = sum(x['current'] for x in statuses)\\n total = sum(x['total'] for x in statuses)\\n if total is not None:\\n task.update(progress=(current, total))\\n task.finish(status=\\\"Done\\\", status_flavor=Task.FLAVOR_GOOD)\",\n \"def add_remote_image(local_image_path, filename):\\n for host in APP.config['REMOTE_HOSTS']:\\n user = APP.config['REMOTE_HOST_USERNAME']\\n directory = APP.config['REMOTE_IMAGE_DIRECTORY']\\n ssh_command = f'''ssh -oStrictHostKeyChecking=accept-new -i /root/.ssh/id_rsa \\\\\\n {user}@{host} mkdir -p {directory}'''\\n subprocess.run(ssh_command, check=True, shell=True)\\n scp_command = f\\\"scp {local_image_path}/{filename} {user}@{host}:{directory}/{filename}\\\"\\n subprocess.run(scp_command, check=True, shell=True)\",\n \"def process_images(self):\\n source_images = self.get_build_images()\\n self.log.info(\\\"Got %s images for publishing. Processing..\\\", len(source_images))\\n\\n for image in source_images:\\n self.fetch_image(image)\\n\\n for target in image.push_registries:\\n for tag in image.release_tags:\\n repository = \\\"%s/%s\\\" % (target, image.repository.name)\\n self.tag_image(image, repository, tag)\\n retry_count = 1\\n while retry_count <= self.retry_limit:\\n self.log.info(\\\"Pushing %s:%s to %s (%d/%d)\\\", repository, tag, target, retry_count, self.retry_limit)\\n try:\\n self.publish_image(target, repository, tag)\\n break\\n except ImagePushError as e:\\n self.log.error(\\\"%s\\\", e.message)\\n retry_count = retry_count + 1\\n else:\\n return False\\n return True\",\n \"def pull_dlc_images(images):\\n for image in images:\\n run(f\\\"docker pull {image}\\\", hide=\\\"out\\\")\",\n \"def RemoteBuild(self, image):\\n raise NotImplementedError()\",\n \"def get_images(name):\\n url = \\\"/\\\".join([REGISTRY_BASE, name, \\\"/tags/list\\\"])\\n response = req(url)\\n image_list = []\\n if response is not None:\\n headers = {\\\"Accept\\\": \\\"application/vnd.docker.distribution.manifest.v2+json\\\"}\\n tags = response[\\\"tags\\\"]\\n for tag in tags:\\n url = \\\"/\\\".join([REGISTRY_BASE, name, \\\"/manifests\\\", tag])\\n response = req(url, headers)\\n if response is not None:\\n image = {}\\n image[\\\"size\\\"] = response[\\\"config\\\"][\\\"size\\\"]\\n for i in response[\\\"layers\\\"]:\\n image[\\\"size\\\"] += i[\\\"size\\\"]\\n image[\\\"size\\\"] = round(float(image[\\\"size\\\"]) / 1024 / 1024, 2)\\n image[\\\"id\\\"] = response[\\\"config\\\"][\\\"digest\\\"][7:19]\\n image[\\\"tag\\\"] = tag\\n image[\\\"cmd\\\"] = \\\"docker pull uk8s.com/\\\" + name + \\\":\\\" + tag\\n image_list.append(image)\\n return sorted(image_list, reverse=True)\",\n \"def _build_docker_images(self):\\n print(f\\\"+ building {len(self.neurodocker_specs)} Docker images\\\")\\n self.docker_status = []\\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\\n try:\\n print(\\\"++ building image: {}\\\".format(neurodocker_dict))\\n cg.docker_main(\\n self.working_dir,\\n neurodocker_dict,\\n sha1,\\n build_context=self.build_context,\\n )\\n self.docker_status.append(\\\"docker ok\\\")\\n except Exception as e:\\n self.docker_status.append(\\n \\\"failed to build image with SHA1 {}: {}\\\".format(sha1, e)\\n )\",\n \"def main():\\n\\n # get AWS credentials\\n aws_credentials = read_aws_credentials()\\n access_key_id = aws_credentials['access_key_id']\\n secret_access_key = aws_credentials['secret_access_key']\\n aws_region = aws_credentials['region']\\n\\n # build Docker image\\n docker_client = docker.from_env()\\n image, build_log = docker_client.images.build(\\n path='.', tag=LOCAL_REPOSITORY, rm=True)\\n\\n # get AWS ECR login token\\n ecr_client = boto3.client(\\n 'ecr', aws_access_key_id=access_key_id, \\n aws_secret_access_key=secret_access_key, region_name=aws_region)\\n\\n ecr_credentials = (\\n ecr_client\\n .get_authorization_token()\\n ['authorizationData'][0])\\n\\n ecr_username = 'AWS'\\n\\n ecr_password = (\\n base64.b64decode(ecr_credentials['authorizationToken'])\\n .replace(b'AWS:', b'')\\n .decode('utf-8'))\\n\\n ecr_url = ecr_credentials['proxyEndpoint']\\n\\n # get Docker to login/authenticate with ECR\\n docker_client.login(\\n username=ecr_username, password=ecr_password, registry=ecr_url)\\n\\n # tag image for AWS ECR\\n ecr_repo_name = '{}/{}'.format(\\n ecr_url.replace('https://', ''), LOCAL_REPOSITORY)\\n\\n image.tag(ecr_repo_name, tag='latest')\\n\\n # push image to AWS ECR\\n push_log = docker_client.images.push(ecr_repo_name, tag='latest')\\n\\n # force new deployment of ECS service\\n ecs_client = boto3.client(\\n 'ecs', aws_access_key_id=access_key_id,\\n aws_secret_access_key=secret_access_key, region_name=aws_region)\\n\\n ecs_client.update_service(\\n cluster=ECS_CLUSTER, service=ECS_SERVICE, forceNewDeployment=True)\\n\\n return None\",\n \"def pull_image(image):\\n\\n subprocess.check_call(['docker', 'pull', image])\",\n \"def push(self) -> None:\\n\\n with ImportExtensions(required=True):\\n import requests\\n\\n pkg_path = Path(self.args.path)\\n if not pkg_path.exists():\\n self.logger.critical(f'`{self.args.path}` is not a valid path!')\\n exit(1)\\n\\n request_headers = self._get_request_header()\\n\\n try:\\n # archive the executor package\\n with TimeContext(f'Packaging {self.args.path}', self.logger):\\n md5_hash = hashlib.md5()\\n bytesio = archive_package(pkg_path)\\n content = bytesio.getvalue()\\n md5_hash.update(content)\\n\\n md5_digest = md5_hash.hexdigest()\\n\\n # upload the archived package\\n form_data = {\\n 'public': self.args.public if hasattr(self.args, 'public') else False,\\n 'private': self.args.private\\n if hasattr(self.args, 'private')\\n else False,\\n 'md5sum': md5_digest,\\n 'force': self.args.force,\\n 'secret': self.args.secret,\\n }\\n\\n method = 'put' if self.args.force else 'post'\\n\\n hubble_url = get_hubble_url()\\n # upload the archived executor to Jina Hub\\n with TimeContext(\\n f'Pushing to {hubble_url} ({method.upper()})',\\n self.logger,\\n ):\\n resp = getattr(requests, method)(\\n hubble_url,\\n files={'file': content},\\n data=form_data,\\n headers=request_headers,\\n )\\n\\n if 200 <= resp.status_code < 300:\\n # TODO: only support single executor now\\n image = resp.json()['executors'][0]\\n\\n uuid8 = image['id']\\n secret = image['secret']\\n visibility = image['visibility']\\n\\n info_table = [\\n f'\\\\t🔑 ID:\\\\t\\\\t' + colored(f'{uuid8}', 'cyan'),\\n f'\\\\t🔒 Secret:\\\\t'\\n + colored(\\n f'{secret}',\\n 'cyan',\\n )\\n + colored(\\n ' (👈 Please store this secret carefully, it wont show up again)',\\n 'red',\\n ),\\n f'\\\\t👀 Visibility:\\\\t' + colored(f'{visibility}', 'cyan'),\\n ]\\n\\n if 'alias' in image:\\n info_table.append(f'\\\\t📛 Alias:\\\\t' + colored(image['alias'], 'cyan'))\\n\\n self.logger.success(f'🎉 Executor `{pkg_path}` is pushed successfully!')\\n self.logger.info('\\\\n' + '\\\\n'.join(info_table))\\n\\n usage = (\\n f'jinahub://{uuid8}'\\n if visibility == 'public'\\n else f'jinahub://{uuid8}:{secret}'\\n )\\n\\n self.logger.info(f'You can use it via `uses={usage}` in the Flow/CLI.')\\n elif resp.text:\\n # NOTE: sometimes resp.text returns empty\\n raise Exception(resp.text)\\n else:\\n resp.raise_for_status()\\n except Exception as e: # IO related errors\\n self.logger.error(\\n f'Error while pushing `{self.args.path}` with session_id={request_headers[\\\"jinameta-session-id\\\"]}: '\\n f'\\\\n{e!r}'\\n )\",\n \"def push(self, repository, stream=False, raise_on_error=False, **kwargs):\\n response = super(DockerClientWrapper, self).push(repository, stream=stream, **kwargs)\\n if stream:\\n result = self._docker_status_stream(response, raise_on_error)\\n else:\\n result = self._docker_status_stream(response.split('\\\\r\\\\n') if response else (), raise_on_error)\\n return result and not result.get('error')\",\n \"def docker_worker():\",\n \"def build_docker(c):\\n tag = c.run('git describe', hide=True)\\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\\n c.run(f'docker build -t {docker_img} .')\",\n \"def publish_release(self, source, config, target, deis_registry):\\n # get the source repository name and tag\\n src_name, src_tag = docker.utils.parse_repository_tag(source)\\n # get the target repository name and tag\\n name, tag = docker.utils.parse_repository_tag(target)\\n # strip any \\\"http://host.domain:port\\\" prefix from the target repository name,\\n # since we always publish to the Deis registry\\n name = strip_prefix(name)\\n\\n # pull the source image from the registry\\n # NOTE: this relies on an implementation detail of deis-builder, that\\n # the image has been uploaded already to deis-registry\\n if deis_registry:\\n repo = \\\"{}/{}\\\".format(self.registry, src_name)\\n else:\\n repo = src_name\\n self.pull(repo, src_tag)\\n\\n # tag the image locally without the repository URL\\n image = \\\"{}:{}\\\".format(repo, src_tag)\\n self.tag(image, src_name, tag=src_tag)\\n\\n # build a Docker image that adds a \\\"last-mile\\\" layer of environment\\n config.update({'DEIS_APP': name, 'DEIS_RELEASE': tag})\\n self.build(source, config, name, tag)\\n\\n # push the image to deis-registry\\n self.push(\\\"{}/{}\\\".format(self.registry, name), tag)\",\n \"def build_container(\\n self, odcs, repo_type, repo, push_to_defaults, additional_registries, terminate_event,\\n scratch=False, retries=3):\\n if self.org_image_name is None or self.org_version is None:\\n if not os.path.isfile(os.path.join(self.distgit_dir, 'Dockerfile')):\\n self.logger.info('No Dockerfile found in {}'.format(self.distgit_dir))\\n else:\\n self.logger.info('Unknown error loading Dockerfile information')\\n return False\\n\\n action = \\\"build\\\"\\n release = self.org_release if self.org_release is not None else '?'\\n record = {\\n \\\"dir\\\": self.distgit_dir,\\n \\\"dockerfile\\\": \\\"%s/Dockerfile\\\" % self.distgit_dir,\\n \\\"distgit\\\": self.metadata.name,\\n \\\"image\\\": self.org_image_name,\\n \\\"version\\\": self.org_version,\\n \\\"release\\\": release,\\n \\\"message\\\": \\\"Unknown failure\\\",\\n \\\"task_id\\\": \\\"n/a\\\",\\n \\\"task_url\\\": \\\"n/a\\\",\\n \\\"status\\\": -1,\\n \\\"push_status\\\": -1,\\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\\n }\\n\\n target_tag = \\\"-\\\".join((self.org_version, release))\\n target_image = \\\":\\\".join((self.org_image_name, target_tag))\\n\\n try:\\n if not scratch and self.org_release is not None \\\\\\n and self.metadata.tag_exists(target_tag):\\n self.logger.info(\\\"Image already built for: {}\\\".format(target_image))\\n else:\\n # If this image is FROM another group member, we need to wait on that group member\\n # Use .get('from',None) since from is a reserved word.\\n image_from = Model(self.config.get('from', None))\\n if image_from.member is not Missing:\\n self._set_wait_for(image_from.member, terminate_event)\\n for builder in image_from.get('builder', []):\\n if 'member' in builder:\\n self._set_wait_for(builder['member'], terminate_event)\\n\\n # Allow an image to wait on an arbitrary image in the group. This is presently\\n # just a workaround for: https://projects.engineering.redhat.com/browse/OSBS-5592\\n if self.config.wait_for is not Missing:\\n self._set_wait_for(self.config.wait_for, terminate_event)\\n\\n def wait(n):\\n self.logger.info(\\\"Async error in image build thread [attempt #{}]\\\".format(n + 1))\\n # Brew does not handle an immediate retry correctly, wait\\n # before trying another build, terminating if interrupted.\\n if terminate_event.wait(timeout=5 * 60):\\n raise KeyboardInterrupt()\\n\\n exectools.retry(\\n retries=3, wait_f=wait,\\n task_f=lambda: self._build_container(\\n target_image, odcs, repo_type, repo, terminate_event,\\n scratch, record))\\n\\n # Just in case someone else is building an image, go ahead and find what was just\\n # built so that push_image will have a fixed point of reference and not detect any\\n # subsequent builds.\\n push_version, push_release = ('','')\\n if not scratch:\\n _, push_version, push_release = self.metadata.get_latest_build_info()\\n record[\\\"message\\\"] = \\\"Success\\\"\\n record[\\\"status\\\"] = 0\\n self.build_status = True\\n\\n except (Exception, KeyboardInterrupt):\\n tb = traceback.format_exc()\\n record[\\\"message\\\"] = \\\"Exception occurred:\\\\n{}\\\".format(tb)\\n self.logger.info(\\\"Exception occurred during build:\\\\n{}\\\".format(tb))\\n # This is designed to fall through to finally. Since this method is designed to be\\n # threaded, we should not throw an exception; instead return False.\\n finally:\\n # Regardless of success, allow other images depending on this one to progress or fail.\\n self.build_lock.release()\\n\\n self.push_status = True # if if never pushes, the status is True\\n if not scratch and self.build_status and (push_to_defaults or additional_registries):\\n # If this is a scratch build, we aren't going to be pushing. We might be able to determine the\\n # image name by parsing the build log, but not worth the effort until we need scratch builds.\\n # The image name for a scratch build looks something like:\\n # brew-pulp-docker01.web.prod.ext.phx2.redhat.com:8888/openshift3/ose-base:rhaos-3.7-rhel-7-docker-candidate-16066-20170829214444\\n\\n # To ensure we don't overwhelm the system building, pull & push synchronously\\n with self.runtime.mutex:\\n self.push_status = False\\n try:\\n self.push_image([], push_to_defaults, additional_registries, version_release_tuple=(push_version, push_release))\\n self.push_status = True\\n except Exception as push_e:\\n self.logger.info(\\\"Error during push after successful build: %s\\\" % str(push_e))\\n self.push_status = False\\n\\n record['push_status'] = '0' if self.push_status else '-1'\\n\\n self.runtime.add_record(action, **record)\\n return self.build_status and self.push_status\",\n \"def test_force_with_local_and_registry_image(self, mock_docker_environment):\\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def build_all_images_for_release(lang, release):\\n docker_images = []\\n build_jobs = []\\n\\n env = {}\\n # If we not using current tree or the sibling for grpc stack, do checkout.\\n stack_base = \\\"\\\"\\n if args.git_checkout:\\n stack_base = checkout_grpc_stack(lang, release)\\n var = {\\n \\\"go\\\": \\\"GRPC_GO_ROOT\\\",\\n \\\"java\\\": \\\"GRPC_JAVA_ROOT\\\",\\n \\\"node\\\": \\\"GRPC_NODE_ROOT\\\",\\n }.get(lang, \\\"GRPC_ROOT\\\")\\n env[var] = stack_base\\n\\n for runtime in client_matrix.get_runtimes_for_lang_release(lang, release):\\n job = build_image_jobspec(runtime, env, release, stack_base)\\n docker_images.append(job.tag)\\n build_jobs.append(job)\\n\\n jobset.message(\\\"START\\\", \\\"Building interop docker images.\\\", do_newline=True)\\n print(\\\"Jobs to run: \\\\n%s\\\\n\\\" % \\\"\\\\n\\\".join(str(j) for j in build_jobs))\\n\\n num_failures, _ = jobset.run(\\n build_jobs, newline_on_success=True, maxjobs=multiprocessing.cpu_count()\\n )\\n if num_failures:\\n jobset.message(\\n \\\"FAILED\\\", \\\"Failed to build interop docker images.\\\", do_newline=True\\n )\\n docker_images_cleanup.extend(docker_images)\\n sys.exit(1)\\n\\n jobset.message(\\n \\\"SUCCESS\\\",\\n \\\"All docker images built for %s at %s.\\\" % (lang, release),\\n do_newline=True,\\n )\\n\\n if release != \\\"master\\\":\\n commit_log = os.path.join(stack_base, \\\"commit_log\\\")\\n if os.path.exists(commit_log):\\n for image in docker_images:\\n add_files_to_image(image, [commit_log], \\\"release=%s\\\" % release)\\n return docker_images\",\n \"def publish_manifest(ctx, name, tag, image, signed_push=False):\\n manifest_spec = {\\\"image\\\": \\\"{}:{}\\\".format(name, tag)}\\n src_images = []\\n\\n for img in image:\\n img_splitted = img.replace(' ', '').split(',')\\n if len(img_splitted) != 2:\\n print(\\\"Impossible to parse source format for: '{}'\\\".format(img))\\n raise Exit(code=1)\\n\\n platform_splitted = img_splitted[1].split('/')\\n if len(platform_splitted) != 2:\\n print(\\\"Impossible to parse platform format for: '{}'\\\".format(img))\\n raise Exit(code=1)\\n\\n src_images.append(\\n {\\\"image\\\": img_splitted[0], \\\"platform\\\": {\\\"architecture\\\": platform_splitted[1], \\\"os\\\": platform_splitted[0]}}\\n )\\n manifest_spec[\\\"manifests\\\"] = src_images\\n\\n with tempfile.NamedTemporaryFile(mode='w', delete=False) as f:\\n temp_file_path = f.name\\n yaml.dump(manifest_spec, f, default_flow_style=False)\\n\\n print(\\\"Using temp file: {}\\\".format(temp_file_path))\\n ctx.run(\\\"cat {}\\\".format(temp_file_path))\\n\\n try:\\n result = retry_run(ctx, \\\"manifest-tool push from-spec {}\\\".format(temp_file_path))\\n if result.stdout:\\n out = result.stdout.split('\\\\n')[0]\\n fields = out.split(\\\" \\\")\\n\\n if len(fields) != 3:\\n print(\\\"Unexpected output when invoking manifest-tool\\\")\\n raise Exit(code=1)\\n\\n digest_fields = fields[1].split(\\\":\\\")\\n\\n if len(digest_fields) != 2 or digest_fields[0] != \\\"sha256\\\":\\n print(\\\"Unexpected digest format in manifest-tool output\\\")\\n raise Exit(code=1)\\n\\n digest = digest_fields[1]\\n length = fields[2]\\n\\n if signed_push:\\n cmd = \\\"\\\"\\\"\\n notary -s https://notary.docker.io -d {home}/.docker/trust addhash \\\\\\n -p docker.io/{name} {tag} {length} --sha256 {sha256} \\\\\\n -r targets/releases\\n \\\"\\\"\\\"\\n retry_run(ctx, cmd.format(home=os.path.expanduser(\\\"~\\\"), name=name, tag=tag, length=length, sha256=digest))\\n finally:\\n os.remove(temp_file_path)\",\n \"def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None, skip_build=False):\\n value_modifications = {}\\n for name, options in images.items():\\n image_path = options.get('contextPath', os.path.join('images', name))\\n image_tag = tag\\n # include chartpress.yaml itself as it can contain build args and\\n # similar that influence the image that would be built\\n paths = list(options.get('paths', [])) + [image_path, 'chartpress.yaml']\\n last_commit = last_modified_commit(*paths)\\n if tag is None:\\n if chart_version:\\n image_tag = \\\"{}-{}\\\".format(chart_version, last_commit)\\n else:\\n image_tag = last_commit\\n image_name = prefix + name\\n image_spec = '{}:{}'.format(image_name, image_tag)\\n\\n value_modifications[options['valuesPath']] = {\\n 'repository': image_name,\\n 'tag': SingleQuotedScalarString(image_tag),\\n }\\n\\n if skip_build:\\n continue\\n\\n template_namespace = {\\n 'LAST_COMMIT': last_commit,\\n 'TAG': image_tag,\\n }\\n\\n if tag or image_needs_building(image_spec):\\n build_args = render_build_args(options, template_namespace)\\n build_image(image_path, image_spec, build_args, options.get('dockerfilePath'))\\n else:\\n print(f\\\"Skipping build for {image_spec}, it already exists\\\")\\n\\n if push:\\n if tag or image_needs_pushing(image_spec):\\n check_call([\\n 'docker', 'push', image_spec\\n ])\\n else:\\n print(f\\\"Skipping push for {image_spec}, already on registry\\\")\\n return value_modifications\",\n \"def test_image_exists_registry(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n build_image_if_needed(TEST_IMAGE_NAME)\\n mock_docker_environment.api.pull.assert_called_with(\\n TEST_IMAGE_NAME, \\\"latest\\\", decode=True, stream=True\\n )\\n mock_docker_environment.images.build.assert_not_called()\",\n \"def test_force_with_registry_image(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def build_container(client):\\n client.images.build(path=os.path.join(os.path.abspath(\\\"\\\"), \\\"docker\\\"), tag=\\\"scrape_light\\\")\",\n \"def pullall():\\n\\tprint(red('\\\\t\\\\tStarting download of QNIBTerminal images\\\\t\\\\t'))\\n\\t# pull all the needed images\\n\\tdocker_images={'fd20','terminal','helixdns','elk','slurm','compute'}\\n\\tfor image in docker_images:\\n\\t\\tprint 'docker pull qnib/'+image\\n\\t\\t# run('docker pull qnib/'+image)\",\n \"def test_unknown_registry(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n build_image_if_needed(\\\"unknown.registry.com/foo/bar\\\")\\n mock_docker_environment.images.build.assert_called_with(\\n dockerfile=\\\"Dockerfile\\\",\\n tag=\\\"unknown.registry.com/foo/bar:latest\\\",\\n path=\\\"/opt/ixian_docker\\\",\\n )\",\n \"def upload():\\n sh('python setup.py register sdist upload')\",\n \"def test_image_exists_registry_no_pull(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n build_image_if_needed(TEST_IMAGE_NAME, pull=False)\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def pull_base_images(ctx, dockerfile, signed_pull=True):\\n images = set()\\n stages = set()\\n\\n with open(dockerfile, \\\"r\\\") as f:\\n for line in f:\\n words = line.split()\\n # Get source images\\n if len(words) < 2 or words[0].lower() != \\\"from\\\":\\n continue\\n images.add(words[1])\\n # Get stage names to remove them from pull\\n if len(words) < 4 or words[2].lower() != \\\"as\\\":\\n continue\\n stages.add(words[3])\\n\\n if stages:\\n print(\\\"Ignoring intermediate stage names: {}\\\".format(\\\", \\\".join(stages)))\\n images -= stages\\n\\n print(\\\"Pulling following base images: {}\\\".format(\\\", \\\".join(images)))\\n\\n pull_env = {}\\n if signed_pull:\\n pull_env[\\\"DOCKER_CONTENT_TRUST\\\"] = \\\"1\\\"\\n\\n for i in images:\\n ctx.run(\\\"docker pull {}\\\".format(i), env=pull_env)\",\n \"def run_in_docker(image, commands): # pragma: no cover\\n repo_dir = os.path.abspath(os.path.join(__file__, '../../'))\\n mount_option = '{}:/mnt:ro'.format(repo_dir)\\n\\n cmd = ('docker', 'run', '-v', mount_option, '-i', image, 'sh')\\n proc = subprocess.Popen(\\n cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,\\n )\\n\\n lines = '\\\\n'.join(commands)\\n return proc.communicate(lines.encode('utf-8'))[0].decode('utf-8')\",\n \"def push(args):\\n if args.type == 'ssh':\\n cache = set(args.remote_cache).union(set(args.cache))\\n for path in sorted(cache):\\n if os.path.exists(os.path.join(args.base, path)) and not remote_exists(args.sftp, os.path.join(args.remote_base, path)):\\n print('push: {}'.format(path))\\n ensure_remote(args.sftp, os.path.dirname(os.path.join(args.remote_base, path)))\\n args.sftp.put(\\n os.path.join(args.base, path),\\n os.path.join(args.remote_base, path)\\n )\\n args.remote_cache.append(path)\\n args.remote_update = True\\n elif args.type == 's3':\\n raise NotImplementedError('s3:// remote type not yet supported!')\\n elif args.type == 'gs':\\n raise NotImplementedError('gs:// remote type not yet supported!')\\n return\",\n \"def assets_push(ctx, metadata, dir, brizo, price, service_endpoint, timeout):\\n try:\\n files = [f for f in os.listdir(dir) if os.path.isfile(dir+'/'+f)]\\n except NotADirectoryError:\\n files = [dir]\\n\\n response = []\\n metadata = json.load(open(metadata, 'r'))\\n\\n for f in files:\\n metadata['base']['files'][0]['url'] = f\\n response += [ctx.invoke(assets_publish,\\n metadata=metadata,\\n brizo=brizo,\\n price=price,\\n service_endpoint=service_endpoint,\\n timeout=timeout)]\",\n \"def test_push(self, mock_docker_environment, snapshot, capsys):\\n push_image(TEST_IMAGE_NAME)\\n out, err = capsys.readouterr()\\n snapshot.assert_match(out)\",\n \"def cli(ctx, paths, **kwds):\\n\\n def upload(realized_repository):\\n return shed.upload_repository(ctx, realized_repository, **kwds)\\n\\n exit_code = shed.for_each_repository(ctx, upload, paths, **kwds)\\n sys.exit(exit_code)\",\n \"def build_jar_and_push(project, branch):\\n Print.GN('Building JAR and pushing image.')\\n current_env = os.environ.copy()\\n current_env['BRANCH'] = branch\\n subprocess.check_call([\\n os.path.join(SCRIPT_DIR, '../../docker/build.sh'),\\n 'jar',\\n '-p', project,\\n '-d', 'push',\\n '-r', 'gcr',\\n ], env=current_env)\",\n \"def upload():\\n\\n # Our credentials are only available from within the main repository and not forks.\\n # We need to prevent uploads from all BUT the branches in the main repository.\\n # Pull requests and master-branches of forks are not allowed to upload.\\n is_pull_request = (\\n (\\\"TRAVIS_PULL_REQUEST\\\" in os.environ and os.environ[\\\"TRAVIS_PULL_REQUEST\\\"] != \\\"false\\\") or\\n \\\"APPVEYOR_PULL_REQUEST_NUMBER\\\" in os.environ\\n )\\n if is_pull_request:\\n click.echo(\\\"Refusing to upload artifacts from a pull request!\\\")\\n return\\n\\n if \\\"AWS_ACCESS_KEY_ID\\\" in os.environ:\\n subprocess.check_call([\\n \\\"aws\\\", \\\"s3\\\", \\\"cp\\\",\\n \\\"--acl\\\", \\\"public-read\\\",\\n DIST_DIR + \\\"/\\\",\\n \\\"s3://snapshots.mitmproxy.org/{}/\\\".format(UPLOAD_DIR),\\n \\\"--recursive\\\",\\n ])\\n\\n upload_pypi = (\\n TAG and\\n \\\"WHEEL\\\" in os.environ and\\n \\\"TWINE_USERNAME\\\" in os.environ and\\n \\\"TWINE_PASSWORD\\\" in os.environ\\n )\\n if upload_pypi:\\n whl = glob.glob(join(DIST_DIR, 'mitmproxy-*-py3-none-any.whl'))[0]\\n click.echo(\\\"Uploading {} to PyPi...\\\".format(whl))\\n subprocess.check_call([\\n \\\"twine\\\",\\n \\\"upload\\\",\\n whl\\n ])\\n\\n upload_docker = (\\n (TAG or BRANCH == \\\"master\\\") and\\n \\\"DOCKER\\\" in os.environ and\\n \\\"DOCKER_USERNAME\\\" in os.environ and\\n \\\"DOCKER_PASSWORD\\\" in os.environ\\n )\\n if upload_docker:\\n docker_tag = \\\"dev\\\" if BRANCH == \\\"master\\\" else VERSION\\n\\n click.echo(\\\"Uploading Docker image to tag={}...\\\".format(docker_tag))\\n subprocess.check_call([\\n \\\"docker\\\",\\n \\\"login\\\",\\n \\\"-u\\\", os.environ[\\\"DOCKER_USERNAME\\\"],\\n \\\"-p\\\", os.environ[\\\"DOCKER_PASSWORD\\\"],\\n ])\\n subprocess.check_call([\\n \\\"docker\\\",\\n \\\"push\\\",\\n \\\"mitmproxy/mitmproxy:{}\\\".format(docker_tag),\\n ])\",\n \"def push(self):\\n out, err, code = self.command( [\\\"git\\\", \\\"push\\\"], self.directory )\",\n \"def release_docker_image(\\n name: str, version: str, docker_image_prefix: str, exit_on_error: bool = True\\n) -> subprocess.CompletedProcess:\\n # Check if docker exists on the system\\n build_utils.command_exists(\\\"docker\\\", exit_on_error=exit_on_error)\\n\\n if not docker_image_prefix:\\n build_utils.log(\\n \\\"The flag --docker-image-prefix cannot be blank when pushing a Docker image.\\\"\\n )\\n build_utils.exit_process(build_utils.EXIT_CODE_GENERAL)\\n\\n versioned_tag = get_image_name(name=name, tag=version)\\n remote_versioned_tag = get_image_name(\\n name=name, tag=version, image_prefix=docker_image_prefix\\n )\\n build_utils.run(\\n \\\"docker tag \\\" + versioned_tag + \\\" \\\" + remote_versioned_tag,\\n exit_on_error=exit_on_error,\\n )\\n completed_process = build_utils.run(\\n \\\"docker push \\\" + remote_versioned_tag, exit_on_error=exit_on_error\\n )\\n\\n if completed_process.returncode > 0:\\n build_utils.log(f\\\"Failed to release Docker image {name}:{version}\\\")\\n\\n # Only push version with latest tag if no suffix is added (pre-release)\\n if \\\"-\\\" not in version:\\n remote_latest_tag = get_image_name(\\n name=name, tag=\\\"latest\\\", image_prefix=docker_image_prefix\\n )\\n\\n build_utils.log(\\n \\\"Release Docker image with latest tag as well: \\\" + remote_latest_tag\\n )\\n\\n build_utils.run(\\n \\\"docker tag \\\" + versioned_tag + \\\" \\\" + remote_latest_tag,\\n exit_on_error=exit_on_error,\\n )\\n build_utils.run(\\\"docker push \\\" + remote_latest_tag, exit_on_error=exit_on_error)\\n\\n return completed_process\",\n \"def deploy():\\n build()\\n collect()\\n commit()\\n push()\",\n \"def test_k8s_docker_registry_local_and_mirror(\\n self, os_conn, env, configure_docker, environment, murano, session,\\n cluster, pod, package, role, murano_k8s_image_username):\\n docker_cfg_f = '/etc/default/docker'\\n vm, vm_floating_ip, keypair = configure_docker\\n\\n # Add DockerHTTPd configuration\\n environment = murano.get_environment(environment)\\n murano.create_service(environment, session, murano.httpd(pod))\\n\\n # Get updated information about services for deploy\\n all_services = murano.murano.services.list(environment.id, session.id)\\n all_services_dict = [i.to_dict() for i in all_services]\\n\\n # Delete from murano env info about services to update them\\n for service in all_services_dict:\\n murano.delete_service(environment, session, service)\\n\\n # Set \\\"Custom Docker registry URL\\\" and \\\"Docker registry mirror URL\\\" in\\n # \\\"Kubernetes Cluster\\\" config.\\n local_reg_path = '{0}:{1}'.format(\\n vm_floating_ip, self.docker_registry_port)\\n local_mirr_path = 'http://{0}:{1}'.format(\\n vm_floating_ip, self.docker_mirror_port)\\n\\n if role == 'custom_registry':\\n all_services_dict[0]['dockerRegistry'] = local_reg_path\\n elif role == 'registry_mirr':\\n all_services_dict[0]['dockerMirror'] = local_mirr_path\\n else:\\n # both\\n all_services_dict[0]['dockerRegistry'] = local_reg_path\\n all_services_dict[0]['dockerMirror'] = local_mirr_path\\n\\n # Add back updated info about KubernetesCluster to murano env\\n for service in all_services_dict:\\n murano.create_service(environment, session, service)\\n\\n if role in ('custom_registry', 'both'):\\n # Add 'Docker Container' service with local docker image\\n docker_app = murano.docker_app(\\n host=pod,\\n image_location='{0}/local_redmine'.format(vm_floating_ip),\\n port=3000,\\n app_name='DockerImageFromLocalRepo')\\n murano.create_service(environment, session, docker_app)\\n\\n # Deploy environment\\n murano.deploy_environment(environment, session)\\n\\n # Get ssh to nodes\\n vms = os_conn.nova.servers.list()\\n minion_vm = [x for x in vms if 'minion' in x.to_dict()['name']][0]\\n minion_vm_ssh = os_conn.ssh_to_instance(\\n env, minion_vm, vm_keypair=keypair,\\n username=murano_k8s_image_username)\\n vm_ssh = os_conn.ssh_to_instance(\\n env, vm, vm_keypair=keypair, username='ubuntu')\\n\\n # Read docker cfg file from minion node\\n with minion_vm_ssh as remote:\\n remote.check_call('sudo chmod 777 %s' % docker_cfg_f)\\n docker_cfg_out = remote.check_call('cat %s' % docker_cfg_f)\\n docker_cfg_out = docker_cfg_out.stdout_string\\n\\n # Check content of docker cfg file\\n if role in ('custom_registry', 'both'):\\n\\n assert ('--insecure-registry {0}'.format(local_reg_path)\\n in docker_cfg_out), (\\n \\\"Docker local registry link does not present in Docker cfg \\\"\\n \\\"file on Minion node.\\\\n{0}\\\".format(docker_cfg_out))\\n\\n if role in ('registry_mirr', 'both'):\\n\\n assert ('--registry-mirror={0}'.format(local_mirr_path)\\n in docker_cfg_out), (\\n \\\"Docker local mirror link does not present on Docker cfg file\\\"\\n \\\"on Minion node.\\\\n{0}\\\".format(docker_cfg_out))\\n\\n # Check local registry and local mirror\\n with minion_vm_ssh as remote:\\n if role in ('registry_mirr', 'both'):\\n remote.check_call('sudo docker pull wordpress')\\n\\n if role in ('custom_registry', 'both'):\\n remote.check_call('sudo docker pull {0}/local_ubuntu'.format(\\n local_reg_path))\\n\\n # Check logs of a docker registry mirror.\\n # Time compare to pull from internet/mirror is not working as the\\n # difference is very low.\\n with vm_ssh as remote:\\n mirr_logs = remote.check_call('docker logs mirror')\\n mirr_logs = mirr_logs.stdout_string\\n\\n if role in ('registry_mirr', 'both'):\\n assert 'wordpress' in mirr_logs, (\\n \\\"Remote image does not present inside Docker Mirror logs\\\")\\n\\n if role in ('custom_registry', 'both'):\\n assert 'local_ubuntu' not in mirr_logs, (\\n \\\"Local image presents inside Docker Mirror logs\\\")\",\n \"def upload():\\n run('mkdir -p /srv/images/'+env.project_name+'/')\\n rsync_project(\\n env.project_dir, './',\\n exclude=(\\n '.git', '.gitignore', '__pycache__', '*.pyc', '.DS_Store', 'environment.yml',\\n 'fabfile.py', 'Makefile', '.idea', 'bower_components', 'node_modules',\\n '.env.example', 'README.md', 'var'\\n ), delete=True)\",\n \"def test_image_exists_registry_pull_not_found(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n mock_docker_environment.api.pull.side_effect = DockerNotFound(\\\"testing\\\")\\n\\n build_image_if_needed(TEST_IMAGE_NAME)\\n mock_docker_environment.api.pull.assert_called_with(\\n TEST_IMAGE_NAME, \\\"latest\\\", decode=True, stream=True\\n )\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def test_image_exists_local_and_registry(self, mock_docker_environment):\\n build_image_if_needed(TEST_IMAGE_NAME)\\n mock_docker_environment.images.build.assert_not_called()\",\n \"def _Build(self, image):\\n image = _ContainerImage(image)\\n build_start = time.time()\\n if not FLAGS.local_container_build:\\n try:\\n # Build the image remotely using an image building service.\\n self.RemoteBuild(image)\\n self.remote_build_times[image.name] = time.time() - build_start\\n return\\n except NotImplementedError:\\n pass\\n\\n self.PrePush(image)\\n # Build the image locally using docker.\\n build_start = time.time()\\n self.LocalBuildAndPush(image)\\n self.local_build_times[image.name] = time.time() - build_start\",\n \"def docker_build(c):\\n cli_tasks.docker_build.run(c)\",\n \"def add_docker():\\n pass\\n # can i query docker for the location of the docker for the location of the\\n # socket?\\n # `docker info` will return \\\"Docker Root Dir: /var/lib/docker\\\"\\n # check CoreOs project for detail on nvidia-docker socket\",\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 addDocker( self, name, **params ):\\n defaults={'dimage': self.dimage, 'hostExchangeFolder': self.hostExchangeFolder}\\n defaults.update(params)\\n return self.addHost( name, cls=Docker, **defaults )\",\n \"def post_image(img_file):\\n img = np.array(Image.open(img_file))\\n data={\\\"images\\\":img.tolist(),\\\"batch_size\\\":1}\\n json_data=json.dumps(data)\\n img=np.array(json.loads(json_data)[\\\"images\\\"])\\n headers={}\\n URL=\\\"http://127.0.0.1:8080/invocations\\\"\\n response = requests.post(URL, data=json_data, headers=headers)\\n return response\",\n \"def init_actions():\\n os.environ['DOCKER_HOST'] = settings.DOCKER_HOST\\n for url, details in settings.DOCKER_REGISTRIES.items():\\n login_registry(url, details)\",\n \"def _build(registry, image_name, git_sha):\\n\\n image = f\\\"{registry}/{image_name}\\\"\\n\\n # If this image is not in our registry, nothing we can do.\\n if registry != config.DOCKER_REGISTRY:\\n return \\\"\\\"\\n\\n # If image:{git_sha} already exists, then return.\\n logger.info(\\n f\\\"Pulling git_sha tag {image}:{git_sha} to check if it already exists\\\"\\n )\\n if (\\n not args.dry_run\\n and docker_silent.pull(f\\\"{image}:{git_sha}\\\").returncode == 0\\n ):\\n # better to find the hash-{hash} of this image, and return hash\\n # but currently, it is not easy to find all tags of the same image digest through registry API.\\n # so we return image digest instead.\\n digest = get_image_digest(f\\\"{image}:{git_sha}\\\")\\n logger.info(\\n f\\\"git_sha tag {image}:{git_sha} already exists, digest: %s\\\", digest\\n )\\n if not digest:\\n raise Exception(\\\"Failed to get digest for existing image\\\")\\n _tag_to_extra_tags(args, image, git_sha)\\n return digest\\n\\n # Enter build context directory if it is specified\\n build_context = enter_build_context(image_name)\\n\\n # Parse .dockerignore in build context\\n dockerignore_files_set = parse_dockerignore(build_context)\\n\\n # Check if the dockerfile exists\\n dockerfile_path = locate_dockerfile(image_name)\\n if not os.path.isfile(dockerfile_path):\\n logger.error(\\n \\\"%s not exists or is not a file, so %s cannot get build\\\",\\n dockerfile_path,\\n image_name,\\n )\\n raise Exception(\\\"Building cannot continue\\\")\\n\\n dockerfile = Dockerfile(dockerfile_path, build_arg=build_arg)\\n\\n hasher = sha256()\\n\\n # Build parents, and calc parents hash\\n for parent_image in dockerfile.parent_images:\\n (\\n parent_image_registry,\\n parent_image_name,\\n parent_image_tag,\\n ) = parse_docker_image_identity(parent_image)\\n parent_digest = _build(\\n parent_image_registry, parent_image_name, parent_image_tag\\n )\\n if parent_digest is None:\\n raise Exception(f\\\"Failed to get parent_digest for {image}\\\")\\n hasher.update(parent_digest.encode())\\n hash_logger.info(\\n \\\"parent: %s, digest: (%s, %s), hash: %s\\\",\\n parent_image,\\n parent_digest,\\n parent_digest.encode(),\\n hasher.hexdigest(),\\n )\\n\\n # Calc current image files hash\\n\\n def update_file_hash(f):\\n if not os.path.isfile(f):\\n return\\n if f in dockerignore_files_set:\\n hash_logger.debug(\\\"ignore: %s\\\", f)\\n return\\n with open(f, \\\"rb\\\") as open_file:\\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\\n hash_logger.info(\\\"update: %s, hash: %s\\\", f, hasher.hexdigest())\\n\\n srcs = [dockerfile_path] + dockerfile.copied_srcs + dockerfile.added_srcs\\n # TODO: if the src is a url, download it and hash it (even crane didn't do that)\\n for src in srcs:\\n for f in sorted(glob.glob(src)):\\n # We match every file in a directory recursively\\n if os.path.isdir(f):\\n for sub_f in sorted(glob.glob(f\\\"{f}/**\\\", recursive=True)):\\n update_file_hash(sub_f)\\n else:\\n update_file_hash(f)\\n\\n files_hash = hasher.hexdigest()\\n hash_logger.info(\\\"image: %s, hash: %s\\\", image, files_hash)\\n\\n hash_tag = config.FILES_HASH_TAG_PATTERN.format(files_hash=files_hash)\\n # FIXME(harry): hack, remove this\\n old_hash_image = f\\\"docker-registry.example.com:5000/{image_name}:{hash_tag}\\\"\\n\\n logger.info(\\n f\\\"Pulling files_hash tag {image}:{hash_tag} to check if it already exists\\\"\\n )\\n # If image:hash-{hash} already exists,\\n # then content didn't change, return.\\n # We just need to tag it to latest code version.\\n if (\\n not args.dry_run\\n and docker_silent.pull(f\\\"{image}:{hash_tag}\\\").returncode == 0\\n ):\\n logger.info(\\n f\\\"files_hash tag {image}:{hash_tag} already exists, \\\"\\n \\\"it means content didn't change, we can just tag the old image to new git_sha version tag\\\"\\n )\\n # FIXME(harry): hack, remove this\\n elif not args.dry_run and docker_silent.pull(old_hash_image).returncode == 0:\\n logger.info(f\\\"NOTE: files_hash tag {old_hash_image} already exists!\\\")\\n # tag and push this hash image\\n if docker.tag(old_hash_image, f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(\\\"Failed to tag old hash image\\\")\\n return\\n if docker.push(f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(\\\"Failed to push hash_tag image\\\")\\n return\\n # If image:hash-{hash} not exists, then build it from Dockerfile.\\n else:\\n logger.info(\\n f\\\"files_hash tag {image}:{hash_tag} dosen't exists, \\\"\\n \\\"it means content may changed, gonna build it from Dockerfile\\\"\\n )\\n if build_with_raw_command(args, image, dockerfile_path, hash_tag) != 0:\\n logger.error(f\\\"Failed to build {image}:{hash_tag}\\\")\\n return\\n if docker.push(f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(f\\\"Failed to push image\\\")\\n return\\n logger.info(f\\\"image files_hash tag {image}:{hash_tag} is pushed\\\")\\n\\n # tag and push this final image\\n if docker.tag(f\\\"{image}:{hash_tag}\\\", f\\\"{image}:{git_sha}\\\").returncode != 0:\\n logger.error(\\\"Failed to tag image\\\")\\n return\\n _tag_to_extra_tags(args, image, git_sha)\\n if docker.push(f\\\"{image}:{git_sha}\\\").returncode != 0:\\n logger.error(\\\"Failed to push image\\\")\\n return\\n digest = get_image_digest(f\\\"{image}:{git_sha}\\\")\\n if not digest:\\n logger.error(\\\"Failed to get digest for image\\\")\\n return\\n logger.info(f\\\"image {image}:{git_sha} is pushed, digest: {digest}\\\")\\n\\n return digest\",\n \"def local_images(self, images):\\n registries = self.known_docker_registries()\\n found_images = []\\n for image in images:\\n # docker could have the image name as-is or prefixed with any registry\\n imglist = [image] + [reg + \\\"/\\\" + image for reg in registries]\\n if self.is_image_local(imglist):\\n found_images.append(image)\\n return found_images\",\n \"def push(targets, **namespace):\",\n \"def create_image(name: str, distribution: str, apt_repo: str, release_track: str, release_label: str, flavour: str,\\n organization: str, docker_registry: str, rosdistro_path: pathlib.Path, timestamp:str,\\n publish: bool = False):\\n\\n # Read configuration files\\n common_config = yaml.safe_load((rosdistro_path / 'config/recipes.yaml').open())['common']\\n recipe = yaml.safe_load((rosdistro_path / 'config/images.yaml').open())['images']\\n distro = recipe[name]['distro']\\n build_type = recipe[name]['build_type']\\n env = source_file(f'{os.environ[\\\"BUNDLE_ROOT\\\"]}/{distro}/setup.bash')\\n today = timestamp\\n extra_vars: List[Any] = []\\n\\n try:\\n package = recipe[name]['package']\\n provision_file = recipe[name]['provision_file']\\n except KeyError:\\n package = '/tailor-image'\\n provision_file = f'{build_type}.yaml'\\n\\n env['ANSIBLE_CONFIG'] = find_package(package, 'ansible.cfg', env)\\n template_path = f'/tailor-image/environment/image_recipes/{build_type}/{build_type}.json'\\n provision_file_path = find_package(package, 'playbooks/' + provision_file, env)\\n\\n optional_vars = []\\n optional_var_names = ['username', 'password', 'extra_arguments_ansible',\\n 'ansible_command', 'description', 'disk_size', 'group']\\n\\n for var in optional_var_names:\\n if var in recipe[name]:\\n optional_vars.extend(['-var', f'{var}={recipe[name][var]}'])\\n\\n if build_type == 'docker':\\n image_name = f'tailor-image-{name}-{distribution}-{release_label}'\\n docker_registry_data = docker_registry.replace('https://', '').split('/')\\n ecr_server = docker_registry_data[0]\\n ecr_repository = docker_registry_data[1]\\n extra_vars = [\\n '-var', f'type={build_type}',\\n '-var', f'bundle_flavour={flavour}',\\n '-var', f'image_name={image_name}',\\n '-var', f'ecr_server={ecr_server}',\\n '-var', f'os_version={distribution}',\\n '-var', f'ecr_repository={ecr_repository}',\\n '-var', f'aws_access_key={os.environ[\\\"AWS_ACCESS_KEY_ID\\\"]}',\\n '-var', f'aws_secret_key={os.environ[\\\"AWS_SECRET_ACCESS_KEY\\\"]}'\\n ]\\n\\n if not publish:\\n extra_vars += ['-except', 'publish']\\n\\n # Make sure we remove old containers before creting new ones\\n run_command(['docker', 'rm', '-f', 'default'], check=False)\\n\\n elif build_type in ['bare_metal', 'lxd'] and publish:\\n # Get information about base image\\n base_image = recipe[name]['base_image'].replace('$distribution', distribution)\\n\\n # Get disk size to use\\n disk_size = recipe[name].get('disk_size', 9) # In GB\\n\\n # Get base image\\n base_image_local_path = '/tmp/' + base_image\\n base_image_key = release_label + '/images/' + base_image\\n click.echo(f'Downloading image from {base_image_key}')\\n try:\\n boto3.resource('s3').Bucket(apt_repo).download_file(base_image_key, base_image_local_path)\\n except botocore.exceptions.ClientError:\\n click.echo(f'Unable to download base image from {base_image_key}, creating a new one')\\n run_command(['bash',\\n '/tailor-image/environment/create_base_image.bash',\\n f'{base_image_local_path}',\\n f'{distribution}'])\\n boto3.resource('s3').Bucket(apt_repo).upload_file(base_image_local_path, base_image_key)\\n\\n # Enable nbd kernel module, necesary for qemu's packer chroot builder\\n run_command(['modprobe', 'nbd'])\\n\\n # Resize image\\n run_command(['qemu-img', 'resize', base_image_local_path, '30G'])\\n\\n # Copy image\\n tmp_image = base_image_local_path.replace('disk1', 'disk1-resized')\\n run_command(['cp', base_image_local_path, tmp_image])\\n\\n # Resize partition inside qcow image\\n run_command(['virt-resize', '--expand', '/dev/sda1', base_image_local_path, tmp_image])\\n run_command(['mv', tmp_image, base_image_local_path])\\n\\n # Generate image name\\n image_name = f'{organization}_{name}_{distribution}_{release_label}_{today}'\\n\\n extra_vars = [\\n '-var', f'image_name={image_name}',\\n '-var', f's3_bucket={apt_repo}',\\n '-var', f'iso_image={base_image_local_path}',\\n '-var', f'distribution={distribution}',\\n '-var', f'disk_size={disk_size}'\\n ]\\n\\n # Make sure to clean old image builds\\n run_command(['rm', '-rf', '/tmp/images'])\\n\\n elif build_type == 'ami':\\n image_name = f'{organization}_{name}_{distribution}_ami_{release_label}'\\n # Get ami-id for base image\\n source_ami_id = recipe[name]['source_ami'].get(distribution)\\n\\n if not source_ami_id:\\n click.echo(f'You need to specify a bas AMI for the desired distribution {distribution}')\\n sys.exit(1)\\n\\n # Increase fow how long we wait for image to be ready. Default is 30 minutes, sometime it might take longer\\n env['AWS_MAX_ATTEMPTS'] = '90' # minutes\\n env['AWS_POLL_DELAY_SECONDS'] = '60' # Poll for status every minute\\n\\n extra_vars = [\\n '-var', f'build_date={today}',\\n '-var', f'image_name={image_name}',\\n '-var', f'name={name}',\\n '-var', f'source_ami_id={source_ami_id}',\\n '-var', f'distribution={distribution}',\\n '-var', f'release_label={release_label}',\\n '-var', f'aws_access_key={os.environ[\\\"AWS_ACCESS_KEY_ID\\\"]}',\\n '-var', f'aws_secret_key={os.environ[\\\"AWS_SECRET_ACCESS_KEY\\\"]}'\\n ]\\n else:\\n return 0\\n\\n extra_vars.extend(optional_vars)\\n\\n click.echo(f'Building {build_type} image with: {provision_file}', err=True)\\n\\n command = ['packer', 'build',\\n '-var', f'playbook_file={provision_file_path}',\\n '-var', f'organization={organization}',\\n '-var', f'bundle_track={release_track}',\\n '-var', f'bundle_version={release_label}'] + extra_vars + ['-timestamp-ui', template_path]\\n\\n run_command(command, env=env, cwd='/tmp')\\n\\n if build_type in ['bare_metal', 'lxd'] and publish:\\n update_image_index(release_label, apt_repo, common_config, image_name)\",\n \"def commit(self, repository, tag=None, push=False, **kwargs):\\n logger.debug('Committing `%s` with container id %s ...', self.fqdn, self.container.short_id)\\n image = self.container.commit(repository=repository, tag=tag, **kwargs)\\n logger.debug('%s repo tags committed with image id as %s', image.tags, image.short_id)\\n if push:\\n logger.debug('Pushing image of `%s` to repository %s ...', self.fqdn, repository)\\n for line in client.api.push(repository, tag, stream=True, decode=True):\\n line.pop('progressDetail', None) # take out too much detail\\n logger.debug(line)\\n logger.debug('%s repo tags pushed for `%s`, whose image id is %s',\\n image.tags, self.fqdn, image.short_id)\",\n \"def pullDockerImage(client, names):\\n imgNotExistList = []\\n for name in names:\\n try:\\n client.images.pull(name)\\n # some invalid image names will not be pulled but the pull method\\n # will not throw an exception so the only way to confirm if a pull\\n # succeeded is to attempt a docker inspect on the image\\n client.images.get(name)\\n except Exception:\\n imgNotExistList.append(name)\\n if len(imgNotExistList) != 0:\\n raise DockerImageNotFoundError('Could not find multiple images ',\\n image_name=imgNotExistList)\",\n \"def repository_create_hosted_docker(ctx: click.Context, **kwargs):\\n _create_repository(ctx, 'hosted', **kwargs)\",\n \"def push(self):\\n self.runtime.logger.info('Pushing config...')\\n with Dir(self.runtime.metadata_dir):\\n exectools.cmd_assert([\\\"git\\\", \\\"push\\\"])\",\n \"def build_docker(params) -> None:\\n print(\\\"Building docker image...\\\")\\n cmd = \\\"cd bg_changer && docker build --tag bg_changer . >/dev/null 2>&1\\\"\\n if os.system(cmd) == 0:\\n print(\\\" Success !\\\")\\n else:\\n print(\\\" Failure !\\\")\",\n \"def start_upload(self, group_name=None):\\n rmt = BossRemote(cfg_file_or_dict=self.args.config)\\n\\n type_to_dtype = {'image': 'uint16', 'annotation': 'uint64'}\\n\\n img = tf.imread(os.path.expanduser(self.args.tif_stack))\\n if self.args.type == 'annotation' and img.dtype != 'uint64':\\n img = np.asarray(img, dtype='uint64')\\n\\n coll_name = self.args.collection\\n exp_name = self.args.experiment\\n chan_name = self.args.channel\\n source_chan = []\\n\\n if self.args.source_channel != None:\\n source_chan = [self.args.source_channel]\\n\\n # upload image back to boss\\n channel_rsc = self._get_channel_resource(rmt, chan_name, coll_name, exp_name, type=self.args.type, sources=source_chan, datatype=type_to_dtype[self.args.type], new_channel=self.args.new_channel)\\n\\n if img.dtype != 'uint64' or img.dtype != 'uint16':\\n if self.args.type == 'image':\\n img = img.astype('uint16')\\n else:\\n img = img.astype('uint64')\\n\\n if not self.args.chunk:\\n self._upload_to_boss(rmt, img, channel_rsc)\\n else:\\n self._upload_chunk_to_boss(rmt, img, channel_rsc, x_range=self.args.x_range, y_range=self.args.y_range, z_range=self.args.z_range)\\n\\n url = 'https://ndwebtools.neurodata.io/ndviz_url/{}/{}/'.format(coll_name, exp_name)\\n\\n if group_name:\\n self._change_permissions(group_name)\\n\\n return url\",\n \"def build(obj):\\n logger.info(ASCII_LOGO)\\n logger.info(\\\"Started building SageMaker Docker image. It will take some minutes...\\\\n\\\")\\n\\n try:\\n config_file_path = os.path.join('.sagify.json')\\n if not os.path.isfile(config_file_path):\\n raise ValueError()\\n\\n config = ConfigManager(config_file_path).get_config()\\n api_build.build(\\n source_dir=config.sagify_module_dir,\\n requirements_dir=config.requirements_dir,\\n docker_tag=obj['docker_tag'],\\n image_name=config.image_name,\\n python_version=config.python_version)\\n\\n logger.info(\\\"Docker image built successfully!\\\")\\n except ValueError:\\n logger.info(\\\"This is not a sagify directory: {}\\\".format(dir))\\n sys.exit(-1)\\n except subprocess.CalledProcessError as e:\\n logger.debug(e.output)\\n raise\\n except Exception as e:\\n logger.info(\\\"{}\\\".format(e))\\n sys.exit(-1)\",\n \"def test_push_tag(self, mock_docker_environment, snapshot, capsys):\\n mock_docker_environment.api.push = mock.Mock(\\n return_value=event_streams.PUSH_SUCCESSFUL_CUSTOM_TAG\\n )\\n push_image(TEST_IMAGE_NAME, \\\"custom_tag\\\")\\n out, err = capsys.readouterr()\\n snapshot.assert_match(out)\",\n \"def setup(self):\\n\\n exists = [i for i in self.client.images() if self.image in i['RepoTags']]\\n\\n # Only pull the image if we don't have it\\n if not exists or self.pull:\\n self.client.pull(self.image)\\n self.logger.debug(\\\"Pulled {}\\\".format(self.image))\\n\\n self.container = self.client.create_container(\\n image=self.image,\\n host_config=self.host_config,\\n name=self.name,\\n command=self.command,\\n environment=self.environment\\n )\\n self.logger.debug(\\\"Created container {}\\\".format(self.container['Id']))\",\n \"def _ensure_image(testkit_path, branch_name, artifacts_path):\\n # Construct Docker image name from branch name\\n image_name = \\\"runner:%s\\\" % branch_name\\n image_path = os.path.join(testkit_path, \\\"runner_image\\\")\\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\\n\\n return image_name\",\n \"def add_files_to_image(image, with_files, label=None):\\n tag_idx = image.find(\\\":\\\")\\n if tag_idx == -1:\\n jobset.message(\\n \\\"FAILED\\\", \\\"invalid docker image %s\\\" % image, do_newline=True\\n )\\n sys.exit(1)\\n orig_tag = \\\"%s_\\\" % image\\n subprocess.check_output([\\\"docker\\\", \\\"tag\\\", image, orig_tag])\\n\\n lines = [\\\"FROM \\\" + orig_tag]\\n if label:\\n lines.append(\\\"LABEL %s\\\" % label)\\n\\n temp_dir = tempfile.mkdtemp()\\n atexit.register(lambda: subprocess.call([\\\"rm\\\", \\\"-rf\\\", temp_dir]))\\n\\n # Copy with_files inside the tmp directory, which will be the docker build\\n # context.\\n for f in with_files:\\n shutil.copy(f, temp_dir)\\n lines.append(\\\"COPY %s %s/\\\" % (os.path.basename(f), _BUILD_INFO))\\n\\n # Create a Dockerfile.\\n with open(os.path.join(temp_dir, \\\"Dockerfile\\\"), \\\"w\\\") as f:\\n f.write(\\\"\\\\n\\\".join(lines))\\n\\n jobset.message(\\\"START\\\", \\\"Repackaging %s\\\" % image, do_newline=True)\\n build_cmd = [\\\"docker\\\", \\\"build\\\", \\\"--rm\\\", \\\"--tag\\\", image, temp_dir]\\n subprocess.check_output(build_cmd)\\n dockerjob.remove_image(orig_tag, skip_nonexistent=True)\",\n \"def PrePush(self, image):\\n pass\",\n \"def push_sources():\\n ensure_src_dir()\\n push_rev = getattr(env, 'push_rev', None)\\n if push_rev is None:\\n push_rev = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n local(\\\"git tag -a {0} -m \\\\\\\"Tagged for release\\\\\\\"\\\".format(push_rev))\\n local(\\\"git push origin master --tags\\\")\\n\\n with cd(SRC_DIR):\\n run(\\\"git pull origin master\\\")\\n run(\\\"git fetch -t\\\")\\n run(\\\"git checkout {0}\\\".format(push_rev))\",\n \"def pull_image(self):\\n status = []\\n for key, container in self.containers.items():\\n result = container.pull()\\n status.append(result)\\n return status\",\n \"def build_all_images_for_lang(lang):\\n if not args.git_checkout:\\n if args.release != \\\"master\\\":\\n print(\\n \\\"Cannot use --release without also enabling --git_checkout.\\\\n\\\"\\n )\\n sys.exit(1)\\n releases = [args.release]\\n else:\\n if args.release == \\\"all\\\":\\n releases = client_matrix.get_release_tags(lang)\\n else:\\n # Build a particular release.\\n if args.release not in [\\\"master\\\"] + client_matrix.get_release_tags(\\n lang\\n ):\\n jobset.message(\\n \\\"SKIPPED\\\",\\n \\\"%s for %s is not defined\\\" % (args.release, lang),\\n do_newline=True,\\n )\\n return []\\n releases = [args.release]\\n\\n images = []\\n for release in releases:\\n images += build_all_images_for_release(lang, release)\\n jobset.message(\\n \\\"SUCCESS\\\",\\n \\\"All docker images built for %s at %s.\\\" % (lang, releases),\\n do_newline=True,\\n )\\n return images\",\n \"def pull(self, repo, tag):\\n check_blacklist(repo)\\n logger.info(\\\"Pulling Docker image {}:{}\\\".format(repo, tag))\\n with SimpleFlock(self.FLOCKFILE, timeout=1200):\\n stream = self.client.pull(repo, tag=tag, stream=True, insecure_registry=True)\\n log_output(stream)\",\n \"def run(args):\\n docker(' '.join(args))\",\n \"def pull(release):\\n image = f\\\"breqwatr/rsyslog:{release}\\\"\\n ecr.pull(image)\",\n \"def build(path, tag):\\n print (\\\"*** BUILDING IS INITIATED\\\")\\n try:\\n response = client.images.build(path = path, tag=tag, rm=True)\\n except Exception as ex:\\n template = \\\"An exception of type {0} occured. Arguments:\\\\n{1!r}\\\"\\n message = template.format(type(ex).__name__, ex.args)\\n click.echo(message)\\n print(\\\"*** BUILD OUTPUT:\\\")\\n for object in response[1]:\\n click.echo(object)\\n click.echo('*** BUILDING FINISHED')\",\n \"def store_image(self, image_name):\\n self.images.append(image_name)\",\n \"def get_image_registry_url(self, image_name):\\n c = self._oc_command([\\\"get\\\", \\\"is\\\", image_name,\\n \\\"--output=jsonpath=\\\\'{ .status.dockerImageRepository }\\\\'\\\"])\\n try:\\n internal_registry_name = run_cmd(c, return_output=True)\\n except subprocess.CalledProcessError as ex:\\n raise ConuException(\\\"oc get is failed: %s\\\" % ex)\\n\\n logger.info(\\\"Image registry url: %s\\\", internal_registry_name)\\n\\n return internal_registry_name.replace(\\\"'\\\", \\\"\\\").replace('\\\"', '')\",\n \"def test_docker_image_hint(mock_tools):\\n # Mock the return values for Docker verification\\n mock_tools.subprocess.check_output.side_effect = [\\n VALID_DOCKER_VERSION,\\n VALID_DOCKER_INFO,\\n VALID_BUILDX_VERSION,\\n VALID_USER_MAPPING_IMAGE_CACHE,\\n ]\\n\\n Docker.verify(mock_tools, image_tag=\\\"myimage:tagtorulethemall\\\")\\n\\n mock_tools.subprocess.run.assert_has_calls(\\n [\\n call(\\n [\\n \\\"docker\\\",\\n \\\"run\\\",\\n \\\"--rm\\\",\\n \\\"--volume\\\",\\n f\\\"{Path.cwd() / 'build'}:/host_write_test:z\\\",\\n \\\"myimage:tagtorulethemall\\\",\\n \\\"touch\\\",\\n PurePosixPath(\\\"/host_write_test/container_write_test\\\"),\\n ],\\n check=True,\\n ),\\n call(\\n [\\n \\\"docker\\\",\\n \\\"run\\\",\\n \\\"--rm\\\",\\n \\\"--volume\\\",\\n f\\\"{Path.cwd() / 'build'}:/host_write_test:z\\\",\\n \\\"myimage:tagtorulethemall\\\",\\n \\\"rm\\\",\\n \\\"-f\\\",\\n PurePosixPath(\\\"/host_write_test/container_write_test\\\"),\\n ],\\n check=True,\\n ),\\n ]\\n )\",\n \"def snapshot_image_on_provider(self, builder, provider, credentials, target, template, parameters):\",\n \"def prepare():\\n sh('docker build --rm -t {image} {dir}', image=IMAGE, dir=os.path.dirname(__file__))\",\n \"def push(self):\\n origin = self.git_repo.remotes.origin\\n origin.push()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7688454","0.76087564","0.72781956","0.718321","0.715713","0.7090948","0.7030179","0.6809519","0.6794375","0.6744697","0.65739185","0.6560774","0.6509127","0.6483394","0.6415379","0.6381899","0.6256319","0.6209923","0.6139788","0.61235374","0.6070284","0.60335165","0.5997129","0.5994137","0.59526986","0.5939015","0.59345603","0.5889279","0.58881855","0.5880009","0.5864449","0.5852927","0.58053595","0.57974607","0.5797071","0.5796037","0.5772302","0.5736843","0.5728212","0.57088566","0.56986225","0.5696118","0.5684685","0.56659675","0.5623941","0.55962765","0.55911","0.55850154","0.5539568","0.55190897","0.54939437","0.54632103","0.5448859","0.5447893","0.54450583","0.5439672","0.5418431","0.5412045","0.5410755","0.5408817","0.5403548","0.53696924","0.535797","0.53549063","0.53452516","0.5338289","0.53282154","0.53263324","0.5324876","0.5297888","0.5287912","0.526383","0.5263243","0.5256696","0.5256653","0.52507275","0.5248647","0.5239757","0.5233684","0.52229124","0.52120334","0.519863","0.5195656","0.518499","0.5183564","0.51720005","0.5163757","0.516197","0.5161178","0.5154118","0.5153786","0.5147729","0.5145877","0.5145467","0.51364225","0.5121572","0.510929","0.5103487","0.5098642","0.50980747"],"string":"[\n \"0.7688454\",\n \"0.76087564\",\n \"0.72781956\",\n \"0.718321\",\n \"0.715713\",\n \"0.7090948\",\n \"0.7030179\",\n \"0.6809519\",\n \"0.6794375\",\n \"0.6744697\",\n \"0.65739185\",\n \"0.6560774\",\n \"0.6509127\",\n \"0.6483394\",\n \"0.6415379\",\n \"0.6381899\",\n \"0.6256319\",\n \"0.6209923\",\n \"0.6139788\",\n \"0.61235374\",\n \"0.6070284\",\n \"0.60335165\",\n \"0.5997129\",\n \"0.5994137\",\n \"0.59526986\",\n \"0.5939015\",\n \"0.59345603\",\n \"0.5889279\",\n \"0.58881855\",\n \"0.5880009\",\n \"0.5864449\",\n \"0.5852927\",\n \"0.58053595\",\n \"0.57974607\",\n \"0.5797071\",\n \"0.5796037\",\n \"0.5772302\",\n \"0.5736843\",\n \"0.5728212\",\n \"0.57088566\",\n \"0.56986225\",\n \"0.5696118\",\n \"0.5684685\",\n \"0.56659675\",\n \"0.5623941\",\n \"0.55962765\",\n \"0.55911\",\n \"0.55850154\",\n \"0.5539568\",\n \"0.55190897\",\n \"0.54939437\",\n \"0.54632103\",\n \"0.5448859\",\n \"0.5447893\",\n \"0.54450583\",\n \"0.5439672\",\n \"0.5418431\",\n \"0.5412045\",\n \"0.5410755\",\n \"0.5408817\",\n \"0.5403548\",\n \"0.53696924\",\n \"0.535797\",\n \"0.53549063\",\n \"0.53452516\",\n \"0.5338289\",\n \"0.53282154\",\n \"0.53263324\",\n \"0.5324876\",\n \"0.5297888\",\n \"0.5287912\",\n \"0.526383\",\n \"0.5263243\",\n \"0.5256696\",\n \"0.5256653\",\n \"0.52507275\",\n \"0.5248647\",\n \"0.5239757\",\n \"0.5233684\",\n \"0.52229124\",\n \"0.52120334\",\n \"0.519863\",\n \"0.5195656\",\n \"0.518499\",\n \"0.5183564\",\n \"0.51720005\",\n \"0.5163757\",\n \"0.516197\",\n \"0.5161178\",\n \"0.5154118\",\n \"0.5153786\",\n \"0.5147729\",\n \"0.5145877\",\n \"0.5145467\",\n \"0.51364225\",\n \"0.5121572\",\n \"0.510929\",\n \"0.5103487\",\n \"0.5098642\",\n \"0.50980747\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.82243633"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":80396,"cells":{"query":{"kind":"string","value":"execute unit test on docker images"},"document":{"kind":"string","value":"def unittest(tag, path, custom_cli_subnet, custom_db_subnet, builder):\n manager = Manager(\n 'unittest', tag, images=['server'],\n custom_cli_subnet=custom_cli_subnet, custom_db_subnet=custom_db_subnet,\n unittest_path=path, builder_hostname=builder)\n manager.run()"},"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(params=''):\n docker_exec('py.test {}'.format(params))","def test_units(container):\n short_image_name = container.image_name[container.image_name.rfind(\"/\") + 1 :]\n host_data_dir = os.path.join(THIS_DIR, f\"../{short_image_name}/test/units\")\n LOGGER.info(f\"Searching for units tests in {host_data_dir}\")\n cont_data_dir = \"/home/jovyan/data\"\n\n if not os.path.exists(host_data_dir):\n LOGGER.info(f\"Not found unit tests for image: {container.image_name}\")\n return\n\n for test_file in os.listdir(host_data_dir):\n LOGGER.info(f\"Running unit test: {test_file}\")\n\n c = container.run(\n volumes={host_data_dir: {\"bind\": cont_data_dir, \"mode\": \"ro\"}},\n tty=True,\n command=[\"start.sh\", \"python\", f\"{cont_data_dir}/{test_file}\"],\n )\n rv = c.wait(timeout=30)\n logs = c.logs(stdout=True).decode(\"utf-8\")\n LOGGER.debug(logs)\n assert rv == 0 or rv[\"StatusCode\"] == 0","def docker_test(c, rebuild_venv=False):\n cli_tasks.docker_test.run(c, rebuild_venv)","def test_docker_build(rule_runner: RuleRunner) -> None:\n rule_runner.write_files(\n {\n \"src/BUILD\": \"docker_image(name='test-image', image_tags=['1.0'])\",\n \"src/Dockerfile\": \"FROM python:3.8\",\n }\n )\n target = rule_runner.get_target(Address(\"src\", target_name=\"test-image\"))\n result = run_docker(rule_runner, target)\n assert len(result.artifacts) == 1\n assert len(result.artifacts[0].extra_log_lines) == 2\n assert \"Built docker image: test-image:1.0\" == result.artifacts[0].extra_log_lines[0]\n assert \"Docker image ID:\" in result.artifacts[0].extra_log_lines[1]\n assert \"<unknown>\" not in result.artifacts[0].extra_log_lines[1]","def dockerize_test(ctx, binary, skip_cleanup=False):\n import docker\n\n client = docker.from_env()\n temp_folder = tempfile.mkdtemp(prefix=\"ddtest-\")\n\n ctx.run(\"cp %s %s/test.bin\" % (binary, temp_folder))\n\n with open(\"%s/Dockerfile\" % temp_folder, 'w') as stream:\n stream.write(\n \"\"\"FROM debian:stretch-slim\nENV DOCKER_DD_AGENT=yes\nWORKDIR /\nADD https://github.com/docker/compose/releases/download/1.16.1/docker-compose-Linux-x86_64 /bin/docker-compose\nRUN echo \"1804b0ce6596efe707b9cab05d74b161833ed503f0535a937dd5d17bea8fc50a /bin/docker-compose\" > sum && \\\n sha256sum -c sum && \\\n chmod +x /bin/docker-compose\nCMD /test.bin\nCOPY test.bin /test.bin\n\"\"\"\n )\n # Handle optional testdata folder\n if os.path.isdir(\"./testdata\"):\n ctx.run(\"cp -R testdata %s\" % temp_folder)\n stream.write(\"COPY testdata /testdata\")\n\n test_image, _ = client.images.build(path=temp_folder, rm=True)\n\n scratch_volume = client.volumes.create()\n\n test_container = client.containers.run(\n test_image.id,\n detach=True,\n pid_mode=\"host\", # For origin detection\n environment=[\"SCRATCH_VOLUME_NAME=\" + scratch_volume.name, \"SCRATCH_VOLUME_PATH=/tmp/scratch\",],\n volumes={\n '/var/run/docker.sock': {'bind': '/var/run/docker.sock', 'mode': 'ro'},\n '/proc': {'bind': '/host/proc', 'mode': 'ro'},\n '/sys/fs/cgroup': {'bind': '/host/sys/fs/cgroup', 'mode': 'ro'},\n scratch_volume.name: {'bind': '/tmp/scratch', 'mode': 'rw'},\n },\n )\n\n exit_code = test_container.wait()['StatusCode']\n\n print(test_container.logs(stdout=True, stderr=False, stream=False))\n\n sys.stderr.write(test_container.logs(stdout=False, stderr=True, stream=False).decode(sys.stderr.encoding))\n\n if not skip_cleanup:\n shutil.rmtree(temp_folder)\n test_container.remove(v=True, force=True)\n scratch_volume.remove(force=True)\n client.images.remove(test_image.id)\n\n if exit_code != 0:\n raise Exit(code=exit_code)","def test_sx(params=''):\n docker_exec('py.test -sx {}'.format(params))","def test_docker_image_hint(mock_tools):\n # Mock the return values for Docker verification\n mock_tools.subprocess.check_output.side_effect = [\n VALID_DOCKER_VERSION,\n VALID_DOCKER_INFO,\n VALID_BUILDX_VERSION,\n VALID_USER_MAPPING_IMAGE_CACHE,\n ]\n\n Docker.verify(mock_tools, image_tag=\"myimage:tagtorulethemall\")\n\n mock_tools.subprocess.run.assert_has_calls(\n [\n call(\n [\n \"docker\",\n \"run\",\n \"--rm\",\n \"--volume\",\n f\"{Path.cwd() / 'build'}:/host_write_test:z\",\n \"myimage:tagtorulethemall\",\n \"touch\",\n PurePosixPath(\"/host_write_test/container_write_test\"),\n ],\n check=True,\n ),\n call(\n [\n \"docker\",\n \"run\",\n \"--rm\",\n \"--volume\",\n f\"{Path.cwd() / 'build'}:/host_write_test:z\",\n \"myimage:tagtorulethemall\",\n \"rm\",\n \"-f\",\n PurePosixPath(\"/host_write_test/container_write_test\"),\n ],\n check=True,\n ),\n ]\n )","def _docker_run_pytest(\n self,\n test_image: str,\n keep_container: bool,\n test_xml: str,\n no_coverage: bool = False,\n should_disable_network: bool = False,\n ) -> Tuple[int, str, dict]:\n network_status = \"disabled\" if should_disable_network else \"enabled\"\n\n log_prompt = f\"{self._pack_name} - Pytest - Image {test_image}, network: {network_status}\"\n logger.info(f\"{log_prompt} - Start\")\n container_name = f\"{self._pack_name}-pytest\"\n # Check if previous run left container a live if it does, Remove it\n self._docker_remove_container(container_name)\n # Collect tests\n exit_code = SUCCESS\n output = \"\"\n test_json = {}\n try:\n # Running pytest container\n cov_file_path = Path.joinpath(self._pack_abs_dir, \".coverage\")\n cov = self._pack_abs_dir.stem if not no_coverage else \"\"\n uid = os.getuid() or 4000\n logger.debug(\n f\"{log_prompt} - user uid for running lint/test: {uid}\"\n ) # lgtm[py/clear-text-logging-sensitive-data]\n container: docker.models.containers.Container = (\n get_docker().create_container(\n name=container_name,\n image=test_image,\n command=[\n build_pytest_command(test_xml=test_xml, json=True, cov=cov)\n ],\n user=f\"{uid}:4000\",\n files_to_push=[(self._pack_abs_dir, \"/devwork\")],\n environment=self._facts[\"env_vars\"],\n network_disabled=should_disable_network,\n )\n )\n container.start()\n stream_docker_container_output(container.logs(stream=True))\n # Waiting for container to be finished\n container_status: dict = container.wait()\n # Getting container exit code\n container_exit_code = container_status.get(\"StatusCode\")\n # Getting container logs\n logger.info(f\"{log_prompt} - exit-code: {container_exit_code}\")\n if container_exit_code in [0, 1, 2, 5]:\n # 0-All tests passed\n # 1-Tests were collected and run but some of the tests failed\n # 2-Test execution was interrupted by the user\n # 5-No tests were collected\n\n if test_xml:\n test_data_xml = get_file_from_container(\n container_obj=container,\n container_path=\"/devwork/report_pytest.xml\",\n )\n xml_apth = Path(test_xml) / f\"{self._pack_name}_pytest.xml\"\n\n with open(file=xml_apth, mode=\"bw\") as f:\n f.write(test_data_xml) # type: ignore\n\n if cov:\n cov_data = get_file_from_container(\n container_obj=container, container_path=\"/devwork/.coverage\"\n )\n cov_data = (\n cov_data if isinstance(cov_data, bytes) else cov_data.encode()\n )\n with open(cov_file_path, \"wb\") as coverage_file:\n coverage_file.write(cov_data)\n coverage_report_editor(\n cov_file_path,\n os.path.join(\n self._pack_abs_dir, f\"{self._pack_abs_dir.stem}.py\"\n ),\n )\n\n test_json = json.loads(\n get_file_from_container(\n container_obj=container,\n container_path=\"/devwork/report_pytest.json\",\n encoding=\"utf-8\",\n )\n )\n for test in test_json.get(\"report\", {}).get(\"tests\"):\n if test.get(\"call\", {}).get(\"longrepr\"):\n test[\"call\"][\"longrepr\"] = test[\"call\"][\"longrepr\"].split(\"\\n\")\n if container_exit_code in [0, 5]:\n logger.info(f\"{log_prompt} - Successfully finished\")\n exit_code = SUCCESS\n elif container_exit_code in [2]:\n output = container.logs().decode(\"utf-8\")\n exit_code = FAIL\n else:\n logger.error(f\"{log_prompt} - Finished, errors found\")\n exit_code = FAIL\n elif container_exit_code in [3, 4]:\n # 3-Internal error happened while executing tests\n # 4-pytest command line usage error\n logger.critical(f\"{log_prompt} - Usage error\")\n exit_code = RERUN\n output = container.logs().decode(\"utf-8\")\n else:\n # Any other container exit code\n logger.error(\n f\"{log_prompt} - Finished, docker container error found ({container_exit_code})\"\n )\n exit_code = FAIL\n # Remove container if not needed\n if keep_container:\n logger.info(f\"{log_prompt} - Container name {container_name}\")\n container.commit(repository=container_name.lower(), tag=\"pytest\")\n else:\n try:\n container.remove(force=True)\n except docker.errors.NotFound as e:\n logger.critical(f\"{log_prompt} - Unable to remove container {e}\")\n except (docker.errors.ImageNotFound, docker.errors.APIError) as e:\n logger.critical(f\"{log_prompt} - Unable to run pytest container {e}\")\n exit_code = RERUN\n logger.info(f\"{self._pack_name} - Pytest finished image {test_image}\")\n return exit_code, output, test_json","def check():\n cmake('tests')\n docker('./{build}/tests', build=BUILD)","def main():\n extensions = os.getenv('EXTENSIONS', DEFAULT_EXTENSIONS).split(',')\n extensions.sort()\n docker_contents = []\n contents = travis_contents()\n data = yaml.safe_load(contents)\n\n # set the version\n php_versions = data.get('php', [DEFAULT_VERSION])\n php_version = php_versions[0]\n docker_contents.append('FROM php:{0}'.format(php_version))\n\n # ensure all the php shit exists\n\n # LC_ALL=en_US.UTF-8\n docker_contents.append('ENV DEBIAN_FRONTEND=noninteractive LC_ALL=C DOCKER=1') # noqa\n docker_contents.append('RUN apt-get update')\n docker_contents.append('RUN apt-get -qq install -qq -y php5-cli php-pear')\n # for composer\n docker_contents.append('RUN apt-get -qq install -qq -y git-core')\n # for curl\n docker_contents.append('RUN apt-get -qq install -qq -y libcurl4-openssl-dev')\n # for intl\n docker_contents.append('RUN apt-get -qq install -qq -y libicu-dev')\n\n # installs user-specified packages\n packages = os.getenv('PACKAGES', '')\n if len(os.getenv('PACKAGES', '')) > 0:\n packages = packages.split(',')\n docker_contents.append('RUN apt-get -qq install -qq -y {0}'.format(\n ' '.join(packages)\n ))\n\n for extension in extensions:\n if extension in available_extensions:\n docker_contents.append('RUN docker-php-ext-install {0}'.format(\n extension\n ))\n else:\n docker_contents.append('RUN apt-get -qq install -qq -y php5-{0} && pecl install -o -f {0} && \\\\'.format(extension))\n docker_contents.append(' rm -rf /tmp/pear && \\\\')\n if extension in ZEND_EXTENSIONS:\n docker_contents.append(' echo \"zend_extension=/usr/local/lib/php/extensions/no-debug-non-zts-{0}/xdebug.so\" > /usr/local/etc/php/conf.d/{1}.ini'.format(\n phpextension_paths[php_version],\n extension\n ))\n else:\n docker_contents.append(' echo \"extension={0}.so\" > /usr/local/etc/php/conf.d/{0}.ini'.format(extension))\n\n # ensure we have all the proper php testing stuff\n docker_contents.append('RUN \\\\')\n docker_contents.append(' curl -sSL https://phar.phpunit.de/phpunit-old.phar > phpunit.phar && \\\\')\n docker_contents.append(' curl -sS https://getcomposer.org/installer | php && \\\\')\n docker_contents.append(' mv composer.phar /usr/local/bin/composer && \\\\')\n docker_contents.append(' mv phpunit.phar /usr/local/bin/phpunit && \\\\')\n docker_contents.append(' chmod +x /usr/local/bin/composer /usr/local/bin/phpunit && \\\\')\n docker_contents.append(' phpunit --version')\n\n # set the environment\n environments = data.get('env', {'matrix': 'CI=1'}).get('matrix', [])\n docker_env = environments[0]\n docker_contents.append('ENV {0}'.format(docker_env))\n\n docker_contents.append('ADD composer.json /app/composer.json')\n docker_contents.append('WORKDIR /app')\n docker_contents.append('RUN echo \"date.timezone = UTC\" > /usr/local/etc/php/conf.d/timezone.ini') # noqa\n\n for script in data.get('before_script', []):\n docker_contents.append('RUN {0}'.format(script))\n\n docker_contents.append('ADD . /app')\n\n # HACK\n docker_contents.append('ENV COVERALLS=1 DEFAULT=1 PHPCS=1')\n\n for script in data.get('script', []):\n docker_contents.append('RUN {0}'.format(script))\n\n with open('{0}/Dockerfile'.format(os.getcwd()), 'w') as f:\n for line in docker_contents:\n f.write(\"{0}\\n\\n\".format(line))","def test_in_docker_machine(self):\n new_executor = self.executor.in_docker_machine('confluent')\n output, _err = new_executor.docker.run('a-machine:a-tag', remove=executor.NO_VALUE,\n interactive=executor.NO_VALUE,\n terminal=executor.NO_VALUE).batch()\n self.assertEqual(output, 'hello\\r\\n')","def integration_tests(ctx, skip_image_build=False, skip_build=False, python_command=\"python3\"):\n if not skip_image_build:\n # postpone the import otherwise `image_build` will be added to the docker\n # namespace\n from .dogstatsd import image_build\n\n image_build(ctx, skip_build=skip_build)\n\n print(\"Starting docker integration tests\")\n env = {\"DOCKER_IMAGE\": DOGSTATSD_TAG}\n ctx.run(\"{} ./test/integration/docker/dsd_listening.py\".format(python_command), env=env)","def run_sagemaker_tests(images):\n if not images:\n return\n pool_number = len(images)\n with Pool(pool_number) as p:\n p.map(run_sagemaker_pytest_cmd, images)","def execute_local_tests(image, pytest_cache_params):\n account_id = os.getenv(\"ACCOUNT_ID\", boto3.client(\"sts\").get_caller_identity()[\"Account\"])\n pytest_cache_util = PytestCache(boto3.client(\"s3\"), account_id)\n ec2_client = boto3.client(\"ec2\", config=Config(retries={\"max_attempts\": 10}), region_name=DEFAULT_REGION)\n pytest_command, path, tag, job_type = generate_sagemaker_pytest_cmd(image, SAGEMAKER_LOCAL_TEST_TYPE)\n pytest_command += \" --last-failed --last-failed-no-failures all \"\n print(pytest_command)\n framework, _ = get_framework_and_version_from_tag(image)\n random.seed(f\"{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\")\n ec2_key_name = f\"{job_type}_{tag}_sagemaker_{random.randint(1, 1000)}\"\n region = os.getenv(\"AWS_REGION\", DEFAULT_REGION)\n ec2_ami_id = UBUNTU_18_BASE_DLAMI_US_EAST_1 if region == \"us-east-1\" else UBUNTU_18_BASE_DLAMI_US_WEST_2\n sm_tests_tar_name = \"sagemaker_tests.tar.gz\"\n ec2_test_report_path = os.path.join(UBUNTU_HOME_DIR, \"test\", f\"{job_type}_{tag}_sm_local.xml\")\n instance_id = \"\"\n ec2_conn = None\n try:\n key_file = generate_ssh_keypair(ec2_client, ec2_key_name)\n print(f\"Launching new Instance for image: {image}\")\n instance_id, ip_address = launch_sagemaker_local_ec2_instance(\n image,\n ec2_ami_id,\n ec2_key_name,\n region\n )\n ec2_conn = ec2_utils.get_ec2_fabric_connection(instance_id, key_file, region)\n ec2_conn.put(sm_tests_tar_name, f\"{UBUNTU_HOME_DIR}\")\n ec2_conn.run(f\"$(aws ecr get-login --no-include-email --region {region})\")\n try:\n ec2_conn.run(f\"docker pull {image}\", timeout=600)\n except invoke.exceptions.CommandTimedOut as e:\n output = ec2_conn.run(f\"docker images {image} --format '{{.Repository}}:{{.Tag}}'\").stdout.strip(\"\\n\")\n if output != image:\n raise DLCSageMakerLocalTestFailure(\n f\"Image pull for {image} failed.\\ndocker images output = {output}\"\n ) from e\n ec2_conn.run(f\"tar -xzf {sm_tests_tar_name}\")\n kill_background_processes_and_run_apt_get_update(ec2_conn)\n with ec2_conn.cd(path):\n install_sm_local_dependencies(framework, job_type, image, ec2_conn, ec2_ami_id)\n pytest_cache_util.download_pytest_cache_from_s3_to_ec2(ec2_conn, path, **pytest_cache_params)\n # Workaround for mxnet cpu training images as test distributed\n # causes an issue with fabric ec2_connection\n if framework == \"mxnet\" and job_type == \"training\" and \"cpu\" in image:\n try:\n ec2_conn.run(pytest_command, timeout=1000, warn=True)\n except exceptions.CommandTimedOut as exc:\n print(f\"Ec2 connection timed out for {image}, {exc}\")\n finally:\n print(f\"Downloading Test reports for image: {image}\")\n ec2_conn.close()\n ec2_conn_new = ec2_utils.get_ec2_fabric_connection(instance_id, key_file, region)\n ec2_conn_new.get(ec2_test_report_path,\n os.path.join(\"test\", f\"{job_type}_{tag}_sm_local.xml\"))\n output = subprocess.check_output(f\"cat test/{job_type}_{tag}_sm_local.xml\", shell=True,\n executable=\"/bin/bash\")\n pytest_cache_util.upload_pytest_cache_from_ec2_to_s3(ec2_conn_new, path, **pytest_cache_params)\n if 'failures=\"0\"' not in str(output):\n raise ValueError(f\"Sagemaker Local tests failed for {image}\")\n else:\n ec2_conn.run(pytest_command)\n print(f\"Downloading Test reports for image: {image}\")\n ec2_conn.get(ec2_test_report_path, os.path.join(\"test\", f\"{job_type}_{tag}_sm_local.xml\"))\n finally:\n with ec2_conn.cd(path):\n pytest_cache_util.upload_pytest_cache_from_ec2_to_s3(ec2_conn, path, **pytest_cache_params)\n print(f\"Terminating Instances for image: {image}\")\n ec2_utils.terminate_instance(instance_id, region)\n print(f\"Destroying ssh Key_pair for image: {image}\")\n destroy_ssh_keypair(ec2_client, ec2_key_name)\n # return None here to prevent errors from multiprocessing.map(). Without this it returns some object by default\n # which is causing \"cannot pickle '_thread.lock' object\" error\n return None","def test_build_artifacts_invokes_docker_commands(mocker):\n\n tmp_filename = '/var/folders/xw/yk2rrhks1w72y0zr_7t7b851qlt8b3/T/tmp52bd77s3'\n mock_builder = mocker.patch('juniper.actions.build_compose', return_value=tmp_filename)\n\n # Mocking the dependencies of this action. These three high level packages are\n # needed to invoke docker-compose in the right context!\n mocker.patch('juniper.actions.os')\n mocker.patch('juniper.actions.shutil')\n mock_subprocess_run = mocker.patch('juniper.actions.subprocess.run')\n\n compose_cmd_calls = [\n mocker.call([\"docker-compose\", \"-f\", tmp_filename, '--project-directory', '.', 'down']),\n mocker.call([\"docker-compose\", \"-f\", tmp_filename, '--project-directory', '.', 'up'])\n ]\n\n processor_ctx = reader('./tests/manifests/processor-test.yml')\n actions.build_artifacts(logger, processor_ctx)\n\n mock_subprocess_run.assert_has_calls(compose_cmd_calls)\n mock_builder.assert_called_once()","def run_in_docker(image, commands): # pragma: no cover\n repo_dir = os.path.abspath(os.path.join(__file__, '../../'))\n mount_option = '{}:/mnt:ro'.format(repo_dir)\n\n cmd = ('docker', 'run', '-v', mount_option, '-i', image, 'sh')\n proc = subprocess.Popen(\n cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,\n )\n\n lines = '\\n'.join(commands)\n return proc.communicate(lines.encode('utf-8'))[0].decode('utf-8')","def test_go_docker(mocker):\n mocked_function = mocker.patch(\"os.system\")\n go_docker()\n mocked_function.assert_called_once_with(\"docker-compose up -d\")","def test(tag_name: str):\n\n image_full_name = f\"{GITLAB_IMAGE_URL}:{tag_name}-test\"\n _build(tag_name=tag_name, image_full_name=image_full_name)\n _test(image_full_name=image_full_name)","def build_test_version_of_container_image(\n image_type: ImageType,\n image_builder_cls: Type[ContainerisedAgentBuilder],\n architecture: CpuArch,\n result_image_name: str,\n ready_image_oci_tarball: pl.Path = None,\n install_additional_test_libs: bool = True,\n):\n\n image_builder = image_builder_cls()\n\n registry_container_name = \"agent_image_e2e_test_registry\"\n\n delete_container(container_name=registry_container_name)\n\n # Create temporary local registry to push production image there.\n subprocess.run(\n [\n \"docker\",\n \"run\",\n \"-d\",\n \"--rm\",\n \"-p=5000:5000\",\n f\"--name={registry_container_name}\",\n \"registry:2\",\n ],\n check=True,\n )\n try:\n all_image_tags = image_builder.generate_final_registry_tags(\n image_type=image_type,\n registry=\"localhost:5000\",\n name_prefix=\"user\",\n tags=[\"prod\"],\n )\n\n # Publish image to the local registry\n image_builder.publish(\n image_type=image_type,\n tags=all_image_tags,\n existing_oci_layout_tarball=ready_image_oci_tarball,\n no_verify_tls=True,\n )\n\n prod_image_tag = all_image_tags[0]\n if install_additional_test_libs:\n\n # Build agent image requirements, because it also includes requirements (like coverage) for testing.\n requirement_libs_dir = image_builder.build_requirement_libs(\n architecture=architecture,\n )\n\n # Build testable image.\n buildx_build(\n dockerfile_path=_PARENT_DIR / \"Dockerfile\",\n context_path=_PARENT_DIR,\n architectures=[architecture],\n build_contexts={\n \"prod_image\": f\"docker-image://{prod_image_tag}\",\n \"requirement_libs\": str(requirement_libs_dir),\n },\n output=DockerImageBuildOutput(\n name=result_image_name,\n ),\n )\n else:\n subprocess.run(\n [\n \"docker\",\n \"pull\",\n prod_image_tag,\n ],\n check=True,\n )\n\n subprocess.run(\n [\n \"docker\",\n \"tag\",\n prod_image_tag,\n result_image_name,\n ],\n check=True,\n )\n finally:\n delete_container(container_name=registry_container_name)\n\n return result_image_name","def execute_sagemaker_remote_tests(image):\n pytest_command, path, tag, job_type = generate_sagemaker_pytest_cmd(image, SAGEMAKER_REMOTE_TEST_TYPE)\n context = Context()\n with context.cd(path):\n context.run(f\"virtualenv {tag}\")\n with context.prefix(f\"source {tag}/bin/activate\"):\n context.run(\"pip install -r requirements.txt\", warn=True)\n res = context.run(pytest_command, warn=True)\n metrics_utils.send_test_result_metrics(res.return_code)\n if res.failed:\n raise DLCSageMakerRemoteTestFailure(\n f\"{pytest_command} failed with error code: {res.return_code}\\n\"\n f\"Traceback:\\n{res.stdout}\"\n )","def ping_docker():\n with Docker('unittest-36', image='python:3.6') as tun:\n return tun.call(python_version)[:2]","def test_docker_api(proc):\n assert len(proc.docker_container_id)\n assert proc.docker_inspect()['Id'].startswith(proc.docker_container_id)\n assert proc.docker_stats()['Container'] == proc.docker_container_id","def test_run_container1(client):\n with client.application.app_context():\n pytest.skip(\"Not implemented\")","def _run_lint_on_docker_image(\n self,\n no_pylint: bool,\n no_test: bool,\n no_pwsh_analyze: bool,\n no_pwsh_test: bool,\n keep_container: bool,\n test_xml: str,\n no_coverage: bool,\n no_flake8: bool,\n no_vulture: bool,\n should_disable_network: bool,\n ):\n log_prompt = f\"{self._pack_name} - Run Lint On Docker Image\"\n if self._facts[\"images\"]:\n logger.info(\n f'{log_prompt} - Running lint: Number of images={self._facts[\"images\"]}'\n )\n else:\n logger.info(f\"{log_prompt} - Skipped\")\n for image in self._facts[\"images\"]:\n logger.info(f\"{log_prompt} - Running lint on docker image {image[0]}\")\n # Docker image status - visualize\n status = {\n \"image\": image[0],\n \"image_errors\": \"\",\n \"pylint_errors\": \"\",\n \"pytest_errors\": \"\",\n \"pytest_json\": {},\n \"pwsh_analyze_errors\": \"\",\n \"pwsh_test_errors\": \"\",\n }\n # Creating image if pylint specified or found tests and tests specified\n image_id = \"\"\n errors = \"\"\n for trial in range(2):\n image_id, errors = self._docker_image_create(docker_base_image=image)\n if not errors:\n break\n\n if image_id and not errors:\n # Set image creation status\n for check in [\n \"pylint\",\n \"pytest\",\n \"pwsh_analyze\",\n \"pwsh_test\",\n \"flake8\",\n \"vulture\",\n ]:\n exit_code = SUCCESS\n output = \"\"\n for trial in range(2):\n if self._pkg_lint_status[\"pack_type\"] == TYPE_PYTHON:\n if (\n not no_flake8\n and check == \"flake8\"\n and (\n self._facts[\"lint_files\"]\n or self._facts[\"lint_unittest_files\"]\n )\n ):\n exit_code, output = self._docker_run_linter(\n linter=check,\n test_image=image_id,\n keep_container=keep_container,\n )\n if (\n not no_vulture\n and check == \"vulture\"\n and self._facts[\"lint_files\"]\n ):\n exit_code, output = self._docker_run_linter(\n linter=check,\n test_image=image_id,\n keep_container=keep_container,\n )\n # Perform pylint\n if (\n not no_pylint\n and check == \"pylint\"\n and self._facts[\"lint_files\"]\n ):\n exit_code, output = self._docker_run_linter(\n linter=check,\n test_image=image_id,\n keep_container=keep_container,\n )\n # Perform pytest\n elif (\n not no_test\n and self._facts[\"test\"]\n and check == \"pytest\"\n ):\n exit_code, output, test_json = self._docker_run_pytest(\n test_image=image_id,\n keep_container=keep_container,\n test_xml=test_xml,\n no_coverage=no_coverage,\n should_disable_network=should_disable_network,\n )\n status[\"pytest_json\"] = test_json\n elif self._pkg_lint_status[\"pack_type\"] == TYPE_PWSH:\n # Perform powershell analyze\n if (\n not no_pwsh_analyze\n and check == \"pwsh_analyze\"\n and self._facts[\"lint_files\"]\n ):\n exit_code, output = self._docker_run_pwsh_analyze(\n test_image=image_id, keep_container=keep_container\n )\n # Perform powershell test\n elif not no_pwsh_test and check == \"pwsh_test\":\n exit_code, output = self._docker_run_pwsh_test(\n test_image=image_id, keep_container=keep_container\n )\n # If lint check perform and failed on reason related to environment will run twice,\n # But it failing in second time it will count as test failure.\n if (\n (exit_code == RERUN and trial == 1)\n or exit_code == FAIL\n or exit_code == SUCCESS\n ):\n if exit_code in [RERUN, FAIL]:\n if check in {\"flake8\", \"vulture\"}:\n self._handle_lint_results(exit_code, check, output)\n else:\n self._pkg_lint_status[\"exit_code\"] |= EXIT_CODES[\n check\n ]\n status[f\"{check}_errors\"] = output\n break\n else:\n status[\"image_errors\"] = str(errors)\n self._pkg_lint_status[\"exit_code\"] += EXIT_CODES[\"image\"]\n # Add image status to images\n self._pkg_lint_status[\"images\"].append(status)\n logger.info(f\"{log_prompt} - Finished linting on docker image {image[0]}\")\n\n if self._facts[\"images\"]:\n logger.info(\n f'{log_prompt} - Finished linting. Number of images={self._facts[\"images\"]}'\n )","def test_create_container(self):\n pass","def test_build_image(self):\n labels = {\"apple\": \"red\", \"grape\": \"green\"}\n self.docker.images.build(\n path=\"test/python/docker/build_labels\",\n labels=labels,\n tag=\"labels\",\n isolation=\"default\",\n )\n image = self.docker.images.get(\"labels\")\n self.assertEqual(image.labels[\"apple\"], labels[\"apple\"])\n self.assertEqual(image.labels[\"grape\"], labels[\"grape\"])","def run(args):\n docker(' '.join(args))","def _get_dockerfiles_for_test() -> str:\n project_root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n dockerfiles_dir = os.path.join(project_root_dir, \"dockerfiles\")\n if sys.version_info[0:2] == (3, 6):\n return os.path.join(dockerfiles_dir, \"centos7.Dockerfile\")\n elif sys.version_info[0:2] == (3, 9):\n return os.path.join(dockerfiles_dir, \"rocky8.Dockerfile\")\n else:\n raise Exception(\n \"Running the tests with INMANTA_TEST_INFRA_SETUP=true is only supported using a python3.6 or python3.9 venv\"\n )","def test_run_container2(client):\n with client.application.app_context():\n pytest.skip(\"Not implemented\")","def test_node_start(self, m_attach_and_stream,\n m_find_or_pull_node_image, m_docker,\n m_docker_client, m_client, m_install_kube,\n m_warn_if_hostname_conflict, m_warn_if_unknown_ip,\n m_get_host_ips, m_check_system, m_os_getenv,\n m_os_makedirs, m_os_path_exists):\n # Set up mock objects\n m_os_path_exists.return_value = False\n ip_1 = '1.1.1.1'\n ip_2 = '2.2.2.2'\n m_get_host_ips.return_value = [ip_1, ip_2]\n m_os_getenv.side_effect = iter(['1.1.1.1:80', \"\"])\n m_docker.utils.create_host_config.return_value = 'host_config'\n container = {'Id':666}\n m_docker_client.create_container.return_value = container\n\n # Set up arguments\n node_image = 'node_image'\n log_dir = './log_dir'\n ip = ''\n ip6 = 'aa:bb::zz'\n as_num = ''\n detach = False\n kubernetes = True\n\n # Call method under test\n node.node_start(\n node_image, log_dir, ip, ip6, as_num, detach, kubernetes\n )\n\n # Set up variables used in assertion statements\n environment = [\n \"HOSTNAME=%s\" % node.hostname,\n \"IP=%s\" % ip_2,\n \"IP6=%s\" % ip6,\n \"ETCD_AUTHORITY=1.1.1.1:80\", # etcd host:port\n \"FELIX_ETCDADDR=1.1.1.1:80\", # etcd host:port\n \"POLICY_ONLY_CALICO=\",\n ]\n binds = {\n \"/proc\":\n {\n \"bind\": \"/proc_host\",\n \"ro\": False\n },\n log_dir:\n {\n \"bind\": \"/var/log/calico\",\n \"ro\": False\n },\n \"/run/docker/plugins\":\n {\n \"bind\": \"/usr/share/docker/plugins\",\n \"ro\": False\n }\n }\n\n # Assert\n m_os_path_exists.assert_called_once_with(log_dir)\n m_os_makedirs.assert_called_once_with(log_dir)\n m_check_system.assert_called_once_with(fix=False, quit_if_error=False)\n m_get_host_ips.assert_called_once_with(exclude=[\"docker0\"])\n m_warn_if_unknown_ip.assert_called_once_with(ip_2, ip6)\n m_warn_if_hostname_conflict.assert_called_once_with(ip_2)\n m_install_kube.assert_called_once_with(node.KUBERNETES_PLUGIN_DIR)\n m_client.get_ip_pools.assert_has_calls([call(4), call(6)])\n m_client.ensure_global_config.assert_called_once_with()\n m_client.create_host.assert_called_once_with(\n node.hostname, ip_2, ip6, as_num\n )\n m_docker_client.remove_container.assert_called_once_with(\n 'calico-node', force=True\n )\n\n getenv_calls = [call(ETCD_AUTHORITY_ENV, ETCD_AUTHORITY_DEFAULT),\n call(node.POLICY_ONLY_ENV, \"\")]\n m_os_getenv.assert_has_calls(getenv_calls)\n\n m_docker.utils.create_host_config.assert_called_once_with(\n privileged=True,\n restart_policy={\"Name\":\"Always\"},\n network_mode=\"host\",\n binds=binds\n )\n m_find_or_pull_node_image.assert_called_once_with(\n 'node_image'\n )\n m_docker_client.create_container.assert_called_once_with(\n node_image,\n name='calico-node',\n detach=True,\n environment=environment,\n host_config='host_config',\n volumes=['/proc_host',\n '/var/log/calico',\n '/usr/share/docker/plugins']\n )\n m_docker_client.start.assert_called_once_with(container)\n m_attach_and_stream.assert_called_once_with(container)","def test_basic_run(self):\n self.expect_datatore_lookup('SomeBlobKey', True)\n self.expect_open_image('SomeBlobKey', (1600, 1200))\n self.expect_resize(blob_image._DEFAULT_SERVING_SIZE)\n self.expect_encode_image('SomeImageInJpeg')\n self.run_request('image/jpeg', 'SomeImageInJpeg')","def run(config, webhook, output):\n start_time = datetime.now()\n test_project_name = f'test-{Global.docker_project_name(webhook)}-{config[\"language\"]}-{config[\"version\"]}'\n context = 'test'\n\n # Build the image\n try:\n image = Image.build_image(config, webhook, context)\n image_output = f'Test image created.\\n{image}'\n print(image_output)\n except subprocess.TimeoutExpired:\n final_output = 'Error: Harvey timed out building the Test image.'\n print(final_output)\n Utils.kill(final_output, webhook)\n except subprocess.CalledProcessError as error:\n final_output = f'{output}\\nError: Harvey could not build the Test image.\\n{error.output}'\n print(final_output)\n Utils.kill(final_output, webhook)\n\n # Create a container\n container = Container.create_container(test_project_name)\n if container:\n container_output = 'Test container created.'\n print(container_output)\n else:\n final_output = f'{output}\\n{image_output}\\nError: Harvey could not create the Test container.'\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\n Image.remove_image(test_project_name)\n Utils.kill(final_output, webhook)\n\n # Start the container\n start = Container.start_container(test_project_name)\n if start:\n start_output = 'Test container started.'\n print(start_output)\n else:\n final_output = f'{output}\\n{image_output}\\n{container_output}\\nError: Harvey could not start the container.'\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\n Image.remove_image(image[0])\n Container.remove_container(test_project_name)\n Utils.kill(final_output, webhook)\n\n # Wait for container to exit\n wait = Container.wait_container(test_project_name)\n if wait:\n wait_output = 'Waiting for Test container to exit.'\n print(wait_output)\n else:\n final_output = (\n f'{output}'\n f'\\n{image_output}'\n f'\\n{container_output}'\n f'\\n{start_output}'\n '\\nError: Harvey could not wait for the container.'\n )\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\n Image.remove_image(image[0])\n Container.remove_container(test_project_name)\n Utils.kill(final_output, webhook)\n\n # Return logs\n logs = Container.inspect_container_logs(test_project_name)\n if logs:\n logs_output = (\n '\\nTest logs:\\n'\n '============================================================\\n'\n f'{logs.text}'\n '============================================================\\n'\n )\n print(logs_output)\n else:\n final_output = (\n f'{output}'\n f'\\n{image_output}'\n f'\\n{container_output}'\n f'\\n{start_output}'\n f'\\n{wait_output}'\n '\\nError: Harvey could not create the container logs.'\n )\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\n Image.remove_image(image[0])\n Container.remove_container(test_project_name)\n Utils.kill(final_output, webhook)\n\n # Remove container and image after it's done\n remove = Container.remove_container(test_project_name)\n if remove:\n Image.remove_image(image[0])\n remove_output = 'Test container and image removed.'\n print(remove_output)\n else:\n final_output = (\n f'{output}'\n f'\\n{image_output}'\n f'\\n{container_output}'\n f'\\n{start_output}'\n f'\\n{wait_output}'\n f'\\n{logs_output}'\n '\\nError: Harvey could not remove the container and/or image.'\n )\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\n Image.remove_image(image[0])\n Container.remove_container(test_project_name)\n Utils.kill(final_output, webhook)\n\n execution_time = f'\\nTest stage execution time: {datetime.now() - start_time}'\n final_output = (\n f'{image_output}'\n f'\\n{container_output}'\n f'\\n{start_output}'\n f'\\n{wait_output}'\n f'\\n{logs_output}'\n f'\\n{remove_output}'\n f'\\n{execution_time}'\n )\n print(execution_time)\n\n return final_output","def do_run(cs, args):\n opts = {}\n opts['name'] = args.name\n opts['image'] = args.image\n opts['memory'] = args.memory\n opts['cpu'] = args.cpu\n opts['environment'] = zun_utils.format_args(args.environment)\n opts['workdir'] = args.workdir\n opts['auto_remove'] = args.auto_remove\n opts['labels'] = zun_utils.format_args(args.label)\n opts['image_pull_policy'] = args.image_pull_policy\n opts['image_driver'] = args.image_driver\n opts['hints'] = zun_utils.format_args(args.hint)\n opts['nets'] = zun_utils.parse_nets(args.net)\n opts['mounts'] = zun_utils.parse_mounts(args.mount)\n opts['runtime'] = args.runtime\n opts['hostname'] = args.hostname\n opts['disk'] = args.disk\n opts['availability_zone'] = args.availability_zone\n opts['command'] = args.command\n opts['registry'] = args.registry\n opts['host'] = args.host\n if args.entrypoint:\n opts['entrypoint'] = zun_utils.parse_entrypoint(args.entrypoint)\n if args.healthcheck:\n opts['healthcheck'] = zun_utils.parse_health(args.healthcheck)\n\n if args.auto_heal:\n opts['auto_heal'] = args.auto_heal\n if args.security_group:\n opts['security_groups'] = args.security_group\n if args.expose_port:\n opts['exposed_ports'] = zun_utils.parse_exposed_ports(args.expose_port)\n if args.restart:\n opts['restart_policy'] = zun_utils.check_restart_policy(args.restart)\n if args.interactive:\n opts['interactive'] = True\n if args.privileged:\n opts['privileged'] = True\n opts = zun_utils.remove_null_parms(**opts)\n container = cs.containers.run(**opts)\n _show_container(container)\n container_uuid = getattr(container, 'uuid', None)\n if args.interactive:\n ready_for_attach = False\n while True:\n container = cs.containers.get(container_uuid)\n if zun_utils.check_container_status(container, 'Running'):\n ready_for_attach = True\n break\n if zun_utils.check_container_status(container, 'Error'):\n raise exceptions.ContainerStateError(container_uuid)\n print(\"Waiting for container start\")\n time.sleep(1)\n if ready_for_attach is True:\n response = cs.containers.attach(container_uuid)\n websocketclient.do_attach(cs, response, container_uuid, \"~\", 0.5)\n else:\n raise exceptions.InvalidWebSocketLink(container_uuid)","def test_run_container3(client):\n with client.application.app_context():\n pytest.skip(\"Not implemented\")","def test_matplotlib(container, test_file, expected_file, description):\n host_data_dir = os.path.join(os.path.dirname(\n os.path.realpath(__file__)), \"data\")\n cont_data_dir = \"/home/jovyan/data\"\n output_dir = \"/tmp\"\n LOGGER.info(description)\n command = \"sleep infinity\"\n running_container = container.run(\n volumes={host_data_dir: {\"bind\": cont_data_dir, \"mode\": \"ro\"}},\n tty=True,\n command=[\"start.sh\", \"bash\", \"-c\", command],\n )\n command = f\"python {cont_data_dir}/{test_file}\"\n cmd = running_container.exec_run(command)\n assert cmd.exit_code == 0, f\"Command {command} failed\"\n LOGGER.debug(cmd.output.decode(\"utf-8\"))\n # Checking if the file is generated\n # https://stackoverflow.com/a/15895594/4413446\n command = f\"test -s {output_dir}/{expected_file}\"\n cmd = running_container.exec_run(command)\n assert cmd.exit_code == 0, f\"Command {command} failed\"\n LOGGER.debug(cmd.output.decode(\"utf-8\"))","def test_push(self, mock_docker_environment, snapshot, capsys):\n push_image(TEST_IMAGE_NAME)\n out, err = capsys.readouterr()\n snapshot.assert_match(out)","def test_docker_failure(mock_tools, user_mapping_run_calls, capsys):\n # Mock Docker cannot be found\n mock_tools.subprocess.check_output.side_effect = [\n subprocess.CalledProcessError(\n returncode=1,\n cmd=\"docker --version\",\n ),\n \"Success!\",\n VALID_BUILDX_VERSION,\n VALID_USER_MAPPING_IMAGE_CACHE,\n ]\n\n # Invoke Docker verify\n result = Docker.verify(mock_tools)\n\n # The verify call should return the Docker wrapper\n assert isinstance(result, Docker)\n\n # Docker version and plugins were verified\n mock_tools.subprocess.check_output.assert_has_calls(DOCKER_VERIFICATION_CALLS)\n\n # Docker user mapping inspection occurred\n mock_tools.subprocess.run.assert_has_calls(user_mapping_run_calls)\n\n # console output\n output = capsys.readouterr()\n assert \"** WARNING: Unable to determine if Docker is installed\" in output.out\n assert output.err == \"\"","def run_in_docker(docker_image: str,\n source_code_path: str,\n *,\n environment_vars: List[str] = None,\n command: str = None,\n result_path: str =\"/tmp/results\") -> str:\n\n #\n # Create temporal directory in the local host.\n #\n # We use dir=\"/tmp\" because in OSX, TemporaryDirectory function create the\n # temporal dir in path \"/var/...\" and, by default, Docker hasn't permission\n # to mount this directory, raising a error like that:\n #\n # The path /var/folders/_h/b5wqbhtn4zvcmsshf8nv1kcr0000gn/T/deeptracykyoc\n # is not shared from OS X and is not known to Docker.\n # You can configure shared paths from Docker ->\n # Preferences... -> File Sharing.\n # See https://docs.docker.com/docker-for-mac/osxfs/#namespaces for\n # more info.\n # -------------------------------------------------------------------------\n with tempfile.TemporaryDirectory(prefix=\"deeptracy\",\n dir=\"/tmp\") as tmp_dir:\n\n docker_client = docker.from_env()\n\n result_file_name = \"\".join(str(random.randint(0, 9))\n for _ in range(30))\n host_result_file = os.path.join(tmp_dir, result_file_name)\n\n # Choice volumes\n docker_volumes = {\n tmp_dir: {\n 'bind': result_path,\n 'mode': 'rw'\n },\n source_code_path: {\n 'bind': \"/opt/app\",\n 'mode': 'ro'\n },\n }\n\n # --------------------------------------------------------------------------\n # Build function call options\n # --------------------------------------------------------------------------\n envs = environment_vars or []\n envs.extend([\n # \"OUTPUT_FILE={}\".format(container_result_file)\n \"OUTPUT_FILE={}\".format(result_file_name)\n ])\n\n # Run content inside docker\n docker_client.containers.run(\n image=docker_image,\n remove=True,\n command=command,\n environment=envs,\n volumes=docker_volumes\n )\n\n # Read result file\n with open(host_result_file, \"r\") as f:\n yield f.read()","def docker_exec(cmdline):\n local('docker exec -ti {} {}'.format(project_name, cmdline))","def generate_sagemaker_pytest_cmd(image, sagemaker_test_type):\n region = os.getenv(\"AWS_REGION\", DEFAULT_REGION)\n account_id = os.getenv(\"ACCOUNT_ID\", image.split(\".\")[0])\n print(\"image name {}\".format(image))\n sm_remote_docker_base_name, tag = image.split(\"/\")[1].split(\":\")\n sm_local_docker_repo_uri = image.split(\":\")[0]\n\n # Assign instance type\n instance_type = assign_sagemaker_remote_job_instance_type(image)\n\n # Get path to test directory\n find_path = sm_remote_docker_base_name.split(\"-\")\n\n # NOTE: We are relying on the fact that repos are defined as <context>-<framework>-<job_type> in our infrastructure\n framework, framework_version = get_framework_and_version_from_tag(image)\n framework_major_version = framework_version.split(\".\")[0]\n job_type = get_job_type_from_image(image)\n path = os.path.join(\"test\", \"sagemaker_tests\", framework, job_type)\n aws_id_arg = \"--aws-id\"\n docker_base_arg = \"--docker-base-name\"\n instance_type_arg = \"--instance-type\"\n accelerator_type_arg = \"--accelerator-type\"\n framework_version_arg = \"--framework-version\"\n eia_arg = \"ml.eia1.large\"\n processor = \"neuron\" if \"neuron\" in image else \"gpu\" if \"gpu\" in image else \"eia\" if \"eia\" in image else \"cpu\"\n py_version = re.search(r\"py\\d+\", tag).group()\n sm_local_py_version = \"37\" if py_version == \"py37\" else \"38\" if py_version == \"py38\" else \"2\" if py_version == \"py27\" else \"3\"\n if framework == \"tensorflow\" and job_type == \"inference\":\n # Tf Inference tests have an additional sub directory with test\n integration_path = os.path.join(\"test\", \"integration\", sagemaker_test_type)\n else:\n integration_path = os.path.join(\"integration\", sagemaker_test_type)\n\n # Conditions for modifying tensorflow SageMaker pytest commands\n if framework == \"tensorflow\" and sagemaker_test_type == SAGEMAKER_REMOTE_TEST_TYPE:\n if job_type == \"inference\":\n aws_id_arg = \"--registry\"\n docker_base_arg = \"--repo\"\n instance_type_arg = \"--instance-types\"\n framework_version_arg = \"--versions\"\n integration_path = os.path.join(integration_path, \"test_tfs.py\") if processor != \"eia\" else os.path.join(\n integration_path, \"test_ei.py\")\n\n if framework == \"tensorflow\" and job_type == \"training\":\n aws_id_arg = \"--account-id\"\n\n test_report = os.path.join(os.getcwd(), \"test\", f\"{job_type}_{tag}.xml\")\n local_test_report = os.path.join(UBUNTU_HOME_DIR, \"test\", f\"{job_type}_{tag}_sm_local.xml\")\n\n # Explanation of why we need the if-condition below:\n # We have separate Pipeline Actions that run EFA tests, which have the env variable \"EFA_DEDICATED=True\" configured\n # so that those Actions only run the EFA tests.\n # However, there is no such dedicated CB job dedicated to EFA tests in the PR context. This means that when in the\n # PR context, setting \"DISABLE_EFA_TESTS\" to True should skip EFA tests, but setting it to False should enable\n # not just the EFA tests, but also all other tests as well.\n if is_pr_context():\n efa_tests_disabled = os.getenv(\"DISABLE_EFA_TESTS\", \"False\").lower() == \"true\"\n efa_flag = \"-m \\\"not efa\\\"\" if efa_tests_disabled else \"\"\n else:\n efa_dedicated = os.getenv(\"EFA_DEDICATED\", \"False\").lower() == \"true\"\n efa_flag = '--efa' if efa_dedicated else '-m \\\"not efa\\\"'\n\n region_list = \",\".join(SAGEMAKER_EXECUTION_REGIONS)\n\n sagemaker_regions_list = f\"--sagemaker-regions {region_list}\"\n\n remote_pytest_cmd = (\n f\"pytest -rA {integration_path} --region {region} --processor {processor} {docker_base_arg} \"\n f\"{sm_remote_docker_base_name} --tag {tag} {framework_version_arg} {framework_version} \"\n f\"{aws_id_arg} {account_id} {instance_type_arg} {instance_type} {efa_flag} {sagemaker_regions_list} --junitxml {test_report}\"\n )\n\n if processor == \"eia\":\n remote_pytest_cmd += f\"{accelerator_type_arg} {eia_arg}\"\n\n local_pytest_cmd = (f\"pytest -s -v {integration_path} {docker_base_arg} \"\n f\"{sm_local_docker_repo_uri} --tag {tag} --framework-version {framework_version} \"\n f\"--processor {processor} {aws_id_arg} {account_id} --junitxml {local_test_report}\")\n\n if framework == \"tensorflow\" and job_type != \"inference\":\n local_pytest_cmd = f\"{local_pytest_cmd} --py-version {sm_local_py_version} --region {region}\"\n if framework == \"tensorflow\" and job_type == \"training\":\n path = os.path.join(os.path.dirname(path), f\"{framework}{framework_major_version}_training\")\n if \"huggingface\" in framework and job_type == \"inference\":\n path = os.path.join(\"test\", \"sagemaker_tests\", \"huggingface\", \"inference\")\n\n return (\n remote_pytest_cmd if sagemaker_test_type == SAGEMAKER_REMOTE_TEST_TYPE else local_pytest_cmd,\n path,\n tag,\n job_type,\n )","def run_build_image(image_list):\n success_image_list = list()\n failed_image_list = list()\n for image in image_list:\n docker_build_success_flag = image.run_docker_build()\n if not docker_build_success_flag:\n failed_image_list.append(image)\n continue\n smoke_test_sccess_flag = image.run_smoke_test()\n if not smoke_test_sccess_flag:\n failed_image_list.append(image)\n continue\n success_image_list.append(image)\n\n logger.info(\"--- docker build summary ---\")\n logger.info(\"following image succeed:\")\n for image in success_image_list:\n logger.info(f\" {image.local_identifier}\")\n logger.info(\"following image failed:\")\n for image in failed_image_list:\n logger.info(f\" {image.local_identifier}\")\n return success_image_list, failed_image_list","def build_container(client):\n client.images.build(path=os.path.join(os.path.abspath(\"\"), \"docker\"), tag=\"scrape_light\")","def run(self, test, env):\n\n raise NotImplementedError","def tests_pull_workflow(self):\n daemon_client = {}\n client = self.get_client(daemon_client)\n\n for container in client.containers(all=True):\n if 'test-container' in \\\n ''.join([name for name in container.get('Names')]):\n client.remove_container('test-container')\n\n if ['{0}:latest'.format(TEST_IMAGE)] in \\\n [i.get('RepoTags') for i in client.images()]:\n client.remove_image(TEST_IMAGE, force=True)\n\n # execute install workflow\n self.env.execute('install', task_retries=0)\n\n container_instance = {}\n\n for instance in self.env.storage.get_node_instances():\n if 'container_id' in instance.runtime_properties.keys():\n container_instance = instance\n\n self.assertTrue(container_instance is not None,\n 'Failed getting container.')\n\n container_id = container_instance.runtime_properties.get(\n 'container_id')\n containers = client.containers(all=True)\n self.assertTrue(container_id in [c.get('Id') for c in containers])\n\n self.env.execute('uninstall', task_retries=3)\n repotags = []\n for i in client.images():\n repotags.append(i.get('RepoTags'))\n\n self.assertFalse(TEST_IMAGE in [tag for tag in repotags])\n if ['{0}:latest'.format(TEST_IMAGE)] in \\\n [i.get('RepoTags') for i in client.images()]:\n client.remove_image(TEST_IMAGE, force=True)","def docker_worker():","def _run_ci_integration_test():\n _run_install(False)\n _run_integration_tests_on_github(False)","def test_force_with_local_and_registry_image(self, mock_docker_environment):\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def run_tests():\n with open('buildspec.yml', 'r') as stream:\n buildspec: dict = yaml.load(stream)\n test_commands = buildspec.get('tests', [])\n if len(test_commands) == 0:\n exit(0)\n\n results = []\n\n for test_command in test_commands:\n print(f'Running `{test_command}`')\n p = subprocess.Popen(test_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n output = ''\n for c in iter(p.stdout.readline, b''):\n str_line = c if type(c) is str else c.decode('UTF-8')\n sys.stdout.write(str_line)\n output += str_line\n\n p.communicate()\n results.append({\n 'command': test_command,\n 'status': 'passed' if p.returncode == 0 else 'failed',\n 'output': output\n })\n\n post_to_github(results)","def prepare():\n sh('docker build --rm -t {image} {dir}', image=IMAGE, dir=os.path.dirname(__file__))","def _build_docker_images(self):\n print(f\"+ building {len(self.neurodocker_specs)} Docker images\")\n self.docker_status = []\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\n try:\n print(\"++ building image: {}\".format(neurodocker_dict))\n cg.docker_main(\n self.working_dir,\n neurodocker_dict,\n sha1,\n build_context=self.build_context,\n )\n self.docker_status.append(\"docker ok\")\n except Exception as e:\n self.docker_status.append(\n \"failed to build image with SHA1 {}: {}\".format(sha1, e)\n )","def main():\n parser = argparse.ArgumentParser(\n epilog=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\n )\n parser.add_argument(\n \"-d\", \"--dry-run\", action=\"store_true\", default=0, help=\"Dry run mode.\"\n )\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"count\",\n default=0,\n help=\"Verbosity. Default is WARNING level.\",\n )\n\n subparsers = parser.add_subparsers(help=\"Sub commands\", dest=\"subparser\")\n subparsers.required = True\n\n build_parser = subparsers.add_parser(\n \"build\",\n description=\"Build an image from Dockerfile, caching image hierarchy\",\n help=\"Build an image from a Dockerfile\",\n )\n build_parser.add_argument(\n \"path\", metavar=\"PATH\", help=\"The build context directory\"\n )\n build_parser.add_argument(\n \"-f\",\n \"--file\",\n help=\"Name of the Dockerfile. If not provided, \"\n \"will use config.DOCKERFILE_PATH_PATTERN to compute. \",\n )\n build_parser.add_argument(\n \"-v\",\n \"--git-sha\",\n required=True,\n help=\"The version of code to build against, \" \"will pass as GIT_SHA variable\",\n )\n build_parser.add_argument(\n \"-n\", \"--name\", required=True, help=\"The name of the image to build\"\n )\n build_parser.add_argument(\n \"--build-arg\",\n metavar=\"ARG=VALUE\",\n nargs=\"*\",\n default=[],\n help=\"Set extra build-time variables. GIT_SHA, TIMESTAMP will be passed by default.\",\n )\n build_parser.add_argument(\n \"-r\",\n \"--raw\",\n action=\"store_true\",\n help=\"Whether to use raw docker build command to build, skipping caching logic\",\n )\n build_parser.add_argument(\n \"--registry\",\n default=config.DOCKER_REGISTRY,\n help=\"Docker registry use to determine the image identity, \"\n \"can be set via IMAGE_BUILDER_DOCKER_REGISTRY environment variable, \"\n 'or set DOCKER_REGISTRY in config.py. Default is \"%(default)s\"',\n )\n build_parser.add_argument(\n \"-t\",\n \"--tag-pattern\",\n default=config.GIT_SHA_TAG_PATTERN,\n help=\"Tag pattern, can only include one `{git_sha}` placeholder, \"\n 'such as \"{git_sha}-new\". If the tag exists, we won\\'t rebuild it. '\n 'Default is \"%(default)s\"',\n )\n build_parser.add_argument(\n \"-e\",\n \"--extra-tag\",\n nargs=\"*\",\n default=[],\n help=\"Extra tags to tag to the final images\",\n )\n build_parser.add_argument(\n \"--extra-name\",\n nargs=\"*\",\n default=[],\n help=\"Extra name and optionally with a tag in the 'name:tag' format\",\n )\n build_parser.add_argument(\n \"-o\", \"--output-hash\", help=\"The output filename of the files hash log.\"\n )\n build_parser.set_defaults(func=build)\n\n args = parser.parse_args()\n if args.dry_run:\n # DRY_RUN env will be read in image_builder.libs.process\n os.environ[\"DRY_RUN\"] = \"1\"\n\n if args.func == build:\n args.path = expand_path(args.path)\n if args.output_hash:\n args.output_hash = expand_path(args.output_hash)\n\n args.file = args.file or locate_dockerfile(args.name)\n args.file = expand_path(args.file)\n # set environ for main dockerfile for possibly retrieving later\n os.environ[\n config.DOCKERFILE_ENV_PATTERN.format(image_name=args.name)\n ] = args.file\n\n # change CWD to PATH\n os.chdir(args.path)\n\n if not args.registry:\n parser.error(\n \"--registry should be provied \"\n \"or specified by IMAGE_BUILDER_DOCKER_REGISTRY environment variable or set DOCKER_REGISTRY in config.py\"\n )\n if not all(\"=\" in kv for kv in args.build_arg):\n parser.error(\"--build_arg must be in ARG=VALUE format\")\n\n # set git_sha_tag\n try:\n args.git_sha_tag = args.tag_pattern.format(git_sha=args.git_sha)\n except KeyError:\n parser.error(\n 'Wrong --tag-pattern provided. Can only include one `{git_sha}` placeholder, such as \"{git_sha}-new\"'\n )\n\n # setup logging\n level = logging.WARNING - args.verbose * 10\n logging.basicConfig(\n level=level, format=\"%(asctime)s %(name)s %(levelname)s %(message)s\"\n )\n\n if args.output_hash:\n h = logging.FileHandler(args.output_hash)\n h.setLevel(logging.DEBUG)\n h.setFormatter(logging.Formatter(\"%(message)s\"))\n hash_logger.addHandler(h)\n\n # Suppress warning when we don't verify ssl\n import urllib3\n\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n return args.func(args)","def test_build_image_default_context(self, mock_docker_environment):\n build_image(\"Dockerfile.test\", TEST_IMAGE_NAME, context=None)\n mock_docker_environment.images.build.assert_called_with(\n dockerfile=\"Dockerfile.test\", path=pwd(), tag=TEST_IMAGE_NAME\n )","def _ensure_image(testkit_path, branch_name, artifacts_path):\n # Construct Docker image name from branch name\n image_name = \"runner:%s\" % branch_name\n image_path = os.path.join(testkit_path, \"runner_image\")\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\n\n return image_name","def test_pep8():\n docker_exec('py.test tests/test_pep8.py')","def test_testutils():\n build()\n sh(\"%s psutil\\\\tests\\\\test_testutils.py\" % PYTHON)","def run_docker(m):\n\n import botocore.session\n from subprocess import Popen, PIPE, STDOUT\n\n raise NotImplementedError(\"No longer have access to raw_args\")\n\n session = botocore.session.get_session()\n\n args = ['docker', 'run', '--rm', '-t', '-i',\n '-eAWS_ACCESS_KEY_ID={}'.format(session.get_credentials().access_key),\n '-eAWS_SECRET_ACCESS_KEY={}'.format(session.get_credentials().secret_key),\n 'civicknowledge/metatab',\n 'metasync']\n\n for a in ('-D', '--docker'):\n try:\n m.raw_args.remove(a)\n except ValueError:\n pass\n\n args.extend(m.raw_args[1:])\n\n if m.args.verbose:\n prt(\"Running Docker Command: \", ' '.join(args))\n else:\n prt(\"Running In Docker\")\n\n process = Popen(args, stdout=PIPE, stderr=STDOUT)\n with process.stdout:\n for line in iter(process.stdout.readline, b''):\n prt(line.decode('ascii'), end='')\n\n exitcode = process.wait() # 0 means success\n\n exit(exitcode)","def testRunRecipe(self):\n warnings.filterwarnings(\n action=\"ignore\", message=\"unclosed\", category=ResourceWarning)\n\n # Load the recipe, set the arguments, and run\n self.test_state.LoadRecipe(RECIPE, TEST_MODULES)\n self.test_state.command_line_options = {\n 'aws_region': self.aws_region,\n 'gcp_zone': self.gcp_zone,\n 'volumes': self.aws_volume,\n 'aws_bucket': self.aws_bucket,\n 'gcp_bucket': self.gcp_bucket,\n 'subnet': self.aws_subnet,\n 'gcp_project': self.gcp_project_id\n }\n self.test_state.SetupModules()\n self.test_state.RunModules()\n\n # AWS Volume in count should equal GCE Disk out count, and be at least 1\n self.assertGreaterEqual(\n len(self.test_state.GetContainers(containers.AWSVolume)), 1)\n self.assertEqual(len(self.test_state.GetContainers(containers.AWSVolume)),\n len(self.test_state.GetContainers(containers.GCEDisk)))\n\n disks = compute.GoogleCloudCompute(self.gcp_project_id).Disks()\n real_gce_disk_names = [disks[k].name for k in disks.keys()]\n\n for d in self.test_state.GetContainers(containers.GCEDisk):\n self.assertIn(d.name, real_gce_disk_names)\n real_disk = compute.GoogleComputeDisk(\n self.gcp_project_id, self.gcp_zone, d.name)\n self.assertEqual(real_disk.GetDiskType(), 'pd-standard')\n # Make an API call to the service that will fail if the disk doesn't exist","def test_force_with_local_image(self, mock_docker_environment):\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def build_docker(params) -> None:\n print(\"Building docker image...\")\n cmd = \"cd bg_changer && docker build --tag bg_changer . >/dev/null 2>&1\"\n if os.system(cmd) == 0:\n print(\" Success !\")\n else:\n print(\" Failure !\")","def check_image(\n image: str, trivy: bool = True, exit_on_error: bool = True\n) -> subprocess.CompletedProcess:\n build_utils.log(\"Run vulnerability checks on docker image:\")\n\n if trivy and build_utils.command_exists(\"trivy\", exit_on_error=exit_on_error):\n return build_utils.run(\n f\"trivy image --timeout=20m0s --exit-code 1 --severity HIGH,CRITICAL {image}\",\n exit_on_error=exit_on_error,\n )\n\n return subprocess.CompletedProcess(args=\"\", returncode=-1, stdout=\"\", stderr=\"\")\n # TODO: Implement dockl container scan","def test_docker_push_target_ref(tmpdir, docker_client, registry):\n tmp = tmpdir.join('shipwright-localhost-sample')\n path = str(tmp)\n source = pkg_resources.resource_filename(\n __name__,\n 'examples/shipwright-localhost-sample',\n )\n repo = create_repo(path, source)\n tag = repo.head.ref.commit.hexsha[:12]\n\n tmp.join('service1/base.txt').write('Hi mum')\n commit_untracked(repo)\n new_tag = repo.head.ref.commit.hexsha[:12]\n\n client_cfg = docker_utils.kwargs_from_env()\n cli = docker_client\n\n defaults = get_defaults()\n defaults['push'] = True\n try:\n shipw_cli.run(\n path=path,\n client_cfg=client_cfg,\n arguments=defaults,\n environ={},\n )\n\n # Remove the build images:\n old_images = (\n cli.images(name='localhost:5000/service1', quiet=True) +\n cli.images(name='localhost:5000/shared', quiet=True) +\n cli.images(name='localhost:5000/base', quiet=True)\n )\n for image in old_images:\n cli.remove_image(image, force=True)\n\n images_after_delete = (\n cli.images(name='localhost:5000/service1') +\n cli.images(name='localhost:5000/shared') +\n cli.images(name='localhost:5000/base')\n )\n assert images_after_delete == []\n\n # Pull them again:\n for t in ['master', 'latest', new_tag, tag]:\n for repo in ['service1', 'shared', 'base']:\n cli.pull('localhost:5000/' + repo, t)\n\n service1, shared, base = (\n cli.images(name='localhost:5000/service1') +\n cli.images(name='localhost:5000/shared') +\n cli.images(name='localhost:5000/base')\n )\n\n assert set(service1['RepoTags']) == {\n 'localhost:5000/service1:master',\n 'localhost:5000/service1:latest',\n 'localhost:5000/service1:' + new_tag,\n }\n\n assert set(shared['RepoTags']) == {\n 'localhost:5000/shared:master',\n 'localhost:5000/shared:latest',\n 'localhost:5000/shared:' + new_tag,\n 'localhost:5000/shared:' + tag,\n }\n\n assert set(base['RepoTags']) == {\n 'localhost:5000/base:master',\n 'localhost:5000/base:latest',\n 'localhost:5000/base:' + new_tag,\n 'localhost:5000/base:' + tag,\n }\n finally:\n old_images = (\n cli.images(name='localhost:5000/service1', quiet=True) +\n cli.images(name='localhost:5000/shared', quiet=True) +\n cli.images(name='localhost:5000/base', quiet=True)\n )\n for image in old_images:\n cli.remove_image(image, force=True)","def test_docker_exists(mock_tools, user_mapping_run_calls, capsys, tmp_path):\n # Mock the return value of Docker Version\n mock_tools.subprocess.check_output.side_effect = [\n VALID_DOCKER_VERSION,\n VALID_DOCKER_INFO,\n VALID_BUILDX_VERSION,\n VALID_USER_MAPPING_IMAGE_CACHE,\n ]\n\n # Invoke docker verify\n result = Docker.verify(mock_tools)\n\n # The verify call should return the Docker wrapper\n assert isinstance(result, Docker)\n\n # Docker version and plugins were verified\n mock_tools.subprocess.check_output.assert_has_calls(DOCKER_VERIFICATION_CALLS)\n\n # Docker user mapping inspection occurred\n mock_tools.subprocess.run.assert_has_calls(user_mapping_run_calls)\n\n # No console output\n output = capsys.readouterr()\n assert output.out == \"\"\n assert output.err == \"\"","def test_container_exists():\n return exec_fn(_test_container_exists)","def docker_build(c):\n cli_tasks.docker_build.run(c)","def generate_sagemaker_pytest_cmd(image):\n reruns = 4\n region = os.getenv(\"AWS_REGION\", \"us-west-2\")\n integration_path = os.path.join(\"integration\", \"sagemaker\")\n account_id = os.getenv(\"ACCOUNT_ID\", image.split(\".\")[0])\n docker_base_name, tag = image.split(\"/\")[1].split(\":\")\n\n # Assign instance type\n instance_type = assign_sagemaker_instance_type(image)\n\n # Get path to test directory\n find_path = docker_base_name.split(\"-\")\n\n # NOTE: We are relying on the fact that repos are defined as <context>-<framework>-<job_type> in our infrastructure\n framework = find_path[1]\n job_type = find_path[2]\n path = os.path.join(\"test\", \"sagemaker_tests\", framework, job_type)\n aws_id_arg = \"--aws-id\"\n docker_base_arg = \"--docker-base-name\"\n instance_type_arg = \"--instance-type\"\n\n # Conditions for modifying tensorflow SageMaker pytest commands\n if framework == \"tensorflow\":\n if job_type == \"training\":\n aws_id_arg = \"--account-id\"\n\n # NOTE: We rely on Framework Version being in \"major.minor.patch\" format\n tf_framework_version = re.search(r\"\\d+(\\.\\d+){2}\", tag).group()\n tf_major_version = tf_framework_version.split(\".\")[0]\n path = os.path.join(os.path.dirname(path), f\"{framework}{tf_major_version}_training\")\n else:\n aws_id_arg = \"--registry\"\n docker_base_arg = \"--repo\"\n integration_path = os.path.join(integration_path, \"test_tfs.py\")\n instance_type_arg = \"--instance-types\"\n\n test_report = os.path.join(os.getcwd(), \"test\", f\"{tag}.xml\")\n return (\n f\"pytest --reruns {reruns} {integration_path} --region {region} {docker_base_arg} \"\n f\"{docker_base_name} --tag {tag} {aws_id_arg} {account_id} {instance_type_arg} {instance_type} \"\n f\"--junitxml {test_report}\",\n path,\n tag,\n )","def cli(context):\n dev = f\"docker run -it -v {PWD}:/local {IMAGE_NAME}:{IMAGE_VER} /bin/bash\"\n print(f\"{dev}\")\n context.run(f\"{dev}\", pty=True)","def test(command, options=\"\"):\n\n print(\n \"\"\"\nRunning pytest the test framework\n=================================\n\"\"\"\n )\n for dir_ in TEST_DIRECTORIES:\n test_dir(command, options=options, dir_=dir_)\n # command.run(f\"python -m pytest {options} {' '.join(dir_ for dir_ in TEST_DIRECTORIES)}\", echo=True, pty=POSIX)\n\n print(\n \"\"\"\nAll Testing Directories Passed Successfully\n===========================================\n\"\"\"\n )","def main():\n client = docker.from_env()\n parser = argparse.ArgumentParser(description='')\n parser.add_argument('--package_id', default='0',\n help='provide id for the work package, comma separated if multiple')\n parser.add_argument('--load_quicksave', default=\"no\", help='wanna load? -> yes/no')\n args = parser.parse_args()\n packages = args.package_id.split(\",\")\n print('Building docker container. This might take a while.')\n build_container(client)\n print('Build finished. Starting containers.')\n for package in packages:\n start_container(client, package, args.load_quicksave)\n print('Containers are running. Check Docker Dashboard for container health. Script will exit.')","def test_container_cycle(self):\n # Before Create\n print(\"Create\")\n rep = post(self.url + \"/search\", data={'name': name})\n self.errorCatch(rep)\n\n # Create\n rep = post(self.url + \"/create\", data={\n 'image': default_image,\n 'homepath': \"/nashome/guest/test\",\n 'naspath': \"/home/nas/test\",\n 'command': \"tail -f /dev/null\",\n 'name': name})\n self.checkRunning()\n\n # Double create\n rep = post(self.url + \"/create\", data={\n 'image': default_image,\n 'homepath': \"/nashome/guest/test\",\n 'naspath': \"/home/nas/test\",\n 'command': \"tail -f /dev/null\",\n 'name': name})\n self.errorCatch(rep)\n\n # Check by api\n con = client.containers.get(name)\n self.assertIn(\"tmp0\", con.exec_run(\"ls /home/nas\").output.decode())\n self.assertIn(\"tmp1\", con.exec_run(\"ls /home/ubuntu\").output.decode())\n self.assertEqual(con.status, \"running\")\n\n # Stop\n con.exec_run(\"touch /opt/tmp2\").output.decode()\n print(\"Stop\")\n rep = post(self.url + \"/stop\", data={'name': name})\n self.checkOK(rep)\n\n # check stop\n rep = post(self.url + \"/search\", data={'name': name})\n self.checkOK(rep)\n rep = rep.json()\n self.assertIsInstance(rep[\"data\"], dict)\n self.assertEqual(rep['data']['status'], \"exited\")\n\n # start\n print(\"Resume\")\n rep = post(self.url + \"/start\", data={'name': name})\n self.checkOK(rep)\n self.checkRunning()\n con = client.containers.get(name)\n self.assertIn(\"tmp2\", con.exec_run(\"ls /opt\").output.decode())\n\n # change pw\n print(\"Change Password\")\n con.exec_run(\"adduser ubuntu\")\n rep = post(self.url + \"/passwd\", data={'name': name,\n 'pw': \"tmpPW\"})\n self.checkOK(rep)\n self.assertIn(\"tmpPW\", con.exec_run(\"cat /etc/shadow\").output.decode())\n\n # commit\n print(\"Commit\")\n rep = post(self.url + \"/commit\", data={'name': name,\n 'newname': name})\n self.checkOK(rep)\n\n # search image\n rep = post(self.url + \"/search/image\", data={'name': name})\n rep = rep.json()\n self.assertIsInstance(rep['data'], dict)\n\n # delete\n print(\"Delete\")\n rep = post(self.url + \"/delete\", data={'name': name})\n self.checkOK(rep)\n\n # check delete\n rep = post(self.url + \"/search\", data={'name': name})\n self.errorCatch(rep)\n\n # Delete Image\n print(\"Delete Image\")\n rep = post(self.url + \"/delete/image\", data={'name': name})\n self.checkOK(rep)\n\n # Check if delete it\n rep = post(self.url + \"/search/image\", data={'name': name})\n self.errorCatch(rep)","def run_sagemaker_pytest_cmd(image):\n pytest_command, path, tag = generate_sagemaker_pytest_cmd(image)\n\n context = Context()\n with context.cd(path):\n context.run(f\"virtualenv {tag}\")\n with context.prefix(f\"source {tag}/bin/activate\"):\n context.run(\"pip install -r requirements.txt\", warn=True)\n context.run(pytest_command)","def run_docker_container():\n creds = {\n 'user': 'root',\n 'password': 'root',\n 'host': '127.0.0.1',\n 'db': 'test',\n 'port': 3307\n }\n client = from_env()\n client.containers.list()\n container = client.containers.run('mariadb:10.3', name='test-mariadb-database',\n ports={3306: creds['port']}, environment={'MYSQL_ROOT_PASSWORD': creds['password'], 'MYSQL_DATABASE': creds['db']},\n detach=True)\n url = (\"mysql+pymysql://{}:{}@{}:{}/{}\".format(creds['user'], creds['password'], creds['host'], creds['port'], creds['db']))\n return url, container","def build_docker(c):\n tag = c.run('git describe', hide=True)\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\n c.run(f'docker build -t {docker_img} .')","def _docker_run_pwsh_test(\n self, test_image: str, keep_container: bool\n ) -> Tuple[int, str]:\n log_prompt = f\"{self._pack_name} - Powershell test - Image {test_image}\"\n logger.info(f\"{log_prompt} - Start\")\n container_name = f\"{self._pack_name}-pwsh-test\"\n # Check if previous run left container a live if it do, we remove it\n self._docker_remove_container(container_name)\n\n # Run container\n exit_code = SUCCESS\n output = \"\"\n try:\n uid = os.getuid() or 4000\n logger.debug(\n f\"{log_prompt} - user uid for running lint/test: {uid}\"\n ) # lgtm[py/clear-text-logging-sensitive-data]\n container: docker.models.containers.Container = (\n get_docker().create_container(\n files_to_push=[(self._pack_abs_dir, \"/devwork\")],\n name=container_name,\n image=test_image,\n command=build_pwsh_test_command(),\n user=f\"{uid}:4000\",\n environment=self._facts[\"env_vars\"],\n )\n )\n container.start()\n stream_docker_container_output(container.logs(stream=True))\n # wait for container to finish\n container_status = container.wait()\n # Get container exit code\n container_exit_code = container_status.get(\"StatusCode\")\n # Getting container logs\n container_log = container.logs().decode(\"utf-8\")\n logger.info(f\"{log_prompt} - exit-code: {container_exit_code}\")\n if container_exit_code:\n # 1-fatal message issued\n # 2-Error message issued\n logger.error(f\"{log_prompt} - Finished, errors found\")\n output = container_log\n exit_code = FAIL\n else:\n logger.info(f\"{log_prompt} - Successfully finished\")\n # Keeping container if needed or remove it\n if keep_container:\n logger.info(f\"{log_prompt} - container name {container_name}\")\n container.commit(repository=container_name.lower(), tag=\"pwsh_test\")\n else:\n try:\n container.remove(force=True)\n except docker.errors.NotFound as e:\n logger.critical(f\"{log_prompt} - Unable to delete container - {e}\")\n except (\n docker.errors.ImageNotFound,\n docker.errors.APIError,\n requests.exceptions.ReadTimeout,\n ) as e:\n logger.critical(f\"{log_prompt} - Unable to run powershell test - {e}\")\n exit_code = RERUN\n\n return exit_code, output","def test_create_image(self):\n pass","def runTests(self):\n \n pass","def run(self, container_config: ContainerConfig) -> Container:","def test_build_containers_when_dependency_container_is_built(self, set_context, repo_urls, build_image):\n build_image.side_effect = [1, 2, 3]\n repo_urls.return_value = [\"url\"]\n e1 = models.Event.create(db.session, \"test_msg_id\", \"RHSA-2018-001\", events.TestingEvent)\n event = self.get_event_from_msg(get_fedmsg('brew_container_task_closed'))\n\n base_build = models.ArtifactBuild.create(db.session, e1, 'test-product-docker', ArtifactType.IMAGE, event.task_id)\n\n build_0 = models.ArtifactBuild.create(db.session, e1, 'docker-up-0', ArtifactType.IMAGE, 0,\n dep_on=base_build, state=ArtifactBuildState.PLANNED)\n build_1 = models.ArtifactBuild.create(db.session, e1, 'docker-up-1', ArtifactType.IMAGE, 0,\n dep_on=base_build, state=ArtifactBuildState.PLANNED)\n build_2 = models.ArtifactBuild.create(db.session, e1, 'docker-up-2', ArtifactType.IMAGE, 0,\n dep_on=base_build, state=ArtifactBuildState.PLANNED)\n\n self.handler.handle(event)\n self.assertEqual(base_build.state, ArtifactBuildState.DONE.value)\n build_image.assert_has_calls([\n mock.call(build_0, ['url']), mock.call(build_1, ['url']),\n mock.call(build_2, ['url']),\n ])\n\n set_context.assert_has_calls([\n mock.call(build_0), mock.call(build_1), mock.call(build_2)])\n\n self.assertEqual(build_0.build_id, 1)\n self.assertEqual(build_1.build_id, 2)\n self.assertEqual(build_2.build_id, 3)","def test(self):\n for arch, python in self.python:\n self.run(f\"{python} -m pytest\")","def run_enrichr_docker(self, cmdargs, temp_dir=None):\n cmd = [os.getenv('CDENRICHR_DOCKER'), 'run', '--rm', '-v',\n temp_dir+':'+temp_dir,\n os.getenv('CDENRICHR_DOCKER_IMAGE')]\n cmd.extend(cmdargs)\n p = subprocess.Popen(cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n\n out, err = p.communicate()\n\n return p.returncode, out, err","def main():\n\n # get AWS credentials\n aws_credentials = read_aws_credentials()\n access_key_id = aws_credentials['access_key_id']\n secret_access_key = aws_credentials['secret_access_key']\n aws_region = aws_credentials['region']\n\n # build Docker image\n docker_client = docker.from_env()\n image, build_log = docker_client.images.build(\n path='.', tag=LOCAL_REPOSITORY, rm=True)\n\n # get AWS ECR login token\n ecr_client = boto3.client(\n 'ecr', aws_access_key_id=access_key_id, \n aws_secret_access_key=secret_access_key, region_name=aws_region)\n\n ecr_credentials = (\n ecr_client\n .get_authorization_token()\n ['authorizationData'][0])\n\n ecr_username = 'AWS'\n\n ecr_password = (\n base64.b64decode(ecr_credentials['authorizationToken'])\n .replace(b'AWS:', b'')\n .decode('utf-8'))\n\n ecr_url = ecr_credentials['proxyEndpoint']\n\n # get Docker to login/authenticate with ECR\n docker_client.login(\n username=ecr_username, password=ecr_password, registry=ecr_url)\n\n # tag image for AWS ECR\n ecr_repo_name = '{}/{}'.format(\n ecr_url.replace('https://', ''), LOCAL_REPOSITORY)\n\n image.tag(ecr_repo_name, tag='latest')\n\n # push image to AWS ECR\n push_log = docker_client.images.push(ecr_repo_name, tag='latest')\n\n # force new deployment of ECS service\n ecs_client = boto3.client(\n 'ecs', aws_access_key_id=access_key_id,\n aws_secret_access_key=secret_access_key, region_name=aws_region)\n\n ecs_client.update_service(\n cluster=ECS_CLUSTER, service=ECS_SERVICE, forceNewDeployment=True)\n\n return None","def test_docker_unknown_version(mock_tools, user_mapping_run_calls, capsys):\n # Mock a bad return value of `docker --version`\n mock_tools.subprocess.check_output.return_value = \"ceci nest pas un Docker\\n\"\n\n # Invoke Docker verify\n result = Docker.verify(mock_tools)\n\n # The verify call should return the Docker wrapper\n assert isinstance(result, Docker)\n\n # Docker version and plugins were verified\n mock_tools.subprocess.check_output.assert_has_calls(DOCKER_VERIFICATION_CALLS)\n\n # Docker user mapping inspection occurred\n mock_tools.subprocess.run.assert_has_calls(user_mapping_run_calls)\n\n # console output\n output = capsys.readouterr()\n assert \"** WARNING: Unable to determine the version of Docker\" in output.out\n assert output.err == \"\"","def runTest(self):\n self.setUp()\n self.test_FlexCrop1()","def test_docker_doesnt_exist(mock_tools):\n # Mock Docker not installed on system\n mock_tools.subprocess.check_output.side_effect = FileNotFoundError\n\n # Invoke Docker verify\n with pytest.raises(BriefcaseCommandError):\n Docker.verify(mock_tools)\n\n # But docker was invoked\n mock_tools.subprocess.check_output.assert_called_with([\"docker\", \"--version\"])","def dockerfile_with_path_map() -> co.Exec:\n path_map = {\"./code\": \"/root/code\"}\n image = co.Image(\n dockerfile=\"./docker/Dockerfile.copy\", context=\".\", path_map=path_map\n )\n return co.Exec(\"python /root/code/test.py\", image=image, doc=co.util.magic_doc())","def _run_ci_test():\n _run_install(False)\n _run_coverage_html(False)\n _run_typecheck_xml(False)\n _run_lint(True)","def test_mock_is_accurate(self, case):\n commands = case.setup_script + (\n 'python -c \"import platform\\nprint(platform.linux_distribution())\"', # noqa\n )\n stdout = run_in_docker(case.docker_image, commands)\n assert stdout.strip() == str(case.mock_linux_dist)","def test_retag_valid_image(self):\n alpine = self.docker.images.get(constant.ALPINE)\n self.assertTrue(alpine.tag(\"demo\", \"rename\"))\n\n alpine = self.docker.images.get(constant.ALPINE)\n self.assertNotIn(\"demo:test\", alpine.tags)","def run_docker_push(image_list, docker_client):\n logger.info(\"--- push image to registry ---\")\n if runtime == Runtime.local:\n logger.info(\"Detected local runtime, stop here.\")\n return\n\n if not len(success_image_list):\n logger.info(\"No success image to push, stop here.\")\n return\n\n if GIT_BRANCH != \"master\":\n logger.info(\"Not master branch, stop here\")\n return\n\n docker_client.login(username=DOCKER_HUB_USERNAME, password=DOCKER_HUB_PASSWORD)\n for image in image_list:\n image.run_docker_push(docker_client)\n print(\"Finished.\")","def test_defaults_centos(self):\n c = catalyst()\n self.assertEqual(str(c),\nr'''# ParaView Catalyst version 5.6.1\nRUN yum install -y \\\n git \\\n gzip \\\n libICE-devel \\\n libSM-devel \\\n libX11-devel \\\n libXau-devel \\\n libXext-devel \\\n libXt-devel \\\n libglvnd-devel \\\n make \\\n mesa-libGL-devel \\\n tar \\\n wget \\\n which && \\\n rm -rf /var/cache/yum/*\nRUN mkdir -p /var/tmp && wget -q -nc --no-check-certificate -O /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base.tar.gz -P /var/tmp https://www.paraview.org/paraview-downloads/download.php?submit=Download\\&version=v5.6\\&type=catalyst\\&os=Sources\\&downloadFile=Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base.tar.gz && \\\n mkdir -p /var/tmp && tar -x -f /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base.tar.gz -C /var/tmp -z && \\\n mkdir -p /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/build && cd /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/build && /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/cmake.sh -DCMAKE_INSTALL_PREFIX=/usr/local/catalyst /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base && \\\n cmake --build /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/build --target all -- -j$(nproc) && \\\n cmake --build /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/build --target install -- -j$(nproc) && \\\n rm -rf /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base.tar.gz /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base\nENV LD_LIBRARY_PATH=/usr/local/catalyst/lib:$LD_LIBRARY_PATH \\\n PATH=/usr/local/catalyst/bin:$PATH''')","def run(ws):\n ws.docker_run()","def push_image(image):\n\n subprocess.check_call(['docker', 'push', image])","def test_start(set_env, container: Container, docker_client: DockerClient):\n # pylint: disable=unused-argument\n assert container\n container.stop()\n\n from dockerdb.commands.start import start\n\n start()\n\n assert container.status == \"running\"","def test_defaults_centos8(self):\n o = ofed()\n self.assertEqual(str(o),\nr'''# OFED\nRUN yum install -y dnf-utils && \\\n yum-config-manager --set-enabled powertools && \\\n yum install -y --disablerepo=mlnx\\* \\\n libibmad \\\n libibmad-devel \\\n libibumad \\\n libibverbs \\\n libibverbs-utils \\\n libmlx5 \\\n librdmacm \\\n rdma-core \\\n rdma-core-devel && \\\n rm -rf /var/cache/yum/*''')","def run_sm_perf_test(image_uri, num_nodes, region):\n _, framework_version = get_framework_and_version_from_tag(image_uri)\n if framework_version.startswith(\"1.\"):\n pytest.skip(\"Skipping benchmark test on TF 1.x images.\")\n\n processor = \"gpu\" if \"gpu\" in image_uri else \"cpu\"\n device_cuda_str = (\n f\"{processor}-{get_cuda_version_from_tag(image_uri)}\" if processor == \"gpu\" else processor\n )\n\n ec2_instance_type = \"p3.16xlarge\" if processor == \"gpu\" else \"c5.18xlarge\"\n\n py_version = \"py2\" if \"py2\" in image_uri else \"py37\" if \"py37\" in image_uri else \"py3\"\n\n time_str = time.strftime(\"%Y-%m-%d-%H-%M-%S\")\n commit_info = os.getenv(\"CODEBUILD_RESOLVED_SOURCE_VERSION\")\n target_upload_location = os.path.join(\n BENCHMARK_RESULTS_S3_BUCKET,\n \"tensorflow\",\n framework_version,\n \"sagemaker\",\n \"training\",\n device_cuda_str,\n py_version,\n )\n training_job_name = f\"tf{framework_version[0]}-tr-bench-{device_cuda_str}-{num_nodes}-node-{py_version}-{commit_info[:7]}-{time_str}\"\n\n # Inserting random sleep because this test starts multiple training jobs around the same time, resulting in\n # a throttling error for SageMaker APIs.\n time.sleep(Random(x=training_job_name).random() * 60)\n\n test_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"resources\")\n venv_dir = os.path.join(test_dir, \"sm_benchmark_venv\")\n\n ctx = Context()\n\n with ctx.cd(test_dir), ctx.prefix(f\"source {venv_dir}/bin/activate\"):\n log_file = f\"results-{commit_info}-{time_str}-{framework_version}-{device_cuda_str}-{py_version}-{num_nodes}-node.txt\"\n run_out = ctx.run(\n f\"timeout 45m python tf_sm_benchmark.py \"\n f\"--framework-version {framework_version} \"\n f\"--image-uri {image_uri} \"\n f\"--instance-type ml.{ec2_instance_type} \"\n f\"--node-count {num_nodes} \"\n f\"--python {py_version} \"\n f\"--region {region} \"\n f\"--job-name {training_job_name}\"\n f\"2>&1 | tee {log_file}\",\n warn=True,\n echo=True,\n )\n\n if not (run_out.ok or run_out.return_code == 124):\n target_upload_location = os.path.join(target_upload_location, \"failure_log\")\n\n ctx.run(\n f\"aws s3 cp {os.path.join(test_dir, log_file)} {os.path.join(target_upload_location, log_file)}\"\n )\n\n LOGGER.info(f\"Test results can be found at {os.path.join(target_upload_location, log_file)}\")\n\n result_statement, throughput = _print_results_of_test(\n os.path.join(test_dir, log_file), processor\n )\n throughput /= num_nodes\n\n assert run_out.ok, (\n f\"Benchmark Test failed with return code {run_out.return_code}. \"\n f\"Test results can be found at {os.path.join(target_upload_location, log_file)}\"\n )\n\n threshold_table = (\n (\n TENSORFLOW_SM_TRAINING_CPU_1NODE_THRESHOLD\n if num_nodes == 1\n else TENSORFLOW_SM_TRAINING_CPU_4NODE_THRESHOLD\n )\n if processor == \"cpu\"\n else TENSORFLOW_SM_TRAINING_GPU_1NODE_THRESHOLD\n if num_nodes == 1\n else TENSORFLOW_SM_TRAINING_GPU_4NODE_THRESHOLD\n )\n threshold = get_threshold_for_image(framework_version, threshold_table)\n LOGGER.info(\n f\"tensorflow {framework_version} sagemaker training {device_cuda_str} {py_version} \"\n f\"imagenet {num_nodes} nodes Throughput: {throughput} images/sec, threshold: {threshold} images/sec\"\n )\n assert throughput > threshold, (\n f\"tensorflow {framework_version} sagemaker training {processor} {py_version} imagenet {num_nodes} nodes \"\n f\"Benchmark Result {throughput} does not reach the threshold {threshold}\"\n )","def docker_services(\n docker_compose_file, docker_allow_fallback, docker_compose_project_name,\n docker_compose_project_dir\n):\n\n docker_compose = DockerComposeExecutor(\n docker_compose_file,\n docker_compose_project_name,\n docker_compose_project_dir\n )\n\n # If we allowed to run without Docker, check it's presence\n if docker_allow_fallback is True:\n try:\n with open(os.devnull, 'w') as devnull:\n subprocess.call(['docker', 'ps'],\n stdout=devnull, stderr=devnull)\n except Exception:\n yield Services(None, docker_allow_fallback=True)\n return\n\n project = project_from_options(\n docker_compose._compose_project_dir,\n options=docker_compose.as_dict_options()\n )\n cmd = TopLevelCommand(project)\n\n # Spawn containers.\n up_options = docker_compose.defaults_opts('up')\n up_options['-d'] = True\n up_options['--build'] = True\n cmd.up(up_options)\n\n # Let test(s) run.\n yield Services(project)\n\n # Clean up.\n down_option = docker_compose.defaults_opts('down')\n down_option['-v'] = True\n cmd.down(down_option)","def test_unknown_registry(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n build_image_if_needed(\"unknown.registry.com/foo/bar\")\n mock_docker_environment.images.build.assert_called_with(\n dockerfile=\"Dockerfile\",\n tag=\"unknown.registry.com/foo/bar:latest\",\n path=\"/opt/ixian_docker\",\n )","def test_tag_valid_image(self):\n alpine = self.docker.images.get(constant.ALPINE)\n self.assertTrue(alpine.tag(\"demo\", constant.ALPINE_SHORTNAME))\n\n alpine = self.docker.images.get(constant.ALPINE)\n for tag in alpine.tags:\n self.assertIn(\"alpine\", tag)","def test(args):\n startTimeStamp = time.monotonic()\n #for GKE platform\n if (f\"{args.platform}\" == \"GKE\" and (f\"{args.type}\" == \"all\")):\n experiments = sorted(os.listdir('./litmus'))\n\n elif (f\"{args.platform}\" == \"GKE\" and (f\"{args.type}\" == \"pod\")):\n experiments = [\"container-kill.yaml\",\"disk-fill\",\"pod-cpu-hog\",\"pod-delete\",\"pod-memory-hog\",\"pod-network-corruption\",\"pod-network-latency\",\"pod-network-loss\"]\n\n elif (f\"{args.platform}\" == \"GKE\" and (f\"{args.type}\" == \"node\")):\n kind_supported = [\"node-cpu-hog\",\"node-memory-hog\"]\n\n #for kind platform\n if ((f\"{args.platform}\" == \"kind\") and (f\"{args.type}\" == \"all\")):\n kind_supported = [\"pod-delete\",\"container-kill\",\"node-cpu-hog\",\"node-memory-hog\"]\n experiments=[s + \".yaml\" for s in kind_supported]\n\n elif ((f\"{args.platform}\" == \"kind\") and (f\"{args.type}\" == \"pod\")):\n experiments = [\"node-cpu-hog.yaml\",\"node-memory-hog.yaml\"]\n\n elif ((f\"{args.platform}\" == \"kind\") and (f\"{args.type}\" == \"node\")):\n experiments = [\"node-cpu-hog.yaml\",\"node-memory-hog.yaml\"]\n\n # for EKS platform\n if (f\"{args.platform}\" == \"EKS\" and (f\"{args.type}\" == \"all\")):\n experiments = sorted(os.listdir('./litmus'))\n\n elif (f\"{args.platform}\" == \"EKS\" and (f\"{args.type}\" == \"pod\")):\n experiments = [\"container-kill.yaml\", \"disk-fill\", \"pod-cpu-hog\", \"pod-delete\", \"pod-memory-hog\",\n \"pod-network-corruption\", \"pod-network-latency\", \"pod-network-loss\"]\n\n elif (f\"{args.platform}\" == \"EKS\" and (f\"{args.type}\" == \"node\")):\n kind_supported = [\"node-cpu-hog\", \"node-memory-hog\"]\n\n experiment_results = []\n\n if args.test == '*':\n # Run all experiments in /litmus directory with wait time between them\n print_color(f\"Running all Litmus ChaosEngine Experiments with {args.wait} mins wait time between each one...\")\n lstindex = len(experiments)\n for experiment_file in experiments:\n result = run_experiment(experiment_file.replace('.yaml', ''))\n experiment_results.append(result)\n print_color(f\"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Waiting {args.wait} mins before running next experiment...\", bcolors.WARNING)\n lstindex -= 1\n if lstindex != 0:\n time.sleep(args.wait * 60)\n else:\n # Check experiment exists\n experiment_file = args.test + \".yaml\"\n if experiment_file in experiments:\n result = run_experiment(args.test)\n experiment_results.append(result)\n else:\n print_color(f\"ERROR: {experiment_file} not found in ./litmus directory. Please check the name and try again.\", bcolors.FAIL)\n sys.exit(2)\n\n # Print out experiment result summary\n print_color(\"***************************************************************************************************\", bcolors.OKBLUE)\n print_color(\"* Experiments Result Summary\", bcolors.OKBLUE)\n print_color(\"***************************************************************************************************\\n\", bcolors.OKBLUE)\n headers = [\"#\", \"Start Time\", \"Experiment\", \"Status\"]\n row_format = \"{:>25}\" * (len(headers) + 1)\n print_color(row_format.format(\"\", *headers), bcolors.OKBLUE)\n i = 1\n for result in experiment_results:\n if result.status == \"Pass\":\n print_color(row_format.format(\"\", str(i), result.startTime.strftime('%Y-%m-%d %H:%M:%S'), result.name,\" \"+ result.status + \" 'carts-db' Service is up and Running after chaos\"), bcolors.OKBLUE)\n i += 1\n else:\n print_color(row_format.format(\"\", str(i), result.startTime.strftime('%Y-%m-%d %H:%M:%S'), result.name, result.status), bcolors.OKBLUE)\n i += 1\n print(\"\\n\")\n currentTimeStamp= time.monotonic()\n diffTimeStamp = currentTimeStamp - startTimeStamp\n ty_res = time.gmtime(diffTimeStamp)\n totalTime = time.strftime(\"%H:%M:%S\",ty_res)\n\n if (f\"{args.report}\" == \"yes\"):\n\n print_color(\"Creating PDF Report\", bcolors.OKBLUE)\n\n fileName = 'chaos-report.pdf'\n pdf = SimpleDocTemplate(fileName,pagesize=LETTER)\n styles = getSampleStyleSheet()\n data = [None]\n data[0] = [\"S.No.\", \"Start Time\", \"Experiment\", \"Status\"]\n\n i =1\n expPassed = 0\n expFailed = 0\n for result in experiment_results:\n if result.status == \"Pass\":\n data.append([str(i), result.startTime.strftime('%Y-%m-%d %H:%M:%S'), result.name,\" \"+ result.status + \" 'carts-db' Service is up and Running after chaos\"])\n i += 1\n expPassed +=1\n else:\n data.append([str(i), result.startTime.strftime('%Y-%m-%d %H:%M:%S'), result.name,result.status])\n i += 1\n expFailed +=1\n\n table = Table(data)\n\n picture = Image(\"images/litmus.png\")\n picture.drawWidth = 100\n picture.drawHeight = 100\n picTable = Table([[picture]],100,100)\n\n elems = []\n\n # Adding logo\n elems.append(picTable)\n # Adding title\n text = \"LitmusChaos Report <br/> <br/>Experiments Result Summary\"\n para = Paragraph(text, styles['Title'])\n elems.append(para)\n\n ## Adding result table\n elems.append(table)\n\n style = TableStyle([\n ('BACKGROUND',(0,0),(3,0), colors.green),\n ('TEXTCOLOR',(0,0),(-1,0), colors.whitesmoke),\n ('ALIGN',(0,0),(-1,-1), 'CENTER'),\n ('FONTNAME',(0,0),(-1,0), 'Courier'),\n ('FONTSIZE',(0,0),(-1,0), 14),\n ('BOTTOMPADDING',(0,0),(-1,0), 12),\n ('BACKGROUND',(0,1),(-1,-1), colors.beige),\n ])\n\n ts = TableStyle([\n ('BOX',(0,0),(-1,-1),1,colors.black)\n ])\n\n ## Adding table style\n table.setStyle(style)\n table.setStyle(ts)\n\n para1 = Paragraph(\"The total number of passed experiments: %s \" % str(expPassed), styles['Heading3'])\n elems.append(para1)\n para2 = Paragraph(\"The total number of failed experiments: %s \" % str(expFailed), styles['Heading3'])\n elems.append(para2)\n para3 = Paragraph(\"The total experiment execution time: %s (HH:MM:SS)\" % str(totalTime), styles['Heading3'])\n elems.append(para3)\n\n pdf.build(elems)\n print_color(\"PDF Report Created Successfully \", bcolors.OKBLUE)","def dockerfile() -> co.Exec:\n image = co.Image(dockerfile=\"./docker/Dockerfile.simple\")\n return co.Exec(\n f\"python -c '{pretty_table_script}'\", image=image, doc=co.util.magic_doc()\n )","def test_baseimage_first(self):\n s = Stage()\n s += shell(commands=['abc'])\n s.name = 'bar'\n s.baseimage('foo')\n self.assertEqual(str(s), 'FROM foo AS bar\\n\\nRUN abc')"],"string":"[\n \"def test(params=''):\\n docker_exec('py.test {}'.format(params))\",\n \"def test_units(container):\\n short_image_name = container.image_name[container.image_name.rfind(\\\"/\\\") + 1 :]\\n host_data_dir = os.path.join(THIS_DIR, f\\\"../{short_image_name}/test/units\\\")\\n LOGGER.info(f\\\"Searching for units tests in {host_data_dir}\\\")\\n cont_data_dir = \\\"/home/jovyan/data\\\"\\n\\n if not os.path.exists(host_data_dir):\\n LOGGER.info(f\\\"Not found unit tests for image: {container.image_name}\\\")\\n return\\n\\n for test_file in os.listdir(host_data_dir):\\n LOGGER.info(f\\\"Running unit test: {test_file}\\\")\\n\\n c = container.run(\\n volumes={host_data_dir: {\\\"bind\\\": cont_data_dir, \\\"mode\\\": \\\"ro\\\"}},\\n tty=True,\\n command=[\\\"start.sh\\\", \\\"python\\\", f\\\"{cont_data_dir}/{test_file}\\\"],\\n )\\n rv = c.wait(timeout=30)\\n logs = c.logs(stdout=True).decode(\\\"utf-8\\\")\\n LOGGER.debug(logs)\\n assert rv == 0 or rv[\\\"StatusCode\\\"] == 0\",\n \"def docker_test(c, rebuild_venv=False):\\n cli_tasks.docker_test.run(c, rebuild_venv)\",\n \"def test_docker_build(rule_runner: RuleRunner) -> None:\\n rule_runner.write_files(\\n {\\n \\\"src/BUILD\\\": \\\"docker_image(name='test-image', image_tags=['1.0'])\\\",\\n \\\"src/Dockerfile\\\": \\\"FROM python:3.8\\\",\\n }\\n )\\n target = rule_runner.get_target(Address(\\\"src\\\", target_name=\\\"test-image\\\"))\\n result = run_docker(rule_runner, target)\\n assert len(result.artifacts) == 1\\n assert len(result.artifacts[0].extra_log_lines) == 2\\n assert \\\"Built docker image: test-image:1.0\\\" == result.artifacts[0].extra_log_lines[0]\\n assert \\\"Docker image ID:\\\" in result.artifacts[0].extra_log_lines[1]\\n assert \\\"<unknown>\\\" not in result.artifacts[0].extra_log_lines[1]\",\n \"def dockerize_test(ctx, binary, skip_cleanup=False):\\n import docker\\n\\n client = docker.from_env()\\n temp_folder = tempfile.mkdtemp(prefix=\\\"ddtest-\\\")\\n\\n ctx.run(\\\"cp %s %s/test.bin\\\" % (binary, temp_folder))\\n\\n with open(\\\"%s/Dockerfile\\\" % temp_folder, 'w') as stream:\\n stream.write(\\n \\\"\\\"\\\"FROM debian:stretch-slim\\nENV DOCKER_DD_AGENT=yes\\nWORKDIR /\\nADD https://github.com/docker/compose/releases/download/1.16.1/docker-compose-Linux-x86_64 /bin/docker-compose\\nRUN echo \\\"1804b0ce6596efe707b9cab05d74b161833ed503f0535a937dd5d17bea8fc50a /bin/docker-compose\\\" > sum && \\\\\\n sha256sum -c sum && \\\\\\n chmod +x /bin/docker-compose\\nCMD /test.bin\\nCOPY test.bin /test.bin\\n\\\"\\\"\\\"\\n )\\n # Handle optional testdata folder\\n if os.path.isdir(\\\"./testdata\\\"):\\n ctx.run(\\\"cp -R testdata %s\\\" % temp_folder)\\n stream.write(\\\"COPY testdata /testdata\\\")\\n\\n test_image, _ = client.images.build(path=temp_folder, rm=True)\\n\\n scratch_volume = client.volumes.create()\\n\\n test_container = client.containers.run(\\n test_image.id,\\n detach=True,\\n pid_mode=\\\"host\\\", # For origin detection\\n environment=[\\\"SCRATCH_VOLUME_NAME=\\\" + scratch_volume.name, \\\"SCRATCH_VOLUME_PATH=/tmp/scratch\\\",],\\n volumes={\\n '/var/run/docker.sock': {'bind': '/var/run/docker.sock', 'mode': 'ro'},\\n '/proc': {'bind': '/host/proc', 'mode': 'ro'},\\n '/sys/fs/cgroup': {'bind': '/host/sys/fs/cgroup', 'mode': 'ro'},\\n scratch_volume.name: {'bind': '/tmp/scratch', 'mode': 'rw'},\\n },\\n )\\n\\n exit_code = test_container.wait()['StatusCode']\\n\\n print(test_container.logs(stdout=True, stderr=False, stream=False))\\n\\n sys.stderr.write(test_container.logs(stdout=False, stderr=True, stream=False).decode(sys.stderr.encoding))\\n\\n if not skip_cleanup:\\n shutil.rmtree(temp_folder)\\n test_container.remove(v=True, force=True)\\n scratch_volume.remove(force=True)\\n client.images.remove(test_image.id)\\n\\n if exit_code != 0:\\n raise Exit(code=exit_code)\",\n \"def test_sx(params=''):\\n docker_exec('py.test -sx {}'.format(params))\",\n \"def test_docker_image_hint(mock_tools):\\n # Mock the return values for Docker verification\\n mock_tools.subprocess.check_output.side_effect = [\\n VALID_DOCKER_VERSION,\\n VALID_DOCKER_INFO,\\n VALID_BUILDX_VERSION,\\n VALID_USER_MAPPING_IMAGE_CACHE,\\n ]\\n\\n Docker.verify(mock_tools, image_tag=\\\"myimage:tagtorulethemall\\\")\\n\\n mock_tools.subprocess.run.assert_has_calls(\\n [\\n call(\\n [\\n \\\"docker\\\",\\n \\\"run\\\",\\n \\\"--rm\\\",\\n \\\"--volume\\\",\\n f\\\"{Path.cwd() / 'build'}:/host_write_test:z\\\",\\n \\\"myimage:tagtorulethemall\\\",\\n \\\"touch\\\",\\n PurePosixPath(\\\"/host_write_test/container_write_test\\\"),\\n ],\\n check=True,\\n ),\\n call(\\n [\\n \\\"docker\\\",\\n \\\"run\\\",\\n \\\"--rm\\\",\\n \\\"--volume\\\",\\n f\\\"{Path.cwd() / 'build'}:/host_write_test:z\\\",\\n \\\"myimage:tagtorulethemall\\\",\\n \\\"rm\\\",\\n \\\"-f\\\",\\n PurePosixPath(\\\"/host_write_test/container_write_test\\\"),\\n ],\\n check=True,\\n ),\\n ]\\n )\",\n \"def _docker_run_pytest(\\n self,\\n test_image: str,\\n keep_container: bool,\\n test_xml: str,\\n no_coverage: bool = False,\\n should_disable_network: bool = False,\\n ) -> Tuple[int, str, dict]:\\n network_status = \\\"disabled\\\" if should_disable_network else \\\"enabled\\\"\\n\\n log_prompt = f\\\"{self._pack_name} - Pytest - Image {test_image}, network: {network_status}\\\"\\n logger.info(f\\\"{log_prompt} - Start\\\")\\n container_name = f\\\"{self._pack_name}-pytest\\\"\\n # Check if previous run left container a live if it does, Remove it\\n self._docker_remove_container(container_name)\\n # Collect tests\\n exit_code = SUCCESS\\n output = \\\"\\\"\\n test_json = {}\\n try:\\n # Running pytest container\\n cov_file_path = Path.joinpath(self._pack_abs_dir, \\\".coverage\\\")\\n cov = self._pack_abs_dir.stem if not no_coverage else \\\"\\\"\\n uid = os.getuid() or 4000\\n logger.debug(\\n f\\\"{log_prompt} - user uid for running lint/test: {uid}\\\"\\n ) # lgtm[py/clear-text-logging-sensitive-data]\\n container: docker.models.containers.Container = (\\n get_docker().create_container(\\n name=container_name,\\n image=test_image,\\n command=[\\n build_pytest_command(test_xml=test_xml, json=True, cov=cov)\\n ],\\n user=f\\\"{uid}:4000\\\",\\n files_to_push=[(self._pack_abs_dir, \\\"/devwork\\\")],\\n environment=self._facts[\\\"env_vars\\\"],\\n network_disabled=should_disable_network,\\n )\\n )\\n container.start()\\n stream_docker_container_output(container.logs(stream=True))\\n # Waiting for container to be finished\\n container_status: dict = container.wait()\\n # Getting container exit code\\n container_exit_code = container_status.get(\\\"StatusCode\\\")\\n # Getting container logs\\n logger.info(f\\\"{log_prompt} - exit-code: {container_exit_code}\\\")\\n if container_exit_code in [0, 1, 2, 5]:\\n # 0-All tests passed\\n # 1-Tests were collected and run but some of the tests failed\\n # 2-Test execution was interrupted by the user\\n # 5-No tests were collected\\n\\n if test_xml:\\n test_data_xml = get_file_from_container(\\n container_obj=container,\\n container_path=\\\"/devwork/report_pytest.xml\\\",\\n )\\n xml_apth = Path(test_xml) / f\\\"{self._pack_name}_pytest.xml\\\"\\n\\n with open(file=xml_apth, mode=\\\"bw\\\") as f:\\n f.write(test_data_xml) # type: ignore\\n\\n if cov:\\n cov_data = get_file_from_container(\\n container_obj=container, container_path=\\\"/devwork/.coverage\\\"\\n )\\n cov_data = (\\n cov_data if isinstance(cov_data, bytes) else cov_data.encode()\\n )\\n with open(cov_file_path, \\\"wb\\\") as coverage_file:\\n coverage_file.write(cov_data)\\n coverage_report_editor(\\n cov_file_path,\\n os.path.join(\\n self._pack_abs_dir, f\\\"{self._pack_abs_dir.stem}.py\\\"\\n ),\\n )\\n\\n test_json = json.loads(\\n get_file_from_container(\\n container_obj=container,\\n container_path=\\\"/devwork/report_pytest.json\\\",\\n encoding=\\\"utf-8\\\",\\n )\\n )\\n for test in test_json.get(\\\"report\\\", {}).get(\\\"tests\\\"):\\n if test.get(\\\"call\\\", {}).get(\\\"longrepr\\\"):\\n test[\\\"call\\\"][\\\"longrepr\\\"] = test[\\\"call\\\"][\\\"longrepr\\\"].split(\\\"\\\\n\\\")\\n if container_exit_code in [0, 5]:\\n logger.info(f\\\"{log_prompt} - Successfully finished\\\")\\n exit_code = SUCCESS\\n elif container_exit_code in [2]:\\n output = container.logs().decode(\\\"utf-8\\\")\\n exit_code = FAIL\\n else:\\n logger.error(f\\\"{log_prompt} - Finished, errors found\\\")\\n exit_code = FAIL\\n elif container_exit_code in [3, 4]:\\n # 3-Internal error happened while executing tests\\n # 4-pytest command line usage error\\n logger.critical(f\\\"{log_prompt} - Usage error\\\")\\n exit_code = RERUN\\n output = container.logs().decode(\\\"utf-8\\\")\\n else:\\n # Any other container exit code\\n logger.error(\\n f\\\"{log_prompt} - Finished, docker container error found ({container_exit_code})\\\"\\n )\\n exit_code = FAIL\\n # Remove container if not needed\\n if keep_container:\\n logger.info(f\\\"{log_prompt} - Container name {container_name}\\\")\\n container.commit(repository=container_name.lower(), tag=\\\"pytest\\\")\\n else:\\n try:\\n container.remove(force=True)\\n except docker.errors.NotFound as e:\\n logger.critical(f\\\"{log_prompt} - Unable to remove container {e}\\\")\\n except (docker.errors.ImageNotFound, docker.errors.APIError) as e:\\n logger.critical(f\\\"{log_prompt} - Unable to run pytest container {e}\\\")\\n exit_code = RERUN\\n logger.info(f\\\"{self._pack_name} - Pytest finished image {test_image}\\\")\\n return exit_code, output, test_json\",\n \"def check():\\n cmake('tests')\\n docker('./{build}/tests', build=BUILD)\",\n \"def main():\\n extensions = os.getenv('EXTENSIONS', DEFAULT_EXTENSIONS).split(',')\\n extensions.sort()\\n docker_contents = []\\n contents = travis_contents()\\n data = yaml.safe_load(contents)\\n\\n # set the version\\n php_versions = data.get('php', [DEFAULT_VERSION])\\n php_version = php_versions[0]\\n docker_contents.append('FROM php:{0}'.format(php_version))\\n\\n # ensure all the php shit exists\\n\\n # LC_ALL=en_US.UTF-8\\n docker_contents.append('ENV DEBIAN_FRONTEND=noninteractive LC_ALL=C DOCKER=1') # noqa\\n docker_contents.append('RUN apt-get update')\\n docker_contents.append('RUN apt-get -qq install -qq -y php5-cli php-pear')\\n # for composer\\n docker_contents.append('RUN apt-get -qq install -qq -y git-core')\\n # for curl\\n docker_contents.append('RUN apt-get -qq install -qq -y libcurl4-openssl-dev')\\n # for intl\\n docker_contents.append('RUN apt-get -qq install -qq -y libicu-dev')\\n\\n # installs user-specified packages\\n packages = os.getenv('PACKAGES', '')\\n if len(os.getenv('PACKAGES', '')) > 0:\\n packages = packages.split(',')\\n docker_contents.append('RUN apt-get -qq install -qq -y {0}'.format(\\n ' '.join(packages)\\n ))\\n\\n for extension in extensions:\\n if extension in available_extensions:\\n docker_contents.append('RUN docker-php-ext-install {0}'.format(\\n extension\\n ))\\n else:\\n docker_contents.append('RUN apt-get -qq install -qq -y php5-{0} && pecl install -o -f {0} && \\\\\\\\'.format(extension))\\n docker_contents.append(' rm -rf /tmp/pear && \\\\\\\\')\\n if extension in ZEND_EXTENSIONS:\\n docker_contents.append(' echo \\\"zend_extension=/usr/local/lib/php/extensions/no-debug-non-zts-{0}/xdebug.so\\\" > /usr/local/etc/php/conf.d/{1}.ini'.format(\\n phpextension_paths[php_version],\\n extension\\n ))\\n else:\\n docker_contents.append(' echo \\\"extension={0}.so\\\" > /usr/local/etc/php/conf.d/{0}.ini'.format(extension))\\n\\n # ensure we have all the proper php testing stuff\\n docker_contents.append('RUN \\\\\\\\')\\n docker_contents.append(' curl -sSL https://phar.phpunit.de/phpunit-old.phar > phpunit.phar && \\\\\\\\')\\n docker_contents.append(' curl -sS https://getcomposer.org/installer | php && \\\\\\\\')\\n docker_contents.append(' mv composer.phar /usr/local/bin/composer && \\\\\\\\')\\n docker_contents.append(' mv phpunit.phar /usr/local/bin/phpunit && \\\\\\\\')\\n docker_contents.append(' chmod +x /usr/local/bin/composer /usr/local/bin/phpunit && \\\\\\\\')\\n docker_contents.append(' phpunit --version')\\n\\n # set the environment\\n environments = data.get('env', {'matrix': 'CI=1'}).get('matrix', [])\\n docker_env = environments[0]\\n docker_contents.append('ENV {0}'.format(docker_env))\\n\\n docker_contents.append('ADD composer.json /app/composer.json')\\n docker_contents.append('WORKDIR /app')\\n docker_contents.append('RUN echo \\\"date.timezone = UTC\\\" > /usr/local/etc/php/conf.d/timezone.ini') # noqa\\n\\n for script in data.get('before_script', []):\\n docker_contents.append('RUN {0}'.format(script))\\n\\n docker_contents.append('ADD . /app')\\n\\n # HACK\\n docker_contents.append('ENV COVERALLS=1 DEFAULT=1 PHPCS=1')\\n\\n for script in data.get('script', []):\\n docker_contents.append('RUN {0}'.format(script))\\n\\n with open('{0}/Dockerfile'.format(os.getcwd()), 'w') as f:\\n for line in docker_contents:\\n f.write(\\\"{0}\\\\n\\\\n\\\".format(line))\",\n \"def test_in_docker_machine(self):\\n new_executor = self.executor.in_docker_machine('confluent')\\n output, _err = new_executor.docker.run('a-machine:a-tag', remove=executor.NO_VALUE,\\n interactive=executor.NO_VALUE,\\n terminal=executor.NO_VALUE).batch()\\n self.assertEqual(output, 'hello\\\\r\\\\n')\",\n \"def integration_tests(ctx, skip_image_build=False, skip_build=False, python_command=\\\"python3\\\"):\\n if not skip_image_build:\\n # postpone the import otherwise `image_build` will be added to the docker\\n # namespace\\n from .dogstatsd import image_build\\n\\n image_build(ctx, skip_build=skip_build)\\n\\n print(\\\"Starting docker integration tests\\\")\\n env = {\\\"DOCKER_IMAGE\\\": DOGSTATSD_TAG}\\n ctx.run(\\\"{} ./test/integration/docker/dsd_listening.py\\\".format(python_command), env=env)\",\n \"def run_sagemaker_tests(images):\\n if not images:\\n return\\n pool_number = len(images)\\n with Pool(pool_number) as p:\\n p.map(run_sagemaker_pytest_cmd, images)\",\n \"def execute_local_tests(image, pytest_cache_params):\\n account_id = os.getenv(\\\"ACCOUNT_ID\\\", boto3.client(\\\"sts\\\").get_caller_identity()[\\\"Account\\\"])\\n pytest_cache_util = PytestCache(boto3.client(\\\"s3\\\"), account_id)\\n ec2_client = boto3.client(\\\"ec2\\\", config=Config(retries={\\\"max_attempts\\\": 10}), region_name=DEFAULT_REGION)\\n pytest_command, path, tag, job_type = generate_sagemaker_pytest_cmd(image, SAGEMAKER_LOCAL_TEST_TYPE)\\n pytest_command += \\\" --last-failed --last-failed-no-failures all \\\"\\n print(pytest_command)\\n framework, _ = get_framework_and_version_from_tag(image)\\n random.seed(f\\\"{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\\\")\\n ec2_key_name = f\\\"{job_type}_{tag}_sagemaker_{random.randint(1, 1000)}\\\"\\n region = os.getenv(\\\"AWS_REGION\\\", DEFAULT_REGION)\\n ec2_ami_id = UBUNTU_18_BASE_DLAMI_US_EAST_1 if region == \\\"us-east-1\\\" else UBUNTU_18_BASE_DLAMI_US_WEST_2\\n sm_tests_tar_name = \\\"sagemaker_tests.tar.gz\\\"\\n ec2_test_report_path = os.path.join(UBUNTU_HOME_DIR, \\\"test\\\", f\\\"{job_type}_{tag}_sm_local.xml\\\")\\n instance_id = \\\"\\\"\\n ec2_conn = None\\n try:\\n key_file = generate_ssh_keypair(ec2_client, ec2_key_name)\\n print(f\\\"Launching new Instance for image: {image}\\\")\\n instance_id, ip_address = launch_sagemaker_local_ec2_instance(\\n image,\\n ec2_ami_id,\\n ec2_key_name,\\n region\\n )\\n ec2_conn = ec2_utils.get_ec2_fabric_connection(instance_id, key_file, region)\\n ec2_conn.put(sm_tests_tar_name, f\\\"{UBUNTU_HOME_DIR}\\\")\\n ec2_conn.run(f\\\"$(aws ecr get-login --no-include-email --region {region})\\\")\\n try:\\n ec2_conn.run(f\\\"docker pull {image}\\\", timeout=600)\\n except invoke.exceptions.CommandTimedOut as e:\\n output = ec2_conn.run(f\\\"docker images {image} --format '{{.Repository}}:{{.Tag}}'\\\").stdout.strip(\\\"\\\\n\\\")\\n if output != image:\\n raise DLCSageMakerLocalTestFailure(\\n f\\\"Image pull for {image} failed.\\\\ndocker images output = {output}\\\"\\n ) from e\\n ec2_conn.run(f\\\"tar -xzf {sm_tests_tar_name}\\\")\\n kill_background_processes_and_run_apt_get_update(ec2_conn)\\n with ec2_conn.cd(path):\\n install_sm_local_dependencies(framework, job_type, image, ec2_conn, ec2_ami_id)\\n pytest_cache_util.download_pytest_cache_from_s3_to_ec2(ec2_conn, path, **pytest_cache_params)\\n # Workaround for mxnet cpu training images as test distributed\\n # causes an issue with fabric ec2_connection\\n if framework == \\\"mxnet\\\" and job_type == \\\"training\\\" and \\\"cpu\\\" in image:\\n try:\\n ec2_conn.run(pytest_command, timeout=1000, warn=True)\\n except exceptions.CommandTimedOut as exc:\\n print(f\\\"Ec2 connection timed out for {image}, {exc}\\\")\\n finally:\\n print(f\\\"Downloading Test reports for image: {image}\\\")\\n ec2_conn.close()\\n ec2_conn_new = ec2_utils.get_ec2_fabric_connection(instance_id, key_file, region)\\n ec2_conn_new.get(ec2_test_report_path,\\n os.path.join(\\\"test\\\", f\\\"{job_type}_{tag}_sm_local.xml\\\"))\\n output = subprocess.check_output(f\\\"cat test/{job_type}_{tag}_sm_local.xml\\\", shell=True,\\n executable=\\\"/bin/bash\\\")\\n pytest_cache_util.upload_pytest_cache_from_ec2_to_s3(ec2_conn_new, path, **pytest_cache_params)\\n if 'failures=\\\"0\\\"' not in str(output):\\n raise ValueError(f\\\"Sagemaker Local tests failed for {image}\\\")\\n else:\\n ec2_conn.run(pytest_command)\\n print(f\\\"Downloading Test reports for image: {image}\\\")\\n ec2_conn.get(ec2_test_report_path, os.path.join(\\\"test\\\", f\\\"{job_type}_{tag}_sm_local.xml\\\"))\\n finally:\\n with ec2_conn.cd(path):\\n pytest_cache_util.upload_pytest_cache_from_ec2_to_s3(ec2_conn, path, **pytest_cache_params)\\n print(f\\\"Terminating Instances for image: {image}\\\")\\n ec2_utils.terminate_instance(instance_id, region)\\n print(f\\\"Destroying ssh Key_pair for image: {image}\\\")\\n destroy_ssh_keypair(ec2_client, ec2_key_name)\\n # return None here to prevent errors from multiprocessing.map(). Without this it returns some object by default\\n # which is causing \\\"cannot pickle '_thread.lock' object\\\" error\\n return None\",\n \"def test_build_artifacts_invokes_docker_commands(mocker):\\n\\n tmp_filename = '/var/folders/xw/yk2rrhks1w72y0zr_7t7b851qlt8b3/T/tmp52bd77s3'\\n mock_builder = mocker.patch('juniper.actions.build_compose', return_value=tmp_filename)\\n\\n # Mocking the dependencies of this action. These three high level packages are\\n # needed to invoke docker-compose in the right context!\\n mocker.patch('juniper.actions.os')\\n mocker.patch('juniper.actions.shutil')\\n mock_subprocess_run = mocker.patch('juniper.actions.subprocess.run')\\n\\n compose_cmd_calls = [\\n mocker.call([\\\"docker-compose\\\", \\\"-f\\\", tmp_filename, '--project-directory', '.', 'down']),\\n mocker.call([\\\"docker-compose\\\", \\\"-f\\\", tmp_filename, '--project-directory', '.', 'up'])\\n ]\\n\\n processor_ctx = reader('./tests/manifests/processor-test.yml')\\n actions.build_artifacts(logger, processor_ctx)\\n\\n mock_subprocess_run.assert_has_calls(compose_cmd_calls)\\n mock_builder.assert_called_once()\",\n \"def run_in_docker(image, commands): # pragma: no cover\\n repo_dir = os.path.abspath(os.path.join(__file__, '../../'))\\n mount_option = '{}:/mnt:ro'.format(repo_dir)\\n\\n cmd = ('docker', 'run', '-v', mount_option, '-i', image, 'sh')\\n proc = subprocess.Popen(\\n cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,\\n )\\n\\n lines = '\\\\n'.join(commands)\\n return proc.communicate(lines.encode('utf-8'))[0].decode('utf-8')\",\n \"def test_go_docker(mocker):\\n mocked_function = mocker.patch(\\\"os.system\\\")\\n go_docker()\\n mocked_function.assert_called_once_with(\\\"docker-compose up -d\\\")\",\n \"def test(tag_name: str):\\n\\n image_full_name = f\\\"{GITLAB_IMAGE_URL}:{tag_name}-test\\\"\\n _build(tag_name=tag_name, image_full_name=image_full_name)\\n _test(image_full_name=image_full_name)\",\n \"def build_test_version_of_container_image(\\n image_type: ImageType,\\n image_builder_cls: Type[ContainerisedAgentBuilder],\\n architecture: CpuArch,\\n result_image_name: str,\\n ready_image_oci_tarball: pl.Path = None,\\n install_additional_test_libs: bool = True,\\n):\\n\\n image_builder = image_builder_cls()\\n\\n registry_container_name = \\\"agent_image_e2e_test_registry\\\"\\n\\n delete_container(container_name=registry_container_name)\\n\\n # Create temporary local registry to push production image there.\\n subprocess.run(\\n [\\n \\\"docker\\\",\\n \\\"run\\\",\\n \\\"-d\\\",\\n \\\"--rm\\\",\\n \\\"-p=5000:5000\\\",\\n f\\\"--name={registry_container_name}\\\",\\n \\\"registry:2\\\",\\n ],\\n check=True,\\n )\\n try:\\n all_image_tags = image_builder.generate_final_registry_tags(\\n image_type=image_type,\\n registry=\\\"localhost:5000\\\",\\n name_prefix=\\\"user\\\",\\n tags=[\\\"prod\\\"],\\n )\\n\\n # Publish image to the local registry\\n image_builder.publish(\\n image_type=image_type,\\n tags=all_image_tags,\\n existing_oci_layout_tarball=ready_image_oci_tarball,\\n no_verify_tls=True,\\n )\\n\\n prod_image_tag = all_image_tags[0]\\n if install_additional_test_libs:\\n\\n # Build agent image requirements, because it also includes requirements (like coverage) for testing.\\n requirement_libs_dir = image_builder.build_requirement_libs(\\n architecture=architecture,\\n )\\n\\n # Build testable image.\\n buildx_build(\\n dockerfile_path=_PARENT_DIR / \\\"Dockerfile\\\",\\n context_path=_PARENT_DIR,\\n architectures=[architecture],\\n build_contexts={\\n \\\"prod_image\\\": f\\\"docker-image://{prod_image_tag}\\\",\\n \\\"requirement_libs\\\": str(requirement_libs_dir),\\n },\\n output=DockerImageBuildOutput(\\n name=result_image_name,\\n ),\\n )\\n else:\\n subprocess.run(\\n [\\n \\\"docker\\\",\\n \\\"pull\\\",\\n prod_image_tag,\\n ],\\n check=True,\\n )\\n\\n subprocess.run(\\n [\\n \\\"docker\\\",\\n \\\"tag\\\",\\n prod_image_tag,\\n result_image_name,\\n ],\\n check=True,\\n )\\n finally:\\n delete_container(container_name=registry_container_name)\\n\\n return result_image_name\",\n \"def execute_sagemaker_remote_tests(image):\\n pytest_command, path, tag, job_type = generate_sagemaker_pytest_cmd(image, SAGEMAKER_REMOTE_TEST_TYPE)\\n context = Context()\\n with context.cd(path):\\n context.run(f\\\"virtualenv {tag}\\\")\\n with context.prefix(f\\\"source {tag}/bin/activate\\\"):\\n context.run(\\\"pip install -r requirements.txt\\\", warn=True)\\n res = context.run(pytest_command, warn=True)\\n metrics_utils.send_test_result_metrics(res.return_code)\\n if res.failed:\\n raise DLCSageMakerRemoteTestFailure(\\n f\\\"{pytest_command} failed with error code: {res.return_code}\\\\n\\\"\\n f\\\"Traceback:\\\\n{res.stdout}\\\"\\n )\",\n \"def ping_docker():\\n with Docker('unittest-36', image='python:3.6') as tun:\\n return tun.call(python_version)[:2]\",\n \"def test_docker_api(proc):\\n assert len(proc.docker_container_id)\\n assert proc.docker_inspect()['Id'].startswith(proc.docker_container_id)\\n assert proc.docker_stats()['Container'] == proc.docker_container_id\",\n \"def test_run_container1(client):\\n with client.application.app_context():\\n pytest.skip(\\\"Not implemented\\\")\",\n \"def _run_lint_on_docker_image(\\n self,\\n no_pylint: bool,\\n no_test: bool,\\n no_pwsh_analyze: bool,\\n no_pwsh_test: bool,\\n keep_container: bool,\\n test_xml: str,\\n no_coverage: bool,\\n no_flake8: bool,\\n no_vulture: bool,\\n should_disable_network: bool,\\n ):\\n log_prompt = f\\\"{self._pack_name} - Run Lint On Docker Image\\\"\\n if self._facts[\\\"images\\\"]:\\n logger.info(\\n f'{log_prompt} - Running lint: Number of images={self._facts[\\\"images\\\"]}'\\n )\\n else:\\n logger.info(f\\\"{log_prompt} - Skipped\\\")\\n for image in self._facts[\\\"images\\\"]:\\n logger.info(f\\\"{log_prompt} - Running lint on docker image {image[0]}\\\")\\n # Docker image status - visualize\\n status = {\\n \\\"image\\\": image[0],\\n \\\"image_errors\\\": \\\"\\\",\\n \\\"pylint_errors\\\": \\\"\\\",\\n \\\"pytest_errors\\\": \\\"\\\",\\n \\\"pytest_json\\\": {},\\n \\\"pwsh_analyze_errors\\\": \\\"\\\",\\n \\\"pwsh_test_errors\\\": \\\"\\\",\\n }\\n # Creating image if pylint specified or found tests and tests specified\\n image_id = \\\"\\\"\\n errors = \\\"\\\"\\n for trial in range(2):\\n image_id, errors = self._docker_image_create(docker_base_image=image)\\n if not errors:\\n break\\n\\n if image_id and not errors:\\n # Set image creation status\\n for check in [\\n \\\"pylint\\\",\\n \\\"pytest\\\",\\n \\\"pwsh_analyze\\\",\\n \\\"pwsh_test\\\",\\n \\\"flake8\\\",\\n \\\"vulture\\\",\\n ]:\\n exit_code = SUCCESS\\n output = \\\"\\\"\\n for trial in range(2):\\n if self._pkg_lint_status[\\\"pack_type\\\"] == TYPE_PYTHON:\\n if (\\n not no_flake8\\n and check == \\\"flake8\\\"\\n and (\\n self._facts[\\\"lint_files\\\"]\\n or self._facts[\\\"lint_unittest_files\\\"]\\n )\\n ):\\n exit_code, output = self._docker_run_linter(\\n linter=check,\\n test_image=image_id,\\n keep_container=keep_container,\\n )\\n if (\\n not no_vulture\\n and check == \\\"vulture\\\"\\n and self._facts[\\\"lint_files\\\"]\\n ):\\n exit_code, output = self._docker_run_linter(\\n linter=check,\\n test_image=image_id,\\n keep_container=keep_container,\\n )\\n # Perform pylint\\n if (\\n not no_pylint\\n and check == \\\"pylint\\\"\\n and self._facts[\\\"lint_files\\\"]\\n ):\\n exit_code, output = self._docker_run_linter(\\n linter=check,\\n test_image=image_id,\\n keep_container=keep_container,\\n )\\n # Perform pytest\\n elif (\\n not no_test\\n and self._facts[\\\"test\\\"]\\n and check == \\\"pytest\\\"\\n ):\\n exit_code, output, test_json = self._docker_run_pytest(\\n test_image=image_id,\\n keep_container=keep_container,\\n test_xml=test_xml,\\n no_coverage=no_coverage,\\n should_disable_network=should_disable_network,\\n )\\n status[\\\"pytest_json\\\"] = test_json\\n elif self._pkg_lint_status[\\\"pack_type\\\"] == TYPE_PWSH:\\n # Perform powershell analyze\\n if (\\n not no_pwsh_analyze\\n and check == \\\"pwsh_analyze\\\"\\n and self._facts[\\\"lint_files\\\"]\\n ):\\n exit_code, output = self._docker_run_pwsh_analyze(\\n test_image=image_id, keep_container=keep_container\\n )\\n # Perform powershell test\\n elif not no_pwsh_test and check == \\\"pwsh_test\\\":\\n exit_code, output = self._docker_run_pwsh_test(\\n test_image=image_id, keep_container=keep_container\\n )\\n # If lint check perform and failed on reason related to environment will run twice,\\n # But it failing in second time it will count as test failure.\\n if (\\n (exit_code == RERUN and trial == 1)\\n or exit_code == FAIL\\n or exit_code == SUCCESS\\n ):\\n if exit_code in [RERUN, FAIL]:\\n if check in {\\\"flake8\\\", \\\"vulture\\\"}:\\n self._handle_lint_results(exit_code, check, output)\\n else:\\n self._pkg_lint_status[\\\"exit_code\\\"] |= EXIT_CODES[\\n check\\n ]\\n status[f\\\"{check}_errors\\\"] = output\\n break\\n else:\\n status[\\\"image_errors\\\"] = str(errors)\\n self._pkg_lint_status[\\\"exit_code\\\"] += EXIT_CODES[\\\"image\\\"]\\n # Add image status to images\\n self._pkg_lint_status[\\\"images\\\"].append(status)\\n logger.info(f\\\"{log_prompt} - Finished linting on docker image {image[0]}\\\")\\n\\n if self._facts[\\\"images\\\"]:\\n logger.info(\\n f'{log_prompt} - Finished linting. Number of images={self._facts[\\\"images\\\"]}'\\n )\",\n \"def test_create_container(self):\\n pass\",\n \"def test_build_image(self):\\n labels = {\\\"apple\\\": \\\"red\\\", \\\"grape\\\": \\\"green\\\"}\\n self.docker.images.build(\\n path=\\\"test/python/docker/build_labels\\\",\\n labels=labels,\\n tag=\\\"labels\\\",\\n isolation=\\\"default\\\",\\n )\\n image = self.docker.images.get(\\\"labels\\\")\\n self.assertEqual(image.labels[\\\"apple\\\"], labels[\\\"apple\\\"])\\n self.assertEqual(image.labels[\\\"grape\\\"], labels[\\\"grape\\\"])\",\n \"def run(args):\\n docker(' '.join(args))\",\n \"def _get_dockerfiles_for_test() -> str:\\n project_root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\\n dockerfiles_dir = os.path.join(project_root_dir, \\\"dockerfiles\\\")\\n if sys.version_info[0:2] == (3, 6):\\n return os.path.join(dockerfiles_dir, \\\"centos7.Dockerfile\\\")\\n elif sys.version_info[0:2] == (3, 9):\\n return os.path.join(dockerfiles_dir, \\\"rocky8.Dockerfile\\\")\\n else:\\n raise Exception(\\n \\\"Running the tests with INMANTA_TEST_INFRA_SETUP=true is only supported using a python3.6 or python3.9 venv\\\"\\n )\",\n \"def test_run_container2(client):\\n with client.application.app_context():\\n pytest.skip(\\\"Not implemented\\\")\",\n \"def test_node_start(self, m_attach_and_stream,\\n m_find_or_pull_node_image, m_docker,\\n m_docker_client, m_client, m_install_kube,\\n m_warn_if_hostname_conflict, m_warn_if_unknown_ip,\\n m_get_host_ips, m_check_system, m_os_getenv,\\n m_os_makedirs, m_os_path_exists):\\n # Set up mock objects\\n m_os_path_exists.return_value = False\\n ip_1 = '1.1.1.1'\\n ip_2 = '2.2.2.2'\\n m_get_host_ips.return_value = [ip_1, ip_2]\\n m_os_getenv.side_effect = iter(['1.1.1.1:80', \\\"\\\"])\\n m_docker.utils.create_host_config.return_value = 'host_config'\\n container = {'Id':666}\\n m_docker_client.create_container.return_value = container\\n\\n # Set up arguments\\n node_image = 'node_image'\\n log_dir = './log_dir'\\n ip = ''\\n ip6 = 'aa:bb::zz'\\n as_num = ''\\n detach = False\\n kubernetes = True\\n\\n # Call method under test\\n node.node_start(\\n node_image, log_dir, ip, ip6, as_num, detach, kubernetes\\n )\\n\\n # Set up variables used in assertion statements\\n environment = [\\n \\\"HOSTNAME=%s\\\" % node.hostname,\\n \\\"IP=%s\\\" % ip_2,\\n \\\"IP6=%s\\\" % ip6,\\n \\\"ETCD_AUTHORITY=1.1.1.1:80\\\", # etcd host:port\\n \\\"FELIX_ETCDADDR=1.1.1.1:80\\\", # etcd host:port\\n \\\"POLICY_ONLY_CALICO=\\\",\\n ]\\n binds = {\\n \\\"/proc\\\":\\n {\\n \\\"bind\\\": \\\"/proc_host\\\",\\n \\\"ro\\\": False\\n },\\n log_dir:\\n {\\n \\\"bind\\\": \\\"/var/log/calico\\\",\\n \\\"ro\\\": False\\n },\\n \\\"/run/docker/plugins\\\":\\n {\\n \\\"bind\\\": \\\"/usr/share/docker/plugins\\\",\\n \\\"ro\\\": False\\n }\\n }\\n\\n # Assert\\n m_os_path_exists.assert_called_once_with(log_dir)\\n m_os_makedirs.assert_called_once_with(log_dir)\\n m_check_system.assert_called_once_with(fix=False, quit_if_error=False)\\n m_get_host_ips.assert_called_once_with(exclude=[\\\"docker0\\\"])\\n m_warn_if_unknown_ip.assert_called_once_with(ip_2, ip6)\\n m_warn_if_hostname_conflict.assert_called_once_with(ip_2)\\n m_install_kube.assert_called_once_with(node.KUBERNETES_PLUGIN_DIR)\\n m_client.get_ip_pools.assert_has_calls([call(4), call(6)])\\n m_client.ensure_global_config.assert_called_once_with()\\n m_client.create_host.assert_called_once_with(\\n node.hostname, ip_2, ip6, as_num\\n )\\n m_docker_client.remove_container.assert_called_once_with(\\n 'calico-node', force=True\\n )\\n\\n getenv_calls = [call(ETCD_AUTHORITY_ENV, ETCD_AUTHORITY_DEFAULT),\\n call(node.POLICY_ONLY_ENV, \\\"\\\")]\\n m_os_getenv.assert_has_calls(getenv_calls)\\n\\n m_docker.utils.create_host_config.assert_called_once_with(\\n privileged=True,\\n restart_policy={\\\"Name\\\":\\\"Always\\\"},\\n network_mode=\\\"host\\\",\\n binds=binds\\n )\\n m_find_or_pull_node_image.assert_called_once_with(\\n 'node_image'\\n )\\n m_docker_client.create_container.assert_called_once_with(\\n node_image,\\n name='calico-node',\\n detach=True,\\n environment=environment,\\n host_config='host_config',\\n volumes=['/proc_host',\\n '/var/log/calico',\\n '/usr/share/docker/plugins']\\n )\\n m_docker_client.start.assert_called_once_with(container)\\n m_attach_and_stream.assert_called_once_with(container)\",\n \"def test_basic_run(self):\\n self.expect_datatore_lookup('SomeBlobKey', True)\\n self.expect_open_image('SomeBlobKey', (1600, 1200))\\n self.expect_resize(blob_image._DEFAULT_SERVING_SIZE)\\n self.expect_encode_image('SomeImageInJpeg')\\n self.run_request('image/jpeg', 'SomeImageInJpeg')\",\n \"def run(config, webhook, output):\\n start_time = datetime.now()\\n test_project_name = f'test-{Global.docker_project_name(webhook)}-{config[\\\"language\\\"]}-{config[\\\"version\\\"]}'\\n context = 'test'\\n\\n # Build the image\\n try:\\n image = Image.build_image(config, webhook, context)\\n image_output = f'Test image created.\\\\n{image}'\\n print(image_output)\\n except subprocess.TimeoutExpired:\\n final_output = 'Error: Harvey timed out building the Test image.'\\n print(final_output)\\n Utils.kill(final_output, webhook)\\n except subprocess.CalledProcessError as error:\\n final_output = f'{output}\\\\nError: Harvey could not build the Test image.\\\\n{error.output}'\\n print(final_output)\\n Utils.kill(final_output, webhook)\\n\\n # Create a container\\n container = Container.create_container(test_project_name)\\n if container:\\n container_output = 'Test container created.'\\n print(container_output)\\n else:\\n final_output = f'{output}\\\\n{image_output}\\\\nError: Harvey could not create the Test container.'\\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\\n Image.remove_image(test_project_name)\\n Utils.kill(final_output, webhook)\\n\\n # Start the container\\n start = Container.start_container(test_project_name)\\n if start:\\n start_output = 'Test container started.'\\n print(start_output)\\n else:\\n final_output = f'{output}\\\\n{image_output}\\\\n{container_output}\\\\nError: Harvey could not start the container.'\\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\\n Image.remove_image(image[0])\\n Container.remove_container(test_project_name)\\n Utils.kill(final_output, webhook)\\n\\n # Wait for container to exit\\n wait = Container.wait_container(test_project_name)\\n if wait:\\n wait_output = 'Waiting for Test container to exit.'\\n print(wait_output)\\n else:\\n final_output = (\\n f'{output}'\\n f'\\\\n{image_output}'\\n f'\\\\n{container_output}'\\n f'\\\\n{start_output}'\\n '\\\\nError: Harvey could not wait for the container.'\\n )\\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\\n Image.remove_image(image[0])\\n Container.remove_container(test_project_name)\\n Utils.kill(final_output, webhook)\\n\\n # Return logs\\n logs = Container.inspect_container_logs(test_project_name)\\n if logs:\\n logs_output = (\\n '\\\\nTest logs:\\\\n'\\n '============================================================\\\\n'\\n f'{logs.text}'\\n '============================================================\\\\n'\\n )\\n print(logs_output)\\n else:\\n final_output = (\\n f'{output}'\\n f'\\\\n{image_output}'\\n f'\\\\n{container_output}'\\n f'\\\\n{start_output}'\\n f'\\\\n{wait_output}'\\n '\\\\nError: Harvey could not create the container logs.'\\n )\\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\\n Image.remove_image(image[0])\\n Container.remove_container(test_project_name)\\n Utils.kill(final_output, webhook)\\n\\n # Remove container and image after it's done\\n remove = Container.remove_container(test_project_name)\\n if remove:\\n Image.remove_image(image[0])\\n remove_output = 'Test container and image removed.'\\n print(remove_output)\\n else:\\n final_output = (\\n f'{output}'\\n f'\\\\n{image_output}'\\n f'\\\\n{container_output}'\\n f'\\\\n{start_output}'\\n f'\\\\n{wait_output}'\\n f'\\\\n{logs_output}'\\n '\\\\nError: Harvey could not remove the container and/or image.'\\n )\\n # Cleanup items as a result of failure so we don't leave orphaned images/containers\\n Image.remove_image(image[0])\\n Container.remove_container(test_project_name)\\n Utils.kill(final_output, webhook)\\n\\n execution_time = f'\\\\nTest stage execution time: {datetime.now() - start_time}'\\n final_output = (\\n f'{image_output}'\\n f'\\\\n{container_output}'\\n f'\\\\n{start_output}'\\n f'\\\\n{wait_output}'\\n f'\\\\n{logs_output}'\\n f'\\\\n{remove_output}'\\n f'\\\\n{execution_time}'\\n )\\n print(execution_time)\\n\\n return final_output\",\n \"def do_run(cs, args):\\n opts = {}\\n opts['name'] = args.name\\n opts['image'] = args.image\\n opts['memory'] = args.memory\\n opts['cpu'] = args.cpu\\n opts['environment'] = zun_utils.format_args(args.environment)\\n opts['workdir'] = args.workdir\\n opts['auto_remove'] = args.auto_remove\\n opts['labels'] = zun_utils.format_args(args.label)\\n opts['image_pull_policy'] = args.image_pull_policy\\n opts['image_driver'] = args.image_driver\\n opts['hints'] = zun_utils.format_args(args.hint)\\n opts['nets'] = zun_utils.parse_nets(args.net)\\n opts['mounts'] = zun_utils.parse_mounts(args.mount)\\n opts['runtime'] = args.runtime\\n opts['hostname'] = args.hostname\\n opts['disk'] = args.disk\\n opts['availability_zone'] = args.availability_zone\\n opts['command'] = args.command\\n opts['registry'] = args.registry\\n opts['host'] = args.host\\n if args.entrypoint:\\n opts['entrypoint'] = zun_utils.parse_entrypoint(args.entrypoint)\\n if args.healthcheck:\\n opts['healthcheck'] = zun_utils.parse_health(args.healthcheck)\\n\\n if args.auto_heal:\\n opts['auto_heal'] = args.auto_heal\\n if args.security_group:\\n opts['security_groups'] = args.security_group\\n if args.expose_port:\\n opts['exposed_ports'] = zun_utils.parse_exposed_ports(args.expose_port)\\n if args.restart:\\n opts['restart_policy'] = zun_utils.check_restart_policy(args.restart)\\n if args.interactive:\\n opts['interactive'] = True\\n if args.privileged:\\n opts['privileged'] = True\\n opts = zun_utils.remove_null_parms(**opts)\\n container = cs.containers.run(**opts)\\n _show_container(container)\\n container_uuid = getattr(container, 'uuid', None)\\n if args.interactive:\\n ready_for_attach = False\\n while True:\\n container = cs.containers.get(container_uuid)\\n if zun_utils.check_container_status(container, 'Running'):\\n ready_for_attach = True\\n break\\n if zun_utils.check_container_status(container, 'Error'):\\n raise exceptions.ContainerStateError(container_uuid)\\n print(\\\"Waiting for container start\\\")\\n time.sleep(1)\\n if ready_for_attach is True:\\n response = cs.containers.attach(container_uuid)\\n websocketclient.do_attach(cs, response, container_uuid, \\\"~\\\", 0.5)\\n else:\\n raise exceptions.InvalidWebSocketLink(container_uuid)\",\n \"def test_run_container3(client):\\n with client.application.app_context():\\n pytest.skip(\\\"Not implemented\\\")\",\n \"def test_matplotlib(container, test_file, expected_file, description):\\n host_data_dir = os.path.join(os.path.dirname(\\n os.path.realpath(__file__)), \\\"data\\\")\\n cont_data_dir = \\\"/home/jovyan/data\\\"\\n output_dir = \\\"/tmp\\\"\\n LOGGER.info(description)\\n command = \\\"sleep infinity\\\"\\n running_container = container.run(\\n volumes={host_data_dir: {\\\"bind\\\": cont_data_dir, \\\"mode\\\": \\\"ro\\\"}},\\n tty=True,\\n command=[\\\"start.sh\\\", \\\"bash\\\", \\\"-c\\\", command],\\n )\\n command = f\\\"python {cont_data_dir}/{test_file}\\\"\\n cmd = running_container.exec_run(command)\\n assert cmd.exit_code == 0, f\\\"Command {command} failed\\\"\\n LOGGER.debug(cmd.output.decode(\\\"utf-8\\\"))\\n # Checking if the file is generated\\n # https://stackoverflow.com/a/15895594/4413446\\n command = f\\\"test -s {output_dir}/{expected_file}\\\"\\n cmd = running_container.exec_run(command)\\n assert cmd.exit_code == 0, f\\\"Command {command} failed\\\"\\n LOGGER.debug(cmd.output.decode(\\\"utf-8\\\"))\",\n \"def test_push(self, mock_docker_environment, snapshot, capsys):\\n push_image(TEST_IMAGE_NAME)\\n out, err = capsys.readouterr()\\n snapshot.assert_match(out)\",\n \"def test_docker_failure(mock_tools, user_mapping_run_calls, capsys):\\n # Mock Docker cannot be found\\n mock_tools.subprocess.check_output.side_effect = [\\n subprocess.CalledProcessError(\\n returncode=1,\\n cmd=\\\"docker --version\\\",\\n ),\\n \\\"Success!\\\",\\n VALID_BUILDX_VERSION,\\n VALID_USER_MAPPING_IMAGE_CACHE,\\n ]\\n\\n # Invoke Docker verify\\n result = Docker.verify(mock_tools)\\n\\n # The verify call should return the Docker wrapper\\n assert isinstance(result, Docker)\\n\\n # Docker version and plugins were verified\\n mock_tools.subprocess.check_output.assert_has_calls(DOCKER_VERIFICATION_CALLS)\\n\\n # Docker user mapping inspection occurred\\n mock_tools.subprocess.run.assert_has_calls(user_mapping_run_calls)\\n\\n # console output\\n output = capsys.readouterr()\\n assert \\\"** WARNING: Unable to determine if Docker is installed\\\" in output.out\\n assert output.err == \\\"\\\"\",\n \"def run_in_docker(docker_image: str,\\n source_code_path: str,\\n *,\\n environment_vars: List[str] = None,\\n command: str = None,\\n result_path: str =\\\"/tmp/results\\\") -> str:\\n\\n #\\n # Create temporal directory in the local host.\\n #\\n # We use dir=\\\"/tmp\\\" because in OSX, TemporaryDirectory function create the\\n # temporal dir in path \\\"/var/...\\\" and, by default, Docker hasn't permission\\n # to mount this directory, raising a error like that:\\n #\\n # The path /var/folders/_h/b5wqbhtn4zvcmsshf8nv1kcr0000gn/T/deeptracykyoc\\n # is not shared from OS X and is not known to Docker.\\n # You can configure shared paths from Docker ->\\n # Preferences... -> File Sharing.\\n # See https://docs.docker.com/docker-for-mac/osxfs/#namespaces for\\n # more info.\\n # -------------------------------------------------------------------------\\n with tempfile.TemporaryDirectory(prefix=\\\"deeptracy\\\",\\n dir=\\\"/tmp\\\") as tmp_dir:\\n\\n docker_client = docker.from_env()\\n\\n result_file_name = \\\"\\\".join(str(random.randint(0, 9))\\n for _ in range(30))\\n host_result_file = os.path.join(tmp_dir, result_file_name)\\n\\n # Choice volumes\\n docker_volumes = {\\n tmp_dir: {\\n 'bind': result_path,\\n 'mode': 'rw'\\n },\\n source_code_path: {\\n 'bind': \\\"/opt/app\\\",\\n 'mode': 'ro'\\n },\\n }\\n\\n # --------------------------------------------------------------------------\\n # Build function call options\\n # --------------------------------------------------------------------------\\n envs = environment_vars or []\\n envs.extend([\\n # \\\"OUTPUT_FILE={}\\\".format(container_result_file)\\n \\\"OUTPUT_FILE={}\\\".format(result_file_name)\\n ])\\n\\n # Run content inside docker\\n docker_client.containers.run(\\n image=docker_image,\\n remove=True,\\n command=command,\\n environment=envs,\\n volumes=docker_volumes\\n )\\n\\n # Read result file\\n with open(host_result_file, \\\"r\\\") as f:\\n yield f.read()\",\n \"def docker_exec(cmdline):\\n local('docker exec -ti {} {}'.format(project_name, cmdline))\",\n \"def generate_sagemaker_pytest_cmd(image, sagemaker_test_type):\\n region = os.getenv(\\\"AWS_REGION\\\", DEFAULT_REGION)\\n account_id = os.getenv(\\\"ACCOUNT_ID\\\", image.split(\\\".\\\")[0])\\n print(\\\"image name {}\\\".format(image))\\n sm_remote_docker_base_name, tag = image.split(\\\"/\\\")[1].split(\\\":\\\")\\n sm_local_docker_repo_uri = image.split(\\\":\\\")[0]\\n\\n # Assign instance type\\n instance_type = assign_sagemaker_remote_job_instance_type(image)\\n\\n # Get path to test directory\\n find_path = sm_remote_docker_base_name.split(\\\"-\\\")\\n\\n # NOTE: We are relying on the fact that repos are defined as <context>-<framework>-<job_type> in our infrastructure\\n framework, framework_version = get_framework_and_version_from_tag(image)\\n framework_major_version = framework_version.split(\\\".\\\")[0]\\n job_type = get_job_type_from_image(image)\\n path = os.path.join(\\\"test\\\", \\\"sagemaker_tests\\\", framework, job_type)\\n aws_id_arg = \\\"--aws-id\\\"\\n docker_base_arg = \\\"--docker-base-name\\\"\\n instance_type_arg = \\\"--instance-type\\\"\\n accelerator_type_arg = \\\"--accelerator-type\\\"\\n framework_version_arg = \\\"--framework-version\\\"\\n eia_arg = \\\"ml.eia1.large\\\"\\n processor = \\\"neuron\\\" if \\\"neuron\\\" in image else \\\"gpu\\\" if \\\"gpu\\\" in image else \\\"eia\\\" if \\\"eia\\\" in image else \\\"cpu\\\"\\n py_version = re.search(r\\\"py\\\\d+\\\", tag).group()\\n sm_local_py_version = \\\"37\\\" if py_version == \\\"py37\\\" else \\\"38\\\" if py_version == \\\"py38\\\" else \\\"2\\\" if py_version == \\\"py27\\\" else \\\"3\\\"\\n if framework == \\\"tensorflow\\\" and job_type == \\\"inference\\\":\\n # Tf Inference tests have an additional sub directory with test\\n integration_path = os.path.join(\\\"test\\\", \\\"integration\\\", sagemaker_test_type)\\n else:\\n integration_path = os.path.join(\\\"integration\\\", sagemaker_test_type)\\n\\n # Conditions for modifying tensorflow SageMaker pytest commands\\n if framework == \\\"tensorflow\\\" and sagemaker_test_type == SAGEMAKER_REMOTE_TEST_TYPE:\\n if job_type == \\\"inference\\\":\\n aws_id_arg = \\\"--registry\\\"\\n docker_base_arg = \\\"--repo\\\"\\n instance_type_arg = \\\"--instance-types\\\"\\n framework_version_arg = \\\"--versions\\\"\\n integration_path = os.path.join(integration_path, \\\"test_tfs.py\\\") if processor != \\\"eia\\\" else os.path.join(\\n integration_path, \\\"test_ei.py\\\")\\n\\n if framework == \\\"tensorflow\\\" and job_type == \\\"training\\\":\\n aws_id_arg = \\\"--account-id\\\"\\n\\n test_report = os.path.join(os.getcwd(), \\\"test\\\", f\\\"{job_type}_{tag}.xml\\\")\\n local_test_report = os.path.join(UBUNTU_HOME_DIR, \\\"test\\\", f\\\"{job_type}_{tag}_sm_local.xml\\\")\\n\\n # Explanation of why we need the if-condition below:\\n # We have separate Pipeline Actions that run EFA tests, which have the env variable \\\"EFA_DEDICATED=True\\\" configured\\n # so that those Actions only run the EFA tests.\\n # However, there is no such dedicated CB job dedicated to EFA tests in the PR context. This means that when in the\\n # PR context, setting \\\"DISABLE_EFA_TESTS\\\" to True should skip EFA tests, but setting it to False should enable\\n # not just the EFA tests, but also all other tests as well.\\n if is_pr_context():\\n efa_tests_disabled = os.getenv(\\\"DISABLE_EFA_TESTS\\\", \\\"False\\\").lower() == \\\"true\\\"\\n efa_flag = \\\"-m \\\\\\\"not efa\\\\\\\"\\\" if efa_tests_disabled else \\\"\\\"\\n else:\\n efa_dedicated = os.getenv(\\\"EFA_DEDICATED\\\", \\\"False\\\").lower() == \\\"true\\\"\\n efa_flag = '--efa' if efa_dedicated else '-m \\\\\\\"not efa\\\\\\\"'\\n\\n region_list = \\\",\\\".join(SAGEMAKER_EXECUTION_REGIONS)\\n\\n sagemaker_regions_list = f\\\"--sagemaker-regions {region_list}\\\"\\n\\n remote_pytest_cmd = (\\n f\\\"pytest -rA {integration_path} --region {region} --processor {processor} {docker_base_arg} \\\"\\n f\\\"{sm_remote_docker_base_name} --tag {tag} {framework_version_arg} {framework_version} \\\"\\n f\\\"{aws_id_arg} {account_id} {instance_type_arg} {instance_type} {efa_flag} {sagemaker_regions_list} --junitxml {test_report}\\\"\\n )\\n\\n if processor == \\\"eia\\\":\\n remote_pytest_cmd += f\\\"{accelerator_type_arg} {eia_arg}\\\"\\n\\n local_pytest_cmd = (f\\\"pytest -s -v {integration_path} {docker_base_arg} \\\"\\n f\\\"{sm_local_docker_repo_uri} --tag {tag} --framework-version {framework_version} \\\"\\n f\\\"--processor {processor} {aws_id_arg} {account_id} --junitxml {local_test_report}\\\")\\n\\n if framework == \\\"tensorflow\\\" and job_type != \\\"inference\\\":\\n local_pytest_cmd = f\\\"{local_pytest_cmd} --py-version {sm_local_py_version} --region {region}\\\"\\n if framework == \\\"tensorflow\\\" and job_type == \\\"training\\\":\\n path = os.path.join(os.path.dirname(path), f\\\"{framework}{framework_major_version}_training\\\")\\n if \\\"huggingface\\\" in framework and job_type == \\\"inference\\\":\\n path = os.path.join(\\\"test\\\", \\\"sagemaker_tests\\\", \\\"huggingface\\\", \\\"inference\\\")\\n\\n return (\\n remote_pytest_cmd if sagemaker_test_type == SAGEMAKER_REMOTE_TEST_TYPE else local_pytest_cmd,\\n path,\\n tag,\\n job_type,\\n )\",\n \"def run_build_image(image_list):\\n success_image_list = list()\\n failed_image_list = list()\\n for image in image_list:\\n docker_build_success_flag = image.run_docker_build()\\n if not docker_build_success_flag:\\n failed_image_list.append(image)\\n continue\\n smoke_test_sccess_flag = image.run_smoke_test()\\n if not smoke_test_sccess_flag:\\n failed_image_list.append(image)\\n continue\\n success_image_list.append(image)\\n\\n logger.info(\\\"--- docker build summary ---\\\")\\n logger.info(\\\"following image succeed:\\\")\\n for image in success_image_list:\\n logger.info(f\\\" {image.local_identifier}\\\")\\n logger.info(\\\"following image failed:\\\")\\n for image in failed_image_list:\\n logger.info(f\\\" {image.local_identifier}\\\")\\n return success_image_list, failed_image_list\",\n \"def build_container(client):\\n client.images.build(path=os.path.join(os.path.abspath(\\\"\\\"), \\\"docker\\\"), tag=\\\"scrape_light\\\")\",\n \"def run(self, test, env):\\n\\n raise NotImplementedError\",\n \"def tests_pull_workflow(self):\\n daemon_client = {}\\n client = self.get_client(daemon_client)\\n\\n for container in client.containers(all=True):\\n if 'test-container' in \\\\\\n ''.join([name for name in container.get('Names')]):\\n client.remove_container('test-container')\\n\\n if ['{0}:latest'.format(TEST_IMAGE)] in \\\\\\n [i.get('RepoTags') for i in client.images()]:\\n client.remove_image(TEST_IMAGE, force=True)\\n\\n # execute install workflow\\n self.env.execute('install', task_retries=0)\\n\\n container_instance = {}\\n\\n for instance in self.env.storage.get_node_instances():\\n if 'container_id' in instance.runtime_properties.keys():\\n container_instance = instance\\n\\n self.assertTrue(container_instance is not None,\\n 'Failed getting container.')\\n\\n container_id = container_instance.runtime_properties.get(\\n 'container_id')\\n containers = client.containers(all=True)\\n self.assertTrue(container_id in [c.get('Id') for c in containers])\\n\\n self.env.execute('uninstall', task_retries=3)\\n repotags = []\\n for i in client.images():\\n repotags.append(i.get('RepoTags'))\\n\\n self.assertFalse(TEST_IMAGE in [tag for tag in repotags])\\n if ['{0}:latest'.format(TEST_IMAGE)] in \\\\\\n [i.get('RepoTags') for i in client.images()]:\\n client.remove_image(TEST_IMAGE, force=True)\",\n \"def docker_worker():\",\n \"def _run_ci_integration_test():\\n _run_install(False)\\n _run_integration_tests_on_github(False)\",\n \"def test_force_with_local_and_registry_image(self, mock_docker_environment):\\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def run_tests():\\n with open('buildspec.yml', 'r') as stream:\\n buildspec: dict = yaml.load(stream)\\n test_commands = buildspec.get('tests', [])\\n if len(test_commands) == 0:\\n exit(0)\\n\\n results = []\\n\\n for test_command in test_commands:\\n print(f'Running `{test_command}`')\\n p = subprocess.Popen(test_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\\n output = ''\\n for c in iter(p.stdout.readline, b''):\\n str_line = c if type(c) is str else c.decode('UTF-8')\\n sys.stdout.write(str_line)\\n output += str_line\\n\\n p.communicate()\\n results.append({\\n 'command': test_command,\\n 'status': 'passed' if p.returncode == 0 else 'failed',\\n 'output': output\\n })\\n\\n post_to_github(results)\",\n \"def prepare():\\n sh('docker build --rm -t {image} {dir}', image=IMAGE, dir=os.path.dirname(__file__))\",\n \"def _build_docker_images(self):\\n print(f\\\"+ building {len(self.neurodocker_specs)} Docker images\\\")\\n self.docker_status = []\\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\\n try:\\n print(\\\"++ building image: {}\\\".format(neurodocker_dict))\\n cg.docker_main(\\n self.working_dir,\\n neurodocker_dict,\\n sha1,\\n build_context=self.build_context,\\n )\\n self.docker_status.append(\\\"docker ok\\\")\\n except Exception as e:\\n self.docker_status.append(\\n \\\"failed to build image with SHA1 {}: {}\\\".format(sha1, e)\\n )\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n epilog=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\\n )\\n parser.add_argument(\\n \\\"-d\\\", \\\"--dry-run\\\", action=\\\"store_true\\\", default=0, help=\\\"Dry run mode.\\\"\\n )\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--verbose\\\",\\n action=\\\"count\\\",\\n default=0,\\n help=\\\"Verbosity. Default is WARNING level.\\\",\\n )\\n\\n subparsers = parser.add_subparsers(help=\\\"Sub commands\\\", dest=\\\"subparser\\\")\\n subparsers.required = True\\n\\n build_parser = subparsers.add_parser(\\n \\\"build\\\",\\n description=\\\"Build an image from Dockerfile, caching image hierarchy\\\",\\n help=\\\"Build an image from a Dockerfile\\\",\\n )\\n build_parser.add_argument(\\n \\\"path\\\", metavar=\\\"PATH\\\", help=\\\"The build context directory\\\"\\n )\\n build_parser.add_argument(\\n \\\"-f\\\",\\n \\\"--file\\\",\\n help=\\\"Name of the Dockerfile. If not provided, \\\"\\n \\\"will use config.DOCKERFILE_PATH_PATTERN to compute. \\\",\\n )\\n build_parser.add_argument(\\n \\\"-v\\\",\\n \\\"--git-sha\\\",\\n required=True,\\n help=\\\"The version of code to build against, \\\" \\\"will pass as GIT_SHA variable\\\",\\n )\\n build_parser.add_argument(\\n \\\"-n\\\", \\\"--name\\\", required=True, help=\\\"The name of the image to build\\\"\\n )\\n build_parser.add_argument(\\n \\\"--build-arg\\\",\\n metavar=\\\"ARG=VALUE\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Set extra build-time variables. GIT_SHA, TIMESTAMP will be passed by default.\\\",\\n )\\n build_parser.add_argument(\\n \\\"-r\\\",\\n \\\"--raw\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Whether to use raw docker build command to build, skipping caching logic\\\",\\n )\\n build_parser.add_argument(\\n \\\"--registry\\\",\\n default=config.DOCKER_REGISTRY,\\n help=\\\"Docker registry use to determine the image identity, \\\"\\n \\\"can be set via IMAGE_BUILDER_DOCKER_REGISTRY environment variable, \\\"\\n 'or set DOCKER_REGISTRY in config.py. Default is \\\"%(default)s\\\"',\\n )\\n build_parser.add_argument(\\n \\\"-t\\\",\\n \\\"--tag-pattern\\\",\\n default=config.GIT_SHA_TAG_PATTERN,\\n help=\\\"Tag pattern, can only include one `{git_sha}` placeholder, \\\"\\n 'such as \\\"{git_sha}-new\\\". If the tag exists, we won\\\\'t rebuild it. '\\n 'Default is \\\"%(default)s\\\"',\\n )\\n build_parser.add_argument(\\n \\\"-e\\\",\\n \\\"--extra-tag\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Extra tags to tag to the final images\\\",\\n )\\n build_parser.add_argument(\\n \\\"--extra-name\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Extra name and optionally with a tag in the 'name:tag' format\\\",\\n )\\n build_parser.add_argument(\\n \\\"-o\\\", \\\"--output-hash\\\", help=\\\"The output filename of the files hash log.\\\"\\n )\\n build_parser.set_defaults(func=build)\\n\\n args = parser.parse_args()\\n if args.dry_run:\\n # DRY_RUN env will be read in image_builder.libs.process\\n os.environ[\\\"DRY_RUN\\\"] = \\\"1\\\"\\n\\n if args.func == build:\\n args.path = expand_path(args.path)\\n if args.output_hash:\\n args.output_hash = expand_path(args.output_hash)\\n\\n args.file = args.file or locate_dockerfile(args.name)\\n args.file = expand_path(args.file)\\n # set environ for main dockerfile for possibly retrieving later\\n os.environ[\\n config.DOCKERFILE_ENV_PATTERN.format(image_name=args.name)\\n ] = args.file\\n\\n # change CWD to PATH\\n os.chdir(args.path)\\n\\n if not args.registry:\\n parser.error(\\n \\\"--registry should be provied \\\"\\n \\\"or specified by IMAGE_BUILDER_DOCKER_REGISTRY environment variable or set DOCKER_REGISTRY in config.py\\\"\\n )\\n if not all(\\\"=\\\" in kv for kv in args.build_arg):\\n parser.error(\\\"--build_arg must be in ARG=VALUE format\\\")\\n\\n # set git_sha_tag\\n try:\\n args.git_sha_tag = args.tag_pattern.format(git_sha=args.git_sha)\\n except KeyError:\\n parser.error(\\n 'Wrong --tag-pattern provided. Can only include one `{git_sha}` placeholder, such as \\\"{git_sha}-new\\\"'\\n )\\n\\n # setup logging\\n level = logging.WARNING - args.verbose * 10\\n logging.basicConfig(\\n level=level, format=\\\"%(asctime)s %(name)s %(levelname)s %(message)s\\\"\\n )\\n\\n if args.output_hash:\\n h = logging.FileHandler(args.output_hash)\\n h.setLevel(logging.DEBUG)\\n h.setFormatter(logging.Formatter(\\\"%(message)s\\\"))\\n hash_logger.addHandler(h)\\n\\n # Suppress warning when we don't verify ssl\\n import urllib3\\n\\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\\n\\n return args.func(args)\",\n \"def test_build_image_default_context(self, mock_docker_environment):\\n build_image(\\\"Dockerfile.test\\\", TEST_IMAGE_NAME, context=None)\\n mock_docker_environment.images.build.assert_called_with(\\n dockerfile=\\\"Dockerfile.test\\\", path=pwd(), tag=TEST_IMAGE_NAME\\n )\",\n \"def _ensure_image(testkit_path, branch_name, artifacts_path):\\n # Construct Docker image name from branch name\\n image_name = \\\"runner:%s\\\" % branch_name\\n image_path = os.path.join(testkit_path, \\\"runner_image\\\")\\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\\n\\n return image_name\",\n \"def test_pep8():\\n docker_exec('py.test tests/test_pep8.py')\",\n \"def test_testutils():\\n build()\\n sh(\\\"%s psutil\\\\\\\\tests\\\\\\\\test_testutils.py\\\" % PYTHON)\",\n \"def run_docker(m):\\n\\n import botocore.session\\n from subprocess import Popen, PIPE, STDOUT\\n\\n raise NotImplementedError(\\\"No longer have access to raw_args\\\")\\n\\n session = botocore.session.get_session()\\n\\n args = ['docker', 'run', '--rm', '-t', '-i',\\n '-eAWS_ACCESS_KEY_ID={}'.format(session.get_credentials().access_key),\\n '-eAWS_SECRET_ACCESS_KEY={}'.format(session.get_credentials().secret_key),\\n 'civicknowledge/metatab',\\n 'metasync']\\n\\n for a in ('-D', '--docker'):\\n try:\\n m.raw_args.remove(a)\\n except ValueError:\\n pass\\n\\n args.extend(m.raw_args[1:])\\n\\n if m.args.verbose:\\n prt(\\\"Running Docker Command: \\\", ' '.join(args))\\n else:\\n prt(\\\"Running In Docker\\\")\\n\\n process = Popen(args, stdout=PIPE, stderr=STDOUT)\\n with process.stdout:\\n for line in iter(process.stdout.readline, b''):\\n prt(line.decode('ascii'), end='')\\n\\n exitcode = process.wait() # 0 means success\\n\\n exit(exitcode)\",\n \"def testRunRecipe(self):\\n warnings.filterwarnings(\\n action=\\\"ignore\\\", message=\\\"unclosed\\\", category=ResourceWarning)\\n\\n # Load the recipe, set the arguments, and run\\n self.test_state.LoadRecipe(RECIPE, TEST_MODULES)\\n self.test_state.command_line_options = {\\n 'aws_region': self.aws_region,\\n 'gcp_zone': self.gcp_zone,\\n 'volumes': self.aws_volume,\\n 'aws_bucket': self.aws_bucket,\\n 'gcp_bucket': self.gcp_bucket,\\n 'subnet': self.aws_subnet,\\n 'gcp_project': self.gcp_project_id\\n }\\n self.test_state.SetupModules()\\n self.test_state.RunModules()\\n\\n # AWS Volume in count should equal GCE Disk out count, and be at least 1\\n self.assertGreaterEqual(\\n len(self.test_state.GetContainers(containers.AWSVolume)), 1)\\n self.assertEqual(len(self.test_state.GetContainers(containers.AWSVolume)),\\n len(self.test_state.GetContainers(containers.GCEDisk)))\\n\\n disks = compute.GoogleCloudCompute(self.gcp_project_id).Disks()\\n real_gce_disk_names = [disks[k].name for k in disks.keys()]\\n\\n for d in self.test_state.GetContainers(containers.GCEDisk):\\n self.assertIn(d.name, real_gce_disk_names)\\n real_disk = compute.GoogleComputeDisk(\\n self.gcp_project_id, self.gcp_zone, d.name)\\n self.assertEqual(real_disk.GetDiskType(), 'pd-standard')\\n # Make an API call to the service that will fail if the disk doesn't exist\",\n \"def test_force_with_local_image(self, mock_docker_environment):\\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def build_docker(params) -> None:\\n print(\\\"Building docker image...\\\")\\n cmd = \\\"cd bg_changer && docker build --tag bg_changer . >/dev/null 2>&1\\\"\\n if os.system(cmd) == 0:\\n print(\\\" Success !\\\")\\n else:\\n print(\\\" Failure !\\\")\",\n \"def check_image(\\n image: str, trivy: bool = True, exit_on_error: bool = True\\n) -> subprocess.CompletedProcess:\\n build_utils.log(\\\"Run vulnerability checks on docker image:\\\")\\n\\n if trivy and build_utils.command_exists(\\\"trivy\\\", exit_on_error=exit_on_error):\\n return build_utils.run(\\n f\\\"trivy image --timeout=20m0s --exit-code 1 --severity HIGH,CRITICAL {image}\\\",\\n exit_on_error=exit_on_error,\\n )\\n\\n return subprocess.CompletedProcess(args=\\\"\\\", returncode=-1, stdout=\\\"\\\", stderr=\\\"\\\")\\n # TODO: Implement dockl container scan\",\n \"def test_docker_push_target_ref(tmpdir, docker_client, registry):\\n tmp = tmpdir.join('shipwright-localhost-sample')\\n path = str(tmp)\\n source = pkg_resources.resource_filename(\\n __name__,\\n 'examples/shipwright-localhost-sample',\\n )\\n repo = create_repo(path, source)\\n tag = repo.head.ref.commit.hexsha[:12]\\n\\n tmp.join('service1/base.txt').write('Hi mum')\\n commit_untracked(repo)\\n new_tag = repo.head.ref.commit.hexsha[:12]\\n\\n client_cfg = docker_utils.kwargs_from_env()\\n cli = docker_client\\n\\n defaults = get_defaults()\\n defaults['push'] = True\\n try:\\n shipw_cli.run(\\n path=path,\\n client_cfg=client_cfg,\\n arguments=defaults,\\n environ={},\\n )\\n\\n # Remove the build images:\\n old_images = (\\n cli.images(name='localhost:5000/service1', quiet=True) +\\n cli.images(name='localhost:5000/shared', quiet=True) +\\n cli.images(name='localhost:5000/base', quiet=True)\\n )\\n for image in old_images:\\n cli.remove_image(image, force=True)\\n\\n images_after_delete = (\\n cli.images(name='localhost:5000/service1') +\\n cli.images(name='localhost:5000/shared') +\\n cli.images(name='localhost:5000/base')\\n )\\n assert images_after_delete == []\\n\\n # Pull them again:\\n for t in ['master', 'latest', new_tag, tag]:\\n for repo in ['service1', 'shared', 'base']:\\n cli.pull('localhost:5000/' + repo, t)\\n\\n service1, shared, base = (\\n cli.images(name='localhost:5000/service1') +\\n cli.images(name='localhost:5000/shared') +\\n cli.images(name='localhost:5000/base')\\n )\\n\\n assert set(service1['RepoTags']) == {\\n 'localhost:5000/service1:master',\\n 'localhost:5000/service1:latest',\\n 'localhost:5000/service1:' + new_tag,\\n }\\n\\n assert set(shared['RepoTags']) == {\\n 'localhost:5000/shared:master',\\n 'localhost:5000/shared:latest',\\n 'localhost:5000/shared:' + new_tag,\\n 'localhost:5000/shared:' + tag,\\n }\\n\\n assert set(base['RepoTags']) == {\\n 'localhost:5000/base:master',\\n 'localhost:5000/base:latest',\\n 'localhost:5000/base:' + new_tag,\\n 'localhost:5000/base:' + tag,\\n }\\n finally:\\n old_images = (\\n cli.images(name='localhost:5000/service1', quiet=True) +\\n cli.images(name='localhost:5000/shared', quiet=True) +\\n cli.images(name='localhost:5000/base', quiet=True)\\n )\\n for image in old_images:\\n cli.remove_image(image, force=True)\",\n \"def test_docker_exists(mock_tools, user_mapping_run_calls, capsys, tmp_path):\\n # Mock the return value of Docker Version\\n mock_tools.subprocess.check_output.side_effect = [\\n VALID_DOCKER_VERSION,\\n VALID_DOCKER_INFO,\\n VALID_BUILDX_VERSION,\\n VALID_USER_MAPPING_IMAGE_CACHE,\\n ]\\n\\n # Invoke docker verify\\n result = Docker.verify(mock_tools)\\n\\n # The verify call should return the Docker wrapper\\n assert isinstance(result, Docker)\\n\\n # Docker version and plugins were verified\\n mock_tools.subprocess.check_output.assert_has_calls(DOCKER_VERIFICATION_CALLS)\\n\\n # Docker user mapping inspection occurred\\n mock_tools.subprocess.run.assert_has_calls(user_mapping_run_calls)\\n\\n # No console output\\n output = capsys.readouterr()\\n assert output.out == \\\"\\\"\\n assert output.err == \\\"\\\"\",\n \"def test_container_exists():\\n return exec_fn(_test_container_exists)\",\n \"def docker_build(c):\\n cli_tasks.docker_build.run(c)\",\n \"def generate_sagemaker_pytest_cmd(image):\\n reruns = 4\\n region = os.getenv(\\\"AWS_REGION\\\", \\\"us-west-2\\\")\\n integration_path = os.path.join(\\\"integration\\\", \\\"sagemaker\\\")\\n account_id = os.getenv(\\\"ACCOUNT_ID\\\", image.split(\\\".\\\")[0])\\n docker_base_name, tag = image.split(\\\"/\\\")[1].split(\\\":\\\")\\n\\n # Assign instance type\\n instance_type = assign_sagemaker_instance_type(image)\\n\\n # Get path to test directory\\n find_path = docker_base_name.split(\\\"-\\\")\\n\\n # NOTE: We are relying on the fact that repos are defined as <context>-<framework>-<job_type> in our infrastructure\\n framework = find_path[1]\\n job_type = find_path[2]\\n path = os.path.join(\\\"test\\\", \\\"sagemaker_tests\\\", framework, job_type)\\n aws_id_arg = \\\"--aws-id\\\"\\n docker_base_arg = \\\"--docker-base-name\\\"\\n instance_type_arg = \\\"--instance-type\\\"\\n\\n # Conditions for modifying tensorflow SageMaker pytest commands\\n if framework == \\\"tensorflow\\\":\\n if job_type == \\\"training\\\":\\n aws_id_arg = \\\"--account-id\\\"\\n\\n # NOTE: We rely on Framework Version being in \\\"major.minor.patch\\\" format\\n tf_framework_version = re.search(r\\\"\\\\d+(\\\\.\\\\d+){2}\\\", tag).group()\\n tf_major_version = tf_framework_version.split(\\\".\\\")[0]\\n path = os.path.join(os.path.dirname(path), f\\\"{framework}{tf_major_version}_training\\\")\\n else:\\n aws_id_arg = \\\"--registry\\\"\\n docker_base_arg = \\\"--repo\\\"\\n integration_path = os.path.join(integration_path, \\\"test_tfs.py\\\")\\n instance_type_arg = \\\"--instance-types\\\"\\n\\n test_report = os.path.join(os.getcwd(), \\\"test\\\", f\\\"{tag}.xml\\\")\\n return (\\n f\\\"pytest --reruns {reruns} {integration_path} --region {region} {docker_base_arg} \\\"\\n f\\\"{docker_base_name} --tag {tag} {aws_id_arg} {account_id} {instance_type_arg} {instance_type} \\\"\\n f\\\"--junitxml {test_report}\\\",\\n path,\\n tag,\\n )\",\n \"def cli(context):\\n dev = f\\\"docker run -it -v {PWD}:/local {IMAGE_NAME}:{IMAGE_VER} /bin/bash\\\"\\n print(f\\\"{dev}\\\")\\n context.run(f\\\"{dev}\\\", pty=True)\",\n \"def test(command, options=\\\"\\\"):\\n\\n print(\\n \\\"\\\"\\\"\\nRunning pytest the test framework\\n=================================\\n\\\"\\\"\\\"\\n )\\n for dir_ in TEST_DIRECTORIES:\\n test_dir(command, options=options, dir_=dir_)\\n # command.run(f\\\"python -m pytest {options} {' '.join(dir_ for dir_ in TEST_DIRECTORIES)}\\\", echo=True, pty=POSIX)\\n\\n print(\\n \\\"\\\"\\\"\\nAll Testing Directories Passed Successfully\\n===========================================\\n\\\"\\\"\\\"\\n )\",\n \"def main():\\n client = docker.from_env()\\n parser = argparse.ArgumentParser(description='')\\n parser.add_argument('--package_id', default='0',\\n help='provide id for the work package, comma separated if multiple')\\n parser.add_argument('--load_quicksave', default=\\\"no\\\", help='wanna load? -> yes/no')\\n args = parser.parse_args()\\n packages = args.package_id.split(\\\",\\\")\\n print('Building docker container. This might take a while.')\\n build_container(client)\\n print('Build finished. Starting containers.')\\n for package in packages:\\n start_container(client, package, args.load_quicksave)\\n print('Containers are running. Check Docker Dashboard for container health. Script will exit.')\",\n \"def test_container_cycle(self):\\n # Before Create\\n print(\\\"Create\\\")\\n rep = post(self.url + \\\"/search\\\", data={'name': name})\\n self.errorCatch(rep)\\n\\n # Create\\n rep = post(self.url + \\\"/create\\\", data={\\n 'image': default_image,\\n 'homepath': \\\"/nashome/guest/test\\\",\\n 'naspath': \\\"/home/nas/test\\\",\\n 'command': \\\"tail -f /dev/null\\\",\\n 'name': name})\\n self.checkRunning()\\n\\n # Double create\\n rep = post(self.url + \\\"/create\\\", data={\\n 'image': default_image,\\n 'homepath': \\\"/nashome/guest/test\\\",\\n 'naspath': \\\"/home/nas/test\\\",\\n 'command': \\\"tail -f /dev/null\\\",\\n 'name': name})\\n self.errorCatch(rep)\\n\\n # Check by api\\n con = client.containers.get(name)\\n self.assertIn(\\\"tmp0\\\", con.exec_run(\\\"ls /home/nas\\\").output.decode())\\n self.assertIn(\\\"tmp1\\\", con.exec_run(\\\"ls /home/ubuntu\\\").output.decode())\\n self.assertEqual(con.status, \\\"running\\\")\\n\\n # Stop\\n con.exec_run(\\\"touch /opt/tmp2\\\").output.decode()\\n print(\\\"Stop\\\")\\n rep = post(self.url + \\\"/stop\\\", data={'name': name})\\n self.checkOK(rep)\\n\\n # check stop\\n rep = post(self.url + \\\"/search\\\", data={'name': name})\\n self.checkOK(rep)\\n rep = rep.json()\\n self.assertIsInstance(rep[\\\"data\\\"], dict)\\n self.assertEqual(rep['data']['status'], \\\"exited\\\")\\n\\n # start\\n print(\\\"Resume\\\")\\n rep = post(self.url + \\\"/start\\\", data={'name': name})\\n self.checkOK(rep)\\n self.checkRunning()\\n con = client.containers.get(name)\\n self.assertIn(\\\"tmp2\\\", con.exec_run(\\\"ls /opt\\\").output.decode())\\n\\n # change pw\\n print(\\\"Change Password\\\")\\n con.exec_run(\\\"adduser ubuntu\\\")\\n rep = post(self.url + \\\"/passwd\\\", data={'name': name,\\n 'pw': \\\"tmpPW\\\"})\\n self.checkOK(rep)\\n self.assertIn(\\\"tmpPW\\\", con.exec_run(\\\"cat /etc/shadow\\\").output.decode())\\n\\n # commit\\n print(\\\"Commit\\\")\\n rep = post(self.url + \\\"/commit\\\", data={'name': name,\\n 'newname': name})\\n self.checkOK(rep)\\n\\n # search image\\n rep = post(self.url + \\\"/search/image\\\", data={'name': name})\\n rep = rep.json()\\n self.assertIsInstance(rep['data'], dict)\\n\\n # delete\\n print(\\\"Delete\\\")\\n rep = post(self.url + \\\"/delete\\\", data={'name': name})\\n self.checkOK(rep)\\n\\n # check delete\\n rep = post(self.url + \\\"/search\\\", data={'name': name})\\n self.errorCatch(rep)\\n\\n # Delete Image\\n print(\\\"Delete Image\\\")\\n rep = post(self.url + \\\"/delete/image\\\", data={'name': name})\\n self.checkOK(rep)\\n\\n # Check if delete it\\n rep = post(self.url + \\\"/search/image\\\", data={'name': name})\\n self.errorCatch(rep)\",\n \"def run_sagemaker_pytest_cmd(image):\\n pytest_command, path, tag = generate_sagemaker_pytest_cmd(image)\\n\\n context = Context()\\n with context.cd(path):\\n context.run(f\\\"virtualenv {tag}\\\")\\n with context.prefix(f\\\"source {tag}/bin/activate\\\"):\\n context.run(\\\"pip install -r requirements.txt\\\", warn=True)\\n context.run(pytest_command)\",\n \"def run_docker_container():\\n creds = {\\n 'user': 'root',\\n 'password': 'root',\\n 'host': '127.0.0.1',\\n 'db': 'test',\\n 'port': 3307\\n }\\n client = from_env()\\n client.containers.list()\\n container = client.containers.run('mariadb:10.3', name='test-mariadb-database',\\n ports={3306: creds['port']}, environment={'MYSQL_ROOT_PASSWORD': creds['password'], 'MYSQL_DATABASE': creds['db']},\\n detach=True)\\n url = (\\\"mysql+pymysql://{}:{}@{}:{}/{}\\\".format(creds['user'], creds['password'], creds['host'], creds['port'], creds['db']))\\n return url, container\",\n \"def build_docker(c):\\n tag = c.run('git describe', hide=True)\\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\\n c.run(f'docker build -t {docker_img} .')\",\n \"def _docker_run_pwsh_test(\\n self, test_image: str, keep_container: bool\\n ) -> Tuple[int, str]:\\n log_prompt = f\\\"{self._pack_name} - Powershell test - Image {test_image}\\\"\\n logger.info(f\\\"{log_prompt} - Start\\\")\\n container_name = f\\\"{self._pack_name}-pwsh-test\\\"\\n # Check if previous run left container a live if it do, we remove it\\n self._docker_remove_container(container_name)\\n\\n # Run container\\n exit_code = SUCCESS\\n output = \\\"\\\"\\n try:\\n uid = os.getuid() or 4000\\n logger.debug(\\n f\\\"{log_prompt} - user uid for running lint/test: {uid}\\\"\\n ) # lgtm[py/clear-text-logging-sensitive-data]\\n container: docker.models.containers.Container = (\\n get_docker().create_container(\\n files_to_push=[(self._pack_abs_dir, \\\"/devwork\\\")],\\n name=container_name,\\n image=test_image,\\n command=build_pwsh_test_command(),\\n user=f\\\"{uid}:4000\\\",\\n environment=self._facts[\\\"env_vars\\\"],\\n )\\n )\\n container.start()\\n stream_docker_container_output(container.logs(stream=True))\\n # wait for container to finish\\n container_status = container.wait()\\n # Get container exit code\\n container_exit_code = container_status.get(\\\"StatusCode\\\")\\n # Getting container logs\\n container_log = container.logs().decode(\\\"utf-8\\\")\\n logger.info(f\\\"{log_prompt} - exit-code: {container_exit_code}\\\")\\n if container_exit_code:\\n # 1-fatal message issued\\n # 2-Error message issued\\n logger.error(f\\\"{log_prompt} - Finished, errors found\\\")\\n output = container_log\\n exit_code = FAIL\\n else:\\n logger.info(f\\\"{log_prompt} - Successfully finished\\\")\\n # Keeping container if needed or remove it\\n if keep_container:\\n logger.info(f\\\"{log_prompt} - container name {container_name}\\\")\\n container.commit(repository=container_name.lower(), tag=\\\"pwsh_test\\\")\\n else:\\n try:\\n container.remove(force=True)\\n except docker.errors.NotFound as e:\\n logger.critical(f\\\"{log_prompt} - Unable to delete container - {e}\\\")\\n except (\\n docker.errors.ImageNotFound,\\n docker.errors.APIError,\\n requests.exceptions.ReadTimeout,\\n ) as e:\\n logger.critical(f\\\"{log_prompt} - Unable to run powershell test - {e}\\\")\\n exit_code = RERUN\\n\\n return exit_code, output\",\n \"def test_create_image(self):\\n pass\",\n \"def runTests(self):\\n \\n pass\",\n \"def run(self, container_config: ContainerConfig) -> Container:\",\n \"def test_build_containers_when_dependency_container_is_built(self, set_context, repo_urls, build_image):\\n build_image.side_effect = [1, 2, 3]\\n repo_urls.return_value = [\\\"url\\\"]\\n e1 = models.Event.create(db.session, \\\"test_msg_id\\\", \\\"RHSA-2018-001\\\", events.TestingEvent)\\n event = self.get_event_from_msg(get_fedmsg('brew_container_task_closed'))\\n\\n base_build = models.ArtifactBuild.create(db.session, e1, 'test-product-docker', ArtifactType.IMAGE, event.task_id)\\n\\n build_0 = models.ArtifactBuild.create(db.session, e1, 'docker-up-0', ArtifactType.IMAGE, 0,\\n dep_on=base_build, state=ArtifactBuildState.PLANNED)\\n build_1 = models.ArtifactBuild.create(db.session, e1, 'docker-up-1', ArtifactType.IMAGE, 0,\\n dep_on=base_build, state=ArtifactBuildState.PLANNED)\\n build_2 = models.ArtifactBuild.create(db.session, e1, 'docker-up-2', ArtifactType.IMAGE, 0,\\n dep_on=base_build, state=ArtifactBuildState.PLANNED)\\n\\n self.handler.handle(event)\\n self.assertEqual(base_build.state, ArtifactBuildState.DONE.value)\\n build_image.assert_has_calls([\\n mock.call(build_0, ['url']), mock.call(build_1, ['url']),\\n mock.call(build_2, ['url']),\\n ])\\n\\n set_context.assert_has_calls([\\n mock.call(build_0), mock.call(build_1), mock.call(build_2)])\\n\\n self.assertEqual(build_0.build_id, 1)\\n self.assertEqual(build_1.build_id, 2)\\n self.assertEqual(build_2.build_id, 3)\",\n \"def test(self):\\n for arch, python in self.python:\\n self.run(f\\\"{python} -m pytest\\\")\",\n \"def run_enrichr_docker(self, cmdargs, temp_dir=None):\\n cmd = [os.getenv('CDENRICHR_DOCKER'), 'run', '--rm', '-v',\\n temp_dir+':'+temp_dir,\\n os.getenv('CDENRICHR_DOCKER_IMAGE')]\\n cmd.extend(cmdargs)\\n p = subprocess.Popen(cmd,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n\\n out, err = p.communicate()\\n\\n return p.returncode, out, err\",\n \"def main():\\n\\n # get AWS credentials\\n aws_credentials = read_aws_credentials()\\n access_key_id = aws_credentials['access_key_id']\\n secret_access_key = aws_credentials['secret_access_key']\\n aws_region = aws_credentials['region']\\n\\n # build Docker image\\n docker_client = docker.from_env()\\n image, build_log = docker_client.images.build(\\n path='.', tag=LOCAL_REPOSITORY, rm=True)\\n\\n # get AWS ECR login token\\n ecr_client = boto3.client(\\n 'ecr', aws_access_key_id=access_key_id, \\n aws_secret_access_key=secret_access_key, region_name=aws_region)\\n\\n ecr_credentials = (\\n ecr_client\\n .get_authorization_token()\\n ['authorizationData'][0])\\n\\n ecr_username = 'AWS'\\n\\n ecr_password = (\\n base64.b64decode(ecr_credentials['authorizationToken'])\\n .replace(b'AWS:', b'')\\n .decode('utf-8'))\\n\\n ecr_url = ecr_credentials['proxyEndpoint']\\n\\n # get Docker to login/authenticate with ECR\\n docker_client.login(\\n username=ecr_username, password=ecr_password, registry=ecr_url)\\n\\n # tag image for AWS ECR\\n ecr_repo_name = '{}/{}'.format(\\n ecr_url.replace('https://', ''), LOCAL_REPOSITORY)\\n\\n image.tag(ecr_repo_name, tag='latest')\\n\\n # push image to AWS ECR\\n push_log = docker_client.images.push(ecr_repo_name, tag='latest')\\n\\n # force new deployment of ECS service\\n ecs_client = boto3.client(\\n 'ecs', aws_access_key_id=access_key_id,\\n aws_secret_access_key=secret_access_key, region_name=aws_region)\\n\\n ecs_client.update_service(\\n cluster=ECS_CLUSTER, service=ECS_SERVICE, forceNewDeployment=True)\\n\\n return None\",\n \"def test_docker_unknown_version(mock_tools, user_mapping_run_calls, capsys):\\n # Mock a bad return value of `docker --version`\\n mock_tools.subprocess.check_output.return_value = \\\"ceci nest pas un Docker\\\\n\\\"\\n\\n # Invoke Docker verify\\n result = Docker.verify(mock_tools)\\n\\n # The verify call should return the Docker wrapper\\n assert isinstance(result, Docker)\\n\\n # Docker version and plugins were verified\\n mock_tools.subprocess.check_output.assert_has_calls(DOCKER_VERIFICATION_CALLS)\\n\\n # Docker user mapping inspection occurred\\n mock_tools.subprocess.run.assert_has_calls(user_mapping_run_calls)\\n\\n # console output\\n output = capsys.readouterr()\\n assert \\\"** WARNING: Unable to determine the version of Docker\\\" in output.out\\n assert output.err == \\\"\\\"\",\n \"def runTest(self):\\n self.setUp()\\n self.test_FlexCrop1()\",\n \"def test_docker_doesnt_exist(mock_tools):\\n # Mock Docker not installed on system\\n mock_tools.subprocess.check_output.side_effect = FileNotFoundError\\n\\n # Invoke Docker verify\\n with pytest.raises(BriefcaseCommandError):\\n Docker.verify(mock_tools)\\n\\n # But docker was invoked\\n mock_tools.subprocess.check_output.assert_called_with([\\\"docker\\\", \\\"--version\\\"])\",\n \"def dockerfile_with_path_map() -> co.Exec:\\n path_map = {\\\"./code\\\": \\\"/root/code\\\"}\\n image = co.Image(\\n dockerfile=\\\"./docker/Dockerfile.copy\\\", context=\\\".\\\", path_map=path_map\\n )\\n return co.Exec(\\\"python /root/code/test.py\\\", image=image, doc=co.util.magic_doc())\",\n \"def _run_ci_test():\\n _run_install(False)\\n _run_coverage_html(False)\\n _run_typecheck_xml(False)\\n _run_lint(True)\",\n \"def test_mock_is_accurate(self, case):\\n commands = case.setup_script + (\\n 'python -c \\\"import platform\\\\nprint(platform.linux_distribution())\\\"', # noqa\\n )\\n stdout = run_in_docker(case.docker_image, commands)\\n assert stdout.strip() == str(case.mock_linux_dist)\",\n \"def test_retag_valid_image(self):\\n alpine = self.docker.images.get(constant.ALPINE)\\n self.assertTrue(alpine.tag(\\\"demo\\\", \\\"rename\\\"))\\n\\n alpine = self.docker.images.get(constant.ALPINE)\\n self.assertNotIn(\\\"demo:test\\\", alpine.tags)\",\n \"def run_docker_push(image_list, docker_client):\\n logger.info(\\\"--- push image to registry ---\\\")\\n if runtime == Runtime.local:\\n logger.info(\\\"Detected local runtime, stop here.\\\")\\n return\\n\\n if not len(success_image_list):\\n logger.info(\\\"No success image to push, stop here.\\\")\\n return\\n\\n if GIT_BRANCH != \\\"master\\\":\\n logger.info(\\\"Not master branch, stop here\\\")\\n return\\n\\n docker_client.login(username=DOCKER_HUB_USERNAME, password=DOCKER_HUB_PASSWORD)\\n for image in image_list:\\n image.run_docker_push(docker_client)\\n print(\\\"Finished.\\\")\",\n \"def test_defaults_centos(self):\\n c = catalyst()\\n self.assertEqual(str(c),\\nr'''# ParaView Catalyst version 5.6.1\\nRUN yum install -y \\\\\\n git \\\\\\n gzip \\\\\\n libICE-devel \\\\\\n libSM-devel \\\\\\n libX11-devel \\\\\\n libXau-devel \\\\\\n libXext-devel \\\\\\n libXt-devel \\\\\\n libglvnd-devel \\\\\\n make \\\\\\n mesa-libGL-devel \\\\\\n tar \\\\\\n wget \\\\\\n which && \\\\\\n rm -rf /var/cache/yum/*\\nRUN mkdir -p /var/tmp && wget -q -nc --no-check-certificate -O /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base.tar.gz -P /var/tmp https://www.paraview.org/paraview-downloads/download.php?submit=Download\\\\&version=v5.6\\\\&type=catalyst\\\\&os=Sources\\\\&downloadFile=Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base.tar.gz && \\\\\\n mkdir -p /var/tmp && tar -x -f /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base.tar.gz -C /var/tmp -z && \\\\\\n mkdir -p /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/build && cd /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/build && /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/cmake.sh -DCMAKE_INSTALL_PREFIX=/usr/local/catalyst /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base && \\\\\\n cmake --build /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/build --target all -- -j$(nproc) && \\\\\\n cmake --build /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base/build --target install -- -j$(nproc) && \\\\\\n rm -rf /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base.tar.gz /var/tmp/Catalyst-v5.6.1-Base-Enable-Python-Essentials-Extras-Rendering-Base\\nENV LD_LIBRARY_PATH=/usr/local/catalyst/lib:$LD_LIBRARY_PATH \\\\\\n PATH=/usr/local/catalyst/bin:$PATH''')\",\n \"def run(ws):\\n ws.docker_run()\",\n \"def push_image(image):\\n\\n subprocess.check_call(['docker', 'push', image])\",\n \"def test_start(set_env, container: Container, docker_client: DockerClient):\\n # pylint: disable=unused-argument\\n assert container\\n container.stop()\\n\\n from dockerdb.commands.start import start\\n\\n start()\\n\\n assert container.status == \\\"running\\\"\",\n \"def test_defaults_centos8(self):\\n o = ofed()\\n self.assertEqual(str(o),\\nr'''# OFED\\nRUN yum install -y dnf-utils && \\\\\\n yum-config-manager --set-enabled powertools && \\\\\\n yum install -y --disablerepo=mlnx\\\\* \\\\\\n libibmad \\\\\\n libibmad-devel \\\\\\n libibumad \\\\\\n libibverbs \\\\\\n libibverbs-utils \\\\\\n libmlx5 \\\\\\n librdmacm \\\\\\n rdma-core \\\\\\n rdma-core-devel && \\\\\\n rm -rf /var/cache/yum/*''')\",\n \"def run_sm_perf_test(image_uri, num_nodes, region):\\n _, framework_version = get_framework_and_version_from_tag(image_uri)\\n if framework_version.startswith(\\\"1.\\\"):\\n pytest.skip(\\\"Skipping benchmark test on TF 1.x images.\\\")\\n\\n processor = \\\"gpu\\\" if \\\"gpu\\\" in image_uri else \\\"cpu\\\"\\n device_cuda_str = (\\n f\\\"{processor}-{get_cuda_version_from_tag(image_uri)}\\\" if processor == \\\"gpu\\\" else processor\\n )\\n\\n ec2_instance_type = \\\"p3.16xlarge\\\" if processor == \\\"gpu\\\" else \\\"c5.18xlarge\\\"\\n\\n py_version = \\\"py2\\\" if \\\"py2\\\" in image_uri else \\\"py37\\\" if \\\"py37\\\" in image_uri else \\\"py3\\\"\\n\\n time_str = time.strftime(\\\"%Y-%m-%d-%H-%M-%S\\\")\\n commit_info = os.getenv(\\\"CODEBUILD_RESOLVED_SOURCE_VERSION\\\")\\n target_upload_location = os.path.join(\\n BENCHMARK_RESULTS_S3_BUCKET,\\n \\\"tensorflow\\\",\\n framework_version,\\n \\\"sagemaker\\\",\\n \\\"training\\\",\\n device_cuda_str,\\n py_version,\\n )\\n training_job_name = f\\\"tf{framework_version[0]}-tr-bench-{device_cuda_str}-{num_nodes}-node-{py_version}-{commit_info[:7]}-{time_str}\\\"\\n\\n # Inserting random sleep because this test starts multiple training jobs around the same time, resulting in\\n # a throttling error for SageMaker APIs.\\n time.sleep(Random(x=training_job_name).random() * 60)\\n\\n test_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \\\"resources\\\")\\n venv_dir = os.path.join(test_dir, \\\"sm_benchmark_venv\\\")\\n\\n ctx = Context()\\n\\n with ctx.cd(test_dir), ctx.prefix(f\\\"source {venv_dir}/bin/activate\\\"):\\n log_file = f\\\"results-{commit_info}-{time_str}-{framework_version}-{device_cuda_str}-{py_version}-{num_nodes}-node.txt\\\"\\n run_out = ctx.run(\\n f\\\"timeout 45m python tf_sm_benchmark.py \\\"\\n f\\\"--framework-version {framework_version} \\\"\\n f\\\"--image-uri {image_uri} \\\"\\n f\\\"--instance-type ml.{ec2_instance_type} \\\"\\n f\\\"--node-count {num_nodes} \\\"\\n f\\\"--python {py_version} \\\"\\n f\\\"--region {region} \\\"\\n f\\\"--job-name {training_job_name}\\\"\\n f\\\"2>&1 | tee {log_file}\\\",\\n warn=True,\\n echo=True,\\n )\\n\\n if not (run_out.ok or run_out.return_code == 124):\\n target_upload_location = os.path.join(target_upload_location, \\\"failure_log\\\")\\n\\n ctx.run(\\n f\\\"aws s3 cp {os.path.join(test_dir, log_file)} {os.path.join(target_upload_location, log_file)}\\\"\\n )\\n\\n LOGGER.info(f\\\"Test results can be found at {os.path.join(target_upload_location, log_file)}\\\")\\n\\n result_statement, throughput = _print_results_of_test(\\n os.path.join(test_dir, log_file), processor\\n )\\n throughput /= num_nodes\\n\\n assert run_out.ok, (\\n f\\\"Benchmark Test failed with return code {run_out.return_code}. \\\"\\n f\\\"Test results can be found at {os.path.join(target_upload_location, log_file)}\\\"\\n )\\n\\n threshold_table = (\\n (\\n TENSORFLOW_SM_TRAINING_CPU_1NODE_THRESHOLD\\n if num_nodes == 1\\n else TENSORFLOW_SM_TRAINING_CPU_4NODE_THRESHOLD\\n )\\n if processor == \\\"cpu\\\"\\n else TENSORFLOW_SM_TRAINING_GPU_1NODE_THRESHOLD\\n if num_nodes == 1\\n else TENSORFLOW_SM_TRAINING_GPU_4NODE_THRESHOLD\\n )\\n threshold = get_threshold_for_image(framework_version, threshold_table)\\n LOGGER.info(\\n f\\\"tensorflow {framework_version} sagemaker training {device_cuda_str} {py_version} \\\"\\n f\\\"imagenet {num_nodes} nodes Throughput: {throughput} images/sec, threshold: {threshold} images/sec\\\"\\n )\\n assert throughput > threshold, (\\n f\\\"tensorflow {framework_version} sagemaker training {processor} {py_version} imagenet {num_nodes} nodes \\\"\\n f\\\"Benchmark Result {throughput} does not reach the threshold {threshold}\\\"\\n )\",\n \"def docker_services(\\n docker_compose_file, docker_allow_fallback, docker_compose_project_name,\\n docker_compose_project_dir\\n):\\n\\n docker_compose = DockerComposeExecutor(\\n docker_compose_file,\\n docker_compose_project_name,\\n docker_compose_project_dir\\n )\\n\\n # If we allowed to run without Docker, check it's presence\\n if docker_allow_fallback is True:\\n try:\\n with open(os.devnull, 'w') as devnull:\\n subprocess.call(['docker', 'ps'],\\n stdout=devnull, stderr=devnull)\\n except Exception:\\n yield Services(None, docker_allow_fallback=True)\\n return\\n\\n project = project_from_options(\\n docker_compose._compose_project_dir,\\n options=docker_compose.as_dict_options()\\n )\\n cmd = TopLevelCommand(project)\\n\\n # Spawn containers.\\n up_options = docker_compose.defaults_opts('up')\\n up_options['-d'] = True\\n up_options['--build'] = True\\n cmd.up(up_options)\\n\\n # Let test(s) run.\\n yield Services(project)\\n\\n # Clean up.\\n down_option = docker_compose.defaults_opts('down')\\n down_option['-v'] = True\\n cmd.down(down_option)\",\n \"def test_unknown_registry(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n build_image_if_needed(\\\"unknown.registry.com/foo/bar\\\")\\n mock_docker_environment.images.build.assert_called_with(\\n dockerfile=\\\"Dockerfile\\\",\\n tag=\\\"unknown.registry.com/foo/bar:latest\\\",\\n path=\\\"/opt/ixian_docker\\\",\\n )\",\n \"def test_tag_valid_image(self):\\n alpine = self.docker.images.get(constant.ALPINE)\\n self.assertTrue(alpine.tag(\\\"demo\\\", constant.ALPINE_SHORTNAME))\\n\\n alpine = self.docker.images.get(constant.ALPINE)\\n for tag in alpine.tags:\\n self.assertIn(\\\"alpine\\\", tag)\",\n \"def test(args):\\n startTimeStamp = time.monotonic()\\n #for GKE platform\\n if (f\\\"{args.platform}\\\" == \\\"GKE\\\" and (f\\\"{args.type}\\\" == \\\"all\\\")):\\n experiments = sorted(os.listdir('./litmus'))\\n\\n elif (f\\\"{args.platform}\\\" == \\\"GKE\\\" and (f\\\"{args.type}\\\" == \\\"pod\\\")):\\n experiments = [\\\"container-kill.yaml\\\",\\\"disk-fill\\\",\\\"pod-cpu-hog\\\",\\\"pod-delete\\\",\\\"pod-memory-hog\\\",\\\"pod-network-corruption\\\",\\\"pod-network-latency\\\",\\\"pod-network-loss\\\"]\\n\\n elif (f\\\"{args.platform}\\\" == \\\"GKE\\\" and (f\\\"{args.type}\\\" == \\\"node\\\")):\\n kind_supported = [\\\"node-cpu-hog\\\",\\\"node-memory-hog\\\"]\\n\\n #for kind platform\\n if ((f\\\"{args.platform}\\\" == \\\"kind\\\") and (f\\\"{args.type}\\\" == \\\"all\\\")):\\n kind_supported = [\\\"pod-delete\\\",\\\"container-kill\\\",\\\"node-cpu-hog\\\",\\\"node-memory-hog\\\"]\\n experiments=[s + \\\".yaml\\\" for s in kind_supported]\\n\\n elif ((f\\\"{args.platform}\\\" == \\\"kind\\\") and (f\\\"{args.type}\\\" == \\\"pod\\\")):\\n experiments = [\\\"node-cpu-hog.yaml\\\",\\\"node-memory-hog.yaml\\\"]\\n\\n elif ((f\\\"{args.platform}\\\" == \\\"kind\\\") and (f\\\"{args.type}\\\" == \\\"node\\\")):\\n experiments = [\\\"node-cpu-hog.yaml\\\",\\\"node-memory-hog.yaml\\\"]\\n\\n # for EKS platform\\n if (f\\\"{args.platform}\\\" == \\\"EKS\\\" and (f\\\"{args.type}\\\" == \\\"all\\\")):\\n experiments = sorted(os.listdir('./litmus'))\\n\\n elif (f\\\"{args.platform}\\\" == \\\"EKS\\\" and (f\\\"{args.type}\\\" == \\\"pod\\\")):\\n experiments = [\\\"container-kill.yaml\\\", \\\"disk-fill\\\", \\\"pod-cpu-hog\\\", \\\"pod-delete\\\", \\\"pod-memory-hog\\\",\\n \\\"pod-network-corruption\\\", \\\"pod-network-latency\\\", \\\"pod-network-loss\\\"]\\n\\n elif (f\\\"{args.platform}\\\" == \\\"EKS\\\" and (f\\\"{args.type}\\\" == \\\"node\\\")):\\n kind_supported = [\\\"node-cpu-hog\\\", \\\"node-memory-hog\\\"]\\n\\n experiment_results = []\\n\\n if args.test == '*':\\n # Run all experiments in /litmus directory with wait time between them\\n print_color(f\\\"Running all Litmus ChaosEngine Experiments with {args.wait} mins wait time between each one...\\\")\\n lstindex = len(experiments)\\n for experiment_file in experiments:\\n result = run_experiment(experiment_file.replace('.yaml', ''))\\n experiment_results.append(result)\\n print_color(f\\\"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Waiting {args.wait} mins before running next experiment...\\\", bcolors.WARNING)\\n lstindex -= 1\\n if lstindex != 0:\\n time.sleep(args.wait * 60)\\n else:\\n # Check experiment exists\\n experiment_file = args.test + \\\".yaml\\\"\\n if experiment_file in experiments:\\n result = run_experiment(args.test)\\n experiment_results.append(result)\\n else:\\n print_color(f\\\"ERROR: {experiment_file} not found in ./litmus directory. Please check the name and try again.\\\", bcolors.FAIL)\\n sys.exit(2)\\n\\n # Print out experiment result summary\\n print_color(\\\"***************************************************************************************************\\\", bcolors.OKBLUE)\\n print_color(\\\"* Experiments Result Summary\\\", bcolors.OKBLUE)\\n print_color(\\\"***************************************************************************************************\\\\n\\\", bcolors.OKBLUE)\\n headers = [\\\"#\\\", \\\"Start Time\\\", \\\"Experiment\\\", \\\"Status\\\"]\\n row_format = \\\"{:>25}\\\" * (len(headers) + 1)\\n print_color(row_format.format(\\\"\\\", *headers), bcolors.OKBLUE)\\n i = 1\\n for result in experiment_results:\\n if result.status == \\\"Pass\\\":\\n print_color(row_format.format(\\\"\\\", str(i), result.startTime.strftime('%Y-%m-%d %H:%M:%S'), result.name,\\\" \\\"+ result.status + \\\" 'carts-db' Service is up and Running after chaos\\\"), bcolors.OKBLUE)\\n i += 1\\n else:\\n print_color(row_format.format(\\\"\\\", str(i), result.startTime.strftime('%Y-%m-%d %H:%M:%S'), result.name, result.status), bcolors.OKBLUE)\\n i += 1\\n print(\\\"\\\\n\\\")\\n currentTimeStamp= time.monotonic()\\n diffTimeStamp = currentTimeStamp - startTimeStamp\\n ty_res = time.gmtime(diffTimeStamp)\\n totalTime = time.strftime(\\\"%H:%M:%S\\\",ty_res)\\n\\n if (f\\\"{args.report}\\\" == \\\"yes\\\"):\\n\\n print_color(\\\"Creating PDF Report\\\", bcolors.OKBLUE)\\n\\n fileName = 'chaos-report.pdf'\\n pdf = SimpleDocTemplate(fileName,pagesize=LETTER)\\n styles = getSampleStyleSheet()\\n data = [None]\\n data[0] = [\\\"S.No.\\\", \\\"Start Time\\\", \\\"Experiment\\\", \\\"Status\\\"]\\n\\n i =1\\n expPassed = 0\\n expFailed = 0\\n for result in experiment_results:\\n if result.status == \\\"Pass\\\":\\n data.append([str(i), result.startTime.strftime('%Y-%m-%d %H:%M:%S'), result.name,\\\" \\\"+ result.status + \\\" 'carts-db' Service is up and Running after chaos\\\"])\\n i += 1\\n expPassed +=1\\n else:\\n data.append([str(i), result.startTime.strftime('%Y-%m-%d %H:%M:%S'), result.name,result.status])\\n i += 1\\n expFailed +=1\\n\\n table = Table(data)\\n\\n picture = Image(\\\"images/litmus.png\\\")\\n picture.drawWidth = 100\\n picture.drawHeight = 100\\n picTable = Table([[picture]],100,100)\\n\\n elems = []\\n\\n # Adding logo\\n elems.append(picTable)\\n # Adding title\\n text = \\\"LitmusChaos Report <br/> <br/>Experiments Result Summary\\\"\\n para = Paragraph(text, styles['Title'])\\n elems.append(para)\\n\\n ## Adding result table\\n elems.append(table)\\n\\n style = TableStyle([\\n ('BACKGROUND',(0,0),(3,0), colors.green),\\n ('TEXTCOLOR',(0,0),(-1,0), colors.whitesmoke),\\n ('ALIGN',(0,0),(-1,-1), 'CENTER'),\\n ('FONTNAME',(0,0),(-1,0), 'Courier'),\\n ('FONTSIZE',(0,0),(-1,0), 14),\\n ('BOTTOMPADDING',(0,0),(-1,0), 12),\\n ('BACKGROUND',(0,1),(-1,-1), colors.beige),\\n ])\\n\\n ts = TableStyle([\\n ('BOX',(0,0),(-1,-1),1,colors.black)\\n ])\\n\\n ## Adding table style\\n table.setStyle(style)\\n table.setStyle(ts)\\n\\n para1 = Paragraph(\\\"The total number of passed experiments: %s \\\" % str(expPassed), styles['Heading3'])\\n elems.append(para1)\\n para2 = Paragraph(\\\"The total number of failed experiments: %s \\\" % str(expFailed), styles['Heading3'])\\n elems.append(para2)\\n para3 = Paragraph(\\\"The total experiment execution time: %s (HH:MM:SS)\\\" % str(totalTime), styles['Heading3'])\\n elems.append(para3)\\n\\n pdf.build(elems)\\n print_color(\\\"PDF Report Created Successfully \\\", bcolors.OKBLUE)\",\n \"def dockerfile() -> co.Exec:\\n image = co.Image(dockerfile=\\\"./docker/Dockerfile.simple\\\")\\n return co.Exec(\\n f\\\"python -c '{pretty_table_script}'\\\", image=image, doc=co.util.magic_doc()\\n )\",\n \"def test_baseimage_first(self):\\n s = Stage()\\n s += shell(commands=['abc'])\\n s.name = 'bar'\\n s.baseimage('foo')\\n self.assertEqual(str(s), 'FROM foo AS bar\\\\n\\\\nRUN abc')\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.58483183\",\n \"0.5847421\",\n \"0.58329225\",\n \"0.58315045\",\n \"0.5813572\",\n \"0.5811272\",\n \"0.58070385\",\n \"0.58055985\",\n \"0.5804632\",\n \"0.58003443\",\n \"0.57948834\",\n \"0.5789104\",\n \"0.57848877\",\n \"0.57835513\",\n \"0.5782444\",\n \"0.57585377\",\n \"0.5758397\",\n \"0.5749861\",\n \"0.57473123\",\n \"0.5743178\",\n \"0.5728303\",\n \"0.57209736\",\n \"0.5716744\",\n \"0.5715361\",\n \"0.57025176\",\n \"0.57012683\",\n \"0.5700717\",\n \"0.5697808\",\n \"0.5691024\",\n \"0.56883943\",\n \"0.5685633\",\n \"0.5682641\",\n \"0.56811225\",\n \"0.5680725\",\n \"0.56806\",\n \"0.5676561\",\n \"0.56757987\",\n \"0.5664943\",\n \"0.5630124\",\n \"0.5628875\",\n \"0.5628337\",\n \"0.5617312\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60226077"},"document_rank":{"kind":"string","value":"34"}}},{"rowIdx":80397,"cells":{"query":{"kind":"string","value":"Entry point, executes the root group."},"document":{"kind":"string","value":"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()"},"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 run(self, root):\r\n pass","def root():\n pass","async def _group(self, ctx):\r\n\t\tif ctx.invoked_subcommand is None:\r\n\t\t\tprint (\"Ryhmä komento annettiin ilman alakomentoa\")","def exec(self):\n if self._root.master is None:\n self._root.mainloop()","def execute(self, context):\n global array_nodes\n sub_tree = bpy.data.node_groups.new('Armory group', 'ArmGroupTree') # creating subtree\n sub_tree.use_fake_user = True\n group_node = array_nodes[self.node_index]\n group_node.group_tree = sub_tree # link subtree to group node\n sub_tree.nodes.new('LNGroupInputsNode').location = (-250, 0) # create node for putting data into subtree\n sub_tree.nodes.new('LNGroupOutputsNode').location = (250, 0) # create node for getting data from subtree\n context.space_data.path.append(sub_tree, node=group_node)\n sub_tree.group_node_name = group_node.name\n return {'FINISHED'}","def createMainGroup(self):\n\t\tmc.group( n = self.grp.name, em = True )","def ui_root1():\n return send_build()","def cli_run_group(log_level, endpoint, email, password, org_name, grp_name):\n logging.basicConfig(\n level=log_level,\n format='%(levelname)s: %(message)s',\n )\n knex = Knex(endpoint)\n User(knex, email, password).login()\n org = Organization(knex, org_name).get()\n logger.info(f'Loaded {org}')\n grp = org.sample_group(grp_name).get()\n logger.info(f'Loaded {grp}')\n auto_metadata(list(grp.get_samples()), grp)\n logger.info(f'Processed Metadata')\n run_group(grp, lambda x: click.echo(x, err=True))","def cli() -> None:\n try:\n group() # pylint: disable=no-value-for-parameter\n except forml.AnyError as err:\n print(err, file=sys.stderr)","def main():\n\n # Register all command groups.\n cli.add_command(groups.submit_group)\n cli.add_command(groups.config_group)\n cli.add_command(groups.jobs_group)\n cli.add_command(groups.result_group)\n cli.add_command(groups.status_group)\n\n try:\n cli.main()\n except (ConnectionRefusedError, urllib3_exceptions.MaxRetryError,\n urllib3_exceptions.NewConnectionError) as exception:\n log.error(f'Error connecting to the Turbinia API server: {exception!s}')\n sys.exit(-1)","def main():\n\n # Define the path to the config file, and read it.\n conf_file = os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'conf', 'config.toml')\n configuration = read_config_file(conf_file)\n\n # Set our variables based on what is read from the config file.\n rs_url = configuration['platform']['url']\n api_key = configuration['platform']['api_key']\n client_id = configuration['platform']['client_id']\n\n # Specify ID for group that you would like to move your hosts to.\n group_id = 0 # UPDATE THIS WITH YOUR DESIRED GROUP ID\n\n # Move the hosts to the new group.\n print(f\"Moving host(s) to new group({group_id}).\")\n print()\n\n # Call the function to move your hosts\n successful = move_hosts_to_new_group(rs_url, api_key, client_id, group_id)\n\n if successful:\n print(\"The move was successful.\")\n\n else:\n print(\"The attempted move was not successful.\")","def main(self, session: Session) -> None:\n buttons = []\n for group in groups_api.get_user_groups(session.user):\n if session.user in group.admins:\n buttons.append(self.ui.create_button_view(group.name,\n lambda s: self.show_small_menu(group,\n session)))\n self.ui.create_button_group_view(session, \"What group do you want to change?\",\n buttons).draw()","def do_Root (self, line):\r\n GenRoot(self.stdin, self.tracking).do_cmdloop()","def main():\n try:\n root()\n except errors.ControllerConnectionError as error:\n handle_controller_connection_error(error)\n except errors.NodeConnectionError as error:\n handle_node_connection_error(error)","def RootCodeGroup(self) -> CodeGroup:","def exit_group():\n global group_length\n group_length = group_length - 1\n if group_length < 0:\n group_length = 0\n error(\"Attempting to reduce groups even though you're already at root\")\n logline(\"/\", indent=False)","def _post_chroot_block(self):\n pass","def root(self):\n # method here","def main():\n snap_name = 'REST_Snap_' + strftime('%d%m%Y%H%M%S')\n ru.replication.create_storagegroup_snap(sg_id, snap_name)\n print('Check the Gui now or REST Client to see if snapshot '\n '{snap_name} was created for Storge Group {sg_id}'\n .format(snap_name=snap_name, sg_id=sg_id))","async def github_group(self, ctx: commands.Context) -> None:\n if ctx.invoked_subcommand is None:\n await invoke_help_command(ctx)","def main():\n lc = db.getCursorForDB(localDb)\n rc = db.getCursorForDB(\"enwiki_p\")\n\n # Drop the old groups\n query = \"DELETE FROM ts_users_groups\"\n out(\"Deleting old user groups\")\n lc = db.execute(lc, query)\n\n # Fetch the updated groups\n query = \"SELECT * FROM user_groups\"\n out(\"Selecting user groups\")\n rc = db.execute(rc, query)\n rows = rc.fetchall()\n space = []\n values = []\n for r in rows:\n space.append(\"(%s,%s)\")\n values += [str(r[\"ug_user\"]), str(r[\"ug_group\"])]\n\n # Add them to the local table\n query = \"INSERT INTO ts_users_groups (tug_uid, tug_group) VALUES %s\" % (','.join(space))\n out(\"Inserting user groups\")\n lc = db.execute(lc, query, values)","def wrapper(callback):\n if self.subcommand_groups.get(base_names[0]) is None:\n self.subcommand_groups[base_names[0]] = {}\n self.subcommand_groups[base_names[0]][name] = SubSlashCommandGroup(callback, base_names, name, description, options=options, guild_ids=guild_ids, default_permission=default_permission, guild_permissions=guild_permission)","def root():\n return render_template('root.html')","def main(self):\r\n pass","def main():\n challenge = Challenge()\n # Get the number of saved files on last execution\n last_saved = challenge.get_last_execution()\n # Get the total of products to save\n total_objects = len(challenge.get_products(\"product_groups.json\"))\n\n # While there are products to be saved\n while last_saved < total_objects:\n create_products()\n # Updates last_saved number\n last_saved = challenge.get_last_execution()\n\n logging.info(\"[INFO] Execution done with no errors!\")\n # Sends to runner a signal different from the crash signal\n # Indicates terminated execution\n os._exit(1)","def run(self):\n self.run()","def main(self):\n self.root.mainloop()","def run():\n main()","def _execute_stage(self, index, stage, stop):\n if stop.is_set():\n _LOGGER.info(\"Stopped pipeline on group %s\", self._group)\n return\n _LOGGER.info(\" -> Running stage '%s' on group %s\", stage, self._group)\n if stage.name == 'on':\n self._group.on = True\n elif stage.name == 'off':\n self._group.on = False\n elif stage.name == 'hue':\n self._group.hue = stage.args[0]\n elif stage.name == 'saturation':\n self._group.saturation = stage.args[0]\n elif stage.name == 'color':\n self._group.color = Color(*stage.args)\n elif stage.name == 'brightness':\n self._group.brightness = stage.args[0]\n elif stage.name == 'temperature':\n self._group.temperature = stage.args[0]\n elif stage.name == 'transition':\n self._group.transition(*stage.args, **stage.kwargs)\n elif stage.name == 'white':\n self._group.white()\n elif stage.name == 'white_up':\n self._group.white_up()\n elif stage.name == 'white_down':\n self._group.white_down()\n elif stage.name == 'red_up':\n self._group.red_up()\n elif stage.name == 'red_down':\n self._group.red_down()\n elif stage.name == 'green_up':\n self._group.green_up()\n elif stage.name == 'green_down':\n self._group.green_down()\n elif stage.name == 'blue_up':\n self._group.blue_up()\n elif stage.name == 'blue_down':\n self._group.blue_down()\n elif stage.name == 'night_light':\n self._group.night_light()\n elif stage.name == 'link':\n self._group.link()\n elif stage.name == 'unlink':\n self._group.unlink()\n elif stage.name == 'flash':\n self._group.flash(**stage.kwargs)\n elif stage.name == 'repeat':\n self._repeat(index, stage, stop)\n elif stage.name == 'wait':\n time.sleep(*stage.args)\n elif stage.name == 'callback':\n stage.args[0](*stage.args[1:], **stage.kwargs)","def main():\n ensure_not_root()\n config.setup()\n model.init_db()\n manager.run()","def main():\n print(\"def main\")\n return APP.run()","def main():\n\n # Setup `pysc` to use BASIC auth, with a username, and password. Also sets the endpoint to use.\n setup_sensorcloud_basic(CONSTS['SC_USERNAME'], CONSTS['SC_PASSWORD'],\n CONSTS['SC_ENDPOINT'], CONSTS['PYSC_DEBUG'])\n\n org_id = CONSTS['ORG_ID']\n\n # Ensure the organisation exists on the SensorCloud endpoint.\n try:\n organisation = pysc.models.Organisation.single(org_id)\n except KeyError:\n raise RuntimeWarning(\"\"\"The organisation named {:s} was not found.\\n\"\"\"\n \"\"\"Although the `pysc` api has functionality to create an organisation, it cannot \"\"\"\n \"\"\"do so on the sensor-cloud.io instance on AWS.\"\"\".format(org_id))\n # Ensure sanity, check we got the organisation that we asked for.\n assert (org_id == organisation.id)\n\n # Here we use the Group.resolve_all helper with organisation_id param to filter groups based on id\n # The resolve_all command is similar to .index() however it also calls .follow() on found link automatically,\n # _and_ it converts the resulting HAL objects into real valid `pysc` Group() objects.\n org_groups = pysc.models.Group.resolve_all(params={'organisation_id': org_id})\n # We are not likely to have more than 1000 groups, so we don't need to do return doc pagination here.\n for g in org_groups:\n group_id = g.id\n print(\"Found group: {:s}\".format(group_id))\n\n print(\"Found a total of {:d} groups for {:s} on that SensorCloud endpoint.\".format(len(org_groups), org_id))","def call_main(session, tmpdir, *args):\n # type: (Session, LocalPath, *str) -> None\n config_path = src_path(\"config\", \"test.yaml\")\n argv = [\"grouper-ctl\", \"-c\", config_path, \"-d\", db_url(tmpdir)] + list(args)\n main(sys_argv=argv, session=session)","def Run(self):\n \n if not self.FolderPath == None:\n \n #run the folder manipulation routines...\n self.InitialList, out_0 = self.ReturnRamanFiles(self.FolderPath,'.txt')\n self.Condensensed = self.RamanDBCondenser(out_0)\n self.SetFilters()\n self.Gatherer(self.InitialList,out_0)\n self.BuildTree()","def main():\n\n payload = {'include': 'systems'}\n insights_request = InsightsRequest(SYS_API, payload)\n cute_output(insights_request)\n\n groups = insights_request.get_insights()\n found = False\n\n for elem in groups:\n if not elem['systems']:\n found = True\n print('Deleting empty group ' + elem['display_name'])\n clean_empty_group(elem['id'])\n if not found:\n print('No empty groups found, nothing to delete!!!')\n\n payload = {'expand': 'system'}\n insights_request = InsightsRequest(URL + '/v2/reports', payload)\n reports = create_maint_plan(insights_request)\n \"\"\"\n print(reports)\n\n result = get_json(URL + '/v2/reports', payload)\n print(result)\n for elem in result['resources']:\n print('Report: ' + str(elem['id']) + ' Rule id: ' + str(elem['rule_id']) + '\\tSystem: ' + str(elem['system']['hostname']))\n \"\"\"\n #create_maint_plan('gherkin')","def main():\n print(\"Call your main application code here\")","def main():\n print(\"Call your main application code here\")","def main():\n print(\"Call your main application code here\")","def main(self):\n self.validate()\n self.root.mainloop()","def cmd():\n # Importing the engine\n from sqla_json.sqla_management import engine\n from sqlalchemy.orm import sessionmaker\n from sqla_json.models.node import DbNode\n from sqla_json.models.group import DbGroup\n\n from sqla_json.commands import DEFAULT_GROUP_NAME\n\n import time\n import sys\n\n # Creating the needed session\n Session = sessionmaker(bind=engine)\n session = Session()\n\n # Getting the one and only group with that name\n dbgroup = session.query(DbGroup).filter_by(name=DEFAULT_GROUP_NAME).first()\n\n if dbgroup is None:\n print \"No group is found, exiting\"\n sys.exit()\n\n # Find all nodes\n print \"Getting all the nodes\"\n start_time = time.time()\n\n list_node = list()\n for dbn in session.query(DbNode).all():\n list_node.append(dbn)\n\n print \"Found #{} nodes\".format(len(list_node))\n\n # Add the nodes to the group\n print \"Adding the nodes to the group\"\n # session.bulk_update_mappings(DbGroup, {'id': dbgroup.id,\n # 'dbnodes': list_node})\n dbgroup.dbnodes = list_node\n\n session.expunge_all()\n\n session.bulk_save_objects([dbgroup])\n # session.add(dbgroup)\n\n # print \"LALLLALALA \", {'id': dbgroup.id, 'dbnodes': list_node}\n # session.add(dbgroup)\n session.commit()\n end_time = time.time()\n\n print \"Added to group\"\n print \"Elapsed time --- {} seconds --- \".format(end_time - start_time)","def main():\n global HEALTH_AGGREGATOR\n\n os.makedirs(PEM_DIR, exist_ok=True)\n\n if not os.path.exists(f'{os.path.join(os.getcwd(), \"database.db\")}'):\n DB_User.create_all()\n DB_Resource.create_all()\n\n User.add_user('SpiceWorks', 'HealthChecker')\n\n HEALTH_AGGREGATOR = HealthAggregator()\n app.run(port=5000)\n HEALTH_AGGREGATOR.cleanup()","def run(self):\r\n self.log(texto=f\"Executando {self._name}\")","def do_standalone_display(self):\n stage = clutter.Stage()\n stage.connect('destroy', clutter.main_quit)\n stage.connect('key-press-event', lambda x,y: clutter.main_quit())\n stage.set_fullscreen(True)\n stage.set_color(clutter.color_from_string('black'))\n stage.add(self.group)\n stage.show_all()\n clutter.main()","def main():\n CLI_APP.run()","def _setRoot(self):\n if self.state == BaseEditMode.ES_None:\n sheet = self._logic._getSheet()\n layout = sheet.getLayoutGroup()\n \n import suit.core.layout.LayoutGroupRadial as radialLayout\n if not isinstance(layout, radialLayout.LayoutGroupRadialSimple): \n return\n \n objs = sheet.getSelected()\n # get object for root changing \n if len(objs) == 1: \n layout.setRoot(objs[0])","def main(self):\n cmd, path, args = self._parse_args()\n if cmd == \"shell\":\n print \"You are now in ubs shell.\"\n print \"Use \\\"python %s help\\\" to see other choice.\" % sys.argv[0]\n self.shell()\n elif cmd == \"help\":\n self.print_path_help(path)\n sys.exit(0) \n elif cmd == \"run\":\n self.route(path, args)\n else:\n raise Exception(\"unknown CMD %s\" % cmd)","def handle(self, *args, **kwargs):\n self.check_settings_existence()\n self._load_backend()\n logger.info('Fetch nested groups...')\n self.nested_groups = NestedGroups()\n logger.info('Syncing...')\n if not ldap_module_exists:\n logger.error('ldap module not installed')\n raise ImportError('No module named ldap')\n synced = self.populate_users()\n logger.info('LDAP users synced: %s', synced)","def main(argv: list[str]) -> int:\n args = parse_args(argv)\n logging.basicConfig(format=LOG_FORMAT, level=logging.INFO)\n\n with SchrootSession(args.chroot) as session:\n if not session.check_directory(args.directory):\n args.directory = session.get_home_directory(args.user)\n if not session.check_directory(args.directory):\n session.create_directory(args.directory, args.user)\n\n if args.enable_proposed:\n session.enable_proposed([args.proposed_uri], args.proposed_components)\n if args.ppa:\n session.add_ppas(args.ppa)\n if args.packages:\n session.install_packages(args.packages)\n return session.run(args.directory, args.user, check=False).returncode","def command(self):\n from tg import config\n self.basic_setup()\n\n # reset LDAP database and chroot home folders (if supported)\n self.reset_LDAP(config)\n self.reset_chroot()\n\n # exclude these users from the command\n special_users = ['*anonymous', 'root']\n\n # add users to LDAP and asynchronously create chroot user home folder\n count = 0\n users = User.query.find({'username': {'$nin': special_users}}).all()\n task = None\n for user in users:\n self.add_LDAP_user(user, config)\n if self.HAS_CHROOT:\n task = register_ldap.post(user.username)\n count += 1\n print \"Refreshed {} users.\".format(count)\n\n # wait for last task to complete\n if task:\n MonQTask.wait_for_tasks(query={\n '_id': task._id, 'state': {'$in': ['ready', 'busy']}\n }, timeout=240000)\n\n # asynchronously upload user public keys\n count = 0\n if self.HAS_CHROOT:\n for user in users:\n public_key = getattr(user, 'public_key', None)\n if public_key:\n upload_ssh_ldap.post(user.username, public_key)\n count += 1\n print \"Uploaded {} public keys.\".format(count)","def main(self: object) -> None:\n print(\"[View] main\")\n self.mainloop()","def main(request):\n users = get_channel_users(MEMBER_CHANNEL)\n human_users = filter_bots(users)\n buddy_groups = group_users(human_users)\n if buddy_groups:\n resp = post_message(buddy_groups, POST_CHANNEL)\n ret = str(resp)\n else:\n ret = 'Not enough members to run'\n return ret","def nodes_run_cmd(self, group, cmd):\n path_inventory = u'%s/inventories/%s' % (self.ansible_path, self.environment)\n path_lib = u'%s/library/beehive/' % (self.ansible_path)\n runner = Runner(inventory=path_inventory, verbosity=self.verbosity, \n module=path_lib)\n tasks = [\n dict(action=dict(module=u'shell', args=cmd), register=u'shell_out'),\n ]\n runner.run_task(group, tasks=tasks, frmt=u'json')","def onGroup(a):\n data = a.data()\n if data:\n try:\n Gui.doCommand('Gui.activateWorkbench(\"' + data + '\")')\n except KeyError:\n pass","def ui_root(filename):\n return send_build(filename)","def main(self) -> None:\n pass","def main():\n pass","def execute(self):\n if len(self._tree) > 0:\n return self._tour(self._tree.root(),0,[]) # start the recursion","def Run():\r\n pass","def run(self):\n\t\t\n\t\tpass","def main():\n return","def __on_group_created(self, logger, *args):","def index():\n return \"This is root!!!!\"","def backMain():\n rootStat.destroy()\n main()","def _do_group(self, req):\n # process forms/commands\n message=''\n args={}\n if req.method == 'POST':\n group = req.args.get('group')\n user = req.args.get('user')\n if req.args.get('add'):\n if group and not self.has_group(group):\n self.add_group(group)\n message+=u'added group %s\\n'%group\n if user and not group in self.get_permission_groups(user):\n self.add_user_to_group(user,group)\n message+=u'added %s to %s' % (user, group)\n args['message'] = message\n elif req.args.get('remove') and req.args.get('group'):\n sel = req.args.get('sel')\n sel = isinstance(sel, list) and sel or [sel]\n for user in sel:\n self.del_user_from_group(user,group)\n args['message'] = u'removed %s from %s' % (sel, req.args.get('group'))\n # lists and other info\n all_groups = sorted(self.get_groups())\n args['groups'] = all_groups\n if req.args.get('group') and self.has_group(req.args.get('group')):\n group_name=req.args.get('group')\n elif len(all_groups) > 0:\n group_name=all_groups[0]\n else:\n group_name = None\n if group_name:\n args['group'] = group_name\n args['members'] = sorted(self.group_get_members(group_name))\n args['listing_enabled'] = True\n args['selection_enabled'] = len(all_groups)>0\n return 'sqlflexibleauthstore_group_editor.html', args","def groupId(self):\r\n return 'examplescripts'","def main():\n\tpass","def run(self, distribution_root):\n self.result = self._compile_and_execute(distribution_root)\n return self.result","def main(ctx, root_project_dir, verbose):\n # Subcommands should use the click.pass_context decorator to get this\n # ctx.obj object as the first argument.\n ctx.obj = {\"root_project_dir\": root_project_dir, \"verbose\": verbose}\n\n # Set up application logging. This ensures that only documenteer's\n # logger is activated. If necessary, we can add other app's loggers too.\n if verbose:\n log_level = logging.DEBUG\n else:\n log_level = logging.INFO\n logger = logging.getLogger(\"documenteer\")\n logger.addHandler(logging.StreamHandler())\n logger.setLevel(log_level)","def main():\n\n run_lap()","def main():\n run_program()","def run(self):\n self.cmdloop()","def execute(self):\n if len(self._tree) > 0:\n return self.tour(self._tree.root(), 0, [])","def mainloop(self):\n self.master.mainloop()","def run(self):\n self.render_templates()\n self.create_docker_context()\n self.build()\n self.push()","def _go_to_root_menu(self):\n log.debug(\"Returning to root menu...\")\n # Issue an enter or two off the bat to get out of any display screens\n # and confirm command mode\n try:\n response = self._do_cmd_resp(Command.BLANK, expected_prompt=Prompt.CMD_PROMPT)\n while not str(response).lstrip().endswith(Prompt.CMD_PROMPT):\n response = self._do_cmd_resp(Command.BLANK,\n expected_prompt=Prompt.CMD_PROMPT)\n time.sleep(1)\n except InstrumentTimeoutException:\n raise InstrumentProtocolException(\"Not able to get valid command prompt. Is instrument in command mode?\")\n \n # When you get a --> prompt, do 9's until you get back to the root\n response = self._do_cmd_resp(Command.BACK_MENU,\n expected_prompt=MENU_PROMPTS)\n while not str(response).lstrip().endswith(Prompt.MAIN_MENU):\n response = self._do_cmd_resp(Command.BACK_MENU,\n expected_prompt=MENU_PROMPTS)","def run(self):\n # Get the child element iterator.\n if hasattr(self.thistag, 'iterfind'):\n childiter = self.thistag.iterfind(r'./*')\n else:\n childiter = self.thistag.findall(r'./*')\n\n # Execute the child actions.\n exitcode = 0\n for childtag in childiter:\n logger.debug('child tag = \"{0}\"'.format(childtag.tag))\n\n # Look for an action handler class that matches the\n # child tag name.\n action = None\n for module in actionmodules:\n try:\n action = getattr(module, childtag.tag)\n break\n except AttributeError:\n continue\n\n # Ignore non-action (parameter) tags.\n if not action: continue\n\n # Construct and run the child action.\n logger.debug('Running \"{0}\"...'.format(childtag.tag))\n try:\n exitcode = action(self.context, childtag).run()\n except Exception as e:\n logger.exception(e)\n sys.stderr.write('Internal hook error.'\n + ' Please notify administrator.')\n return -1\n\n # If the child action generated a non-zero exit code, stop\n # processing child actions.\n\n if exitcode != 0: break\n\n # Return the current exit code.\n return exitcode","def main():\n\n browser = initialize()\n process_directory(browser, \"data\")\n browser.close()","def _main_helper(self):\n asyncio.create_task(self._main())","def main():\n print(\"Everythin is ok\")","def main(self, **kwargs) -> None:\n ...","def groupManager(request):\n \n return render_to_response('centers.html',context_instance=RequestContext(request))","def main():\n\n merch_bot = MerchBot()\n merch_bot.run_bot()","def main() -> None:\n try:\n # ServerManager expects cwd to be the server dir (containing\n # dist/, config.yaml, etc.)\n # Let's change our working directory to the location of this file\n # so we can run this script from anywhere and it'll work.\n os.chdir(os.path.abspath(os.path.dirname(__file__)))\n\n ServerManagerApp().run_interactive()\n except CleanError as exc:\n # For clean errors, do a simple print and fail; no tracebacks/etc.\n exc.pretty_print()\n sys.exit(1)","def launch(self):\n self._fork()\n self._lock()\n os.setegid(self._user[1])\n os.seteuid(self._user[0])\n self._loop = True\n signal.signal(signal.SIGTERM, self.__signalHandler)\n sys.stdout = self._output\n sys.stderr = self._error\n self._run()\n sys.stdout = self._stdout\n sys.stderr = self._stderr\n os.setegid(0)\n os.seteuid(0)\n self._unlock()","def main():\n # group_id = get_group_id() This would be used if I had\n # the appropriate privileges\n group_id = 15000022833\n setup_logger()\n ticket_ids = get_newhire_tickets(group_id)\n for ticket_id in ticket_ids:\n update_ticket_info(ticket_id)","def root():\n return 'Hello', 200","def main():\n parser = argparse.ArgumentParser(description=\"Wrapper of the scikit-learn AgglomerativeClustering method. \", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))\n parser.add_argument('--config', required=False, help='Configuration file')\n\n # Specific args of each building block\n required_args = parser.add_argument_group('required arguments')\n required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')\n required_args.add_argument('--output_results_path', required=True, help='Path to the clustered dataset. Accepted formats: csv.')\n parser.add_argument('--output_plot_path', required=False, help='Path to the clustering plot. Accepted formats: png.')\n\n args = parser.parse_args()\n args.config = args.config or \"{}\"\n properties = settings.ConfReader(config=args.config).get_prop_dic()\n\n # Specific call of each building block\n agglomerative_clustering(input_dataset_path=args.input_dataset_path,\n output_results_path=args.output_results_path,\n output_plot_path=args.output_plot_path,\n properties=properties)","def execute(self, root_actor_class, root_instance_name, log_hierarchy, *args, **kwargs):\n loop = self._event_loop\n log = self._log\n shutdown_grace_sec = self._shutdown_grace_seconds\n\n def ask_exit(signame, loop, actor, actor_task):\n log.info(\"got signal {}\".format(signame))\n\n async def wait_for_termination(loop, actor, actor_task):\n try:\n log.info(\"Waiting {} seconds for actors to stop...\".format(\n SHUTDOWN_GRACE_SECONDS))\n await actor.terminate()\n await asyncio.wait_for(actor_task, shutdown_grace_sec)\n log.info(\"actors terminated. stopping loop...\")\n loop.call_later(0.1, lambda: loop.stop())\n except asyncio.TimeoutError:\n log.info(\"Actors did not stop. Cancelling tasks...\")\n actor_task.cancel()\n try:\n await asyncio.wait_for(actor_task, shutdown_grace_sec)\n except asyncio.TimeoutError:\n log.error(\"Fatal error! Unable to cancel tasks.\")\n finally:\n loop.call_later(0.1, lambda: loop.stop())\n exv = actor_task.exception()\n if exv is not None and not isinstance(exv, asyncio.CancelledError):\n raise exv\n\n loop.create_task(wait_for_termination(loop, actor, actor_task))\n\n loop = asyncio.get_event_loop()\n\n root_actor, root_task = Actor.Spawn(\n root_actor_class, loop, None, root_instance_name, log_hierarchy, *args, **kwargs)\n\n for signame in ('SIGINT', 'SIGTERM'):\n loop.add_signal_handler(getattr(signal, signame),\n functools.partial(ask_exit, signame, loop, root_actor, root_task))\n\n def stop_loop(task):\n loop.call_soon(lambda: loop.stop())\n root_task.add_done_callback(stop_loop)\n\n loop.run_forever()","def main():\n print(\"Everything is ok.\")","def main():\n dispatch = {}\n\n parser = argparse.ArgumentParser()\n subs = parser.add_subparsers(dest=\"command_name\", required=True)\n\n # Configure logger\n logger = logging.getLogger()\n handler = logging.StreamHandler()\n handler.setFormatter(ColoredFormatter(fmt=\"%(message)s\"))\n logger.addHandler(handler)\n logger.setLevel(logging.INFO)\n\n for command in sys.modules[__name__].__dict__.values():\n if not hasattr(command, \"command_name\"):\n continue\n\n dispatch[command.command_name] = command()\n\n doc = getattr(command, \"__doc__\", \"\")\n subparser = subs.add_parser(\n command.command_name, help=doc, description=doc)\n\n add_args = getattr(command, \"add_args\", None)\n if add_args:\n command.add_args(subparser)\n\n args = parser.parse_args()\n\n command = dispatch[args.command_name]\n errors = getattr(command, \"check_args\", lambda x: [])(args)\n if errors:\n logger.error(\"\\n\".join(errors))\n # Ugly hack, gets the help for the subcommand, no public API for this\n parser._actions[1]._name_parser_map[args.command_name].print_help()\n else:\n command.run(args, find_repo_root(), logger)","def group_home(request, group_slug):\n user = request.user\n group = GroupProfile.objects.get(slug=group_slug)\n user_permissions = ''\n if not group:\n raise Http404(\"Can't find a group with the slug: %s\" % group_slug)\n else:\n user_permission_type = get_user_permissions(user, group)\n if user_permission_type != 3 and user_permission_type != \"Not logged in\":\n user_permissions = 'allowed'\n query = group.mission_statements.all().order_by(\"-created_at\")\n if query.count() > 0:\n mission_statement = query[0]\n else:\n mission_statement = \"\"\n discussions = group.group.discussions.all()\n members = group.get_group_members()\n user_in_group = group.is_user_in_group(user)\n if group.is_private and not user_in_group:\n messages.error(request, \"The group is private. You're not allowed to see it.\" )\n #initializing the forms\n discussion_form = DiscussionForm()\n mission_statement_form = MissionStatementForm()\n return render_to_response('group_home.html', locals(), context_instance=RequestContext(request))","def restart_with_root():\n if platform.system() == 'Windows':\n os.execv('cmd', join_path(os.path.pardir(os.path.pardir(__file__)), 'start_root.cmd'))\n else:\n os.execv(sys.executable, ['sudo python'] + sys.argv)","def run(self): \r\n return","def doRoot(self,action):\n #--Root directory is Textures directory?\n dirExts = ResourceReplacer.dirExts\n textureExts = ResourceReplacer.textureExts\n srcDir = os.path.join(self.replacerDir,self.file)\n destDir = modInfos.dir\n isTexturesDir = True #--Assume true for now.\n for srcFile in os.listdir(srcDir):\n srcPath = os.path.join(srcDir,srcFile)\n if os.path.isdir(srcPath) and srcFile.lower() in dirExts:\n isTexturesDir = False\n destPath = os.path.join(destDir,srcFile)\n action(srcPath,destPath,dirExts[srcFile.lower()])\n if isTexturesDir:\n destPath = os.path.join(destDir,'Textures')\n action(srcDir,destPath,textureExts)","def main():\n # Add your main code here\n display_menu()\n pass","def run():\n from cgl.plugins.blender.tasks.rig import parent_mdl_to_rig\n parent_mdl_to_rig()","def run_as_root():\n # Need to be root\n if os.geteuid() != 0:\n print(\"You need to be root to run this script. Relaunching with \"\n \"sudo...\\n\")\n subprocess.call([\"sudo\", sys.executable] + sys.argv)\n exit()","def mainloop(self):\n self.root.mainloop()","def mainloop(self):\n self.root.mainloop()","def test_6_1_1_script_group(host):\n assert host.file(AUDIT_SYSTEM_SCRIPT).group == 'root'"],"string":"[\n \"def run(self, root):\\r\\n pass\",\n \"def root():\\n pass\",\n \"async def _group(self, ctx):\\r\\n\\t\\tif ctx.invoked_subcommand is None:\\r\\n\\t\\t\\tprint (\\\"Ryhmä komento annettiin ilman alakomentoa\\\")\",\n \"def exec(self):\\n if self._root.master is None:\\n self._root.mainloop()\",\n \"def execute(self, context):\\n global array_nodes\\n sub_tree = bpy.data.node_groups.new('Armory group', 'ArmGroupTree') # creating subtree\\n sub_tree.use_fake_user = True\\n group_node = array_nodes[self.node_index]\\n group_node.group_tree = sub_tree # link subtree to group node\\n sub_tree.nodes.new('LNGroupInputsNode').location = (-250, 0) # create node for putting data into subtree\\n sub_tree.nodes.new('LNGroupOutputsNode').location = (250, 0) # create node for getting data from subtree\\n context.space_data.path.append(sub_tree, node=group_node)\\n sub_tree.group_node_name = group_node.name\\n return {'FINISHED'}\",\n \"def createMainGroup(self):\\n\\t\\tmc.group( n = self.grp.name, em = True )\",\n \"def ui_root1():\\n return send_build()\",\n \"def cli_run_group(log_level, endpoint, email, password, org_name, grp_name):\\n logging.basicConfig(\\n level=log_level,\\n format='%(levelname)s: %(message)s',\\n )\\n knex = Knex(endpoint)\\n User(knex, email, password).login()\\n org = Organization(knex, org_name).get()\\n logger.info(f'Loaded {org}')\\n grp = org.sample_group(grp_name).get()\\n logger.info(f'Loaded {grp}')\\n auto_metadata(list(grp.get_samples()), grp)\\n logger.info(f'Processed Metadata')\\n run_group(grp, lambda x: click.echo(x, err=True))\",\n \"def cli() -> None:\\n try:\\n group() # pylint: disable=no-value-for-parameter\\n except forml.AnyError as err:\\n print(err, file=sys.stderr)\",\n \"def main():\\n\\n # Register all command groups.\\n cli.add_command(groups.submit_group)\\n cli.add_command(groups.config_group)\\n cli.add_command(groups.jobs_group)\\n cli.add_command(groups.result_group)\\n cli.add_command(groups.status_group)\\n\\n try:\\n cli.main()\\n except (ConnectionRefusedError, urllib3_exceptions.MaxRetryError,\\n urllib3_exceptions.NewConnectionError) as exception:\\n log.error(f'Error connecting to the Turbinia API server: {exception!s}')\\n sys.exit(-1)\",\n \"def main():\\n\\n # Define the path to the config file, and read it.\\n conf_file = os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'conf', 'config.toml')\\n configuration = read_config_file(conf_file)\\n\\n # Set our variables based on what is read from the config file.\\n rs_url = configuration['platform']['url']\\n api_key = configuration['platform']['api_key']\\n client_id = configuration['platform']['client_id']\\n\\n # Specify ID for group that you would like to move your hosts to.\\n group_id = 0 # UPDATE THIS WITH YOUR DESIRED GROUP ID\\n\\n # Move the hosts to the new group.\\n print(f\\\"Moving host(s) to new group({group_id}).\\\")\\n print()\\n\\n # Call the function to move your hosts\\n successful = move_hosts_to_new_group(rs_url, api_key, client_id, group_id)\\n\\n if successful:\\n print(\\\"The move was successful.\\\")\\n\\n else:\\n print(\\\"The attempted move was not successful.\\\")\",\n \"def main(self, session: Session) -> None:\\n buttons = []\\n for group in groups_api.get_user_groups(session.user):\\n if session.user in group.admins:\\n buttons.append(self.ui.create_button_view(group.name,\\n lambda s: self.show_small_menu(group,\\n session)))\\n self.ui.create_button_group_view(session, \\\"What group do you want to change?\\\",\\n buttons).draw()\",\n \"def do_Root (self, line):\\r\\n GenRoot(self.stdin, self.tracking).do_cmdloop()\",\n \"def main():\\n try:\\n root()\\n except errors.ControllerConnectionError as error:\\n handle_controller_connection_error(error)\\n except errors.NodeConnectionError as error:\\n handle_node_connection_error(error)\",\n \"def RootCodeGroup(self) -> CodeGroup:\",\n \"def exit_group():\\n global group_length\\n group_length = group_length - 1\\n if group_length < 0:\\n group_length = 0\\n error(\\\"Attempting to reduce groups even though you're already at root\\\")\\n logline(\\\"/\\\", indent=False)\",\n \"def _post_chroot_block(self):\\n pass\",\n \"def root(self):\\n # method here\",\n \"def main():\\n snap_name = 'REST_Snap_' + strftime('%d%m%Y%H%M%S')\\n ru.replication.create_storagegroup_snap(sg_id, snap_name)\\n print('Check the Gui now or REST Client to see if snapshot '\\n '{snap_name} was created for Storge Group {sg_id}'\\n .format(snap_name=snap_name, sg_id=sg_id))\",\n \"async def github_group(self, ctx: commands.Context) -> None:\\n if ctx.invoked_subcommand is None:\\n await invoke_help_command(ctx)\",\n \"def main():\\n lc = db.getCursorForDB(localDb)\\n rc = db.getCursorForDB(\\\"enwiki_p\\\")\\n\\n # Drop the old groups\\n query = \\\"DELETE FROM ts_users_groups\\\"\\n out(\\\"Deleting old user groups\\\")\\n lc = db.execute(lc, query)\\n\\n # Fetch the updated groups\\n query = \\\"SELECT * FROM user_groups\\\"\\n out(\\\"Selecting user groups\\\")\\n rc = db.execute(rc, query)\\n rows = rc.fetchall()\\n space = []\\n values = []\\n for r in rows:\\n space.append(\\\"(%s,%s)\\\")\\n values += [str(r[\\\"ug_user\\\"]), str(r[\\\"ug_group\\\"])]\\n\\n # Add them to the local table\\n query = \\\"INSERT INTO ts_users_groups (tug_uid, tug_group) VALUES %s\\\" % (','.join(space))\\n out(\\\"Inserting user groups\\\")\\n lc = db.execute(lc, query, values)\",\n \"def wrapper(callback):\\n if self.subcommand_groups.get(base_names[0]) is None:\\n self.subcommand_groups[base_names[0]] = {}\\n self.subcommand_groups[base_names[0]][name] = SubSlashCommandGroup(callback, base_names, name, description, options=options, guild_ids=guild_ids, default_permission=default_permission, guild_permissions=guild_permission)\",\n \"def root():\\n return render_template('root.html')\",\n \"def main(self):\\r\\n pass\",\n \"def main():\\n challenge = Challenge()\\n # Get the number of saved files on last execution\\n last_saved = challenge.get_last_execution()\\n # Get the total of products to save\\n total_objects = len(challenge.get_products(\\\"product_groups.json\\\"))\\n\\n # While there are products to be saved\\n while last_saved < total_objects:\\n create_products()\\n # Updates last_saved number\\n last_saved = challenge.get_last_execution()\\n\\n logging.info(\\\"[INFO] Execution done with no errors!\\\")\\n # Sends to runner a signal different from the crash signal\\n # Indicates terminated execution\\n os._exit(1)\",\n \"def run(self):\\n self.run()\",\n \"def main(self):\\n self.root.mainloop()\",\n \"def run():\\n main()\",\n \"def _execute_stage(self, index, stage, stop):\\n if stop.is_set():\\n _LOGGER.info(\\\"Stopped pipeline on group %s\\\", self._group)\\n return\\n _LOGGER.info(\\\" -> Running stage '%s' on group %s\\\", stage, self._group)\\n if stage.name == 'on':\\n self._group.on = True\\n elif stage.name == 'off':\\n self._group.on = False\\n elif stage.name == 'hue':\\n self._group.hue = stage.args[0]\\n elif stage.name == 'saturation':\\n self._group.saturation = stage.args[0]\\n elif stage.name == 'color':\\n self._group.color = Color(*stage.args)\\n elif stage.name == 'brightness':\\n self._group.brightness = stage.args[0]\\n elif stage.name == 'temperature':\\n self._group.temperature = stage.args[0]\\n elif stage.name == 'transition':\\n self._group.transition(*stage.args, **stage.kwargs)\\n elif stage.name == 'white':\\n self._group.white()\\n elif stage.name == 'white_up':\\n self._group.white_up()\\n elif stage.name == 'white_down':\\n self._group.white_down()\\n elif stage.name == 'red_up':\\n self._group.red_up()\\n elif stage.name == 'red_down':\\n self._group.red_down()\\n elif stage.name == 'green_up':\\n self._group.green_up()\\n elif stage.name == 'green_down':\\n self._group.green_down()\\n elif stage.name == 'blue_up':\\n self._group.blue_up()\\n elif stage.name == 'blue_down':\\n self._group.blue_down()\\n elif stage.name == 'night_light':\\n self._group.night_light()\\n elif stage.name == 'link':\\n self._group.link()\\n elif stage.name == 'unlink':\\n self._group.unlink()\\n elif stage.name == 'flash':\\n self._group.flash(**stage.kwargs)\\n elif stage.name == 'repeat':\\n self._repeat(index, stage, stop)\\n elif stage.name == 'wait':\\n time.sleep(*stage.args)\\n elif stage.name == 'callback':\\n stage.args[0](*stage.args[1:], **stage.kwargs)\",\n \"def main():\\n ensure_not_root()\\n config.setup()\\n model.init_db()\\n manager.run()\",\n \"def main():\\n print(\\\"def main\\\")\\n return APP.run()\",\n \"def main():\\n\\n # Setup `pysc` to use BASIC auth, with a username, and password. Also sets the endpoint to use.\\n setup_sensorcloud_basic(CONSTS['SC_USERNAME'], CONSTS['SC_PASSWORD'],\\n CONSTS['SC_ENDPOINT'], CONSTS['PYSC_DEBUG'])\\n\\n org_id = CONSTS['ORG_ID']\\n\\n # Ensure the organisation exists on the SensorCloud endpoint.\\n try:\\n organisation = pysc.models.Organisation.single(org_id)\\n except KeyError:\\n raise RuntimeWarning(\\\"\\\"\\\"The organisation named {:s} was not found.\\\\n\\\"\\\"\\\"\\n \\\"\\\"\\\"Although the `pysc` api has functionality to create an organisation, it cannot \\\"\\\"\\\"\\n \\\"\\\"\\\"do so on the sensor-cloud.io instance on AWS.\\\"\\\"\\\".format(org_id))\\n # Ensure sanity, check we got the organisation that we asked for.\\n assert (org_id == organisation.id)\\n\\n # Here we use the Group.resolve_all helper with organisation_id param to filter groups based on id\\n # The resolve_all command is similar to .index() however it also calls .follow() on found link automatically,\\n # _and_ it converts the resulting HAL objects into real valid `pysc` Group() objects.\\n org_groups = pysc.models.Group.resolve_all(params={'organisation_id': org_id})\\n # We are not likely to have more than 1000 groups, so we don't need to do return doc pagination here.\\n for g in org_groups:\\n group_id = g.id\\n print(\\\"Found group: {:s}\\\".format(group_id))\\n\\n print(\\\"Found a total of {:d} groups for {:s} on that SensorCloud endpoint.\\\".format(len(org_groups), org_id))\",\n \"def call_main(session, tmpdir, *args):\\n # type: (Session, LocalPath, *str) -> None\\n config_path = src_path(\\\"config\\\", \\\"test.yaml\\\")\\n argv = [\\\"grouper-ctl\\\", \\\"-c\\\", config_path, \\\"-d\\\", db_url(tmpdir)] + list(args)\\n main(sys_argv=argv, session=session)\",\n \"def Run(self):\\n \\n if not self.FolderPath == None:\\n \\n #run the folder manipulation routines...\\n self.InitialList, out_0 = self.ReturnRamanFiles(self.FolderPath,'.txt')\\n self.Condensensed = self.RamanDBCondenser(out_0)\\n self.SetFilters()\\n self.Gatherer(self.InitialList,out_0)\\n self.BuildTree()\",\n \"def main():\\n\\n payload = {'include': 'systems'}\\n insights_request = InsightsRequest(SYS_API, payload)\\n cute_output(insights_request)\\n\\n groups = insights_request.get_insights()\\n found = False\\n\\n for elem in groups:\\n if not elem['systems']:\\n found = True\\n print('Deleting empty group ' + elem['display_name'])\\n clean_empty_group(elem['id'])\\n if not found:\\n print('No empty groups found, nothing to delete!!!')\\n\\n payload = {'expand': 'system'}\\n insights_request = InsightsRequest(URL + '/v2/reports', payload)\\n reports = create_maint_plan(insights_request)\\n \\\"\\\"\\\"\\n print(reports)\\n\\n result = get_json(URL + '/v2/reports', payload)\\n print(result)\\n for elem in result['resources']:\\n print('Report: ' + str(elem['id']) + ' Rule id: ' + str(elem['rule_id']) + '\\\\tSystem: ' + str(elem['system']['hostname']))\\n \\\"\\\"\\\"\\n #create_maint_plan('gherkin')\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main(self):\\n self.validate()\\n self.root.mainloop()\",\n \"def cmd():\\n # Importing the engine\\n from sqla_json.sqla_management import engine\\n from sqlalchemy.orm import sessionmaker\\n from sqla_json.models.node import DbNode\\n from sqla_json.models.group import DbGroup\\n\\n from sqla_json.commands import DEFAULT_GROUP_NAME\\n\\n import time\\n import sys\\n\\n # Creating the needed session\\n Session = sessionmaker(bind=engine)\\n session = Session()\\n\\n # Getting the one and only group with that name\\n dbgroup = session.query(DbGroup).filter_by(name=DEFAULT_GROUP_NAME).first()\\n\\n if dbgroup is None:\\n print \\\"No group is found, exiting\\\"\\n sys.exit()\\n\\n # Find all nodes\\n print \\\"Getting all the nodes\\\"\\n start_time = time.time()\\n\\n list_node = list()\\n for dbn in session.query(DbNode).all():\\n list_node.append(dbn)\\n\\n print \\\"Found #{} nodes\\\".format(len(list_node))\\n\\n # Add the nodes to the group\\n print \\\"Adding the nodes to the group\\\"\\n # session.bulk_update_mappings(DbGroup, {'id': dbgroup.id,\\n # 'dbnodes': list_node})\\n dbgroup.dbnodes = list_node\\n\\n session.expunge_all()\\n\\n session.bulk_save_objects([dbgroup])\\n # session.add(dbgroup)\\n\\n # print \\\"LALLLALALA \\\", {'id': dbgroup.id, 'dbnodes': list_node}\\n # session.add(dbgroup)\\n session.commit()\\n end_time = time.time()\\n\\n print \\\"Added to group\\\"\\n print \\\"Elapsed time --- {} seconds --- \\\".format(end_time - start_time)\",\n \"def main():\\n global HEALTH_AGGREGATOR\\n\\n os.makedirs(PEM_DIR, exist_ok=True)\\n\\n if not os.path.exists(f'{os.path.join(os.getcwd(), \\\"database.db\\\")}'):\\n DB_User.create_all()\\n DB_Resource.create_all()\\n\\n User.add_user('SpiceWorks', 'HealthChecker')\\n\\n HEALTH_AGGREGATOR = HealthAggregator()\\n app.run(port=5000)\\n HEALTH_AGGREGATOR.cleanup()\",\n \"def run(self):\\r\\n self.log(texto=f\\\"Executando {self._name}\\\")\",\n \"def do_standalone_display(self):\\n stage = clutter.Stage()\\n stage.connect('destroy', clutter.main_quit)\\n stage.connect('key-press-event', lambda x,y: clutter.main_quit())\\n stage.set_fullscreen(True)\\n stage.set_color(clutter.color_from_string('black'))\\n stage.add(self.group)\\n stage.show_all()\\n clutter.main()\",\n \"def main():\\n CLI_APP.run()\",\n \"def _setRoot(self):\\n if self.state == BaseEditMode.ES_None:\\n sheet = self._logic._getSheet()\\n layout = sheet.getLayoutGroup()\\n \\n import suit.core.layout.LayoutGroupRadial as radialLayout\\n if not isinstance(layout, radialLayout.LayoutGroupRadialSimple): \\n return\\n \\n objs = sheet.getSelected()\\n # get object for root changing \\n if len(objs) == 1: \\n layout.setRoot(objs[0])\",\n \"def main(self):\\n cmd, path, args = self._parse_args()\\n if cmd == \\\"shell\\\":\\n print \\\"You are now in ubs shell.\\\"\\n print \\\"Use \\\\\\\"python %s help\\\\\\\" to see other choice.\\\" % sys.argv[0]\\n self.shell()\\n elif cmd == \\\"help\\\":\\n self.print_path_help(path)\\n sys.exit(0) \\n elif cmd == \\\"run\\\":\\n self.route(path, args)\\n else:\\n raise Exception(\\\"unknown CMD %s\\\" % cmd)\",\n \"def handle(self, *args, **kwargs):\\n self.check_settings_existence()\\n self._load_backend()\\n logger.info('Fetch nested groups...')\\n self.nested_groups = NestedGroups()\\n logger.info('Syncing...')\\n if not ldap_module_exists:\\n logger.error('ldap module not installed')\\n raise ImportError('No module named ldap')\\n synced = self.populate_users()\\n logger.info('LDAP users synced: %s', synced)\",\n \"def main(argv: list[str]) -> int:\\n args = parse_args(argv)\\n logging.basicConfig(format=LOG_FORMAT, level=logging.INFO)\\n\\n with SchrootSession(args.chroot) as session:\\n if not session.check_directory(args.directory):\\n args.directory = session.get_home_directory(args.user)\\n if not session.check_directory(args.directory):\\n session.create_directory(args.directory, args.user)\\n\\n if args.enable_proposed:\\n session.enable_proposed([args.proposed_uri], args.proposed_components)\\n if args.ppa:\\n session.add_ppas(args.ppa)\\n if args.packages:\\n session.install_packages(args.packages)\\n return session.run(args.directory, args.user, check=False).returncode\",\n \"def command(self):\\n from tg import config\\n self.basic_setup()\\n\\n # reset LDAP database and chroot home folders (if supported)\\n self.reset_LDAP(config)\\n self.reset_chroot()\\n\\n # exclude these users from the command\\n special_users = ['*anonymous', 'root']\\n\\n # add users to LDAP and asynchronously create chroot user home folder\\n count = 0\\n users = User.query.find({'username': {'$nin': special_users}}).all()\\n task = None\\n for user in users:\\n self.add_LDAP_user(user, config)\\n if self.HAS_CHROOT:\\n task = register_ldap.post(user.username)\\n count += 1\\n print \\\"Refreshed {} users.\\\".format(count)\\n\\n # wait for last task to complete\\n if task:\\n MonQTask.wait_for_tasks(query={\\n '_id': task._id, 'state': {'$in': ['ready', 'busy']}\\n }, timeout=240000)\\n\\n # asynchronously upload user public keys\\n count = 0\\n if self.HAS_CHROOT:\\n for user in users:\\n public_key = getattr(user, 'public_key', None)\\n if public_key:\\n upload_ssh_ldap.post(user.username, public_key)\\n count += 1\\n print \\\"Uploaded {} public keys.\\\".format(count)\",\n \"def main(self: object) -> None:\\n print(\\\"[View] main\\\")\\n self.mainloop()\",\n \"def main(request):\\n users = get_channel_users(MEMBER_CHANNEL)\\n human_users = filter_bots(users)\\n buddy_groups = group_users(human_users)\\n if buddy_groups:\\n resp = post_message(buddy_groups, POST_CHANNEL)\\n ret = str(resp)\\n else:\\n ret = 'Not enough members to run'\\n return ret\",\n \"def nodes_run_cmd(self, group, cmd):\\n path_inventory = u'%s/inventories/%s' % (self.ansible_path, self.environment)\\n path_lib = u'%s/library/beehive/' % (self.ansible_path)\\n runner = Runner(inventory=path_inventory, verbosity=self.verbosity, \\n module=path_lib)\\n tasks = [\\n dict(action=dict(module=u'shell', args=cmd), register=u'shell_out'),\\n ]\\n runner.run_task(group, tasks=tasks, frmt=u'json')\",\n \"def onGroup(a):\\n data = a.data()\\n if data:\\n try:\\n Gui.doCommand('Gui.activateWorkbench(\\\"' + data + '\\\")')\\n except KeyError:\\n pass\",\n \"def ui_root(filename):\\n return send_build(filename)\",\n \"def main(self) -> None:\\n pass\",\n \"def main():\\n pass\",\n \"def execute(self):\\n if len(self._tree) > 0:\\n return self._tour(self._tree.root(),0,[]) # start the recursion\",\n \"def Run():\\r\\n pass\",\n \"def run(self):\\n\\t\\t\\n\\t\\tpass\",\n \"def main():\\n return\",\n \"def __on_group_created(self, logger, *args):\",\n \"def index():\\n return \\\"This is root!!!!\\\"\",\n \"def backMain():\\n rootStat.destroy()\\n main()\",\n \"def _do_group(self, req):\\n # process forms/commands\\n message=''\\n args={}\\n if req.method == 'POST':\\n group = req.args.get('group')\\n user = req.args.get('user')\\n if req.args.get('add'):\\n if group and not self.has_group(group):\\n self.add_group(group)\\n message+=u'added group %s\\\\n'%group\\n if user and not group in self.get_permission_groups(user):\\n self.add_user_to_group(user,group)\\n message+=u'added %s to %s' % (user, group)\\n args['message'] = message\\n elif req.args.get('remove') and req.args.get('group'):\\n sel = req.args.get('sel')\\n sel = isinstance(sel, list) and sel or [sel]\\n for user in sel:\\n self.del_user_from_group(user,group)\\n args['message'] = u'removed %s from %s' % (sel, req.args.get('group'))\\n # lists and other info\\n all_groups = sorted(self.get_groups())\\n args['groups'] = all_groups\\n if req.args.get('group') and self.has_group(req.args.get('group')):\\n group_name=req.args.get('group')\\n elif len(all_groups) > 0:\\n group_name=all_groups[0]\\n else:\\n group_name = None\\n if group_name:\\n args['group'] = group_name\\n args['members'] = sorted(self.group_get_members(group_name))\\n args['listing_enabled'] = True\\n args['selection_enabled'] = len(all_groups)>0\\n return 'sqlflexibleauthstore_group_editor.html', args\",\n \"def groupId(self):\\r\\n return 'examplescripts'\",\n \"def main():\\n\\tpass\",\n \"def run(self, distribution_root):\\n self.result = self._compile_and_execute(distribution_root)\\n return self.result\",\n \"def main(ctx, root_project_dir, verbose):\\n # Subcommands should use the click.pass_context decorator to get this\\n # ctx.obj object as the first argument.\\n ctx.obj = {\\\"root_project_dir\\\": root_project_dir, \\\"verbose\\\": verbose}\\n\\n # Set up application logging. This ensures that only documenteer's\\n # logger is activated. If necessary, we can add other app's loggers too.\\n if verbose:\\n log_level = logging.DEBUG\\n else:\\n log_level = logging.INFO\\n logger = logging.getLogger(\\\"documenteer\\\")\\n logger.addHandler(logging.StreamHandler())\\n logger.setLevel(log_level)\",\n \"def main():\\n\\n run_lap()\",\n \"def main():\\n run_program()\",\n \"def run(self):\\n self.cmdloop()\",\n \"def execute(self):\\n if len(self._tree) > 0:\\n return self.tour(self._tree.root(), 0, [])\",\n \"def mainloop(self):\\n self.master.mainloop()\",\n \"def run(self):\\n self.render_templates()\\n self.create_docker_context()\\n self.build()\\n self.push()\",\n \"def _go_to_root_menu(self):\\n log.debug(\\\"Returning to root menu...\\\")\\n # Issue an enter or two off the bat to get out of any display screens\\n # and confirm command mode\\n try:\\n response = self._do_cmd_resp(Command.BLANK, expected_prompt=Prompt.CMD_PROMPT)\\n while not str(response).lstrip().endswith(Prompt.CMD_PROMPT):\\n response = self._do_cmd_resp(Command.BLANK,\\n expected_prompt=Prompt.CMD_PROMPT)\\n time.sleep(1)\\n except InstrumentTimeoutException:\\n raise InstrumentProtocolException(\\\"Not able to get valid command prompt. Is instrument in command mode?\\\")\\n \\n # When you get a --> prompt, do 9's until you get back to the root\\n response = self._do_cmd_resp(Command.BACK_MENU,\\n expected_prompt=MENU_PROMPTS)\\n while not str(response).lstrip().endswith(Prompt.MAIN_MENU):\\n response = self._do_cmd_resp(Command.BACK_MENU,\\n expected_prompt=MENU_PROMPTS)\",\n \"def run(self):\\n # Get the child element iterator.\\n if hasattr(self.thistag, 'iterfind'):\\n childiter = self.thistag.iterfind(r'./*')\\n else:\\n childiter = self.thistag.findall(r'./*')\\n\\n # Execute the child actions.\\n exitcode = 0\\n for childtag in childiter:\\n logger.debug('child tag = \\\"{0}\\\"'.format(childtag.tag))\\n\\n # Look for an action handler class that matches the\\n # child tag name.\\n action = None\\n for module in actionmodules:\\n try:\\n action = getattr(module, childtag.tag)\\n break\\n except AttributeError:\\n continue\\n\\n # Ignore non-action (parameter) tags.\\n if not action: continue\\n\\n # Construct and run the child action.\\n logger.debug('Running \\\"{0}\\\"...'.format(childtag.tag))\\n try:\\n exitcode = action(self.context, childtag).run()\\n except Exception as e:\\n logger.exception(e)\\n sys.stderr.write('Internal hook error.'\\n + ' Please notify administrator.')\\n return -1\\n\\n # If the child action generated a non-zero exit code, stop\\n # processing child actions.\\n\\n if exitcode != 0: break\\n\\n # Return the current exit code.\\n return exitcode\",\n \"def main():\\n\\n browser = initialize()\\n process_directory(browser, \\\"data\\\")\\n browser.close()\",\n \"def _main_helper(self):\\n asyncio.create_task(self._main())\",\n \"def main():\\n print(\\\"Everythin is ok\\\")\",\n \"def main(self, **kwargs) -> None:\\n ...\",\n \"def groupManager(request):\\n \\n return render_to_response('centers.html',context_instance=RequestContext(request))\",\n \"def main():\\n\\n merch_bot = MerchBot()\\n merch_bot.run_bot()\",\n \"def main() -> None:\\n try:\\n # ServerManager expects cwd to be the server dir (containing\\n # dist/, config.yaml, etc.)\\n # Let's change our working directory to the location of this file\\n # so we can run this script from anywhere and it'll work.\\n os.chdir(os.path.abspath(os.path.dirname(__file__)))\\n\\n ServerManagerApp().run_interactive()\\n except CleanError as exc:\\n # For clean errors, do a simple print and fail; no tracebacks/etc.\\n exc.pretty_print()\\n sys.exit(1)\",\n \"def launch(self):\\n self._fork()\\n self._lock()\\n os.setegid(self._user[1])\\n os.seteuid(self._user[0])\\n self._loop = True\\n signal.signal(signal.SIGTERM, self.__signalHandler)\\n sys.stdout = self._output\\n sys.stderr = self._error\\n self._run()\\n sys.stdout = self._stdout\\n sys.stderr = self._stderr\\n os.setegid(0)\\n os.seteuid(0)\\n self._unlock()\",\n \"def main():\\n # group_id = get_group_id() This would be used if I had\\n # the appropriate privileges\\n group_id = 15000022833\\n setup_logger()\\n ticket_ids = get_newhire_tickets(group_id)\\n for ticket_id in ticket_ids:\\n update_ticket_info(ticket_id)\",\n \"def root():\\n return 'Hello', 200\",\n \"def main():\\n parser = argparse.ArgumentParser(description=\\\"Wrapper of the scikit-learn AgglomerativeClustering method. \\\", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))\\n parser.add_argument('--config', required=False, help='Configuration file')\\n\\n # Specific args of each building block\\n required_args = parser.add_argument_group('required arguments')\\n required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')\\n required_args.add_argument('--output_results_path', required=True, help='Path to the clustered dataset. Accepted formats: csv.')\\n parser.add_argument('--output_plot_path', required=False, help='Path to the clustering plot. Accepted formats: png.')\\n\\n args = parser.parse_args()\\n args.config = args.config or \\\"{}\\\"\\n properties = settings.ConfReader(config=args.config).get_prop_dic()\\n\\n # Specific call of each building block\\n agglomerative_clustering(input_dataset_path=args.input_dataset_path,\\n output_results_path=args.output_results_path,\\n output_plot_path=args.output_plot_path,\\n properties=properties)\",\n \"def execute(self, root_actor_class, root_instance_name, log_hierarchy, *args, **kwargs):\\n loop = self._event_loop\\n log = self._log\\n shutdown_grace_sec = self._shutdown_grace_seconds\\n\\n def ask_exit(signame, loop, actor, actor_task):\\n log.info(\\\"got signal {}\\\".format(signame))\\n\\n async def wait_for_termination(loop, actor, actor_task):\\n try:\\n log.info(\\\"Waiting {} seconds for actors to stop...\\\".format(\\n SHUTDOWN_GRACE_SECONDS))\\n await actor.terminate()\\n await asyncio.wait_for(actor_task, shutdown_grace_sec)\\n log.info(\\\"actors terminated. stopping loop...\\\")\\n loop.call_later(0.1, lambda: loop.stop())\\n except asyncio.TimeoutError:\\n log.info(\\\"Actors did not stop. Cancelling tasks...\\\")\\n actor_task.cancel()\\n try:\\n await asyncio.wait_for(actor_task, shutdown_grace_sec)\\n except asyncio.TimeoutError:\\n log.error(\\\"Fatal error! Unable to cancel tasks.\\\")\\n finally:\\n loop.call_later(0.1, lambda: loop.stop())\\n exv = actor_task.exception()\\n if exv is not None and not isinstance(exv, asyncio.CancelledError):\\n raise exv\\n\\n loop.create_task(wait_for_termination(loop, actor, actor_task))\\n\\n loop = asyncio.get_event_loop()\\n\\n root_actor, root_task = Actor.Spawn(\\n root_actor_class, loop, None, root_instance_name, log_hierarchy, *args, **kwargs)\\n\\n for signame in ('SIGINT', 'SIGTERM'):\\n loop.add_signal_handler(getattr(signal, signame),\\n functools.partial(ask_exit, signame, loop, root_actor, root_task))\\n\\n def stop_loop(task):\\n loop.call_soon(lambda: loop.stop())\\n root_task.add_done_callback(stop_loop)\\n\\n loop.run_forever()\",\n \"def main():\\n print(\\\"Everything is ok.\\\")\",\n \"def main():\\n dispatch = {}\\n\\n parser = argparse.ArgumentParser()\\n subs = parser.add_subparsers(dest=\\\"command_name\\\", required=True)\\n\\n # Configure logger\\n logger = logging.getLogger()\\n handler = logging.StreamHandler()\\n handler.setFormatter(ColoredFormatter(fmt=\\\"%(message)s\\\"))\\n logger.addHandler(handler)\\n logger.setLevel(logging.INFO)\\n\\n for command in sys.modules[__name__].__dict__.values():\\n if not hasattr(command, \\\"command_name\\\"):\\n continue\\n\\n dispatch[command.command_name] = command()\\n\\n doc = getattr(command, \\\"__doc__\\\", \\\"\\\")\\n subparser = subs.add_parser(\\n command.command_name, help=doc, description=doc)\\n\\n add_args = getattr(command, \\\"add_args\\\", None)\\n if add_args:\\n command.add_args(subparser)\\n\\n args = parser.parse_args()\\n\\n command = dispatch[args.command_name]\\n errors = getattr(command, \\\"check_args\\\", lambda x: [])(args)\\n if errors:\\n logger.error(\\\"\\\\n\\\".join(errors))\\n # Ugly hack, gets the help for the subcommand, no public API for this\\n parser._actions[1]._name_parser_map[args.command_name].print_help()\\n else:\\n command.run(args, find_repo_root(), logger)\",\n \"def group_home(request, group_slug):\\n user = request.user\\n group = GroupProfile.objects.get(slug=group_slug)\\n user_permissions = ''\\n if not group:\\n raise Http404(\\\"Can't find a group with the slug: %s\\\" % group_slug)\\n else:\\n user_permission_type = get_user_permissions(user, group)\\n if user_permission_type != 3 and user_permission_type != \\\"Not logged in\\\":\\n user_permissions = 'allowed'\\n query = group.mission_statements.all().order_by(\\\"-created_at\\\")\\n if query.count() > 0:\\n mission_statement = query[0]\\n else:\\n mission_statement = \\\"\\\"\\n discussions = group.group.discussions.all()\\n members = group.get_group_members()\\n user_in_group = group.is_user_in_group(user)\\n if group.is_private and not user_in_group:\\n messages.error(request, \\\"The group is private. You're not allowed to see it.\\\" )\\n #initializing the forms\\n discussion_form = DiscussionForm()\\n mission_statement_form = MissionStatementForm()\\n return render_to_response('group_home.html', locals(), context_instance=RequestContext(request))\",\n \"def restart_with_root():\\n if platform.system() == 'Windows':\\n os.execv('cmd', join_path(os.path.pardir(os.path.pardir(__file__)), 'start_root.cmd'))\\n else:\\n os.execv(sys.executable, ['sudo python'] + sys.argv)\",\n \"def run(self): \\r\\n return\",\n \"def doRoot(self,action):\\n #--Root directory is Textures directory?\\n dirExts = ResourceReplacer.dirExts\\n textureExts = ResourceReplacer.textureExts\\n srcDir = os.path.join(self.replacerDir,self.file)\\n destDir = modInfos.dir\\n isTexturesDir = True #--Assume true for now.\\n for srcFile in os.listdir(srcDir):\\n srcPath = os.path.join(srcDir,srcFile)\\n if os.path.isdir(srcPath) and srcFile.lower() in dirExts:\\n isTexturesDir = False\\n destPath = os.path.join(destDir,srcFile)\\n action(srcPath,destPath,dirExts[srcFile.lower()])\\n if isTexturesDir:\\n destPath = os.path.join(destDir,'Textures')\\n action(srcDir,destPath,textureExts)\",\n \"def main():\\n # Add your main code here\\n display_menu()\\n pass\",\n \"def run():\\n from cgl.plugins.blender.tasks.rig import parent_mdl_to_rig\\n parent_mdl_to_rig()\",\n \"def run_as_root():\\n # Need to be root\\n if os.geteuid() != 0:\\n print(\\\"You need to be root to run this script. Relaunching with \\\"\\n \\\"sudo...\\\\n\\\")\\n subprocess.call([\\\"sudo\\\", sys.executable] + sys.argv)\\n exit()\",\n \"def mainloop(self):\\n self.root.mainloop()\",\n \"def mainloop(self):\\n self.root.mainloop()\",\n \"def test_6_1_1_script_group(host):\\n assert host.file(AUDIT_SYSTEM_SCRIPT).group == 'root'\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7180917","0.6376066","0.6170935","0.6146936","0.60530406","0.60025346","0.59464896","0.59450406","0.5913414","0.590124","0.58364505","0.574756","0.5686958","0.5659579","0.5629004","0.5579367","0.5568521","0.556469","0.5518852","0.55107117","0.54983646","0.5454988","0.53643245","0.5345876","0.5337711","0.5304412","0.5299068","0.5298349","0.5297473","0.52949727","0.5288338","0.52720946","0.5263958","0.5263686","0.5239011","0.52335113","0.52335113","0.52335113","0.5227588","0.522137","0.52137536","0.5206041","0.5200457","0.5195836","0.51946735","0.51827395","0.5182331","0.5179627","0.517061","0.5169567","0.5168565","0.5146946","0.51456165","0.5142648","0.51419306","0.51387614","0.5131732","0.51295346","0.5126763","0.51259875","0.51258636","0.5113606","0.50984395","0.50963986","0.508909","0.5079766","0.5078663","0.507801","0.50706285","0.5067139","0.50513905","0.5048208","0.50475556","0.5042598","0.5041307","0.50380415","0.5035244","0.50335056","0.50330836","0.503121","0.5026182","0.5011184","0.50099206","0.5007426","0.50070685","0.50045145","0.50044626","0.5003628","0.50004184","0.49902505","0.49884778","0.4987132","0.4986526","0.49844036","0.4983819","0.49831688","0.49825004","0.49745554","0.49745554","0.49730873"],"string":"[\n \"0.7180917\",\n \"0.6376066\",\n \"0.6170935\",\n \"0.6146936\",\n \"0.60530406\",\n \"0.60025346\",\n \"0.59464896\",\n \"0.59450406\",\n \"0.5913414\",\n \"0.590124\",\n \"0.58364505\",\n \"0.574756\",\n \"0.5686958\",\n \"0.5659579\",\n \"0.5629004\",\n \"0.5579367\",\n \"0.5568521\",\n \"0.556469\",\n \"0.5518852\",\n \"0.55107117\",\n \"0.54983646\",\n \"0.5454988\",\n \"0.53643245\",\n \"0.5345876\",\n \"0.5337711\",\n \"0.5304412\",\n \"0.5299068\",\n \"0.5298349\",\n \"0.5297473\",\n \"0.52949727\",\n \"0.5288338\",\n \"0.52720946\",\n \"0.5263958\",\n \"0.5263686\",\n \"0.5239011\",\n \"0.52335113\",\n \"0.52335113\",\n \"0.52335113\",\n \"0.5227588\",\n \"0.522137\",\n \"0.52137536\",\n \"0.5206041\",\n \"0.5200457\",\n \"0.5195836\",\n \"0.51946735\",\n \"0.51827395\",\n \"0.5182331\",\n \"0.5179627\",\n \"0.517061\",\n \"0.5169567\",\n \"0.5168565\",\n \"0.5146946\",\n \"0.51456165\",\n \"0.5142648\",\n \"0.51419306\",\n \"0.51387614\",\n \"0.5131732\",\n \"0.51295346\",\n \"0.5126763\",\n \"0.51259875\",\n \"0.51258636\",\n \"0.5113606\",\n \"0.50984395\",\n \"0.50963986\",\n \"0.508909\",\n \"0.5079766\",\n \"0.5078663\",\n \"0.507801\",\n \"0.50706285\",\n \"0.5067139\",\n \"0.50513905\",\n \"0.5048208\",\n \"0.50475556\",\n \"0.5042598\",\n \"0.5041307\",\n \"0.50380415\",\n \"0.5035244\",\n \"0.50335056\",\n \"0.50330836\",\n \"0.503121\",\n \"0.5026182\",\n \"0.5011184\",\n \"0.50099206\",\n \"0.5007426\",\n \"0.50070685\",\n \"0.50045145\",\n \"0.50044626\",\n \"0.5003628\",\n \"0.50004184\",\n \"0.49902505\",\n \"0.49884778\",\n \"0.4987132\",\n \"0.4986526\",\n \"0.49844036\",\n \"0.4983819\",\n \"0.49831688\",\n \"0.49825004\",\n \"0.49745554\",\n \"0.49745554\",\n \"0.49730873\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62378114"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":80398,"cells":{"query":{"kind":"string","value":"This function allows for fancy object assigment."},"document":{"kind":"string","value":"def __lt__(self, other):\n name, value = other\n\n self[name] = value"},"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 _from_other(cls, obj):","def obj(self) -> object:\n pass","def new(self, obj):\n pass","def add(obj):","def _setup_object(self) -> object:\n raise NotImplementedError","def __setitem__(self, name, obj):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __setattr__(*args):","def __call__(obj):","def __setattr__(*args, **kwargs):\n \n pass","def __setattr__(*args, **kwargs):\n \n pass","def __set__(self, obj, value):\r\n pass","def new_object(self):\r\n\t\tpass","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n obj = kwargs.get(\"obj\")\n if obj:\n self.obj = obj","def __init__(object):","def __init__(self, **params):\n self.__object = object_param(**params)","def object(self):","def init_obj(obj_name):\n ret = type(obj_name, (object,), {})\n return ret","def contribute_to_object(self, obj):\n pass","def __init__(self, obj=None, key=None):\n d = _get(self, \"__dict__\")\n d[\"_obj\"] = obj\n d[\"__key__\"] = key","def __init__(self, obj, adapted_methods):\n self.obj = obj\n self.__dict__.update(adapted_methods)","def __init__(self, obj, *args, **kwargs):\n self.obj_ = obj\n super(ArtificialRV, self).__init__(*args, **kwargs)","def __setattr__(self, ???):","def __init__(self, data):\n # add play_guid as it sometimes doesn't exist\n if 'play_guid' not in data:\n data['play_guid'] = ''\n # loop through data\n for x in data:\n # set information as correct data type\n mlbgame.object.setobjattr(self, x, data[x])","def make_objects(self):\n pass","def attach(self, obj):\n self.Object = obj.Object","def __init__ (self, d):\n try:\n self.__dict__.update (d.__dict__)\n except:\n self.__dict__.update (d)","def _set_attributes(self):","def attach(self, obj):\n return","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, 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__setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ...","def __setattr__(self, *args, **kwargs):\n ..."],"string":"[\n \"def _from_other(cls, obj):\",\n \"def obj(self) -> object:\\n pass\",\n \"def new(self, obj):\\n pass\",\n \"def add(obj):\",\n \"def _setup_object(self) -> object:\\n raise NotImplementedError\",\n \"def __setitem__(self, name, obj):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __setattr__(*args):\",\n \"def __call__(obj):\",\n \"def __setattr__(*args, **kwargs):\\n \\n pass\",\n \"def __setattr__(*args, **kwargs):\\n \\n pass\",\n \"def __set__(self, obj, value):\\r\\n pass\",\n \"def new_object(self):\\r\\n\\t\\tpass\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n obj = kwargs.get(\\\"obj\\\")\\n if obj:\\n self.obj = obj\",\n \"def __init__(object):\",\n \"def __init__(self, **params):\\n self.__object = object_param(**params)\",\n \"def object(self):\",\n \"def init_obj(obj_name):\\n ret = type(obj_name, (object,), {})\\n return ret\",\n \"def contribute_to_object(self, obj):\\n pass\",\n \"def __init__(self, obj=None, key=None):\\n d = _get(self, \\\"__dict__\\\")\\n d[\\\"_obj\\\"] = obj\\n d[\\\"__key__\\\"] = key\",\n \"def __init__(self, obj, adapted_methods):\\n self.obj = obj\\n self.__dict__.update(adapted_methods)\",\n \"def __init__(self, obj, *args, **kwargs):\\n self.obj_ = obj\\n super(ArtificialRV, self).__init__(*args, **kwargs)\",\n \"def __setattr__(self, ???):\",\n \"def __init__(self, data):\\n # add play_guid as it sometimes doesn't exist\\n if 'play_guid' not in data:\\n data['play_guid'] = ''\\n # loop through data\\n for x in data:\\n # set information as correct data type\\n mlbgame.object.setobjattr(self, x, data[x])\",\n \"def make_objects(self):\\n pass\",\n \"def attach(self, obj):\\n self.Object = obj.Object\",\n \"def __init__ (self, d):\\n try:\\n self.__dict__.update (d.__dict__)\\n except:\\n self.__dict__.update (d)\",\n \"def _set_attributes(self):\",\n \"def attach(self, obj):\\n return\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\",\n \"def __setattr__(self, *args, **kwargs):\\n ...\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.68794894\",\n \"0.6405043\",\n \"0.63679534\",\n \"0.63675547\",\n \"0.63258046\",\n \"0.6255525\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.62520456\",\n \"0.6229607\",\n \"0.6213365\",\n \"0.6213365\",\n \"0.6208713\",\n \"0.61871505\",\n \"0.6175197\",\n \"0.61643463\",\n \"0.6157222\",\n \"0.6109127\",\n \"0.6071654\",\n \"0.60351694\",\n \"0.60351694\",\n \"0.6034657\",\n \"0.60219616\",\n \"0.6021519\",\n \"0.60062176\",\n \"0.6001522\",\n \"0.5979102\",\n \"0.5978734\",\n \"0.5965998\",\n \"0.595919\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\",\n \"0.5926466\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":80399,"cells":{"query":{"kind":"string","value":"This decorator adds an object instance when the object is initiated."},"document":{"kind":"string","value":"def register_instance(cls):\n\n @functools.wraps(cls)\n def wrapper_decorator(*args, **kwargs):\n\n instance = cls(*args, **kwargs)\n\n Register[cls.__name__] = instance\n\n return instance\n\n return wrapper_decorator"},"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, decoratedObj):\n\n self.__decoratedObj = decoratedObj","def register_instance(self, obj):\n self.__instances.append(obj)\n self._proxy_class_methods(obj)","def register_instance(self, instance):\n self.instance = instance","def register_object(self, obj):\n self.modules.append(obj)","def _attach_to_instance(self, instance):\n self._instance = instance","def contribute_to_object(self, obj):\n pass","def __init__(self, decorated):\n self.decorated = decorated","def __init__(self, decorated):\n self.decorated = decorated","def __init__(self, decorated):\n self.decorated = decorated","def _setup_object(self) -> object:\n raise NotImplementedError","def __init__(self, obj):\n obj.Proxy = self","def __init__(self, obj):\n obj.Proxy = self","def attach(self, obj):\n return","def __init__(self, add_intercept=True):\n super().__init__(add_intercept)\n return","def decorate_init(cls, f):\n def wrap(*args, **kwargs):\n if not hasattr(cls, '_init'):\n f(*args, **kwargs)\n cls._init = True\n return wrap","def __call__ (self, cls):\n # Define a wrapper function to capture the actual instantiation and __init__ params\n @wraps(cls)\n def wrapper_f(*args, **kwargs):\n #print(f'type of cls is {type(cls)}')\n peripheral = self.peripheral_type(**self.kwargs)\n o = cls(*args, **kwargs)\n o.message_debug(f\"Decorating class {cls.__name__} with {self.peripheral_type.__name__}\")\n o.attach_sensor(peripheral)\n return o\n return wrapper_f","def instance(self, instance):\n self._instance = instance","def attach(self, obj):\n self.Object = obj.Object","def __init__(self, obj):\n self._store = {}\n self.obj = weakref.proxy(obj)","def __post_init__(self, *args, **kwargs) -> None:\n # add other __init__ items here ...\n pass","def new(self, obj):\n pass","def __init__(self, function, instance):\r\n self.instance = instance\r\n self.function = function","def __init__(self, name, obj=None):\n super().__init__(name)\n if obj != None:\n self.appendPhandle(obj)","def __call__(cls, *args, **kwargs):\n obj = type.__call__(cls, *args, **kwargs)\n obj.initialize()\n return obj","def after_class_creation(cls):\n pass","def add_object(self, obj):\n\t\tself.objects.append(obj)","def register(self, obj):\n if not callable(obj):\n raise ValueError(f\"object must be callable\")\n\n obj_name = obj.__name__\n if obj_name in self._obj_dict:\n pass\n # print(f\"{obj_name} is already registered in {self.name}\")\n # raise KeyError(f'{obj_name} is already registered in {self.name}')\n\n self._obj_dict[obj_name] = obj\n return obj","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n obj = kwargs.get(\"obj\")\n if obj:\n self.obj = obj","def on_start(self):\n\n def decorator(coro):\n self._hooks.append((\"start\", coro))\n return coro\n\n return decorator","def instance(self, instance):\n\n self._instance = instance","def __init__(object):","def __call__(cls, inst = None, initDict = None, *args, **kwargs):\n if not inst:\n inst = blue.classes.CreateInstance(cls.__cid__)\n inst.__klass__ = cls\n if initDict:\n for k, v in initDict.iteritems():\n setattr(inst, k, v)\n\n try:\n inst.__init__()\n except AttributeError:\n pass\n\n return inst","def add(self, obj):\n raise NotImplementedError","def on_load(self):\n self.__init__()","def extend_instance(obj, cls):\n base_cls = obj.__class__\n base_cls_name = obj.__class__.__name__\n obj.__class__ = type(base_cls_name, (base_cls, cls), {})","def register(cls):\n register(cls, cls.provided_class)","def setup_method(self, objectCreation):","def _singleton_init(self, *args, **kwargs):\r\n pass","def decorator(cls):\n\n instance = cls()\n auto_validator_services.register_hook(instance)\n\n return cls","def new_object(self):\r\n\t\tpass","def add_instance(self, context):\r\n self.instance_contexts.append(context)\r\n self.total_count += 1","def __init__(self, **params):\n self.__object = object_param(**params)","def decorator(obj):\n\t\tDRIVERS[name] = obj\n\t\treturn obj","def bind(self, obj: object):\n new_inst = self.__class__(\n func=self._func,\n self_obj=obj,\n unique=self._unique,\n timeout_seconds=self._timeout_seconds,\n expire_seconds=self._expire_seconds,\n **self._kwargs,\n )\n setattr(obj, self._func.__name__, new_inst)\n return new_inst","def __init__(self):\n Redrawable.all_instantiated_widgets.append(self)","async def new(self, obj: Obj):\n if obj not in self.ref_counts:\n self.ref_counts[obj] = 1\n await self.init(obj)\n else:\n self.ref_counts[obj] += 1","def __init__(self):\n self._inst = {}","def wrapper(self, *args, **kwargs):\n self.__wrapped_init__(*args, **kwargs)\n _process_dependencies(self)","def wrapper(self, *args, **kwargs):\n self.__wrapped_init__(*args, **kwargs)\n _process_dependencies(self)","def set_init_args(self, args_obj):\n if self and self[0][0] == '__init__':\n print(\"Only one __init__ step is allowed\")\n return\n self.insert(0, ('__init__', args_obj))","def decorator(cls):\n\n instance = cls(*args, **kwargs)\n serializer_services.register_serializer(instance, **kwargs)\n\n return cls","def on_register(cls):","def __call__(obj):","def __init__(self, obj, *args, **kwargs):\n self.obj_ = obj\n super(ArtificialRV, self).__init__(*args, **kwargs)","def __new__(cls, *args, **kwds):\n cls.__init_lock__.acquire()\n try:\n if not cls.__decorated__:\n cls._decorate()\n cls.__decorated__ = True\n \n return object.__new__(cls, *args, **kwds)\n finally:\n cls.__init_lock__.release()","def __call__(self, *args, **kwargs):\n if not self.instance:\n self.instance = super().__call__(*args, **kwargs)\n return self.instance","def __init__(self, set_on_create=False):\n self.set_on_create = set_on_create","def __init__(self, set_on_create=False):\n self.set_on_create = set_on_create","def __init__(self, set_on_create=False):\n self.set_on_create = set_on_create","def augment(self, *args, **kwargs):\n pass","def __init__(self, decorated_model):\n Logging.__init__(self, decorated_model)","def __init__(self, decorated_model):\n Logging.__init__(self, decorated_model)","def __init__(self, decorated_model):\n Logging.__init__(self, decorated_model)","def __init__(self, decorated_model):\n Logging.__init__(self, decorated_model)","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def add(obj):","def set_observer(active):\n if StorableObject.observe_objects is active:\n return\n\n if active:\n # activate and add __init__\n\n def _init(self):\n StorableObject._weak_cache[self] = StorableObject._weak_index\n StorableObject._weak_index += 1\n\n StorableObject.__init__ = MethodType(_init, None, StorableObject)\n StorableObject.observe_objects = True\n\n if not active:\n del StorableObject.__init__","def new(self, obj):\n key = obj.__class__.__name__+'.'+obj.id\n self.__objects[key] = obj","def __post_init__(self):\n super().__post_init__()","def singleton(cls):\n print('Entering the decorator')\n @functools.wraps(cls)\n def wrapper_singleton(*args, **kwargs):\n if not wrapper_singleton.instance:\n \tprint('Creating instance of the class')\n \twrapper_singleton.instance = cls(*args, **kwargs)\n return wrapper_singleton.instance\n \n wrapper_singleton.instance = None\n print(f'{wrapper_singleton.instance}')\n return wrapper_singleton","def __init__(self):\n this = _patchExtractor.new_patchExtractor()\n try:\n self.this.append(this)\n except Exception:\n self.this = this","def set_instance(self, instance):\n self.instance = instance","def set_instance(self, instance):\n self.instance = instance","def set_instance(self, instance):\n self.instance = instance","def set_instance(self, instance):\n self.instance = instance","def set_instance(self, instance):\n self.instance = instance","def new(self, obj):\n if obj:\n key = obj.__class__.__name__ + \".\" + obj.id\n self.__objects[key] = obj","def new(self, obj):\n if obj:\n self.__session.add(obj)","def new(self, obj):\n if obj:\n self.__session.add(obj)","def __init__():","def _get_instance(self):","def _get_instance(self):","def new(self, obj):\n self.__session.add(obj)","def new(self, obj):\n self.__session.add(obj)","def new(self, obj):\n self.__session.add(obj)","def new(self, obj):\n self.__session.add(obj)","def new(self, obj):\n self.__session.add(obj)","def new(self, obj):\n self.__session.add(obj)","def new(self, obj):\n self.__session.add(obj)","def new(self, obj):\n self.__session.add(obj)","def hydrate_instance(func):\n\n @functools.wraps(func)\n def wrapped(*args):\n new_args = list(args)\n new_args[1] = KolibriInstance.from_dict(args[1])\n return func(*new_args)\n\n # for py2.7\n if not hasattr(wrapped, \"__wrapped__\"):\n setattr(wrapped, \"__wrapped__\", func)\n\n return wrapped"],"string":"[\n \"def __init__(self, decoratedObj):\\n\\n self.__decoratedObj = decoratedObj\",\n \"def register_instance(self, obj):\\n self.__instances.append(obj)\\n self._proxy_class_methods(obj)\",\n \"def register_instance(self, instance):\\n self.instance = instance\",\n \"def register_object(self, obj):\\n self.modules.append(obj)\",\n \"def _attach_to_instance(self, instance):\\n self._instance = instance\",\n \"def contribute_to_object(self, obj):\\n pass\",\n \"def __init__(self, decorated):\\n self.decorated = decorated\",\n \"def __init__(self, decorated):\\n self.decorated = decorated\",\n \"def __init__(self, decorated):\\n self.decorated = decorated\",\n \"def _setup_object(self) -> object:\\n raise NotImplementedError\",\n \"def __init__(self, obj):\\n obj.Proxy = self\",\n \"def __init__(self, obj):\\n obj.Proxy = self\",\n \"def attach(self, obj):\\n return\",\n \"def __init__(self, add_intercept=True):\\n super().__init__(add_intercept)\\n return\",\n \"def decorate_init(cls, f):\\n def wrap(*args, **kwargs):\\n if not hasattr(cls, '_init'):\\n f(*args, **kwargs)\\n cls._init = True\\n return wrap\",\n \"def __call__ (self, cls):\\n # Define a wrapper function to capture the actual instantiation and __init__ params\\n @wraps(cls)\\n def wrapper_f(*args, **kwargs):\\n #print(f'type of cls is {type(cls)}')\\n peripheral = self.peripheral_type(**self.kwargs)\\n o = cls(*args, **kwargs)\\n o.message_debug(f\\\"Decorating class {cls.__name__} with {self.peripheral_type.__name__}\\\")\\n o.attach_sensor(peripheral)\\n return o\\n return wrapper_f\",\n \"def instance(self, instance):\\n self._instance = instance\",\n \"def attach(self, obj):\\n self.Object = obj.Object\",\n \"def __init__(self, obj):\\n self._store = {}\\n self.obj = weakref.proxy(obj)\",\n \"def __post_init__(self, *args, **kwargs) -> None:\\n # add other __init__ items here ...\\n pass\",\n \"def new(self, obj):\\n pass\",\n \"def __init__(self, function, instance):\\r\\n self.instance = instance\\r\\n self.function = function\",\n \"def __init__(self, name, obj=None):\\n super().__init__(name)\\n if obj != None:\\n self.appendPhandle(obj)\",\n \"def __call__(cls, *args, **kwargs):\\n obj = type.__call__(cls, *args, **kwargs)\\n obj.initialize()\\n return obj\",\n \"def after_class_creation(cls):\\n pass\",\n \"def add_object(self, obj):\\n\\t\\tself.objects.append(obj)\",\n \"def register(self, obj):\\n if not callable(obj):\\n raise ValueError(f\\\"object must be callable\\\")\\n\\n obj_name = obj.__name__\\n if obj_name in self._obj_dict:\\n pass\\n # print(f\\\"{obj_name} is already registered in {self.name}\\\")\\n # raise KeyError(f'{obj_name} is already registered in {self.name}')\\n\\n self._obj_dict[obj_name] = obj\\n return obj\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n obj = kwargs.get(\\\"obj\\\")\\n if obj:\\n self.obj = obj\",\n \"def on_start(self):\\n\\n def decorator(coro):\\n self._hooks.append((\\\"start\\\", coro))\\n return coro\\n\\n return decorator\",\n \"def instance(self, instance):\\n\\n self._instance = instance\",\n \"def __init__(object):\",\n \"def __call__(cls, inst = None, initDict = None, *args, **kwargs):\\n if not inst:\\n inst = blue.classes.CreateInstance(cls.__cid__)\\n inst.__klass__ = cls\\n if initDict:\\n for k, v in initDict.iteritems():\\n setattr(inst, k, v)\\n\\n try:\\n inst.__init__()\\n except AttributeError:\\n pass\\n\\n return inst\",\n \"def add(self, obj):\\n raise NotImplementedError\",\n \"def on_load(self):\\n self.__init__()\",\n \"def extend_instance(obj, cls):\\n base_cls = obj.__class__\\n base_cls_name = obj.__class__.__name__\\n obj.__class__ = type(base_cls_name, (base_cls, cls), {})\",\n \"def register(cls):\\n register(cls, cls.provided_class)\",\n \"def setup_method(self, objectCreation):\",\n \"def _singleton_init(self, *args, **kwargs):\\r\\n pass\",\n \"def decorator(cls):\\n\\n instance = cls()\\n auto_validator_services.register_hook(instance)\\n\\n return cls\",\n \"def new_object(self):\\r\\n\\t\\tpass\",\n \"def add_instance(self, context):\\r\\n self.instance_contexts.append(context)\\r\\n self.total_count += 1\",\n \"def __init__(self, **params):\\n self.__object = object_param(**params)\",\n \"def decorator(obj):\\n\\t\\tDRIVERS[name] = obj\\n\\t\\treturn obj\",\n \"def bind(self, obj: object):\\n new_inst = self.__class__(\\n func=self._func,\\n self_obj=obj,\\n unique=self._unique,\\n timeout_seconds=self._timeout_seconds,\\n expire_seconds=self._expire_seconds,\\n **self._kwargs,\\n )\\n setattr(obj, self._func.__name__, new_inst)\\n return new_inst\",\n \"def __init__(self):\\n Redrawable.all_instantiated_widgets.append(self)\",\n \"async def new(self, obj: Obj):\\n if obj not in self.ref_counts:\\n self.ref_counts[obj] = 1\\n await self.init(obj)\\n else:\\n self.ref_counts[obj] += 1\",\n \"def __init__(self):\\n self._inst = {}\",\n \"def wrapper(self, *args, **kwargs):\\n self.__wrapped_init__(*args, **kwargs)\\n _process_dependencies(self)\",\n \"def wrapper(self, *args, **kwargs):\\n self.__wrapped_init__(*args, **kwargs)\\n _process_dependencies(self)\",\n \"def set_init_args(self, args_obj):\\n if self and self[0][0] == '__init__':\\n print(\\\"Only one __init__ step is allowed\\\")\\n return\\n self.insert(0, ('__init__', args_obj))\",\n \"def decorator(cls):\\n\\n instance = cls(*args, **kwargs)\\n serializer_services.register_serializer(instance, **kwargs)\\n\\n return cls\",\n \"def on_register(cls):\",\n \"def __call__(obj):\",\n \"def __init__(self, obj, *args, **kwargs):\\n self.obj_ = obj\\n super(ArtificialRV, self).__init__(*args, **kwargs)\",\n \"def __new__(cls, *args, **kwds):\\n cls.__init_lock__.acquire()\\n try:\\n if not cls.__decorated__:\\n cls._decorate()\\n cls.__decorated__ = True\\n \\n return object.__new__(cls, *args, **kwds)\\n finally:\\n cls.__init_lock__.release()\",\n \"def __call__(self, *args, **kwargs):\\n if not self.instance:\\n self.instance = super().__call__(*args, **kwargs)\\n return self.instance\",\n \"def __init__(self, set_on_create=False):\\n self.set_on_create = set_on_create\",\n \"def __init__(self, set_on_create=False):\\n self.set_on_create = set_on_create\",\n \"def __init__(self, set_on_create=False):\\n self.set_on_create = set_on_create\",\n \"def augment(self, *args, **kwargs):\\n pass\",\n \"def __init__(self, decorated_model):\\n Logging.__init__(self, decorated_model)\",\n \"def __init__(self, decorated_model):\\n Logging.__init__(self, decorated_model)\",\n \"def __init__(self, decorated_model):\\n Logging.__init__(self, decorated_model)\",\n \"def __init__(self, decorated_model):\\n Logging.__init__(self, decorated_model)\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def add(obj):\",\n \"def set_observer(active):\\n if StorableObject.observe_objects is active:\\n return\\n\\n if active:\\n # activate and add __init__\\n\\n def _init(self):\\n StorableObject._weak_cache[self] = StorableObject._weak_index\\n StorableObject._weak_index += 1\\n\\n StorableObject.__init__ = MethodType(_init, None, StorableObject)\\n StorableObject.observe_objects = True\\n\\n if not active:\\n del StorableObject.__init__\",\n \"def new(self, obj):\\n key = obj.__class__.__name__+'.'+obj.id\\n self.__objects[key] = obj\",\n \"def __post_init__(self):\\n super().__post_init__()\",\n \"def singleton(cls):\\n print('Entering the decorator')\\n @functools.wraps(cls)\\n def wrapper_singleton(*args, **kwargs):\\n if not wrapper_singleton.instance:\\n \\tprint('Creating instance of the class')\\n \\twrapper_singleton.instance = cls(*args, **kwargs)\\n return wrapper_singleton.instance\\n \\n wrapper_singleton.instance = None\\n print(f'{wrapper_singleton.instance}')\\n return wrapper_singleton\",\n \"def __init__(self):\\n this = _patchExtractor.new_patchExtractor()\\n try:\\n self.this.append(this)\\n except Exception:\\n self.this = this\",\n \"def set_instance(self, instance):\\n self.instance = instance\",\n \"def set_instance(self, instance):\\n self.instance = instance\",\n \"def set_instance(self, instance):\\n self.instance = instance\",\n \"def set_instance(self, instance):\\n self.instance = instance\",\n \"def set_instance(self, instance):\\n self.instance = instance\",\n \"def new(self, obj):\\n if obj:\\n key = obj.__class__.__name__ + \\\".\\\" + obj.id\\n self.__objects[key] = obj\",\n \"def new(self, obj):\\n if obj:\\n self.__session.add(obj)\",\n \"def new(self, obj):\\n if obj:\\n self.__session.add(obj)\",\n \"def __init__():\",\n \"def _get_instance(self):\",\n \"def _get_instance(self):\",\n \"def new(self, obj):\\n self.__session.add(obj)\",\n \"def new(self, obj):\\n self.__session.add(obj)\",\n \"def new(self, obj):\\n self.__session.add(obj)\",\n \"def new(self, obj):\\n self.__session.add(obj)\",\n \"def new(self, obj):\\n self.__session.add(obj)\",\n \"def new(self, obj):\\n self.__session.add(obj)\",\n \"def new(self, obj):\\n self.__session.add(obj)\",\n \"def new(self, obj):\\n self.__session.add(obj)\",\n \"def hydrate_instance(func):\\n\\n @functools.wraps(func)\\n def wrapped(*args):\\n new_args = list(args)\\n new_args[1] = KolibriInstance.from_dict(args[1])\\n return func(*new_args)\\n\\n # for py2.7\\n if not hasattr(wrapped, \\\"__wrapped__\\\"):\\n setattr(wrapped, \\\"__wrapped__\\\", func)\\n\\n return wrapped\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70706284","0.687097","0.6671335","0.6357395","0.6352234","0.6292987","0.62336093","0.62336093","0.62336093","0.6214509","0.62070495","0.62070495","0.61912227","0.61553884","0.6097399","0.60896945","0.6085249","0.6073874","0.6045348","0.6041891","0.6041726","0.60382634","0.6028722","0.60156345","0.60120726","0.59913224","0.5990223","0.59774554","0.5947366","0.59455335","0.59139407","0.5912167","0.59047025","0.59035903","0.58979577","0.58844525","0.58830297","0.5848022","0.58386874","0.58250535","0.5810892","0.5798772","0.5777839","0.57752943","0.57727724","0.5768155","0.5765432","0.5761275","0.5761275","0.57596815","0.5755123","0.57204324","0.5690059","0.56895214","0.56872565","0.56792307","0.5662397","0.5662397","0.5662397","0.56518203","0.5651464","0.5651464","0.5651464","0.5651464","0.5651001","0.5651001","0.5651001","0.5651001","0.5651001","0.5651001","0.5651001","0.5651001","0.5651001","0.5651001","0.56452346","0.5633289","0.563311","0.5628345","0.5624236","0.5622514","0.561789","0.561789","0.561789","0.561789","0.561789","0.56117266","0.56065845","0.56065845","0.56061167","0.56018317","0.56018317","0.5596902","0.5596902","0.5596902","0.5596902","0.5596902","0.5596902","0.5596902","0.5596902","0.55949223"],"string":"[\n \"0.70706284\",\n \"0.687097\",\n \"0.6671335\",\n \"0.6357395\",\n \"0.6352234\",\n \"0.6292987\",\n \"0.62336093\",\n \"0.62336093\",\n \"0.62336093\",\n \"0.6214509\",\n \"0.62070495\",\n \"0.62070495\",\n \"0.61912227\",\n \"0.61553884\",\n \"0.6097399\",\n \"0.60896945\",\n \"0.6085249\",\n \"0.6073874\",\n \"0.6045348\",\n \"0.6041891\",\n \"0.6041726\",\n \"0.60382634\",\n \"0.6028722\",\n \"0.60156345\",\n \"0.60120726\",\n \"0.59913224\",\n \"0.5990223\",\n \"0.59774554\",\n \"0.5947366\",\n \"0.59455335\",\n \"0.59139407\",\n \"0.5912167\",\n \"0.59047025\",\n \"0.59035903\",\n \"0.58979577\",\n \"0.58844525\",\n \"0.58830297\",\n \"0.5848022\",\n \"0.58386874\",\n \"0.58250535\",\n \"0.5810892\",\n \"0.5798772\",\n \"0.5777839\",\n \"0.57752943\",\n \"0.57727724\",\n \"0.5768155\",\n \"0.5765432\",\n \"0.5761275\",\n \"0.5761275\",\n \"0.57596815\",\n \"0.5755123\",\n \"0.57204324\",\n \"0.5690059\",\n \"0.56895214\",\n \"0.56872565\",\n \"0.56792307\",\n \"0.5662397\",\n \"0.5662397\",\n \"0.5662397\",\n \"0.56518203\",\n \"0.5651464\",\n \"0.5651464\",\n \"0.5651464\",\n \"0.5651464\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.5651001\",\n \"0.56452346\",\n \"0.5633289\",\n \"0.563311\",\n \"0.5628345\",\n \"0.5624236\",\n \"0.5622514\",\n \"0.561789\",\n \"0.561789\",\n \"0.561789\",\n \"0.561789\",\n \"0.561789\",\n \"0.56117266\",\n \"0.56065845\",\n \"0.56065845\",\n \"0.56061167\",\n \"0.56018317\",\n \"0.56018317\",\n \"0.5596902\",\n \"0.5596902\",\n \"0.5596902\",\n \"0.5596902\",\n \"0.5596902\",\n \"0.5596902\",\n \"0.5596902\",\n \"0.5596902\",\n \"0.55949223\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6157864"},"document_rank":{"kind":"string","value":"13"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":803,"numItemsPerPage":100,"numTotalItems":95772,"offset":80300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjIzMzI2MCwianRpIjoiMTkwOTA0YTUtZDNlZS00NjkyLTgwNmEtMzU3NDViMTJlOGVmIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyMzY4NjAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.25Y9y2aCUAYqhttvipBBweC-9rxu9p13G2JoQ1qjPMkFnW9uRnVS0QvBtK2kA4UOBN1dag-Wfa92Ps2iV1IVCQ","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 |
|---|---|---|---|---|---|---|
User or group that owns a resource are granted full access to it. | def __acl__(self):
# type: () -> AccessControlListType
acl = []
if self.owner_user_id:
acl.append((Allow, self.owner_user_id, ALL_PERMISSIONS))
if self.owner_group_id:
acl.append((Allow, "group:%s" % self.owner_group_id, ALL_PERMISSIONS))
return acl | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def raise_for_ownership(self, resource: Model) -> None:\n\n # pylint: disable=import-outside-toplevel\n from superset import db\n\n if self.is_admin():\n return\n\n # Set of wners that works across ORM models.\n owners: List[User] = []\n\n orig_resource = db.ses... | [
"0.68418175",
"0.6817819",
"0.6789879",
"0.6402916",
"0.63664275",
"0.63395506",
"0.62749887",
"0.62603414",
"0.6227944",
"0.61531776",
"0.61475277",
"0.6117667",
"0.6108478",
"0.6108478",
"0.60827714",
"0.6073788",
"0.6063891",
"0.6059636",
"0.6057281",
"0.6044391",
"0.60342... | 0.0 | -1 |
List all groups a user belongs to, filtered by UserGroup status type. | def get_groups_by_status(self, status, db_session=None):
# type: (UserGroupStatus, Session) -> Set[Str]
from magpie.api.management.user.user_utils import get_user_groups_checked
cur_session = get_db_session(session=db_session) if db_session else get_db_session(obj=self)
group_names = s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_groups_by_status(self, status, db_session=None):\n # type: (UserGroupStatus, Session) -> List[Str]\n return []",
"def list_user_groups(self, user_id):\n resp, body = self.get('users/%s/groups' % user_id)\n self.expected_success(200, resp.status)\n body = json.loads(body... | [
"0.7415561",
"0.73563224",
"0.7105298",
"0.6973147",
"0.69514817",
"0.69147885",
"0.6911052",
"0.6895397",
"0.6881288",
"0.6803313",
"0.67458916",
"0.6714019",
"0.6666444",
"0.66410464",
"0.662373",
"0.66146386",
"0.6581608",
"0.65467393",
"0.6506116",
"0.6485276",
"0.6473893... | 0.74114037 | 1 |
Unique identifier of user. | def id(self): # pylint: disable=C0103,invalid-name # considered too short
return sa.Column(sa.Integer, primary_key=True, autoincrement=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_id(self):\n return text_type(hash(self.username))",
"def user_id(self):\n # type: () -> string_types\n return self._user_id",
"def user_id(self) -> str:\n return self._user_id",
"def user_id(self) -> str:\n return self._user_id",
"def user_id(self) -> pulumi.Outp... | [
"0.83980703",
"0.83578485",
"0.82583636",
"0.82583636",
"0.80136585",
"0.79326326",
"0.793035",
"0.7891192",
"0.7879836",
"0.787526",
"0.787526",
"0.78698146",
"0.78698146",
"0.78485024",
"0.78485024",
"0.78485024",
"0.7817112",
"0.7817112",
"0.7817112",
"0.77998596",
"0.7780... | 0.0 | -1 |
Password hash of the user. | def user_password(self):
return sa.Column(sa.Unicode(256), nullable=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _hash_password(self, password):\n passwordhash = bcrypt.hashpw(password.encode('utf8'), bcrypt.gensalt())\n return passwordhash",
"def get_password_hash(password):\n\n return pwd_context.hash(password)",
"def hash_password(self):\n self.__password = self.str_to_hash(self.__password)... | [
"0.8001551",
"0.78925467",
"0.78462327",
"0.77544147",
"0.77429557",
"0.76942587",
"0.76517755",
"0.7613748",
"0.75445867",
"0.75285655",
"0.7505567",
"0.74446845",
"0.7390829",
"0.73609084",
"0.7333976",
"0.73171544",
"0.73148537",
"0.73145485",
"0.7168403",
"0.7168022",
"0.... | 0.0 | -1 |
Email of the user. | def email(self):
return sa.Column(sa.Unicode(100), nullable=False, unique=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_useremail(self):\n return self.user.email",
"def get_user_email(self):\n member = self.get_user()\n if member:\n return member.getProperty('email')",
"def user_email(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user_email\")",
"def email(self, instan... | [
"0.85852766",
"0.8581432",
"0.85670006",
"0.85598457",
"0.8300198",
"0.8293188",
"0.8293188",
"0.8293188",
"0.8182699",
"0.81291264",
"0.8074925",
"0.8074925",
"0.8074925",
"0.8074925",
"0.8064869",
"0.80555576",
"0.7989175",
"0.797051",
"0.79486954",
"0.7945147",
"0.79444593... | 0.0 | -1 |
Date of user's registration. | def registered_date(self):
return sa.Column(sa.TIMESTAMP(timezone=False), default=datetime.datetime.utcnow, server_default=sa.func.now()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_date(self):\n\n return self._register_date",
"def getUserSignupDate(self):\r\n userinfo = self.getUserInfo()\r\n timestamp = int(float(userinfo[\"signupTimeSec\"]))\r\n return time.strftime(\"%m/%d/%Y %H:%M\", time.gmtime(timestamp))",
"def date_registered(self) -> date... | [
"0.77629924",
"0.7628828",
"0.75674725",
"0.7104303",
"0.7104303",
"0.7104303",
"0.7044613",
"0.6900152",
"0.6883736",
"0.6846401",
"0.67712206",
"0.6744589",
"0.65643835",
"0.6496458",
"0.637149",
"0.637149",
"0.637149",
"0.637149",
"0.6331268",
"0.6331268",
"0.6331268",
"... | 0.72349703 | 3 |
Pending user status is enforced. | def status(self):
return UserStatuses.Pending.value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_set_user_status(self):\n pass",
"def user_requested_access(user):\r\n user = CourseCreator.objects.get(user=user)\r\n if user.state != CourseCreator.GRANTED:\r\n user.state = CourseCreator.PENDING\r\n user.save()",
"def add_user_with_status_unrequested(user):\r\n _add_use... | [
"0.6903818",
"0.6896614",
"0.67338765",
"0.65549946",
"0.6512543",
"0.64965606",
"0.64672786",
"0.6448087",
"0.63479024",
"0.6328773",
"0.62876517",
"0.6279613",
"0.6199937",
"0.6159553",
"0.6128632",
"0.60768175",
"0.60722935",
"0.60538155",
"0.6050738",
"0.60454774",
"0.604... | 0.7320671 | 0 |
Pending user is not a member of any group. | def groups(self):
return [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_user_is_group_member_status_is_not_approved(self):\n group = mommy.make('groups.Group', moderated=True)\n thread = self.create_thread(group=group)\n message = thread.first_message\n message.status = 'pending'\n message.save()\n user = self.create_user()\n u... | [
"0.7792742",
"0.7228106",
"0.7156203",
"0.68756753",
"0.6828929",
"0.66670585",
"0.6643753",
"0.66180396",
"0.6471787",
"0.6458959",
"0.6455422",
"0.63878495",
"0.6336446",
"0.63234437",
"0.6303333",
"0.62873536",
"0.6276098",
"0.62558997",
"0.62484837",
"0.6246418",
"0.62415... | 0.0 | -1 |
Pending user is not a member of any group. Avoid error in case this method gets accessed when simultaneously | def get_groups_by_status(self, status, db_session=None):
# type: (UserGroupStatus, Session) -> List[Str]
return [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_user_is_group_member_status_is_not_approved(self):\n group = mommy.make('groups.Group', moderated=True)\n thread = self.create_thread(group=group)\n message = thread.first_message\n message.status = 'pending'\n message.save()\n user = self.create_user()\n u... | [
"0.76340216",
"0.7207143",
"0.7110575",
"0.704016",
"0.6954512",
"0.6781277",
"0.66747254",
"0.6671615",
"0.6658352",
"0.6614824",
"0.6579499",
"0.6539072",
"0.65362036",
"0.6525634",
"0.6504851",
"0.6461988",
"0.64528805",
"0.63386667",
"0.63307506",
"0.6271585",
"0.62596774... | 0.0 | -1 |
Representation of all flags combined. | def all(cls):
# type: () -> UserStatuses
return UserStatuses(sum(cls.values())) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flags(self):\n flags = self.Flags\n return [x for x in self.FLAGS_VALUES if flags & x]",
"def get_flags(self):\n\n if self.raw.flags not in [0, 1, 2, 3]:\n raise ValueError(\"Invalid raw flags: {}\".format(self.raw.flags))\n\n flags = set()\n\n if (self.raw.flags... | [
"0.72928613",
"0.7054312",
"0.7045637",
"0.69635427",
"0.69510907",
"0.686271",
"0.6807425",
"0.646746",
"0.6458346",
"0.6447968",
"0.6439035",
"0.63748443",
"0.63699955",
"0.62179524",
"0.6199411",
"0.6160184",
"0.61336243",
"0.6100112",
"0.60833067",
"0.6059165",
"0.6054686... | 0.0 | -1 |
Retrieves the user matching the given name. | def by_user_name(cls, user_name, status=None, db_session=None): # pylint: disable=W0237,W0221
# type: (Str, Optional[UserStatuses], Optional[Session]) -> Optional[AnyUser]
if status is None or UserStatuses.Pending not in status:
return super(UserSearchService, cls).by_user_name(user_name, d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_user(self, instance, name):\n return instance.get_user(name)",
"def get_user(name):\n try:\n return User.objects.get(name=name)\n except ObjectDoesNotExist:\n raise ObjectDoesNotFound(\"There is no user '{}'.\".format(name))",
"def get_user(self, name):\n try:\n ... | [
"0.8306725",
"0.82802016",
"0.81780684",
"0.8119794",
"0.8105071",
"0.8051432",
"0.8029481",
"0.7894524",
"0.78694415",
"0.778063",
"0.7762436",
"0.77618915",
"0.7470238",
"0.7445575",
"0.7416449",
"0.74154556",
"0.73773164",
"0.7369097",
"0.7361986",
"0.73617166",
"0.7350588... | 0.7004488 | 43 |
Retrieves the first matched user by either name or email, whichever comes first. | def by_name_or_email(cls, user_name, email, status=None, db_session=None):
# type: (Str, Str, Optional[UserStatuses], Optional[Session]) -> Optional[AnyUser]
db_session = get_db_session(db_session)
if status is None or UserStatuses.Pending not in status:
query = db_session.query(cls.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_by_name_or_email(self, name_or_email):\n User = tables.User\n user = (\n self.session\n .query(User)\n .filter(or_(\n func.lower(User.user_name) == name_or_email.lower(),\n func.lower(User.email) == name_or_email.lower()\n ... | [
"0.7823106",
"0.7675339",
"0.7577323",
"0.7559361",
"0.75093603",
"0.7479534",
"0.7464798",
"0.74502665",
"0.7408632",
"0.738302",
"0.73796195",
"0.72978586",
"0.72766876",
"0.72718614",
"0.72718614",
"0.72500736",
"0.7216743",
"0.71987",
"0.7193397",
"0.71775645",
"0.7133463... | 0.7112545 | 22 |
Administrators have all permissions, user/groupspecific permissions added if user is logged in. | def __acl__(self):
# type: () -> AccessControlListType
user = self.request.user
# allow if role MAGPIE_ADMIN_PERMISSION is somehow directly set instead of inferred via members of admin-group
acl = [(Allow, get_constant("MAGPIE_ADMIN_PERMISSION", self.request), ALL_PERMISSIONS)]
a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_super_access():\n current_user = frappe.get_doc('User', frappe.session.user)\n roles = set([role.role for role in current_user.roles])\n return bool(roles & {'Administrator', 'Instructor', 'Education Manager', 'System Manager', 'Academic User'})",
"def canDo(self,request):\n request.needA... | [
"0.6800246",
"0.6705577",
"0.6697742",
"0.6697566",
"0.6669926",
"0.66422534",
"0.6610092",
"0.66090006",
"0.6608209",
"0.6583466",
"0.6532305",
"0.64970964",
"0.64970964",
"0.64784324",
"0.6450758",
"0.64066356",
"0.640475",
"0.640475",
"0.63832355",
"0.6371056",
"0.6360135"... | 0.7004168 | 0 |
Configuration modifiers for parsing access to resources and permissions. | def configuration(self):
return sa.Column(sa.JSON(), nullable=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __acl__():",
"def __base_acl__(self) -> list:\n _acls = [\n (Allow, 'g:professionals', ['list', 'view', 'edit']),\n ]\n return _acls",
"def permissions():\n pass",
"def __acl__(self):\n yield 'Allow', 'system.Everyone', 'none'\n yield security.DENY_ALL",
"def __... | [
"0.6182829",
"0.6077709",
"0.607764",
"0.596731",
"0.5722942",
"0.57069737",
"0.5703722",
"0.5693177",
"0.56347257",
"0.5550067",
"0.55394423",
"0.55283564",
"0.55238104",
"0.5513653",
"0.5499519",
"0.54761636",
"0.5459978",
"0.5409127",
"0.5384876",
"0.5371545",
"0.53407276"... | 0.0 | -1 |
\ Create a sourmash.MinHash object. | def __init__(
self,
n,
ksize,
*,
is_protein=False,
dayhoff=False,
hp=False,
track_abundance=False,
seed=MINHASH_DEFAULT_SEED,
max_hash=0,
mins=None,
scaled=0,
):
# support max_hash in constructor, for now.
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_min_hash(shingles: set) -> MinHash:\n track_min_hash = MinHash(num_perm=128)\n for shin in shingles:\n track_min_hash.update(str(shin).encode('utf-8'))\n return track_min_hash",
"def _new_lsh_index(self):\n minhashes = {}\n lsh = MinHashLSH(self._config.threshold, self._conf... | [
"0.6668846",
"0.6138378",
"0.60524726",
"0.5971202",
"0.58725375",
"0.57037383",
"0.564662",
"0.55130583",
"0.5496498",
"0.5492963",
"0.5479173",
"0.54515046",
"0.53829676",
"0.53736293",
"0.5366463",
"0.52517533",
"0.52490145",
"0.52490145",
"0.52197444",
"0.52121085",
"0.51... | 0.5461433 | 11 |
Convert sequence to hashes without adding to the sketch. If input sequence is DNA and this is a protein, dayhoff, or hp MinHash, translate the DNA appropriately before hashing. If input sequence is protein, set is_protein=True. If `force = True` and `bad_kmers_as_zeroes = True`, invalid kmers hashes will be represented... | def seq_to_hashes(self, sequence, *, force=False, bad_kmers_as_zeroes=False, is_protein=False):
if is_protein and self.moltype not in ("protein", "dayhoff", "hp"):
raise ValueError("cannot add protein sequence to DNA MinHash")
if bad_kmers_as_zeroes and not force:
raise ValueEr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def kmers_and_hashes(self, sequence, *, force=False, is_protein=False):\n import screed\n\n bad_kmers_as_zeroes = False\n if force:\n bad_kmers_as_zeroes = True\n\n sequence = sequence.upper()\n hashvals = self.seq_to_hashes(sequence,\n ... | [
"0.70536274",
"0.52695596",
"0.4618468",
"0.46054563",
"0.45912695",
"0.45135024",
"0.45131776",
"0.44705614",
"0.4454002",
"0.441342",
"0.43388784",
"0.43345582",
"0.4292265",
"0.42891702",
"0.4284157",
"0.42824477",
"0.42541516",
"0.42230168",
"0.4209773",
"0.42078424",
"0.... | 0.79699576 | 0 |
Convert sequence into (kmer, hashval) tuples without adding it to the sketch. If input sequence is DNA and this is a protein, dayhoff, or hp MinHash, translate the DNA appropriately before hashing. If input sequence is protein, set is_protein=True. If 'force' is True, invalid kmers will be represented with 'None'. | def kmers_and_hashes(self, sequence, *, force=False, is_protein=False):
import screed
bad_kmers_as_zeroes = False
if force:
bad_kmers_as_zeroes = True
sequence = sequence.upper()
hashvals = self.seq_to_hashes(sequence,
force=for... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def seq_to_hashes(self, sequence, *, force=False, bad_kmers_as_zeroes=False, is_protein=False):\n\n if is_protein and self.moltype not in (\"protein\", \"dayhoff\", \"hp\"):\n raise ValueError(\"cannot add protein sequence to DNA MinHash\")\n\n if bad_kmers_as_zeroes and not force:\n ... | [
"0.72178686",
"0.46786237",
"0.4610649",
"0.44296953",
"0.43184823",
"0.43025377",
"0.42989746",
"0.42802525",
"0.42648515",
"0.4238355",
"0.41944683",
"0.41212335",
"0.4107931",
"0.40976414",
"0.40808073",
"0.40715283",
"0.4070102",
"0.40576768",
"0.40311107",
"0.4011284",
"... | 0.7367316 | 0 |
Add many hashes to the sketch at once. ``hashes`` can be either an iterable (list, set, etc.), or another ``MinHash`` object. | def add_many(self, hashes):
if isinstance(hashes, MinHash):
self._methodcall(lib.kmerminhash_add_from, hashes._objptr)
else:
self._methodcall(lib.kmerminhash_add_many, list(hashes), len(hashes)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_hashes(self, song_id: int, hashes, batch_size: int = 1000):\n values = [(song_id, hsh, int(offset)) for hsh, offset in hashes]\n for index in range(0, len(hashes), batch_size):\n helpers.bulk(self.cursor, self.gen_dicts(values[index: index + batch_size]))",
"def add_geo_hashes... | [
"0.7200702",
"0.6982986",
"0.6840817",
"0.6506755",
"0.6467743",
"0.644392",
"0.58145267",
"0.5784946",
"0.56172",
"0.5566877",
"0.55600774",
"0.55239296",
"0.54653585",
"0.5456146",
"0.54462945",
"0.5420798",
"0.54167813",
"0.5377261",
"0.5369092",
"0.5362217",
"0.5318342",
... | 0.86459774 | 0 |
Remove many hashes from a sketch at once. ``hashes`` can be either an iterable (list, set, etc.), or another ``MinHash`` object. | def remove_many(self, hashes):
if isinstance(hashes, MinHash):
self._methodcall(lib.kmerminhash_remove_from, hashes._objptr)
else:
self._methodcall(lib.kmerminhash_remove_many, list(hashes), len(hashes)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_from_directory_by_hashes(directory, hashes):\n files = os.listdir(directory)\n if hashes == '*':\n for f in files:\n os.unlink(os.path.join(directory, f))\n for f in files:\n for h in hashes:\n if h in f:\n os.unlink(os.path.join(directory, f))... | [
"0.6154905",
"0.5956718",
"0.58561975",
"0.5804606",
"0.57617533",
"0.5427621",
"0.5317934",
"0.52789366",
"0.5230689",
"0.5153228",
"0.51266813",
"0.5125787",
"0.5074242",
"0.50469494",
"0.4990306",
"0.49507",
"0.49342737",
"0.4907175",
"0.48971403",
"0.48789588",
"0.4843024... | 0.87233436 | 0 |
Return list of hashes or if ``with_abundance`` a list of (hash, abund). | def get_mins(self, with_abundance=False):
mins = self.hashes
if not with_abundance:
return mins.keys()
return mins | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAbundances(self, species): \n return self.abundance_df[self.abundance_df[self.tax_levels[0]]==species]",
"def get_fingerprintable_for_scope(self, bottom_scope, include_passthru=False):\n pairs = []\n if include_passthru:\n pu_args = self.passthru_args_for_scope(bottom_scope)\n ... | [
"0.5129425",
"0.5110695",
"0.4949431",
"0.49324703",
"0.48975635",
"0.48139",
"0.4699067",
"0.46907076",
"0.46503294",
"0.46276388",
"0.46026292",
"0.4587694",
"0.45864993",
"0.45608535",
"0.45470384",
"0.45394257",
"0.45306677",
"0.44996083",
"0.44410548",
"0.44238195",
"0.4... | 0.41494182 | 79 |
\ Return the number of hashes in common between ``self`` and ``other``. Optionally downsample ``scaled`` objects to highest ``scaled`` value. | def count_common(self, other, downsample=False):
if not isinstance(other, MinHash):
raise TypeError("Must be a MinHash!")
return self._methodcall(lib.kmerminhash_count_common, other._get_objptr(), downsample) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def contained_by(self, other, downsample=False):\n if not (self.scaled and other.scaled):\n raise TypeError(\"Error: can only calculate containment for scaled MinHashes\")\n denom = len(self)\n if not denom:\n return 0.0\n total_denom = float(denom * self.scaled) #... | [
"0.6858488",
"0.58855665",
"0.5778567",
"0.55397224",
"0.54475987",
"0.5431119",
"0.53659403",
"0.53243905",
"0.52780986",
"0.52514344",
"0.5219435",
"0.5210827",
"0.5207767",
"0.5174138",
"0.51515955",
"0.5135636",
"0.51316446",
"0.5123332",
"0.5106239",
"0.5092781",
"0.5088... | 0.7180194 | 0 |
Copy this object and downsample new object to either `num` or `scaled`. | def downsample(self, *, num=None, scaled=None):
# first, evaluate provided parameters --
# at least one must be specified!
if num is None and scaled is None:
raise ValueError('must specify either num or scaled to downsample')
# both cannot be specified
if num is not... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def downsample(self, number):\n for num, ss in enumerate(self.samples):\n self.samples[num], self.extra_kwargs[num] = _downsample(\n ss, number, extra_kwargs=self.extra_kwargs[num]\n )",
"def downsample(self, number):\n self.samples, self.extra_kwargs = _downsam... | [
"0.64299905",
"0.6275884",
"0.6191262",
"0.59493554",
"0.5921856",
"0.5814289",
"0.5767988",
"0.5739846",
"0.56866455",
"0.56520945",
"0.55760133",
"0.5561418",
"0.5542622",
"0.55036783",
"0.54945916",
"0.54945916",
"0.54655623",
"0.54654604",
"0.5453377",
"0.5451266",
"0.541... | 0.7158274 | 0 |
If track_abundance=True, return a new flattened MinHash. | def flatten(self):
if self.track_abundance:
# create new object:
a = MinHash(
self.num, self.ksize,
is_protein=self.is_protein, dayhoff=self.dayhoff, hp=self.hp,
track_abundance=False, seed=self.seed, max_hash=self._max_hash
)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inflate(self, from_mh):\n if not self.track_abundance and from_mh.track_abundance:\n orig_abunds = from_mh.hashes\n abunds = { h: orig_abunds.get(h, 0) for h in self.hashes }\n\n abund_mh = from_mh.copy_and_clear()\n\n abund_mh.downsample(scaled=self.scaled)\n... | [
"0.718268",
"0.5613122",
"0.48945636",
"0.48383635",
"0.47822806",
"0.46781078",
"0.4643958",
"0.46218306",
"0.4583534",
"0.45498845",
"0.4548925",
"0.45155597",
"0.45114824",
"0.45092195",
"0.4481805",
"0.44715574",
"0.44415337",
"0.4435643",
"0.4407659",
"0.43675295",
"0.43... | 0.81907636 | 0 |
Calculate similarity of two sketches. If the sketches are not abundance weighted, or ignore_abundance=True, compute Jaccard similarity. If the sketches are abundance weighted, calculate the angular similarity, a distance metric based on the cosine similarity. Note, because the term frequencies (tfidf weights) cannot be... | def similarity(self, other, ignore_abundance=False, downsample=False):
return self._methodcall(lib.kmerminhash_similarity,
other._get_objptr(),
ignore_abundance, downsample) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def jacaard(clusters_a, clusters_b):\n return jaccard_similarity_score(clusters_a, clusters_b)",
"def jaccard_similarity_score(x, y):\n intersection_cardinality = len(set(x).intersection(set(y)))\n union_cardinality = len(set(x).union(set(y)))\n return intersection_cardinality / float(union_cardi... | [
"0.66255057",
"0.65805817",
"0.656553",
"0.656553",
"0.6544001",
"0.64989215",
"0.6419974",
"0.64119095",
"0.6368101",
"0.634537",
"0.63030934",
"0.62782055",
"0.6275815",
"0.6216541",
"0.6167886",
"0.61136615",
"0.60486466",
"0.60355633",
"0.6034516",
"0.6033509",
"0.601564"... | 0.6267294 | 13 |
Calculate how much of self is contained by other. | def contained_by(self, other, downsample=False):
if not (self.scaled and other.scaled):
raise TypeError("Error: can only calculate containment for scaled MinHashes")
denom = len(self)
if not denom:
return 0.0
total_denom = float(denom * self.scaled) # would be bet... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare(self, other):\n return len(self & other) / max(len(self | other), 1)",
"def measure_overlap(self, other):\n return np.dot(self.vector, other.vector)",
"def belongs_to(self, other):\n #if isinstance(other, line.Line):\n print other, other.p1, other.p2\n v1 = Vector... | [
"0.7142372",
"0.7042324",
"0.70301497",
"0.66929966",
"0.6680324",
"0.66532564",
"0.6617386",
"0.6563072",
"0.6559819",
"0.6547346",
"0.6523871",
"0.6453149",
"0.64438856",
"0.6440018",
"0.6414049",
"0.64024335",
"0.63960457",
"0.6393788",
"0.63170385",
"0.6314169",
"0.630946... | 0.73098785 | 0 |
Calculate average containment ANI. | def avg_containment_ani(self, other, *, downsample=False, prob_threshold=1e-3):
if not (self.scaled and other.scaled):
raise TypeError("Error: can only calculate ANI for scaled MinHashes")
a1 = self.containment_ani(other, downsample=downsample, prob_threshold=prob_threshold).ani
a2 =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_adv_anisotropy(self):\n num_atoms = 0\n adv_aniso = 0.0\n\n for atm in self:\n try:\n adv_aniso += atm.calc_anisotropy()\n except ZeroDivisionError:\n pass\n else:\n num_atoms += 1\n\n return adv_anis... | [
"0.6131125",
"0.61068237",
"0.6103669",
"0.59636074",
"0.59305656",
"0.5822768",
"0.5814111",
"0.58053005",
"0.5790873",
"0.5778367",
"0.5771074",
"0.57672966",
"0.5677806",
"0.567333",
"0.5669914",
"0.56641656",
"0.56623226",
"0.5660298",
"0.56546426",
"0.5651552",
"0.564000... | 0.65388435 | 0 |
Set abundances for hashes from ``values``, where ``values[hash] = abund`` If ``abund`` value is set to zero, the ``hash`` will be removed from the sketch. ``abund`` cannot be set to a negative value. | def set_abundances(self, values, clear=True):
if self.track_abundance:
hashes = []
abunds = []
for h, v in values.items():
hashes.append(h)
if v < 0:
raise ValueError("Abundance cannot be set to a negative v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fill(self, value, indices=None):\n # TODO deal with underflow and overflow and do the doc + optimize the function\n\n # change the value array to an array of Histogram index to be modified\n hist_indices = ((value - self.bin_edges[0]) // self.bin_width).astype(int)\n\n # treat overf... | [
"0.49861008",
"0.49133873",
"0.4886975",
"0.48074695",
"0.47927505",
"0.4771163",
"0.4770385",
"0.4706151",
"0.47045323",
"0.46872026",
"0.46551254",
"0.46551254",
"0.46293232",
"0.46113256",
"0.4601353",
"0.4601353",
"0.45550853",
"0.45334214",
"0.45271662",
"0.45200193",
"0... | 0.8501885 | 0 |
return a new MinHash object with abundances taken from 'from_mh' | def inflate(self, from_mh):
if not self.track_abundance and from_mh.track_abundance:
orig_abunds = from_mh.hashes
abunds = { h: orig_abunds.get(h, 0) for h in self.hashes }
abund_mh = from_mh.copy_and_clear()
abund_mh.downsample(scaled=self.scaled)
a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flatten(self):\n if self.track_abundance:\n # create new object:\n a = MinHash(\n self.num, self.ksize,\n is_protein=self.is_protein, dayhoff=self.dayhoff, hp=self.hp,\n track_abundance=False, seed=self.seed, max_hash=self._max_hash\n ... | [
"0.6652322",
"0.5897634",
"0.56669325",
"0.5633293",
"0.55306464",
"0.5507406",
"0.53579575",
"0.5319949",
"0.52097255",
"0.5134658",
"0.511404",
"0.5101854",
"0.5089661",
"0.5068611",
"0.4992265",
"0.49728736",
"0.49419758",
"0.49392864",
"0.49388367",
"0.48303932",
"0.48239... | 0.7799836 | 0 |
Approximate total number of hashes (num_hashes scaled). | def unique_dataset_hashes(self):
if not self.scaled:
raise TypeError("can only approximate unique_dataset_hashes for scaled MinHashes")
# TODO: replace set_size with HLL estimate when that gets implemented
return len(self) * self.scaled # + (self.ksize - 1) for bp estimation | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def totalhashes(self):\n return np.sum(self.counts)",
"def num_hashes(self):\n # see comment in constructor for self.hashes\n return self.config.num_hashes",
"def get_total_rehashes(self):\n return self.count_rehashes",
"def getSize(hash) -> int:\r\n sum = 0\r\n i = 0\r\n while i... | [
"0.80154234",
"0.7362063",
"0.71793133",
"0.67978984",
"0.66877574",
"0.6673941",
"0.6402338",
"0.6303968",
"0.62771624",
"0.6252882",
"0.6237176",
"0.6233178",
"0.61219335",
"0.6053896",
"0.6004548",
"0.59454817",
"0.5944629",
"0.58806014",
"0.58799136",
"0.58773786",
"0.587... | 0.6069469 | 13 |
Init POE client with Client. | def __init__(self, client):
self.__route_tag = "wallet-ng"
self.__path = "poe"
self.__client = client | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def client_setup(self):\n self.client = Client()",
"def init_client(self, client):\n self.client = client",
"def init_client():\n init_config()\n begin_sending_packets()",
"def __init__(self, client):\n\n self.client = client",
"def __init__(self, client):\n self._client =... | [
"0.7610452",
"0.71959406",
"0.6672331",
"0.66443336",
"0.6611263",
"0.6593158",
"0.6565757",
"0.6509013",
"0.6351874",
"0.6317083",
"0.631571",
"0.6284267",
"0.62508565",
"0.62507164",
"0.62332183",
"0.61497575",
"0.6126292",
"0.6111684",
"0.61055666",
"0.6097409",
"0.6090317... | 0.6434195 | 8 |
Set wallet client header | def __set_header(self, header):
if APIKEYHEADER not in header:
header[APIKEYHEADER] = self.__client.get_apikey()
if ROUTETAG not in header:
header[ROUTETAG] = self.__route_tag
if FABIOROUTETAGHEADER not in header:
header[FABIOROUTETAGHEADER] = self.__route_ta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_header( name, value ):",
"def setHeader(k, v):",
"def SetHeader(self, window):\n window.SetName(\"header\")\n window.SetBackgroundColour(wx.GetApp().settings.header_bg_color)\n window.SetForegroundColour(wx.GetApp().settings.header_fg_color)\n window.SetFont(wx.GetApp().sett... | [
"0.63789934",
"0.6275574",
"0.61759233",
"0.61481667",
"0.6141458",
"0.6005986",
"0.59728193",
"0.5948228",
"0.5886777",
"0.5886777",
"0.57172865",
"0.57135797",
"0.5706888",
"0.5656401",
"0.5647001",
"0.5627201",
"0.55987096",
"0.5559345",
"0.55211395",
"0.55181426",
"0.5504... | 0.61821043 | 2 |
Create a POE with ed25519 signed body. | def create_with_sign(self, header, creator, created,
privateB64, payload, nonce=""):
payload = json.dumps(payload)
req_path = "create"
method = self.__client.do_post
signature = build_signature_body(
creator,
created,
nonce,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_pose(self, position, orientation, frame):\n\n pose = PoseStamped()\n pose.header.frame_id = frame\n pose.pose.position.x = position[0]\n pose.pose.position.y = position[1]\n pose.pose.position.z = position[2]\n pose.pose.orientation.w = orientation[0]\n pose.pose.orientation.x = orien... | [
"0.536952",
"0.5362856",
"0.5151448",
"0.5143484",
"0.51104677",
"0.5083384",
"0.50697607",
"0.50141406",
"0.4996712",
"0.49757397",
"0.49587664",
"0.49465784",
"0.48925436",
"0.48520038",
"0.4849136",
"0.48143065",
"0.48096073",
"0.48053464",
"0.47664648",
"0.47503638",
"0.4... | 0.45374873 | 38 |
Update a POE with ed25519 signed body. | def update_with_sign(self, header, creator,
created, privateB64, payload, nonce=""):
payload = json.dumps(payload)
req_path = "update"
method = self.__client.do_put
signature = build_signature_body(
creator,
created,
nonce,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, es, **kwargs):\n pass",
"def PUT(self):\n # Check upload size\n body = self.request.get('BODYFILE')\n checkUploadSize(self.context, body)\n\n # If the module is published, do a transparent checkout\n if self.context.state == 'published':\n sel... | [
"0.5396008",
"0.5233669",
"0.51176083",
"0.5099346",
"0.50415975",
"0.5001394",
"0.48802826",
"0.48533717",
"0.48500893",
"0.48342696",
"0.48286116",
"0.47969967",
"0.47862893",
"0.476743",
"0.47657263",
"0.47657263",
"0.47657263",
"0.47657263",
"0.4737093",
"0.47227287",
"0.... | 0.51079416 | 3 |
Fit an LDA on corpus of reviews, apply to corpus of labeled reviews, show a heatmap comparing the two, and print the best ROC AUC scores associated with each topic. | def compareToManual(reviews, vectorized_revs, coded_reviews_df,
vectorized_coded_revs, n_topics=20, alpha=0.1, eta=0.01):
n = n_topics
a = alpha
b = eta
print "Topics: {}, Alpha: {}, Beta: {}".format(n, a, b)
ldalda = lda.LDA(n_topics=n, alpha=a, eta=b, refresh=1000, random_stat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compareToManual2(reviews, vectorized_revs, coded_reviews_df,\n vectorized_coded_revs, n_topics=20, alpha=0.1, eta=0.01):\n n = n_topics\n a = alpha\n b = eta\n print \"Topics: {}, Alpha: {}, Beta: {}\".format(n, a, b)\n\n ldalda = lda.LDA(n_topics=n, alpha=a, eta=b, refresh=1... | [
"0.7269275",
"0.6819381",
"0.66611135",
"0.63078994",
"0.6253986",
"0.6162204",
"0.59881806",
"0.5941095",
"0.5862071",
"0.5853435",
"0.5849609",
"0.58387995",
"0.5799318",
"0.57985353",
"0.57822347",
"0.57719165",
"0.5741003",
"0.57404876",
"0.5738016",
"0.5728622",
"0.56738... | 0.71987164 | 1 |
Fit an LDA on corpus of reviews, apply to corpus of labeled reviews, show a heatmap comparing the two, and print the best average precision scores associated with each topic. | def compareToManual2(reviews, vectorized_revs, coded_reviews_df,
vectorized_coded_revs, n_topics=20, alpha=0.1, eta=0.01):
n = n_topics
a = alpha
b = eta
print "Topics: {}, Alpha: {}, Beta: {}".format(n, a, b)
ldalda = lda.LDA(n_topics=n, alpha=a, eta=b, refresh=1000, random_st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compareToManual(reviews, vectorized_revs, coded_reviews_df,\n vectorized_coded_revs, n_topics=20, alpha=0.1, eta=0.01):\n n = n_topics\n a = alpha\n b = eta\n print \"Topics: {}, Alpha: {}, Beta: {}\".format(n, a, b)\n\n ldalda = lda.LDA(n_topics=n, alpha=a, eta=b, refresh=100... | [
"0.6815605",
"0.6638938",
"0.6487869",
"0.64365166",
"0.631679",
"0.6159404",
"0.6149328",
"0.61167663",
"0.60183966",
"0.59977394",
"0.59427494",
"0.5931884",
"0.5887719",
"0.5854915",
"0.58348775",
"0.5807241",
"0.57867366",
"0.57603186",
"0.5757726",
"0.5748361",
"0.574495... | 0.7218424 | 0 |
Fit an LDA on corpus of reviews, apply to corpus of labeled reviews, show a heatmap comparing the two, and print the best ROC AUC scores associated with each topic. | def compareToManualNoPrint(reviews, vectorized_revs, coded_reviews_df,
vectorized_coded_revs, n_topics=20, alpha=0.1, eta=0.01):
n = n_topics
a = alpha
b = eta
ldalda = lda.LDA(n_topics=n, alpha=a, eta=b, refresh=1000, random_state=1)
ldalda.fit(vectorized_revs)
cat_array =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compareToManual2(reviews, vectorized_revs, coded_reviews_df,\n vectorized_coded_revs, n_topics=20, alpha=0.1, eta=0.01):\n n = n_topics\n a = alpha\n b = eta\n print \"Topics: {}, Alpha: {}, Beta: {}\".format(n, a, b)\n\n ldalda = lda.LDA(n_topics=n, alpha=a, eta=b, refresh=1... | [
"0.72696346",
"0.71990746",
"0.66610795",
"0.63089925",
"0.6251594",
"0.61621875",
"0.598813",
"0.5940928",
"0.5862699",
"0.5852273",
"0.5847392",
"0.58371717",
"0.58000183",
"0.5799821",
"0.57811666",
"0.5771259",
"0.5742007",
"0.57394266",
"0.57394177",
"0.5728231",
"0.5671... | 0.68196964 | 2 |
Fit an LDA on corpus of reviews, apply to corpus of labeled reviews, show a heatmap comparing the two, and print the best average precision scores associated with each topic. | def compareToManual2(reviews, vectorized_revs, coded_reviews_df,
vectorized_coded_revs, n_topics=20, alpha=0.1, eta=0.01):
n = n_topics
a = alpha
b = eta
ldalda = lda.LDA(n_topics=n, alpha=a, eta=b, refresh=1000, random_state=1)
ldalda.fit(vectorized_revs)
cat_array = ldal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compareToManual2(reviews, vectorized_revs, coded_reviews_df,\n vectorized_coded_revs, n_topics=20, alpha=0.1, eta=0.01):\n n = n_topics\n a = alpha\n b = eta\n print \"Topics: {}, Alpha: {}, Beta: {}\".format(n, a, b)\n\n ldalda = lda.LDA(n_topics=n, alpha=a, eta=b, refresh=1... | [
"0.7218726",
"0.6816323",
"0.6489072",
"0.6436522",
"0.631909",
"0.6159944",
"0.6148784",
"0.61179227",
"0.60166854",
"0.599941",
"0.59446496",
"0.5932613",
"0.5886966",
"0.58527917",
"0.5836098",
"0.5808099",
"0.5787791",
"0.5760561",
"0.5759848",
"0.5748608",
"0.57465607",
... | 0.6640452 | 2 |
Return a dictionary with the best topic(s) for each manuallycoded category in a given LDA implementation. | def bestTopics(lda_imlementation, vectorized_coded_revs, coded_reviews_df):
cat_array = lda_imlementation.transform(vectorized_coded_revs)
topic_array = []
for i in range(lda_imlementation.n_topics):
coded_reviews_df['topic_'+str(i)] = cat_array[:, i]
topic_array += ['topic_'+str(i)]
be... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def classify(listOfTopics):\n result = []\n\n def catForTop(regexes, topic):\n for pattern in regexes:\n if re.match(pattern, topic):\n return True\n return False\n\n for category, regexes in categ.iteritems():\n for topic in listOfTopics:\n if cat... | [
"0.62541837",
"0.615619",
"0.59741247",
"0.5844138",
"0.5826542",
"0.56770694",
"0.5632976",
"0.56197286",
"0.5603382",
"0.5563707",
"0.55546796",
"0.5553891",
"0.55335194",
"0.54931045",
"0.5483634",
"0.54618126",
"0.5451501",
"0.5444643",
"0.5411199",
"0.54071236",
"0.54065... | 0.62596416 | 0 |
Print the ROC and PrecisionRecall curves for a given manuallycoded topic | def getCurves(lda_imlementation, vectorized_coded_revs, column, best_tops,
coded_reviews_df):
cat_array = lda_imlementation.transform(vectorized_coded_revs)
topic_array = []
for i in range(lda_imlementation.n_topics):
coded_reviews_df['topic_'+str(i)] = cat_array[:, i]
topic_a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metrics(labels, preds_proba, print_metrics=True, plot=False, threshold=0.5, rounded=4):\n \n\n test_fpr, test_tpr, test_thresholds = roc_curve(labels, preds_proba)\n roc_auc = auc(test_fpr, test_tpr)\n preds = preds_proba_to_preds_class(preds_proba,threshold)\n precision = precision_score(labels... | [
"0.6867683",
"0.65134686",
"0.64943665",
"0.6467826",
"0.6446851",
"0.6406306",
"0.63933426",
"0.6356165",
"0.6293545",
"0.6268827",
"0.626617",
"0.62420744",
"0.62159014",
"0.61956275",
"0.61634284",
"0.61632425",
"0.6136987",
"0.6112013",
"0.6014178",
"0.601159",
"0.5978724... | 0.65519303 | 1 |
Get the threshold associated with the lowest distance from (0,1) on the ROC curve on the test set of data | def bestThreshold(lda_imlementation, vectorized_coded_revs, column, best_tops,
coded_reviews_df):
cat_array = lda_imlementation.transform(vectorized_coded_revs)
topic_array = []
for i in range(lda_imlementation.n_topics):
coded_reviews_df['topic_'+str(i)] = cat_array[:, i]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_threshold(true_values, predictions, target_fpr=None, target_tpr=None):\n if target_fpr is None and target_tpr is None:\n raise AttributeError('Specify one of TPR and FPR')\n if target_fpr and target_tpr:\n raise AttributeError('Specify only one of TPR and FPR')\n prev_threshold = Non... | [
"0.66580945",
"0.6444057",
"0.6400157",
"0.63673306",
"0.63546443",
"0.6320526",
"0.63043606",
"0.63043606",
"0.6285651",
"0.6224342",
"0.6223859",
"0.6221799",
"0.614567",
"0.6099748",
"0.6095427",
"0.6085509",
"0.60432005",
"0.6024577",
"0.6018095",
"0.6017837",
"0.60176045... | 0.6188567 | 12 |
Get the threshold associated with the lowest distance from (1,1) on the PR curve on the test set of data | def bestThreshold2(lda_imlementation, vectorized_coded_revs, column, best_tops,
coded_reviews_df):
cat_array = lda_imlementation.transform(vectorized_coded_revs)
topic_array = []
for i in range(lda_imlementation.n_topics):
coded_reviews_df['topic_'+str(i)] = cat_array[:, i]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def selectThreshold(yval, pval):\n bestEpsilon = 0\n bestF1 = 0\n F1 = 0\n\n stepsize = (pval.max()-pval.min())/1000\n for epsilon in np.arange(pval.min(), pval.max()+stepsize/2, stepsize):\n predictions = (pval < epsilon)\n tp = ((predictions == 1) & (yval == 1)).sum()\n fp = (... | [
"0.6609041",
"0.6518175",
"0.6476939",
"0.64539623",
"0.6442797",
"0.6442797",
"0.64084864",
"0.6396371",
"0.63451254",
"0.62821597",
"0.62765723",
"0.6247047",
"0.6234266",
"0.6234266",
"0.6234266",
"0.6234266",
"0.6234266",
"0.62273365",
"0.6191945",
"0.61529285",
"0.614345... | 0.0 | -1 |
Get the threshold associated with the highest precision score with a recall over 0.5 on the test set. | def bestThreshold3(lda_imlementation, vectorized_coded_revs, column, best_tops,
coded_reviews_df):
cat_array = lda_imlementation.transform(vectorized_coded_revs)
topic_array = []
for i in range(lda_imlementation.n_topics):
coded_reviews_df['topic_'+str(i)] = cat_array[:, i]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sensitivity(\n targets: List[int], preds: List[float], threshold: float = 0.5\n) -> float:\n return recall(targets, preds, threshold)",
"def compute_average_precision_with_recall_thresholds(precision, recall, recall_thresholds):\n average_precision = 0.\n for t in recall_thresholds:\n p = ... | [
"0.6814696",
"0.67940784",
"0.67078286",
"0.66985404",
"0.6654105",
"0.66287464",
"0.662756",
"0.6626417",
"0.6616618",
"0.6520469",
"0.6428051",
"0.6424526",
"0.64114165",
"0.6362104",
"0.63491356",
"0.6346132",
"0.63300794",
"0.6323249",
"0.63190925",
"0.6309129",
"0.628664... | 0.609557 | 36 |
Return the accuracy, precision, and recall scores for the LDA best topics at a specified threshold. | def restaurantSummary(lda_imlementation, vectorized_coded_revs, column,
best_tops, coded_reviews_df, threshold):
cat_array = lda_imlementation.transform(vectorized_coded_revs)
topic_array = []
for i in range(lda_imlementation.n_topics):
coded_reviews_df['topic_'+str(i)] = cat_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def accuracy(targets: List[int], preds: List[float], threshold: float = 0.5) -> float:\n hard_preds = [1 if p > threshold else 0 for p in preds]\n return accuracy_score(targets, hard_preds)",
"def accuracy(targets: List[int], preds: Union[List[float], List[List[float]]], \n threshold: float = 0... | [
"0.64222395",
"0.6375575",
"0.63511735",
"0.63081896",
"0.62514776",
"0.6224346",
"0.6222285",
"0.62132895",
"0.6194228",
"0.60833067",
"0.60274947",
"0.59365594",
"0.58683616",
"0.5866562",
"0.5864479",
"0.5833923",
"0.58299327",
"0.5820002",
"0.57736564",
"0.57727057",
"0.5... | 0.0 | -1 |
Helper function to parse FASTA format. | def _parseHelper(fastaInput):
desc, sequence = "", []
for rline in fastaInput:
line = rline.rstrip()
if not line:
break
elif line[0] == ">":
if sequence:
yield desc, ''.join(sequence)
sequence = []
desc = line[1:]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(record):\n\n #Extract individual parts of the FASTA record\n\n identifier = record.id #The sequence's Id\n sequence = record.seq #The sequence itself\n sequence = sequence.upper() #Turns all the nucleotides to upper case\n\n return identifier, sequence",
"def fasta_parser(filename):\n ... | [
"0.7274786",
"0.69850767",
"0.68240386",
"0.681288",
"0.67535675",
"0.67449456",
"0.6639561",
"0.6625132",
"0.6602403",
"0.6532851",
"0.6463741",
"0.64551425",
"0.6444978",
"0.64182776",
"0.64099056",
"0.6380498",
"0.6380498",
"0.6379367",
"0.6350089",
"0.6333042",
"0.6317286... | 0.6463049 | 11 |
Takes a file or stdin of FASTA format and returns a generator of tuples of the form (description, sequence). | def parse(f=None):
f = f if f else sys.stdin
return _parseHelper(f) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fasta_read_generator(file_handler):\r\n seq = []\r\n name = ''\r\n for line in file_handler:\r\n if line[0] == '>':\r\n sequence = ''.join(seq)\r\n if name: # only yield when we already have all data for the first sequence\r\n yield name, sequence\r\n ... | [
"0.7582308",
"0.75391984",
"0.74770594",
"0.74419016",
"0.7362299",
"0.7289525",
"0.72521764",
"0.72396266",
"0.71881384",
"0.7154771",
"0.7140987",
"0.7096524",
"0.705887",
"0.7015823",
"0.6997083",
"0.6976023",
"0.695372",
"0.67821014",
"0.67700106",
"0.6726168",
"0.6718865... | 0.0 | -1 |
Takes a string in FASTA format and returns a generator of tuples of the form (description, sequence). | def parseStr(s):
return _parseHelper(s.split("\n")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parseHelper(fastaInput):\n\n desc, sequence = \"\", []\n\n for rline in fastaInput:\n line = rline.rstrip()\n if not line:\n break\n elif line[0] == \">\":\n if sequence: \n yield desc, ''.join(sequence)\n sequence = []\n ... | [
"0.7326887",
"0.7128683",
"0.7113521",
"0.7091112",
"0.69642293",
"0.6934219",
"0.6915862",
"0.69089323",
"0.6875257",
"0.6860623",
"0.6812091",
"0.6750901",
"0.6702442",
"0.6691692",
"0.66584575",
"0.66439056",
"0.65764606",
"0.65017074",
"0.6489449",
"0.6401183",
"0.6375456... | 0.0 | -1 |
Get absolute path to resource, works for dev and for PyInstaller | def resource_path(relative_path):
try:
# PyInstaller creates a temp folder and stores path in _MEIPASS
base_path = sys._MEIPASS
except Exception:
base_path = os.path.abspath(".")
return os.path.join(base_path, relative_path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_resource_path():\n return os.path.join(os.path.dirname(__file__), \"resources\") + os.path.sep",
"def resourcePath(relative):\r\n try:\r\n # PyInstaller creates a temp folder and stores path in _MEIPASS\r\n base_path = sys._MEIPASS\r\n except Exception:\r\n base_path = os.pa... | [
"0.8369502",
"0.82807344",
"0.82486343",
"0.8139249",
"0.81141466",
"0.80987823",
"0.8094279",
"0.8079923",
"0.8067883",
"0.80675626",
"0.806267",
"0.806267",
"0.806267",
"0.806267",
"0.806267",
"0.806267",
"0.806267",
"0.8057104",
"0.8054725",
"0.8040472",
"0.802546",
"0.8... | 0.8045005 | 30 |
creation d'une matrice TF | def create_Tf_matrix(
corpus,
filename_npz="../data/tfidf/data_tf.npz",
filename_features="../data/tfidf/data_feature_names.pkl",
):
vectorizer = CountVectorizer(max_features=len(corpus))
X = vectorizer.fit_transform(corpus)
print("-Vectorized matrix, ", X.toarray().shape)
print(" first lin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __create_tf_matrix(alpha, a, d, q):\n\n tf_matrix = Matrix([[cos(q), -sin(q), 0., a],\n [sin(q) * cos(alpha), cos(q) * cos(alpha), -sin(alpha), -sin(alpha) * d],\n [sin(q) * sin(alpha), cos(q) * sin(alpha), cos(alpha), cos(alpha) * d],\n ... | [
"0.65090847",
"0.6471617",
"0.63259995",
"0.6289696",
"0.6289696",
"0.61118096",
"0.60890025",
"0.5977189",
"0.59724253",
"0.5962874",
"0.59621626",
"0.59424025",
"0.59026086",
"0.5890263",
"0.5879501",
"0.5878053",
"0.5868499",
"0.58639187",
"0.5857934",
"0.5849375",
"0.5841... | 0.61494327 | 5 |
clean dataset and create tf and tfidf | def load_clean_and_generate_tf_idf(fname="../data/tfidf/data_gbif.json"):
print(" load dataset, and clean")
dataset = read_clean_dataset(fname)
print("- Verification:")
display(dataset.head())
dataset["freq"] = dataset["terms"].apply(lambda x: FreqDist(x))
# fdist1 : frequence dans le tableau... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_tfidf(self):\n\n # Add extra columns to clean_dataset from orignial's one\n self.clean_dataframe['overview'], self.clean_dataframe['id'] = self.original_dataframe['overview'], self.original_dataframe['id']\n\n # Define a TF-IDF Vectorizer Object\n tfidf = TfidfVectorizer(s... | [
"0.7173557",
"0.7045647",
"0.67401963",
"0.6647575",
"0.6620768",
"0.65987486",
"0.6561512",
"0.65604633",
"0.6551854",
"0.6520853",
"0.65108556",
"0.6459472",
"0.64554656",
"0.644694",
"0.64379877",
"0.6422224",
"0.6319287",
"0.63025856",
"0.6291027",
"0.62812567",
"0.628116... | 0.6759573 | 2 |
Load tfidf weights function | def load_tfidf(
filename_npz="../data/tfidf/data_tfidf.npz",
filename_features="../data/tfidf/data_tfidf_feature_names.pkl",
vectorizer_path="../data/tfidf/tfidf_vectorizer.pickle",
transform_example=True,
):
""" Input filenames: tfidf, feature names, vectorizer
Output:
- data name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fwordweights(self):\n return self.prefix + 'wordweights.txt'",
"def tf_idf_weight(self, term: str, doc_path: str) -> float:\n return self.tf(term, doc_path) * self.idf(term)",
"def compute_tfidf_weights(train_set, test_set, vocab_size):\n\n transformer = TfidfTransformer()\n\n # fit idf... | [
"0.65269595",
"0.63434577",
"0.62860996",
"0.62207264",
"0.6178997",
"0.6118679",
"0.60200256",
"0.59942776",
"0.59112453",
"0.5907915",
"0.58879375",
"0.5880849",
"0.5867318",
"0.5844076",
"0.5825634",
"0.57969457",
"0.5782009",
"0.57659227",
"0.57593393",
"0.57479084",
"0.5... | 0.6539936 | 0 |
transform query in vector | def transform_query(vectorizer_model, query):
x_request = vectorizer_model.transform(query)
x0 = x_request.toarray()
return x0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_vector(self, query: list):\n if len(query) == 0:\n raise BadQueryParameter(\"Query (list) can not be empty.\")\n\n return self.vectorizer.transform(query)",
"def _to_full_vector(self, query_vector: List[Tuple[str, float]]) -> np.array:\n terms = list(self.index.get_terms()... | [
"0.7272707",
"0.7058692",
"0.67857337",
"0.66777563",
"0.6566718",
"0.59476966",
"0.5946633",
"0.5905197",
"0.58603555",
"0.58545816",
"0.5836491",
"0.58132166",
"0.5806235",
"0.5802026",
"0.57763594",
"0.5758559",
"0.5726149",
"0.5724695",
"0.5701889",
"0.56733346",
"0.56565... | 0.7367226 | 0 |
Makes a Java method signature dictionary from an index name. | def index_signature(self, name, max_occurs, level, dummy=False):
sig = super(Java, self).index_signature(name, max_occurs, level, dummy)
sig['argtype'] = 'int'
return sig | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _index_key(self, sig, codegen):\n return (sig, codegen.magic_tuple())",
"def _index_key(self, sig, codegen):\n codebytes = self._py_func.__code__.co_code\n if self._py_func.__closure__ is not None:\n cvars = tuple([x.cell_contents for x in self._py_func.__closure__])\n ... | [
"0.6284271",
"0.60923684",
"0.5586627",
"0.53634715",
"0.5330007",
"0.5301372",
"0.5275142",
"0.52688384",
"0.52358294",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993",
"0.51993... | 0.6338364 | 0 |
Create a language by name. | def create(language, namespace, templatepath):
lang = None
if language == "Java":
lang = Java(namespace, templatepath)
else:
raise ModelProcessingError(
"Invalid language: %s" % language)
return lang | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def newlang(self, context, *args):\n if not can_manage_roles(context):\n await context.channel.send(MODO_FORBIDDEN)\n elif len(args) < 1:\n await context.channel.send(LANG_MISSING)\n else:\n server = context.message.guild\n lang = normalize(arg... | [
"0.6586048",
"0.6550716",
"0.6433656",
"0.6432657",
"0.6195935",
"0.61141795",
"0.59539646",
"0.58287704",
"0.57442343",
"0.56215405",
"0.55952775",
"0.5560401",
"0.5523971",
"0.54916435",
"0.5489181",
"0.5480538",
"0.54681754",
"0.5450814",
"0.5428346",
"0.5425264",
"0.54123... | 0.69454414 | 0 |
that takes a string k and an int OR float v as arguments and returns a tuple. The first element of the tuple is the string k. The second element is the square of the int/float v and should be annotated as a float. | def to_kv(k: str, v: typing.Union[int, float]) -> typing.Tuple[str, float]:
return k, v * v | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_kv(k: str, v: Union[int, float]) -> Tuple[str, float]:\n my_tuple = (k, v*v)\n\n return my_tuple",
"def to_kv(k: str, v: Union[int, float]) -> Tuple[str, float]:\n return (k, v**2)",
"def to_kv(k: str, v: Union[int, float]) -> Tuple[str, float]:\n return (k, pow(v, 2))",
"def scale_tuple(t... | [
"0.75295675",
"0.7492524",
"0.7131809",
"0.618519",
"0.57764864",
"0.577548",
"0.57631207",
"0.5656854",
"0.56116706",
"0.5492086",
"0.54710186",
"0.54518414",
"0.54417",
"0.5429412",
"0.5414349",
"0.53929144",
"0.53553355",
"0.5290045",
"0.5274288",
"0.5266954",
"0.52394044"... | 0.7365849 | 2 |
Generates a reasonable testname for parameterized function tests | def name_func(func, num, params):
return "%s_%s_%s" % (
func.__name__, int(num),
parameterized.to_safe_name('_'.join((params.args[0].__name__, params.args[1].__name__)))
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_test_name(source):\n out = source.replace(' ', '_').replace(':', '').replace(',', '').lower()\n return \"test_%s\" % out",
"def _testcase_name(testcase):\n name = os.path.splitext(os.path.basename(testcase))[0]\n name = name.replace('-', '_')\n name = 'test_{name}'.format(name=name)\... | [
"0.7779143",
"0.76314366",
"0.71898866",
"0.70460093",
"0.6980619",
"0.6900053",
"0.68684584",
"0.68013304",
"0.6687702",
"0.66859245",
"0.6642913",
"0.65910894",
"0.6530609",
"0.6525812",
"0.6522183",
"0.6518864",
"0.64357144",
"0.63761574",
"0.63758713",
"0.63667077",
"0.63... | 0.61440754 | 30 |
Expected to be close to fairly high accuracy | def test_t(self):
assert np.isclose(self.stepper.t, self.final_t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def accuracy(self):",
"def test_compute_unnormalized_scores(self):\n # todo: implement this test!\n pass",
"def get_expected_cost(self):",
"def test_calculate_all_metrics_precision():\n pass",
"def test_analytical_vs_numerical():\n pass",
"def test_a_variety_of_precisions(self):\n\n ... | [
"0.7370364",
"0.6453732",
"0.6180218",
"0.61739737",
"0.61366445",
"0.61108375",
"0.6097134",
"0.5979966",
"0.59543765",
"0.5939013",
"0.59265757",
"0.5920299",
"0.5920299",
"0.58981127",
"0.58981127",
"0.58801347",
"0.5879768",
"0.5876902",
"0.58555824",
"0.5842415",
"0.5839... | 0.0 | -1 |
Expected to be close, but only within a few digits | def test_q(self):
assert np.allclose(self.stepper.q, self.ODE.exact(self.stepper.t), rtol=1e-3, atol=1e-5) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testConvert(self):\n for i in range(100):\n AB = 10 + i/10.0\n wave = 1000 + 100*i\n f = (10**(-2.406/2.5))*(10**(-0.4*AB))/(wave**2)\n AB_new = -2.5*math.log10(f) - 5*math.log10(wave) - 2.406\n #print \"AB=\",AB,\" wave=\",wave, \" f=\",f, \" AB_... | [
"0.6254778",
"0.6233245",
"0.62250596",
"0.6141581",
"0.6082517",
"0.60696006",
"0.6049742",
"0.60286385",
"0.6021646",
"0.6015881",
"0.6014569",
"0.60016376",
"0.60005903",
"0.5984762",
"0.5972035",
"0.59383065",
"0.59322846",
"0.59322846",
"0.5924956",
"0.59069437",
"0.5893... | 0.0 | -1 |
Tests whether the rate of convergence is close enough to what's expected | def test_convergence(ODE, alg, expected_rate):
final_t = 5*ODE.dt_init
dts = [ODE.dt_init/2**i for i in range(4)]
steppers = [alg(0, dt, ODE.q_init, ODE.A) for dt in dts]
for s in steppers:
s.stepUntil(final_t)
errs = [np.linalg.norm(s.q - ODE.exact(s.t), ord=np.inf) for s in steppers]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_convergenceRates():\n dt_start = 1.0; num_dt = 10\n E_values = zeros(num_dt)\n T = 10.; g = 9.81; m = 50.; Cd = 1.2; rho = 1.0; A = 0.5;\n a = Cd*rho*A/(2.*m)\n \n dt = zeros(num_dt); dt[0] = dt_start\n for i in range(1,len(dt)):\n dt[i] = dt[i-1]/2.\n \n D = -0.39; B = 0... | [
"0.73033756",
"0.71997285",
"0.70893884",
"0.67220837",
"0.6642169",
"0.66098773",
"0.6596448",
"0.6596448",
"0.6596448",
"0.65688455",
"0.6530523",
"0.65262824",
"0.6523298",
"0.64186007",
"0.64098674",
"0.64098674",
"0.6359748",
"0.6358357",
"0.62825835",
"0.62649417",
"0.6... | 0.72973317 | 1 |
Implicit algorithms will detect whether or not A is sparse and build an identity matrix that matches. This may fail. | def test_sparsity_detection(ODE, alg):
stepper = alg(0, ODE.dt_init, ODE.q_init, ODE.A)
assert ODE.__name__.startswith('Sparse') == sp.issparse(stepper.I) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _identity_sparse(d, stype=\"csr\", dtype=complex):\n return sp.eye(d, dtype=dtype, format=stype)",
"def test_build_classification_matrix_sparse():\n import scnym\n\n # generate a sparse matrix with ~10% of elements filled\n B = np.zeros((100, 10))\n ridx = np.random.choice(B.size, size=100, re... | [
"0.68242675",
"0.6718077",
"0.66813797",
"0.64510334",
"0.6438231",
"0.6424529",
"0.63288724",
"0.6323402",
"0.6150004",
"0.61323166",
"0.61043763",
"0.60686755",
"0.60384846",
"0.60135704",
"0.59981954",
"0.5988093",
"0.59876406",
"0.59853727",
"0.5976748",
"0.5964551",
"0.5... | 0.0 | -1 |
Look for (letter) that does not follow a reasonable pattern A reasonable pattern here is 1,2,3...22,1,2,3 Report on lines that go "off" This can be done by subtracting both indices. If the result % 22 is not 1 and the second index is not 1, then an issue can be reported. | def reasonble_order(index_a, index_b):
if (index_b - index_a) % 22 != 1:
if index_b != 1:
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sticky_count_wrapper(fwd_str):\n length = len(fwd_str)\n count = 0\n rev_index = length-1\n for index in range(length/2):\n # print fwd_str[index], \" \", fwd_str[rev_index - index]\n if get_opposite_character(fwd_str, index) is not fwd_str[rev_index-index]:\n # print \"Bre... | [
"0.5752483",
"0.5689012",
"0.5653587",
"0.5587004",
"0.54908335",
"0.5340193",
"0.5302039",
"0.53004813",
"0.5293347",
"0.52346474",
"0.5197106",
"0.5185929",
"0.51823324",
"0.5180201",
"0.5173208",
"0.5169704",
"0.5165643",
"0.5148156",
"0.5119101",
"0.5116015",
"0.51105726"... | 0.6369563 | 0 |
Locate Potential Siman transitions in source file. | def find_transitions(self):
def transition(index_a, index_b):
if None in (index_a, index_b):
return False
return (index_b - index_a) % 22 != 1 and index_b == 1
simanim = []
seif_values = (None, None)
siman_start, count = 0, 0
new_seif_ref ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def FindSourceForPath(self, path):\n ret = self._FindSourceForPathInternal(path)\n if not ret and path not in self._unmatched_paths:\n if self.unmatched_paths_count < 10:\n logging.warning('Could not find source path for %s', path)\n self._unmatched_paths.add(path)\n return ret",
"def m... | [
"0.49302799",
"0.48568365",
"0.47693655",
"0.46251163",
"0.4614407",
"0.46104053",
"0.45840618",
"0.4575503",
"0.45740053",
"0.45691812",
"0.45679146",
"0.45640233",
"0.45638812",
"0.4530316",
"0.45296425",
"0.452781",
"0.4522902",
"0.4509619",
"0.44888696",
"0.44862807",
"0.... | 0.54480284 | 0 |
1) Get number of Seifim in each siman from base 2) Find locations where a split can be made 3) For each split location calculate deviation from base text 4) Get location with lowest deviation 5) Check that the split is within a reasonable distance from expected 6) Make the split | def resolve_merge(self, merge_index, max_diff=3):
merged_siman = self.commentary_simanim[merge_index]
expected_refs = (self.base_simanim[merge_index]['total_refs'], self.base_simanim[merge_index+1]['total_refs'])
split_locations, count = [], 0
for line_num, line in enumerate(self.source_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def minimum_spanning_arborescence(sol):",
"def get_split_positions(read, min_gap):\n cigar = read.cigar\n # Cigar string is a list of tuples:\n if len(read.cigar) <= 1:\n return [] # no break points = empty list of break point positions\n\n ##\n # read has break points if cigar string is lo... | [
"0.58702636",
"0.58141214",
"0.56263816",
"0.56098175",
"0.56064355",
"0.5591489",
"0.5505442",
"0.54941434",
"0.5477648",
"0.546505",
"0.5457885",
"0.5432776",
"0.54293835",
"0.54233766",
"0.53762805",
"0.5371227",
"0.53563607",
"0.5327634",
"0.53155303",
"0.530765",
"0.5300... | 0.0 | -1 |
Creates directories for each interval if it doesn't exist. | def create_directories(intervals):
if not os.path.exists(DIR_PATH):
os.mkdir(DIR_PATH)
for interval in intervals:
path_to_interval = os.path.join(DIR_PATH, interval)
if not os.path.exists(path_to_interval):
os.mkdir(path_to_interval) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dirs(self):\n for new_directory in [self.event_dir, self.event_dir / 'videos']:\n new_directory.mkdir(exist_ok=self.overwrite)\n logger.debug('Dir {} created', new_directory)",
"def create_directories(working_dir, timesteps):\n\tfor time in timesteps:\n\t\tpath = working_d... | [
"0.6597959",
"0.6580007",
"0.65335095",
"0.63984984",
"0.6259434",
"0.62133235",
"0.61995745",
"0.61928344",
"0.6172793",
"0.6109969",
"0.6108993",
"0.60512817",
"0.6040164",
"0.60076576",
"0.5994967",
"0.5981499",
"0.5962351",
"0.5959587",
"0.5959587",
"0.59394664",
"0.59265... | 0.862203 | 0 |
Requests the data from the binance API and save them into a csv file. | def request_binance_data(tickers_intervals, starting_date, ending_date):
for ticker, interval in tickers_intervals:
print(
"Getting historical data for the ticker {} with {} interval".format(
ticker, interval
)
)
try:
csvfile = open(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n page = self.fetch_data(self.url)\n stock_list = self.pop_stock_list(page)\n self.write_csv(stock_list)",
"def get_tweet_data(session, analytics_account, start_time, end_time, user_agent):\n\n export_url = \"https://analytics.twitter.com/user/\" + analytics_account + \"/tw... | [
"0.6569897",
"0.6283327",
"0.62760055",
"0.61615914",
"0.61234707",
"0.6120113",
"0.6115126",
"0.6093527",
"0.6017526",
"0.5874016",
"0.58038586",
"0.5790349",
"0.5780595",
"0.5764296",
"0.57519084",
"0.5732458",
"0.5716796",
"0.5682941",
"0.5639221",
"0.56356066",
"0.5628201... | 0.62043154 | 3 |
Requests the data from the binance API and updates the corresponding csv file. | def update_binance_data(tickers_intervals):
for ticker, interval in tickers_intervals:
try:
path_to_file = os.path.join(DIR_PATH, interval, f"{ticker}.csv")
csvfile = pd.read_csv(path_to_file, index_col=False, delimiter=",")
starting_date = datetime.utcfromtimestamp(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n page = self.fetch_data(self.url)\n stock_list = self.pop_stock_list(page)\n self.write_csv(stock_list)",
"def update(self):\n try:\n response = requests.get(\n self.API_URL, headers=self.API_HEADERS, timeout=15)\n except requests.excep... | [
"0.6643283",
"0.66069555",
"0.6505497",
"0.6037143",
"0.60138357",
"0.5998581",
"0.5954024",
"0.5906363",
"0.578054",
"0.5707935",
"0.5688188",
"0.565734",
"0.5653172",
"0.5602184",
"0.55642736",
"0.5554812",
"0.5539957",
"0.5532375",
"0.55239487",
"0.5520941",
"0.5510955",
... | 0.6307683 | 3 |
Return a pandas.DataFrame index by (bid, cid) that describes all bundles sent by this generator | def list_bundles(self):
if not self.monitor or len(self.sent) == 0:
idx = pd.MultiIndex(levels=[[],[]], labels=[[],[]], names=['bid', 'cid'])
df = pd.DataFrame(index=idx)
else:
df = pd.DataFrame([b.to_dict() for b in self.sent]) if self.monitor else pd.DataFrame()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bc_records_dataframe(self, run_idxs):\n\n return pd.DataFrame(self.bc_records(run_idxs))",
"def bidlist_dataset_0():\n\n bm = BidManager()\n bm.add_bid(1, 3, 0, True, 0)\n bm.add_bid(2, 4, 1, True, 0)\n bm.add_bid(5, 1, 2, True, 0)\n\n bm.add_bid(4, 2, 3, False, 0)\n bm.add_bid(1, 1,... | [
"0.5892857",
"0.5550518",
"0.55504996",
"0.54173326",
"0.54075223",
"0.53586096",
"0.52566284",
"0.52546877",
"0.5214352",
"0.5155566",
"0.51374453",
"0.51234156",
"0.5113576",
"0.49608386",
"0.49301657",
"0.4924111",
"0.49180183",
"0.49081022",
"0.48970145",
"0.48886904",
"0... | 0.8060786 | 0 |
The predicted data volume [bits] that this generator should generate over the course of the simulation | def predicted_data_vol(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predicted_data_vol(self):\n return sum(f['DataRate']*((f['EndTime']-f['StartTime']).total_seconds())\n for f in self.flows.values())",
"def generated_data_vol(self):\n return sum(b.data_vol for b in self.sent) if self.monitor else -1",
"def get_bias(self):",
"def predictio... | [
"0.6459967",
"0.62028074",
"0.5957427",
"0.5926649",
"0.5706994",
"0.5704624",
"0.56800926",
"0.56749916",
"0.5606429",
"0.5591035",
"0.5588136",
"0.5583251",
"0.55683047",
"0.5554941",
"0.55469924",
"0.55372185",
"0.55228543",
"0.55228543",
"0.5499534",
"0.54864234",
"0.5485... | 0.74574953 | 0 |
Return the total data volume in [bits] generated during the simulation | def generated_data_vol(self):
return sum(b.data_vol for b in self.sent) if self.monitor else -1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def total_volume(self):",
"def total_volume(self):\n return self.length * self.width * self.height",
"def total_volume(self):\n v = self.cell_edges\n v = np.abs(v[-1] - v[0])\n return v",
"def volume(self):\n return sum([x[\"counter_volume\"] for x in self.usage])",
"def ... | [
"0.7522965",
"0.7364082",
"0.6998253",
"0.67592776",
"0.6741069",
"0.6725422",
"0.6710091",
"0.6601255",
"0.6586164",
"0.6522993",
"0.6516385",
"0.63422596",
"0.63369596",
"0.6237777",
"0.620324",
"0.6192584",
"0.61785847",
"0.6159002",
"0.61426187",
"0.61349785",
"0.60980326... | 0.6779526 | 3 |
Determines whether a digraph has an Eulerian cycle using necessary | def __satisfies_necessary_and_sufficient_conditions(g):
# Condition 0: at least 1 Edge
if g.get_E() == 0:
return False
# Condition 1: indegree(v) == outdegree(v) for every vertex
for v in range(g.get_V()):
if g.outdegree() != g.indegree(v):
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def IsEulerGraph(self):\n\n for node in self.nodes:\n if ((len(node.neighbours) % 2) == 1) or (len(node.neighbours) == 0):\n return False\n return True",
"def has_cycle(self):\n traversed = dict()\n for i in self.adj_list:\n traversed[i] = False\n\... | [
"0.7150784",
"0.6967263",
"0.6805899",
"0.66196024",
"0.65940124",
"0.6437873",
"0.6399398",
"0.6397124",
"0.6361732",
"0.6266571",
"0.6244261",
"0.6220773",
"0.62039936",
"0.6185866",
"0.6182098",
"0.61817515",
"0.6156103",
"0.61271423",
"0.6117882",
"0.61110204",
"0.6077214... | 0.0 | -1 |
Applies the transformation to the given record (each channel individually). | def __call__(self, record):
filtered_signal = self.filter_signal(record.p_signal, record.fs)
record.p_signal = filtered_signal
return record | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform(self, record):\n if not record:\n return None\n\n # If we've got a list, hope it's a list of records. Recurse,\n # calling transform() on each of the list elements in order and\n # return the resulting list.\n if type(record) is list:\n results... | [
"0.682318",
"0.6734652",
"0.5955765",
"0.5545773",
"0.55429924",
"0.5540069",
"0.5540069",
"0.55038536",
"0.5415301",
"0.533934",
"0.53311086",
"0.5329576",
"0.532138",
"0.5289746",
"0.523335",
"0.52302307",
"0.51941043",
"0.5190431",
"0.51837724",
"0.51816636",
"0.5160706",
... | 0.5125071 | 23 |
Initializes the lowpass filter transform. | def __init__(self,
filter_order=DEFAULT_ORDER, cutoff_freq_hz=DEFAULT_CUTOFF_HZ,
zero_phase=False):
self.filter_order = filter_order
self.cutoff_freq_hz = cutoff_freq_hz
self.zero_phase = zero_phase | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_filter(self):\n shape = self.filter_size + (self.input_shape[-1], self.channels)\n self.filter = self.filter_initializer(shape)",
"def init_lowpass(self, lowpass_cutoff, lowpass_order):\n if lowpass_cutoff > 0 or self._fs_out < self._fs_in:\n nyquist = self._fs_out / 2\n ... | [
"0.71257824",
"0.69304144",
"0.6555534",
"0.6120017",
"0.6084653",
"0.60113204",
"0.5995528",
"0.59895056",
"0.5938458",
"0.59226346",
"0.5891828",
"0.5877485",
"0.58576745",
"0.5845463",
"0.5776792",
"0.5764046",
"0.5754724",
"0.5754182",
"0.57263845",
"0.5714722",
"0.571025... | 0.54970545 | 38 |
Applies the transformation to the given record (each channel individually). | def __call__(self, record):
fs = record.fs
signal = record.p_signal
filtered_signal = self.filter_signal(signal, fs)
record.p_signal = filtered_signal
return record | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform(self, record):\n if not record:\n return None\n\n # If we've got a list, hope it's a list of records. Recurse,\n # calling transform() on each of the list elements in order and\n # return the resulting list.\n if type(record) is list:\n results... | [
"0.68219125",
"0.6733875",
"0.59566206",
"0.5545259",
"0.55415004",
"0.55399793",
"0.55399793",
"0.55021054",
"0.5416756",
"0.53393054",
"0.5331073",
"0.532867",
"0.5321212",
"0.5288621",
"0.52311057",
"0.5193767",
"0.51904297",
"0.51828617",
"0.5181918",
"0.5160857",
"0.5149... | 0.5231336 | 14 |
Encodes a tree to a single string. | def serialize(self, root):
output = []
stack = []
depth = self.depth(root)
if depth == 0:
return []
if depth == 1:
return [root.val]
nodes = sum([ pow(2, i) for i in range(depth)])
#print(depth, nodes)
stack.append(root)
whi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tree_to_string(tree):\n if type(tree) == Tree:\n return sum(list(map(tree_to_string, tree.children)), [])\n else:\n return [str(tree)]",
"def serialize(node, tree=\"\"):\n \n \n if (not node): #Base case\n tree += \"# \"\n return tree\n tree += (str(node.val) + \" ... | [
"0.72254354",
"0.7174656",
"0.70803744",
"0.706792",
"0.7032909",
"0.7030312",
"0.7020698",
"0.69704413",
"0.6961067",
"0.69334626",
"0.692108",
"0.6879218",
"0.6797347",
"0.6790119",
"0.6790119",
"0.6789466",
"0.67704153",
"0.6743968",
"0.6743425",
"0.6740732",
"0.67361337",... | 0.0 | -1 |
Decodes your encoded data to tree. | def deserialize(self, data):
if len(data) == 0:
return None
levels = math.log(len(data) + 1)/math.log(2)
#print("Total levels", levels)
root = TreeNode(data[0])
stack = [root]
curr_elem = 1
if levels == 1:
return root
while len(stac... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deserialize(self, data):\n # if data:\n # root = TreeNode(data.pop(0))\n # # root.val = data.pop\n if not data:\n return None\n data = data.split(' ')\n data = iter(data)\n\n def resucsbuild():\n try:\n val = next(dat... | [
"0.75936663",
"0.7466042",
"0.70945126",
"0.7064568",
"0.70590705",
"0.7052731",
"0.7051215",
"0.70471644",
"0.70444036",
"0.7037396",
"0.6991945",
"0.6987029",
"0.69762975",
"0.6933306",
"0.6910262",
"0.6891147",
"0.68765485",
"0.6839685",
"0.6830137",
"0.6817978",
"0.681678... | 0.6914585 | 14 |
[push Uart cmd from tx queue] low level thread will pop from queue and send out through UART | def ll_uart_write(self,payload):
self._uart_tx_queue.put(payload) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _writeloop(self):\r\n while self._ll_alive:\r\n ## Add a thread lock\r\n if not self._uart_tx_queue.empty():\r\n data = self._uart_tx_queue.get()\r\n #clear the response list before send the command\r\n #self._uart_rx_queue.clear()\r\n ... | [
"0.66928667",
"0.6398725",
"0.63527435",
"0.62069845",
"0.61590314",
"0.6157856",
"0.60016",
"0.5952931",
"0.5904423",
"0.5897935",
"0.588016",
"0.5866585",
"0.58618385",
"0.5804746",
"0.5801713",
"0.5795519",
"0.57902974",
"0.57520497",
"0.5742806",
"0.5721507",
"0.570523",
... | 0.7002497 | 0 |
[read UART cmd response from rx queue] low level thread will push the cmd response to cmd response queue read response timeout {[int]} is defined in uart_set_cmd_timeout | def ll_uart_read(self,length):
data=''
time_begin = time.time()
while True:
#time_cnt = time.time() - time_begin
# if int(time_cnt) >= self._ll_rx_timeout:
# print(time_cnt)
# print(len(data))
# r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_response(self):\n counter = 0\n rx_pkt_done = 0\n while not rx_pkt_done:\n uart_status, tx_buff_full, tx_buff_empty, rx_buff_full, rx_buff_empty, rx_pkt_done = self.x10g_rdma.read_uart_status()\n counter += 1\n if counter == 15001:\n pri... | [
"0.7008674",
"0.66590804",
"0.65142477",
"0.6502266",
"0.6422981",
"0.6408763",
"0.63308805",
"0.631603",
"0.62394845",
"0.62173307",
"0.6190415",
"0.6115748",
"0.60996723",
"0.6096799",
"0.6092437",
"0.60453486",
"0.59815246",
"0.5939977",
"0.5918316",
"0.5904503",
"0.589894... | 0.66863877 | 1 |
[Set the UART cmd response timeout in seconds] | def ll_uart_set_cmd_timeout(self,timeout = 10):
self._ll_rx_timeout = timeout | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ll_uart_get_cmd_timeout(self):\r\n\r\n return self._ll_rx_timeout",
"def timeout_soft_cmd(cmd, timeout):\n return \"timeout %us stdbuf -o0 -e0 %s\" % (timeout, cmd)",
"def set_timeout(self, timeout):\n self.timeout = timeout",
"def settimeout(self,timeout=10):\r\n # Update\r\n self.tim... | [
"0.66830313",
"0.6530053",
"0.64408445",
"0.62874025",
"0.62863076",
"0.6161473",
"0.6140921",
"0.6140921",
"0.61132824",
"0.61115474",
"0.6086067",
"0.60817987",
"0.6053637",
"0.60268223",
"0.60268223",
"0.6000394",
"0.59900683",
"0.5971973",
"0.5971272",
"0.5958285",
"0.595... | 0.8114786 | 0 |
[Get the UART cmd response timeout in seconds] | def ll_uart_get_cmd_timeout(self):
return self._ll_rx_timeout | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_timeout(self) -> int:",
"def ReceiveTimeout(self) -> int:",
"def ReceiveTimeout(self) -> int:",
"def PortStatResponseTimeOut(self):\n\t\treturn self._get_attribute('portStatResponseTimeOut')",
"def gettimeout(self):\r\n return self.sock.gettimeout()",
"def gettimeout(self):\r\n retu... | [
"0.7125122",
"0.7082056",
"0.7082056",
"0.6860464",
"0.6838888",
"0.6838888",
"0.67672056",
"0.67672056",
"0.67382956",
"0.66543394",
"0.66427517",
"0.6604122",
"0.66004556",
"0.65860885",
"0.658583",
"0.6533469",
"0.6510287",
"0.65024114",
"0.6463812",
"0.6463812",
"0.645507... | 0.83621186 | 0 |
Stops the read thread, waits for it to exit cleanly, then closes the underlying serial port | def ll_uart_close(self):
self._ll_alive = False
self._rxThread.join(0.5)
self._txThread.join(0.5)
self._uart_rx_queue.queue.clear()
self._uart_tx_queue.queue.clear() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n self._stop.set()\n self.logger.debug('stop reading from serial port')\n if self.is_alive():\n self.join()\n self.logger.info('reader has terminated')",
"def stop_read_thread(self):\r\n\r\n if self._serial_thread is not None:\r\n self._rea... | [
"0.83227324",
"0.81050825",
"0.80037993",
"0.76284707",
"0.74462587",
"0.72237104",
"0.6966307",
"0.68550265",
"0.6703806",
"0.6591638",
"0.6576251",
"0.65576845",
"0.6527528",
"0.6520647",
"0.65014255",
"0.6499536",
"0.6498317",
"0.64452666",
"0.64118624",
"0.6409018",
"0.63... | 0.64197177 | 18 |
Read thread main loop Reads data from the connected device | def _readloop(self):
while self._ll_alive:
with self._rx_lock:
data = self._Random(1)
if len(data) != 0: # check for timeout
self._uart_rx_queue.put(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_thread(self):\r\n\r\n while self._reading and self._serial_object:\r\n if self._serial_object.in_waiting:\r\n try:\r\n (raw_data, parsed_data) = self._ubxreader.read()\r\n # if raw_data:\r\n # ... | [
"0.7554161",
"0.7444901",
"0.70899904",
"0.7055979",
"0.69975317",
"0.69514054",
"0.6769293",
"0.6760793",
"0.6735775",
"0.6703721",
"0.6666021",
"0.6625009",
"0.66217315",
"0.66095257",
"0.6576406",
"0.6538009",
"0.6517359",
"0.6484256",
"0.6460147",
"0.644426",
"0.64310706"... | 0.70762557 | 3 |
Read cmd from TX FIFO queue and send to DUT through UART write the cmd to the connected device | def _writeloop(self):
while self._ll_alive:
## Add a thread lock
if not self._uart_tx_queue.empty():
data = self._uart_tx_queue.get()
#clear the response list before send the command
#self._uart_rx_queue.clear()
#self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def comTxRx(self, cmd, dataOut, length):\n if DEBUG > 1: sys.stderr.write(\"* comTxRx()\\n\")\n txFrame = b''\n rxHeader = 0\n rxNum = 0\n\n #Transmitting part ----------------------------------------\n #Prepare data for transmit\n if (length % 2) != 0:... | [
"0.65904266",
"0.6548552",
"0.651658",
"0.64164895",
"0.6373682",
"0.6316957",
"0.6248871",
"0.62212783",
"0.61636835",
"0.6133203",
"0.6131311",
"0.60935014",
"0.6089493",
"0.59446883",
"0.58937997",
"0.5891209",
"0.5850071",
"0.58347493",
"0.57909673",
"0.57822806",
"0.5753... | 0.6315484 | 6 |
http request post method decorator | def post_required(func):
def post_wrapper(request,*args,**kwds):
res = http.ResponseBuilder()
if request.method != 'POST':
return res.error("post is required").build_json()
return func(request,*args,**kwds)
return post_wrapper | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def http_post(self, **kwargs):\n return self.rabjcallable.post(**kwargs)",
"def _post(self, *args, **kwargs):\n return self._request('post', *args, **kwargs)",
"def post(self, *args, **kwargs):\n self.request(\"post\", *args, **kwargs)",
"def post(url):\n url = add_slash(url)\n\n d... | [
"0.7253687",
"0.7175989",
"0.7127153",
"0.7121864",
"0.71176344",
"0.6982174",
"0.6923865",
"0.6902044",
"0.6831165",
"0.680928",
"0.6719412",
"0.66592175",
"0.6649926",
"0.6649604",
"0.663869",
"0.66328883",
"0.65493155",
"0.654111",
"0.6518876",
"0.6516656",
"0.6516092",
... | 0.7152784 | 2 |
Register two NIFTI volumes containing landmarks | def register_landmarks(fname_src, fname_dest, dof, fname_affine='affine.txt', verbose=1, path_qc='./'):
from msct_image import Image
# open src label
im_src = Image(fname_src)
# coord_src = im_src.getNonZeroCoordinates(sorting='value') # landmarks are sorted by value
coord_src = im_src.getCoordinat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_registered_image_layers(\n viewer: napari.Viewer, *, registration_directory: pathlib.Path\n) -> Tuple[napari.layers.Image, napari.layers.Labels]:\n layers: List[LayerDataTuple] = []\n\n meta_file = (registration_directory / \"brainreg.json\").resolve()\n if meta_file.exists():\n with ope... | [
"0.50881785",
"0.5056109",
"0.49519038",
"0.4912301",
"0.4879457",
"0.4813697",
"0.4787528",
"0.4785143",
"0.46455887",
"0.46359158",
"0.46346763",
"0.46140108",
"0.45900747",
"0.4589501",
"0.45878723",
"0.45546374",
"0.4543802",
"0.45378584",
"0.45247677",
"0.45247677",
"0.4... | 0.54319185 | 0 |
Compute sum of squared error between pairwise landmarks | def SSE(pointsA, pointsB):
return sum(array(pointsA[:, 0:3]-pointsB[:, 0:3])**2.0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mse(self):\n xs, ys = self.R.nonzero()\n predicted = self.full_matrix()\n error = 0\n for x, y in zip(xs, ys):\n # print(predicted[x, y], self.R[x, y] )\n error += pow(self.R[x, y] - predicted[x, y], 2)\n return np.sqrt(error)",
"def rmse2 (a, p) :\n ... | [
"0.65895677",
"0.6588133",
"0.6415059",
"0.6338098",
"0.6315696",
"0.631417",
"0.6205466",
"0.6133671",
"0.6129634",
"0.6095342",
"0.608772",
"0.6043297",
"0.60194534",
"0.6008844",
"0.5990452",
"0.59714705",
"0.59372824",
"0.59342045",
"0.5901768",
"0.58844507",
"0.5880896",... | 0.0 | -1 |
Cost function to minimize | def minimize_transform(params, points_dest, points_src, constraints):
# initialize dof
dof = [0, 0, 0, 0, 0, 0, 1, 1, 1]
# initialize dictionary to relate constraints index to dof
dict_dof = {'Tx': 0, 'Ty': 1, 'Tz': 2, 'Rx': 3, 'Ry': 4, 'Rz': 5, 'Sx': 6, 'Sy': 7, 'Sz': 8}
# extract constraints
l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def costFun(self, S, x):",
"def cost(self) -> float:",
"def compute_cost(AL, Y):\n pass",
"def compute_cost(AL, Y):\n pass",
"def costFun(self, x):\n\ttmp = x.reshape(self.inp_shape)\n\tc = np.float64(self.calcCost(np.asarray(tmp,dtype=np.float32))) + self.alpha * np.dot(x.T, x)\n\treturn c",
"... | [
"0.7898446",
"0.71774364",
"0.71197855",
"0.7037681",
"0.7032501",
"0.6972743",
"0.69694555",
"0.6966542",
"0.6868792",
"0.68444115",
"0.68282765",
"0.6827418",
"0.67984587",
"0.6764421",
"0.6761392",
"0.6750837",
"0.67058235",
"0.66833335",
"0.66763395",
"0.6667288",
"0.6642... | 0.0 | -1 |
Compute affine transformation to register landmarks | def getRigidTransformFromLandmarks(points_dest, points_src, constraints='Tx_Ty_Tz_Rx_Ry_Rz', verbose=0, path_qc='./'):
# TODO: check input constraints
from scipy.optimize import minimize
# initialize default parameters
init_param = [0, 0, 0, 0, 0, 0, 1, 1, 1]
# initialize parameters for optimizer
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform(self,image,landmarks,s0=None):\n if s0 is None:\n s0 = np.array([[127.6475, 227.8161], [79.1608, 87.0376], [176.8392, 87.0376]], np.float32)\n idx = [8,36,45] #\"\"\"Anchor points\"\"\"\n pts = np.float32(landmarks[idx,:])\n M = cv2.getAffineTransform(pts,s0)\n ... | [
"0.6571072",
"0.641823",
"0.6383142",
"0.634022",
"0.62523025",
"0.62054753",
"0.62054753",
"0.61710006",
"0.6121375",
"0.6112866",
"0.6077915",
"0.6075933",
"0.60051006",
"0.5994395",
"0.59378403",
"0.59053254",
"0.58875793",
"0.58756226",
"0.5864575",
"0.5862957",
"0.583515... | 0.0 | -1 |
This is the identity block | def identity_block(self,X,stage,block):
conv_name_base='res'+str(stage)+block+'_branch'
bn_name_base='bn'+str(stage)+block+'_branch'
#retrieve the filters
#F1,F2,F3=filters
X_shortcut=X
#first component of main path
X=Conv3D(self.filter,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeIdentity(self) -> None:\n ...",
"def setIdentity(self) -> None:\n ...",
"def test_create_identity(self):\n pass",
"def identity(cls):\n return super().identity(4, 4)",
"def confirm_identity(self):\n self.__not_implemented()",
"def next_identity(self) -> UserId:\... | [
"0.71393317",
"0.65090305",
"0.6266525",
"0.6132871",
"0.6096772",
"0.60785687",
"0.6077503",
"0.6056983",
"0.6046447",
"0.59905106",
"0.5987439",
"0.5978849",
"0.5946789",
"0.5917424",
"0.5910534",
"0.589202",
"0.58327717",
"0.5799545",
"0.57756495",
"0.5773348",
"0.57540077... | 0.0 | -1 |
Onehot encoding is applied to columns specified in a pandas DataFrame. | def encode_onehot(df, cols):
vec = DictVectorizer()
vec_data = pd.DataFrame(vec.fit_transform(df[cols].to_dict(orient='records')).toarray())
vec_data.columns = vec.get_feature_names()
vec_data.index = df.index
df = df.drop(cols, axis=1)
df = df.join(vec_data)
return df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def one_hot_encode(df, colnames):\r\n for col in colnames:\r\n oh_df = get_dummies(df[col], prefix=col)\r\n df = concat([oh_df, df], axis=1)\r\n df = df.drop([col], axis=1)\r\n return df",
"def one_hot(input_df, columns):\n df = input_df.copy()\n\n for col in columns:\n du... | [
"0.83890945",
"0.83782166",
"0.8353686",
"0.8352055",
"0.83429927",
"0.83196855",
"0.8312187",
"0.8312064",
"0.82263744",
"0.8203636",
"0.78942597",
"0.7849938",
"0.78145325",
"0.7612664",
"0.7542009",
"0.7526136",
"0.75124633",
"0.7379414",
"0.7350976",
"0.72332495",
"0.7226... | 0.8227697 | 8 |
execute all ci stages in one run | def all_(tag, field_tests, img_passwd_file, registry, install_server_hostname,
quiet, builder):
manager = Manager(
'all', tag, registry_url=registry, field_tests=field_tests,
img_passwd_file=img_passwd_file,
install_server_hostname=install_server_hostname, verbose=not quiet,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _run_ci_test():\n _run_install(False)\n _run_coverage_html(False)\n _run_typecheck_xml(False)\n _run_lint(True)",
"def run(self):\n self._build_docker_images()\n self._build_docker_image_test()\n self._run_workflow_in_matrix_of_envs()",
"def _run_ci_publish():\n _run_ins... | [
"0.7215023",
"0.6739235",
"0.67389643",
"0.6676614",
"0.63964593",
"0.6364255",
"0.6358811",
"0.6307575",
"0.62078595",
"0.5984018",
"0.5947946",
"0.59442085",
"0.59306777",
"0.5906521",
"0.5804627",
"0.57899594",
"0.5749922",
"0.57092535",
"0.56613225",
"0.56181085",
"0.5615... | 0.0 | -1 |
build one or more images | def build(images, tag, quiet):
# images to build
build_images = None
# list of available images
image_list = build_image_map().keys()
# image specified: check if it exists
if images:
build_images = []
for check_name in images:
if check_name in image_list:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_filler_images(self):",
"def _build_docker_images(self):\n print(f\"+ building {len(self.neurodocker_specs)} Docker images\")\n self.docker_status = []\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\n try:\n print(\"++ building image: {}\".... | [
"0.7279872",
"0.7093156",
"0.7073595",
"0.6907964",
"0.67056745",
"0.66842973",
"0.66243804",
"0.66157806",
"0.6573795",
"0.651644",
"0.64935124",
"0.64864373",
"0.6419387",
"0.63917524",
"0.63883257",
"0.63883257",
"0.63883257",
"0.6385622",
"0.637723",
"0.63701296",
"0.6363... | 0.7616135 | 0 |
execute the cleanup stage | def cleanup(tag, keepimg, builder):
manager = Manager('cleanup', tag, keepimg=keepimg,
builder_hostname=builder)
manager.run() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup():",
"def cleanup(self):\r\n logging.info(\"entered the cleanup\")",
"def cleanup(self):",
"def cleanup(self):",
"def cleanup(self):",
"def cleanup(self):\n\n pass",
"def cleanup(self):\n pass",
"def cleanup(self):\n pass",
"def cleanup(self):\n pass",... | [
"0.8597375",
"0.8467043",
"0.8215665",
"0.8215665",
"0.8215665",
"0.81636655",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81482863",
"0.81438214",
"0.8142126",
"0.8112586",
"0.... | 0.0 | -1 |
execute the client integration tests | def clitest(tag, field_tests, img_passwd_file, install_server_hostname,
custom_cli_subnet, custom_db_subnet, builder):
manager = Manager(
'clitest', tag, field_tests=field_tests,
img_passwd_file=img_passwd_file,
install_server_hostname=install_server_hostname,
custom_cli_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_for_client():",
"def runTests(self):\n \n pass",
"def runtest(self):",
"def test_basic_execution(self):",
"def test_integration1(self):\n self._test_integration(1)",
"def run_integration_tests():\n run_pytest('integration')\n return redirect(url_for('main.test_reports', re... | [
"0.7719503",
"0.7161856",
"0.7011778",
"0.69300866",
"0.6815633",
"0.67563385",
"0.67350364",
"0.6734357",
"0.6718839",
"0.6702629",
"0.6696539",
"0.6628397",
"0.65992844",
"0.65992844",
"0.6576469",
"0.65701425",
"0.65472716",
"0.65330935",
"0.6529378",
"0.6528179",
"0.65236... | 0.0 | -1 |
start and keep services running until manually stopped | def run(tag, devmode, img_passwd_file, install_server_hostname,
custom_cli_subnet, custom_db_subnet, clitests, builder):
manager = Manager(
'run', tag, devmode=devmode, img_passwd_file=img_passwd_file,
install_server_hostname=install_server_hostname,
custom_cli_subnet=custom_cli_subn... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ensure_running_service_daemon(context):\n svc = Management()\n if svc._check_if_its_up(usutil.UBERSERVER):\n context.temp_service_daemon = None\n else:\n svc.start()\n context.temp_service_daemon = svc",
"def startServices():\n # dnsmasq\n out_dnsmasq = subprocess.run([\"s... | [
"0.7066118",
"0.6894916",
"0.6701344",
"0.66644454",
"0.6636169",
"0.6594103",
"0.65772367",
"0.6504517",
"0.6455384",
"0.64464915",
"0.6408886",
"0.6372164",
"0.63390625",
"0.63344496",
"0.6302941",
"0.6277452",
"0.62744546",
"0.62490195",
"0.6241172",
"0.62357146",
"0.62187... | 0.0 | -1 |
list the docker images available for build | def list_images():
image_map = build_image_map()
click.echo('')
click.echo('List of available images (Name - Description)')
click.echo('')
for name in image_map:
click.echo('{} -> {}'.format(name, image_map[name])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_docker_images():\n raw_result = subprocess.getstatusoutput('docker images')\n return result_handler(raw_result)",
"def _build_docker_images(self):\n print(f\"+ building {len(self.neurodocker_specs)} Docker images\")\n self.docker_status = []\n for sha1, neurodocker_dict in sel... | [
"0.79035133",
"0.75807214",
"0.7104596",
"0.6980693",
"0.69144505",
"0.6904989",
"0.6883383",
"0.6788377",
"0.6781191",
"0.67198014",
"0.6546041",
"0.653512",
"0.6484546",
"0.6475429",
"0.642328",
"0.6303564",
"0.63027793",
"0.62536365",
"0.6235247",
"0.6217119",
"0.6206234",... | 0.691039 | 5 |
list the available ci stages for execution | def list_stages():
for name in Manager.STAGES:
click.echo('{}'.format(name)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stages() -> List[Tuple[str, str]]:\n return [TestStatus.preparation, TestStatus.testing, TestStatus.completed]",
"def stage_list(args):\n\n for stage in args.stages:\n print stage",
"def stages(self):\n return self._stages",
"def stages(self):\n return self._stages",
"def... | [
"0.71553665",
"0.6959344",
"0.67542017",
"0.67542017",
"0.6622961",
"0.6178401",
"0.6178401",
"0.6178401",
"0.6153931",
"0.6117432",
"0.60700995",
"0.59009725",
"0.5881856",
"0.5803326",
"0.57920223",
"0.5780718",
"0.57623285",
"0.5628671",
"0.56034106",
"0.5523403",
"0.55116... | 0.75736964 | 0 |
push images to a docker registry | def push(images, tag, registry):
manager = Manager('push', tag, images=images, registry_url=registry)
manager.run() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def push_image(image):\n\n subprocess.check_call(['docker', 'push', image])",
"def run_docker_push(image_list, docker_client):\n logger.info(\"--- push image to registry ---\")\n if runtime == Runtime.local:\n logger.info(\"Detected local runtime, stop here.\")\n return\n\n if not len(s... | [
"0.7688454",
"0.76087564",
"0.72781956",
"0.718321",
"0.715713",
"0.7090948",
"0.7030179",
"0.6809519",
"0.6794375",
"0.6744697",
"0.65739185",
"0.6560774",
"0.6509127",
"0.6483394",
"0.6415379",
"0.6381899",
"0.6256319",
"0.6209923",
"0.6139788",
"0.61235374",
"0.6070284",
... | 0.82243633 | 0 |
execute unit test on docker images | def unittest(tag, path, custom_cli_subnet, custom_db_subnet, builder):
manager = Manager(
'unittest', tag, images=['server'],
custom_cli_subnet=custom_cli_subnet, custom_db_subnet=custom_db_subnet,
unittest_path=path, builder_hostname=builder)
manager.run() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(params=''):\n docker_exec('py.test {}'.format(params))",
"def test_units(container):\n short_image_name = container.image_name[container.image_name.rfind(\"/\") + 1 :]\n host_data_dir = os.path.join(THIS_DIR, f\"../{short_image_name}/test/units\")\n LOGGER.info(f\"Searching for units tests i... | [
"0.7846933",
"0.7412047",
"0.7258366",
"0.72149086",
"0.7151974",
"0.71193075",
"0.7115684",
"0.6868198",
"0.6772459",
"0.67464954",
"0.6740845",
"0.66953695",
"0.6591307",
"0.6566432",
"0.64860165",
"0.64527285",
"0.6415776",
"0.640852",
"0.6380202",
"0.6326416",
"0.6318264"... | 0.60226077 | 34 |
Entry point, executes the root group. | def main():
logging.basicConfig(
level=logging.DEBUG, format="%(levelname)s: %(message)s")
root.add_command(all_)
root.add_command(build)
root.add_command(cleanup)
root.add_command(clitest)
root.add_command(run)
root.add_command(push)
root.add_command(unittest)
root.add_comma... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self, root):\r\n pass",
"def root():\n pass",
"async def _group(self, ctx):\r\n\t\tif ctx.invoked_subcommand is None:\r\n\t\t\tprint (\"Ryhmä komento annettiin ilman alakomentoa\")",
"def exec(self):\n if self._root.master is None:\n self._root.mainloop()",
"def execute(... | [
"0.7180917",
"0.6376066",
"0.6170935",
"0.6146936",
"0.60530406",
"0.60025346",
"0.59464896",
"0.59450406",
"0.5913414",
"0.590124",
"0.58364505",
"0.574756",
"0.5686958",
"0.5659579",
"0.5629004",
"0.5579367",
"0.5568521",
"0.556469",
"0.5518852",
"0.55107117",
"0.54983646",... | 0.62378114 | 2 |
This function allows for fancy object assigment. | def __lt__(self, other):
name, value = other
self[name] = value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _from_other(cls, obj):",
"def obj(self) -> object:\n pass",
"def new(self, obj):\n pass",
"def add(obj):",
"def _setup_object(self) -> object:\n raise NotImplementedError",
"def __setitem__(self, name, obj):",
"def __setattr__(*args):",
"def __setattr__(*args):",
"def __set... | [
"0.68794894",
"0.6405043",
"0.63679534",
"0.63675547",
"0.63258046",
"0.6255525",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
"0.62520456",
... | 0.0 | -1 |
This decorator adds an object instance when the object is initiated. | def register_instance(cls):
@functools.wraps(cls)
def wrapper_decorator(*args, **kwargs):
instance = cls(*args, **kwargs)
Register[cls.__name__] = instance
return instance
return wrapper_decorator | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, decoratedObj):\n\n self.__decoratedObj = decoratedObj",
"def register_instance(self, obj):\n self.__instances.append(obj)\n self._proxy_class_methods(obj)",
"def register_instance(self, instance):\n self.instance = instance",
"def register_object(self, obj):\n ... | [
"0.70706284",
"0.687097",
"0.6671335",
"0.6357395",
"0.6352234",
"0.6292987",
"0.62336093",
"0.62336093",
"0.62336093",
"0.6214509",
"0.62070495",
"0.62070495",
"0.61912227",
"0.61553884",
"0.6097399",
"0.60896945",
"0.6085249",
"0.6073874",
"0.6045348",
"0.6041891",
"0.60417... | 0.6157864 | 13 |
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